Assessing the Financial Burden of Multimorbidity Among Patients Aged 30 and above in India

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Despite this, there is limited research on the financial burden of multimorbidity in outpatient and inpatient care, and cross-state comparisons of CHE are underexplored. Methods We conducted a cross-sectional analysis using nationally representative data from the National Sample Survey 75th Round ‘Social Consumption in India: Health (2017-18)’, focusing on patients aged 30 and above in outpatient and inpatient care in India. We assessed multimorbidity prevalence, OOPE, CHE incidence, and CHE intensity. Statistical models, including linear, log-linear, and logistic regressions, were used to examine the financial risk, with a focus on non-communicable diseases (NCDs), healthcare facility choice, and socioeconomic status and Epidemiological Transition Levels (ETLs). Results Multimorbidity prevalence in outpatient care (6.1%) was six times higher than in inpatient care (1.1%). It was most prevalent among older adults, higher MPCE quintiles, urban patients, and those with NCDs. Multimorbidity was associated with higher OOPE, particularly in the rich quintile, patients seeking care from private providers, low ETL states, and rural areas. CHE incidence was highest in low ETL states, private healthcare users, poorest quintile, males, and patients aged 70 + years. CHE intensity, measured by mean positive overshoot, was greatest among the poorest quintile, low ETL states, rural, and male patients. Log-linear and logistic regressions indicated that multimorbidity patients with NCDs, those seeking private care, and those in low ETL states had higher OOPE and CHE risk. The poorest rural multimorbidity patients had the greatest likelihood of experiencing CHE. Furthermore, CHE intensity was significantly elevated among multimorbidity patients with NCDs (95% CI: 19.29–45.79), patients seeking care in private, poorest, and from low ETL states (95% CI: 7.36–35.79). Conclusions The high financial burden of OOPE and CHE among multimorbidity patients, particularly those with NCDs, underscores the urgent need for comprehensive health policies that address financial risk at the primary care level. To alleviate the financial burden among multimorbidity patients, especially in low-resource settings, it is crucial to expand public healthcare coverage, incorporate outpatient care into financial protection schemes, advocate for integrated care models and preventive strategies, establish standardized treatment protocols for reducing unnecessary medications linked to polypharmacy, and leverage the support of digital health technologies. Multimorbidity NCDs Chronic OOPE Catastrophic Health Expenditure India Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction The global increase in the prevalence of chronic diseases, compounded by multimorbidity, the coexistence of two or more chronic conditions in the same individual, poses significant management challenges for health systems worldwide, contributing to increased healthcare expenditures and reduced health outcomes for individuals ( 1 – 3 ). Multimorbidity poses unique challenges compared to comorbidity where secondary ailments accompany a primary condition. Each condition within multimorbidity exerts a significant and distinct impact on an individual's overall health status, contributing to the complexity of their management ( 4 ). Approximately 37.2% of the global population experiences multimorbidity, with its implications varying widely across regions and socioeconomic groups ( 5 ). The coexistence of multiple conditions escalates healthcare costs, particularly for the uninsured ( 6 , 7 ). In India, where total health expenditure accounts for 3.83% of the gross domestic product (GDP) and the level of out-of-pocket expenditure (OOPE) is 39.4% of total health expenditures.( 8 ) The financial burden on individuals managing multiple chronic diseases is immense. The rising prevalence of non-communicable diseases (NCDs) has compounded this issue, contributing significantly to out-of-pocket expenditures (OOPE) and catastrophic health expenditure (CHE). Studies highlight that multimorbidity can intensify OOPE, ranging between five to ten times higher than the costs associated with treating singular conditions, thus accentuating equity concerns, particularly for lower socioeconomic groups ( 9 ). This disparity burdens individuals with increased hospital admissions, premature deaths, and fragmented care, impinging on their quality of life ( 2 , 3 ). The COVID-19 pandemic has further exposed vulnerabilities among individuals with multimorbidity, emphasizing the importance of comprehensive primary care in reducing hospital admissions ( 10 ). From a public health perspective, the growing prevalence of multimorbidity, combined with increased susceptibility to infectious diseases, reiterates the critical need to strengthen basic healthcare services to reduce the adverse impact of future epidemics and pandemics ( 11 ). In India, the complexities of multimorbidity are exacerbated by disparities in healthcare access and financial inequalities, affecting various demographic segments, including the elderly, urban population, affluent, and those residing in low-income regions ( 12 – 14 ). Over time, the age differences in multimorbidity prevalence have narrowed largely due to its increase among younger adults over 30 years of age who are significant contributors to the workforce and household income thereby, necessitating targeted interventions due to its economic implications ( 15 ). Despite the extensive literature on single chronic diseases, research examining multimorbidity’s distinct financial impact across outpatient and inpatient care is limited. Moreover, there is a notable gap in cross-state comparisons of CHE burden in India, particularly across states with differing Epidemiological Transition Levels (ETLs). While studies have examined the costs associated with multimorbidity, most focus on health system costs rather than individual expenditure burden ( 9 ). Existing studies in India have often restricted the definition of multimorbidity to the coexistence of NCDs, overlooking chronic communicable and infectious diseases ( 16 ). Further, the healthcare landscape in India, comprising both public and private sectors, poses varied financial implications. The public healthcare system, challenged with limited budgets, inadequate infrastructure, insufficient human resources, and shortages of crucial medical supplies and equipment, struggles to cater to rural and marginalized populations. As a result, individuals seek care at private facilities that operate largely without regulation, imposing significant financial burdens on individuals and families, particularly for patients with multiple chronic conditions, more so for people from weaker economic backgrounds ( 13 ). This study seeks to address these gaps by leveraging nationally representative data from the National Sample Survey (2017–2018), providing a comprehensive analysis of the financial implications of managing multimorbidity in India. This research adopts a broader definition of multimorbidity endorsed by the World Health Organization (WHO), aiming to provide a comprehensive national-level analysis encompassing all eligible populations at risk of multimorbidity. We address critical gaps observed in previous studies that are confined to specific states, diseases, and older populations ( 1 – 4 , 13 , 17 – 21 ). Our research seeks to assess multimorbidity prevalence, healthcare utilization, and the predictors of out-of-pocket and catastrophic health expenditures for multimorbidity. The research also investigates both inpatient and outpatient care domains, tackling age-specific healthcare challenges and integrating variables such as socioeconomic status, and epidemiological transition level (ETL) state groups to provide a holistic understanding of multimorbidity in India. We hypothesize that patients with multimorbidity experience significantly higher OOPE and CHE compared to those with single chronic conditions or acute illnesses and that economic disparities and ETL state classifications influence these financial outcomes. By adopting a comprehensive approach, this research endeavours to generate evidence-based insights and recommendations aimed at alleviating the financial strain on multimorbidity patients. These insights hold the potential to inform national health programs like the Ayushman Bharat in India, contributing towards the enhancement of healthcare delivery and financial protection for individuals coping with multimorbidity. Methodology Defining multimorbidity The primary focus of this paper revolves around multimorbidity, which denotes the simultaneous presence of two or more chronic health conditions in an individual. Chronic health conditions, as outlined by the WHO, include cardiovascular diseases, cancer, chronic respiratory conditions, and diabetes ( 22 ). These conditions are characterized by their persistent and long-lasting nature, significantly impeding physical, mental, and social well-being, often resulting in prolonged functional limitations. Given their complex nature, chronic health conditions necessitate continuous medical attention and management of symptoms and complications. The definition of chronic health conditions may vary across studies and healthcare settings. For instance, Pless and Douglas (1971) defined chronic health conditions as ailments lasting longer than three months or requiring continuous hospitalization for over a month ( 23 ) Centers for Disease Control and Prevention (CDC) broadly defines chronic diseases as conditions lasting more than a year, requiring ongoing medical attention, or limiting daily activities, or both ( 24 ). The National Sample Survey Office (NSSO) in India identifies an ailment as chronic if symptoms persist for more than a month or if treatment continues for a month or more on the date of the survey( 25 ). For this research, the NSS data was screened, and the following criteria were used to identify patients with chronic ailments: 1. For outpatient care, patients experiencing symptoms persisting for more than one month at the time of the survey, while for inpatient care, patients taking a course of treatment on medical advice for one month or more and continuing on the date of the survey were considered. Cases of acute illnesses like fevers, malaria, diarrhoea, and worm infections were excluded. 2. Patients with definite diagnosis of diseases such as Tuberculosis, Cancers, Bleeding Disorders, Diabetes, Stroke, Hypertension, Heart Disease, Bronchial Asthma etc. irrespective of the duration of illness, if the ailment persists. Subsequently, the data of patients with chronic ailments was further screened to identify patients actively seeking treatment for two or more different chronic conditions in the Outpatient and Inpatient departments. The patients identified after the 2nd level screening of data were identified to be suffering from multimorbidity and were included within the scope of the study. This nuanced approach adopted in the research may facilitate a comprehensive understanding of multimorbidity, along with a deeper understanding of healthcare utilization patterns as well as health expenditures among patients with multimorbidity. Outcome variables The analysis in this research focuses on several crucial outcome variables that are pivotal to assessing the implications of multimorbidity. These include the prevalence of multimorbidity, the associated OOPE, the incidence of CHE, and the intensity of CHE attributed to multimorbidity for both inpatient and outpatient care. These metrics are integral to comprehending the financial impact and burden posed by multimorbidity patients within the healthcare system. Out-of-pocket Expenditure (OOPE) and OOPE Share Total OOPE includes all direct expenses incurred by an individual, both as an inpatient and outpatient, for medical care and transportation (non-medical) costs associated with accessing healthcare services. OOPE is calculated as: \(\:\text{O}\text{O}\text{P}\text{E}=\sum\:_{i=1}^{N}TH{E}_{i}-{R}_{i}\) ( 1 ) Where THE i represents total health expenditure (medical + non-medical) for i th individual and R i is total amount reimbursed by the medical insurance company or employer for i th individual. i is an index denoting the individual, ranging from 1 to N, the sample size. The OOPE share signifies the proportion of an individual's out-of-pocket health expenditure over 30 days relative to their total monthly household consumption expenditure. It serves as a metric to gauge the financial strain of healthcare expenses on an individual's overall monthly budget. The formula for calculating the OOPE share is as follows: $$\:\text{O}\text{O}\text{P}\text{E}\:\text{s}\text{h}\text{a}\text{r}\text{e}=\frac{1}{N}{\sum\:}_{i=1}^{N}{\left(\frac{OO{PE}_{\left(30days\right)i}}{MH{E}_{i}}*100\right)}_{i}$$ 2 where N represents the sample size, OOPE i is Out-of-pocket health expenditure for i th individual over 30 days period and MHE i is the monthly household consumption expenditure for the i th individual and i is an index denoting the individual, ranging from 1 to N. Catastrophic Health Expenditure (CHE) The incidence of CHE is measured using the headcount method, which calculates the proportion of individuals within the sample who experience CHE. CHE is determined by assessing whether an individual's OOPE share surpasses a predefined threshold, typically set at 10% in this study, relative to the monthly household consumption expenditure. The headcount formula is expressed as: $$\:\text{H}\text{e}\text{a}\text{d}\text{c}\text{o}\text{u}\text{n}\text{t}=\frac{1}{N}{\sum\:}_{i=1}^{N}{CHE}_{i}$$ 3 where N represents the sample size, CHE i is 1 when the i th individual incurred CHE, and 0 otherwise. CHE Intensity Measuring the incidence of CHE in silos does not reveal the depth or severity of these costs, specifically how much individual OOPE surpasses the catastrophic threshold typically set at 10%. To understand this phenomenon, the study measures CHE intensity, by employing two indicators: catastrophic overshoot and mean positive overshoot (MPO). Catastrophic overshoot denotes the average extent by which individual OOPE on illness, as a percentage of total individual expenditure, exceeds the set threshold (z). Conversely, the MPO captures the intensity of CHE through the average excess of OOPE on illness beyond the threshold among individuals who reported CHE incidence. The catastrophic overshoot and mean positive overshoot are mathematically represented as follows: $$\:\text{O}\text{v}\text{e}\text{r}\text{s}\text{h}\text{o}\text{o}\text{t}=\frac{1}{N}{\sum\:}_{i=1}^{N}CH{E}_{i}\left(\frac{OO{PE}_{i}}{MH{E}_{i}}-z\right)$$ 4 $$\:\text{M}\text{e}\text{a}\text{n}\:\text{P}\text{o}\text{s}\text{i}\text{t}\text{i}\text{v}\text{e}\:\text{O}\text{v}\text{e}\text{r}\text{s}\text{h}\text{o}\text{o}\text{t}=\frac{1}{U}{\sum\:}_{i=1}^{U}CH{E}_{i}\left(\frac{OO{PE}_{i}}{MH{E}_{i}}-z\right)$$ 5 Here N represents the total sample size of individuals receiving outpatient or inpatient care, while U is the count of individuals experiencing CHE, OOPE i signifies an individual's out-of-pocket health expenditure, MHE i is the Monthly Household Consumption Expenditure of the i th individual, and z signifies the 10% threshold for CHE. This threshold indicates that households allocating more than 10% of their monthly household consumption expenditure to healthcare expenses face a significant financial burden. Explanatory variables The independent variables include the type of health care provider (public/private), NCD status, age, gender, monthly per capita consumption expenditure (MPCE), place of residence (rural/urban) and ETL States ( 26 ). ETL is described based on the ratio of disability-adjusted life years (DALYs) attributed to communicable diseases and against DALYs from NCDs and injuries combined. A low ratio signifies high ETL and vice versa ( 26 , 27 ). The interaction between 'illness type' and 'NCD occurrence' was incorporated into the model to explore how the presence of NCDs influences OOPE (and CHE in subsequent regression analysis) across different illness types (acute, single chronic, and multimorbidity). Similarly, the interaction between 'place of residence' and 'MPCE quintile' was examined to determine how the urban or rural status of the residence affects OOPE (and CHE) across various MPCE quintiles. Statistical Models The analytical methods employed in this study include log-linear, logistic, linear regression models, aimed at comprehensively examining the influence of independent factors on OOPE, CHE and CHE intensity. Log-linear Regression for OOPE A log-linear regression model is used to analyze the impact of various explanatory variables on the logarithmic transformation of OOPE for each patient (i) in the study. The model is defined as: Where α is the intercept, β1 to β6 are coefficients for the respective predictors, and ϵ i is the error term. Interaction_IllnessType_NCD_Occurrence represents the combined effect of illness type (Acute, Single Chronic, Multimorbidity) and NCD occurrence. LevelOfCare distinguishes between public and private healthcare facilities. ETL_States refers to Epidemiological Transition Level State Groups. Interaction_PlaceOfResidence_MPCE_Quintile combines residence (rural/urban) and economic status (MPCE Quintile). Sex and Age_Group denote the patient's gender and age group, respectively. The subscript ‘i’ is used for i th patient. Logistic Regression for CHE The logistic regression model employed to analyze the incidence of CHE aims to evaluate the impact of various explanatory variables on the likelihood of CHE occurrence. The regression model for CHE is articulated as: where CHE i is the probability of patients incurring catastrophic health expenditure for outpatient and inpatient care. The model estimates the log odds of incurring CHE adjusted for a set of explanatory variables. All explanatory variables remain consistent with those of the log-linear regression equation. Linear Regression for CHE Intensity The linear regression model employed to analyze Mean Positive Overshoot (MPO), which indicates the intensity of CHE aims to evaluate the impact of various explanatory variables. This model plays a crucial role in quantifying the extent of financial burden experienced by patients due to CHE, based on a range of explanatory factors. The linear regression model for MPO is defined as follows: Where MPO i is the Mean Positive Overshoot for the i th patient, which serves as an indicator of the intensity of CHE experienced. All explanatory variables remain consistent with those utilized in the log-linear regression equation. Data This study utilizes unit-level data sourced from the 75th round of the ‘Social Consumption in India: Health’ survey conducted by the National Sample Survey Office (NSSO), Government of India, spanning from July 2017 to June 2018. The dataset captures information from 113,823 households, representing a total of 555,352 individuals, inclusive of 2,537 deceased members. Employing stratified random sampling techniques, the survey documented 43,240 instances of illness, encompassing 42,107 outpatient cases and 93,925 inpatient cases within the preceding 365 days. A wide array of data was collected spanning demographics, morbidity, mortality, hospitalizations, health insurance coverage, OOPE and healthcare utilization patterns( 25 ). However, our study narrows its focus to explore the prevalence of multimorbidity and the associated expenditures incurred for outpatient care (within the last 15 days) and inpatient care (within the last 365 days), with an emphasis on discerning disparities between single chronic conditions and acute illnesses vis-à-vis multimorbidity conditions. Notably, the NSS data is available for inpatient or outpatient visits and is not aggregated at the individual level. In our analysis, we aggregate case-level data to individual levels thereby, individuals serving as the primary unit for estimating the outcome variables. All data analyses have been conducted utilizing STATA 16 software. Sampling weights are applied to ensure the representativeness of the sample, whereas the samples provided in the survey remain unweighted. The composition of the sample included in the study is presented in Fig. 1 . Results Prevalence of Multimorbidity One in 17 patients in outpatient care have multimorbidity, with a prevalence of 6.1% (95% CI: 5.5–6.7). In comparison, prevalence of single chronic conditions is observed in 63.3% of patients (95% CI: 61.9–64.7), while acute illnesses account for 30.6% (95% CI: 29.3–32.0). In the inpatient setting, the prevalence of multimorbidity is lower at 1.1% (95% CI: 0.9–1.3), with single chronic conditions and acute illnesses reported at 58.0% (95% CI: 56.8–59.1) and 41.0% (95% CI: 39.8–42.1), respectively (Summary Table 1 ). Table 1 Overview of Comprehensive Health Indicators for Outpatient and Inpatient Care among Patients aged 30 and Above, NSS 75th Round Household Social Consumption in India: Health Survey Data, 2017–2018 Type of Care Indicators Acute Illness Single-Chronic Multimorbidity Value 95% CI Value 95% CI Value 95% CI For Outpatient Care (last 15 days reference period) Prevalence % 30.6 29.3–32.0 63.3 61.9–64.7 6.1 5.5–6.7 Public Health Care Utilization % 23.9 21.7–26.2 29.9 28.4–31.4 30.0 25.6–34.8 Private Health Care Utilization % 52.8 50.2–55.5 60.5 58.8–62.1 60.8 55.9–65.5 Medicine OOP (Rs.) 405 381–429 503 489–517 682 643–721 Diagnostic OOP (Rs.) 82 75–89 83 74–93 70 54–85 Total Medical OOP Expenditure (Rs.) 611 577–644 673 651–694 872 813–931 Total Non-Medical OOP Expenditure (Rs.) 99 94–104 85 81–90 78 68–87 Total OOP Health Expenditure (Rs.) 709 673–745 758 734–782 950 887–1014 Incidence of CHE at 10% 43.0 42.0–45.0 40.0 39.0–41.0 51.0 49.0–53.0 Mean Intensity (Positive Overshoot) of CHE (%) 29.0 27.0–31.0 30.0 29.0–32.0 27.0 24.0–30.0 For Inpatient Care (last 365 days reference period) Prevalence % 41.0 39.8–42.1 58.0 56.8–59.1 1.1 0.9–1.3 Public Health Care Utilization % 42.0 40.1–44.0 39.2 37.9–40.6 26.7 20.3–34.2 Private Health Care Utilization % 53.9 51.9–55.9 55.2 53.9–56.5 51.7 43.0-60.4 Medicine OOP (Rs.) 5408 5165–5651 11154 10756–11552 21106 17447–24765 Diagnostic OOP (Rs.) 2182 2087–2276 4364 4210–4517 10807 8994–12620 Total Medical OOP Expenditure (Rs.) 15020 14389–15650 29468 28567–30370 62899 53820–71978 Total Non-Medical OOP Expenditure (Rs.) 2064 1991–2137 3074 3002–3145 6539 5726–7352 Total OOP Health Expenditure (Rs.) 17084 16415–17753 32542 31604–33480 69438 59913–78963 Incidence of CHE at 10% 34.0 33.0–35.0 50.0 50.0–51.0 70.0 65.0–74.0 Mean Intensity (Positive Overshoot) of CHE (%) 32.0 30.0–34.0 42.0 41.0–44.0 66.0 55.0–77.0 Source: Computed from unit-level NSSO 75th round Household Social Consumption in India: Health Survey Data, 2017–2018 Patients with NCDs exhibit higher prevalence of multimorbidity, with 9.4% (95% CI: 8.5–10.3) in outpatient care and 2.5% (95% CI: 2.0-3.1) in inpatient care, compared to those without NCDs (Table 2 ). Multimorbidity is more prevalent in high ETL states, where the rates reach 12.2% (95% CI: 10.9–13.6) in outpatient care and 2.1% (95% CI: 1.6–2.7) in inpatient care, while low ETL states show the lowest prevalence. Urban areas report higher multimorbidity prevalence than rural areas in both outpatient and inpatient settings. Across MPCE quintiles, the richest quintile experiences the highest prevalence, whereas the poorest quintile shows the lowest for both care types. Gender differences are also observed: in outpatient care, males have a slightly lower multimorbidity prevalence (5.9%; 95% CI: 5.1–6.8) compared to females (6.2%; 95% CI: 5.4–7.1), while in inpatient care, males exhibit a higher prevalence (1.2%; 95% CI: 0.9–1.5) compared to females (1.0%; 95% CI: 0.8–1.2). Table 2: Prevalence of Acute, Single Chronic and Multimorbidity Illnesses in outpatient and inpatient care among patients aged 30 and above by Background Characteristics, NSS 75th Round Household Social Consumption in India: Health Survey Data, 2017–2018 Background Characteristics For Outpatient Care (last 15 days reference period) For Inpatient Care (last 365 days reference period) Acute Illness Single-Chronic Multimorbidity Outpatient Sample (n) Acute Illness Single-Chronic Multimorbidity Inpatient Sample (n) % 95% CI % 95% CI % 95% CI % 95% CI % 95% CI % 95% CI Type of Illness Non-NCD 64.3 61.9-66.5 35.4 33.1-37.8 0.3 0.2-0.6 8272 54.9 53.6-56.2 44.6 43.3-46.0 0.5 0.4-0.6 26392 Have NCD 11.2 10.2-12.4 79.4 78.0-80.7 9.4 8.5-10.3 18633 10.4 9.0-12.0 87.1 85.5-88.6 2.5 2.0-3.1 12430 ETL State Group Low ETL State 47.5 44.7-50.3 51.8 49.0-54.6 0.7 0.4-1.2 6419 39.8 37.7-41.9 59.6 57.5-61.7 0.6 0.4-0.8 12768 Lower-middle ETL State 33.3 27.9-39.2 63.6 57.6-69.2 3.1 1.6-6.2 1655 45.7 42.4-49.0 53.3 50.0-56.6 1.0 0.5-2.0 4907 Higher-middle ETL State 24.9 23.0-26.9 68.3 66.2-70.3 6.9 5.9-8.0 11497 42.5 40.6-44.5 56.5 54.6-58.4 1.0 0.7-1.4 14004 High ETL State 20.1 18.1-22.3 67.7 65.4-70.0 12.2 10.9-13.6 7334 38.4 36.4-40.5 59.5 57.4-61.6 2.1 1.6-2.7 7143 Sector Rural 0.0 33.1-36.7 60.1 58.2-61.9 5.0 4.3-5.8 13402 42.1 40.6-43.6 57.0 55.5-58.5 0.9 0.7-1.2 21258 Urban 23.8 22.0-25.7 68.5 66.6-70.4 7.7 6.8-8.7 13503 38.9 37.3-40.6 59.7 58.0-61.3 1.4 1.2-1.7 17564 MPCE Quintile Poorest 41.8 38.3-45.3 55.9 52.4-59.3 2.3 1.6-3.4 4167 41.6 38.8-44.5 57.6 54.8-60.4 0.8 0.5-1.2 6900 Poor 37.8 34.4-41.3 57.2 53.7-60.7 5.0 3.7-6.7 4348 41.2 38.0-44.4 58.0 54.8-61.1 0.9 0.6-1.2 6538 Middle 31.3 28.4-34.3 64.5 61.5-67.5 4.2 3.3-5.3 5355 42.1 39.5-44.6 57.0 54.5-59.5 0.9 0.7-1.3 7864 Rich 27.4 24.7-30.3 66.3 63.3-69.2 6.3 5.1-7.7 5474 41.5 38.9-44.1 57.4 54.8-60.0 1.1 0.6-1.8 7979 Richest 21.5 19.3-23.8 68.3 65.8-70.7 10.3 8.9-11.8 7561 38.9 37.0-40.8 59.5 