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Year : 2020  |  Volume : 64  |  Issue : 3  |  Page : 223-228  

Determinants of out-of-pocket and catastrophic health expenditure in rural population: A community-based study in a block of Purba Barddhaman, West Bengal

1 Senior Resident, Department of Community Medicine, Rampurhat Government Medical College and Hospital, Rampurhat, West Bengal, India
2 Senior Resident, Department of Community Medicine, AIIMS, Nagpur, Maharashtra, India
3 Professor, Department of Community Medicine, Diamond Harbour Medical College and Hospital, Diamond Harbour, West Bengal, India

Date of Submission06-Jul-2020
Date of Decision11-Jul-2020
Date of Acceptance21-Aug-2020
Date of Web Publication22-Sep-2020

Correspondence Address:
Anirban Dalui
20C, Sri Gopal Mullick Lane, Flat 3, 1st Floor, Kolkata - 700 012, West Bengal
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/ijph.IJPH_848_20

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Background: In India, health expenditure accounts for <5% of the Gross domestic product and the level of out-of-pocket (OOP) spending is 69.5% of total health expenditures. OOP expenditure (OOPE) has a negative impact on equity and can increase the risk of vulnerable groups slipping into poverty. Objectives: The study aimed to estimate the OOPE on health and catastrophic health expenditure (CHE) and their sociodemographic determinants in a rural area of Purba Barddhaman. Methods: A community-based cross-sectional study was conducted between July 2018 and February 2019 in Bhatar Block of Purba Bardhaman district, West Bengal. Required sample of 235 households, selected randomly were primary study units. One respondent from each household was interviewed with a predesigned, pretested schedule for sociodemographic and health-care expenditure-related variables. Mann–Whitney U test/Kruskal Wallis H test and multivariable logistic regression was applied. Results: The median OOP health expenditure was Rs. 3870 (inter quartile range: 2156–4952). Of 235 families, 38 (16.2%) had CHE over a period of 1 year. The significant correlates for CHE were type of village according to the presence of public health-care facility (adjusted odds ratio [AOR] = 4.748; 95% confidence interval [CI]: 1.886–11.956), presence of health insurance (AOR = 11.124; 95% CI: 3.690–33.535) and gender of the head of the family (AOR = 18.176; 95% CI: 3.353–98.534). Concentration curve suggested a higher concentration of CHE among poor households. Conclusion: CHE is substantially high in the area. The efforts are required to make the services available as close to the households as possible and to increase awareness about health facilities.

Keywords: Catastrophic health expenditure, concentration curve, determinants, out-of-pocket expenditure

How to cite this article:
Dalui A, Banerjee S, Roy R. Determinants of out-of-pocket and catastrophic health expenditure in rural population: A community-based study in a block of Purba Barddhaman, West Bengal. Indian J Public Health 2020;64:223-8

How to cite this URL:
Dalui A, Banerjee S, Roy R. Determinants of out-of-pocket and catastrophic health expenditure in rural population: A community-based study in a block of Purba Barddhaman, West Bengal. Indian J Public Health [serial online] 2020 [cited 2021 Jun 25];64:223-8. Available from:

   Introduction Top

In developing countries, out-of-pocket expenditure (OOPE) is the primary means of financing healthcare. Health expenditure in India accounts for <5% of the gross domestic product and the level of out-of-pocket (OOP) spending is 69.5% of total health expenditures.[1] In some areas of the country, the services of a primary health center (PHC) are not accessible to a majority of the population due to inconvenient distance and people in these areas are more likely to avail facilities in the private sector which may lead to higher per capita OOP health expenditure. OOPE exacerbates poverty and has a negative impact on equity and can increase the risk of vulnerable groups slipping into poverty.[2]

In the absence of a strong social security system, low-income level and insurance coverage, increasing longevity and noncommunicable diseases in these countries, the OOPE on health care is large relative to household income. The high OOPE reduces the consumption of nonhealth goods and services of the households, disrupts the household level of living and pushes many families into the medical poverty trap and distress financing.[3],[4],[5] OOP health expenditures were those made by households at the point of receiving health services and include cash payments reported in the surveys. Catastrophic spending on health occurs when a household must reduce its basic expenses over acertain period of time to cope with healthcare expenses on one or more of its members. Health expenditure has been defined catastrophic if 5%–20% of total household income is spent on health care.[6]

Many studies on health-care expenditure have been conducted in India,[7],[8],[9],[10] but study on OOPE in this part of the country is lacking. Although several studies found out factors for OOPE in rural population and government is also trying to give financial support for healthcare delivery, OOPE in India is still high.

