Evaluating Health Related Quality of Life and Healthcare Utilization Among Adults with Concurrent Cancer and Chronic Kidney Disease: Medical Expenditure Panel Survey

Evaluating Health Related Quality of Life and Healthcare Utilization Among Adults with Concurrent Cancer and Chronic Kidney Disease: Medical Expenditure Panel Survey | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Evaluating Health Related Quality of Life and Healthcare Utilization Among Adults with Concurrent Cancer and Chronic Kidney Disease: Medical Expenditure Panel Survey Keyuri Adhikari, Mohammad A. Al-Mamun This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8058673/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Objectives Cancer patients may suffer from kidney injury due to cancer itself and cancer-related therapy. But the burden of concurrent cancer and CKD on Health-Related Quality of Life (HRQOL) and Health Resource Utilization (HRU) remains to be explored. This study aims to identify the patterns and factors associated with HRQOL and HRU in adults having Cancer and CKD. Methods This was a cross-sectional study using Medical Expenditure Panel Survey 2011 to 2019, with participants (> 18 years of age) with 5 cancers (Lung, Breast, Prostate, and Kidney) and CKD. To measure HRQoL, a Physical Component Summary (PCS) and a Mental Component Summary (MCS) were utilized. Multivariable linear and logistic regressions were used to evaluate factors associated with HRQoL and HRU, respectively. Results A total of 441 patients with Cancer and CKD (weighted n = 235,057) were consisted of > 45 years of age (81.1%), White (63.4%), and fitted into middle to high income group (67%). People at 45–64 years had significantly lowest MCS scores (beta=-1.62 [-1.91, -1.32], p = 0.001) when compared to 18–64 years of age. Sociodemographic factors such as low income (PCS vs MCS: -2.50[-3.38, -1.61] vs -1.17[-1.52, -0.81], p < 0.001), uninsured (-2.42[-2.83, -1.99] vs -2.38[-2.92, -1.83],p < 0.01), less than high school education (-2.5[-2.99, -2.01] vs -1.87[-2.28, -1.45], p < 0.001] are significantly associated with lower HRQoL. Both Cancer and CKD had significantly lower HRQOL when compared to with neither CKD nor Cancer group. Conclusion Our study suggests the importance of monitoring the renal functions among the Cancer patients to improve health care delivery through understanding the disease burden. Cancer CKD HRQOL HRU MEPS Figures Figure 1 BACKGROUND Cancer and Chronic Kidney Disease (CKD) are both associated with significant morbidity and mortality and are among the rapidly growing health problems both nationally and globally 1 – 2 . Approximately,1 million new cancer cases were diagnosed in the United States (U.S.) in 2020, out of which approximately 600,000 have died from the disease 4 . According to National Cancer Institute data from 2017–2019, approximately 40.5 percent of men and women will be diagnosed with any types of cancer at some point during their lifetime. 4 . On the other hand, about 35.5 million people in the US are currently estimated to have CKD and this number is expected to increase to about 37 million by 2030 3 . In 2019, the economic burden associated with cancer and CKD care was $ 50.4 and $ 21.09 billion, respectively. 5 – 7 Cancer patients may suffer from kidney injury due to cancer itself and cancer-related therapy 34 . Both acute kidney injury (AKI) and CKD are prevalent in patients with cancer 35 – 37 . Incidence and severity of kidney injury varies depending on type/stage of cancer, treatment regimen, and underlying comorbidities. Literature suggests that cancer patients have 1- and 5-year AKI risk of 17.5% and 27%, respectively and they are even at higher risk with ~ 13% to 54% of patients developing AKI and 8% to 60% requiring dialysis when they are critically ill 38 – 40 . Several studies reported that stage 3 CKD prevalence varies in a range of ~ 13% to 30% among cancer patients 41 – 42 . However, the current literature is impeded with lack of information available on the burden of concurrent cancer and CKD on Health-Related Quality of Life (HRQOL) and Health Resource Utilization (HRU) (i.e., usage of healthcare resources and treatment services) in the US. Prevalence of CKD was found to be the highest among adults with kidney cancer (48.7%), followed by adults with urinary bladder cancer (34.1%), liver cancer (20%) 12 , and pancreatic cancer (19.6%) 12 . Even among adults with a diagnosis of uterine, brain, and testicular cancer, the prevalence of CKD was observed to be 5.7%, 7.1% and 8.8%, respectively 12 . Adults with moderate-to-severe CKD have over 50% increased risk of cancer death for abdominal solid organs and more than 3 times the risk of death from kidney cancers 8 . This incidence may be related to chronic inflammation, accumulation of carcinogenic compounds, oxidative stress, impairment of DNA repair, excessive parathyroid hormone, and changes in intestinal microbiota 8 . On the contrary, it was observed that concomitant CKD diagnosis was increased among the cancer patients regardless of their tumour types or anticancer treatment 8 . It suggests that Cancer and CKD may have a complex inter-relationship, and that may have an impact on their quality of life and usage of healthcare resources 8 . One way to estimate HRQOL and HRU is to explore Patient Reported Outcomes (PRO) in adults having concurrent Cancer and CKD. These outcomes are becoming increasingly important in determining the direct cost of chronic diseases and determining the efficacy of therapeutic interventions. Measures of HRQOL have become a commonly used tool to define and identify the patient reported health outcomes in the health-care delivery paradigms 20 . A recent study identified that the healthcare resource use (e.g., admissions, prescription refills, office visits, and lab services) and cost (e.g., hospital, outpatient and total) were significantly higher in multiple myeloma adults with CKD than those without CKD 6 . In another study, health costs and utilization were associated with renal diseases in adults with Cancer in the US. It also identified that the healthcare expenditure and utilization was significantly higher in Cancer adults with renal diseases than those without renal diseases 21 . However, to the best of our knowledge, there are no studies that identify the factors associated with HRQOL and HRU in adults having concurrent Cancer and CKD. This study thus aims to identify the patterns of HRQOL and HRU in adults having concurrent Cancer and CKD. Our primary objective was to identify the factors associated with low HRQOL and high HRU in adults with concurrent Cancer and CKD. We will examine how the HRQOL and HRU differs across age, gender, race and other socioeconomic categories like education, income, and insurance status. Our secondary objective is to compare HRQOL and HRU for adults having concurrent Cancer and CKD with those having only Cancer, only CKD or neither of the two conditions. We hypothesized that having concurrent Cancer and CKD will be associated with lower HRQOL and higher HRU as compared to those having only Cancer, only CKD or neither of the two conditions. METHODS STUDY DESIGN We conducted a cross-sectional study using Medical Expenditure Panel Survey (MEPS) data from 2011 to 2019. MEPS is a collection of large-scale surveys of families and people, as well as their medical providers and employers, conducted across the United States. The database is derived from the Healthcare Cost and Utilization Project (HCUP) - a family of health care databases and related software tools and products developed through a Federal-State-Industry partnership and sponsored by the Agency for Healthcare Research and Quality (AHRQ). MEPS has been conducted annually since 1996 and it chooses US houses and families using a sophisticated sample design that is intended to be representative of the non-institutionalized civilian population 30 . Face-to-face computer-assisted interviews are used to collect data for the survey, which includes information on demographics, socioeconomic status, physical and mental health conditions, insurance coverage, usage of health services, prescription drug costs, and health status. MEPS is a comprehensive and reliable data source to evaluate national estimates of health expenditures and health status. It offers data for a two-year reference period with an overlapping panel survey. In this survey, a new cohort, or "panel," is started every year and comprises people who are in-person questioned five times, or "rounds," over a period of 2.5 years. MEPS gathers information on the costs associated with medical events, including ambulatory, emergency room, and inpatient visits, as well as prescribed medication. It comprises a medical provider component in addition to household interviews. This component is a follow-back survey that gathers expenditure data from a sample of medical providers that survey participants use. It is prioritized in expenditure estimation and is thought to be more accurate than a household survey. Throughout the MEPS interview, respondents are asked which "health problems" had "bothered" each member of the family during the observation period in order to gather information on specific medical disorders. Respondents also provide the cause of each medical incident. The Clinical Classification Software system, a tool for clustering the approximately 17, 000 International Classification of Diseases (ICD), Ninth and Tenth Revision condition codes into 285 mutually exclusive and homogeneous categories, were used to identify respondents