57.6-61.4 1.6 1.2-2.2 9541 Sex Male 28.7 26.8-30.7 65.5 63.4-67.4 5.9 5.1-6.8 12367 36.6 35.0-38.2 62.3 60.6-63.9 1.2 0.9-1.5 19464 Female 32.1 30.4-34.0 61.7 59.8-63.5 6.2 5.4-7.1 14538 45.2 43.6-46.9 53.7 52.1-55.4 1.0 0.8-1.2 19358 Age Group 30-44 52.5 49.5-55.4 46.3 43.4-49.3 1.2 0.8-1.8 5187 47.4 45.2-49.6 51.7 49.5-53.9 0.9 0.7-1.1 11799 45-59 30.0 27.9-32.1 64.1 61.9-66.3 5.9 5.0-7.0 10151 42.4 40.6-44.2 57.0 55.2-58.7 0.7 0.5-0.9 15028 60-69 18.2 16.0-20.6 73.8 71.2-76.2 8.0 6.8-9.5 6897 34.3 31.8-36.8 64.4 61.9-66.8 1.3 1.0-1.8 6929 70+ 17.4 14.8-20.5 71.7 68.4-74.7 10.9 9.1-13 4670 30.5 27.6-33.5 67.1 64.0-70.0 2.5 1.6-3.8 5066 Total 30.6 29.3-32.0 63.3 61.9-64.7 6.1 5.5-6.7 26905 41.0 39.8-42.1 58.0 56.8-59.1 1.1 0.9-1.3 38822 Source: Computed from unit-level NSSO 75th round Household Social Consumption in India: Health Survey Data, 2017–2018 Age was found to be a crucial factor, with multimorbidity prevalence increasing as patients get older. The lowest prevalence in outpatient care is observed in the 30–44 age group (1.2%; 95% CI: 0.8–1.8), while the highest is among those aged 70 and above (10.9%; 95% CI: 9.1–13.0). Inpatient care follows a similar trend, highlighting the growing healthcare needs and hospitalization risks faced by older adults with multimorbidity (Table 2 ). Across ETL regions, a high variation between states can be seen for multimorbidity prevalence for both outpatient and inpatient care. In outpatient care, Assam reports the highest multimorbidity prevalence at 2.0% (95% CI: 0.5–7.3) among low ETL states, followed by Madhya Pradesh at 1.6% (95% CI: 0.4–6.3). Among lower-middle ETL states, Gujarat exhibits the highest prevalence at 3.5% (95% CI: 1.8–6.9). In higher-middle ETL states, Lakshadweep stands out with a prevalence of 18.7% (95% CI: 9.6–33.2), followed closely by Andhra Pradesh at 14.2% (95% CI: 11.3–17.8). In high ETL states, Kerala has the highest prevalence of multimorbidity at 20.3% (95% CI: 18.2–22.6), with Tamil Nadu following at 5.6% (95% CI: 3.6–8.5). For inpatient care, in low ETL states, Rajasthan has the highest multimorbidity prevalence at 1.3% (95% CI: 0.6–2.6), followed by Odisha at 1.1% (95% CI: 0.6-2.0). Among lower-middle ETL states, Gujarat reports the highest prevalence at 1.3% (95% CI: 0.6–2.7), followed by Mizoram at 0.5% (95% CI: 0.1–2.1). In higher-middle ETL states, Lakshadweep leads with a prevalence of 2.3% (95% CI: 0.5–9.6), followed by West Bengal at 1.9% (95% CI: 1-3.4). In high ETL states, Kerala again shows the highest prevalence at 3.3% (95% CI: 2.4–4.6), followed by Goa at 1.1% (95% CI: 0.2–5.3) (Fig. 2 ). Health Seeking Behavior Healthcare utilization patterns among multimorbidity patients aged 30 and above highlight disparities in the preference for private as opposed to public healthcare facilities. In outpatient care, most patients with multimorbidity opt for private healthcare services, with 60.8% (95% CI: 55.9–65.5) using private providers, compared to 30.0% (95% CI: 25.6–34.8) who access public healthcare. A similar trend is observed in inpatient care, where 51.7% (95% CI: 43.0-60.4) of multimorbidity patients choose private facilities, while only 26.7% (95% CI: 20.3–34.2) utilize public hospitals (Summary Table 1 ). Among MPCE quintiles, there is a significant disparity in the utilization of public and private healthcare services. In outpatient care, most patients from the richest quintile use private health care facilities (65%) compared poorest quintiles (41%). This disparity is even more pronounced in inpatient care, where 64% of the richest patients utilize private facilities, compared to 38% of the poorest patients (Fig. 3 ). Out-of-Pocket-Expenditures Out-of-pocket expenditures (OOPE) are higher for patients with multimorbidity in both outpatient and inpatient care. In outpatient care, multimorbidity patients incur the highest OOPE, averaging Rs. 950 (95% CI: 887–1014), compared to Rs. 758 (95% CI: 734–782) for single chronic conditions and Rs. 709 (95% CI: 673–745) for acute illnesses. Similarly, in inpatient care, the OOPE for multimorbidity is considerably higher at Rs. 69,438 (95% CI: 59,913 − 78,963) compared to Rs. 32,542 (95% CI: 31,604 − 33,480) for single chronic conditions and Rs. 17,084 (95% CI: 16,415 − 17,753) for acute illnesses. The higher costs of medicines and diagnostics play a crucial role in the high OOPE for multimorbidity patients. In outpatient care, the OOPE for medicines is Rs. 682 (95% CI: 643–721) for multimorbidity patients, compared to Rs. 503 (95% CI: 489–517) for single chronic conditions and Rs. 405 (95% CI: 381–429) for acute illnesses. For inpatient care, medicine costs are similarly burdensome, with multimorbidity patients spending Rs. 21,106 (95% CI: 17,447 − 24,765), much higher than the Rs. 11,154 (95% CI: 10,756 − 11,552) spent by patients with single chronic conditions and Rs. 5,408 (95% CI: 5,165-5,651) spent by those with acute illnesses. Diagnostic expenses were also higher with multimorbidity patients in inpatient care incurring Rs. 10,807 (95% CI: 8,994 − 12,620), compared to Rs. 4,364 (95% CI: 4,210-4,517) for single chronic conditions and Rs. 2,182 (95% CI: 2,087 − 2,276) for acute illnesses (Summary Table 1 ). Patients with NCDs incur higher OOPE for both outpatient and inpatient care across all illness categories. For multimorbidity in inpatient care, the OOPE for those with NCDs is Rs. 75,882, compared to Rs. 53,196 for patients without NCDs. There is a considerable disparity between the OOPE in private and public healthcare facilities. In outpatient care, multimorbidity patients using private healthcare services face an OOPE of Rs. 1,159 compared to just Rs. 485 in public facilities. In inpatient care, the difference is even more striking, with private healthcare costing Rs. 97,211, while public care amounts to Rs. 13,876. Rural-urban disparities in OOPE are evident, with urban residents facing higher expenses in outpatient care across all illness categories. For multimorbidity, urban patients incur Rs. 1,181 compared to Rs. 731 for their rural counterparts. However, in inpatient care, rural patients with multimorbidity bear a heavier burden, with OOPE reaching Rs. 80,822, compared to Rs. 56,171 for urban patients. Socioeconomic disparities are also pronounced, with patients from the richest MPCE quintile incurring significantly higher OOPE for multimorbidity compared to the poorest quintile. Moreover, male patients and those in the older age group (70 + years) report higher OOPE for multimorbidity, reflecting their increased healthcare needs and associated financial burden (Table 3 ). Table 3 Average Out of pocket expenditure for outpatient care in last 15 days and for inpatient care in the last 365 days among patients aged 30 and above by type of illnesses and background characteristics, NSS 75th Round Household Social Consumption in India: Health Survey Data, 2017–2018 Background Characteristics OOPE for Outpatient Care (Rs.) (last 15 days reference period) OOPE for Inpatient Care (Rs.) (last 365 days reference period) Acute Illness Single Chronic Multimorbidity Overall Outpatient Care Acute Illness Single Chronic Multimorbidity Overall Inpatient Care Type of Illness Non-NCD 694 1007 1180 807 15461 26683 53196 20641 Have NCD 758 694 946 725 35864 39117 75882 39694 Level of Care Public 484 493 485 490 4054 8837 13876 6828 Private 929 950 1159 957 27032 46890 97211 39382 ETL State Group Low ETL State 719 1042 1627 893 17520 33420 75370 27350 Lower-middle ETL State 577 607 901 607 12842 26853 43823 20618 Higher-middle ETL State 671 686 923 698 17654 31804 79835 26256 High ETL State 835 660 935 729 16496 33896 60372 27761 Sector Rural 650 733 731 704 15110 28609 80822 23406 Urban 851 794 1181 837 20857 39191 56171 32287 MPCE Quintile Poorest 572 720 918 663 13440 23927 52306 19783 Poor 636 909 798 801 17784 28278 41240 24070 Middle 782 660 909 708 17251 29493 46371 24498 Rich 707 647 1169 696 14878 31645 116459 25611 Richest 870 852 909 862 21226 44728 69057 35986 Sex Male 766 783 962 789 18911 37211 85212 31071 Female 671 737 942 729 15635 27243 51968 22245 Age Group 30–44 656 883 1085 766 14995 27886 45850 21928 45–59 782 685 812 721 17429 31108 74280 25604 60–69 607 759 1036 753 21070 36253 64009 31427 70+ 812 793 1017 821 17552 39816 89130 34251 India-Total 709 758 950 755 17084 32542 69438 26613 Source: Computed from unit-level NSSO 75th round Household Social Consumption in India: Health Survey Data, 2017–2018 OOPE for multimorbidity varies significantly across states with differing ETL and places of residence, revealing disparities in healthcare costs and accessibility. Patients in lower ETL states face higher financial burdens for outpatient care, with an average OOPE of Rs. 1,627, compared to Rs. 935 in higher ETL states. In inpatient care, the trend continues, with patients in lower ETL states incurring higher OOPE (Rs. 75,370) compared to those in higher ETL states (Rs. 60,372). Among low ETL states, Uttar Pradesh reports the highest OOPE for outpatient care at Rs. 2,392, followed by Assam at Rs. 996. For inpatient care, Assam stands out with an exceptionally high OOPE of Rs. 148,545, while Uttar Pradesh follows with Rs. 100,693. In lower-middle ETL states, Sikkim has the highest outpatient OOPE (Rs. 3,533), while Tripura shows the most substantial inpatient care burden (Rs. 2,52,668). Higher-middle ETL states report higher costs, with Delhi leading in outpatient OOPE (Rs. 2,349) and the Andaman & Nicobar Islands showing the highest inpatient OOPE (Rs. 3,07,008). In contrast, high ETL states demonstrate relatively lower variability in OOPE. Goa has the highest OOPE at Rs. 1,252 in outpatient while Punjab leads in inpatient care with an average OOPE of Rs. 1,14,233. These findings highlight the significant regional differences in healthcare costs for patients with multimorbidity, particularly between lower and higher ETL states (Fig. 4 ). Determinants of Out-of-pocket expenditure The log-linear regression analysis for overall outpatient care (26,905 patients) revealed significant increases in healthcare spending among multimorbidity patients. Multimorbidity patients with NCDs faced a 42% (exp(0.35)-1) higher likelihood of increased expenditure. Private healthcare utilization was a key driver of higher OOPE. Patients residing in low (Coeff: 0.41, 95% CI: 0.37, 0.46) and lower-middle ETL (Coeff: 0.38, 95% CI: 0.31, 0.44) states also experienced a greater financial burden than those in high ETL states. Among multimorbidity patients in outpatient care (2,055 observations), patients who use private healthcare facilities incur 146% (exp(0.90)-1) significantly higher OOPE compared to those using public healthcare. Multimorbidity patients in low ETL states experience 97% (exp(0.68)-1) higher OOPE for outpatient care compared to multimorbidity patients in high ETL states and the result is statistically significant (p < 0.001, 95% CI: 0.43, 0.93). The poorest rural multimorbidity patients incur 60% (exp(-0.93) − 1) lower OOPE compared to the richest urban patients. Female multimorbidity patients experience 15% (exp(-0.16)-1) lower OOPE compared to male multimorbidity patients. For inpatient care (38,822 patients), multimorbidity patients with NCDs are 2.3 times (exp (1.2)-1) more likely to bear higher healthcare costs compared to those without NCDs. Further regression among multimorbidity patients (363 observations) shows that patients who sought inpatient care in private institutions experienced 4.87 times (exp(1.77)-1) higher OOPE compared to those who utilized public healthcare facilities (95% CI: 1.44, 2.10) and result is highly statistically significant, indicating the substantial financial burden of private care. Multimorbidity patients from low ETL states have 51% (exp(0.41)-1) higher OOPE among inpatient care compared to those in high ETL states. The urban poor multimorbidity patients experience 53% (exp(-0.77)-1) lower OOPE compared to the richest urban residents. Female multimorbidity patients in inpatient care incur 35% (exp(-0.43)-1) lower OOPE compared to male patients. Multimorbidity patients aged 70 and above face 68% (exp(0.52)-1) higher OOPE compared to those aged 30–44 (Table 4 ). Table 4 Log-linear Regression for Out of Pocket Health Expenditure for Outpatient (15 days reference period) and Inpatient Care (365days) among patients aged 30 and above, NSS 75th Round Household Social Consumption in India: Health Survey Data, 2017–2018 Explanatory Variables For Outpatient Care (last 15 days reference period) For Inpatient Care (last 365 days reference period) Only Multimorbidity Patient Overall Illness Only Multimorbidity Patient Overall Illness Coef. 95% CI Coef. 95% CI Coef. 95% CI Coef. 95% CI Interaction of Illnesses Type and NCDs Occurrence Acute#Non-NCDs® 0 0.00,0.00 0 0.00,0.00 Acute#NCDs -0.13*** -0.20,-0.06 0.35*** 0.28,0.43 Single-Chronic#Non-NCDs 0.09** 0.03,0.14 0.45*** 0.42,0.48 Single-Chronic#NCDs -0.09*** -0.14,-0.05 0.67*** 0.64,0.71 Multimorbidity# Non-NCDs 0.51** 0.14,0.88 0.88*** 0.65,1.11 Multimorbidity# NCDs 0.35*** 0.28,0.42 1.20*** 1.04,1.36 Type of Illness Non-NCD® 0 0.00,0.00 0 0.00,0.00 Have NCD -0.13 -0.47,0.21 0.16 -0.14,0.46 Level of Care Public® 0 0.00,0.00 0 0.00,0.00 0 0.00,0.00 0 0.00,0.00 Private 0.90*** 0.79,1.02 0.82*** 0.78,0.85 1.77*** 1.44,2.10 1.84*** 1.82,1.87 ETL State Group Low ETL State 0.68*** 0.43,0.93 0.41*** 0.37,0.46 0.41* 0.02,0.81 0.07*** 0.03,0.10 Lower-middle ETL State 0.17 -0.16,0.51 0.38*** 0.31,0.44 -0.12 -0.73,0.50 0.16*** 0.12,0.21 Higher-middle ETL State -0.01 -0.12,0.10 0.02 -0.02,0.06 -0.03 -0.35,0.30 -0.06** -0.09,-0.02 High ETL State® 0 0.00,0.00 0 0.00,0.00 0 0.00,0.00 0 0.00,0.00 Interaction of Place of Residence and MPCE Quintile Rural # Poorest -0.93*** -1.40,-0.45 -0.36*** -0.44,-0.28 -0.19 -1.07,0.68 -0.21*** -0.27,-0.14 Rural # Poor -0.51** -0.84,-0.18 -0.34*** -0.42,-0.26 -0.14 -0.88,0.60 -0.12*** -0.18,-0.05 Rural # Middle -0.40** -0.66,-0.15 -0.28*** -0.34,-0.21 -0.53 -1.18,0.11 -0.14*** -0.20,-0.08 Rural # Rich -0.30** -0.51,-0.09 -0.21*** -0.28,-0.15 0 -0.60,0.60 -0.13*** -0.19,-0.08 Rural # Richest -0.21* -0.37,-0.04 -0.11*** -0.17,-0.05 -0.25 -0.72,0.21 0 -0.06,0.05 Urban # Poorest -0.25* -0.49,-0.02 -0.33*** -0.39,-0.26 -0.31 -0.90,0.28 -0.20*** -0.26,-0.14 Urban # Poor -0.31** -0.51,-0.11 -0.26*** -0.33,-0.19 -0.77** -1.34,-0.21 -0.15*** -0.21,-0.09 Urban # Middle -0.29** -0.47,-0.11 -0.24*** -0.30,-0.17 -0.3 -0.86,0.27 -0.06* -0.12,-0.00 Urban # Rich -0.23* -0.41,-0.05 -0.14*** -0.20,-0.07 -0.15 -0.68,0.39 -0.02 -0.08,0.04 Urban # Richest® 0 0.00,0.00 0 0.00,0.00 0 0.00,0.00 0 0.00,0.00 Sex Male® 0 0.00,0.00 0 0.00,0.00 0 0.00,0.00 0 0.00,0.00 Female -0.16** -0.26,-0.07 -0.10*** -0.13,-0.07 -0.43** -0.71,-0.15 -0.14*** -0.16,-0.11 Age Group 30–44® 0 0.00,0.00 0 0.00,0.00 0 0.00,0.00 0 0.00,0.00 45–59 -0.07 -0.30,0.16 0 -0.05,0.04 0.57** 0.16,0.97 0 -0.03,0.03 60–69 0 -0.23,0.23 0.02 -0.03,0.07 0.39 -0.02,0.79 0.09*** 0.05,0.13 70+ 0.09 -0.15,0.32 0.09*** 0.04,0.15 0.52* 0.12,0.92 0.17*** 0.12,0.21 Observations 2055 26905 363 38822 Source: Computed from unit-level NSSO 75th round Household Social Consumption in India: Health Survey Data, 2017–2018; Note: * p < 0.05, ** p < 0.01, *** p < 0.001; and ® is reference category Incidence and intensity of CHE The incidence of CHE is highest among multimorbidity patients, particularly in outpatient care, where 51.3% of these patients experience CHE compared to those with acute or single chronic conditions. In inpatient care, the burden is even more significant, with 69.6% of multimorbidity patients facing CHE, and the mean positive overshoot (MPO) reaching 66.0%. For multimorbidity patients with NCDs, the incidence rises to 75%, with an intensity of 66% MPO in inpatient care. Geographic and socioeconomic disparities further intensify the incidence of CHE. Multimorbidity patients in rural areas face higher CHE in inpatient care (79.9%) compared to their urban counterparts (58%). Elderly individuals experience the highest CHE incidence in inpatient care (80%), while those in the lowest economic quintiles report the highest CHE rates in both outpatient (75%) and inpatient care (71%). For the poorest quintile, the MPO is for inpatient care. Across all ETL state groups, multimorbidity patients consistently experience the highest CHE incidence compared to those with acute and single chronic conditions. In low ETL states, outpatient CHE for multimorbidity patients is the most severe, with incidences reaching 89% and an MPO of 25% (Table 5 ). In states like Assam and Odisha, multimorbidity patients face a staggering 100% CHE incidence in both outpatient and inpatient care. Similarly, in Bihar, 100% of patients encounter CHE in inpatient settings. Lower-middle ETL states, including Sikkim and Manipur, also report a 100% CHE incidence across both care types. In higher-middle ETL states, Telangana and Karnataka show alarming inpatient CHE incidences of 100% and 92%, respectively. While high ETL states such as Punjab and Himachal Pradesh report lower outpatient CHE incidences (13% and 26%), they still exhibit high inpatient CHE rates, with 94% and 84% of multimorbidity patients facing CHE (Fig. 5 ). Determinants of CHE The logistic regression analysis revealed key predictors of catastrophic health expenditure (CHE) among patients in outpatient care and inpatient care in India (Table 6 ). Among outpatient cases (26,905 patients), multimorbidity patients with NCDs had a 33% higher likelihood of incurring CHE (OR: 1.33, 95% CI: 1.18–1.50) than those with acute conditions. For inpatient care (38,822 patients), the financial burden was even more pronounced, with multimorbidity patients having NCDs being 5.9 times more likely to face CHE (p < 0.001, 95% CI: 4.25–8.20). The type of healthcare facility chosen was also a critical factor influencing CHE. Outpatients opting for private healthcare were 2.6 times more likely to experience CHE than those seeking care in public facilities (OR: 2.60, 95% CI: 2.45–2.76). Inpatient care at private facilities posed an even greater risk, with patients being 9.85 times more likely to encounter CHE compared to those using public healthcare. Geographical disparities further impacted the CHE incidence. Patients residing in low ETL states had a 67% higher likelihood of incurring CHE in outpatient care compared to those in high ETL states. Also, the economic and residential settings played a significant role. The poorest rural outpatients were 5.5 times more likely to experience CHE than their wealthier urban counterparts (OR: 5.49, 95% CI: 4.73–6.36), indicating the financial vulnerability of disadvantaged populations in India. The analysis highlighted a significant disparity in the likelihood of CHE among different demographic groups. The poorest rural residents were found to have 6.73 times greater likelihood of incurring CHE compared to the wealthiest urban residents. Gender differences also emerged, with females demonstrating a 15% lower likelihood of facing CHE compared to their male counterparts (OR: 0.85, 95% CI: 0.81–0.89). Age was another significant factor influencing the likelihood of CHE. Individuals aged 60 to 69 were 22% more likely to experience CHE, while those aged 70 and above faced a 20% higher likelihood compared to individuals aged 30 to 44. Table 6 Logistic Regression for Catastrophic Health Expenditure (CHE) Incidence (at 10% threshold) for Outpatient Care (in last 15 days) and Inpatient Care (365 days) among patients aged 30 and above, NSS 75th Round Household Social Consumption in India: Health Survey Data, 2017–2018 Explanatory Variables For Outpatient Care (last 15 days reference period) For Inpatient Care (last 365 days reference period) Only Multimorbidity Patient Overall Illness Only Multimorbidity Patient Overall Illness OR 95% CI OR 95% CI OR 95% CI OR 95% CI Interaction of Illnesses Type and NCDs Occurrence Acute#Non-NCDs® 1 1.00,1.00 1 1.00,1.00 Acute#NCDs 0.82** 0.72,0.94 1.48*** 1.29,1.70 Single-Chronic#Non-NCDs 0.96 0.88,1.06 1.91*** 1.81,2.02 Single-Chronic#NCDs 0.76*** 0.70,0.82 2.46*** 2.31,2.61 Multimorbidity# Non-NCDs 2.01* 1.06,3.79 2.48*** 1.60,3.84 Multimorbidity# NCDs 1.33*** 1.18,1.50 5.90*** 4.25,8.20 Type of Illness Non-NCD® 1 1.00,1.00 1 1.00,1.00 Have NCD 0.7 0.36,1.35 2.02* 1.06,3.84 Level of Care Public® 1 1.00,1.00 1 1.00,1.00 1 1.00,1.00 1 1.00,1.00 Private 4.31*** 3.44,5.40 2.60*** 2.45,2.76 14.06*** 6.88,28.73 9.85*** 9.33,10.40 Epidemiological Transition Level (ETL) State Low ETL State 2.27** 1.34,3.84 1.67*** 1.55,1.81 2.33 0.93,5.85 1.09* 1.02,1.17 Lower-middle ETL State 0.92 0.48,1.76 1.34*** 1.19,1.51 0.8 0.22,2.98 1.01 0.92,1.10 Higher-middle ETL State 1.05 0.86,1.29 1.09* 1.02,1.16 0.77 0.39,1.52 0.99 0.93,1.06 High ETL State® 1 1.00,1.00 1 1.00,1.00 1 1.00,1.00 1 1.00,1.00 Interaction of Place of Residence and MPCE Quintile Rural # Poorest 3.64* 1.36,9.73 5.49*** 4.73,6.36 4.21 0.34,52.67 6.73*** 5.95,7.61 Rural # Poor 2.96*** 1.57,5.57 3.26*** 2.85,3.73 6.01 0.70,51.58 4.19*** 3.73,4.71 Rural # Middle 4.10*** 2.47,6.81 3.26*** 2.89,3.69 1.34 0.33,5.47 3.34*** 3.00,3.72 Rural # Rich 2.70*** 1.81,4.04 2.62*** 2.33,2.95 5.55* 1.24,24.85 2.77*** 2.49,3.07 Rural # Richest 2.21*** 1.62,3.02 2.12*** 1.90,2.36 1.08 0.40,2.90 2.23*** 2.03,2.46 Urban # Poorest 4.58*** 2.84,7.38 2.51*** 2.23,2.83 1.59 0.45,5.67 3.08*** 2.77,3.44 Urban # Poor 1.76** 1.20,2.59 1.79*** 1.59,2.02 0.33 0.10,1.05 2.01*** 1.80,2.25 Urban # Middle 1.48* 1.04,2.10 1.51*** 1.34,1.70 0.7 0.22,2.23 1.75*** 1.57,1.95 Urban # Rich 1.56* 1.11,2.20 1.40*** 1.24,1.57 0.98 0.33,2.95 1.41*** 1.27,1.58 Urban # Richest® 1 1.00,1.00 1 1.00,1.00 1 1.00,1.00 1 1.00,1.00 Sex Male® 1 1.00,1.00 1 1.00,1.00 1 1.00,1.00 1 1.00,1.00 Female 0.85 0.71,1.03 0.89*** 0.84,0.93 0.58 0.31,1.05 0.85*** 0.81,0.89 Age Group 30–44® 1 1.00,1.00 1 1.00,1.00 1 1.00,1.00 1 1.00,1.00 45–59 0.76 0.48,1.19 0.96 0.89,1.04 2.89* 1.19,7.02 1.07* 1.01,1.13 60–69 0.8 0.51,1.25 0.97 0.90,1.05 2.41* 1.02,5.66 1.22*** 1.14,1.31 70+ 0.78 0.50,1.24 1.05 0.96,1.15 2.60* 1.14,5.94 1.20*** 1.11,1.30 Observations 2055 26905 363 38822 Source: Computed from unit-level NSSO 75th round Household Social Consumption in India: Health Survey Data, 2017–2018; Note: * p < 0.05, ** p < 0.01, *** p < 0.001; and ® is reference category Determinants of Intensity of CHE The linear regression analysis examined the factors that affect the intensity of CHE, focusing on the positive overshoot, among patients aged 30 and above in both outpatient and inpatient care in India (Table 7 ). Multimorbidity patients having NCDs were significantly associated with a higher CHE intensity in inpatient care (Coef: 32.54, 95% CI: 19.29–45.79), which indicates that the combination of multimorbidity and NCDs places patients at a much higher risk of catastrophic health expenditure, reflecting the compounded financial burden due to more complex care needs and long-term treatments. Low ETL states (Coef: 21.57, 95% CI: 7.36–35.79) show the most substantial impact on CHE intensity among multimorbidity patients in outpatient care, which underscores the geographical disparities in healthcare costs, with lower ETL states facing disproportionately high financial burdens for multimorbidity patients. Private care usage statistically significantly increased CHE intensity in both outpatient (Coef: 5.73, 95% CI: 2.62–8.84) and inpatient care (Coef: 24.75, 95% CI: 21.15–28.34), suggesting that those seeking care from private providers are more likely to experience higher out-of-pocket spending. The interaction analysis between MPCE quintiles and sectors reveals that patients from the rural poorest MPCE quintile have a higher likelihood of having a higher intensity of CHE followed by the rural poor and rural middle MPCE, compared to their wealthier urban counterparts, both in outpatient (Coefficient: 25.63, 95% CI: 18.80, 32.47) and inpatient care settings (Coefficient: 26.65, 95% CI: 19.72, 33.58). The poorest urban also faced significant financial burdens compared to the urban richest. Table 7 Linear Regression for Intensity of Catastrophic Health Expenditure for Outpatient (15 days) and Inpatient Care (365 days) among patients aged 30 and above, NSS 75th Round Household Social Consumption in India: Health Survey Data, 2017–2018 Explanatory Variables Outpatient Care (last 15 days reference period) Inpatient Care (last 365 days reference period) Only Multimorbidity Patient Overall Illness Only Multimorbidity Patient Overall Illness Coef. 95% CI Coef. 95% CI Coef. 95% CI Coef. 95% CI Interaction of Illnesses Type and NCDs Occurrence Acute#Non-NCDs® 0 0.00,0.00 0 0.00,0.00 Acute#NCDs -2.73 -9.17,3.71 9.77* 1.48,18.06 Single-Chronic#Non-NCDs 4.26 -0.18,8.70 9.16*** 5.71,12.61 Single-Chronic#NCDs -1.53 -5.23,2.16 23.88*** 20.30,27.46 Multimorbidity# Non-NCDs 17.71 -8.52,43.94 37.76*** 16.47,59.05 Multimorbidity# NCDs 0.39 -5.30,6.08 32.54*** 19.29,45.79 Have NCDs? Non-NCDs® 0 0.00,0.00 0 0.00,0.00 NCDs -9.45 -30.08,11.17 -8.04 -38.96,22.88 Level of Care Public® 0 0.00,0.00 0 0.00,0.00 0 0.00,0.00 0 0.00,0.00 Private 0 -8.67,8.68 5.73*** 2.62,8.84 25.69 -18.10,69.47 24.75*** 21.15,28.34 Epidemiological Transition Level (ETL) State Low ETL State 21.57** 7.36,35.79 6.75*** 2.96,10.53 12.08 -26.77,50.92 -2.64 -6.75,1.47 Lower-middle ETL State -4.08 -27.79,19.63 5.14 -0.78,11.05 -22.29 -89.89,45.31 -5.79* -11.20,-0.37 Higher-middle ETL State -2.14 -9.27,4.99 -1.82 -5.22,1.57 -15.23 -48.96,18.50 -3.76 -7.71,0.19 High ETL State® 0 0.00,0.00 0 0.00,0.00 0 0.00,0.00 0 0.00,0.00 Interaction of Place of Residence and MPCE Quintile Rural # Poorest 17.9 -10.17,45.97 25.63*** 18.80,32.47 59.74 -18.22,137.70 26.65*** 19.72,33.58 Rural # Poor 11.92 -8.48,32.32 14.47*** 7.77,21.16 38.5 -29.55,106.56 17.36*** 10.54,24.18 Rural # Middle 4.02 -12.06,20.09 11.23*** 5.03,17.43 -4.61 -67.66,58.44 13.58*** 7.11,20.05 Rural # Rich 4.82 -8.94,18.59 9.15** 3.05,15.24 44.14 -11.49,99.77 8.13* 1.80,14.46 Rural # Richest -7.52 -18.86,3.83 10.60*** 4.86,16.35 -4.24 -50.50,42.02 5.45 -0.59,11.48 Urban # Poorest -6.88 -21.37,7.61 9.65** 3.47,15.83 47.81 -10.62,106.24 14.88*** 8.41,21.35 Urban # Poor -9.88 -24.01,4.25 4.4 -2.04,10.84 -21 -85.15,43.15 9.69** 2.78,16.60 Urban # Middle -12.25 -25.44,0.93 2.29 -4.08,8.67 5.42 -54.01,64.84 4.74 -1.99,11.47 Urban # Rich -3.62 -16.30,9.06 1.2 -5.33,7.73 5.99 -46.30,58.27 1.49 -5.39,8.36 Urban # Richest® 0 0.00,0.00 0 0.00,0.00 0 0.00,0.00 0 0.00,0.00 Sex Male® 0 0.00,0.00 0 0.00,0.00 0 0.00,0.00 0 0.00,0.00 Female 0.66 -5.87,7.19 -1.73 -4.29,0.83 -20.99 -47.85,5.86 -8.78*** -11.48,-6.07 Age Group 30–44® 0 0.00,0.00 0 0.00,0.00 0 0.00,0.00 0 0.00,0.00 45–59 -3.81 -18.28,10.67 -1.22 -4.84,2.39 32.96 -8.24,74.16 0.9 -2.46,4.27 60–69 -0.5 -15.05,14.06 1.69 -2.31,5.69 27.76 -13.60,69.11 3.39 -0.67,7.44 70+ 2.52 -12.47,17.52 -0.7 -5.07,3.67 12.09 -28.30,52.47 1.75 -2.75,6.24 Observations 984 10844 247 16513 Source: Computed from unit-level NSSO 75th round Household Social Consumption in India: Health Survey Data, 2017–2018; Note: * p < 0.05, ** p < 0.01, *** p < 0.001; and ® is reference category Discussions This study highlights the substantial financial burden faced by patients with multimorbidity in India, with OOPE and CHE far exceeding those incurred by individuals with single chronic conditions or acute illnesses. To date, there has been no peer-reviewed research offering a thorough examination of multimorbidity for both outpatient and inpatient cases separately. Furthermore, the absence of cross-state comparisons regarding CHE associated with OOPE has limited a comprehensive understanding of health system resilience and the development of policies aimed at mitigating financial hardship. Our research seeks to address these gaps by utilizing data from the National Sample Survey (2017-18), which is nationally representative. Our findings reveal that the prevalence of multimorbidity in outpatient care is six times higher than that in inpatient care. This observation aligns closely with the findings of Varanasi et al. (2024) study that conducted a systematic review and meta-analysis indicating a multimorbidity prevalence range of 1.16–65.9%. The significantly lower prevalence of multimorbidity among inpatients suggests an underutilization of hospital services, despite the evident healthcare needs. Barriers such as prohibitive costs, significant travel distances and prior high out-of-pocket expenses may contribute to this underutilization ( 14 , 28 ). Our research also highlights that multimorbidity prevalence is higher in urban areas compared to rural regions, with increases noted alongside rising income and age for both outpatient and inpatient care consistent with previous findings from nationally representative studies in India ( 16 , 29 – 31 ). The epidemiological transition plays a significant role in the observed prevalence of multimorbidity; our study indicates that individuals with NCDs exhibit higher rates of multimorbidity in both outpatient (9.4%) and inpatient care (2.5%). These findings are corroborated by a study, which analyzed nationally representative data from the World Health Organization ( 32 ). Multimorbidity presents distinct challenges in healthcare access, affordability, and financial protection across different levels of care. Patients with multimorbidity predominantly utilize private healthcare services for both outpatient and inpatient care, suggesting significant barriers to accessing public healthcare. Factors such as long wait times, perceived lower quality of care, and the lack of specialized services in the public sector contribute to this preference for private healthcare ( 33 , 34 ). This reliance on private healthcare exacerbates the financial burden on patients with multimorbidity, as private care is often associated with higher OOPE, placing a significant strain on these individuals ( 35 ). Our findings demonstrate substantial variability in multimorbidity prevalence across states, with economic factors and ETL classifications significantly influencing this condition in India. For instance, Kerala, classified as an economically advanced and high ETL state, shows a higher prevalence of multimorbidity, while states like Meghalaya and Nagaland, categorized as low ETL and economically disadvantaged, exhibit lower prevalence rates. This discrepancy can be partly explained by increased awareness and the inclusion of NCDs tracking in routine healthcare practices in more developed states, leading to better detection and diagnosis. Our findings are consistent with previous research that highlights the role of improved healthcare systems in detecting chronic conditions ( 36 , 37 ). Numerous studies indicate that OOPE serves as the primary means of healthcare financing in low- and middle-income countries ( 38 ). India mirrors this trend, with health expenditure accounting for 3.83% of GDP, leading to significant implications for healthcare funding in the country. Our findings align with previous studies indicating that patients with multimorbidity often resort to private healthcare, which leads to higher OOPE due to the lack of specialized public healthcare services ( 33 , 34 ), compared to those with single chronic conditions or acute illnesses. Specifically, OOPE for multimorbidity patients with NCDs (Rs. 75,882) in inpatient care is far exceeding the costs for patients with acute illnesses. Log-linear regression analysis indicates that multimorbidity patients with NCDs have a 42% higher likelihood of incurring higher outpatient expenses, while in inpatient care, these patients are 2 times more likely to experience higher costs compared to those with acute conditions. These results highlight the compounded financial strain faced by multimorbidity patients, particularly when NCDs are involved. A key factor contributing to these elevated costs is polypharmacy, as managing multiple chronic conditions often requires numerous medications. Polypharmacy increases the risks of adverse drug reactions, medication non-adherence, and harmful drug interactions ( 39 – 41 ), which can worsen patient outcomes, lead to more frequent hospital admissions, and escalate overall healthcare costs ( 42 ). Our analysis further identifies medication and diagnostic expenditures as the main drivers of high OOPE, consistent with previous research findings ( 43 ). The stark urban-rural and socioeconomic disparities in OOPE, particularly for inpatient care, underscore the need for targeted interventions. Patients from urban areas experience higher OOPE for outpatient care than rural across all illness categories, with the disparity most pronounced among multimorbidity patients. However, rural multimorbidity patients faced significantly higher inpatient OOPE (Rs. 80,822) than their urban counterparts (Rs. 56,171), likely due to travel and accommodation expenses, as well as limited local service availability and greater indirect costs ( 44 ). Socioeconomic inequalities manifest in OOPE for multimorbidity, with the wealthiest MPCE quintile incurring significantly higher costs compared to the poorest quintile. This discrepancy likely stems from the wealthiest quintile’s consistent preference for private healthcare facilities, which charge higher treatment costs than public services, in both outpatient and inpatient contexts. Previous research has suggested that the limited availability of NCD services in the public sector drives patients towards private hospitals, where inadequate health insurance coverage compounds their out-of-pocket costs ( 36 ). Additionally, males and older age groups (70 + years) report elevated OOPE for multimorbidity, reflecting increased healthcare needs arising from higher multimorbidity incidence. These findings align with global patterns where healthcare costs are closely linked to socioeconomic status, leading to increased OOPE disparities ( 45 , 46 ). Our analysis of CHE reinforces the argument that multimorbidity disproportionately affects the poorest quintiles, with high OOPE often leading to financial catastrophe, exacerbating the difficulties in managing complex health conditions ( 36 ). Specifically, our findings indicate that multimorbidity patients experience the highest CHE incidences in both outpatient (51.3%) and inpatient care (69.6%) settings due to a high proportion of OOPE. Demographic and epidemiological transitions further influence the distribution of health expenditures across disease and age groups. Patients with NCDs experience an even higher CHE incidence and intensity, particularly in inpatient settings, where CHE incidence reaches 75%, and the mean positive overshoot—a measure of financial intensity-is 66%. Logistic regression analysis indicates that multimorbidity patients with NCDs are 33% more likely to incur CHE in outpatient care and 5.9 times more likely in inpatient care compared to those without multimorbidity. Moreover, linear regression analysis underscores that multimorbidity significantly increases the severity of CHE. Additionally, the increased CHE incidence among patients utilizing private healthcare facilities demonstrates the inadequacy of current public healthcare infrastructure in managing complex cases of multimorbidity, especially in low-ETL states, with CHE incidence in private healthcare facilities reaching 63% in outpatient care and 87% in inpatient care, compared to 24% and 25%, respectively, in public facilities. Our logistic regression findings further reveal that outpatients opting for private care are 2.6 times more likely to incur CHE, while inpatients using private facilities face a 9.8 times higher likelihood of experiencing CHE. The study acknowledges a few limitations that should be considered when interpreting the findings. The first limitation of the present study included the morbidities and expenditures were self-reported which might have brought in measurement bias. Moreover, the collection of data over the last 15 days for outpatient care and 365 days for inpatient care may result in limited time frames for outpatient data and potential recall bias for inpatient data. Another limitation pertains to the findings related to average health expenditures, which should be approached cautiously due to observed skewness and high variability in the data. Conclusions Understanding health expenditure dynamics at the national level is crucial for informing effective policy responses. Our study sheds light on the prevalence of multimorbidity in India and its significant financial implications, particularly emphasizing the striking disparities in OOPE and CHE. It illustrates the financial strain that the increasing burden of multimorbidity imposes across various healthcare settings, socioeconomic groups and geographic regions. This complex scenario necessitates a comprehensive, multifaceted strategy to address the challenges of multimorbidity, particularly at the primary care level. The study highlights the importance of improving access to affordable, high-quality public healthcare services, especially for NCD management. With outpatient care contributing significantly to OOPE, enhancing primary care systems emerges as a critical intervention. Furthermore, extending financial protection schemes to cover outpatient care is essential to reduce OOPE and CHE for patients managing multiple chronic conditions. By bringing healthcare services closer to communities, this approach aims to prevent disease progression and reduce the financial burdens faced by individuals. Furthermore, expanding the Pradhan Mantri Jan Aarogya Yojana (PMJAY) to include outpatient care can help the healthcare system address a significant portion of CHE arising from outpatient expenditures. Such an expansion could include coverage for outpatient consultations, diagnostic tests, medications and routine procedures essential for managing multimorbidity. The compounded financial burden from polypharmacy, diagnostic expenses, and frequent hospitalizations highlights the urgent need for financial protection mechanisms and the inclusion of outpatient care under schemes like PMJAY ( 35 ). Medication costs represent a substantial portion of OOPE for those with multimorbidity, primarily due to the prevalence of polypharmacy, which can lead to increased morbidity, hospitalizations and higher overall healthcare expenditures. Managing polypharmacy demands careful consideration of the benefits and risks associated with multiple medications in patients who already bear significant financial and healthcare burdens ( 47 ). To mitigate the risks of polypharmacy, it is essential to conduct regular medication reviews, facilitate coordinated care among healthcare providers, educate patients on effective medication management and employ comprehensive medication management strategies that optimize therapeutic outcomes while minimizing potential adverse effects. Effective approaches for managing polypharmacy include routine medication evaluations, deprescribing when appropriate and adherence to clinical guidelines to refine pharmacotherapy ( 48 ). Implementing standardized treatment protocols and reducing unnecessary medications could also lower the risks associated with polypharmacy, thereby decreasing OOPE and improving treatment adherence. Integrating preventive strategies, such as early detection and effective management of NCDs and multimorbidity, into the existing Comprehensive Primary Health Care (CPHC) framework( 49 ) is essential. Emphasizing these measures through public health campaigns and regular screenings can help prevent the progression to multimorbidity, improving overall health outcomes. At a health systems level, there is an urgent need to integrate various health programs and interventions. Leveraging support of digital health technologies under the Ayushman Bharat Digital Health Mission like Electronic Health Records (EHR) and Telemedicine consultations with Specialists can bridge existing gaps in the continuum of care and availability of Human Resources for Health. By strategically implementing these technologies, patient care can be streamlined, facilitating remote consultations with specialists and thereby bringing specialized care closer to communities. This initiative has the potential to enhance both the affordability and accessibility of healthcare for patients managing multimorbidity. Declarations Ethics approval and consent to participate Ethical approval was not required for this study as it used only anonymized data from secondary sources, publicly available from the National Sample Survey Office (NSSO). Therefore, no ethical issues or approval from an ethics committee, nor consent to participate, were necessary. All methods were conducted in accordance with relevant guidelines and regulations. Consent for publication Not applicable. Availability of data and materials The datasets were derived from sources in the public domain: NSSO: Social Consumption and Health 75th round and can be downloaded upon registration and filling in basic details at https://microdata.gov.in/nada43/index.php/catalog/152 Competing interests We declare no conflict of interest. Funding Not applicable. Acknowledgement Not applicable. 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Reducing inappropriate polypharmacy: The process of deprescribing. Vol. 175, JAMA Internal Medicine. 2015. Ministry of Health and Family Welfare. Ayushman Bharat: comprehensive primary health care through health and wellness centers operational guidelines. [Internet]. New Delhi; 2018 [cited 2024 Nov 10]. Available from: https://www.nhm.gov.in/New_Updates_2018/NHM_Components/ Health_System_Stregthening/Comprehensive_primary_health_care/letter/Operational_Guidelines_For_CPHC.pdf Table 5 Table 5 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table5.docx Cite Share Download PDF Status: Published Journal Publication published 15 Jan, 2025 Read the published version in BMC Health Services Research → Version 1 posted Editorial decision: Revision requested 13 Nov, 2024 Editor assigned by journal 12 Nov, 2024 Submission checks completed at journal 12 Nov, 2024 First submitted to journal 10 Nov, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5425175","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":377557870,"identity":"aa433dc3-d885-47b6-be08-ffd320880950","order_by":0,"name":"Sudheer Kumar Shukla","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYHACMzDJxgwkPoAY7KRoYZwB00uUFhBg5gGTBNTLz0je9uBDxT0GPnbew59tfm2T52NmYPzwMQe3FoMbaeWGM84UA93Dlyad23fbsI2ZgVly5jY8WiRyzKR52xKAWnjMmHN7bjMCtbAx8+LRIj8DqOXvP7AW48+WPbftCWphuAHUwtgA1mIgzfDjdiJBLQZnnpVJ9hxL4AH5RbK34XZyGzNjM16/yLcnb5P4UZMgJ99/9vCHH39u285vbz744SM+h0EBDxgxtoHYjA2E1cN1MfwhVvEoGAWjYBSMJAAAXRNE3J03PmAAAAAASUVORK5CYII=","orcid":"","institution":"Health Systems Transformation Platform","correspondingAuthor":true,"prefix":"","firstName":"Sudheer","middleName":"Kumar","lastName":"Shukla","suffix":""},{"id":377557872,"identity":"61246169-321c-43d1-84d3-ad2b3c900708","order_by":1,"name":"Pratheeba John","email":"","orcid":"","institution":"Health Systems Transformation Platform","correspondingAuthor":false,"prefix":"","firstName":"Pratheeba","middleName":"","lastName":"John","suffix":""},{"id":377557874,"identity":"cadcea5e-65d3-468a-b38e-e1832e98ac01","order_by":2,"name":"Sakshi Khemani","email":"","orcid":"","institution":"Health Systems Transformation Platform","correspondingAuthor":false,"prefix":"","firstName":"Sakshi","middleName":"","lastName":"Khemani","suffix":""},{"id":377557876,"identity":"dbe6108e-f9f1-4b88-96f6-90793683bf1a","order_by":3,"name":"Ankur Shaji Nair","email":"","orcid":"","institution":"Health Systems Transformation Platform","correspondingAuthor":false,"prefix":"","firstName":"Ankur","middleName":"Shaji","lastName":"Nair","suffix":""},{"id":377557878,"identity":"a2231a63-c01f-433e-b010-7c2ae7e12aa3","order_by":4,"name":"Nishikant Singh","email":"","orcid":"","institution":"Health Systems Transformation Platform","correspondingAuthor":false,"prefix":"","firstName":"Nishikant","middleName":"","lastName":"Singh","suffix":""},{"id":377557879,"identity":"f829b9d2-6714-4c8e-8923-283f295dc339","order_by":5,"name":"Rajeev Sadanandan","email":"","orcid":"","institution":"Health Systems Transformation Platform","correspondingAuthor":false,"prefix":"","firstName":"Rajeev","middleName":"","lastName":"Sadanandan","suffix":""}],"badges":[],"createdAt":"2024-11-10 09:23:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5425175/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5425175/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12913-025-12206-w","type":"published","date":"2025-01-15T15:57:16+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":71610143,"identity":"53810ebc-99c6-43c6-9701-e51744a41dfc","added_by":"auto","created_at":"2024-12-17 06:41:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":413146,"visible":true,"origin":"","legend":"\u003cp\u003eFlow Chart of sample size for outpatient and inpatient care setting, NSS 75th Round Household Social Consumption in India: Health Survey Data, 2017–2018\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5425175/v1/f66f5a3f2091b4473488819f.png"},{"id":71608271,"identity":"527da975-aa4b-4e7c-8736-de475d23b1e8","added_by":"auto","created_at":"2024-12-17 06:33:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":124998,"visible":true,"origin":"","legend":"\u003cp\u003eMultimorbidity Prevalence in outpatient and inpatient care among patients aged 30 and above across Indian states, stratified by Epidemiological Transition Level (ETL), NSS 75th Round Household Social Consumption in India: Health Survey Data, 2017–2018\u003c/p\u003e\n\u003cp\u003eSource: Computed from unit-level NSSO 75th round Household Social Consumption in India: Health Survey Data, 2017–2018\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5425175/v1/fe91744136d8d0694f0080fd.png"},{"id":71608273,"identity":"d8972df8-5ec8-4164-9481-fe966364aa72","added_by":"auto","created_at":"2024-12-17 06:33:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":103353,"visible":true,"origin":"","legend":"\u003cp\u003eHealth Care Utilization by level of care among multimorbidity patients aged 30 and above in India, NSS 75th Round Household Social Consumption in India: Health Survey Data, 2017–2018\u003c/p\u003e\n\u003cp\u003eSource: Computed from unit-level NSSO 75th round Household Social Consumption in India: Health Survey Data, 2017–2018\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5425175/v1/af2bab57c1cd5d406eb4f88b.png"},{"id":71608275,"identity":"6aa8110d-ed95-4b0e-84f5-ac7904d85963","added_by":"auto","created_at":"2024-12-17 06:33:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":122269,"visible":true,"origin":"","legend":"\u003cp\u003eOOPE (Rs.) for Multimorbidity Patients in outpatient and inpatient care among patients aged 30 and above across Indian states, stratified by their Epidemiological Transition Level (ETL), NSS 75th Round Household Social Consumption in India: Health Survey Data, 2017–2018\u003c/p\u003e\n\u003cp\u003eSource: Computed from unit-level NSSO 75th round Household Social Consumption in India: Health Survey Data, 2017–2018\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5425175/v1/5f13580ea9628de707e6a49b.png"},{"id":71610144,"identity":"162e635f-a0fd-4f2c-8575-f0321527fb4e","added_by":"auto","created_at":"2024-12-17 06:41:44","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":132353,"visible":true,"origin":"","legend":"\u003cp\u003eCHE Incidence (%) for multimorbidity patients in outpatient and inpatient care among patients aged 30 and above across Indian states, stratified by their epidemiological transition level (ETL), NSS 75th Round Household Social Consumption in India: Health Survey Data, 2017–2018\u003c/p\u003e\n\u003cp\u003eSource: Computed from unit-level NSSO 75th round Household Social Consumption in India: Health Survey Data, 2017–2018\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5425175/v1/e898ebaea5d8b2c2065ce651.png"},{"id":74284538,"identity":"2c6eb7d3-61f2-435f-a5c9-479af0c9c643","added_by":"auto","created_at":"2025-01-20 16:08:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3650736,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5425175/v1/4400289c-74aa-42f9-8851-b9f01bb07c8a.pdf"},{"id":71608276,"identity":"649f08d7-e544-4e41-8ca8-6b56e0099d4f","added_by":"auto","created_at":"2024-12-17 06:33:44","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":228637,"visible":true,"origin":"","legend":"","description":"","filename":"Table5.docx","url":"https://assets-eu.researchsquare.com/files/rs-5425175/v1/8a1c452b16bf22d75dd1d28e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing the Financial Burden of Multimorbidity Among Patients Aged 30 and above in India","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe global increase in the prevalence of chronic diseases, compounded by multimorbidity, the coexistence of two or more chronic conditions in the same individual, poses significant management challenges for health systems worldwide, contributing to increased healthcare expenditures and reduced health outcomes for individuals (\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Multimorbidity poses unique challenges compared to comorbidity where secondary ailments accompany a primary condition. Each condition within multimorbidity exerts a significant and distinct impact on an individual's overall health status, contributing to the complexity of their management (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Approximately 37.2% of the global population experiences multimorbidity, with its implications varying widely across regions and socioeconomic groups (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). The coexistence of multiple conditions escalates healthcare costs, particularly for the uninsured (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn India, where total health expenditure accounts for 3.83% of the gross domestic product (GDP) and the level of out-of-pocket expenditure (OOPE) is 39.4% of total health expenditures.