In this perspective, the present study aimed to determine the magnitude of OOP health expenditure and catastrophic health expenditure (CHE) in the past 1 year in rural population of Bhatar Block as well as their sociodemographic determinants.

   Materials and Methods Top

Study design and area

It was a community-based observational study with cross-sectional design. The study was conducted between July 2018 and February 2019 in Bhatar Block of Purba Bardhaman district of West Bengal. Bhatar is one of the 23 blocks of the district comprising of 14 Gram Panchayats and 107 villages.

Study units/population: Sampling and selection

Households of the study area, residing for at least 1 year were considered as the primary study units. Adult people, both males and females (more than 18 years) belonging to the study units were the study population and included as respondents.

Considering the prevalence of CHE as 39.43%, reported by the previous study[11] in rural population in India, 95% confidence interval (CI), 20% relative error sample size was calculated using formula: N = z2α pq/d2 and minimum required sample was 147. Applying design effect of 1.5 for multi stage sampling and considering 10% nonrespondent rate, the sample size became (147 × 1.5) + 14.7 = 235.

Required households were selected from the study area by multistage sampling. One village from each of the 14 g panchayats of the block was selected randomly and thus a total 14 villages were identified. After that probability proportion to size sampling method was applied to decide the number of house-holds from each village. Then, the center of the village with confluence of multiple roads was identified and one of the roads was selected randomly. The number of households on both side of that road was enumerated and the first household was selected randomly from them. Thereafter, the required number of samples was taken consecutively along the road. Thus, finally, 235 households were included from 14 g panchayats of the block.

One adult member from each the selected households was identified as respondent for the study.

Tools and techniques: Data collection

Data were collected by interviewing the respondent using a predesigned, pretested schedule and also reviewing relevant records/documents regarding sociodemographic and health expenditure-related variables.

Sociodemographic and background variables included – gender, education, and occupation of the head of the family; type of family; socioeconomic status; coverage with any health insurance; and presence of health facility in the village. Health expenditure-related variables included – sources of expenditure; direct health expenditure (doctor's consultation fee, purchase of medicine, diagnostic charges, and hospital charges); indirect health expenditure (transportation charge, lodging charges, and loss of wages for both the patients and the family members).

Prior to data collection, ethical approval was obtained and the district health authority was intimated about the purpose of the study, their permission and cooperation were sought. The data were collected at the household level. Before interviewing, the subjects were briefed about the purpose and process of the study and their informed consent was obtained.

Definitions and measurement of outcome variables

The following definitions were used to measure the OOP and CHE of the families:

  1. OOP health expenditure: These are made by households at the point of receiving health services and include cash payments reported
  2. CHE: It is defined as the household's annual health expenditure when exceeds 10% of the total annual household income[12]
  3. Direct health expenditure: It includes all annual medical expenditure toward treatment which includes doctor's fee, purchase of medicine, diagnostic charges, and hospital charges[13]
  4. Indirect health expenditure: It includes the other annual expenses incurred by a household which includes transportation charge, lodging charges, and loss of wages for both the patients and the family members.[13]

Ethical considerations

Ethical clearance for the study was obtained from the Institutional Ethics Committee of Burdwan Medical College and Hospital, Purba Barddhaman (BMC-2581). Participation of the subjects in the study was voluntary and informed consents were obtained from all participants. Confidentiality and anonymity of information were maintained.

Data analysis

The collected data were checked for completeness and consistency and entered into the computer in the excel data sheets. The data analysis was done using SPSS version 20.0 (SPSS Inc., Chicago, Illinois, USA). Annual OOP health expenditure was expressed as median [inter quartile range (IQR)]. Mann–Whitney U test as well as Kruskal–Wallis H test was done to identify the main covariates of the annual OOP health expenditure. CHE was expressed as a binary variable to find out its association with different sociodemographic variables. Multivariable logistic regression analysis was conducted to identify determinants of CHE. Odds ratio with its 95% CI was calculated to express the strength of association. The concentration curve was drawn taking cumulative percentage of CHE in y-axis and cumulative percentage of the households in x-axis, beginning with the poorest, and ending with the richest (x-axis) to analyze household income-related inequalities in the distribution of CHE.

