Financial Burden of Out-of-pocket Health Expenditures in Türki̇ye: Under Covid-19

Financial Burden of Out-of-pocket Health Expenditures in Türki̇ye: Under Covid-19 | 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 Financial Burden of Out-of-pocket Health Expenditures in Türki̇ye: Under Covid-19 Seher Nur Sulku, Yagmur Tokatlioglu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7924397/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Apr, 2026 Read the published version in BMC Health Services Research → Version 1 posted 11 You are reading this latest preprint version Abstract Background In alignment with the UN Sustainable Development Goal 3.8 on Universal Health Coverage, this study assesses the extent to which Türkiye’s health insurance system protected households from out-of-pocket ( OOP ) health expenditures before and during the Covid-19 pandemic. Despite the implementation of the Health Transformation Program and Universal Health Insurance, concerns remain about financial protection, particularly amid economic downturns and the rapid expansion of private healthcare. Methods Using nationally representative Household Budget Survey data from 2019 (pre-pandemic) and 2022 (pandemic), the study analyzes health expenditure patterns through four models: (1) a logit model predicting any health expenditure, (2) an OLS model estimating the share of health spending in household budgets, (3) a logit model identifying catastrophic health expenditure (CHE), and (4) an OLS model assessing the elasticity of OOP spending. Results Key findings reveal that nominal OOP spending more than doubled from 98.79 TL in 2019 to 245.37 TL in 2022, yet declined slightly in PPP-adjusted USD. The proportion of households facing CHE rose marginally from 4.28% to 4.37%, well below the global average of 13%. However, CHE intensity worsened, with OOP spending among affected households increasing from 19.5% to 21.9% of total consumption. Pharmaceutical, dental, and hospital costs were the main contributors. Insurance coverage slightly increased the likelihood of spending but reduced CHE risk by 13%, indicating partial protection. Income was a strong predictor of both spending and CHE risk, highlighting limitations in the progressivity of health financing. Vulnerable groups—women, households with young children, elderly, or disabled members—faced higher CHE risk. Barriers to access, such as transportation difficulties, also increased financial burden. Conclusions The study concludes that while Türkiye’s health insurance system offers some protection, it remains insufficient for disadvantaged populations, particularly in times of crisis. The absence of 2020–2021 data due to the pandemic limits insights into the immediate effects of Covid-19 but underscores the need for more resilient, equitable health financing mechanisms. Universal Health Coverage Out-of-Pocket Expenditure Catastrophic Health Expenditure Türkiye Covid-19 Pandemic Figures Figure 1 Figure 2 Background In 2015, all member nations of the United Nations reached a consensus on the Sustainable Development Goals (SDGs) with the objective of creating a more equitable and healthier world by the year 2030. SDG Target 3.8 seeks to achieve Universal Health Coverage (UHC), which encompasses financial risk protection, access to quality essential health-care services, and availability of safe, effective, quality, and affordable essential medicines and vaccines for everyone [ 1 ]. Contemporarily, nations have either embraced or are in different phases of embracing and executing some version of UHC [ 2 ]. The connotation of these endeavours is highlighted by the fact that every year, almost 150 million people encounter severe financial hardship due to healthcare spending and about 100 million are forced into poverty because of these expenditures [ 3 ]. This makes every effort to attain UHC valuable. The concept of insurance, in its most basic form, entails the reduction of financial risk. Health insurance, on the other hand, is expected to alleviate the financial burden on individuals/households or governments under current or future adverse health conditions [ 4 ]. The most fundamental financial burden on individuals is the out-of-pocket ( OOP ) health expenditures [ 5 , 6 ]. Therefore, reforms directed towards the healthcare system are expected to result in a reduction of OOP expenses for individuals or households [ 7 ]. Nonetheless, it is not always clear that health insurance mitigates risk. Since factors such as the demographics of the population, the overall organization of the health system, the balance between demand and supply of healthcare services, the healthcare workforce of the country, as well as the extent and scope of healthcare coverage, influence the outcomes, and OOP payments may increase even when insured [ 8 – 10 ]. Therefore, the question of whether health insurance shields individuals from financial risk is an issue that warrants further investigation. In this research, we assess the extent to which the health insurance mechanism in Türkiye offered suitable safeguards against OOP health expenditures of the individuals using the 2019 and 2022 Household Budget Survey’s (HBS) nationally representative micro data sets, collected by Turkish Statistical Institute (TurkStat), via descriptive statistics, logit and regression models considering households socioeconomic disparities. Since the pandemic, TurkStat was unable to collect data in 2020 and 2021, hence we use available data to infer Türkiye’s experience and assess the pre-Covid period of 2019 to the Covid-era period of 2022. We construct four models: (1) The first model is a logit model predicting whether a household has made any healthcare expenditure, (2) The second model is an OLS model estimating the share of healthcare expenditures in total household expenditures, (3) The third model is a logit model predicting whether a household has faced catastrophic healthcare expenditures, (4) The fourth model is an OLS model estimating the elasticity of OOP healthcare expenditures. In international literature there is no study assessed how the catastrophic health expenditure (CHE) was supposed to change during the Covid-19 era relative to historical patterns and the underlying determinants across countries, only Haakenstad et al. [ 11 ] examines the OOP trends for before and during the Covid-19 period of 2020 for five countries Mexico, Belarus, Russia, Peru and Vietnam and finds out that CHE did not universally increase during the pandemic, but Mexico and Belarus had increases higher than expected based on pre-2020 trends. Indeed, in recent literature on CHE in Türkiye [ 12 – 15 ] there is no study examine the 2019–2022 period and compares the results of post-pandemic year with respect to a pre-pandemic year. Hence we fill this crucial gap via our analysis. Türkiye commenced the execution of the 'Health Transformation Program' (HTP) in 2003. The goal of the HTP was to create an equitable system that delivers high-quality and modern healthcare services to the populace, guarantees effective financial protection against high healthcare expenses, and achieves financial sustainability [ 16 ]. With the HTP, diverse insurance systems were consolidated under the Social Security Institution (SSI), leading to the establishment of the Universal Health Insurance (UHI) System. The UHI system, providing health services under one scheme, was implemented in October 2008 and the variations in benefit provisions among diverse health insurance programs were harmonized under the UHI umbrella [ 17 – 18 ]. From 2003 to 2013, notable advancements, were accomplished within the Turkish healthcare system through the HTP, with these reforms being recognized as a model of excellence on a global scale [ 19 – 20 ]. Despite the fact that, in Türkiye, UHI aimed to offer coverage for the entire population since 2012, the financial costs, prolonged waiting times and a nationwide shortage of physicians threaten the equitable access and effectiveness of the healthcare system [ 21 ]. During the HTP the number of private of hospitals increased enormously, currently constituting 37% of total number of hospitals [ 22 ]. As the private hospitals contracted by the SSI are permitted to impose fees that can be as much as 200% higher than the standard rates. While SSI does cover a portion of the expenses incurred at these private facilities, any remaining costs must be settled as OOP expenses. Consequently, an increasing number of individuals are opting to acquire complementary health insurance to help offset the costs associated with treatment in private hospitals [ 23 ]. Therefore, it is interesting to study Türkiye experience in providing financial protection against severe healthcare expenditure. Indeed, the most serious global health crisis of the last century, Covid-19, first emerged in December 2019 in the city of Wuhan, located in China's Hubei province, and was declared a pandemic on March 11, 2020 [ 24 ] of which the first case in Türkiye was seen. Since the onset of the Covid-19 pandemic in March 2020 in Türkiye, akin to global trends, the health system's capacity to provide services was significantly impacted, leading to the postponement of hospital admissions except for urgent cases [ 25 , 26 ]. Due to reductions in health services from both the supply and demand perspectives, attributed to curfews, fear of infection, and other factors [ 27 , 28 ], there was a rapid reorganization of healthcare systems, including the implementation of telemedicine applications [ 29 ]. A substantial portion of outpatient and primary healthcare transitioned to telemedicine, which is regarded as beneficial since it addresses many non-urgent needs [ 30 ]; however, it has also faced criticism for potentially contributing to decreased access to services [ 31 ]. In Türkiye, a controlled return to normalcy commenced in June 2020, following a reduction in the first wave of Covid-19 due to strict restrictions and measures. During the active combat phase (March 2020-May 2020), the expansion of diagnostic laboratories, early diagnosis and treatment, contact tracing, and the management of medications and protective equipment became paramount [ 32 ]. After a gradual reopening process from May to June, by June 2020, the majority of pandemic related restrictions were lifted [ 30 ]. As a result, many hospitals resumed routine outpatient services and surgeries. Türkiye's Covid-19 vaccination initiated on January 14, 2021, with over 72% of the adult population having received their initial dose; by early July 2021, the Ministry of Health administered a third dose to at-risk groups [ 33 ]. Türkiye has been recognized as one of the most organized and effective nations in combating the Covid-19 pandemic [ 34 ]. It is important to highlight that a robust healthcare infrastructure, along with a substantial number of hospital beds and incentive unit beds, played a crucial role in managing the Covid-19 crisis. A total of 794 hospitals were designated as pandemic facilities, and 11,269 hospital beds were allocated for isolation purposes, with field hospitals established at border crossings [ 30 ]. As it enters its third year since the first reported case, in 2022, Türkiye has moved past the most challenging phases of the coronavirus pandemic, and the WHO has recognized Türkiye for its effective response to the outbreak [ 35 ]. The WHO ended the Covid-19 pandemic on 5 May 2023 [ 36 ]. Turkish Ministry of Health has completely stopped publishing the coronavirus table as of November 27, 2022. According to the latest data released, there have been 17,042,722 coronavirus cases in the country, and 101,492 people have died due to the pandemic. According to WHO data, Türkiye ranks 12th among other countries in terms of total cases and 20th in terms of total deaths [ 37 ]. Furthermore, Türkiye's macroeconomic indicators were deteriorating even before the pandemic. The 2017–2019 period can be briefly described as one in which growth and per capita national income declined steadily, unemployment rose, price stability deteriorated, the Turkish lira excessively depreciated against the US Dollar, the budget deficit and public debt grew, and risk premiums increased [ 38 ]. Furthermore, akin to other nations, the Covid-19 pandemic exacerbated the already struggling Turkish economy through curfews, quarantines, and both national and international travel restrictions, leading to increased inflation, job losses, and diminished income [ 39 , 40 ]. Since 2021, although per capita GDP has begun to recover, reaching a level of US $ 10,659 in 2022 [ 41 ], the distribution of income has worsened. The most recent World Inequality Report, published by the World Inequality Lab at the Paris School of Economics, indicates that the top 10% of the Turkish population receives 54.5% of the total income, while the bottom 50% only receives 11.9% [ 42 ]. During the HTP, the proportion of private health expenditures relative to total health expenditures fell to 21% in 2020, down from 28.69% in 2003; but it has reached to 24% level in 2022 [ 43 ]. Hence, it is also crucial to measure how the Covid-19 pandemic and the economic deterioration cause burden on household’s income in order to access the health services especially for the disadvantaged ones in Türkiye. Brief Health Statistics Facts for Türkiye Health outcomes in Türkiye during the reform period improved successfully: Infant mortality rate per 1000 live births declined to 9.8 in 2022 from 22.6 in 2005, the maternal mortality per 100,000 live births decreased from 64.0 in 2002 to 13.5 in 2022 which is close to the WHO European Region’s average of 11.4 (per 100,000 live births in 2022) [ 22 , 44 ], in 2019 the life expectancy at birth reached 78.6 years in Türkiye but dropped to 77.3 during the Covid-19 period still not far away from the WHO European Region’s average of 79 years. Although the Turkish population is aging with the proportion of individuals aged 65 and older rose from 5.7% in 2000 to 9.7% in 2021, it remains younger in comparison to the OECD, where the average share of the elderly was 17.7% in 2021 [ 22 , 44 ]. In Türkiye, currently, compulsory UHI covers nearly 94% of the population [ 45 ]. Furthermore, the