with concurrent Cancer and CKD. STUDY SAMPLE Adults with Lung, Breast, Colorectal, Prostate, and Kidney Cancers were identified from the MEPS dataset using ICD-9-CM codes, C34 (Lung Cancer), C50 (Breast Cancer), C18 (Colorectal Cancer), C61 (Prostate Cancer), and C64 (Kidney Cancer) by years 2011 through 2015. For years 2016 through 2019, we used ICD-10-CM codes, 162 (Lung Cancer), 174 (Breast Cancer), 153 (Colorectal Cancer), 185 (Prostate Cancer), and 189 (Kidney Cancer). CKD adults were identified from the medical condition files from 2011 to 2015 and 2016 to 2019 using ICD-9-CM code 585, and ICD-10-CM code N18, respectively. Patients with ESRD and/or undergoing dialysis were removed from the study sample. This is depicted in Figure 1. STUDY MEASURES Our primary and secondary study outcomes were HRQOL and HRU, respectively. HRQOL was measured using the Physical Component Summary (PCS) and Mental Component Summary (MCS) scores available in MEPS Full year consolidated files. HRU was measured by calculating four separate variables which included inpatient, outpatient, emergency department visits, and as well as prescription medications filled. Prescription medications filled was defined as the number of medications refilled by the patients for the conditions related to their concurrent Cancer and CKD diagnosis and subsequent related events. Covariates included biological factors of gender (male and female) and age in categories (18–44 years, 45–64 years, and 64 and older). Socio-cultural factors included marital status (married, widowed, divorced/separated, and never married) and race/ethnicity (White, African American, and other racial minorities). Other races included individuals who identified as Asian, Native Hawaiian, Pacific Islander and Multiple Races. Socioeconomic status included income categorized as low, middle, and high. Other factors included insurance coverage (private, public, and uninsured) and prescription drug coverage (yes/no). Also, for each patient the number of comorbidities were identified. Apart from the covariates, complex survey variables were also used in selected subgroup analyses for calculating unbiased estimates which would account for the survey design and survey nonresponse. Complex survey variables included a pooled person-level weight, variance estimation primary sampling unit, and a sampling stratum required for variance estimation 30 . STATISTICAL ANALYSES Descriptive statistics were conducted to summarize the study cohorts. Rao Scott chi square test was used to examine the differences in the demographics for four cohorts – Individuals with CKD, Individuals with Cancer, Individuals with both Cancer and CKD, Individuals with neither CKD nor Cancer. A linear regression model was used to estimate the significant factors associated with HRQOL and HRU in adults with concurrent Cancer and CKD. The model was controlled for explanatory variables such as sex, age, race, income level, prescription drug coverage, health insurance coverage, education, and comorbidities. A linear regression was also conducted to identify the differences in HRQOL and HRU between those having concurrent Cancer and CKD with those having only Cancer, only CKD or neither of the two conditions. However, it was observed that there is a variability in the sample size among the four depicted groups. We therefore decided to conduct propensity score matching technique to reduce potential biases. Inverse probability weighting (IPTW) based on the propensity score (PS) was used to address the nonrandomized allocation of the groups. PS weight was used to create a pseudo-population so that the distribution of baseline characteristics between groups are similar, thereby minimizing confounding bias. A maximum of 10,000 regression trees were used in the generalized boosted model to estimate the PS for different groups in order to achieve the best possible balance between them. The weights were derived to obtain the estimates for the average group effect in the population. Generalized boosted model (GBM) were used to estimate the propensity score weights for each group of the disease condition. GBM is a nonparametric machine learning method which involves an iterative process to capture complex and nonlinear relationships between treatment assignment and patient characteristics 43 . The iterative estimation can be tuned to find the propensity score model with optimal balance between groups. The "twang" package in R was utilized to calculate the GBM. We used a maximum of 10,000 regression trees (iterations) in the generalized boosted model to estimate the PS for different groups to achieve the best possible balance between them. The predictor variables in the PS generation included sex, age, race, marital status, income, education, and comorbidities. Standardized differences were calculated to assess the balance of variables in the treatment groups. Proposed cut-offs for acceptable standardized differences have ranged from 0.1 to 0.25. All analyses were conducted using SAS software, version 9.4 (SAS institute. Cary, NC) except IPTW and GBM which were conducted using R software. This study was considered exempt by West Virginia University IRB since MEPS is a publicly available national representative dataset with deidentified individuals. RESULTS Description of Study Sample Table 1 displays the patient characteristics four disease groups: Neither CKD nor Cancer, Cancer only, CKD only, Both Cancer and CKD. Among total 235,057 participants, 229,468 had neither CKD nor Cancer, 4,268 had Cancer only, 862 had CKD only, and 441 had both Cancer and CKD. This meant that 97.6% of the individuals had neither CKD nor Cancer, individuals with Cancer represented 1.8% of the total sample population, 0.4% of the total sample population had CKD while individuals with both Cancer and CKD comprised of only 0.2% of the sample population. The sample had an equal blend of male (50.6%) and females (49.4%) and consisted of a major number of individuals >45 years of age (81.1%). The sample population was predominantly White (63.4%), married (61.6%) and fitted into middle to high income group (67%). A significant difference is observed among the four groups with regards to age (p=0.02), race (p<0.001), marital status (p=0.04), income (p<0.001), health insurance (p<0.001), prescription drug coverage (p<0.001), and presence of comorbidities (p<0.001) which was confirmed using chi square tests. The sample consisted of an almost equal proportion of Males (50.6%) and Females (49.4%). Comorbidities within the sample included congestive heart failure (18.3%), stroke (17.8%), fall (13.8%), hypertension (11.9%), diabetes (8.1%) and COPD (7.6%). Also, a majority of the population had either public or private insurance (80.8%) and prescription drug coverage (78.7%). Factors affecting HRQOL in adults with Cancer and CKD Table 2 presents the association between socioeconomic factors and HRQOL scores including both PCS and MCS scores. Age group, race/ethnicity, income level, health insurance, education level and smoking status were all significantly (p < 0.05) associated with lower HRQOL (i.e., PCS and MCS score) among the patient with concurrent Cancer and CKD. As compared to individuals aged between 18-44 years, those aged 65 and above had twice as lower PCS scores (β = − 1.99, p = 0.002). Surprisingly, individuals aged between the ages of 45-64 had lowest MC score (β = − 1.74, p = 0.001) when compared to the age group 18-44 years. African Americans rated significantly lower in PCS (β = − 1.16, p = 0.001) and MCS (β = − 1.11, p = <0.001) scores as compared to Caucasians. Unmarried individuals had lower PCS and MCS scores when compared to those who were married but was not significant. It was evident that individuals with low income had significantly lower PCS (β=-2.50, p=0.001) and MCS (β=-1.17, p=0.001) scores compared to high income individuals. Additionally, individuals who were uninsured had twice as much lower PCS (β = − 2.42, p = <0.001) and MCS (β = − 2.38, p = <0.001) scores than those covered by private insurance. PCS and MCS scores were significantly lower for those who were found to have less than college education as compared to individuals who did have college education. Also, both PCS and MCS scores were almost twice as much lower for current smokers [PCS (β = − 2.08, p = 0.002) and MCS (β = − 1.99, p = <0.001)]. Factors affecting HRU in individuals with Cancer and CKD The association between socioeconomic factors and HRU varied substantially across the four parameters of HRU (e.g., prescriptions filled, inpatient service use, outpatient service use and emergency department use) (Table 3). As compared to individuals aged between 18-44 years, those aged 45-64 years had highest HRU use in prescription refills category (β = − 1.46, p = 0.001) as compared to the other three categories. Individuals aged 65 and above utilized twice as much outpatient services (β = − 1.99, p = 0.001). African Americans showed consistently lower HRU in four categories compared to Whites. Individuals with low income significantly (p<0.01) utilized highest HRU in all categories. Interestingly, those with middle income (β = 1.41, p = <0.001) and low income (β = 1.50, p = <0.001) had more prescriptions filled than those with high income. Also, those with middle income (β = 1.11, p = <0.001) and low income (β = 1.50, p = <0.001) utilized more outpatient services than those with higher income. This trend was similar when comparing uninsured individuals to those who had private insurance. Those who were uninsured utilized more emergency