(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) The financial burden on individuals managing multiple chronic diseases is immense. The rising prevalence of non-communicable diseases (NCDs) has compounded this issue, contributing significantly to out-of-pocket expenditures (OOPE) and catastrophic health expenditure (CHE). Studies highlight that multimorbidity can intensify OOPE, ranging between five to ten times higher than the costs associated with treating singular conditions, thus accentuating equity concerns, particularly for lower socioeconomic groups (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). This disparity burdens individuals with increased hospital admissions, premature deaths, and fragmented care, impinging on their quality of life (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). The COVID-19 pandemic has further exposed vulnerabilities among individuals with multimorbidity, emphasizing the importance of comprehensive primary care in reducing hospital admissions (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). From a public health perspective, the growing prevalence of multimorbidity, combined with increased susceptibility to infectious diseases, reiterates the critical need to strengthen basic healthcare services to reduce the adverse impact of future epidemics and pandemics (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn India, the complexities of multimorbidity are exacerbated by disparities in healthcare access and financial inequalities, affecting various demographic segments, including the elderly, urban population, affluent, and those residing in low-income regions (\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Over time, the age differences in multimorbidity prevalence have narrowed largely due to its increase among younger adults over 30 years of age who are significant contributors to the workforce and household income thereby, necessitating targeted interventions due to its economic implications (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite the extensive literature on single chronic diseases, research examining multimorbidity\u0026rsquo;s distinct financial impact across outpatient and inpatient care is limited. Moreover, there is a notable gap in cross-state comparisons of CHE burden in India, particularly across states with differing Epidemiological Transition Levels (ETLs). While studies have examined the costs associated with multimorbidity, most focus on health system costs rather than individual expenditure burden (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Existing studies in India have often restricted the definition of multimorbidity to the coexistence of NCDs, overlooking chronic communicable and infectious diseases (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Further, the healthcare landscape in India, comprising both public and private sectors, poses varied financial implications. The public healthcare system, challenged with limited budgets, inadequate infrastructure, insufficient human resources, and shortages of crucial medical supplies and equipment, struggles to cater to rural and marginalized populations. As a result, individuals seek care at private facilities that operate largely without regulation, imposing significant financial burdens on individuals and families, particularly for patients with multiple chronic conditions, more so for people from weaker economic backgrounds (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study seeks to address these gaps by leveraging nationally representative data from the National Sample Survey (2017\u0026ndash;2018), providing a comprehensive analysis of the financial implications of managing multimorbidity in India. This research adopts a broader definition of multimorbidity endorsed by the World Health Organization (WHO), aiming to provide a comprehensive national-level analysis encompassing all eligible populations at risk of multimorbidity. We address critical gaps observed in previous studies that are confined to specific states, diseases, and older populations (\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan additionalcitationids=\"CR18 CR19 CR20\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Our research seeks to assess multimorbidity prevalence, healthcare utilization, and the predictors of out-of-pocket and catastrophic health expenditures for multimorbidity.\u003c/p\u003e \u003cp\u003eThe research also investigates both inpatient and outpatient care domains, tackling age-specific healthcare challenges and integrating variables such as socioeconomic status, and epidemiological transition level (ETL) state groups to provide a holistic understanding of multimorbidity in India. We hypothesize that patients with multimorbidity experience significantly higher OOPE and CHE compared to those with single chronic conditions or acute illnesses and that economic disparities and ETL state classifications influence these financial outcomes. By adopting a comprehensive approach, this research endeavours to generate evidence-based insights and recommendations aimed at alleviating the financial strain on multimorbidity patients. These insights hold the potential to inform national health programs like the Ayushman Bharat in India, contributing towards the enhancement of healthcare delivery and financial protection for individuals coping with multimorbidity.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eDefining multimorbidity\u003c/h2\u003e\n \u003cp\u003eThe primary focus of this paper revolves around multimorbidity, which denotes the simultaneous presence of two or more chronic health conditions in an individual. Chronic health conditions, as outlined by the WHO, include cardiovascular diseases, cancer, chronic respiratory conditions, and diabetes (\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e). These conditions are characterized by their persistent and long-lasting nature, significantly impeding physical, mental, and social well-being, often resulting in prolonged functional limitations. Given their complex nature, chronic health conditions necessitate continuous medical attention and management of symptoms and complications.\u003c/p\u003e\n \u003cp\u003eThe definition of chronic health conditions may vary across studies and healthcare settings. For instance, Pless and Douglas (1971) defined chronic health conditions as ailments lasting longer than three months or requiring continuous hospitalization for over a month (\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e) Centers for Disease Control and Prevention (CDC) broadly defines chronic diseases as conditions lasting more than a year, requiring ongoing medical attention, or limiting daily activities, or both (\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e). The National Sample Survey Office (NSSO) in India identifies an ailment as chronic if symptoms persist for more than a month or if treatment continues for a month or more on the date of the survey(\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eFor this research, the NSS data was screened, and the following criteria were used to identify patients with chronic ailments:\u003c/p\u003e\n \u003cp\u003e\u003cspan\u003e1. For outpatient care, patients experiencing symptoms persisting for more than one month at the time of the survey, while for inpatient care, patients taking a course of treatment on medical advice for one month or more and continuing on the date of the survey were considered. Cases of acute illnesses like fevers, malaria, diarrhoea, and worm infections were excluded.\u003cbr\u003e\u003c/span\u003e\u003cspan\u003e2. Patients with definite diagnosis of diseases such as Tuberculosis, Cancers, Bleeding Disorders, Diabetes, Stroke, Hypertension, Heart Disease, Bronchial Asthma etc. irrespective of the duration of illness, if the ailment persists.\u003cbr\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003eSubsequently, the data of patients with chronic ailments was further screened to identify patients actively seeking treatment for two or more different chronic conditions in the Outpatient and Inpatient departments. The patients identified after the 2nd level screening of data were identified to be suffering from multimorbidity and were included within the scope of the study.\u003c/p\u003e\n \u003cp\u003eThis nuanced approach adopted in the research may facilitate a comprehensive understanding of multimorbidity, along with a deeper understanding of healthcare utilization patterns as well as health expenditures among patients with multimorbidity.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eOutcome variables\u003c/h3\u003e\n\u003cp\u003eThe analysis in this research focuses on several crucial outcome variables that are pivotal to assessing the implications of multimorbidity. These include the prevalence of multimorbidity, the associated OOPE, the incidence of CHE, and the intensity of CHE attributed to multimorbidity for both inpatient and outpatient care. These metrics are integral to comprehending the financial impact and burden posed by multimorbidity patients within the healthcare system.\u003c/p\u003e\n\u003ch3\u003eOut-of-pocket Expenditure (OOPE) and OOPE Share\u003c/h3\u003e\n\u003cp\u003eTotal OOPE includes all direct expenses incurred by an individual, both as an inpatient and outpatient, for medical care and transportation (non-medical) costs associated with accessing healthcare services. OOPE is calculated as:\u003c/p\u003e\n\u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{O}\\text{O}\\text{P}\\text{E}=\\sum\\:_{i=1}^{N}TH{E}_{i}-{R}_{i}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWhere THE\u003csub\u003ei\u003c/sub\u003e represents total health expenditure (medical\u0026thinsp;+\u0026thinsp;non-medical) for i\u003csup\u003eth\u003c/sup\u003e individual and R\u003csub\u003ei\u003c/sub\u003e is total amount reimbursed by the medical insurance company or employer for i\u003csup\u003eth\u003c/sup\u003e individual. i is an index denoting the individual, ranging from 1 to N, the sample size.\u003c/p\u003e\n\u003cp\u003eThe OOPE share signifies the proportion of an individual\u0026apos;s out-of-pocket health expenditure over 30 days relative to their total monthly household consumption expenditure. It serves as a metric to gauge the financial strain of healthcare expenses on an individual\u0026apos;s overall monthly budget. The formula for calculating the OOPE share is as follows:\u003c/p\u003e\n\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e$$\\:\\text{O}\\text{O}\\text{P}\\text{E}\\:\\text{s}\\text{h}\\text{a}\\text{r}\\text{e}=\\frac{1}{N}{\\sum\\:}_{i=1}^{N}{\\left(\\frac{OO{PE}_{\\left(30days\\right)i}}{MH{E}_{i}}*100\\right)}_{i}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003ewhere N represents the sample size, OOPE\u003csub\u003ei\u003c/sub\u003e is Out-of-pocket health expenditure for i\u003csup\u003eth\u003c/sup\u003e individual over 30 days period and MHE\u003csub\u003ei\u003c/sub\u003e is the monthly household consumption expenditure for the i\u003csup\u003eth\u003c/sup\u003e individual and i is an index denoting the individual, ranging from 1 to N.\u003c/p\u003e\n\u003ch3\u003eCatastrophic Health Expenditure (CHE)\u003c/h3\u003e\n\u003cp\u003eThe incidence of CHE is measured using the headcount method, which calculates the proportion of individuals within the sample who experience CHE. CHE is determined by assessing whether an individual\u0026apos;s OOPE share surpasses a predefined threshold, typically set at 10% in this study, relative to the monthly household consumption expenditure.\u003c/p\u003e\n\u003cp\u003eThe headcount formula is expressed as:\u003c/p\u003e\n\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e$$\\:\\text{H}\\text{e}\\text{a}\\text{d}\\text{c}\\text{o}\\text{u}\\text{n}\\text{t}=\\frac{1}{N}{\\sum\\:}_{i=1}^{N}{CHE}_{i}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003ewhere N represents the sample size, CHE\u003csub\u003ei\u003c/sub\u003e is 1 when the i\u003csup\u003eth\u003c/sup\u003e individual incurred CHE, and 0 otherwise.\u003c/p\u003e\n\u003ch3\u003eCHE Intensity\u003c/h3\u003e\n\u003cp\u003eMeasuring the incidence of CHE in silos does not reveal the depth or severity of these costs, specifically how much individual OOPE surpasses the catastrophic threshold typically set at 10%. To understand this phenomenon, the study measures CHE intensity, by employing two indicators: catastrophic overshoot and mean positive overshoot (MPO). Catastrophic overshoot denotes the average extent by which individual OOPE on illness, as a percentage of total individual expenditure, exceeds the set threshold (z). Conversely, the MPO captures the intensity of CHE through the average excess of OOPE on illness beyond the threshold among individuals who reported CHE incidence.\u003c/p\u003e\n\u003cp\u003eThe catastrophic overshoot and mean positive overshoot are mathematically represented as follows:\u003c/p\u003e\n\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e$$\\:\\text{O}\\text{v}\\text{e}\\text{r}\\text{s}\\text{h}\\text{o}\\text{o}\\text{t}=\\frac{1}{N}{\\sum\\:}_{i=1}^{N}CH{E}_{i}\\left(\\frac{OO{PE}_{i}}{MH{E}_{i}}-z\\right)$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e$$\\:\\text{M}\\text{e}\\text{a}\\text{n}\\:\\text{P}\\text{o}\\text{s}\\text{i}\\text{t}\\text{i}\\text{v}\\text{e}\\:\\text{O}\\text{v}\\text{e}\\text{r}\\text{s}\\text{h}\\text{o}\\text{o}\\text{t}=\\frac{1}{U}{\\sum\\:}_{i=1}^{U}CH{E}_{i}\\left(\\frac{OO{PE}_{i}}{MH{E}_{i}}-z\\right)$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eHere N represents the total sample size of individuals receiving outpatient or inpatient care, while U is the count of individuals experiencing CHE, OOPE\u003csub\u003ei\u003c/sub\u003e signifies an individual\u0026apos;s out-of-pocket health expenditure, MHE\u003csub\u003ei\u003c/sub\u003e is the Monthly Household Consumption Expenditure of the i\u003csup\u003eth\u003c/sup\u003e individual, and z signifies the 10% threshold for CHE. This threshold indicates that households allocating more than 10% of their monthly household consumption expenditure to healthcare expenses face a significant financial burden.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eExplanatory variables\u003c/h2\u003e\n \u003cp\u003eThe independent variables include the type of health care provider (public/private), NCD status, age, gender, monthly per capita consumption expenditure (MPCE), place of residence (rural/urban) and ETL States (\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e). ETL is described based on the ratio of disability-adjusted life years (DALYs) attributed to communicable diseases and against DALYs from NCDs and injuries combined. A low ratio signifies high ETL and vice versa (\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e). The interaction between \u0026apos;illness type\u0026apos; and \u0026apos;NCD occurrence\u0026apos; was incorporated into the model to explore how the presence of NCDs influences OOPE (and CHE in subsequent regression analysis) across different illness types (acute, single chronic, and multimorbidity). Similarly, the interaction between \u0026apos;place of residence\u0026apos; and \u0026apos;MPCE quintile\u0026apos; was examined to determine how the urban or rural status of the residence affects OOPE (and CHE) across various MPCE quintiles.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eStatistical Models\u003c/h3\u003e\n\u003cp\u003eThe analytical methods employed in this study include log-linear, logistic, linear regression models, aimed at comprehensively examining the influence of independent factors on OOPE, CHE and CHE intensity.\u003c/p\u003e\n\u003ch3\u003eLog-linear Regression for OOPE\u003c/h3\u003e\n\u003cp\u003eA log-linear regression model is used to analyze the impact of various explanatory variables on the logarithmic transformation of OOPE for each patient (i) in the study. The model is defined as:\u003c/p\u003e\n\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\u003cimg 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\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e\u003cbr\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eWhere \u0026alpha; is the intercept, \u0026beta;1 to \u0026beta;6 are coefficients for the respective predictors, and ϵ\u003csub\u003ei\u003c/sub\u003e is the error term. \u003cem\u003eInteraction_IllnessType_NCD_Occurrence\u003c/em\u003e represents the combined effect of illness type (Acute, Single Chronic, Multimorbidity) and NCD occurrence. \u003cem\u003eLevelOfCare\u003c/em\u003e distinguishes between public and private healthcare facilities. \u003cem\u003eETL_States\u003c/em\u003e refers to Epidemiological Transition Level State Groups. \u003cem\u003eInteraction_PlaceOfResidence_MPCE_Quintile\u003c/em\u003e combines residence (rural/urban) and economic status (MPCE Quintile). \u003cem\u003eSex\u003c/em\u003e and \u003cem\u003eAge_Group\u003c/em\u003e denote the patient\u0026apos;s gender and age group, respectively. The subscript \u0026lsquo;i\u0026rsquo; is used for i\u003csup\u003eth\u003c/sup\u003e patient.\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eLogistic Regression for CHE\u003c/h2\u003e\n \u003cp\u003eThe logistic regression model employed to analyze the incidence of CHE aims to evaluate the impact of various explanatory variables on the likelihood of CHE occurrence. The regression model for CHE is articulated as:\u003c/p\u003e\n \u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\u003cimg src=\"https://myfiles.space/user_files/122228_c8a1650c59388082/122228_custom_files/img1732879006.png\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere CHE\u003csub\u003ei\u003c/sub\u003e is the probability of patients incurring catastrophic health expenditure for outpatient and inpatient care. The model estimates the log odds of incurring CHE adjusted for a set of explanatory variables. All explanatory variables remain consistent with those of the log-linear regression equation.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eLinear Regression for CHE Intensity\u003c/h2\u003e\n \u003cp\u003eThe linear regression model employed to analyze Mean Positive Overshoot (MPO), which indicates the intensity of CHE aims to evaluate the impact of various explanatory variables. This model plays a crucial role in quantifying the extent of financial burden experienced by patients due to CHE, based on a range of explanatory factors. The linear regression model for MPO is defined as follows:\u003c/p\u003e\n \u003cdiv id=\"Equ7\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ7\" name=\"EquationSource\"\u003e\u003cimg src=\"https://myfiles.space/user_files/122228_c8a1650c59388082/122228_custom_files/img1732879005.png\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere MPO\u003csub\u003ei\u003c/sub\u003e is the Mean Positive Overshoot for the i\u003csup\u003eth\u003c/sup\u003e patient, which serves as an indicator of the intensity of CHE experienced. All explanatory variables remain consistent with those utilized in the log-linear regression equation.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eData\u003c/h2\u003e\n \u003cp\u003eThis study utilizes unit-level data sourced from the 75th round of the \u0026lsquo;Social Consumption in India: Health\u0026rsquo; survey conducted by the National Sample Survey Office (NSSO), Government of India, spanning from July 2017 to June 2018. The dataset captures information from 113,823 households, representing a total of 555,352 individuals, inclusive of 2,537 deceased members. Employing stratified random sampling techniques, the survey documented 43,240 instances of illness, encompassing 42,107 outpatient cases and 93,925 inpatient cases within the preceding 365 days. A wide array of data was collected spanning demographics, morbidity, mortality, hospitalizations, health insurance coverage, OOPE and healthcare utilization patterns(\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eHowever, our study narrows its focus to explore the prevalence of multimorbidity and the associated expenditures incurred for outpatient care (within the last 15 days) and inpatient care (within the last 365 days), with an emphasis on discerning disparities between single chronic conditions and acute illnesses vis-\u0026agrave;-vis multimorbidity conditions. Notably, the NSS data is available for inpatient or outpatient visits and is not aggregated at the individual level. In our analysis, we aggregate case-level data to individual levels thereby, individuals serving as the primary unit for estimating the outcome variables. All data analyses have been conducted utilizing STATA 16 software. Sampling weights are applied to ensure the representativeness of the sample, whereas the samples provided in the survey remain unweighted. The composition of the sample included in the study is presented in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003e\u0026lt;Figure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026gt;\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec15\"\u003e\n \u003ch2\u003ePrevalence of Multimorbidity\u003c/h2\u003e\n \u003cp\u003eOne in 17 patients in outpatient care have multimorbidity, with a prevalence of 6.1% (95% CI: 5.5\u0026ndash;6.7). In comparison, prevalence of single chronic conditions is observed in 63.3% of patients (95% CI: 61.9\u0026ndash;64.7), while acute illnesses account for 30.6% (95% CI: 29.3\u0026ndash;32.0). In the inpatient setting, the prevalence of multimorbidity is lower at 1.1% (95% CI: 0.9\u0026ndash;1.3), with single chronic conditions and acute illnesses reported at 58.0% (95% CI: 56.8\u0026ndash;59.1) and 41.0% (95% CI: 39.8\u0026ndash;42.1), respectively (Summary Table\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eOverview of Comprehensive Health Indicators for Outpatient and Inpatient Care among Patients aged 30 and Above, NSS 75th Round Household Social Consumption in India: Health Survey Data, 2017\u0026ndash;2018\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eType of Care\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eIndicators\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAcute Illness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSingle-Chronic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMultimorbidity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"10\"\u003e\n \u003cp\u003eFor Outpatient Care (last 15 days reference period)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrevalence %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.3\u0026ndash;32.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.9\u0026ndash;64.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.5\u0026ndash;6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePublic Health Care Utilization %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.7\u0026ndash;26.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.4\u0026ndash;31.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.6\u0026ndash;34.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrivate Health Care Utilization %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.2\u0026ndash;55.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.8\u0026ndash;62.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.9\u0026ndash;65.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedicine OOP (Rs.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e381\u0026ndash;429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e489\u0026ndash;517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e643\u0026ndash;721\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiagnostic OOP (Rs.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75\u0026ndash;89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74\u0026ndash;93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54\u0026ndash;85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal Medical OOP Expenditure (Rs.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e611\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e577\u0026ndash;644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e651\u0026ndash;694\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e813\u0026ndash;931\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal Non-Medical OOP Expenditure (Rs.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94\u0026ndash;104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81\u0026ndash;90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68\u0026ndash;87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal OOP Health Expenditure (Rs.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e673\u0026ndash;745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e734\u0026ndash;782\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e887\u0026ndash;1014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIncidence of CHE at 10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.0\u0026ndash;45.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.0\u0026ndash;41.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.0\u0026ndash;53.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean Intensity (Positive Overshoot) of CHE (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.0\u0026ndash;31.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.0\u0026ndash;32.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.0\u0026ndash;30.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"10\"\u003e\n \u003cp\u003eFor Inpatient Care (last 365 days reference period)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrevalence %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.8\u0026ndash;42.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.8\u0026ndash;59.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9\u0026ndash;1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePublic Health Care Utilization %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.1\u0026ndash;44.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.9\u0026ndash;40.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.3\u0026ndash;34.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrivate Health Care Utilization %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.9\u0026ndash;55.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.9\u0026ndash;56.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.0-60.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedicine OOP (Rs.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5165\u0026ndash;5651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10756\u0026ndash;11552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17447\u0026ndash;24765\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiagnostic OOP (Rs.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2087\u0026ndash;2276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4210\u0026ndash;4517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10807\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8994\u0026ndash;12620\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal Medical OOP Expenditure (Rs.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14389\u0026ndash;15650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28567\u0026ndash;30370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53820\u0026ndash;71978\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal Non-Medical OOP Expenditure (Rs.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1991\u0026ndash;2137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3002\u0026ndash;3145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5726\u0026ndash;7352\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal OOP Health Expenditure (Rs.