share of OOP health expenditures among total health expenditures was only 16% in 2020, but rose to 18.5% in 2022 becoming close to the OECD average of 18% [ 22 ]. In fact, the public share of total health expenditure in Türkiye is considerably high at 75% compared to developing countries [ 46 ], though slightly around the OECD average of 76% in 2022 [ 22 ]. Furthermore, the population's satisfaction with health services peaked at 75.9% in 2011, up from 39.5% in 2003, and was recorded at 65.6% in 2021 [ 47 ]. Moreover, in Türkiye, by 2018, the current health expenditures had decreased to US $ 32.88 billion, representing 4.1% of GDP, but saw a slight increase in 2020, and reached to US $ 33.55 billion in 2022 [ 22 ]. Nevertheless, the share of current health expenditures in Türkiye's GDP for 2020 and 2022 were 4.6% and 3.7% respectively, which remains significantly lower than the OECD average of 9.7% [ 22 , 48 ]. Methods Data Source In this study, we evaluate the degree to which the health insurance system in Türkiye provided adequate protections against OOP health expenses for individuals, utilizing the nationally representative micro data sets from the 2019 and 2022 Household Budget Surveys (HBS) conducted by the TurkStat 1 employing descriptive statistics, logit and regression models while taking into account the socioeconomic disparities among households. Due to the pandemic, TurkStat was unable to gather data in 2020 and 2021; therefore, we consider and compare the pre-Covid period of 2019 with the post-Covid period of 2022. The HBS micro data sets consist of Household Data Set (dwelling conditions, availability of household goods and facilities, transport vehicle ownership, real estate ownership), Individual Data Set (household composition, economic activity status, employment status, types of income, income), Consumption Expenditure (HBS Code-5). The HBS of Turkstat takes into account all kinds of OOP health expenditures including co-payments but excluding insurance premiums and transportation costs. Health expenditure data cover the following 14 items: Dental services; pharmaceutical products; hospital services; specialist practice; services of medical analysis laboratories and X-ray centres; other medical products n.e.c; corrective eye-glasses and contact lenses; other paramedical services; other therapeutic appliances and equipment; hearing aids; repair of therapeutic appliances and equipment; thermal-baths, corrective-gymnastic therapy, ambulance services and hire of therapeutic equipment; pregnancy tests and mechanical contraceptive devices; general practice. In the 2019 and 2022 HBSs, a total of 15,552 sample households were surveyed over the course of one year, from January 1 to December 31, with a varying sample of 1,296 households each month. Finally, HBS responses were obtained from 11,521 households covering 38,744 individuals for 2019, and 11,922 households covering 39,188 individuals [ 45 , 49 ]. In our regression models, the 2019 and 2022 datasets were combined to create a pooled dataset of 77,932 individuals’ observations. The data management and analyses were conducted using the Stata 17 software package. Multivariate Analysis Models and Variables Employed In this study we built four models presented from Eq. (1) to Eq. (4). Description and summary statistics of the dependent and independent variables used in these models are presented in Table 1 . First, analysis of the presence of any OOP healthcare expenditure with control variables is conducted using logit methodology as given in Eq. (1): \(\:{any\_health}_{i}={\beta\:}_{0}+{\beta\:}_{1}\text{l}\text{n}\left({\text{e}\text{x}\text{p}}_{i}\right)+\sum\:_{i=1}^{\text{k}}{\alpha\:}_{i}{Z}_{i}+{\epsilon\:}_{i}\) (1) here the dependent variable is a dummy, any_health , showing any presence of such expenditure and \(\:{\epsilon\:}_{i}\) is the idiosyncratic error term; in the logit model, the error term captures the unobserved factors that influence the probability of an outcome and accounts for the stochastic component of the utility function The independent variables chosen according to literature [ 50 – 59 ] and the availability in the HBS data set. Since one of an important question is the relationship between individuals’ income level and presence and the magnitude of OOP health expenditure [ 51 , 60 ], we introduced household’s monthly total expenditure, exp , as a proxy of household income. The total consumption expenditure of a household is considered to represent effective income, based on the assumption that consumption provides a more precise indication of purchasing power than the income reported in household surveys [ 6 , 50 ]. Since in accordance with the Turkish family structure, the need for healthcare services by a household member affects not only that individual but also the entire family's budget [ 53 ], while using OOP healthcare expenditures and total expenditures, we assigned household’s aggregate level to individuals within the family. The other explanatory variables, \(\:{Z}_{i}\) , in the model are gender, education level, marital status, employment status, insurance status, household size, the number of individuals aged 5 and under and 65 and over in the household, whether there are individuals with disabilities in the household, household's monthly total expenditure ( exp ), household's transportation barrier to access to health center and a dummy variable for the year 2022, intended to measure the impact of the pandemic-era year. These independent variables were employed also in the other models defined below. The second model, Eq. (2), is an ordinary least square (OLS) regression model estimating the share of OOP healthcare expenditures in total household expenditures, Share = ( OOP Health expenditure/Total expenditure)*100: \(\:{share}_{i}={\beta\:}_{0}+{\beta\:}_{1}\text{l}\text{n}\left({\text{e}\text{x}\text{p}}_{i}\right)+\sum\:_{i=1}^{\text{k}}{\alpha\:}_{i}{Z}_{i}+{\epsilon\:}_{i}\) (2) here the dependent variable is percent defines as \(\:share\) = ( OOP Health Expenditure / Total Expenditure)*100, and \(\:{\epsilon\:}_{i}\) is the idiosyncratic error term; in the OLS model, the error term represents the portion of the dependent variable that is not explained by the independent variables, including omitted factors, measurement errors, and random disturbances. OOP Health Expenditure is defined as summation of all subcategories: (1) Dental services; (2) Pharmaceutical products; (3) Hospital services; (4) Specialist practice; (5) Services of medical analysis laboratories and X-ray centres; (6) Other medical products n.e.c; (7) Corrective eye-glasses and contact lenses; (8) Other paramedical services; (9) Other = Other therapeutic appliances and equipment + Hearing aids + Repair of therapeutic appliances and equipment + Thermal-baths, corrective-gymnastic therapy, ambulance services and hire of therapeutic equipment + Pregnancy tests and mechanical contraceptive devices + General practice. The third model, Eq. 3, is a logit model predicting catastrophic healthcare expenditures. UN the Sustainable Development Goals (SDGs) indicator 3.8.2 defines the OOP health expenditure as catastrophic if a household spends 10% or more of its total household expenditure or income on healthcare services [ 61 ]. Thus, we mainly follow 10% threshold in order to define catastrophe even though in literature there are alternative thresholds [ 5 , 53 ], such as more than 40% of capacity to pay [ 50 ]. \(\:{catastrophic}_{i}\:={\beta\:}_{0}+{\beta\:}_{1}\text{l}\text{n}\left({\text{e}\text{x}\text{p}}_{i}\right)+\sum\:_{i=1}^{\text{k}}{\alpha\:}_{i}{Z}_{i}+{\epsilon\:}_{i}\) (3) Finally, the fourth model is an OLS model estimating the elasticity of OOP healthcare expenditures given in Eq. (4) as follows: \(\:lnOOP={\beta\:}_{0}+{\beta\:}_{1}\text{l}\text{n}\left({\text{e}\text{x}\text{p}}_{i}\right)+{\beta\:}_{2}\text{l}\text{n}\left({\text{e}\text{x}\text{p}}_{i}\right)\:x\:year\:2022+\sum\:_{i=1}^{k}{\alpha\:}_{i}{Z}_{i}+{\epsilon\:}_{i}\) (4) here \(\:lnOOP\) is natural logarithm of the household's monthly OOP health expenditure and we have defined the interaction term \(\:\text{l}\text{n}\left({\text{e}\text{x}\text{p}}_{i}\right)\:x\:year\:2022\) in order to distinguish the elasticity in the Covid-era of 2022 from 2019, thus \(\:({\:\beta\:}_{1}+{\beta\:}_{2})\) measures the elascitiy in 2022 while \(\:{\beta\:}_{1}\) in 2019. In Türkiye since the inflation rate jump to 72.31% in 2022 from 15.18% in 2019, all Turkish lira (TL) values adjusted using purchasing power poverty (PPP) USD $ rate. Eventhough, in nominal terms, mean OOP expenditures more than doubled between 2019 and 2022, rising from 98.79 TL to 245.37 TL, in PPP $ terms it has decreased to 49.07 $ in 2022 from 51.99 $ in 2019. Similarly, in PPP $ terms it has decreased to 2,440 $ in 2022 from 2,589 $ in 2019 (See: Appendix Table 1 ). Therefore, in Table 1 , the averages of natural logarithms of total and health expenditures in PPP $ , ln(exp) and ln OOP , are slightly decreased from 2019 to 2022. Indeed, from Table 1 we see that percentage of people having any_heath expenditure decreased to 52.72% in 2022 from 57.41% in 2019. Since in Türkiye the co-payment is mandatory, this decrease could be attributed to reduction in health demand during the Covid-19 era. Furthermore, average ratio of OOP to total expenditures, share , slightly decreased to 1.79% in 2022 from 1.85% in 2019. However, the percentage of people face with CHE has somewhat increased to 4.37% during the Covid-19 era of 2022 with respect to 4.28% in 2019. Indeed, among households with OOP expenditure above 10%, the burden intensified, as their OOP expenditure ratio increased from 19.53% in 2019 to 21.88% in 2022 (See Appendix Table 1 ). This suggests that financial pressure from health expenditures became more concentrated among already vulnerable households, even though the general distribution of OOP burden across the entire sample did not show substantial change. Table 1 Description of the variables used in the 2019 and 2022 data sets Dependent variables (ratio or mean) 2019 2022 any_health = 1, if the household's health expenditures are greater than 0 57.41% 52.72% Share =( OOP Health expenditure/Total expenditure)*100 1.85% 1.79% Catastrophic = 1, if the share of the household's health expenditures in total expenditures is greater than 10% 4.28% 4.37% ln( OOP ) The logarithm of the household's monthly health expenditure adjusted for PPP 3.427 $ 3.190 $ Explanatory variables ln( exp ) The logarithm of the household's monthly total expenditure adjusted for PPP 7.646 $ 7.522 $ Insured = 1, if individual’s health insurance is SSI ('4A'+'4B'+'4C') or 'UHI' 93.65% 93.87% Difficult Access = 1, if individual has transportation barrier to access to the services related to “health center” considering the location of dwelling is 'difficult' or 'very difficult' 27.61% 29.57% Female = 1, Female 50.56% 50.63% High school and above = 1, if individual’s education level is high level and above 27.60% 29.86% Married = 1, if the individual is 'married' 66.10% 65.64% Employed = 1, if the individual employment status as of survey month (in the last week of the survey month in 2019/2022) is 'worked' 45.27% 45.71% HH size Household size 4.285 4.127 Number of children (0–5) The total number of individuals aged 0–5 in the household 0.465 0.407 Number of elderly (+ 65) The number of individuals aged 65 and over in the household 0.308 0.289 Disable = 1, if there is a member in the household has been limited in activities related to work because of a health or mental problem 14.85% 12.52% Year 2022 = 0, if the year is 2019 n = 38,744 = 1, if the year is 2022 n = 39,188 Source: Our calculations from HBS 2019 & 2022. The variable "Insured" is a binary variable coded as 0, if individual’s health insurance is 'private associations (Bank, etc.)', 'private health insurance' or 'other'; and coded as 1 if the individual possesses UHI and 0 otherwise. The social security categories '4A', '4B', and '4C' under the Social Security Institution in Türkiye have all been consolidated under the umbrella of UHI. The education variable is coded as 1 if the individual’s education level is high school or above, and 0 otherwise. This is because high school represents the most significant breakpoint in the education system in Türkiye. Having a high school education or higher creates a substantial difference in labor force participation, income level, and social status [ 62 ]. Results Distribution of OOP health expenditure for individuals with/out catastrophic expenditures The distribution of healthcare expenditures shown in Figs. 1 and 2 is as follows: Fig. 1, panels (a) and (b), present the distribution of health expenditures among households with a financial burden due to health expenditure in 2019 and 2022, respectively. In 2019, the largest share within the distribution was hospital services with 26%, whereas in 2022, dental services accounted for the highest share with 34%. The second-largest expenditure category was dental services with 20% in 2019, while hospital services ranked second with 25% in 2022. In fact, from 2019 to 2022, the share of hospital services expenditures remained relatively stable, whereas dental services experienced a remarkable increase of 14 percentage points. Additionally, pharmaceutical products ranked as the third major expenditure category in both years; however, their share decreased by 4 percentage points from 2019 to 2022. Figure 2 , panels (a) and (b), illustrate the distribution of health expenditures among households without a financial burden due to health expenditure in 2019 and 2022, respectively. In both years, pharmaceutical products represented the largest share of expenditures, and in contrast to Fig. 1, their share increased by 4 percentage points from 2019 to 2022. Compared to households with a financial burden, these households allocated a substantially higher share of their health expenditures to pharmaceutical products, while the shares of hospital services and dental services markedly declined, particularly in the post-Covid period. Figure 1: Share of health expenditure items in total health expenditure (%), burden greater than 10% of family expenditure (a) 2019, n = 1,657 (b) 2022, n = 1,713 (a) 2019, n = 37,087 (b) 2022, n = 37,475 Model Findings The results for the determinants of OOP health expenditures for the pre-Covid 2019 and the Covid-era 2022 are given in Table 2 . The models used here were defined in equations (1), (2), (3), and (4). Table 2 Regression results for Model (1), (2), (3) & (4) any_health (1) share (2) catastrophic (3) ln(OOP) (4) Odds ratio Coefficient Odds ratio Coefficient ln(exp) 2.072*** 0.720*** 1.578*** 0.738*** (0.031) (0.036) (0.042) (0.019) Insured 1.195*** 0.008 0.868* -0.026 (0.041) (0.078) (0.071) (0.035) Difficult Access 0.873*** 0.058 1.124** 0.002 (0.017) (0.049) (0.051) (0.019) Female 1.074*** 0.093** 1.069 0.044** (0.020) (0.044) (0.045) (0.017) High school and above 1.112*** 0.085* 1.036 0.143*** (0.022) (0.048) (0.048) (0.019) Married 1.161*** 0.189*** 1.088* 0.065*** (0.022) (0.045) (0.049) (0.018) Employed 0.939*** -0.203*** 0.879*** -0.046** (0.018) (0.046) (0.040) (0.018) HH size 0.946*** -0.267*** 0.757*** -0.091*** (0.005) (0.012) (0.011) (0.005) Number of children (0–5) 1.261*** 0.429*** 1.452*** 0.176*** (0.020) (0.034) (0.054) (0.014) Number of elderly (+ 65) 1.079*** 0.493*** 1.389*** 0.156*** (0.015) (0.040) (0.040) (0.014) Disable 1.546*** 1.020*** 2.000*** 0.338*** (0.040) (0.070) (0.101) (0.024) Year 2022 0.905*** 0.033 1.058 -0.681*** (0.016) (0.042) (0.042) (0.194) \(\:\text{l}\text{n}\left({\text{e}\text{x}\text{p}}_{i}\right)\:x\:year\:2022\) - - - 0.067*** (0.025) Constant 0.004*** -3.149*** 0.003*** -2.183*** (0.000) (0.264) (0.001) (0.147) n = 59,529; Pseudo \(\:{\text{R}}^{2}\) =0.0486 n = 59,529; \(\:{\text{R}}^{2}\) =0.0233 n = 59,529; Pseudo \(\:{\text{R}}^{2}\) =0.0393 n = 32,436 \(\:{\text{R}}^{2}\) =0.125 Wald \(\:{\chi\:}^{2}\) (12) = 3466.75 ( p = 0.000) F = 87.54 ( p = 0.000) Wald \(\:{\chi\:}^{2}\) (12) = 961.03 ( p = 0.000) F = 336.37 (p = 0.000) ***, **, * statistical significance at 1%, 5%, and 10% levels, respectively. Robust standard errors are in parentheses. The results show that household expenditure (ln(exp)) is a strong predictor; it more than doubles the odds of any health spending (OR = 2.072) and also increases the likelihood of catastrophic expenditures by about 58% (OR = 1.578). Insurance coverage, while increasing the odds of any expenditure by nearly 20% (OR = 1.195), reduces the risk of catastrophic spending by around 13% (OR = 0.868). This finding underlines the partial protective role of insurance. Households reporting transportation barriers to access healthcare, i.e. difficult access, were less likely to have any health expenditure (OR = 0.873), yet paradoxically more likely to face catastrophic costs (OR = 1.124). Gender and education exert moderate effects, with women and more educated individuals slightly more likely to report any health spending. Marital status has a pronounced effect, as married individuals are 16% more likely to incur expenditures. Employees are less likely to have catastrophic expenditures (OR = 0.879). Larger household size reduces risks substantially, lowering the odds of catastrophic expenditures by nearly 25%; we believe this result is related to the fact that large households in Türkiye tend to have lower levels of education [ 63 ]. By contrast, vulnerable subgroups face severe risks: households with children under five have a 45% higher chance of catastrophic spending, and those with elderly members a 39% higher risk. The most striking one, households with disabled members are twice as likely to face catastrophic expenditures (OR = 2.000). By 2022, the odds of any health spending fell by about 10% while catastrophic expenditures showed no significant change. Taken together, the results underscore that while income and insurance are crucial, household composition—especially the presence of young children, elderly, or disabled members—remains a critical driver of CHEs. The last, (4)th, model explores the income elasticities of out-of-pocket health expenditures with respect to pre-Covid and after Covid years under control of other socio-economic variables. According to our findings the estimated elasticity in 2019 was \(\:\widehat{{\beta\:}_{1}}=0.738\) and rose to \(\:\left(\widehat{{\:\beta\:}_{1}}+\widehat{{\beta\:}_{2}}=0.738+0.067\right)=0.806\) in 2022. Even though all coefficients were statistically significant at 1% level OOP health expenditure stayed inelastic during both years. Discussion This research examines how effectively the health insurance framework in Türkiye has safeguarded individuals from OOP health expenditures during the times of the pandemic and the country specific economic instabilities. We utilize the nationally representative HBS micro data sets to extrapolate Türkiye's experience and evaluate the pre-Covid period of 2019 to the Covid-era period of 2022 because TurkStat was unable to collect data in 2020 and 2021 due to the pandemic. We construct four models: First we predict whether a household has spent any money on healthcare, then an OLS model that calculates the proportion of healthcare spending to total household spending; third, a logit model is used to predict whether a household has experienced catastrophic medical expenses; the fourth model assesses the elasticity of OOP health expenses. Our descriptive analysis indicates that the average OOP expenditures more than doubled from 2019 to 2022, rising from 98.79 TL to 245.37 TL in nominal terms. Nevertheless, when adjusted for purchasing power parity (PPP), the expenditures decreased from 51.99 $ in 2019 to 49.07 $ in 2022. This situation can be attributed to the deteriorated macroeconomics indicators of Türkiye that have been long taken place even before the Covid-19 breakout [ 38 ]. In Türkiye since the inflation rate jump to 72.31% in 2022 from 15.18% in 2019, and the US dollar rocketed to during the same period. Furthermore, it is observed that the proportion of individuals incurring any health expenditure declined to 52.72% in 2022 from 57.41% in 2019. As the co-payment is compulsory in Türkiye, this reduction could be linked to a decrease in healthcare demand during the Covid-19 pandemic. We followed UN SDGs indicator 3.8.2’s CHE definition as spending more than 10% of household consumption on OOP health care expenditure. We see that in Türkiye, the average share of OOP health expenditures to total expenditures decreased to 1.79% in 2022 from 1.85% in 2019. Moreover, similar to Haakenstad et al. [ 11 ], in our findings the percentage of people face with CHE has slightly (not significantly) increased to 4.37% during the Covid-19 era of 2022 with respect to 4.28% in 2019. Cinaroglu [ 15 ], using HBSs, found that the proportion of OOP health expenditure in the effective income of the household, i.e. non-subsistence effective income, rose to 2.66 in 2019 from 2.41 in 2015. Thus, this increasing trend in CHE was already going on and may not be totally attributed to the pandemic. Indeed, in Türkiye there was a decrease of 39% in outpatient services in 2020 and a further decline of 21% in 2021, alongside a reduction of 29% in total inpatient services in 2020 and a 17% decrease in 2021 with respect to 2019 realizations [ 26 ]. As the demand for healthcare services has fallen significantly, that may prevent the expected surge in OOP healthcare expenditures. According to the WHO’s [ 64 ] proportion of the population spending more than 10% of household consumption (or income) on OOP health care expenditure’s the global average was 13% in 2019. Thus, the respective rates in Türkiye for the pre-Covid year of 2019 and the Covid-era of 2022 were 4.28% and 4.37%, around one-third of the world average. However, our findings indicate that among households with CHE their OOP expenditure ratio rose from 19.53% in 2019 to 21.88% in 2022, suggesting that vulnerable ones that are already at risk are subject to an increasing amount of financial strain due to health care costs. Furthermore, we consider the distribution of OOP health expenditure for individuals with/out catastrophic expenditures. We found that for the individuals with CHE the primary reasons of OOP expenditures were dental, hospital services and pharmaceutical expenses both before and during the Covid-19 era. Meanwhile, among those with burdens less than 10% of expenditure, pharmaceutical products constitute the highest proportion of OOP expenditures. In fact, the supply of pharmaceutical and medical items in Türkiye is largely dependent on imports [ 65 ], and the depreciation of the Turkish Lira has increased the expense of funding healthcare. Next, we construct the logit and OLS models to analyse the determinants of the OOP health expenditures and the catastrophic health expenditures (CHEs) via four models (1)-(4). Similar to literature Xu et al. [ 50 ] and van Doorslaer et al. [ 66 ], we found that Income (household expenditure) was a strong predictor: it more than doubles the odds of any health spending and increases the risk of CHEs by 58%. However, unlike findings in high-income countries where higher income reduces CHE risk due to better financial protection [ 67 ], the results here suggest that the progressivity of health financing in Türkiye may be limited. Furthermore, with respect to our findings the insurance coverage increases the likelihood of spending slightly (+ 20%), but reduces CHE risk by 13%, showing partial financial protection, similar to findings in countries with comprehensive coverage (e.g., France, Canada), where insurance significantly lowers CHE incidence [ 68 ]. Moreover, our models indicate that transportation barriers to access healthcare lowers the likelihood of spending but increases CHE risk. Households reporting difficult access to health care were less likely to spend but more likely to experience CHEs—a paradox found in other studies as well [ 69 ]. The disadvantaged people face up barriers to access such as transportation have to pay more for health as indicated in Syed et al. [ 70 ]. Additional we see that women and more educated individuals are slightly more likely to spend on health, consistent with studies linking education and gender with higher health awareness and service use [ 71 ]. According to our findings, married individuals are 16% more likely to spend on health echoing earlier work that marriage enhances health outcomes and access [ 72 ]. Indeed, we see that employees are less likely to face CHEs, aligning with research showing that formal sector workers are more likely to have stable incomes and health benefits [ 73 ]. Furthermore, our finding that larger households had a 25% lower risk of CHEs, potentially due to pooled financial resources or shared caregiving. However, other studies (e.g., UNICEF, [ 74 ]) caution that larger household size can also indicate hidden vulnerability, especially when dependents outnumber earners—a nuance observed in Türkiye where large households often have lower education levels. Moreover, we see that vulnerable groups at higher risk are households with children under 5, with elderly members and with disabled members. This reflects findings from Russell [ 75 ] and Saksena, Smith, and Tediosi [ 76 ], who highlight the economic burden of dependency and the lack of institutional care, particularly in low- and middle-income settings. Furthermore, we figured out that OOP health spending was income inelastic in both 2019 and 2022, though elasticity slightly increased from 0.738 to 0.806 in Türkiye. Our finding is consistent with the World Bank [ 77 ], which classify health care as a necessity good: spending does not rise proportionally with income. Limitation: TurkStat was unable to conduct Household Budget Surveys in 2020 and 2021 due to Covid-related restrictions. Therefore, unfortunately, we cannot measure the impact of Covid-19 on OOP health expenditures in 2020, when the Covid-19 pandemic broke out and when the country and people were caught unprepared, and in 2021, while pandemic measures continued. Despite the fact that 2022 remains a pandemic year for Türkiye, the country has progressed past the most difficult stages of the coronavirus pandemic, as most restrictions associated with the pandemic were removed by June 2020 [ 30 ]. We recognize this as a limitation of our study. Conclusion This study closes a gap in the literature by analyzing OOP medical expenses in Türkiye throughout the 2019–2022 timeframe and contrasting the outcomes of the pandemic year of 2022 with those of a pre-pandemic year of 2019. Türkiye is particularly noteworthy because of the significant progress made and its recognition as a global model of excellence through the health system reforms. But since 2018, its economy has been in turmoil, and the Covid-19 pandemic and economic inequality have put financial security and health care affordability at risk. According to our findings the proportion of population facing CHE for the pre-Covid year of 2019 and the Covid-era of 2022 were respectively 4.28% and 4.37%. Moreover, for individuals with CHE their OOP expenditure ratio rose from 19.53% in 2019 to 21.88% in 2022, implying the financial pressure from health expenditures become more concentrated among already vulnerable households. It is seen from our logit and regression models that income and insurance play central roles in determining health spending behavior and exposure to financial risk. However, household composition, particularly the presence of young children, elderly, or disabled members, critically shapes the risk of catastrophic expenditures. Addressing these disparities requires not only economic interventions but also structural reforms to improve access and protect vulnerable populations. In order to prioritize suitable policies and efforts to safeguard these vulnerable people by providing financial protection, decision makers can benefit from our findings for better understanding the factors that contribute to OOP health expenditure in Türkiye. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding There was no available funding for this study. This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Author Contribution All authors contributed equally, read, and approved the final version of the manuscript. Acknowledgements Not applicable. Data Availability Not required. 