Rising non-communicable diseases (NCDs) coupled with increasing injuries have resulted in a significant increase in health spending in India. While out-of-pocket expenditure remains the major source ...of health care financing in India (two-thirds of the total health spending), the financial burden varies enormously across diseases and by the economic well-being of the households. Though prior studies have examined the variation in disease pattern, little is known about the financial risk to the families by type of diseases in India. In this context, the present study examines disease-specific out-of-pocket expenditure (OOPE), catastrophic health expenditure (CHE) and distress health financing.
Unit data from the 71st round of the National Sample Survey Organization (2014) was used for this study. OOPE is defined as health spending on hospitalization net of reimbursement, and CHE is defined as household health spending exceeding 10% of household consumption expenditure. Distress health financing is defined as a situation when a household has to borrow money or sell their property/assets or when it gets contributions from friends/relatives to meet its health care expenses. OOPE was estimated for 16 selected diseases and across three broad categories- communicable diseases, NCDs and injuries. Multivariate logistic regression was used to understand the determinants of distress financing and CHE.
Mean OOPE on hospitalization was INR 19,210 and was the highest for cancer (INR 57,232) followed by heart diseases (INR 40,947). About 28% of the households incurred CHE and faced distress financing. Among all the diseases, cancer caused the highest CHE (79%) and distress financing (43%). More than one-third of the inpatients reported distressed financing for heart diseases, neurological disorders, genito urinary problems, musculoskeletal diseases, gastro-intestinal problems and injuries. The likelihood of incurring distress financing was 3.2 times higher for those hospitalized for cancer (OR 3.23; 95% CI: 2.62-3.99) and 2.6 times for tuberculosis patients (OR 2.61; 95% CI: 2.06-3.31). A large proportion of households who had reported distress financing also incurred CHE.
Free treatment for cancer and heart diseases is recommended for the vulnerable sections of the society. Risk-pooling and social security mechanisms based on contributions from both households as well as the central and state governments can reduce the financial burden of diseases and avert households from distress health financing.
Background: The National Health Mission (NHM), one of the largest publicly funded maternal health programs worldwide was initiated in 2005 to reduce maternal, neo-natal and infant mortality and ...out-of-pocket expenditure (OOPE) on maternal care in India. Though evidence suggests improvement in maternal and child health, little is known on the change in OOPE and catastrophic health spending (CHS) since the launch of NHM. Aim: The aim of this paper is to provide a comprehensive estimate of OOPE and CHS on maternal care by public and private health providers in pre and post NHM periods. Data and method: The unit data from the 60th and 71st rounds of National Sample Survey (NSS) is used in the analyses. Descriptive statistics is used to understand the differentials in OOPE and CHS. The CHS is estimated based on capacity to pay, derived from household consumption expenditure, the subsistence expenditure (based on state specific poverty line) and household OOPE on maternal care. Data of both rounds are pooled to understand the impact of NHM on OOPE and CHS. The log-linear regression model and the logit regression models adjusted for state fixed effect, clustering and socio-economic and demographic correlates are used in the analyses. Results: Women availing themselves of ante natal, natal and post natal care (all three maternal care services) from public health centres have increased from 11% in 2004 to 31% by 2014 while that from private health centres had increased from 12% to 20% during the same period. The mean OOPE on all three maternal care services from public health centres was US$60 in pre-NHM and US$86 in post-NHM periods while that from private health center was US$170 and US$300 during the same period. Controlling for socioeconomic and demographic correlates, the OOPE on delivery care from public health center had not shown any significant increase in post NHM period. The OOPE on delivery care in private health center had increased by 5.6 times compared to that from public health centers in pre NHM period. Economic well-being of the households and educational attainment of women is positively and significantly associated with OOPE, linking OOPE and ability to pay. The extent of CHS on all three maternal care from public health centers had declined from 56% in pre NHM period to 29% in post NHM period while that from private health centres had declined from 56% to 47% during the same period. The odds of incurring CHS on institutional delivery in public health centers (OR .03, 95% CI 0.02, 06) and maternal care (OR 0.06, 95% CI 0.04, 0.07) suggest decline in CHS in the post NHM period. Women delivering in private health centres, residing in rural areas and poor households are more likely to face CHS on maternal care. Conclusion: NHM has been successful in increasing maternal care and reducing the catastrophic health spending in public health centers. Regulating private health centres and continuing cash incentive under NHM is recommended.