department services (β = 1.28, p = <0.001), outpatient services (β = 1.21, p = <0.001), and inpatient services (β = 1.52, p = <0.001). Individuals who had less than some college education were also more likely to utilise the resources more. For example, those who had less than high school education utilized more emergency department services (β = 1.82, p = <0.001), outpatient services (β = 1.50, p = <0.001), inpatient services (β = 1.24, p = <0.001) and had more prescriptions filled (β = 1.50, p = <0.001). Also, as compared to nonsmokers, current smokers were more likely to utilize emergency departments (β = 1.99, p = <0.001) and inpatient services (β = 1.66, p = <0.001). HRQOL and HRU mong the adults with Neither CKD nor Cancer, Cancer only, CKD only, and Both Cancer and CKD Using IPTW and GBM for propensity score matching, the patients were divided into the same four groups of Neither CKD nor Cancer, Cancer only, CKD only, Both Cancer and CKD. After propensity score matching, the study sample now consisted of 1634 individuals, of which 403 had neither CKD nor Cancer, 411 had Cancer only, 397 had CKD only, and 423 had both Cancer and CKD. Standardised differences across the study sample for individual characteristics of age, gender, race, marital status and comorbidities are depicted in Supplementary Table 1. After controlling for sex, age, race, marital status, and comorbidities, individuals with both Cancer and CKD exhibited significantly lowest PCS (β=-1.49, p=0.015) and MCS (β=-1.58, p=0.01) scores than individuals with Neither CKD nor Cancer (Supplementary Table 2). Also, we found that individuals with Cancer only had significantly lower PCS scores (β = − 1.01, p = 0.001), with CKD only (β = − 1.12, p =0.002), and with both Cancer and CKD (β = − 1.49, p = 0.015) compared to those with neither CKD nor Cancer. Also, lower MCS scores was observed among individuals with Cancer only (β = − 1.14, p = 0.002), with CKD only (β = − 1.28, p =0.003), and with both Cancer and CKD (β = − 1.58, p = 0.011) compared to those with neither CKD nor Cancer. When measuring HRU usage among the four groups, we found that individuals with both Cancer and CKD significantly (p<0.01) utilized highest HRU among all four categories (Supplementary Table 3). Interestingly, individuals with only CKD showed slightly higher inpatient (β=1.15, p=0.001) and emergency (β=-1.18, p=0.002) services uses than cancer only patients when compared to Neither Cancer and CKD. DISCUSSION Our study examines the patterns and factors associated with HRQOL, and HRU among the individuals who have both Cancer and CKD. Our findings suggest that people at 45–64 years had significantly lowest MCS scores when compared to 18–64 years of age group (see Table 2). People without health insurance had the lowest PCS and MCS score (~ 2.4 times) compared to the people with private health insurance. Moreover, not having a high school degree was associated with their lower PCS and MCS score. Additionally, those with lower income had ~ 2.5 times worse PCS scores than the one with higher income. Also, their MCS scores were lower than those with higher incomes. Current smokers also had twice as worse PCS and MCS scores than those who were nonsmokers. Our findings for HRQOL thus suggest that sociodemographic factors such as middle and low income, not having health insurance, less than high school education, and being a smoker are associated with lower PCS and MCS scores for Cancer and CKD patients. This may be related to the burden of chronic conditions such as CHF (19.7%), stroke (19.2%), Fall (15.1%), Hypertension (13.8), and Diabetes (8.8%) (Table 1). Moreover, having both Cancer and CKD with pre-existing conditions may have induced stress and that is why individuals with both Cancer and CKD might have exhibited a greater decline in PCS and MCS. Our findings are consistent with previous studies which have similarly reported that patients with kidney disease have scores below the national standards for burden of kidney disease and PCS and MCS 31 , 32 . According to Lentz et al., people with Cancer and comorbidities like CKD who are undergoing expensive therapy feel physical and emotional distress and have much poorer levels of life satisfaction 25 . In addition, lower health literacy has been reported in patients with a lower education level. Lower health literacy can cause more delayed diagnosis of conditions, poorer access to health care, and less adherence to healthy lifestyles, which results in the association with worse physical and mental functioning thus leading to lower PCS and MCS scores. 26 ,45 On the other hand, patients in the 65 + age group consumed higher HRU in all the categories (e.g., prescription filled, inpatient services used, outpatient services used, and ER use). Interestingly it was also observed that individuals with low income had about 1.5 times more prescriptions filled and more outpatient visits. Also, uninsured individuals utilized about 1.25 times more inpatient services and emergency department use. Those who had a less than high school education were 1.8 times more likely to use emergency departments. Same was the case with current smokers. The findings of this study point to the overriding and dominating impact of socioeconomic factors on individuals' lives with Cancer and CKD. For example, Individuals' health literacy, ability to seek and follow medical advice, adopt healthy behaviours and lifestyles, and even disease-coping skills can all be affected by a lack of education 23 . The need of integrating socioeconomic determinants of HRQOL and utilization evaluation into clinical care is shown by the prevalence of socioeconomic determinants 28 . Our study also found that individuals with both Cancer and CKD reported significantly lower HRQOL (~ 1.5 times less in both PCS and MCS scores) when compared to neither CKD nor Cancer group. Interestingly, patients with only CKD had significantly lower PCS and MCS scores (e.g., 1.12. and 1.28, respectively) compared to neither CKD nor Cancer group. Additionally, both Cancer and CKD patients consumed higher HRU when compared to the neither CKD nor Cancer group. Importantly, Cancer only and CKD only groups had similar HRU usage in all the categories. Given that a substantial proportion of individuals within the sample had low income, there may be financial barriers to accessing/obtaining health care services, including medical specialist services, prescription drugs, and mental health counselling. This may explain why individuals with CKD eventually exhibit higher HRU than those without CKD 27 . Furthermore, published research have shown that persons with CKD use more healthcare resources 29 . The present findings possess implications for healthcare professionals and educators, who often coordinate care for individuals with Cancer and/or CKD-related complications. Understanding the factors affecting the HRQOL and utilization is important to help manage these chronic disease conditions. The previous epidemiological data suggested significant difficulties in cancer treatment, screening, and biopsy in patients with CKD. Treating Cancer is difficult because of the reduced renal excretion of drugs and different pharmacodynamics/pharmacokinetics of anticancer drugs in patients with CKD 2 – 4 . Our results confirmed it by showing that individuals with both cancer and CKD had utilized all four HRUs higher compared to the neither Cancer and CKD group. Our study further suggests the importance of monitoring the renal functions among the Cancer patients. The outcomes of our study can be useful in decision making, improving patient-physician relationship, and improving health care delivery through a comprehensive patient-centred approach. The findings of the current study could also assist decision-makers and healthcare providers in prediction of imminent increased risk for patients with Cancer and CKD and thus enable tailoring custom made economically sensitive programs to improve patient management and improve resource allocation. Moreover, our study results will be utilized for doing further PRO studies in the newly identified research field named “Onco-nephrology 44 ”. The current study has both strengths and limitations. To the best of the authors’ knowledge, this is one of the few studies to examine the humanistic (i.e., HRQOL) and utilization burden of CKD complications among individuals with Cancer. A representative sample of individuals with Cancer and CKD in the United States was used, and the analyses included a full list of confounders. However, there are several limitations to the research. Because all the data was self-reported, it could be prone to recall bias. Furthermore, due to the lack of laboratory data in MEPS, data on the severity of participants' Cancer and CKD was not accessible, hence it was not considered in this study. The design of this study is cross-sectional, thereby precluding the ability to infer causality. it is possible that the HRQoL and HRU of CKD and Cancer patients could be influenced by factors such as the severity of CKD and the type and stages of Cancer or the addition of more comorbidities. However, the assessment of CKD and Cancer severity was hindered since the MEPS data does not provide information on the different stages of CKD and Cancer. CONCLUSION In the present study, the results indicate that the presence of both Cancer and CKD was negatively associated with HRQOL, and HRU as compared to those without Cancer and CKD. Identifying the factors affecting HRQOL and HRU can help guide interventions and treatments to achieve