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16415\u0026ndash;17753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31604\u0026ndash;33480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59913\u0026ndash;78963\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIncidence of CHE at 10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.0\u0026ndash;35.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.0\u0026ndash;51.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65.0\u0026ndash;74.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean Intensity (Positive Overshoot) of CHE (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.0\u0026ndash;34.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.0\u0026ndash;44.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.0\u0026ndash;77.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eSource: Computed from unit-level NSSO 75th round Household Social Consumption in India: Health Survey Data, 2017\u0026ndash;2018\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u0026lt; Summary Table\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e\u0026gt;\u003c/p\u003e\n \u003cp\u003ePatients with NCDs exhibit higher prevalence of multimorbidity, with 9.4% (95% CI: 8.5\u0026ndash;10.3) in outpatient care and 2.5% (95% CI: 2.0-3.1) in inpatient care, compared to those without NCDs (Table\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e). Multimorbidity is more prevalent in high ETL states, where the rates reach 12.2% (95% CI: 10.9\u0026ndash;13.6) in outpatient care and 2.1% (95% CI: 1.6\u0026ndash;2.7) in inpatient care, while low ETL states show the lowest prevalence. Urban areas report higher multimorbidity prevalence than rural areas in both outpatient and inpatient settings. Across MPCE quintiles, the richest quintile experiences the highest prevalence, whereas the poorest quintile shows the lowest for both care types. Gender differences are also observed: in outpatient care, males have a slightly lower multimorbidity prevalence (5.9%; 95% CI: 5.1\u0026ndash;6.8) compared to females (6.2%; 95% CI: 5.4\u0026ndash;7.1), while in inpatient care, males exhibit a higher prevalence (1.2%; 95% CI: 0.9\u0026ndash;1.5) compared to females (1.0%; 95% CI: 0.8\u0026ndash;1.2).\u003c/p\u003e\n \u003cp\u003eTable 2: Prevalence of Acute, Single Chronic and Multimorbidity Illnesses in outpatient and inpatient care among patients aged 30 and above by Background Characteristics, NSS 75th Round Household Social Consumption in India: Health Survey Data, 2017\u0026ndash;2018\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eBackground Characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003eFor Outpatient Care (last 15 days reference period)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003eFor Inpatient Care (last 365 days reference period)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eAcute Illness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eSingle-Chronic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eMultimorbidity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eOutpatient Sample (n)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eAcute Illness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eSingle-Chronic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eMultimorbidity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eInpatient Sample (n)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eType of Illness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNon-NCD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e64.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e61.9-66.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e35.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e33.1-37.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.2-0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e8272\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e54.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e53.6-56.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e44.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e43.3-46.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.4-0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e26392\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHave NCD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.2-12.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e79.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e78.0-80.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.5-10.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e18633\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.0-12.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e87.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e85.5-88.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.0-3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e12430\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eETL State Group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLow ETL State\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e47.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e44.7-50.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e51.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e49.0-54.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.4-1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e6419\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e39.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e37.7-41.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e59.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e57.5-61.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.4-0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e12768\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLower-middle ETL State\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e33.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27.9-39.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e63.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e57.6-69.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.6-6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e1655\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e45.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e42.4-49.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e53.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e50.0-56.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.5-2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e4907\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigher-middle ETL State\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e24.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23.0-26.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e68.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e66.2-70.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.9-8.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e11497\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e42.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e40.6-44.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e56.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e54.6-58.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.7-1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e14004\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigh ETL State\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18.1-22.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e67.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e65.4-70.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.9-13.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e7334\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e36.4-40.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e59.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e57.4-61.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.6-2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e7143\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSector\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e33.1-36.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e58.2-61.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.3-5.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e13402\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e42.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e40.6-43.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e57.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e55.5-58.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.7-1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e21258\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22.0-25.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e68.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e66.6-70.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.8-8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e13503\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e37.3-40.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e59.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e58.0-61.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.2-1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e17564\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMPCE Quintile\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePoorest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e41.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38.3-45.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e55.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e52.4-59.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.6-3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e4167\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e41.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38.8-44.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e57.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e54.8-60.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.5-1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e6900\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e37.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e34.4-41.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e57.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e53.7-60.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.7-6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e4348\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e41.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38.0-44.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e58.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e54.8-61.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.6-1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e6538\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e31.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28.4-34.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e64.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e61.5-67.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.3-5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e5355\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e42.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e39.5-44.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e57.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e54.5-59.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.7-1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e7864\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRich\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e24.7-30.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e66.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e63.3-69.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.1-7.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e5474\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e41.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38.9-44.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e57.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e54.8-60.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.6-1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e7979\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRichest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19.3-23.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e68.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e65.8-70.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.9-11.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e7561\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e37.0-40.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e59.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e57.6-61.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.2-2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e9541\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26.8-30.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e65.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e63.4-67.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.1-6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e12367\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e36.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e35.0-38.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e62.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60.6-63.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.9-1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e19464\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30.4-34.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e61.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e59.8-63.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.4-7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e14538\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e45.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e43.6-46.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e53.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e52.1-55.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.8-1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e19358\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge Group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e52.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e49.5-55.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e46.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e43.4-49.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.8-1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e5187\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e47.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e45.2-49.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e51.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e49.5-53.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.7-1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e11799\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e45-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27.9-32.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e64.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e61.9-66.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.0-7.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e10151\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e42.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e40.6-44.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e57.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e55.2-58.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.5-0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e15028\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60-69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16.0-20.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e73.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e71.2-76.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.8-9.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e6897\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e34.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e31.8-36.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e64.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e61.9-66.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.0-1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e6929\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e70+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14.8-20.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e71.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e68.4-74.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.1-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e4670\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27.6-33.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e67.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e64.0-70.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.6-3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e5066\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e29.3-32.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e63.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e61.9-64.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.5-6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e26905\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e41.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e39.8-42.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e58.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e56.8-59.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.9-1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e38822\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003eSource: Computed from unit-level NSSO 75th round Household Social Consumption in India: Health Survey Data, 2017\u0026ndash;2018\u003c/p\u003e\n \u003cdiv\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cp\u003e\u0026lt;Table\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e\u0026gt;\u003c/p\u003e\n \u003cp\u003eAge was found to be a crucial factor, with multimorbidity prevalence increasing as patients get older. The lowest prevalence in outpatient care is observed in the 30\u0026ndash;44 age group (1.2%; 95% CI: 0.8\u0026ndash;1.8), while the highest is among those aged 70 and above (10.9%; 95% CI: 9.1\u0026ndash;13.0). Inpatient care follows a similar trend, highlighting the growing healthcare needs and hospitalization risks faced by older adults with multimorbidity (Table\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eAcross ETL regions, a high variation between states can be seen for multimorbidity prevalence for both outpatient and inpatient care. In outpatient care, Assam reports the highest multimorbidity prevalence at 2.0% (95% CI: 0.5\u0026ndash;7.3) among low ETL states, followed by Madhya Pradesh at 1.6% (95% CI: 0.4\u0026ndash;6.3). Among lower-middle ETL states, Gujarat exhibits the highest prevalence at 3.5% (95% CI: 1.8\u0026ndash;6.9). In higher-middle ETL states, Lakshadweep stands out with a prevalence of 18.7% (95% CI: 9.6\u0026ndash;33.2), followed closely by Andhra Pradesh at 14.2% (95% CI: 11.3\u0026ndash;17.8). In high ETL states, Kerala has the highest prevalence of multimorbidity at 20.3% (95% CI: 18.2\u0026ndash;22.6), with Tamil Nadu following at 5.6% (95% CI: 3.6\u0026ndash;8.5). For inpatient care, in low ETL states, Rajasthan has the highest multimorbidity prevalence at 1.3% (95% CI: 0.6\u0026ndash;2.6), followed by Odisha at 1.1% (95% CI: 0.6-2.0). Among lower-middle ETL states, Gujarat reports the highest prevalence at 1.3% (95% CI: 0.6\u0026ndash;2.7), followed by Mizoram at 0.5% (95% CI: 0.1\u0026ndash;2.1). In higher-middle ETL states, Lakshadweep leads with a prevalence of 2.3% (95% CI: 0.5\u0026ndash;9.6), followed by West Bengal at 1.9% (95% CI: 1-3.4). In high ETL states, Kerala again shows the highest prevalence at 3.3% (95% CI: 2.4\u0026ndash;4.6), followed by Goa at 1.1% (95% CI: 0.2\u0026ndash;5.3) (Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u0026lt;Figure \u003cspan\u003e2\u003c/span\u003e\u0026gt;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\"\u003e\n \u003ch2\u003eHealth Seeking Behavior\u003c/h2\u003e\n \u003cp\u003eHealthcare utilization patterns among multimorbidity patients aged 30 and above highlight disparities in the preference for private as opposed to public healthcare facilities. In outpatient care, most patients with multimorbidity opt for private healthcare services, with 60.8% (95% CI: 55.9\u0026ndash;65.5) using private providers, compared to 30.0% (95% CI: 25.6\u0026ndash;34.8) who access public healthcare. A similar trend is observed in inpatient care, where 51.7% (95% CI: 43.0-60.4) of multimorbidity patients choose private facilities, while only 26.7% (95% CI: 20.3\u0026ndash;34.2) utilize public hospitals (Summary Table\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e). Among MPCE quintiles, there is a significant disparity in the utilization of public and private healthcare services. In outpatient care, most patients from the richest quintile use private health care facilities (65%) compared poorest quintiles (41%). This disparity is even more pronounced in inpatient care, where 64% of the richest patients utilize private facilities, compared to 38% of the poorest patients (Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u0026lt;Figure \u003cspan\u003e3\u003c/span\u003e\u0026gt;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\"\u003e\n \u003ch2\u003eOut-of-Pocket-Expenditures\u003c/h2\u003e\n \u003cp\u003eOut-of-pocket expenditures (OOPE) are higher for patients with multimorbidity in both outpatient and inpatient care. In outpatient care, multimorbidity patients incur the highest OOPE, averaging Rs. 950 (95% CI: 887\u0026ndash;1014), compared to Rs. 758 (95% CI: 734\u0026ndash;782) for single chronic conditions and Rs. 709 (95% CI: 673\u0026ndash;745) for acute illnesses. Similarly, in inpatient care, the OOPE for multimorbidity is considerably higher at Rs. 69,438 (95% CI: 59,913\u0026thinsp;\u0026minus;\u0026thinsp;78,963) compared to Rs. 32,542 (95% CI: 31,604\u0026thinsp;\u0026minus;\u0026thinsp;33,480) for single chronic conditions and Rs. 17,084 (95% CI: 16,415\u0026thinsp;\u0026minus;\u0026thinsp;17,753) for acute illnesses. The higher costs of medicines and diagnostics play a crucial role in the high OOPE for multimorbidity patients. In outpatient care, the OOPE for medicines is Rs. 682 (95% CI: 643\u0026ndash;721) for multimorbidity patients, compared to Rs. 503 (95% CI: 489\u0026ndash;517) for single chronic conditions and Rs. 405 (95% CI: 381\u0026ndash;429) for acute illnesses. For inpatient care, medicine costs are similarly burdensome, with multimorbidity patients spending Rs. 21,106 (95% CI: 17,447\u0026thinsp;\u0026minus;\u0026thinsp;24,765), much higher than the Rs. 11,154 (95% CI: 10,756\u0026thinsp;\u0026minus;\u0026thinsp;11,552) spent by patients with single chronic conditions and Rs. 5,408 (95% CI: 5,165-5,651) spent by those with acute illnesses. Diagnostic expenses were also higher with multimorbidity patients in inpatient care incurring Rs. 10,807 (95% CI: 8,994\u0026thinsp;\u0026minus;\u0026thinsp;12,620), compared to Rs. 4,364 (95% CI: 4,210-4,517) for single chronic conditions and Rs. 2,182 (95% CI: 2,087\u0026thinsp;\u0026minus;\u0026thinsp;2,276) for acute illnesses (Summary Table\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003ePatients with NCDs incur higher OOPE for both outpatient and inpatient care across all illness categories. For multimorbidity in inpatient care, the OOPE for those with NCDs is Rs. 75,882, compared to Rs. 53,196 for patients without NCDs. There is a considerable disparity between the OOPE in private and public healthcare facilities. In outpatient care, multimorbidity patients using private healthcare services face an OOPE of Rs. 1,159 compared to just Rs. 485 in public facilities. In inpatient care, the difference is even more striking, with private healthcare costing Rs. 97,211, while public care amounts to Rs. 13,876. Rural-urban disparities in OOPE are evident, with urban residents facing higher expenses in outpatient care across all illness categories. For multimorbidity, urban patients incur Rs. 1,181 compared to Rs. 731 for their rural counterparts. However, in inpatient care, rural patients with multimorbidity bear a heavier burden, with OOPE reaching Rs. 80,822, compared to Rs. 56,171 for urban patients. Socioeconomic disparities are also pronounced, with patients from the richest MPCE quintile incurring significantly higher OOPE for multimorbidity compared to the poorest quintile. Moreover, male patients and those in the older age group (70\u0026thinsp;+\u0026thinsp;years) report higher OOPE for multimorbidity, reflecting their increased healthcare needs and associated financial burden (Table\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eAverage Out of pocket expenditure for outpatient care in last 15 days and for inpatient care in the last 365 days among patients aged 30 and above by type of illnesses and background characteristics, NSS 75th Round Household Social Consumption in India: Health Survey Data, 2017\u0026ndash;2018\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eBackground Characteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eOOPE for Outpatient Care (Rs.) (last 15 days reference period)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eOOPE for Inpatient Care (Rs.) (last 365 days reference period)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAcute Illness\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSingle Chronic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMultimorbidity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOverall Outpatient Care\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAcute Illness\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSingle Chronic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMultimorbidity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOverall Inpatient Care\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eType of Illness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-NCD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e694\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e807\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15461\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20641\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHave NCD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e694\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e946\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e75882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39694\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLevel of Care\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePublic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8837\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6828\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrivate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e929\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e957\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39382\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eETL State Group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow ETL State\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e893\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e75370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27350\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLower-middle ETL State\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43823\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20618\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigher-middle ETL State\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e923\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31804\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26256\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh ETL State\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e660\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33896\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27761\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSector\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e80822\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23406\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e837\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e56171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32287\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMPCE Quintile\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePoorest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e720\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e663\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19783\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e798\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e801\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24070\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e782\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e660\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24498\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRich\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e647\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e696\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14878\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31645\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e116459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25611\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRichest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e870\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e862\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44728\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35986\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e85212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31071\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e737\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22245\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge Group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u0026ndash;44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e883\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27886\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21928\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u0026ndash;59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e782\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25604\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u0026ndash;69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31427\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e821\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e89130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34251\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndia-Total\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26613\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003eSource: Computed from unit-level NSSO 75th round Household Social Consumption in India: Health Survey Data, 2017\u0026ndash;2018\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u0026lt;Table\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e\u0026gt;\u003c/p\u003e\n \u003cp\u003eOOPE for multimorbidity varies significantly across states with differing ETL and places of residence, revealing disparities in healthcare costs and accessibility. Patients in lower ETL states face higher financial burdens for outpatient care, with an average OOPE of Rs. 1,627, compared to Rs. 935 in higher ETL states. In inpatient care, the trend continues, with patients in lower ETL states incurring higher OOPE (Rs. 75,370) compared to those in higher ETL states (Rs. 60,372). Among low ETL states, Uttar Pradesh reports the highest OOPE for outpatient care at Rs. 2,392, followed by Assam at Rs. 996. For inpatient care, Assam stands out with an exceptionally high OOPE of Rs. 148,545, while Uttar Pradesh follows with Rs. 100,693. In lower-middle ETL states, Sikkim has the highest outpatient OOPE (Rs. 3,533), while Tripura shows the most substantial inpatient care burden (Rs. 2,52,668). Higher-middle ETL states report higher costs, with Delhi leading in outpatient OOPE (Rs. 2,349) and the Andaman \u0026amp; Nicobar Islands showing the highest inpatient OOPE (Rs. 3,07,008). In contrast, high ETL states demonstrate relatively lower variability in OOPE. Goa has the highest OOPE at Rs. 1,252 in outpatient while Punjab leads in inpatient care with an average OOPE of Rs. 1,14,233. These findings highlight the significant regional differences in healthcare costs for patients with multimorbidity, particularly between lower and higher ETL states (Fig.\u0026nbsp;\u003cspan\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u0026lt;Figure \u003cspan\u003e4\u003c/span\u003e\u0026gt;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\"\u003e\n \u003ch2\u003eDeterminants of Out-of-pocket expenditure\u003c/h2\u003e\n \u003cp\u003eThe log-linear regression analysis for overall outpatient care (26,905 patients) revealed significant increases in healthcare spending among multimorbidity patients. Multimorbidity patients with NCDs faced a 42% (exp(0.35)-1) higher likelihood of increased expenditure. Private healthcare utilization was a key driver of higher OOPE. Patients residing in low (Coeff: 0.41, 95% CI: 0.37, 0.46) and lower-middle ETL (Coeff: 0.38, 95% CI: 0.31, 0.44) states also experienced a greater financial burden than those in high ETL states. Among multimorbidity patients in outpatient care (2,055 observations), patients who use private healthcare facilities incur 146% (exp(0.90)-1) significantly higher OOPE compared to those using public healthcare. Multimorbidity patients in low ETL states experience 97% (exp(0.68)-1) higher OOPE for outpatient care compared to multimorbidity patients in high ETL states and the result is statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, 95% CI: 0.43, 0.93). The poorest rural multimorbidity patients incur 60% (exp(-0.93) \u0026minus;\u0026thinsp;1) lower OOPE compared to the richest urban patients. Female multimorbidity patients experience 15% (exp(-0.16)-1) lower OOPE compared to male multimorbidity patients. For inpatient care (38,822 patients), multimorbidity patients with NCDs are 2.3 times (exp (1.2)-1) more likely to bear higher healthcare costs compared to those without NCDs. Further regression among multimorbidity patients (363 observations) shows that patients who sought inpatient care in private institutions experienced 4.87 times (exp(1.77)-1) higher OOPE compared to those who utilized public healthcare facilities (95% CI: 1.44, 2.10) and result is highly statistically significant, indicating the substantial financial burden of private care. Multimorbidity patients from low ETL states have 51% (exp(0.41)-1) higher OOPE among inpatient care compared to those in high ETL states. The urban poor multimorbidity patients experience 53% (exp(-0.77)-1) lower OOPE compared to the richest urban residents. Female multimorbidity patients in inpatient care incur 35% (exp(-0.43)-1) lower OOPE compared to male patients. Multimorbidity patients aged 70 and above face 68% (exp(0.52)-1) higher OOPE compared to those aged 30\u0026ndash;44 (Table\u0026nbsp;\u003cspan\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eLog-linear Regression for Out of Pocket Health Expenditure for Outpatient (15 days reference period) and Inpatient Care (365days) among patients aged 30 and above, NSS 75th Round Household Social Consumption in India: Health Survey Data, 2017\u0026ndash;2018\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eExplanatory Variables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eFor Outpatient Care (last 15 days reference period)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eFor Inpatient Care (last 365 days reference period)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eOnly Multimorbidity Patient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eOverall Illness\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eOnly Multimorbidity Patient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eOverall Illness\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoef.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoef.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoef.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoef.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eInteraction of Illnesses Type and NCDs Occurrence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAcute#Non-NCDs\u0026reg;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00,0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00,0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAcute#NCDs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.13***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.20,-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.35***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.28,0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSingle-Chronic#Non-NCDs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03,0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.42,0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSingle-Chronic#NCDs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.09***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.14,-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.64,0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMultimorbidity# Non-NCDs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.51**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14,0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.65,1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMultimorbidity# NCDs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.35***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.28,0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.20***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.04,1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eType of Illness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-NCD\u0026reg;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00,0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00,0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHave NCD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.47,0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.14,0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLevel of Care\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePublic\u0026reg;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00,0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00,0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00,0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00,0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrivate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.90***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.79,1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.78,0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.77***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.44,2.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.84***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.82,1.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eETL State Group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow ETL State\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.43,0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.41***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.37,0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.41*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02,0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03,0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLower-middle ETL State\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.16,0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.38***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.31,0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.73,0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.16***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12,0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigher-middle ETL State\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.12,0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.02,0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.35,0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.06**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.09,-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh ETL State\u0026reg;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00,0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00,0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00,0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00,0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eInteraction of Place of Residence and MPCE Quintile\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRural # Poorest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.93***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.40,-0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.36***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.44,-0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.07,0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.21***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.27,-0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRural # Poor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.51**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.84,-0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.34***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.42,-0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.88,0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.12***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.18,-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRural # Middle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.40**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.66,-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.28***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.34,-0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.18,0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.14***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.20,-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRural # Rich\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.30**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.51,-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.21***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.28,-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.60,0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.13***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.19,-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRural # Richest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.21*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.37,-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.11***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.17,-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.72,0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.06,0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban # Poorest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.25*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.49,-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.33***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.39,-0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.90,0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.20***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.26,-0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban # Poor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.31**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.51,-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.26***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.33,-0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.77**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.34,-0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.15***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.21,-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban # Middle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.29**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.47,-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.24***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.30,-0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.86,0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.06*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.12,-0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban # Rich\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.23*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.41,-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.14***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.20,-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.68,0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.08,0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban # Richest\u0026reg;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00,0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00,0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00,0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00,0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u0026reg;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00,0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00,0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00,0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00,0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.16**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.26,-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.10***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.13,-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.43**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.71,-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.14***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.16,-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge Group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u0026ndash;44\u0026reg;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00,0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00,0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00,0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00,0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u0026ndash;59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.30,0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.05,0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.57**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.16,0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.03,0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u0026ndash;69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.23,0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.03,0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.02,0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05,0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.15,0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04,0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.52*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12,0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12,0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38822\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003eSource: Computed from unit-level NSSO 75th round Household Social Consumption in India: Health Survey Data, 2017\u0026ndash;2018; Note: * p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; and \u0026reg; is reference category\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u0026lt;Table\u0026nbsp;\u003cspan\u003e4\u003c/span\u003e\u0026gt;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\"\u003e\n \u003ch2\u003eIncidence and intensity of CHE\u003c/h2\u003e\n \u003cp\u003eThe incidence of CHE is highest among multimorbidity patients, particularly in outpatient care, where 51.3% of these patients experience CHE compared to those with acute or single chronic conditions. In inpatient care, the burden is even more significant, with 69.6% of multimorbidity patients facing CHE, and the mean positive overshoot (MPO) reaching 66.0%. For multimorbidity patients with NCDs, the incidence rises to 75%, with an intensity of 66% MPO in inpatient care. Geographic and socioeconomic disparities further intensify the incidence of CHE. Multimorbidity patients in rural areas face higher CHE in inpatient care (79.9%) compared to their urban counterparts (58%). Elderly individuals experience the highest CHE incidence in inpatient care (80%), while those in the lowest economic quintiles report the highest CHE rates in both outpatient (75%) and inpatient care (71%). For the poorest quintile, the MPO is for inpatient care. Across all ETL state groups, multimorbidity patients consistently experience the highest CHE incidence compared to those with acute and single chronic conditions. In low ETL states, outpatient CHE for multimorbidity patients is the most severe, with incidences reaching 89% and an MPO of 25% (Table\u0026nbsp;\u003cspan\u003e5\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv\u003e\u0026nbsp;\u003c/div\u003e\n \u003cp\u003e\u0026lt;Table\u0026nbsp;\u003cspan\u003e5\u003c/span\u003e\u0026gt;\u003c/p\u003e\n \u003cp\u003eIn states like Assam and Odisha, multimorbidity patients face a staggering 100% CHE incidence in both outpatient and inpatient care. Similarly, in Bihar, 100% of patients encounter CHE in inpatient settings. Lower-middle ETL states, including Sikkim and Manipur, also report a 100% CHE incidence across both care types. In higher-middle ETL states, Telangana and Karnataka show alarming inpatient CHE incidences of 100% and 92%, respectively. While high ETL states such as Punjab and Himachal Pradesh report lower outpatient CHE incidences (13% and 26%), they still exhibit high inpatient CHE rates, with 94% and 84% of multimorbidity patients facing CHE (Fig.\u0026nbsp;\u003cspan\u003e5\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u0026lt;Figure \u003cspan\u003e5\u003c/span\u003e\u0026gt;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\"\u003e\n \u003ch2\u003eDeterminants of CHE\u003c/h2\u003e\n \u003cp\u003eThe logistic regression analysis revealed key predictors of catastrophic health expenditure (CHE) among patients in outpatient care and inpatient care in India (Table\u0026nbsp;\u003cspan\u003e6\u003c/span\u003e). Among outpatient cases (26,905 patients), multimorbidity patients with NCDs had a 33% higher likelihood of incurring CHE (OR: 1.33, 95% CI: 1.18\u0026ndash;1.50) than those with acute conditions. For inpatient care (38,822 patients), the financial burden was even more pronounced, with multimorbidity patients having NCDs being 5.9 times more likely to face CHE (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, 95% CI: 4.25\u0026ndash;8.20). The type of healthcare facility chosen was also a critical factor influencing CHE. Outpatients opting for private healthcare were 2.6 times more likely to experience CHE than those seeking care in public facilities (OR: 2.60, 95% CI: 2.45\u0026ndash;2.76). Inpatient care at private facilities posed an even greater risk, with patients being 9.85 times more likely to encounter CHE compared to those using public healthcare. Geographical disparities further impacted the CHE incidence. Patients residing in low ETL states had a 67% higher likelihood of incurring CHE in outpatient care compared to those in high ETL states. Also, the economic and residential settings played a significant role. The poorest rural outpatients were 5.5 times more likely to experience CHE than their wealthier urban counterparts (OR: 5.49, 95% CI: 4.73\u0026ndash;6.36), indicating the financial vulnerability of disadvantaged populations in India. The analysis highlighted a significant disparity in the likelihood of CHE among different demographic groups. The poorest rural residents were found to have 6.73 times greater likelihood of incurring CHE compared to the wealthiest urban residents. Gender differences also emerged, with females demonstrating a 15% lower likelihood of facing CHE compared to their male counterparts (OR: 0.85, 95% CI: 0.81\u0026ndash;0.89). Age was another significant factor influencing the likelihood of CHE. Individuals aged 60 to 69 were 22% more likely to experience CHE, while those aged 70 and above faced a 20% higher likelihood compared to individuals aged 30 to 44.