   Results Top

Background characteristics

Of the 235 households studied, 198 (84.3%) had male member as the head of family and 37 (15.7%) households had female member as the head of family; 49 (20.9%) were illiterate, 84 (35.7%) had primary education, 79 (33.6%) had completed secondary education, and 23 (9.8%) were educated till higher secondary and above. Hundred and eleven (47.2%) of the household's head were unskilled laborers, 83 (35.3%) were skilled laborers and 41 (17.5%) were employed in service or had a business. While 70 (30.2%) households lived in a joint family, 164 lived in a nuclear family (69.8%). Only 151 (64.3%) had health insurance. 40.4% households were residing in a village with primary health center (PHC) and rest of the households (59.6%) were residing in villages where no PHC were present. Majority of households were below poverty line (70.2%) [Table 1].
Table 1: Factors associated with total annual out-of-pocket health expenditure (n=235)

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Morbidities and health-care seeking

Of the total 1087 morbidity conditions which were classified according to major categories of International Classification of Diseases-10, 258 (23.7%) had disorders of respiratory system, 203 (18.7%) had disorders of cardiological system, 187 (17.3%) had disorders of bones and joints, 121 (11.1%) had disorders of gastrointestinal system including intestinal infections, 68 (6.2%) had injuries other than fractures, 63 (5.8%) had disorders of skin and subcutaneous tissue, while around 187 (17.2%) morbidities were by miscellaneous causes that included disorders of female genital organs, teeth, central nervous system, eye and ear, urinary, and pregnancy-related complications including abortion.

Considering multiple episodes of a disease there were total 1217 episodes of illness. Of these, any type of illness that included all the episodes in 1 year, 478 (39.3%) visited PHC or Block PHC/CHC, 421 (34.6%) visited private practitioners, 153 (12.6%) visited medical college hospitals, 31 (2.5%) had self-treatment, 121 (9.9%) visited others which included AYUSH/Registered Medical Practitioner/Quacks, and 13 (1.1%) visited health worker.

Of 59 hospitalizations for 1 year, 28 (47.5%) were hospitalized in medical college hospital, 14 (23.7%) in a government hospital like BPHC and 17 (28.8%) in private hospital.

Out-of-pocket health expenditure and determinants

The median OOP health expenditure was Rs. 3870 (IQR: 2156–4952). The median direct expenditure was Rs. 1780.00 (IQR: 550.00–3700.00) and median indirect health expenditure was Rs. 2100.00 (IQR: 600.00–3550.00). [Table 2] shows the further distribution of direct and indirect health expenditure.
Table 2: Distribution of direct and indirect health expenditures (rupees) in the study units (n=235)

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In the present study, OOPEs of households did not follow normal distribution so that non parametric tests of significance were done to find out the factors affecting OOPE. Annual OOP health expenditure was expressed as median (IQR Mann–Whitney U test was used to compare differences between two independent groups and Kruskal–Wallis H test was done to compare differences between more than two groups of an independent variable. It was found that OOPE in the past 1 year was associated with caste category (P = 0.005), socioeconomic status of the family (P = 0.040), and presence of PHC in the village (P = 0.020) [Table 1].

Magnitude and correlates of catastrophic health expenditure

Of 235 households, 38 (16.2%) had CHE. The significant independent correlates of catastrophic health expenditure were the type of village they belonged to, presence of any health insurance and gender. The families which belonged to villages other than PHC village had higher odds (adjusted odds ratio [AOR] = 4.748; 95% CI: 1.886–11.956) of having catastrophic health expenditure as compared to those which belonged to PHC village. Families not covered by any kind of health insurance had higher odds (AOR = 11.124; 95% CI: 3.690–33.535) of having CHE as compared to those who were covered under any health insurance. Families headed by female members had higher odds (AOR = 18.176; 95% CI: 3.353–98.534) of having CHE as compared to those families headed by male members [Table 3]. The model was found to be fit assessed by significance of Omnibus test and nonsignificance by Hosmer–Lemeshow Test. 31%–47% variance of the outcome (i.e., CHE) has been explained by this model.
Table 3: Multivariable logistic regression showing factors associated with catastrophic health expenditure (n=235)

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Mobilization of money

To meet out the expenses incurred on health expenditure, of the total 235 households, 201 (85.5%) had enough money, 27 (11.5%) borrowed money, and 7 (3%) of them sold assets.