better patient outcomes. These findings also support the need for comprehensive care for Cancer individuals with CKD-related complications. Declarations No Funding was received for the study and the development of this manuscript. Ethics approval and consent to participate Not Applicable Consent for publication Not Applicable Competing interests The authors declare that they have no competing interests Funding Not Applicable Author Contribution K.A. and M.A. devised the main conceptual idea and designed the study. K.A. wrote the manuscript. M.A. provided critical feedback and supervised the project. All authors helped shape the research, analysis and manuscript. Acknowledgements Not Applicable Data Availability The data utilized in this study were obtained from the Medical Expenditure Panel Survey (MEPS), a publicly accessible database maintained by the Agency for Healthcare Research and Quality (AHRQ), United States Department of Health and Human Services. All datasets analyzed are de-identified and available to the public without restriction. The MEPS data files used in this study can be accessed through the AHRQ website at https://www.meps.ahrq.gov/mepsweb/.No additional or proprietary data were generated or analyzed in this study. References Coresh, J., Selvin, E., Stevens, L. A., Manzi, J., Kusek, J. W., Eggers, P., Van Lente, F., & Levey, A. S. (2007). Prevalence of chronic kidney disease in the United States. JAMA , 298 (17), 2038–2047. https://doi.org/10.1001/jama.298.17.2038 Siegel R, Naishadham D, Jemal A: Cancer statistics, 2012. CA Cancer J Clin 62: 10–29, 2012 Hoerger, T. J., Simpson, S. A., Yarnoff, B. O., Pavkov, M. 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Chronic kidney disease after nephrectomy in individuals with renal cortical tumours: a retrospective cohort study. The Lancet. Oncology , 7 (9), 735–740. https://doi.org/10.1016/S1470-2045(06)70803-8 Qian, Y., Bhowmik, D., Bond, C., Wang, S., Colman, S., Hernandez, R. K., Cheng, P., & Intorcia, M. (2017). Renal impairment and use of nephrotoxic agents in individuals with multiple myeloma in the clinical practice setting in the United States. Cancer medicine , 6 (7), 1523–1530. https://doi.org/10.1002/cam4.1075 Salahudeen, A. K., Doshi, S. M., Pawar, T., Nowshad, G., Lahoti, A., & Shah, P. (2013). Incidence rate, clinical correlates, and outcomes of AKI in individuals admitted to a comprehensive Cancer center. Clinical journal of the American Society of Nephrology: CJASN , 8 (3), 347–354. https://doi.org/10.2215/CJN.03530412 Lahoti, A., Nates, J. L., Wakefield, C. D., Price, K. J., & Salahudeen, A. K. (2011). Costs and outcomes of acute kidney injury in critically ill individuals with Cancer. The journal of supportive oncology , 9 (4), 149–155. https://doi.org/10.1016/j.suponc.2011.03.008 Mujais, S. K., Story, K., Brouillette, J., Takano, T., Soroka, S., Franek, C., Mendelssohn, D., & Finkelstein, F. O. (2009). Health-related quality of life in CKD Individuals: correlates and evolution over time. Clinical journal of the American Society of Nephrology : CJASN , 4 (8), 1293–1301. Dedhia, M., HEALTH OUTCOMES AND UTILIZATION ASSOCIATED WITH RENAL DISEASES IN INDIVIDUALS WITH CANCER IN THE UNITED STATES (2021). Basińska, M. A., & Sołtys, M. (2020). Personal resources and flexibility in coping with stress depending on perceived stress in a group of Cancer individuals. Health Psychology Report, 8(2), 107-119. Jhamb, M., & Roumelioti, M. E. (2020). Socioeconomic Determinants of Quality of Life in Individuals with Kidne y Diseases. Clinical Journal of the American Society of Nephrology, 15(2), 162-164. Lentz, R., Benson III, A. B., & Kircher, S. (2019). Financial toxicity in Cancer care: prevalence, causes, consequences, and reduction strategies. Journal of Surgical Oncology, 120(1), 85-92. Porter, A. C., Lash, J. P., Xie, D., Pan, Q., DeLuca, J., Kanthety, R., ... & CRIC Study Investigators. (2016). Predictors and outcomes of health–related quality of life in individuals with CKD. Clinical Journal of the American Society of Nephrology, 11(7), 1154-1162. Smith, D. H., Gullion, C. M., Nichols, G., Keith, D. S., & Brown, J. B. (2004). Cost of medical care for chronic kidney disease and comorbidity among enrollees in a large HMO population. Journal of the American Society of Nephrology, 15(5), 1300-1306. Teerawattananon, Y., Luz, A., Pilasant, S., Tangsathitkulchai, S., Chootipongchaivat, S., Tritasavit, N., ... & Tantivess, S. (2016). How to meet the demand for good quality renal dialysis as part of universal health coverage in resource-limited settings? Health Research Policy and Systems, 14(1), 1-8. Trifirò, G., Sultana, J., Giorgianni, F., Ingrasciotta, Y., Buemi, M., Muscianisi, M., ... & Santoro, D. (2014). Chronic kidney disease requiring healthcare services: a new approach to evaluate epidemiology of renal disease. BioMed Research International, 2014. Medical Expenditure Panel Survey. https://www.meps.ahrq.gov/mepsweb/ Mazairac, A. H., Grooteman, M. P., Blankestijn, P. J., Penne, E. L., Van Der Weerd, N. C., Den Hoedt, C. H., et al. (2012). Differences in quality of life of hemodialysis patients between dialysis centers. Qual. Life Res. 21, 299–307. doi: 10.1007/s11136-011-9942-3 Erez, G., Selman, L., and Murtagh, F. E. (2016). Measuring health-related quality of life in patients with conservatively managed stage 5 chronic kidney disease: limitations of the medical outcomes study short form 36: SF-36. Qual. Life Res. 25, 2799–2809. doi: 10.1007/s11136-016-1313-7 Palmer, S., Vecchio, M., Craig, J. C., Tonelli, M., Johnson, D. W., Nicolucci, A., et al. (2013). Prevalence of depression in chronic kidney disease: systematic review and meta-analysis of observational studies. Kidney Int. 84, 179–191. doi: 10.1038/ki.2013.77 Epidemiology of Chronic Kidney Disease in Cancer Patients: Lessons From the IRMA Study Group. Summary of the International Conference on Onco-Nephrology: an emerging field in medicine. Janus N, Launay-Vacher V, Byloos E, et al. Cancer and renal insufficiency results of the BIRMA study. Br J Cancer. 2010;103:1815–1821. Benoit DD, Hoste EA. Acute kidney injury in critically ill patients with cancer. Crit Care Clin. 2010;26:151–179. Canet E, Zafrani L, Lambert J, et al. Acute kidney injury in patients with newly diagnosed high-grade hematological malignancies: impact on remission and survival. PLoS One. 2013;8, e55870. Christiansen CF, Johansen MB, Langeberg WJ, et al. Incidence of acute kidney injury in cancer patients: a Danish population-based cohort study. Eur J Intern Med. 2011;22:399–406. Libório AB, Abreu KL, Silva GB Jr, et al. Predicting hospital mortality in critically ill cancer patients according to acute kidney injury severity. Oncology. 2011;80:160–166. Darmon M, Ciroldi M, Thiery G, et al. Clinical review: specific aspects of acute renal failure in cancer patients. Crit Care. 2006;10:211. Iff S, Craig JC, Turner R, et al. Reduced estimated GFR and cancer mortality. Am J Kidney Dis. 2014;63:23–30. Na SY, Sung JY, Chang JH, et al. Chronic kidney disease in cancer: an independent predictor of cancer-specific mortality. Am J Nephrol. 2011;33:121–130. McCaffrey DF, Griffin BA, Almirall D, Slaughter ME, Ramchand R, Burgette LF. A tutorial on propensity score estimation for multiple treatments using generalized boosted models. Stat Med. 2013;32(19):3388-3414. doi:10.1002/sim.5753 Onconephrology: The Intersections Between the Kidney and Cancer. Summary of the International Conference on Onco-Nephrology: an emerging field in medicine Tables Tables 1 to 3 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files TableCKDclean11.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 02 Mar, 2026 Reviewers agreed at journal 10 Feb, 2026 Reviewers invited by journal 09 Feb, 2026 Editor assigned by journal 11 Nov, 2025 Submission checks completed at journal 11 Nov, 2025 First submitted to journal 07 Nov, 2025 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. 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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-8058673","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":589905620,"identity":"5d1cc626-8074-4a90-b0ae-86f692de6a0b","order_by":0,"name":"Keyuri Adhikari","email":"","orcid":"","institution":"West Virginia University","correspondingAuthor":false,"prefix":"","firstName":"Keyuri","middleName":"","lastName":"Adhikari","suffix":""},{"id":589905621,"identity":"ee2ddfff-86a5-484a-8b13-b02574508ea7","order_by":1,"name":"Mohammad A. Al-Mamun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABE0lEQVRIie3OPUvDQBjA8YRCs5xkPZHgJxACB4fQ4mfpQ6BZEpeCi4MH4nWxZM3HqAjPXgLtcjhfiEsWJ4XgJkXxTEERU0o3h/sPx73w4znHsdn+ZaRdXeH0RLvz6c9buIO4G3KY701CvYOcTGeL1zI5C4S3uJm/JcU5q6Z148oChHeNtINw9RAdpRgxQUCWMywm/FEx2hKyvOgkOgkN6YFwQOoDLAB10ndcHIOgCd9C2DrFKxB+Lct3Q+7z+GlDjp+3EW6mmG9QkNXXlDkdcUOG5oZ0E6X4IMUVk7SWVYAx5GYuhY8hk2Q8Oe0iq1tWpXgZZH60LF9wAFke102jaJB5xZ3uIN/1f51Gf25sNpvNtk+fN+ZtZAlmUBgAAAAASUVORK5CYII=","orcid":"","institution":"West Virginia University","correspondingAuthor":true,"prefix":"","firstName":"Mohammad","middleName":"A.","lastName":"Al-Mamun","suffix":""}],"badges":[],"createdAt":"2025-11-07 15:53:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8058673/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8058673/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102503803,"identity":"a7d4c11b-162c-46f3-a235-6ca5949feedc","added_by":"auto","created_at":"2026-02-12 11:06:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":147257,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy Flowchart\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8058673/v1/823b3da38197000b84a3308c.png"},{"id":102746784,"identity":"47ce72af-efb3-4bb4-a44c-de398935ec99","added_by":"auto","created_at":"2026-02-16 09:01:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":704980,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8058673/v1/c49e8bcd-b0c4-49cf-a8d2-fd08d3bcccaa.pdf"},{"id":102503802,"identity":"fe7f8d43-5f2e-4520-bf29-a1e6479c31e9","added_by":"auto","created_at":"2026-02-12 11:06:16","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":156340,"visible":true,"origin":"","legend":"","description":"","filename":"TableCKDclean11.