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 6\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eLogistic Regression for Catastrophic Health Expenditure (CHE) Incidence (at 10% threshold) for Outpatient Care (in last 15 days) and Inpatient Care (365 days) among patients aged 30 and above, NSS 75th Round Household Social Consumption in India: Health Survey Data, 2017\u0026ndash;2018\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eExplanatory Variables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eFor Outpatient Care (last 15 days reference period)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eFor Inpatient Care (last 365 days reference period)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eOnly Multimorbidity Patient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eOverall Illness\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eOnly Multimorbidity Patient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eOverall Illness\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eInteraction of Illnesses Type and NCDs Occurrence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAcute#Non-NCDs\u0026reg;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00,1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00,1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAcute#NCDs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.72,0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.48***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.29,1.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSingle-Chronic#Non-NCDs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.88,1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.91***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.81,2.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSingle-Chronic#NCDs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.76***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.70,0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.46***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.31,2.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMultimorbidity# Non-NCDs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.06,3.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.48***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.60,3.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMultimorbidity# NCDs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.33***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.18,1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.90***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.25,8.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eType of Illness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-NCD\u0026reg;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00,1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00,1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHave NCD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.36,1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.02*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.06,3.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLevel of Care\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePublic\u0026reg;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00,1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00,1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00,1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00,1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrivate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.31***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.44,5.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.60***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.45,2.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.06***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.88,28.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.85***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.33,10.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEpidemiological Transition Level (ETL) State\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow ETL State\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.27**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.34,3.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.67***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.55,1.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.93,5.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.02,1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLower-middle ETL State\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.48,1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.34***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.19,1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.22,2.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.92,1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigher-middle ETL State\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.86,1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.02,1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.39,1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.93,1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh ETL State\u0026reg;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00,1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00,1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00,1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00,1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eInteraction of Place of Residence and MPCE Quintile\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRural # Poorest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.64*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.36,9.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.49***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.73,6.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.34,52.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.73***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.95,7.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRural # Poor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.96***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.57,5.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.26***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.85,3.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.70,51.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.19***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.73,4.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRural # Middle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.10***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.47,6.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.26***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.89,3.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.33,5.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.34***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.00,3.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRural # Rich\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.70***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.81,4.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.62***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.33,2.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.55*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.24,24.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.77***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.49,3.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRural # Richest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.21***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.62,3.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.12***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.90,2.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.40,2.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.23***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.03,2.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban # Poorest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.58***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.84,7.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.51***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.23,2.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.45,5.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.08***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.77,3.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban # Poor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.76**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.20,2.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.79***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.59,2.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.10,1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.01***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.80,2.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban # Middle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.48*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.04,2.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.51***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.34,1.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.22,2.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.75***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.57,1.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban # Rich\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.56*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.11,2.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.40***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.24,1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.33,2.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.41***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.27,1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban # Richest\u0026reg;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00,1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00,1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00,1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00,1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u0026reg;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00,1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00,1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00,1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00,1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.71,1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.84,0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.31,1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.81,0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge Group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u0026ndash;44\u0026reg;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00,1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00,1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00,1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00,1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u0026ndash;59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.48,1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.89,1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.89*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.19,7.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.01,1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u0026ndash;69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.51,1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.90,1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.41*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.02,5.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.22***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.14,1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.50,1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.96,1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.60*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.14,5.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.20***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.11,1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38822\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003eSource: Computed from unit-level NSSO 75th round Household Social Consumption in India: Health Survey Data, 2017\u0026ndash;2018; Note: * p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; and \u0026reg; is reference category\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u0026lt;Table\u0026nbsp;\u003cspan\u003e6\u003c/span\u003e\u0026gt;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\"\u003e\n \u003ch2\u003eDeterminants of Intensity of CHE\u003c/h2\u003e\n \u003cp\u003eThe linear regression analysis examined the factors that affect the intensity of CHE, focusing on the positive overshoot, among patients aged 30 and above in both outpatient and inpatient care in India (Table\u0026nbsp;\u003cspan\u003e7\u003c/span\u003e). Multimorbidity patients having NCDs were significantly associated with a higher CHE intensity in inpatient care (Coef: 32.54, 95% CI: 19.29\u0026ndash;45.79), which indicates that the combination of multimorbidity and NCDs places patients at a much higher risk of catastrophic health expenditure, reflecting the compounded financial burden due to more complex care needs and long-term treatments. Low ETL states (Coef: 21.57, 95% CI: 7.36\u0026ndash;35.79) show the most substantial impact on CHE intensity among multimorbidity patients in outpatient care, which underscores the geographical disparities in healthcare costs, with lower ETL states facing disproportionately high financial burdens for multimorbidity patients. Private care usage statistically significantly increased CHE intensity in both outpatient (Coef: 5.73, 95% CI: 2.62\u0026ndash;8.84) and inpatient care (Coef: 24.75, 95% CI: 21.15\u0026ndash;28.34), suggesting that those seeking care from private providers are more likely to experience higher out-of-pocket spending. The interaction analysis between MPCE quintiles and sectors reveals that patients from the rural poorest MPCE quintile have a higher likelihood of having a higher intensity of CHE followed by the rural poor and rural middle MPCE, compared to their wealthier urban counterparts, both in outpatient (Coefficient: 25.63, 95% CI: 18.80, 32.47) and inpatient care settings (Coefficient: 26.65, 95% CI: 19.72, 33.58). The poorest urban also faced significant financial burdens compared to the urban richest.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 7\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eLinear Regression for Intensity of Catastrophic Health Expenditure for Outpatient (15 days) and Inpatient Care (365 days) among patients aged 30 and above, NSS 75th Round Household Social Consumption in India: Health Survey Data, 2017\u0026ndash;2018\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eExplanatory Variables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eOutpatient Care (last 15 days reference period)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eInpatient Care (last 365 days reference period)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eOnly Multimorbidity Patient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eOverall Illness\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eOnly Multimorbidity Patient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eOverall Illness\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoef.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoef.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoef.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoef.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eInteraction of Illnesses Type and NCDs Occurrence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAcute#Non-NCDs\u0026reg;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00,0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00,0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAcute#NCDs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-9.17,3.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.77*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.48,18.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSingle-Chronic#Non-NCDs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.18,8.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.16***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.71,12.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSingle-Chronic#NCDs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-5.23,2.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.88***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.30,27.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMultimorbidity# Non-NCDs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-8.52,43.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.76***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.47,59.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMultimorbidity# NCDs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-5.30,6.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.54***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.29,45.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHave NCDs?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-NCDs\u0026reg;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00,0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00,0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNCDs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-30.08,11.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-38.96,22.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLevel of Care\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePublic\u0026reg;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00,0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00,0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00,0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00,0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrivate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-8.67,8.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.73***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.62,8.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-18.10,69.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.75***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.15,28.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEpidemiological Transition Level (ETL) State\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow ETL State\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.57**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.36,35.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.75***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.96,10.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-26.77,50.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-6.75,1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLower-middle ETL State\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-27.79,19.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.78,11.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-22.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-89.89,45.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.79*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-11.20,-0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigher-middle ETL State\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-9.27,4.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-5.22,1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-15.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-48.96,18.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-7.71,0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh ETL State\u0026reg;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00,0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00,0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00,0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00,0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eInteraction of Place of Residence and MPCE Quintile\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRural # Poorest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-10.17,45.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.63***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.80,32.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-18.22,137.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.65***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.72,33.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRural # Poor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-8.48,32.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.47***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.77,21.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-29.55,106.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.36***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.54,24.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRural # Middle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-12.06,20.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.23***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.03,17.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-67.66,58.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.58***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.11,20.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRural # Rich\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-8.94,18.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.15**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.05,15.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-11.49,99.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.13*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.80,14.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRural # Richest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-18.86,3.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.60***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.86,16.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-50.50,42.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.59,11.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban # Poorest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-21.37,7.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.65**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.47,15.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-10.62,106.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.88***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.41,21.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban # Poor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-24.01,4.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.04,10.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-85.15,43.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.69**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.78,16.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban # Middle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-12.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-25.44,0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.08,8.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-54.01,64.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.99,11.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban # Rich\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-16.30,9.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-5.33,7.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-46.30,58.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-5.39,8.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban # Richest\u0026reg;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00,0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00,0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00,0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00,0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u0026reg;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00,0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00,0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00,0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00,0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-5.87,7.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.29,0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-20.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-47.85,5.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.78***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-11.48,-6.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge Group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u0026ndash;44\u0026reg;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00,0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00,0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00,0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00,0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u0026ndash;59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-18.28,10.