The desire for children could be considered a reliable predictor of subsequent fertility. At the same time, the sex composition of surviving children, along with other demographic and socioeconomic ...factors, may affect a couple’s fertility desire and, therefore, their subsequent fertility. This study examined the impact of the sex composition of living children and a couple’s agreement on fertility desire on their subsequent fertility in India using data came from two rounds of nationally representative surveys: the India Human Development Survey (IHDS)-I (2004–05) and IHDS-II (2011–12). To understand which factors affect the chances of an additional pregnancy or childbirth, a random effects logistic regression model was applied to the panel data. It was found that the fertility desires of both marital partners were important in determining the chances of subsequent fertility. About 35% of the couples wanting to limit children had undergone pregnancy or childbirth, while 76% of the couples wanting more children had conceived or given birth to children. In the case of discordance between the spouses, subsequent fertility was found to remain intermediate between those agreeing to continue childbirth and those wanting to limit it. The findings also affirmed that child sex preference, specifically son preference, still persists in Indian society. More than 80% of the couples with only daughters in IHDS-I mutually wanted to have additional children, whereas in families that only had sons, the chance of a subsequent pregnancy was inversely associated with the number of sons. Strong patriarchal settings, combined with cultural and socioeconomic factors, affect the persistence of sex preference in India. Programmes aimed at increasing family planning use need to address son preference and should include components that promote the value of girl children.
Reproduction in India is mainly confined to within marriage. The fertility preferences of spouses will not necessarily be the same, but discussion between couples creates scope for understanding ...between spouses after marriage. Knowing each other’s opinions facilitates decision-making on sensitive matters such as contraception use and desired family size. This study used data from the India Human Development Survey-II (2011–12), and was based on a sample of 31,276 currently married women. The aim was to understand the role of pre-marital communication, studied through the choosing of husbands, mutual communication before marriage and duration of time spouses knew each other before marriage on the fertility preferences of couples post-marriage. These preferences included contraception use, who has most say on the number of children and the gap between desired and actual number of offspring. The results showed that wives who knew their husbands or who had any kind of communication with them before marriage had a greater chance of being involved in fertility decisions. However, most fertility decisions were found to be male-driven. Wives who knew their husbands for more than a month before marriage took more decisions on number of children (27%) than those who only knew their husbands from the day of their wedding (20%). Wives were less likely to have more children/sons/daughters than desired if they had some communication with their husbands before marriage. A better understanding of fertility preferences between spouses might help to curb unwanted births through delaying or limiting births by contraception use. Families in India could encourage couples to interact before marriage so they can make collective decisions on contraception use and/or the number of children they have.
ObjectivesThe prime objective of this study is to examine the trends of disease and age pattern of hospitalisation and associated costs in India during 1995–2014.DesignPresent study used nationally ...representative data on morbidity and healthcare from the 52nd (1995) and 71st (2014) rounds of the National Sample Survey.SettingsA total of 120 942 and 65 932 households were surveyed in 1995 and 2014, respectively.MeasuresDescriptive statistics, logistic regression analyses and decomposition analyses were used in examining the changes in patterns of hospitalisation and associated costs. Hospitalisation rates and costs per hospitalisation (out-of-pocket expenditure) were estimated for selected diseases and in four broad categories: communicable diseases, non-communicable diseases (NCDs), injuries and others. All the costs are presented at 2014 prices in US$.ResultsHospitalisation rate in India has increased from 1661 in 1995 to 3699 in 2014 (per 100 000 population). It has more than doubled across all age groups. Hospitalisation among children was primarily because of communicable diseases, while NCDs were the leading cause of hospitalisation for the 40+ population. Costs per hospitalisation have increased from US$177 in 1995 to US$316 in 2014 (an increase of 79%). Costs per hospitalisation for NCDs in 2014 were US$471 compared with US$175 for communicable diseases. It was highest for cancer inpatients (US$942) followed by heart diseases (US$674). Age is the significant predictor of hospitalisation for all the selected diseases. Decomposition results showed that about three-fifth of the increase in unconditional costs per hospitalisation was due to increase in mean hospital costs, and the other two-fifth was due to increase in hospitalisation rates.ConclusionThere has been more than twofold increase in hospitalisation rates in India during the last two decades, and significantly higher rates were observed among infants and older adults. Increasing hospitalisation rates and costs per hospitalisation are contributing substantially to the rising healthcare costs in India.
Purpose
Two of the crucial components of health care service utilisation are the type of health care services received (government, private, others) and the place visited (same village, another ...village, another district/town, a metro city, abroad, etc.). The association between health care facilities and gender is important for understanding the disparities between males and females. Thus, the primary objective of this study was to reassess the gender differences in the type and place of health care utilisation.
Methods
Data from the second round of the India Human Development Survey (2011–2012) were used for this study. Analysis was done using both bi- and multivariate techniques (multinomial logistic regression).
Results
Results indicate an improvement in the female health care-seeking behaviour. We found that females have a higher tendency to visit private health care centres, whereas a higher percentage of males used government health care services for the treatment of both long- and short-term morbidities. Males and females reported visiting health care centres within the village, in another village, in another district/town and in a metro area/abroad for treatment approximately to the same extent.
Conclusion
The analysis of the male and female treatment-seeking behaviour revealed a clear picture of proliferating gender neutrality. The increase in the health care-seeking behaviour of women can be considered an upshot of improved female education and increased awareness among males regarding female empowerment. Government interventions in different sectors have also improved the plight of women directly or indirectly.