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8058673/v1/87f8690cfc1164a84304cf42.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluating Health Related Quality of Life and Healthcare Utilization Among Adults with Concurrent Cancer and Chronic Kidney Disease: Medical Expenditure Panel Survey","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eCancer and Chronic Kidney Disease (CKD) are both associated with significant morbidity and mortality and are among the rapidly growing health problems both nationally and globally\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Approximately,1\u0026nbsp;million new cancer cases were diagnosed in the United States (U.S.) in 2020, out of which approximately 600,000 have died from the disease\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. According to National Cancer Institute data from 2017\u0026ndash;2019, approximately 40.5 percent of men and women will be diagnosed with any types of cancer at some point during their lifetime.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. On the other hand, about 35.5\u0026nbsp;million people in the US are currently estimated to have CKD and this number is expected to increase to about 37\u0026nbsp;million by 2030\u003csup\u003e3\u003c/sup\u003e. In 2019, the economic burden associated with cancer and CKD care was \u003cspan\u003e$\u003c/span\u003e50.4 and \u003cspan\u003e$\u003c/span\u003e21.09\u0026nbsp;billion, respectively.\u003csup\u003e\u003cb\u003e\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e Cancer patients may suffer from kidney injury due to cancer itself and cancer-related therapy\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Both acute kidney injury (AKI) and CKD are prevalent in patients with cancer\u003csup\u003e\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Incidence and severity of kidney injury varies depending on type/stage of cancer, treatment regimen, and underlying comorbidities. Literature suggests that cancer patients have 1- and 5-year AKI risk of 17.5% and 27%, respectively and they are even at higher risk with ~\u0026thinsp;13% to 54% of patients developing AKI and 8% to 60% requiring dialysis when they are critically ill\u003csup\u003e\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Several studies reported that stage 3 CKD prevalence varies in a range of ~\u0026thinsp;13% to 30% among cancer patients\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. However, the current literature is impeded with lack of information available on the burden of concurrent cancer and CKD on Health-Related Quality of Life (HRQOL) and Health Resource Utilization (HRU) (i.e., usage of healthcare resources and treatment services) in the US.\u003c/p\u003e \u003cp\u003ePrevalence of CKD was found to be the highest among adults with kidney cancer (48.7%), followed by adults with urinary bladder cancer (34.1%), liver cancer (20%)\u003csup\u003e12\u003c/sup\u003e, and pancreatic cancer (19.6%)\u003csup\u003e12\u003c/sup\u003e. Even among adults with a diagnosis of uterine, brain, and testicular cancer, the prevalence of CKD was observed to be 5.7%, 7.1% and 8.8%, respectively\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Adults with moderate-to-severe CKD have over 50% increased risk of cancer death for abdominal solid organs and more than 3 times the risk of death from kidney cancers\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. This incidence may be related to chronic inflammation, accumulation of carcinogenic compounds, oxidative stress, impairment of DNA repair, excessive parathyroid hormone, and changes in intestinal microbiota\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. On the contrary, it was observed that concomitant CKD diagnosis was increased among the cancer patients regardless of their tumour types or anticancer treatment\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. It suggests that Cancer and CKD may have a complex inter-relationship, and that may have an impact on their quality of life and usage of healthcare resources\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOne way to estimate HRQOL and HRU is to explore Patient Reported Outcomes (PRO) in adults having concurrent Cancer and CKD. These outcomes are becoming increasingly important in determining the direct cost of chronic diseases and determining the efficacy of therapeutic interventions. Measures of HRQOL have become a commonly used tool to define and identify the patient reported health outcomes in the health-care delivery paradigms\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. A recent study identified that the healthcare resource use (e.g., admissions, prescription refills, office visits, and lab services) and cost (e.g., hospital, outpatient and total) were significantly higher in multiple myeloma adults with CKD than those without CKD\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. In another study, health costs and utilization were associated with renal diseases in adults with Cancer in the US. It also identified that the healthcare expenditure and utilization was significantly higher in Cancer adults with renal diseases than those without renal diseases\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. However, to the best of our knowledge, there are no studies that identify the factors associated with HRQOL and HRU in adults having concurrent Cancer and CKD.\u003c/p\u003e \u003cp\u003eThis study thus aims to identify the patterns of HRQOL and HRU in adults having concurrent Cancer and CKD. Our primary objective was to identify the factors associated with low HRQOL and high HRU in adults with concurrent Cancer and CKD. We will examine how the HRQOL and HRU differs across age, gender, race and other socioeconomic categories like education, income, and insurance status. Our secondary objective is to compare HRQOL and HRU for adults having concurrent Cancer and CKD with those having only Cancer, only CKD or neither of the two conditions. We hypothesized that having concurrent Cancer and CKD will be associated with lower HRQOL and higher HRU as compared to those having only Cancer, only CKD or neither of the two conditions.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e\u003cu\u003eSTUDY DESIGN\u003c/u\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe conducted a cross-sectional study using Medical Expenditure Panel Survey (MEPS) data from 2011 to 2019. MEPS is a collection of large-scale surveys of families and people, as well as their medical providers and employers, conducted across the United States. The database is derived from the Healthcare Cost and Utilization Project (HCUP) - a family of health care databases and related software tools and products developed through a Federal-State-Industry partnership and sponsored by the Agency for Healthcare Research and Quality (AHRQ). MEPS has been conducted annually since 1996 and it\u0026nbsp;chooses US houses and families using a sophisticated sample design that is intended to be representative of the non-institutionalized civilian population\u003csup\u003e30\u003c/sup\u003e. Face-to-face computer-assisted interviews are used to collect data for the survey, which includes information on demographics, socioeconomic status, physical and mental health conditions, insurance coverage, usage of health services, prescription drug costs, and health status. MEPS is a comprehensive and reliable data source to evaluate national estimates of health expenditures and health status. It offers data for a two-year reference period with an overlapping panel survey. In this survey, a new cohort, or \u0026quot;panel,\u0026quot; is started every year and comprises people who are in-person questioned five times, or \u0026quot;rounds,\u0026quot; over a period of 2.5 years.\u003c/p\u003e\n\u003cp\u003eMEPS gathers information on the costs associated with medical events, including ambulatory, emergency room, and inpatient visits, as well as prescribed medication. It comprises a medical provider component in addition to household interviews. This component is a follow-back survey that gathers expenditure data from a sample of medical providers that survey participants use. It is prioritized in expenditure estimation and is thought to be more accurate than a household survey. Throughout the MEPS interview, respondents are asked which \u0026quot;health problems\u0026quot; had \u0026quot;bothered\u0026quot; each member of the family during the observation period in order to gather information on specific medical disorders. Respondents also provide the cause of each medical incident. The Clinical Classification Software system, a tool for clustering the approximately 17,\u0026nbsp;000 International Classification of Diseases (ICD), Ninth and Tenth Revision\u0026nbsp;condition codes into 285 mutually exclusive and homogeneous categories, were used to identify respondents with concurrent Cancer and CKD.