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Spatiotemporal variation and socioeconomic factors of financial hardships of out-of-pocket health expenditure in Pakistan

Background: Financial hardships of out-of-pocket health expenditure (OPHE) is a growing concern for health policy makers in many low and middle-income countries. Spatiotemporal variation between Pakistan’s four provinces over 2001-2015 is discussed, which would help comparing existing health services delivery and financial risk protection plans. Aims: In this paper, we estimate financial hardship of OPHE in Pakistan. Methods: We use the data sets of the household integrated economic surveys 2001-02, 2005-06, 2010-11 and 2015-16. We estimate OPHE share in household total and non-subsistence expenditure, catastrophic headcount at the threshold of OPHE ≥ 10% of total expenditure or OPHE ≥ 25% of non-subsistence expenditure. We estimate impoverishment of OPHE using national poverty lines. Finally, we explore socioeconomic factors of financial hardships of OPHE. Results: Over the years, catastrophic headcount and impoverishment of OPHE had decreased at national level (–1.3% points) and in the provinces of Sindh (-7.8% points) and Khyber Pukhtoonkhawa (KPK), (–2.8% points). The province of KPK and the year 2005-06 witnessed the highest incidence of financial catastrophe (26.89% points) and impoverishment (4.8% points) of OPHE. Households in rural areas, in the middle and rich quintiles and those headed by a male were more likely to encounter financial catastrophe and impoverishment due to OPHE. Conclusion: Inter-provincial variation in financial hardships of OPHE provide aide to provincial level priority setting. The high impact of OPHE in the non-poor, in rural areas, and in KPK calls for enhanced targeting of financial risk protection plans.


Historical background
Health care financing is an area of low priority in health policy formulation and analysis in Pakistan. The country is not included in the Universal Health Coverage Study Series group: low-and middle-income countries (LMICs) that are implementing major reforms to achieve universal health care (1). In contrast with other countries in the region (Bangladesh 1996-97, India 1997, Nepal 1997, Sri Lanka 1995), Pakistan only produced its first national health accounts in 2008 (2). Public expenditure reviews and health care financing analyses are rarely carried out (3). During the 2000s, Pakistan ignored many global and regional efforts to track progress on financial risk protection. For instance, in the World Health Organization (WHO) study on financial catastrophe in 59 countries, Bangladesh and Sri Lanka were included from south Asia (4). In the research collaboration on financial hardships of out-of-pocket health expenditure (OPHE) in 11 Asian countries, Bangladesh, India, Nepal, and Sri Lanka participated from the south Asian region (5,6). In the second round of the WHO study, the number of countries in-creased from 59 to 89, still excluding Pakistan (7). In the section on financial hardships of OPHE, one of the background papers of the 2010 World Health Report included Bangladesh, India, Nepal and Sri Lanka from south Asia (8). The WHO and World Bank joint project on universal health coverage case studies from 13 LMICs included Bangladesh and India (9).
As a backdrop to the fact that financial risk protection and universal health coverage have been included in the United Nations Agenda 2030, since 2015, analyses of financial hardships of OPHE have been common for all United Nations Member States, including Pakistan (10-13). The purpose of such efforts is comparison across countries. Pakistan has significant ethnic/linguistic diversity across provinces (Table 1) and the health sector is a provincial matter, thus, evidence on financial risk protection should include subnational and time trend analysis.

Health care financing trends
Health care financing in Pakistan is predominately private out-of-pocket with some improvement over the years (Figure 1). For example, during 2001-2015, the share EMHJ -Vol. 27 No. 10 -2021 of public health expenditure increased from 23% to 34% of the total health expenditure. This was accompanied by a decrease in the share of OPHE in current health expenditure: 68% (2005) to 58% (2015) (2).
Currently, 2 large-scale financial risk protection schemes are operating in the country managed by the provincial government of Khyber Pakhtunkhwa (KPK) and the federal government (14,15). These schemes target the population living below the poverty line and provide coverage for inpatient care with an enrolment of 2.6 million and 6.9 million families respectively. Independent sources, however, report a coverage of 0-3% for these programmes (Table 1).

Aims
The existing literature on the financial hardships from OPHE in Pakistan is limited to a few grey reports that used very old (2003)(2004) cross-sectional datasets (16,17).
Multi-country analysis provides a national-level estimate of financial hardship in Pakistan but ignores subnational analysis, time trends and socioeconomic determinants of financial hardship of OPHE (10-13).
In this study, we track progress on financial risk protection over the period 2001-2016. We explore whether the economic growth of the country, a decline in the incidence of poverty (Table 2) and an improvement in the texture of health care financing has contained the financial hardships of OPHE. We provide subnationallevel analysis due to the ethnic/cultural diversity of the provinces (Table 1) and the fact that since 2010, the health sector has been a provincial matter. We hypothesize that financial hardship from OPHE has decreased at national and provincial levels. Lastly, we explore the influence of socioeconomic factors on OPHE and its financial hardships on households. Total expenditure of the household in the Household Integrated Economic Survey covers approximately 155 questions using recalls of 2 weeks, 1 month and 1 year.
The yearly total expenditure of the household is the sum of fortnightly expenditures (multiplied by 26), monthly expenditures (multiplied by 12) and yearly expenditures. Non-subsistence expenditure of the household is the total expenditure of the household less subsistence expenditure. We define subsistence expenditure as the expenditure on food items, utilities and fuel, laundry and personal care. To estimate yearly expenditure, we multiply expenditure on 2-week and 4-week recall by a factor of 26 and 12 respectively.
Yearly recall for OPHE was reported as an aggregate at household level. It includes expenditure on medical products and services and excludes health insurance premiums. The recall period for OPHE remained the same over 2001-16, but the questions on OPHE differed.