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The HBS micro data was prepared by TurkStat in accordance with “Regulations on Procedures and Guidelines for Data Privacy and Confidential Data Security at Official Statistics” which came into force after publishing in Official Newspaper No.26204 and dated June 20, 2006 as described in the Turkish Statistical Institute’s Decree No. 5429 and Law 13 [ 45 ]. Turkish Statistical Institute reserves all the rights of these data. Unauthorised duplication or distribution of the data is prohibited under Law No. 5846. Additional Declarations No competing interests reported. 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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-7924397","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":537713194,"identity":"42a6c98f-2650-4781-97c7-1c7c236b82cf","order_by":0,"name":"Seher Nur Sulku","email":"","orcid":"","institution":"Ankara Haci Bayram Veli University","correspondingAuthor":false,"prefix":"","firstName":"Seher","middleName":"Nur","lastName":"Sulku","suffix":""},{"id":537713195,"identity":"e4fc2d0a-9f85-484e-bad9-f25c1bac9eda","order_by":1,"name":"Yagmur Tokatlioglu","email":"data:image/png;base64,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","orcid":"","institution":"Ankara Haci Bayram Veli University","correspondingAuthor":true,"prefix":"","firstName":"Yagmur","middleName":"","lastName":"Tokatlioglu","suffix":""}],"badges":[],"createdAt":"2025-10-22 14:08:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7924397/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7924397/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12913-026-14530-1","type":"published","date":"2026-04-23T15:58:50+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":95503304,"identity":"97f90e45-0ff9-481e-8329-742e72d4e112","added_by":"auto","created_at":"2025-11-10 05:38:51","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":99008,"visible":true,"origin":"","legend":"","description":"","filename":"FinancialBurdenlast1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7924397/v1/839f5d5e1fab653b1290153c.docx"},{"id":95503309,"identity":"82e86beb-de16-4c23-a7ae-119d6c9abed7","added_by":"auto","created_at":"2025-11-10 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05:38:53","extension":"eps","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":126413,"visible":true,"origin":"","legend":"","description":"","filename":"drawingimage4.eps","url":"https://assets-eu.researchsquare.com/files/rs-7924397/v1/6d9b6092c8f6ef1252d0e70f.eps"},{"id":95503275,"identity":"6ca06891-7bd3-4917-856d-e6a8f655af73","added_by":"auto","created_at":"2025-11-10 05:38:49","extension":"xml","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":168565,"visible":true,"origin":"","legend":"","description":"","filename":"f3ea8535920b46359ec91436d597765a1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7924397/v1/fe50a0aa3182ce7412ba2cbc.xml"},{"id":95503274,"identity":"e1c483ed-090d-4bae-b758-73cbfe6f4e5e","added_by":"auto","created_at":"2025-11-10 05:38:49","extension":"html","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":180882,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7924397/v1/b1effb8df487d192da8cc285.html"},{"id":95503306,"identity":"e005377a-64e6-43b6-b458-c64e1b523f89","added_by":"auto","created_at":"2025-11-10 05:38:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":128299,"visible":true,"origin":"","legend":"\u003cp\u003eShare of health expenditure items in total health expenditure (%), burden greater than 10% of family expenditure\u003c/p\u003e\n\u003cp\u003e(a) 2019, n=1,657 \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;(b) 2022, n=1,713\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7924397/v1/d1e416ee0640602cfa90fd09.png"},{"id":95503273,"identity":"afa17a7a-a7ea-4e1a-8fa7-73ed13610b49","added_by":"auto","created_at":"2025-11-10 05:38:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":138961,"visible":true,"origin":"","legend":"\u003cp\u003eShare of health expenditure items in total health expenditure (%), lower than 10% of family expenditure\u003c/p\u003e\n\u003cp\u003e(a) 2019, n=37,087 \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;(b) 2022, n=37,475\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7924397/v1/43b363ef0570f30971df0e86.png"},{"id":107929018,"identity":"99abaab2-1450-41e4-b818-26758016d03c","added_by":"auto","created_at":"2026-04-27 16:13:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":690661,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7924397/v1/fee4ef09-14df-4c15-94d2-1a8a052ebbaf.pdf"},{"id":95503310,"identity":"97fc7fc3-1c38-4d75-a7a0-3d74e1518f30","added_by":"auto","created_at":"2025-11-10 05:38:52","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":16208,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-7924397/v1/4ce34676de8acdbddd21d9ac.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eFinancial Burden of Out-of-pocket Health Expenditures in Türki̇ye: Under Covid-19\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eIn 2015, all member nations of the United Nations reached a consensus on the Sustainable Development Goals (SDGs) with the objective of creating a more equitable and healthier world by the year 2030. SDG Target 3.8 seeks to achieve Universal Health Coverage (UHC), which encompasses financial risk protection, access to quality essential health-care services, and availability of safe, effective, quality, and affordable essential medicines and vaccines for everyone [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Contemporarily, nations have either embraced or are in different phases of embracing and executing some version of UHC [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The connotation of these endeavours is highlighted by the fact that every year, almost 150\u0026nbsp;million people encounter severe financial hardship due to healthcare spending and about 100\u0026nbsp;million are forced into poverty because of these expenditures [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This makes every effort to attain UHC valuable.\u003c/p\u003e\u003cp\u003eThe concept of insurance, in its most basic form, entails the reduction of financial risk. Health insurance, on the other hand, is expected to alleviate the financial burden on individuals/households or governments under current or future adverse health conditions [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The most fundamental financial burden on individuals is the out-of-pocket (\u003cem\u003eOOP\u003c/em\u003e) health expenditures [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Therefore, reforms directed towards the healthcare system are expected to result in a reduction of \u003cem\u003eOOP\u003c/em\u003e expenses for individuals or households [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Nonetheless, it is not always clear that health insurance mitigates risk. Since factors such as the demographics of the population, the overall organization of the health system, the balance between demand and supply of healthcare services, the healthcare workforce of the country, as well as the extent and scope of healthcare coverage, influence the outcomes, and \u003cem\u003eOOP\u003c/em\u003e payments may increase even when insured [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Therefore, the question of whether health insurance shields individuals from financial risk is an issue that warrants further investigation.\u003c/p\u003e\u003cp\u003eIn this research, we assess the extent to which the health insurance mechanism in T\u0026uuml;rkiye offered suitable safeguards against \u003cem\u003eOOP\u003c/em\u003e health expenditures of the individuals using the 2019 and 2022 Household Budget Survey\u0026rsquo;s (HBS) nationally representative micro data sets, collected by Turkish Statistical Institute (TurkStat), via descriptive statistics, logit and regression models considering households socioeconomic disparities. Since the pandemic, TurkStat was unable to collect data in 2020 and 2021, hence we use available data to infer T\u0026uuml;rkiye\u0026rsquo;s experience and assess the pre-Covid period of 2019 to the Covid-era period of 2022. We construct four models: (1) The first model is a logit model predicting whether a household has made any healthcare expenditure, (2) The second model is an OLS model estimating the share of healthcare expenditures in total household expenditures, (3) The third model is a logit model predicting whether a household has faced catastrophic healthcare expenditures, (4) The fourth model is an OLS model estimating the elasticity of \u003cem\u003eOOP\u003c/em\u003e healthcare expenditures. In international literature there is no study assessed how the catastrophic health expenditure (CHE) was supposed to change during the Covid-19 era relative to historical patterns and the underlying determinants across countries, only Haakenstad et al. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] examines the \u003cem\u003eOOP\u003c/em\u003e trends for before and during the Covid-19 period of 2020 for five countries Mexico, Belarus, Russia, Peru and Vietnam and finds out that CHE did not universally increase during the pandemic, but Mexico and Belarus had increases higher than expected based on pre-2020 trends. Indeed, in recent literature on CHE in T\u0026uuml;rkiye [\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] there is no study examine the 2019\u0026ndash;2022 period and compares the results of post-pandemic year with respect to a pre-pandemic year. Hence we fill this crucial gap via our analysis.\u003c/p\u003e\u003cp\u003eT\u0026uuml;rkiye commenced the execution of the 'Health Transformation Program' (HTP) in 2003. The goal of the HTP was to create an equitable system that delivers high-quality and modern healthcare services to the populace, guarantees effective financial protection against high healthcare expenses, and achieves financial sustainability [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. With the HTP, diverse insurance systems were consolidated under the Social Security Institution (SSI), leading to the establishment of the Universal Health Insurance (UHI) System. The UHI system, providing health services under one scheme, was implemented in October 2008 and the variations in benefit provisions among diverse health insurance programs were harmonized under the UHI umbrella [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. From 2003 to 2013, notable advancements, were accomplished within the Turkish healthcare system through the HTP, with these reforms being recognized as a model of excellence on a global scale [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite the fact that, in T\u0026uuml;rkiye, UHI aimed to offer coverage for the entire population since 2012, the financial costs, prolonged waiting times and a nationwide shortage of physicians threaten the equitable access and effectiveness of the healthcare system [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. During the HTP the number of private of hospitals increased enormously, currently constituting 37% of total number of hospitals [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. As the private hospitals contracted by the SSI are permitted to impose fees that can be as much as 200% higher than the standard rates. While SSI does cover a portion of the expenses incurred at these private facilities, any remaining costs must be settled as \u003cem\u003eOOP\u003c/em\u003e expenses. Consequently, an increasing number of individuals are opting to acquire complementary health insurance to help offset the costs associated with treatment in private hospitals [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Therefore, it is interesting to study T\u0026uuml;rkiye experience in providing financial protection against severe healthcare expenditure.\u003c/p\u003e\u003cp\u003eIndeed, the most serious global health crisis of the last century, Covid-19, first emerged in December 2019 in the city of Wuhan, located in China's Hubei province, and was declared a pandemic on March 11, 2020 [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] of which the first case in T\u0026uuml;rkiye was seen. Since the onset of the Covid-19 pandemic in March 2020 in T\u0026uuml;rkiye, akin to global trends, the health system's capacity to provide services was significantly impacted, leading to the postponement of hospital admissions except for urgent cases [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Due to reductions in health services from both the supply and demand perspectives, attributed to curfews, fear of infection, and other factors [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], there was a rapid reorganization of healthcare systems, including the implementation of telemedicine applications [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. A substantial portion of outpatient and primary healthcare transitioned to telemedicine, which is regarded as beneficial since it addresses many non-urgent needs [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]; however, it has also faced criticism for potentially contributing to decreased access to services [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn T\u0026uuml;rkiye, a controlled return to normalcy commenced in June 2020, following a reduction in the first wave of Covid-19 due to strict restrictions and measures. During the active combat phase (March 2020-May 2020), the expansion of diagnostic laboratories, early diagnosis and treatment, contact tracing, and the management of medications and protective equipment became paramount [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. After a gradual reopening process from May to June, by June 2020, the majority of pandemic related restrictions were lifted [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. As a result, many hospitals resumed routine outpatient services and surgeries.