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eSTUDY SAMPLE\u003c/u\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdults with Lung, Breast, Colorectal, Prostate, and Kidney Cancers were identified from the MEPS dataset using ICD-9-CM codes, C34 (Lung Cancer), C50 (Breast Cancer), C18 (Colorectal Cancer), C61 (Prostate Cancer), and C64 (Kidney Cancer) by years 2011 through 2015. For years 2016 through 2019, we used ICD-10-CM codes, 162 (Lung Cancer), 174 (Breast Cancer), 153 (Colorectal Cancer), 185 (Prostate Cancer), and 189 (Kidney Cancer). CKD adults were identified from the medical condition files from 2011 to 2015 and 2016 to 2019 using ICD-9-CM code 585, and ICD-10-CM code N18, respectively. Patients with ESRD and/or undergoing dialysis were removed from the study sample. This is depicted in Figure 1.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eSTUDY MEASURES\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eOur primary and secondary study outcomes were HRQOL and HRU, respectively. HRQOL was measured using the Physical Component Summary (PCS) and Mental Component Summary (MCS) scores available in MEPS Full year consolidated files.\u0026nbsp;HRU was measured by calculating four separate variables which included inpatient, outpatient, emergency department visits, and as well as prescription medications filled. Prescription medications filled was defined as the number of medications refilled by the patients for the conditions related to their concurrent Cancer and CKD diagnosis and subsequent related events.\u003c/p\u003e\n\u003cp\u003eCovariates included biological\u0026nbsp;factors of gender (male and female) and age in categories (18\u0026ndash;44 years, 45\u0026ndash;64 years, and 64 and older). Socio-cultural factors included marital status (married, widowed, divorced/separated, and never married) and race/ethnicity (White, African American, and other racial minorities). Other races included individuals who identified as Asian, Native Hawaiian, Pacific Islander and Multiple Races. Socioeconomic status included income categorized as low, middle, and high. Other factors included insurance coverage (private, public, and uninsured) and prescription drug coverage (yes/no). Also, for each patient the number of comorbidities were identified.\u003c/p\u003e\n\u003cp\u003eApart from the covariates, complex survey variables were also used in selected subgroup analyses for calculating unbiased estimates which would account for the survey design and survey nonresponse. Complex survey variables included a pooled person-level weight, variance estimation\u0026nbsp;primary sampling unit, and a sampling stratum required for variance estimation\u003csup\u003e30\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eSTATISTICAL ANALYSES\u003c/u\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDescriptive statistics were conducted to summarize the study cohorts. Rao Scott chi square test was used to examine the differences in the demographics for four cohorts \u0026ndash; Individuals with CKD, Individuals with Cancer, Individuals with both Cancer and CKD, Individuals with neither CKD nor Cancer. A linear regression model was used to estimate the significant factors associated with HRQOL and HRU in adults with concurrent Cancer and CKD. The model was controlled for explanatory variables such as sex, age, race, income level, prescription drug coverage, health insurance coverage, education, and comorbidities. A linear regression was also conducted to identify the differences in HRQOL and HRU between those having concurrent Cancer and CKD with those having only Cancer, only CKD or neither of the two conditions. However, it was observed that there is a variability in the sample size among the four depicted groups. We therefore decided to conduct propensity score matching technique to reduce potential biases.\u003c/p\u003e\n\u003cp\u003eInverse probability weighting (IPTW) based on the propensity score (PS) was used to address the nonrandomized allocation of the groups. PS weight was used to create a pseudo-population so that the distribution of baseline characteristics between groups are similar, thereby minimizing confounding bias. A maximum of 10,000 regression trees were used in the generalized boosted model to estimate the PS for different groups in order to achieve the best possible balance between them. The weights were derived to obtain the estimates for the average group effect in the population. Generalized boosted model (GBM) were used to estimate the propensity score weights for each group of the disease condition. GBM is a nonparametric machine learning method which involves an iterative process to capture complex and nonlinear relationships between treatment assignment and patient characteristics\u003csup\u003e43\u003c/sup\u003e. The iterative estimation can be tuned to find the propensity score model with optimal balance between groups. The \u0026quot;twang\u0026quot; package in R was utilized to calculate the GBM. We used a maximum of 10,000 regression trees (iterations) in the generalized boosted model to estimate the PS for different groups to achieve the best possible balance between them. The predictor variables in the PS generation included sex, age, race, marital status, income, education, and comorbidities. Standardized differences were calculated to assess the balance of variables in the treatment groups. Proposed cut-offs for acceptable standardized differences have ranged from 0.1 to 0.25.\u003c/p\u003e\n\u003cp\u003eAll analyses were conducted using SAS software, version 9.4 (SAS institute. Cary, NC) except IPTW and GBM which were conducted using R software. This study was considered exempt by West Virginia University IRB since MEPS is a publicly available national representative dataset with deidentified individuals.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003e\u003cu\u003eDescription of Study Sample\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 1 displays the patient characteristics four disease groups: Neither CKD nor Cancer, Cancer only, CKD only, Both Cancer and CKD. Among total 235,057 participants, 229,468 had neither CKD nor Cancer, 4,268 had Cancer only, 862 had CKD only, and 441 had both Cancer and CKD. This meant that 97.6% of the individuals had neither CKD nor Cancer, individuals with Cancer represented 1.8% of the total sample population, 0.4% of the total sample population had CKD while individuals with both Cancer and CKD comprised of only 0.2% of the sample population.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The sample had an equal blend of male (50.6%) and females (49.4%) and consisted of a major number of individuals \u0026gt;45 years of age (81.1%). The sample population was predominantly White (63.4%), married (61.6%) and fitted into middle to high income group (67%). A significant difference is observed among the four groups with regards to age (p=0.02), race (p\u0026lt;0.001), marital status (p=0.04), income (p\u0026lt;0.001), health insurance (p\u0026lt;0.001), prescription drug coverage (p\u0026lt;0.001), and presence of comorbidities (p\u0026lt;0.001) which was confirmed using chi square tests. The sample consisted of an almost equal proportion of Males (50.6%) and Females (49.4%). Comorbidities within the sample included congestive heart failure (18.3%), stroke (17.8%), fall (13.8%), hypertension (11.9%), diabetes (8.1%) and COPD (7.6%). Also, a majority of the population had either public or private insurance (80.8%) and prescription drug coverage (78.7%).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eFactors affecting HRQOL in adults with Cancer and CKD\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 presents the association between socioeconomic factors and HRQOL scores including both PCS and MCS scores. Age group, race/ethnicity, income level, health insurance, education level and smoking status were all significantly (p \u0026lt; 0.05) associated with \u0026nbsp;lower HRQOL (i.e., PCS and MCS score) among the patient with concurrent Cancer and CKD. As compared to individuals aged between 18-44 years, those aged 65 and above had twice as lower PCS scores (\u0026beta; = \u0026minus; 1.99, p = 0.002). Surprisingly, individuals aged between the ages of 45-64 had lowest MC score (\u0026beta; = \u0026minus; 1.74, p = 0.001) \u0026nbsp;when compared to the age group 18-44 years. African Americans rated significantly lower in PCS (\u0026beta; = \u0026minus; 1.16, p = 0.001) and MCS (\u0026beta; = \u0026minus; 1.11, p = \u0026lt;0.001) scores as compared to Caucasians. Unmarried individuals had lower PCS and MCS scores when compared to those who were married but was not significant. It was evident that individuals with low income had significantly lower PCS (\u0026beta;=-2.50, p=0.001) and MCS (\u0026beta;=-1.17, p=0.001) scores compared to high income individuals. Additionally, individuals who were uninsured had twice as much lower PCS (\u0026beta; = \u0026minus; 2.42, p = \u0026lt;0.001) and MCS (\u0026beta; = \u0026minus; 2.38, p = \u0026lt;0.001) scores than those covered by private insurance. PCS and MCS scores were significantly lower for those who were found to have less than college education as compared to individuals who did have college education. Also, both PCS and MCS scores were almost twice as much lower for current smokers [PCS (\u0026beta; = \u0026minus; 2.08, p = 0.002) and MCS (\u0026beta; = \u0026minus; 1.99, p = \u0026lt;0.001)].