Using the Longitudinal Ageing Study in India 2010 pilot survey data, the present study examines the covariates and risk factors associated with functional limitations among older adults (45+ ages) in ...India. Functional limitation is defined as the difficultly in performing some basic activities of daily livings (ADLs) viz. bathing, eating, walking, dressing, toileting and getting in/out of bed. Result suggests that one in every seven older adults in India has at least one of the functional limitations. Among all the activities of daily livings, the most reported problem is difficulty in getting in and out of bed (7 %) followed by walking (6.6 %) and toileting (5.5 %). Age and physical functionality is inversely correlated; older adults aged 60 years report more functional limitations and this becomes more noticeable for older adults aged 75 years and above. We found inverse association between functional limitations and education level and positive association with wealth possession. The multivariate results also corroborate the findings of bivariate results that older adults at higher age, females and older adults with low education are more likely to have functional limitations than their counterpart groups. The likelihood of functional limitations increases significantly in the presence of chronic diseases and smoking tobacco. These finding calls for devising policy to ensure the social security and health care requirements of aged, uneducated, females, poor and those suffering from chronic diseases.
Using the unit data from the 64th round of the National Sample Surveys, 2007–08 on employment, unemployment, and migration, covering 125,578 households, this paper estimates the level, depth, and ...severity of poverty among non-migrants and intra-state migrants, inter-state migrants, and emigrants in India. Based on the out-migration of any members of the household for employment at place of origin and using place of last residence definition, households are classified into intra-state migrants, inter-state migrant, emigrants, and non-migrant households. Economic well-being of migrant’s households at the place of origin is measured by consumption expenditure (income). A set of poverty indices, the poverty headcount ratio, poverty gap ratio, and square poverty gap, are estimated from the household consumption expenditure to measure the level, depth, and severity of poverty among migration categories. The official state-specific poverty line is used in estimating the poverty indices. Descriptive analyses and logistic regression analyses are used in the analyses. Results suggest that the level, depth, and severity of poverty among migrant households is lower than that among non-migrant households; however, it varies across migrant categories. The poverty head count ratio was 41 % among inter-state migrants, 31 % among intra-state migrants, 20 % among emigrants, and 39 % among non-migrants in India. The poverty gap ratio and squared poverty gap were highest among inter-state migrants. Two broad patterns emerge from the state level analyses. Barring Kerala and Punjab that have a higher percentage of emigrants, inter-state migration accounts for a larger share of employment-related migration from the less developed states of Uttar Pradesh, Bihar, Jharkhand, Chhattisgarh, and Odisha while intra-state migration accounts for a larger share in the developed states of Maharashtra, Gujarat, Karnataka, and Tamil Nadu. Second, the level, depth, and severity of inter-state migrants from less developed states is higher than that of intra-state migrants and non-migrants; however, the pattern is reversed in the more developed states of India. Adjusting for socioeconomic correlates, the odds of poor among intra-state migrants are lower than those among inter-state migrant’s households. The study supports the proposition that migration and remittances in India are not panacea to structural development constraints and that poor long-distance migrants need to be integrated in poverty alleviation programs.
Aim
This paper examines the pattern, growth and determinants of household health spending in India and compares the growth of per capita household health spending and per capita consumption ...expenditure over the last two decades.
Subject and methods
The unit data of various rounds of the National Sample Survey (consumption expenditure surveys 1993–1994, 2004–2005 and 2011–2012 and morbidity and health care surveys 1995–1996 and 2004) along with data from other secondary sources are used in the analyses. The patterns and growth of health spending are analyzed by demographic, social and economic attributes and economic well-being is measured using per capita consumption expenditure. Household health spending is subdivided into age structure, population growth, real cost of medical care and increased hospitalization. Descriptive statistics, fixed effect models and simple decomposition methods are used in the analyses.
Results
Results suggest that during 1993–2012, the annual growth rate of real per capita household health spending was twice (6.14 %) the real per capita consumption expenditure (2.60 %). On average, per capita household health spending among the richest consumption quintile was at least eight times higher than that of the poorest consumption quintile, linking household health spending to ability to pay. Household health spending was income inelastic. Though medicine accounts for a larger share of household health spending, household spending on medical tests is growing fast. We found a strong and positive gradient of age on per capita household health spending after controlling for income and other confounders. During 1995–2004, the age structure, hospitalization and real cost of hospitalization accounted for a 14, 42 and 26 % increase in the cost of hospitalization respectively.
Conclusion
Household health spending is growing faster than the consumption expenditure (economic well being) of household and changing age structure is significantly affecting health spending in India. Increased public spending on health, upgrading the public health system and increasing access to health insurance can reduce the household health spending in India.
Work and Health in India Kumar, Vimal; Paramasivan Ganesh, Mangadv; Ganesh, Sarlaksha ...
12/2017
eBook
The rapid economic growth of the past few decades has radically transformed India's labour market, bringing millions of former agricultural workers into manufacturing industries, and, more recently, ...the expanding service industries, such as call centres and IT companies.
Alongside this employment shift has come a change in health and health problems, as communicable diseases have become less common, while non-communicable diseases, like cardiovascular problems, and mental health issues such as stress, have increased.
This interdisciplinary work connects those two trends to offer an analysis of the impact of working conditions on the health of Indian workers that is unprecedented in scope and depth.
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