\u003c/p\u003e\u003cp\u003eT\u0026uuml;rkiye's Covid-19 vaccination initiated on January 14, 2021, with over 72% of the adult population having received their initial dose; by early July 2021, the Ministry of Health administered a third dose to at-risk groups [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. T\u0026uuml;rkiye has been recognized as one of the most organized and effective nations in combating the Covid-19 pandemic [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. It is important to highlight that a robust healthcare infrastructure, along with a substantial number of hospital beds and incentive unit beds, played a crucial role in managing the Covid-19 crisis. A total of 794 hospitals were designated as pandemic facilities, and 11,269 hospital beds were allocated for isolation purposes, with field hospitals established at border crossings [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAs it enters its third year since the first reported case, in 2022, T\u0026uuml;rkiye has moved past the most challenging phases of the coronavirus pandemic, and the WHO has recognized T\u0026uuml;rkiye for its effective response to the outbreak [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The WHO ended the Covid-19 pandemic on 5 May 2023 [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Turkish Ministry of Health has completely stopped publishing the coronavirus table as of November 27, 2022. According to the latest data released, there have been 17,042,722 coronavirus cases in the country, and 101,492 people have died due to the pandemic. According to WHO data, T\u0026uuml;rkiye ranks 12th among other countries in terms of total cases and 20th in terms of total deaths [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFurthermore, T\u0026uuml;rkiye's macroeconomic indicators were deteriorating even before the pandemic. The 2017\u0026ndash;2019 period can be briefly described as one in which growth and per capita national income declined steadily, unemployment rose, price stability deteriorated, the Turkish lira excessively depreciated against the US Dollar, the budget deficit and public debt grew, and risk premiums increased [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Furthermore, akin to other nations, the Covid-19 pandemic exacerbated the already struggling Turkish economy through curfews, quarantines, and both national and international travel restrictions, leading to increased inflation, job losses, and diminished income [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Since 2021, although per capita GDP has begun to recover, reaching a level of US\u003cspan\u003e$\u003c/span\u003e 10,659 in 2022 [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], the distribution of income has worsened. The most recent World Inequality Report, published by the World Inequality Lab at the Paris School of Economics, indicates that the top 10% of the Turkish population receives 54.5% of the total income, while the bottom 50% only receives 11.9% [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. During the HTP, the proportion of private health expenditures relative to total health expenditures fell to 21% in 2020, down from 28.69% in 2003; but it has reached to 24% level in 2022 [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Hence, it is also crucial to measure how the Covid-19 pandemic and the economic deterioration cause burden on household\u0026rsquo;s income in order to access the health services especially for the disadvantaged ones in T\u0026uuml;rkiye.\u003c/p\u003e\n\u003ch3\u003eBrief Health Statistics Facts for Türkiye\u003c/h3\u003e\n\u003cp\u003eHealth outcomes in Türkiye during the reform period improved successfully: Infant mortality rate per 1000 live births declined to 9.8 in 2022 from 22.6 in 2005, the maternal mortality per 100,000 live births decreased from 64.0 in 2002 to 13.5 in 2022 which is close to the WHO European Region’s average of 11.4 (per 100,000 live births in 2022) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], in 2019 the life expectancy at birth reached 78.6 years in Türkiye but dropped to 77.3 during the Covid-19 period still not far away from the WHO European Region’s average of 79 years. Although the Turkish population is aging with the proportion of individuals aged 65 and older rose from 5.7% in 2000 to 9.7% in 2021, it remains younger in comparison to the OECD, where the average share of the elderly was 17.7% in 2021 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. In Türkiye, currently, compulsory UHI covers nearly 94% of the population [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Furthermore, the share of \u003cem\u003eOOP\u003c/em\u003e health expenditures among total health expenditures was only 16% in 2020, but rose to 18.5% in 2022 becoming close to the OECD average of 18% [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In fact, the public share of total health expenditure in Türkiye is considerably high at 75% compared to developing countries [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], though slightly around the OECD average of 76% in 2022 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Furthermore, the population's satisfaction with health services peaked at 75.9% in 2011, up from 39.5% in 2003, and was recorded at 65.6% in 2021 [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Moreover, in Türkiye, by 2018, the current health expenditures had decreased to US\u003cspan\u003e$\u003c/span\u003e 32.88\u0026nbsp;billion, representing 4.1% of GDP, but saw a slight increase in 2020, and reached to US\u003cspan\u003e$\u003c/span\u003e 33.55\u0026nbsp;billion in 2022 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Nevertheless, the share of current health expenditures in Türkiye's GDP for 2020 and 2022 were 4.6% and 3.7% respectively, which remains significantly lower than the OECD average of 9.7% [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003cdiv id=\"Sec4\" class=\"Section3\"\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Methods","content":"\u003ch2\u003eData Source\u003c/h2\u003e\u003cp\u003eIn this study, we evaluate the degree to which the health insurance system in Türkiye provided adequate protections against \u003cem\u003eOOP\u003c/em\u003e health expenses for individuals, utilizing the nationally representative micro data sets from the 2019 and 2022 Household Budget Surveys (HBS) conducted by the TurkStat\u003csup\u003e1\u003c/sup\u003e employing descriptive statistics, logit and regression models while taking into account the socioeconomic disparities among households. Due to the pandemic, TurkStat was unable to gather data in 2020 and 2021; therefore, we consider and compare the pre-Covid period of 2019 with the post-Covid period of 2022.\u003c/p\u003e\u003cp\u003eThe HBS micro data sets consist of Household Data Set (dwelling conditions, availability of household goods and facilities, transport vehicle ownership, real estate ownership), Individual Data Set (household composition, economic activity status, employment status, types of income, income), Consumption Expenditure (HBS Code-5). The HBS of Turkstat takes into account all kinds of \u003cem\u003eOOP\u003c/em\u003e health expenditures including co-payments but excluding insurance premiums and transportation costs. Health expenditure data cover the following 14 items: Dental services; pharmaceutical products; hospital services; specialist practice; services of medical analysis laboratories and X-ray centres; other medical products n.e.c; corrective eye-glasses and contact lenses; other paramedical services; other therapeutic appliances and equipment; hearing aids; repair of therapeutic appliances and equipment; thermal-baths, corrective-gymnastic therapy, ambulance services and hire of therapeutic equipment; pregnancy tests and mechanical contraceptive devices; general practice.\u003c/p\u003e\u003cp\u003eIn the 2019 and 2022 HBSs, a total of 15,552 sample households were surveyed over the course of one year, from January 1 to December 31, with a varying sample of 1,296 households each month. Finally, HBS responses were obtained from 11,521 households covering 38,744 individuals for 2019, and 11,922 households covering 39,188 individuals [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. In our regression models, the 2019 and 2022 datasets were combined to create a pooled dataset of 77,932 individuals’ observations. The data management and analyses were conducted using the Stata 17 software package.\u003c/p\u003e\n\u003ch3\u003eMultivariate Analysis Models and Variables Employed\u003c/h3\u003e\n\u003cp\u003eIn this study we built four models presented from Eq.\u0026nbsp;(1) to Eq.\u0026nbsp;(4). Description and summary statistics of the dependent and independent variables used in these models are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. First, analysis of the presence of any \u003cem\u003eOOP\u003c/em\u003e healthcare expenditure with control variables is conducted using logit methodology as given in Eq.\u0026nbsp;(1):\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{any\\_health}_{i}={\\beta\\:}_{0}+{\\beta\\:}_{1}\\text{l}\\text{n}\\left({\\text{e}\\text{x}\\text{p}}_{i}\\right)+\\sum\\:_{i=1}^{\\text{k}}{\\alpha\\:}_{i}{Z}_{i}+{\\epsilon\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ehere the dependent variable is a dummy, \u003cem\u003eany_health\u003c/em\u003e, showing any presence of such expenditure and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the idiosyncratic error term; in the logit model, the error term captures the unobserved factors that influence the probability of an outcome and accounts for the stochastic component of the utility function The independent variables chosen according to literature [\u003cspan additionalcitationids=\"CR51 CR52 CR53 CR54 CR55 CR56 CR57 CR58\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e] and the availability in the HBS data set. Since one of an important question is the relationship between individuals\u0026rsquo; income level and presence and the magnitude of \u003cem\u003eOOP\u003c/em\u003e health expenditure [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e], we introduced household\u0026rsquo;s monthly total expenditure, \u003cem\u003eexp\u003c/em\u003e, as a proxy of household income. The total consumption expenditure of a household is considered to represent effective income, based on the assumption that consumption provides a more precise indication of purchasing power than the income reported in household surveys [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Since in accordance with the Turkish family structure, the need for healthcare services by a household member affects not only that individual but also the entire family's budget [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], while using \u003cem\u003eOOP\u003c/em\u003e healthcare expenditures and total expenditures, we assigned household\u0026rsquo;s aggregate level to individuals within the family. The other explanatory variables, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Z}_{i}\\)\u003c/span\u003e\u003c/span\u003e, in the model are gender, education level, marital status, employment status, insurance status, household size, the number of individuals aged 5 and under and 65 and over in the household, whether there are individuals with disabilities in the household, household's monthly total expenditure (\u003cem\u003eexp\u003c/em\u003e), household's transportation barrier to access to health center and a dummy variable for the year 2022, intended to measure the impact of the pandemic-era year. These independent variables were employed also in the other models defined below.\u003c/p\u003e\u003cp\u003eThe second model, Eq.\u0026nbsp;(2), is an ordinary least square (OLS) regression model estimating the share of \u003cem\u003eOOP\u003c/em\u003e healthcare expenditures in total household expenditures, Share = (\u003cem\u003eOOP\u003c/em\u003e Health expenditure/Total expenditure)*100:\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{share}_{i}={\\beta\\:}_{0}+{\\beta\\:}_{1}\\text{l}\\text{n}\\left({\\text{e}\\text{x}\\text{p}}_{i}\\right)+\\sum\\:_{i=1}^{\\text{k}}{\\alpha\\:}_{i}{Z}_{i}+{\\epsilon\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ehere the dependent variable is percent defines as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:share\\)\u003c/span\u003e\u003c/span\u003e = (\u003cem\u003eOOP\u003c/em\u003e Health Expenditure / Total Expenditure)*100, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the idiosyncratic error term; in the OLS model, the error term represents the portion of the dependent variable that is not explained by the independent variables, including omitted factors, measurement errors, and random disturbances. \u003cem\u003eOOP\u003c/em\u003e Health Expenditure is defined as summation of all subcategories: (1) Dental services; (2) Pharmaceutical products; (3) Hospital services; (4) Specialist practice; (5) Services of medical analysis laboratories and X-ray centres; (6) Other medical products n.e.c; (7) Corrective eye-glasses and contact lenses; (8) Other paramedical services; (9) Other\u0026thinsp;=\u0026thinsp;Other therapeutic appliances and equipment\u0026thinsp;+\u0026thinsp;Hearing aids\u0026thinsp;+\u0026thinsp;Repair of therapeutic appliances and equipment\u0026thinsp;+\u0026thinsp;Thermal-baths, corrective-gymnastic therapy, ambulance services and hire of therapeutic equipment\u0026thinsp;+\u0026thinsp;Pregnancy tests and mechanical contraceptive devices\u0026thinsp;+\u0026thinsp;General practice.\u003c/p\u003e\u003cp\u003eThe third model, Eq.\u0026nbsp;3, is a logit model predicting catastrophic healthcare expenditures. UN the Sustainable Development Goals (SDGs) indicator 3.8.2 defines the \u003cem\u003eOOP\u003c/em\u003e health expenditure as catastrophic if a household spends 10% or more of its total household expenditure or income on healthcare services [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Thus, we mainly follow 10% threshold in order to define catastrophe even though in literature there are alternative thresholds [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], such as more than 40% of capacity to pay [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabc\" border=\"1\"\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{catastrophic}_{i}\\:={\\beta\\:}_{0}+{\\beta\\:}_{1}\\text{l}\\text{n}\\left({\\text{e}\\text{x}\\text{p}}_{i}\\right)+\\sum\\:_{i=1}^{\\text{k}}{\\alpha\\:}_{i}{Z}_{i}+{\\epsilon\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFinally, the fourth model is an OLS model estimating the elasticity of \u003cem\u003eOOP\u003c/em\u003e healthcare expenditures given in Eq.