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eFactors affecting HRU in individuals with Cancer and CKD\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe association between socioeconomic factors and HRU varied substantially across the four parameters of HRU (e.g., prescriptions filled, inpatient service use, outpatient service use and emergency department use) (Table 3). As compared to individuals aged between 18-44 years, those aged 45-64 years had highest HRU use in prescription refills category (\u0026beta; = \u0026minus; 1.46, p = 0.001) as compared to the other three categories. Individuals aged 65 and above utilized twice as much outpatient services (\u0026beta; = \u0026minus; 1.99, p = 0.001). African Americans showed consistently lower HRU in four categories compared to Whites. Individuals with low income significantly (p\u0026lt;0.01) utilized highest HRU in all categories. \u0026nbsp;Interestingly, those with middle income (\u0026beta; = 1.41, p = \u0026lt;0.001) and low income (\u0026beta; = 1.50, p = \u0026lt;0.001) had more prescriptions filled than those with high income. Also, those with middle income (\u0026beta; = 1.11, p = \u0026lt;0.001) and low income (\u0026beta; = 1.50, p = \u0026lt;0.001) utilized more outpatient services than those with higher income. This trend was similar when comparing uninsured individuals to those who had private insurance. Those who were uninsured utilized more emergency department services (\u0026beta; = 1.28, p = \u0026lt;0.001), outpatient services (\u0026beta; = 1.21, p = \u0026lt;0.001), and inpatient services (\u0026beta; = 1.52, p = \u0026lt;0.001). Individuals who had less than some college education were also more likely to utilise the resources more. For example, those who had less than high school education utilized more emergency department services (\u0026beta; = 1.82, p = \u0026lt;0.001), outpatient services (\u0026beta; = 1.50, p = \u0026lt;0.001), inpatient services (\u0026beta; = 1.24, p = \u0026lt;0.001) and had more prescriptions filled (\u0026beta; = 1.50, p = \u0026lt;0.001). Also, as compared to nonsmokers, current smokers were more likely to utilize emergency departments (\u0026beta; = 1.99, p = \u0026lt;0.001) and inpatient services (\u0026beta; = 1.66, p = \u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eHRQOL and HRU mong the adults with Neither CKD nor Cancer, Cancer only, CKD only, and Both Cancer and CKD\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing IPTW and GBM for propensity score matching, the patients were divided into the same four groups of Neither CKD nor Cancer, Cancer only, CKD only, Both Cancer and CKD. After propensity score matching, the study sample now consisted of 1634 individuals, of which 403 had neither CKD nor Cancer, 411 had Cancer only, 397 had CKD only, and 423 had both Cancer and CKD. Standardised differences across the study sample for individual characteristics of age, gender, race, marital status and comorbidities are depicted in Supplementary Table 1.\u003c/p\u003e\n\u003cp\u003eAfter controlling for sex, age, race, marital status, and comorbidities, individuals with both Cancer and CKD exhibited significantly lowest PCS (\u0026beta;=-1.49, p=0.015) and MCS (\u0026beta;=-1.58, p=0.01) scores than individuals with Neither CKD nor Cancer (Supplementary Table 2). Also, we found that individuals with Cancer only had significantly lower PCS scores (\u0026beta; = \u0026minus; 1.01, p = 0.001), with CKD only (\u0026beta; = \u0026minus; 1.12, p =0.002), and with both Cancer and CKD (\u0026beta; = \u0026minus; 1.49, p = 0.015) compared to those with neither CKD nor Cancer. Also, lower MCS scores was observed among individuals with Cancer only (\u0026beta; = \u0026minus; 1.14, p = 0.002), with CKD only (\u0026beta; = \u0026minus; 1.28, p =0.003), and with both Cancer and CKD (\u0026beta; = \u0026minus; 1.58, p = 0.011) compared to those with neither CKD nor Cancer. When measuring HRU usage among the four groups, we found that individuals with both Cancer and CKD significantly (p\u0026lt;0.01) utilized highest HRU among all four categories (Supplementary Table 3). Interestingly, individuals with only CKD showed slightly higher inpatient (\u0026beta;=1.15, p=0.001) and emergency (\u0026beta;=-1.18, p=0.002) services uses than cancer only patients when compared to Neither Cancer and CKD. \u0026nbsp;\u0026nbsp;\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eOur study examines the patterns and factors associated with HRQOL, and HRU among the individuals who have both Cancer and CKD. Our findings suggest that people at 45\u0026ndash;64 years had significantly lowest MCS scores when compared to 18\u0026ndash;64 years of age group (see Table\u0026nbsp;2). People without health insurance had the lowest PCS and MCS score (~\u0026thinsp;2.4 times) compared to the people with private health insurance. Moreover, not having a high school degree was associated with their lower PCS and MCS score. Additionally, those with lower income had\u0026thinsp;~\u0026thinsp;2.5 times worse PCS scores than the one with higher income. Also, their MCS scores were lower than those with higher incomes. Current smokers also had twice as worse PCS and MCS scores than those who were nonsmokers. Our findings for HRQOL thus suggest that sociodemographic factors such as middle and low income, not having health insurance, less than high school education, and being a smoker are associated with lower PCS and MCS scores for Cancer and CKD patients. This may be related to the burden of chronic conditions such as CHF (19.7%), stroke (19.2%), Fall (15.1%), Hypertension (13.8), and Diabetes (8.8%) (Table\u0026nbsp;1). Moreover, having both Cancer and CKD with pre-existing conditions may have induced stress and that is why individuals with both Cancer and CKD might have exhibited a greater decline in PCS and MCS. Our findings are consistent with previous studies which have similarly reported that patients with kidney disease have scores below the national standards for burden of kidney disease and PCS and MCS\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. According to Lentz et al., people with Cancer and comorbidities like CKD who are undergoing expensive therapy feel physical and emotional distress and have much poorer levels of life satisfaction\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. In addition, lower health literacy has been reported in patients with a lower education level. Lower health literacy can cause more delayed diagnosis of conditions, poorer access to health care, and less adherence to healthy lifestyles, which results in the association with worse physical and mental functioning thus leading to lower PCS and MCS scores.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,45\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eOn the other hand, patients in the 65\u0026thinsp;+\u0026thinsp;age group consumed higher HRU in all the categories (e.g., prescription filled, inpatient services used, outpatient services used, and ER use). Interestingly it was also observed that individuals with low income had about 1.5 times more prescriptions filled and more outpatient visits. Also, uninsured individuals utilized about 1.25 times more inpatient services and emergency department use. Those who had a less than high school education were 1.8 times more likely to use emergency departments. Same was the case with current smokers. The findings of this study point to the overriding and dominating impact of socioeconomic factors on individuals' lives with Cancer and CKD. For example, Individuals' health literacy, ability to seek and follow medical advice, adopt healthy behaviours and lifestyles, and even disease-coping skills can all be affected by a lack of education\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. The need of integrating socioeconomic determinants of HRQOL and utilization evaluation into clinical care is shown by the prevalence of socioeconomic determinants\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur study also found that individuals with both Cancer and CKD reported significantly lower HRQOL (~\u0026thinsp;1.5 times less in both PCS and MCS scores) when compared to neither CKD nor Cancer group. Interestingly, patients with only CKD had significantly lower PCS and MCS scores (e.g., 1.12. and 1.28, respectively) compared to neither CKD nor Cancer group. Additionally, both Cancer and CKD patients consumed higher HRU when compared to the neither CKD nor Cancer group. Importantly, Cancer only and CKD only groups had similar HRU usage in all the categories. Given that a substantial proportion of individuals within the sample had low income, there may be financial barriers to accessing/obtaining health care services, including medical specialist services, prescription drugs, and mental health counselling. This may explain why individuals with CKD eventually exhibit higher HRU than those without CKD\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Furthermore, published research have shown that persons with CKD use more healthcare resources\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe present findings possess implications for healthcare professionals and educators, who often coordinate care for individuals with Cancer and/or CKD-related complications. Understanding the factors affecting the HRQOL and utilization is important to help manage these chronic disease conditions. The previous epidemiological data suggested significant difficulties in cancer treatment, screening, and biopsy in patients with CKD. Treating Cancer is difficult because of the reduced renal excretion of drugs and different pharmacodynamics/pharmacokinetics of anticancer drugs in patients with CKD\u003csup\u003e\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Our results confirmed it by showing that individuals with both cancer and CKD had utilized all four HRUs higher compared to the neither Cancer and CKD group. Our study further suggests the importance of monitoring the renal functions among the Cancer patients. The outcomes of our study can be useful in decision making, improving patient-physician relationship, and improving health care delivery through a comprehensive patient-centred approach. The findings of the current study could also assist decision-makers and healthcare providers in prediction of imminent increased risk for patients with Cancer and CKD and thus enable tailoring custom made economically sensitive programs to improve patient management and improve resource allocation. Moreover, our study results will be utilized for doing further PRO studies in the newly identified research field named \u0026ldquo;Onco-nephrology\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e\u0026rdquo;.