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.84,2.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-8.24,74.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.46,4.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u0026ndash;69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-15.05,14.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.31,5.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-13.60,69.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.67,7.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-12.47,17.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-5.07,3.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-28.30,52.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.75,6.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003eSource: Computed from unit-level NSSO 75th round Household Social Consumption in India: Health Survey Data, 2017\u0026ndash;2018; Note: * p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; and \u0026reg; is reference category\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u0026lt;Table\u0026nbsp;\u003cspan\u003e7\u003c/span\u003e\u0026gt;\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussions","content":"\u003cp\u003eThis study highlights the substantial financial burden faced by patients with multimorbidity in India, with OOPE and CHE far exceeding those incurred by individuals with single chronic conditions or acute illnesses. To date, there has been no peer-reviewed research offering a thorough examination of multimorbidity for both outpatient and inpatient cases separately. Furthermore, the absence of cross-state comparisons regarding CHE associated with OOPE has limited a comprehensive understanding of health system resilience and the development of policies aimed at mitigating financial hardship. Our research seeks to address these gaps by utilizing data from the National Sample Survey (2017-18), which is nationally representative. Our findings reveal that the prevalence of multimorbidity in outpatient care is six times higher than that in inpatient care. This observation aligns closely with the findings of Varanasi et al. (2024) study that conducted a systematic review and meta-analysis indicating a multimorbidity prevalence range of 1.16\u0026ndash;65.9%. The significantly lower prevalence of multimorbidity among inpatients suggests an underutilization of hospital services, despite the evident healthcare needs. Barriers such as prohibitive costs, significant travel distances and prior high out-of-pocket expenses may contribute to this underutilization (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Our research also highlights that multimorbidity prevalence is higher in urban areas compared to rural regions, with increases noted alongside rising income and age for both outpatient and inpatient care consistent with previous findings from nationally representative studies in India (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). The epidemiological transition plays a significant role in the observed prevalence of multimorbidity; our study indicates that individuals with NCDs exhibit higher rates of multimorbidity in both outpatient (9.4%) and inpatient care (2.5%). These findings are corroborated by a study, which analyzed nationally representative data from the World Health Organization (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMultimorbidity presents distinct challenges in healthcare access, affordability, and financial protection across different levels of care. Patients with multimorbidity predominantly utilize private healthcare services for both outpatient and inpatient care, suggesting significant barriers to accessing public healthcare. Factors such as long wait times, perceived lower quality of care, and the lack of specialized services in the public sector contribute to this preference for private healthcare (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). This reliance on private healthcare exacerbates the financial burden on patients with multimorbidity, as private care is often associated with higher OOPE, placing a significant strain on these individuals (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur findings demonstrate substantial variability in multimorbidity prevalence across states, with economic factors and ETL classifications significantly influencing this condition in India. For instance, Kerala, classified as an economically advanced and high ETL state, shows a higher prevalence of multimorbidity, while states like Meghalaya and Nagaland, categorized as low ETL and economically disadvantaged, exhibit lower prevalence rates. This discrepancy can be partly explained by increased awareness and the inclusion of NCDs tracking in routine healthcare practices in more developed states, leading to better detection and diagnosis. Our findings are consistent with previous research that highlights the role of improved healthcare systems in detecting chronic conditions (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNumerous studies indicate that OOPE serves as the primary means of healthcare financing in low- and middle-income countries (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). India mirrors this trend, with health expenditure accounting for 3.83% of GDP, leading to significant implications for healthcare funding in the country. Our findings align with previous studies indicating that patients with multimorbidity often resort to private healthcare, which leads to higher OOPE due to the lack of specialized public healthcare services (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e), compared to those with single chronic conditions or acute illnesses. Specifically, OOPE for multimorbidity patients with NCDs (Rs. 75,882) in inpatient care is far exceeding the costs for patients with acute illnesses. Log-linear regression analysis indicates that multimorbidity patients with NCDs have a 42% higher likelihood of incurring higher outpatient expenses, while in inpatient care, these patients are 2 times more likely to experience higher costs compared to those with acute conditions. These results highlight the compounded financial strain faced by multimorbidity patients, particularly when NCDs are involved. A key factor contributing to these elevated costs is polypharmacy, as managing multiple chronic conditions often requires numerous medications. Polypharmacy increases the risks of adverse drug reactions, medication non-adherence, and harmful drug interactions (\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e), which can worsen patient outcomes, lead to more frequent hospital admissions, and escalate overall healthcare costs (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Our analysis further identifies medication and diagnostic expenditures as the main drivers of high OOPE, consistent with previous research findings (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe stark urban-rural and socioeconomic disparities in OOPE, particularly for inpatient care, underscore the need for targeted interventions. Patients from urban areas experience higher OOPE for outpatient care than rural across all illness categories, with the disparity most pronounced among multimorbidity patients. However, rural multimorbidity patients faced significantly higher inpatient OOPE (Rs. 80,822) than their urban counterparts (Rs. 56,171), likely due to travel and accommodation expenses, as well as limited local service availability and greater indirect costs (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). Socioeconomic inequalities manifest in OOPE for multimorbidity, with the wealthiest MPCE quintile incurring significantly higher costs compared to the poorest quintile. This discrepancy likely stems from the wealthiest quintile\u0026rsquo;s consistent preference for private healthcare facilities, which charge higher treatment costs than public services, in both outpatient and inpatient contexts. Previous research has suggested that the limited availability of NCD services in the public sector drives patients towards private hospitals, where inadequate health insurance coverage compounds their out-of-pocket costs (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Additionally, males and older age groups (70\u0026thinsp;+\u0026thinsp;years) report elevated OOPE for multimorbidity, reflecting increased healthcare needs arising from higher multimorbidity incidence. These findings align with global patterns where healthcare costs are closely linked to socioeconomic status, leading to increased OOPE disparities (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur analysis of CHE reinforces the argument that multimorbidity disproportionately affects the poorest quintiles, with high OOPE often leading to financial catastrophe, exacerbating the difficulties in managing complex health conditions (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Specifically, our findings indicate that multimorbidity patients experience the highest CHE incidences in both outpatient (51.3%) and inpatient care (69.6%) settings due to a high proportion of OOPE. Demographic and epidemiological transitions further influence the distribution of health expenditures across disease and age groups. Patients with NCDs experience an even higher CHE incidence and intensity, particularly in inpatient settings, where CHE incidence reaches 75%, and the mean positive overshoot\u0026mdash;a measure of financial intensity-is 66%. Logistic regression analysis indicates that multimorbidity patients with NCDs are 33% more likely to incur CHE in outpatient care and 5.9 times more likely in inpatient care compared to those without multimorbidity. Moreover, linear regression analysis underscores that multimorbidity significantly increases the severity of CHE.\u003c/p\u003e \u003cp\u003eAdditionally, the increased CHE incidence among patients utilizing private healthcare facilities demonstrates the inadequacy of current public healthcare infrastructure in managing complex cases of multimorbidity, especially in low-ETL states, with CHE incidence in private healthcare facilities reaching 63% in outpatient care and 87% in inpatient care, compared to 24% and 25%, respectively, in public facilities. Our logistic regression findings further reveal that outpatients opting for private care are 2.6 times more likely to incur CHE, while inpatients using private facilities face a 9.8 times higher likelihood of experiencing CHE.\u003c/p\u003e \u003cp\u003eThe study acknowledges a few limitations that should be considered when interpreting the findings. The first limitation of the present study included the morbidities and expenditures were self-reported which might have brought in measurement bias. Moreover, the collection of data over the last 15 days for outpatient care and 365 days for inpatient care may result in limited time frames for outpatient data and potential recall bias for inpatient data. Another limitation pertains to the findings related to average health expenditures, which should be approached cautiously due to observed skewness and high variability in the data.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eUnderstanding health expenditure dynamics at the national level is crucial for informing effective policy responses. Our study sheds light on the prevalence of multimorbidity in India and its significant financial implications, particularly emphasizing the striking disparities in OOPE and CHE. It illustrates the financial strain that the increasing burden of multimorbidity imposes across various healthcare settings, socioeconomic groups and geographic regions. This complex scenario necessitates a comprehensive, multifaceted strategy to address the challenges of multimorbidity, particularly at the primary care level. The study highlights the importance of improving access to affordable, high-quality public healthcare services, especially for NCD management.\u003c/p\u003e \u003cp\u003eWith outpatient care contributing significantly to OOPE, enhancing primary care systems emerges as a critical intervention. Furthermore, extending financial protection schemes to cover outpatient care is essential to reduce OOPE and CHE for patients managing multiple chronic conditions. By bringing healthcare services closer to communities, this approach aims to prevent disease progression and reduce the financial burdens faced by individuals. Furthermore, expanding the Pradhan Mantri Jan Aarogya Yojana (PMJAY) to include outpatient care can help the healthcare system address a significant portion of CHE arising from outpatient expenditures. Such an expansion could include coverage for outpatient consultations, diagnostic tests, medications and routine procedures essential for managing multimorbidity. The compounded financial burden from polypharmacy, diagnostic expenses, and frequent hospitalizations highlights the urgent need for financial protection mechanisms and the inclusion of outpatient care under schemes like PMJAY (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMedication costs represent a substantial portion of OOPE for those with multimorbidity, primarily due to the prevalence of polypharmacy, which can lead to increased morbidity, hospitalizations and higher overall healthcare expenditures. Managing polypharmacy demands careful consideration of the benefits and risks associated with multiple medications in patients who already bear significant financial and healthcare burdens (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). To mitigate the risks of polypharmacy, it is essential to conduct regular medication reviews, facilitate coordinated care among healthcare providers, educate patients on effective medication management and employ comprehensive medication management strategies that optimize therapeutic outcomes while minimizing potential adverse effects. Effective approaches for managing polypharmacy include routine medication evaluations, deprescribing when appropriate and adherence to clinical guidelines to refine pharmacotherapy (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). Implementing standardized treatment protocols and reducing unnecessary medications could also lower the risks associated with polypharmacy, thereby decreasing OOPE and improving treatment adherence.\u003c/p\u003e \u003cp\u003eIntegrating preventive strategies, such as early detection and effective management of NCDs and multimorbidity, into the existing Comprehensive Primary Health Care (CPHC) framework(\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e) is essential. Emphasizing these measures through public health campaigns and regular screenings can help prevent the progression to multimorbidity, improving overall health outcomes. At a health systems level, there is an urgent need to integrate various health programs and interventions. Leveraging support of digital health technologies under the Ayushman Bharat Digital Health Mission like Electronic Health Records (EHR) and Telemedicine consultations with Specialists can bridge existing gaps in the continuum of care and availability of Human Resources for Health. By strategically implementing these technologies, patient care can be streamlined, facilitating remote consultations with specialists and thereby bringing specialized care closer to communities. This initiative has the potential to enhance both the affordability and accessibility of healthcare for patients managing multimorbidity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was not required for this study as it used only anonymized data from secondary sources, publicly available from the National Sample Survey Office (NSSO). Therefore, no ethical issues or approval from an ethics committee, nor consent to participate, were necessary. All methods were conducted in accordance with relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets were derived from sources in the public domain: NSSO: Social Consumption and Health 75th round and can be downloaded upon registration and filling in basic details at https://microdata.gov.in/nada43/index.php/catalog/152\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgement\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eVan den Akker M, Buntix F, Metsemakers JFM, Roos S, Knottnerus JA. Multimorbidity in general practice: Prevalence, incidence, and determinants of co-occurring chronic and recurrent diseases. J Clin Epidemiol. 1998;51(5). \u003c/li\u003e\n\u003cli\u003eBarnett K, Mercer SW, Norbury M, Watt G, Wyke S, Guthrie B. Epidemiology of multimorbidity and implications for health care, research, and medical education: A cross-sectional study. The Lancet. 2012;380(9836). \u003c/li\u003e\n\u003cli\u003eSalisbury C, Johnson L, Purdy S, Valderas JM, Montgomery AA. Epidemiology and impact of multimorbidity in primary care: A retrospective cohort study. British Journal of General Practice. 2011;61(582). \u003c/li\u003e\n\u003cli\u003eWHO. Multimorbidity. Technical Series on Safer Primary Care. Vol. Geneva: Wo, World Health Organisation. 2016. \u003c/li\u003e\n\u003cli\u003eChowdhury SR, Chandra Das D, Sunna TC, Beyene J, Hossain A. Global and regional prevalence of multimorbidity in the adult population in community settings: a systematic review and meta-analysis. EClinicalMedicine. 2023;57. \u003c/li\u003e\n\u003cli\u003eMarengoni A, Angleman S, Melis R, Mangialasche F, Karp A, Garmen A, et al. Aging with multimorbidity: A systematic review of the literature. Vol. 10, Ageing Research Reviews. 2011. \u003c/li\u003e\n\u003cli\u003eViolan C, Foguet-Boreu Q, Flores-Mateo G, Salisbury C, Blom J, Freitag M, et al. Prevalence, determinants and patterns of multimorbidity in primary care: A systematic review of observational studies. PLoS One. 2014;9(7). \u003c/li\u003e\n\u003cli\u003eNational Health Systems Resource Centre. National Health Systems Resource Centre [Internet]. 2024 [cited 2024 Nov 10]. Available from: https://nhsrcindia.org/national-health-accounts-records\u003c/li\u003e\n\u003cli\u003eLarkin J, Walsh B, Moriarty F, Clyne B, Harrington P, Smith SM. What is the impact of multimorbidity on out-of-pocket healthcare expenditure among community-dwelling older adults in Ireland? A cross-sectional study. 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Physical multimorbidity, health service use, and catastrophic health expenditure by socioeconomic groups in China: an analysis of population-based panel data. Lancet Glob Health. 2020;8(6). \u003c/li\u003e\n\u003cli\u003eAggarwal P, Woolford SJ, Patel HP. Multi-morbidity and polypharmacy in older people: Challenges and opportunities for clinical practice. Vol. 5, Geriatrics (Switzerland). 2020. \u003c/li\u003e\n\u003cli\u003ePayne RA, Avery AJ, Duerden M, Saunders CL, Simpson CR, Abel GA. Prevalence of polypharmacy in a Scottish primary care population. Eur J Clin Pharmacol. 2014;70(5). \u003c/li\u003e\n\u003cli\u003eDuerden M, Avery T, Payne R. Polypharmacy and medicines optimisation: Making it safe and sound. https://www.kingsfund.org.uk/publications/polypharmacy-and-medicines-optimisation. The King\u0026rsquo;s Fund. 2013. \u003c/li\u003e\n\u003cli\u003ePirmohamed M, James S, Meakin S, Green C, Scott AK, Walley TJ, et al. Adverse drug reactions as cause of admission to hospital: Prospective analysis of 18 820 patients. Br Med J. 2004;329(7456). \u003c/li\u003e\n\u003cli\u003eSum G, Hone T, Atun R, Millett C, Suhrcke M, Mahal A, et al. Multimorbidity and out-of-pocket expenditure on medicines: A systematic review. Vol. 3, BMJ Global Health. 2018. \u003c/li\u003e\n\u003cli\u003eKornelsen J, Khowaja AR, Av-Gay G, Sullivan E, Parajulee A, Dunnebacke M, et al. The rural tax: comprehensive out-of-pocket costs associated with patient travel in British Columbia. BMC Health Serv Res. 2021;21(1). \u003c/li\u003e\n\u003cli\u003e\u0026Aacute;lvarez-G\u0026aacute;lvez J, Ortega-Mart\u0026iacute;n E, Carretero-Bravo J, P\u0026eacute;rez-Mu\u0026ntilde;oz C, Su\u0026aacute;rez-Lled\u0026oacute; V, Ramos-Fiol B. Social determinants of multimorbidity patterns: A systematic review. Vol. 11, Frontiers in Public Health. 2023. \u003c/li\u003e\n\u003cli\u003eMcMaughan DJ, Oloruntoba O, Smith ML. Socioeconomic Status and Access to Healthcare: Interrelated Drivers for Healthy Aging. Vol. 8, Frontiers in Public Health. 2020. \u003c/li\u003e\n\u003cli\u003eMaher RL, Hanlon J, Hajjar ER. Expert Opinion on Drug Safety Clinical consequences of polypharmacy in elderly Clinical consequences of polypharmacy in elderly. Expert Opin Drug Saf. 2014;13(1). \u003c/li\u003e\n\u003cli\u003eScott IA, Hilmer SN, Reeve E, Potter K, Couteur D Le, Rigby D, et al. Reducing inappropriate polypharmacy: The process of deprescribing. Vol. 175, JAMA Internal Medicine. 2015. \u003c/li\u003e\n\u003cli\u003eMinistry of Health and Family Welfare. Ayushman Bharat: comprehensive primary health care through health and wellness centers operational guidelines. [Internet]. New Delhi; 2018 [cited 2024 Nov 10]. Available from: \u003cbr\u003ehttps://www.nhm.gov.in/New_Updates_2018/NHM_Components/\u003cbr\u003eHealth_System_Stregthening/Comprehensive_primary_health_care/letter/Operational_Guidelines_For_CPHC.pdf\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 5","content":"\u003cp\u003eTable 5 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Multimorbidity, NCDs, Chronic, OOPE, Catastrophic Health Expenditure, India","lastPublishedDoi":"10.21203/rs.3.rs-5425175/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5425175/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMultimorbidity is associated with significant out-of-pocket expenditures (OOPE) and catastrophic health expenditure (CHE), especially in low- and middle-income countries like India. Despite this, there is limited research on the financial burden of multimorbidity in outpatient and inpatient care, and cross-state comparisons of CHE are underexplored.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a cross-sectional analysis using nationally representative data from the National Sample Survey 75th Round \u0026lsquo;Social Consumption in India: Health (2017-18)\u0026rsquo;, focusing on patients aged 30 and above in outpatient and inpatient care in India. We assessed multimorbidity prevalence, OOPE, CHE incidence, and CHE intensity. Statistical models, including linear, log-linear, and logistic regressions, were used to examine the financial risk, with a focus on non-communicable diseases (NCDs), healthcare facility choice, and socioeconomic status and Epidemiological Transition Levels (ETLs).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eMultimorbidity prevalence in outpatient care (6.1%) was six times higher than in inpatient care (1.1%). It was most prevalent among older adults, higher MPCE quintiles, urban patients, and those with NCDs. Multimorbidity was associated with higher OOPE, particularly in the rich quintile, patients seeking care from private providers, low ETL states, and rural areas. CHE incidence was highest in low ETL states, private healthcare users, poorest quintile, males, and patients aged 70\u0026thinsp;+\u0026thinsp;years. CHE intensity, measured by mean positive overshoot, was greatest among the poorest quintile, low ETL states, rural, and male patients. Log-linear and logistic regressions indicated that multimorbidity patients with NCDs, those seeking private care, and those in low ETL states had higher OOPE and CHE risk. The poorest rural multimorbidity patients had the greatest likelihood of experiencing CHE. Furthermore, CHE intensity was significantly elevated among multimorbidity patients with NCDs (95% CI: 19.29\u0026ndash;45.79), patients seeking care in private, poorest, and from low ETL states (95% CI: 7.36\u0026ndash;35.79).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003e The high financial burden of OOPE and CHE among multimorbidity patients, particularly those with NCDs, underscores the urgent need for comprehensive health policies that address financial risk at the primary care level. To alleviate the financial burden among multimorbidity patients, especially in low-resource settings, it is crucial to expand public healthcare coverage, incorporate outpatient care into financial protection schemes, advocate for integrated care models and preventive strategies, establish standardized treatment protocols for reducing unnecessary medications linked to polypharmacy, and leverage the support of digital health technologies.\u003c/p\u003e","manuscriptTitle":"Assessing the Financial Burden of Multimorbidity Among Patients Aged 30 and above in India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-17 06:33:39","doi":"10.21203/rs.3.rs-5425175/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-13T08:24:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-12T12:17:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-12T12:13:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Health Services Research","date":"2024-11-10T09:20:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0a870d61-3c71-4f42-863f-a4d44ebcf380","owner":[],"postedDate":"December 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-01-20T16:00:32+00:00","versionOfRecord":{"articleIdentity":"rs-5425175","link":"https://doi.org/10.1186/s12913-025-12206-w","journal":{"identity":"bmc-health-services-research","isVorOnly":false,"title":"BMC Health Services Research"},"publishedOn":"2025-01-15 15:57:16","publishedOnDateReadable":"January 15th, 2025"},"versionCreatedAt":"2024-12-17 06:33:39","video":"","vorDoi":"10.1186/s12913-025-12206-w","vorDoiUrl":"https://doi.org/10.1186/s12913-025-12206-w","workflowStages":[]},"version":"v1","identity":"rs-5425175","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5425175","identity":"rs-5425175","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}