\u0026nbsp;(4) as follows:\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabd\" border=\"1\"\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:lnOOP={\\beta\\:}_{0}+{\\beta\\:}_{1}\\text{l}\\text{n}\\left({\\text{e}\\text{x}\\text{p}}_{i}\\right)+{\\beta\\:}_{2}\\text{l}\\text{n}\\left({\\text{e}\\text{x}\\text{p}}_{i}\\right)\\:x\\:year\\:2022+\\sum\\:_{i=1}^{k}{\\alpha\\:}_{i}{Z}_{i}+{\\epsilon\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ehere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:lnOOP\\)\u003c/span\u003e\u003c/span\u003e is natural logarithm of the household's monthly \u003cem\u003eOOP\u003c/em\u003e health expenditure and we have defined the interaction term \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{l}\\text{n}\\left({\\text{e}\\text{x}\\text{p}}_{i}\\right)\\:x\\:year\\:2022\\)\u003c/span\u003e\u003c/span\u003e in order to distinguish the elasticity in the Covid-era of 2022 from 2019, thus \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:({\\:\\beta\\:}_{1}+{\\beta\\:}_{2})\\)\u003c/span\u003e\u003c/span\u003e measures the elascitiy in 2022 while \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{1}\\)\u003c/span\u003e\u003c/span\u003ein 2019.\u003c/p\u003e\u003cp\u003eIn T\u0026uuml;rkiye since the inflation rate jump to 72.31% in 2022 from 15.18% in 2019, all Turkish lira (TL) values adjusted using purchasing power poverty (PPP) USD \u003cspan\u003e$\u003c/span\u003e rate. Eventhough, in nominal terms, mean \u003cem\u003eOOP\u003c/em\u003e expenditures more than doubled between 2019 and 2022, rising from 98.79 TL to 245.37 TL, in PPP \u003cspan\u003e$\u003c/span\u003e terms it has decreased to 49.07 \u003cspan\u003e$\u003c/span\u003e in 2022 from 51.99 \u003cspan\u003e$\u003c/span\u003e in 2019. Similarly, in PPP \u003cspan\u003e$\u003c/span\u003e terms it has decreased to 2,440 \u003cspan\u003e$\u003c/span\u003e in 2022 from 2,589 \u003cspan\u003e$\u003c/span\u003e in 2019 (See: Appendix Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Therefore, in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the averages of natural logarithms of total and health expenditures in PPP \u003cspan\u003e$\u003c/span\u003e, ln(exp) and ln\u003cem\u003eOOP\u003c/em\u003e, are slightly decreased from 2019 to 2022. Indeed, from Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e we see that percentage of people having \u003cem\u003eany_heath\u003c/em\u003e expenditure decreased to 52.72% in 2022 from 57.41% in 2019. Since in T\u0026uuml;rkiye the co-payment is mandatory, this decrease could be attributed to reduction in health demand during the Covid-19 era. Furthermore, average ratio of \u003cem\u003eOOP\u003c/em\u003e to total expenditures, \u003cem\u003eshare\u003c/em\u003e, slightly decreased to 1.79% in 2022 from 1.85% in 2019. However, the percentage of people face with CHE has somewhat increased to 4.37% during the Covid-19 era of 2022 with respect to 4.28% in 2019. Indeed, among households with \u003cem\u003eOOP\u003c/em\u003e expenditure above 10%, the burden intensified, as their \u003cem\u003eOOP\u003c/em\u003e expenditure ratio increased from 19.53% in 2019 to 21.88% in 2022 (See Appendix Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This suggests that financial pressure from health expenditures became more concentrated among already vulnerable households, even though the general distribution of \u003cem\u003eOOP\u003c/em\u003e burden across the entire sample did not show substantial change.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescription of the variables used in the 2019 and 2022 data sets\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDependent variables (ratio or mean)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e2019 2022\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eany_health\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e=\u0026thinsp;1, if the household's health expenditures are greater than 0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e57.41%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e52.72%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShare\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e=(\u003cem\u003eOOP\u003c/em\u003e Health expenditure/Total expenditure)*100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.85%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.79%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCatastrophic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e=\u0026thinsp;1, if the share of the household's health expenditures in total expenditures is greater than 10%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.28%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.37%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eln(\u003cem\u003eOOP\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe logarithm of the household's monthly health expenditure adjusted for PPP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.427 \u003cspan\u003e$\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.190 \u003cspan\u003e$\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eExplanatory variables\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eln(\u003cem\u003eexp\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe logarithm of the household's monthly total expenditure adjusted for PPP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.646 \u003cspan\u003e$\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.522 \u003cspan\u003e$\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInsured\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e=\u0026thinsp;1, if individual\u0026rsquo;s health insurance is SSI ('4A'+'4B'+'4C') or 'UHI'\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e93.65%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e93.87%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDifficult Access\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e=\u0026thinsp;1, if individual has transportation barrier to access to the services related to \u0026ldquo;health center\u0026rdquo; considering the location of dwelling is 'difficult' or 'very difficult'\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.61%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29.57%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e=\u0026thinsp;1, Female\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50.56%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50.63%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh school and above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e=\u0026thinsp;1, if individual\u0026rsquo;s education level is high level and above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.60%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29.86%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e=\u0026thinsp;1, if the individual is 'married'\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66.10%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e65.64%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e=\u0026thinsp;1, if the individual employment status as of survey month (in the last week of the survey month in 2019/2022) is 'worked'\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45.27%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e45.71%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHH size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHousehold size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.285\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.127\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of children (0\u0026ndash;5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe total number of individuals aged 0\u0026ndash;5 in the household\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.465\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.407\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of elderly (+\u0026thinsp;65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe number of individuals aged 65 and over in the household\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.308\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.289\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDisable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e=\u0026thinsp;1, if there is a member in the household has been limited in activities related to work because of a health or mental problem\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.85%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.52%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eYear 2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e=\u0026thinsp;0, if the year is 2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;38,744\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e=\u0026thinsp;1, if the year is 2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;39,188\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSource: Our calculations from HBS 2019 \u0026amp; 2022.\u003c/p\u003e\u003cp\u003eThe variable \"Insured\" is a binary variable coded as 0, if individual\u0026rsquo;s health insurance is 'private associations (Bank, etc.)', 'private health insurance' or 'other'; and coded as 1 if the individual possesses UHI and 0 otherwise. The social security categories '4A', '4B', and '4C' under the Social Security Institution in T\u0026uuml;rkiye have all been consolidated under the umbrella of UHI. The education variable is coded as 1 if the individual\u0026rsquo;s education level is high school or above, and 0 otherwise. This is because high school represents the most significant breakpoint in the education system in T\u0026uuml;rkiye. Having a high school education or higher creates a substantial difference in labor force participation, income level, and social status [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e].\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eDistribution of\u003c/b\u003e \u003cb\u003eOOP\u003c/b\u003e \u003cb\u003ehealth expenditure for individuals with/out catastrophic expenditures\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe distribution of healthcare expenditures shown in Figs.\u0026nbsp;1 and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e is as follows: Fig.\u0026nbsp;1, panels (a) and (b), present the distribution of health expenditures among households with a financial burden due to health expenditure in 2019 and 2022, respectively. In 2019, the largest share within the distribution was hospital services with 26%, whereas in 2022, dental services accounted for the highest share with 34%. The second-largest expenditure category was dental services with 20% in 2019, while hospital services ranked second with 25% in 2022. In fact, from 2019 to 2022, the share of hospital services expenditures remained relatively stable, whereas dental services experienced a remarkable increase of 14 percentage points. Additionally, pharmaceutical products ranked as the third major expenditure category in both years; however, their share decreased by 4 percentage points from 2019 to 2022.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e, panels (a) and (b), illustrate the distribution of health expenditures among households without a financial burden due to health expenditure in 2019 and 2022, respectively. In both years, pharmaceutical products represented the largest share of expenditures, and in contrast to Fig.\u0026nbsp;1, their share increased by 4 percentage points from 2019 to 2022. Compared to households with a financial burden, these households allocated a substantially higher share of their health expenditures to pharmaceutical products, while the shares of hospital services and dental services markedly declined, particularly in the post-Covid period.\u003c/p\u003e\u003cp\u003eFigure 1: Share of health expenditure items in total health expenditure (%), burden greater than 10% of family expenditure\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e(a) 2019, n\u0026thinsp;=\u0026thinsp;1,657 (b) 2022, n\u0026thinsp;=\u0026thinsp;1,713\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e(a) 2019, n\u0026thinsp;=\u0026thinsp;37,087 (b) 2022, n\u0026thinsp;=\u0026thinsp;37,475\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eModel Findings\u003c/h3\u003e\n\u003cp\u003eThe results for the determinants of \u003cem\u003eOOP\u003c/em\u003e health expenditures for the pre-Covid 2019 and the Covid-era 2022 are given in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The models used here were defined in equations (1), (2), (3), and (4).