\u003c/p\u003e \u003cp\u003eThe current study has both strengths and limitations. To the best of the authors\u0026rsquo; knowledge, this is one of the few studies to examine the humanistic (i.e., HRQOL) and utilization burden of CKD complications among individuals with Cancer. A representative sample of individuals with Cancer and CKD in the United States was used, and the analyses included a full list of confounders. However, there are several limitations to the research. Because all the data was self-reported, it could be prone to recall bias. Furthermore, due to the lack of laboratory data in MEPS, data on the severity of participants' Cancer and CKD was not accessible, hence it was not considered in this study. The design of this study is cross-sectional, thereby precluding the ability to infer causality. it is possible that the HRQoL and HRU of CKD and Cancer patients could be influenced by factors such as the severity of CKD and the type and stages of Cancer or the addition of more comorbidities. However, the assessment of CKD and Cancer severity was hindered since the MEPS data does not provide information on the different stages of CKD and Cancer.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eIn the present study, the results indicate that the presence of both Cancer and CKD was negatively associated with HRQOL, and HRU as compared to those without Cancer and CKD. Identifying the factors affecting HRQOL and HRU can help guide interventions and treatments to achieve better patient outcomes. These findings also support the need for comprehensive care for Cancer individuals with CKD-related complications.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eNo Funding was received for the study and the development of this manuscript.\u003c/p\u003e\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eK.A. and M.A. devised the main conceptual idea and designed the study. K.A. wrote the manuscript. M.A. provided critical feedback and supervised the project. All authors helped shape the research, analysis and manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe data utilized in this study were obtained from the Medical Expenditure Panel Survey (MEPS), a publicly accessible database maintained by the Agency for Healthcare Research and Quality (AHRQ), United States Department of Health and Human Services. All datasets analyzed are de-identified and available to the public without restriction. The MEPS data files used in this study can be accessed through the AHRQ website at https://www.meps.ahrq.gov/mepsweb/.No additional or proprietary data were generated or analyzed in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCoresh, J., Selvin, E., Stevens, L. A., Manzi, J., Kusek, J. 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Prevalence of depression in chronic kidney disease: systematic review and meta-analysis of observational studies. Kidney Int. 84, 179\u0026ndash;191. doi: 10.1038/ki.2013.77\u003c/li\u003e\n\u003cli\u003eEpidemiology of Chronic Kidney Disease in Cancer Patients: Lessons From the IRMA Study Group. Summary of the International Conference on Onco-Nephrology: an emerging field in medicine.\u003c/li\u003e\n\u003cli\u003eJanus N, Launay-Vacher V, Byloos E, et al. Cancer and renal insufficiency results of the BIRMA study. Br J Cancer. 2010;103:1815\u0026ndash;1821.\u003c/li\u003e\n\u003cli\u003eBenoit DD, Hoste EA. Acute kidney injury in critically ill patients with cancer. Crit Care Clin. 2010;26:151\u0026ndash;179.\u003c/li\u003e\n\u003cli\u003eCanet E, Zafrani L, Lambert J, et al. Acute kidney injury in patients with newly diagnosed high-grade hematological malignancies: impact on remission and survival. 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Am J Nephrol. 2011;33:121\u0026ndash;130.\u003c/li\u003e\n\u003cli\u003eMcCaffrey DF, Griffin BA, Almirall D, Slaughter ME, Ramchand R, Burgette LF. A tutorial on propensity score estimation for multiple treatments using generalized boosted models. Stat Med. 2013;32(19):3388-3414. doi:10.1002/sim.5753\u003c/li\u003e\n\u003cli\u003eOnconephrology: The Intersections Between the Kidney and Cancer. Summary of the International Conference on Onco-Nephrology: an emerging field in medicine\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 3 are 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":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cancer-causes-and-control","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"caco","sideBox":"Learn more about [Cancer Causes \u0026 Control](https://www.springer.com/journal/10552)","snPcode":"10552","submissionUrl":"https://submission.nature.com/new-submission/10552/3","title":"Cancer Causes \u0026 Control","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Cancer, CKD, HRQOL, HRU, MEPS","lastPublishedDoi":"10.21203/rs.3.rs-8058673/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8058673/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eCancer patients may suffer from kidney injury due to cancer itself and cancer-related therapy. But the burden of concurrent cancer and CKD on Health-Related Quality of Life (HRQOL) and Health Resource Utilization (HRU) remains to be explored. This study aims to identify the patterns and factors associated with HRQOL and HRU in adults having Cancer and CKD.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis was a cross-sectional study using Medical Expenditure Panel Survey 2011 to 2019, with participants (\u0026gt;\u0026thinsp;18 years of age) with 5 cancers (Lung, Breast, Prostate, and Kidney) and CKD. To measure HRQoL, a Physical Component Summary (PCS) and a Mental Component Summary (MCS) were utilized. Multivariable linear and logistic regressions were used to evaluate factors associated with HRQoL and HRU, respectively.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 441 patients with Cancer and CKD (weighted n\u0026thinsp;=\u0026thinsp;235,057) were consisted of \u0026gt;\u0026thinsp;45 years of age (81.1%), White (63.4%), and fitted into middle to high income group (67%). People at 45\u0026ndash;64 years had significantly lowest MCS scores (beta=-1.62 [-1.91, -1.32], p\u0026thinsp;=\u0026thinsp;0.001) when compared to 18\u0026ndash;64 years of age. Sociodemographic factors such as low income (PCS vs MCS: -2.50[-3.38, -1.61] vs -1.17[-1.52, -0.81], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), uninsured (-2.42[-2.83, -1.99] vs -2.38[-2.92, -1.83],p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), less than high school education (-2.5[-2.99, -2.01] vs -1.87[-2.28, -1.45], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001] are significantly associated with lower HRQoL. Both Cancer and CKD had significantly lower HRQOL when compared to with neither CKD nor Cancer group.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur study suggests the importance of monitoring the renal functions among the Cancer patients to improve health care delivery through understanding the disease burden.\u003c/p\u003e","manuscriptTitle":"Evaluating Health Related Quality of Life and Healthcare Utilization Among Adults with Concurrent Cancer and Chronic Kidney Disease: Medical Expenditure Panel Survey","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-12 11:06:11","doi":"10.21203/rs.3.rs-8058673/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-03-03T00:16:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"321492344578607951489277121758849749119","date":"2026-02-10T12:56:28+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-09T22:52:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-11T10:45:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-11T10:43:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cancer Causes \u0026 Control","date":"2025-11-07T15:43:12+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cancer-causes-and-control","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"caco","sideBox":"Learn more about [Cancer Causes \u0026 Control](https://www.springer.com/journal/10552)","snPcode":"10552","submissionUrl":"https://submission.nature.com/new-submission/10552/3","title":"Cancer Causes \u0026 Control","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"49fd5f95-7e61-4c1e-9dfd-552d82d15f67","owner":[],"postedDate":"February 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-12T11:06:11+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-12 11:06:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8058673","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8058673","identity":"rs-8058673","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}