Concentration curve

The concentration curve plots the cumulative percentage of, in this case, CHE (y-axis) against the cumulative percentage of the population (in this case, households), ranked by living standards (here proxied by capacity to pay), beginning with the poorest, and ending with the richest (x-axis). Concentration index is the area between the calculated concentration curve and the line of perfect inequality. The value of concentration index is-0.16 which signifies health expenditure is more concentrated among the poor. The blue line here is the line of equity and the concentration curve is above the equality line. This curve signifies that catastrophic health expenditure is more concentrated among poor households [Figure 1].
Figure 1: Inequity in health expenditure: A concentration curve

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   Discussion Top

According to the National Sample Survey Organization (NSSO) 60th round average total expenditure for India was Rs. 6225 and the average total medical expenditure was Rs. 5695.[14] The average cost of care for rural areas for each episode of hospitalization was Rs. 16,956 while the average cost for OPD-based care for ailments in the past 15 days was Rs. 50,918. The health-care seeking behavior seen in the present study was in contrast to that seen in the NSSO 71st and NSSO 60th round surveys while the average cost of care both for OPD and hospitalization was markedly less. These differences could be due to improved rural health infrastructure and services under the National Rural Health Mission and availability of State General Hospital and Burdwan Medical College within 30KM reach.

This study showed that median OOP health expenditure was Rs. 3870 (IQR: 2156–4952). The median direct expenditure was Rs. 1780.00 (IQR: 550.00–3700.00) and median indirect health expenditure was Rs. 2100.00 (IQR: 600.00–3550.00). In the present study, OOPE in the last 1 year was associated with caste category (P = 0.005), socioeconomic status of the family (P = 0.040) and presence of PHC in the village (P = 0.020). According to Loganathan et al., the median direct expenditure was RS. 863.85 (IQR: 358.45–2709.50) and median indirect health expenditure was Rs. 100.00 (IQR: 0–540.00). They also showed that significant correlates for the ratio of OOP health expenditure to total annual income of the family were the occupation of head of family, caste category, and type of village.[15] Although the median OOPE was more in our study, the factors affecting OOPE were similar in nature.

Only 16.2% of the households had catastrophic expenditure in the present study. In one study from Orissa, it was 18.6%[16] and in a study by Ghosh,[13] the catastrophic health-care expenditure among selected 16 states increased from 13.1% in 1993–1994 to about 15.4% in 2004–2005. The findings of current study were comparable with the findings of these studies.

In a study by Pal on CHE in India found that the incidence of catastrophic payments goes down with increased income and improved education. He also identified economic and social status of households as key determinants of incidence of catastrophic health expenditure.[17] The present study showed that type of village they belonged to and presence of any health insurance were found to be associated with CHE. The WHO in their strategy for Health Financing in Asian Pacific Region found that inadequate access to the health-care facility was a determinant of catastrophic health expenditure. The Report also identifies the distance of health facility as a key determinant for access with distance being a greater barrier for women than for men.[18] Shukla et al. showed that lower socioeconomic status was found to be one of the most important predictors for the incurrence of CHE.[19]

Families without health insurance also had higher odds for CHE. This could be due to the fact that the community-based health insurance schemes which the households were availing offered a good coverage. Although Shahrawat and Rao reported that insurance schemes covering only hospital expenses did not adequately protect the poor against impoverishment due to their spending on health as medicines and OOPs for OPD visits were the main share (72%) of total OOP payments.[20]

In the present study, money mobilization was accounted for the households, and it was found that of the total 235 households 85.5% had enough money. In another study on population of Maharashtra, 83.9% had enough money, 15% borrowed money, and 1.1% of them sold assets.[15]

The study could not avoid few limitations, which might be considered while interpreting the observations. The morbidities and expenditure were self-reported which might have brought in measurement bias. Although median health expenditure was reported in analysis, findings related to average health expenditure need to be interpreted appropriately as the data were skewed with high variability. Apart from that household income and expenditure could also be under reported due to respondents' recalls. Although we have taken Annual Household income to draw the concentration curve, wealth index would have been a better measure.

   Conclusion Top

Our study showed that though the health insurance coverage of households was good enough (around 64.3%), around one-sixth of the households had to spend catastrophically on health. Although some reduction in financial burdening or hardship was seen in the study population, it did not protect them against having to spend catastrophically on health. Location or distance from the public health-care facility and gender of head of family were identified as a significant factor for CHE. The significant correlates for OOPE were the education of head of family, caste category, and location of village in respect to government health-care facility. Improved health insurance coverage is not translated into better protection against health impoverishment. The efforts should be needed to make the services available as close to the households as possible and awareness about health facilities should be increased.

Financial support and sponsorship


Conflicts of interest

There are no conflicts of interest.

   References Top

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Loganathan K, Deshmukh PR, Raut AV. Socio-demographic determinants of out-of-pocket health expenditure in a rural area of Wardha district of Maharashtra, India. Indian J Med Res 2017;146:654-61.  Back to cited text no. 15
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  [Table 1], [Table 2], [Table 3]


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