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRegression results for Model (1), (2), (3) \u0026amp; (4)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eany_health\u003c/em\u003e\u003c/p\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eshare\u003c/em\u003e\u003c/p\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ecatastrophic\u003c/em\u003e\u003c/p\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eln(OOP)\u003c/em\u003e\u003c/p\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOdds ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCoefficient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOdds ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCoefficient\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eln(exp)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.072***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.720***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.578***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.738***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.031)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.036)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.042)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.019)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInsured\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.195***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.868*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.026\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.041)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.078)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.071)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.035)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDifficult Access\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.873***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.124**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.017)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.049)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.051)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.019)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.074***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.093**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.044**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.020)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.044)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.045)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.017)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh school and above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.112***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.085*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.143***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.022)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.048)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.048)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.019)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.161***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.189***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.088*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.065***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.022)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.045)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.049)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.018)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.939***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.203***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.879***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.046**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.018)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.046)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.040)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.018)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHH size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.946***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.267***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.757***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.091***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.005)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.012)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.011)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.005)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of children (0\u0026ndash;5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.261***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.429***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.452***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.176***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.020)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.034)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.054)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.014)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of elderly (+\u0026thinsp;65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.079***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.493***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.389***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.156***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.015)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.040)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.040)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.014)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDisable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.546***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.020***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.000***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.338***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.040)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.070)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.101)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.024)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear 2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.905***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.681***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.016)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.042)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.042)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.194)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{l}\\text{n}\\left({\\text{e}\\text{x}\\text{p}}_{i}\\right)\\:x\\:year\\:2022\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.067***\u003c/p\u003e\u003cp\u003e(0.025)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.004***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-3.149***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.003***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.183***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.264)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.147)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;59,529;\u003c/p\u003e\u003cp\u003ePseudo \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{R}}^{2}\\)\u003c/span\u003e\u003c/span\u003e=0.0486\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;59,529;\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{R}}^{2}\\)\u003c/span\u003e\u003c/span\u003e=0.0233\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;59,529;\u003c/p\u003e\u003cp\u003ePseudo \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{R}}^{2}\\)\u003c/span\u003e\u003c/span\u003e=0.0393\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;32,436\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{R}}^{2}\\)\u003c/span\u003e\u003c/span\u003e=0.125\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWald \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}^{2}\\)\u003c/span\u003e\u003c/span\u003e(12)\u0026thinsp;=\u0026thinsp;3466.75\u003c/p\u003e\u003cp\u003e(\u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.000)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF\u0026thinsp;=\u0026thinsp;87.54\u003c/p\u003e\u003cp\u003e(\u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.000)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWald \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}^{2}\\)\u003c/span\u003e\u003c/span\u003e (12)\u0026thinsp;=\u0026thinsp;961.03 (\u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.000)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eF\u0026thinsp;=\u0026thinsp;336.37\u003c/p\u003e\u003cp\u003e\u003cem\u003e(p\u0026thinsp;=\u0026thinsp;0.000)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e***, **, * statistical significance at 1%, 5%, and 10% levels, respectively.\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eRobust standard errors are in parentheses.\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe results show that household expenditure (ln(exp)) is a strong predictor; it more than doubles the odds of any health spending (OR\u0026thinsp;=\u0026thinsp;2.072) and also increases the likelihood of catastrophic expenditures by about 58% (OR\u0026thinsp;=\u0026thinsp;1.578). Insurance coverage, while increasing the odds of any expenditure by nearly 20% (OR\u0026thinsp;=\u0026thinsp;1.195), reduces the risk of catastrophic spending by around 13% (OR\u0026thinsp;=\u0026thinsp;0.868). This finding underlines the partial protective role of insurance.\u003c/p\u003e\u003cp\u003eHouseholds reporting transportation barriers to access healthcare, i.e. difficult access, were less likely to have any health expenditure (OR\u0026thinsp;=\u0026thinsp;0.873), yet paradoxically more likely to face catastrophic costs (OR\u0026thinsp;=\u0026thinsp;1.124). Gender and education exert moderate effects, with women and more educated individuals slightly more likely to report any health spending. Marital status has a pronounced effect, as married individuals are 16% more likely to incur expenditures.\u003c/p\u003e\u003cp\u003eEmployees are less likely to have catastrophic expenditures (OR\u0026thinsp;=\u0026thinsp;0.879). Larger household size reduces risks substantially, lowering the odds of catastrophic expenditures by nearly 25%; we believe this result is related to the fact that large households in T\u0026uuml;rkiye tend to have lower levels of education [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. By contrast, vulnerable subgroups face severe risks: households with children under five have a 45% higher chance of catastrophic spending, and those with elderly members a 39% higher risk. The most striking one, households with disabled members are twice as likely to face catastrophic expenditures (OR\u0026thinsp;=\u0026thinsp;2.000).\u003c/p\u003e\u003cp\u003eBy 2022, the odds of any health spending fell by about 10% while catastrophic expenditures showed no significant change. Taken together, the results underscore that while income and insurance are crucial, household composition\u0026mdash;especially the presence of young children, elderly, or disabled members\u0026mdash;remains a critical driver of CHEs.\u003c/p\u003e\u003cp\u003eThe last, (4)th, model explores the income elasticities of out-of-pocket health expenditures with respect to pre-Covid and after Covid years under control of other socio-economic variables. According to our findings the estimated elasticity in 2019 was \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\widehat{{\\beta\\:}_{1}}=0.738\\)\u003c/span\u003e\u003c/span\u003e and rose to \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left(\\widehat{{\\:\\beta\\:}_{1}}+\\widehat{{\\beta\\:}_{2}}=0.738+0.067\\right)=0.806\\)\u003c/span\u003e\u003c/span\u003e in 2022. Even though all coefficients were statistically significant at 1% level \u003cem\u003eOOP\u003c/em\u003e health expenditure stayed inelastic during both years.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis research examines how effectively the health insurance framework in T\u0026uuml;rkiye has safeguarded individuals from \u003cem\u003eOOP\u003c/em\u003e health expenditures during the times of the pandemic and the country specific economic instabilities. We utilize the nationally representative HBS micro data sets to extrapolate T\u0026uuml;rkiye's experience and evaluate the pre-Covid period of 2019 to the Covid-era period of 2022 because TurkStat was unable to collect data in 2020 and 2021 due to the pandemic. We construct four models: First we predict whether a household has spent any money on healthcare, then an OLS model that calculates the proportion of healthcare spending to total household spending; third, a logit model is used to predict whether a household has experienced catastrophic medical expenses; the fourth model assesses the elasticity of \u003cem\u003eOOP\u003c/em\u003e health expenses.\u003c/p\u003e\u003cp\u003eOur descriptive analysis indicates that the average \u003cem\u003eOOP\u003c/em\u003e expenditures more than doubled from 2019 to 2022, rising from 98.79 TL to 245.37 TL in nominal terms. Nevertheless, when adjusted for purchasing power parity (PPP), the expenditures decreased from 51.99 \u003cspan\u003e$\u003c/span\u003e in 2019 to 49.07 \u003cspan\u003e$\u003c/span\u003e in 2022. This situation can be attributed to the deteriorated macroeconomics indicators of T\u0026uuml;rkiye that have been long taken place even before the Covid-19 breakout [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In T\u0026uuml;rkiye since the inflation rate jump to 72.31% in 2022 from 15.18% in 2019, and the US dollar rocketed to during the same period. Furthermore, it is observed that the proportion of individuals incurring any health expenditure declined to 52.72% in 2022 from 57.41% in 2019. As the co-payment is compulsory in T\u0026uuml;rkiye, this reduction could be linked to a decrease in healthcare demand during the Covid-19 pandemic.\u003c/p\u003e\u003cp\u003eWe followed UN SDGs indicator 3.8.2\u0026rsquo;s CHE definition as spending more than 10% of household consumption on \u003cem\u003eOOP\u003c/em\u003e health care expenditure. We see that in T\u0026uuml;rkiye, the average share of \u003cem\u003eOOP\u003c/em\u003e health expenditures to total expenditures decreased to 1.79% in 2022 from 1.85% in 2019. Moreover, similar to Haakenstad et al. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], in our findings the percentage of people face with CHE has slightly (not significantly) increased to 4.37% during the Covid-19 era of 2022 with respect to 4.28% in 2019. Cinaroglu [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], using HBSs, found that the proportion of \u003cem\u003eOOP\u003c/em\u003e health expenditure in the effective income of the household, i.e. non-subsistence effective income, rose to 2.66 in 2019 from 2.41 in 2015. Thus, this increasing trend in CHE was already going on and may not be totally attributed to the pandemic. Indeed, in T\u0026uuml;rkiye there was a decrease of 39% in outpatient services in 2020 and a further decline of 21% in 2021, alongside a reduction of 29% in total inpatient services in 2020 and a 17% decrease in 2021 with respect to 2019 realizations [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. As the demand for healthcare services has fallen significantly, that may prevent the expected surge in \u003cem\u003eOOP\u003c/em\u003e healthcare expenditures.