Analytical approach
We estimate OPHE in Pakistan rupees (PKR) 2015-16 constant prices. Using the methods of O'Donnell et al., we analysed the absolute and relative financial hardship from OPHE, i.e. impoverishment and financial catastrophe (18).
A financial catastrophe occurs to a household if their OPHE exceeds a certain threshold (proportion) of their income or expenditure. If OPHE i is the out-of-pocket health payment of the ith household and X i is their total (non-subsistence) expenditure, then the ith household encounters a financial catastrophe (E i ) if the share of OPHE i in X i exceeds the threshold value Z, i.e. <equation> where z represents the point where spending on health could create financial hardships for the household.
Due to substantial informal sector and non-market transactions in LMICs, household expenditure is a better proxy of household economic status than household income (18). We used 2 types of household expenditure to estimate financial catastrophe: household total expenditure and non-subsistence expenditure. The argument in favour of using non-subsistence expenditure is that subsistence expenditure is usually non-discretionary and household total expenditure net of subsistence expenditure provides a better understating of the catastrophic impact of OPHE (18). We used 2 threshold values of z to estimate financial catastrophe, OPHE exceeding 10% of the total expenditure (CH 10 ) and OPHE exceeding 25% of non-subsistence expenditure (CH 25 ) (18). The catastrophic headcount (CH) is the proportion of households that had encountered financial catastrophe, i.e. To analyse the distributional aspect of OPHE and catastrophic incidence, we estimated concentration indices (CI) of CH 10 and CH 25 . We used convenient regression methods to compute the concentration indices (18).
In this equation, o i is OPHE of the ith household, μ is the mean of OPHE, δ is the variance of frictional rank and r i is the fractional rank of the household by their total expenditure or non-subsistence expenditure (18).
We estimated the effect of OPHE on poverty headcount (H imp ) as the difference between poverty headcount gross of total expenditure and poverty headcount of total expenditure net of OPHE.
Where poverty headcount is:   (20). In addition to headcount, we estimated catastrophic overshoot and normalized poverty gap (Supplementary Tables 1,2), i.e. the amount a household falls short of the catastrophic threshold or poverty line respectively (18).
Finally, we analysed the influences of the socioeconomic and demographic features of the household on financial hardship of OPHE. We used the Probit regression on each binary outcome (CH 10 , CH 25 and H imp , denoted by Y in the equation below) and including the vector of 9 covariates: gender, age, schooling, marital status of the head of the household, household size, rural households, quintiles, provinces and year of survey.
Where Φ is the non-linear function of X i . We accounted for population-adjusted survey sampling weights and clustering at the level of primary sampling units in our analyses. We used the same set of socioeconomic factors as determinants of OPHE (2015-16 prices) using multiple linear regression. In this case, we transformed OPHE to the natural logarithm due to its positively skewed distribution. We estimated standard errors of all variables and these can be provided on request. We used STATA, version 15.1, and MS Excel, 2013, for all analyses.

Results
Across all years, OPHE at 2015-16 constant prices was highest in 2015-16 in KPK province, in the rural areas and The share of OPHE in the household total and nonsubsistence expenditure slightly decreased over the period of the analysis (Figure 2).
At national level, CH 10 and CH 25 had generally decreased, yet this trend was inconsistent. In 2005-06, CH 10 and CH 25 had increased from in 2001-02, while in 2010-11 it had decreased (Table 4). Although in 2015-16 the incidence of CH decreased from the 2001-02 levels, in comparison with 2010-11, it had increased. Nationally, the incidence of CH was lowest in 2010-11 and highest in 2005-06. Among the provinces, CH 10 and CH 25 were highest in KPK and lowest in Baluchistan. However, in 2 provinces, Punjab and Baluchistan, CH 10 had increased over the years while CH 25 had increased in Baluchistan.
CH 10 and CH 25 had followed similar trends except that in Baluchistan CH 10 had increased and CH 25 had decreased between 2015-16 and 2001-02.
The incidence of financial catastrophe had generally moved away from the rich over the years towards the poor: more of the poor population were encountering CH than the rich population except in Baluchistan where, by 2015, proportionately more non-poor encountered CH than poor. The values of CI were generally more inequitable (pro-poor) for CH 25 then CH 10 . Unlike the incidence of catastrophic headcount: that was lowest, i.e. more inequitable, in 2010-11 than other years.    (Table 5). Figure 3 provides the estimates of CH 10 , CH 25 and H Imp for population. Here the proportion of the population affected by financial catastrophe is generally higher than that impoverished due to OPHE. Financial catastrophe affected more people in 2005-06 and in Punjab province.
Analysis of socioeconomic and demographic factors revealed that: a household in a rural area, in KPK and in 2005-06; and a household with 1-4 members, and the head of the household is male, very young or very old, with fewer years of schooling and unmarried were more likely to encounter CH 10 , CH 25 or H imp then their respective reference groups (Table 6).