\u003c/p\u003e\u003cp\u003eAccording to the WHO\u0026rsquo;s [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e] proportion of the population spending more than 10% of household consumption (or income) on \u003cem\u003eOOP\u003c/em\u003e health care expenditure\u0026rsquo;s the global average was 13% in 2019. Thus, the respective rates in T\u0026uuml;rkiye for the pre-Covid year of 2019 and the Covid-era of 2022 were 4.28% and 4.37%, around one-third of the world average. However, our findings indicate that among households with CHE their \u003cem\u003eOOP\u003c/em\u003e expenditure ratio rose from 19.53% in 2019 to 21.88% in 2022, suggesting that vulnerable ones that are already at risk are subject to an increasing amount of financial strain due to health care costs.\u003c/p\u003e\u003cp\u003eFurthermore, we consider the distribution of \u003cem\u003eOOP\u003c/em\u003e health expenditure for individuals with/out catastrophic expenditures. We found that for the individuals with CHE the primary reasons of \u003cem\u003eOOP\u003c/em\u003e expenditures were dental, hospital services and pharmaceutical expenses both before and during the Covid-19 era. Meanwhile, among those with burdens less than 10% of expenditure, pharmaceutical products constitute the highest proportion of \u003cem\u003eOOP\u003c/em\u003e expenditures. In fact, the supply of pharmaceutical and medical items in T\u0026uuml;rkiye is largely dependent on imports [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e], and the depreciation of the Turkish Lira has increased the expense of funding healthcare.\u003c/p\u003e\u003cp\u003eNext, we construct the logit and OLS models to analyse the determinants of the \u003cem\u003eOOP\u003c/em\u003e health expenditures and the catastrophic health expenditures (CHEs) via four models (1)-(4). Similar to literature Xu et al. [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] and van Doorslaer et al. [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e], we found that Income (household expenditure) was a strong predictor: it more than doubles the odds of any health spending and increases the risk of CHEs by 58%. However, unlike findings in high-income countries where higher income reduces CHE risk due to better financial protection [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e], the results here suggest that the progressivity of health financing in T\u0026uuml;rkiye may be limited.\u003c/p\u003e\u003cp\u003eFurthermore, with respect to our findings the insurance coverage increases the likelihood of spending slightly (+\u0026thinsp;20%), but reduces CHE risk by 13%, showing partial financial protection, similar to findings in countries with comprehensive coverage (e.g., France, Canada), where insurance significantly lowers CHE incidence [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. Moreover, our models indicate that transportation barriers to access healthcare lowers the likelihood of spending but increases CHE risk. Households reporting difficult access to health care were less likely to spend but more likely to experience CHEs\u0026mdash;a paradox found in other studies as well [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. The disadvantaged people face up barriers to access such as transportation have to pay more for health as indicated in Syed et al. [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAdditional we see that women and more educated individuals are slightly more likely to spend on health, consistent with studies linking education and gender with higher health awareness and service use [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. According to our findings, married individuals are 16% more likely to spend on health echoing earlier work that marriage enhances health outcomes and access [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. Indeed, we see that employees are less likely to face CHEs, aligning with research showing that formal sector workers are more likely to have stable incomes and health benefits [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. Furthermore, our finding that larger households had a 25% lower risk of CHEs, potentially due to pooled financial resources or shared caregiving. However, other studies (e.g., UNICEF, [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]) caution that larger household size can also indicate hidden vulnerability, especially when dependents outnumber earners\u0026mdash;a nuance observed in T\u0026uuml;rkiye where large households often have lower education levels.\u003c/p\u003e\u003cp\u003eMoreover, we see that vulnerable groups at higher risk are households with children under 5, with elderly members and with disabled members. This reflects findings from Russell [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e] and Saksena, Smith, and Tediosi [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e], who highlight the economic burden of dependency and the lack of institutional care, particularly in low- and middle-income settings.\u003c/p\u003e\u003cp\u003eFurthermore, we figured out that \u003cem\u003eOOP\u003c/em\u003e health spending was income inelastic in both 2019 and 2022, though elasticity slightly increased from 0.738 to 0.806 in T\u0026uuml;rkiye. Our finding is consistent with the World Bank [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e], which classify health care as a necessity good: spending does not rise proportionally with income.\u003c/p\u003e\u003cp\u003eLimitation: TurkStat was unable to conduct Household Budget Surveys in 2020 and 2021 due to Covid-related restrictions. Therefore, unfortunately, we cannot measure the impact of Covid-19 on \u003cem\u003eOOP\u003c/em\u003e health expenditures in 2020, when the Covid-19 pandemic broke out and when the country and people were caught unprepared, and in 2021, while pandemic measures continued. Despite the fact that 2022 remains a pandemic year for T\u0026uuml;rkiye, the country has progressed past the most difficult stages of the coronavirus pandemic, as most restrictions associated with the pandemic were removed by June 2020 [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. We recognize this as a limitation of our study.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study closes a gap in the literature by analyzing \u003cem\u003eOOP\u003c/em\u003e medical expenses in T\u0026uuml;rkiye throughout the 2019\u0026ndash;2022 timeframe and contrasting the outcomes of the pandemic year of 2022 with those of a pre-pandemic year of 2019. T\u0026uuml;rkiye is particularly noteworthy because of the significant progress made and its recognition as a global model of excellence through the health system reforms. But since 2018, its economy has been in turmoil, and the Covid-19 pandemic and economic inequality have put financial security and health care affordability at risk. According to our findings the proportion of population facing CHE for the pre-Covid year of 2019 and the Covid-era of 2022 were respectively 4.28% and 4.37%. Moreover, for individuals with CHE their \u003cem\u003eOOP\u003c/em\u003e expenditure ratio rose from 19.53% in 2019 to 21.88% in 2022, implying the financial pressure from health expenditures become more concentrated among already vulnerable households.\u003c/p\u003e\u003cp\u003eIt is seen from our logit and regression models that income and insurance play central roles in determining health spending behavior and exposure to financial risk. However, household composition, particularly the presence of young children, elderly, or disabled members, critically shapes the risk of catastrophic expenditures. Addressing these disparities requires not only economic interventions but also structural reforms to improve access and protect vulnerable populations. In order to prioritize suitable policies and efforts to safeguard these vulnerable people by providing financial protection, decision makers can benefit from our findings for better understanding the factors that contribute to \u003cem\u003eOOP\u003c/em\u003e health expenditure in T\u0026uuml;rkiye.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThere was no available funding for this study. This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed equally, read, and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eNot required. The authors used Turkish Statistical Institute\u0026rsquo;s (TurkStat) 2019 and 2022 Household Budget Survey records based on official data. TurkStat is a governmental institution collects Turkish populations\u0026rsquo; data with respect to personal Data Protection\u0026nbsp;Laws and shares the\u0026nbsp;anonymized\u0026nbsp;data with researchers according to agreement and official permission. 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The anonymized data is then shared with researchers based on agreements and official permissions. \u003cem\u003eThe HBS micro data was prepared by TurkStat in accordance with \u0026ldquo;Regulations on Procedures and Guidelines for Data Privacy and Confidential Data Security at Official Statistics\u0026rdquo; which came into force after publishing in Official Newspaper No.26204 and dated June 20, 2006 as described in the Turkish Statistical Institute\u0026rsquo;s Decree No. 5429 and Law 13\u003c/em\u003e [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. \u003cem\u003eTurkish Statistical Institute reserves all the rights of these data. Unauthorised duplication or distribution of the data is prohibited under Law No. 5846.\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\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":"Universal Health Coverage, Out-of-Pocket Expenditure, Catastrophic Health Expenditure, Türkiye, Covid-19 Pandemic","lastPublishedDoi":"10.21203/rs.3.rs-7924397/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7924397/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn alignment with the UN Sustainable Development Goal 3.8 on Universal Health Coverage, this study assesses the extent to which T\u0026uuml;rkiye\u0026rsquo;s health insurance system protected households from out-of-pocket (\u003cem\u003eOOP\u003c/em\u003e) health expenditures before and during the Covid-19 pandemic. Despite the implementation of the Health Transformation Program and Universal Health Insurance, concerns remain about financial protection, particularly amid economic downturns and the rapid expansion of private healthcare.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e\u003cp\u003eUsing nationally representative Household Budget Survey data from 2019 (pre-pandemic) and 2022 (pandemic), the study analyzes health expenditure patterns through four models: (1) a logit model predicting any health expenditure, (2) an OLS model estimating the share of health spending in household budgets, (3) a logit model identifying catastrophic health expenditure (CHE), and (4) an OLS model assessing the elasticity of \u003cem\u003eOOP\u003c/em\u003e spending.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e\u003cp\u003eKey findings reveal that nominal \u003cem\u003eOOP\u003c/em\u003e spending more than doubled from 98.79 TL in 2019 to 245.37 TL in 2022, yet declined slightly in PPP-adjusted USD. The proportion of households facing CHE rose marginally from 4.28% to 4.37%, well below the global average of 13%. However, CHE intensity worsened, with \u003cem\u003eOOP\u003c/em\u003e spending among affected households increasing from 19.5% to 21.9% of total consumption. Pharmaceutical, dental, and hospital costs were the main contributors. Insurance coverage slightly increased the likelihood of spending but reduced CHE risk by 13%, indicating partial protection. Income was a strong predictor of both spending and CHE risk, highlighting limitations in the progressivity of health financing. Vulnerable groups\u0026mdash;women, households with young children, elderly, or disabled members\u0026mdash;faced higher CHE risk. Barriers to access, such as transportation difficulties, also increased financial burden.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe study concludes that while T\u0026uuml;rkiye\u0026rsquo;s health insurance system offers some protection, it remains insufficient for disadvantaged populations, particularly in times of crisis. The absence of 2020\u0026ndash;2021 data due to the pandemic limits insights into the immediate effects of Covid-19 but underscores the need for more resilient, equitable health financing mechanisms.\u003c/p\u003e","manuscriptTitle":"Financial Burden of Out-of-pocket Health Expenditures in Türki̇ye: Under Covid-19","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-10 05:38:36","doi":"10.21203/rs.3.rs-7924397/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-05T10:52:31+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-02T01:00:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-18T16:22:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"43759835634029118868069115801039688630","date":"2025-12-17T14:08:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"214073131131647756569204670271146113638","date":"2025-12-15T10:13:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"332064938351969244980163305209710275779","date":"2025-12-15T09:59:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-28T17:04:31+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-27T18:15:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-24T05:08:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-24T05:07:13+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Health Services Research","date":"2025-10-22T13:56:55+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":"415e6c17-1030-49f0-a560-5e110ee0a4ce","owner":[],"postedDate":"November 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T16:09:48+00:00","versionOfRecord":{"articleIdentity":"rs-7924397","link":"https://doi.org/10.1186/s12913-026-14530-1","journal":{"identity":"bmc-health-services-research","isVorOnly":false,"title":"BMC Health Services Research"},"publishedOn":"2026-04-23 15:58:50","publishedOnDateReadable":"April 23rd, 2026"},"versionCreatedAt":"2025-11-10 05:38:36","video":"","vorDoi":"10.1186/s12913-026-14530-1","vorDoiUrl":"https://doi.org/10.1186/s12913-026-14530-1","workflowStages":[]},"version":"v1","identity":"rs-7924397","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7924397","identity":"rs-7924397","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}