Discussion
We found a small decrease in the financial catastrophe and impoverishment of OPHE in 2001-2015. We verified that national-level estimates of these are not consistent across all provinces. Nationally, the incidence of financial catastrophe of OPHE decreased but in 2 provinces, the trends were reversed. Our findings are robust, using national survey data with a population-representative sample and common methods of tracking progress on financial risk protection (18). Our approach using basic development needs and subsistence expenditure in estimating impoverishment and catastrophe of OPHE respectively draws from consequentialist ethics that poverty is multidimensional and food is just one aspect of it (19,20). We could not analyse the financial hardship of OPHE in the context of disease patterns, demographic features and use of health services because the unit of analysis for OPHE in the Household Integrated Economic Survey is the household. Comparison across years needs careful interpretation due to the difference in the number of items on OPHE in the Household Integrated Economic Surveys.   Index of inequality (7,12).   Table 2). Coupled with the lowest share of the public in total health expenditure, the incidence of financial hardship was highest in 2005-06. These trends were reversed in 2010-11: lowest GDP growth rate, lowest income-related inequalities, highest share of the public in total health care expenditure and lowest incidence of financial hardship. The declining trend for financial catastrophe in our analysis contrasts with analyses from India (2003-2010) and the Islamic Republic of Iran (1993/94-2011/12), where the incidence of financial catastrophe had increased (24,32).

Xu et al. and Wagstaff et al. found a positive relationship between catastrophic incidence and Gini
The high incidence of financial hardships from OPHE in KPK is similar to the findings of earlier research in 2004-05 (23). The majority of the population lives in rural and mountainous areas where access is poor and this delays care seeking, complicating the illness and escalating the costs of treatment. Another aspect to consider could be better health-seeking behaviour in KPK (23): among all the provinces, life expectancy at birth is highest in KPK and under-5 mortality is lowest (Table 1).
Our findings have particular relevance to the existing financial risk protection schemes that target families living below the poverty line although they do indicate that the non-poor are almost equally affected by the financial hardships of OPHE. To strengthen our argument in favour of the non-poor, we distributed CH 10 and CH 25 by adult equivalent total expenditure (AETE) quintiles (Table 7) but no clear direction emerged in the catastrophic incidence and AETE quintiles. The incidence of financial catastrophe (CH 25 Table 7).
Some of our findings conflict with common trends reported in previous research. For example, we found that small-sized households and households headed by a male are positive predictors of financial hardship while other studies have reported this to be true of families headed by females and large households (24)(25)(26)(27). In the case of small-sized household, our findings are consistent with Van Minh (34); for male-headed households, we agree with the findings of Kumar et al. and Lu et al. (28,29).
Methodological consideration for comparison across the literature includes: features of the data, for example recall period; number of items of OPHE and unit of data collection; and methods of estimating financial hardship, for example, threshold of financial catastrophe and poverty line in impoverishment. The literature on survey design indicates that by increasing the recall period the reported expenditure decreases consistently (30,31). The recall period in the Household Integrated Economic Survey is similar to the recall period of OPHE in expenditure surveys used in analyses from the Islamic Republic of Iran, India and China (28,32). Due to the difference in frequencies of need for outpatient and inpatient care, separate recall periods for outpatient (2-4 weeks) and inpatient (1 year) care are more appropriate, such as were used in studies from Rwanda, India, Bangladesh and Vietnam (26,29,33,34). Lu et al. found that a lower level of disintegration of OPHE items creates negative reporting bias (29). We speculate an increase in the incidence of financial hardships from OPHE in 2015-16 for this factor.
Due to differences in the catastrophic thresholds, poverty lines and years of analysis, our estimates at national level need careful comparison with current research. For example, we used the national poverty line [PKR 2333 (US$ 27.38) per capita per month] while the WHO report used the poverty line (US$ 2 a day, i.e. PKR 5112 per capita per month) (10). In the catastrophic EMHJ -Vol. 27 No. 10 -2021 analysis, we used non-subsistence expenditure whereas other research commonly used non-food expenditure (10-12).

Conclusion
Our analysis provides some directions towards reviewing existing policies and practices; specifically: • evaluate the impact of the financial risk protection scheme on financial hardship of OPHE; • revisit the methods of data collection on the OPHE section of the Household Integrated Economic Survey, especially the recall period and number of items; • explore factors of high incidence of financial hardship of OPHE in the province of KPK and in rural areas.
We expect that variation in financial hardship among provinces and in different years will assist the national and provincial governments in their priority-setting in the health sector in Pakistan.

Acknowledgement
Authors would like to thank Professor Eddy Van Doorslaer, Erasmus University for his comments.