Socio-demographic determinants of out-of-pocket health expenditure in a rural area of Wardha district of Maharashtra, India.
TL;DR: Estimation of the OOP expenditure on health and catastrophic health expenditure and their socio-demographic determinants in a rural area of Maharashtra, India found people with no healthcare facility located in their village had higher odds of having catastrophic health Expenditure.
Abstract: Background & objectives: In India, health expenditure accounts for less than 5 per cent of the Gross Domestic Product and the level of out-of-pocket (OOP) spending is 69.5 per cent of total health expenditures. OOP expenditure exacerbates poverty and has a negative impact on equity and can increase the risk of vulnerable groups slipping into poverty. This study was conducted to estimate the OOP expenditure on health and catastrophic health expenditure and their socio-demographic determinants in a rural area of Maharashtra, India. Methods: This was a prospective observational study involving monthly follow up visits, done in 180 households of three villages under a primary health centre in Wardha district, India. Results: Of the 180 families, 18.9 per cent had catastrophic health expenditure over a period of one year. The median total out-of-pocket health expenditure was '1105.00 with median medical expenditure being '863.85 and median non-medical health expenditure being '100.00. A total of 151 (83.9%) had enough money, 27 (15%) borrowed money and two (1.1%) of them sold assets. The significant correlates for the ratio of out-of-pocket health expenditure to total annual income of the family were the occupation of head of family, caste category and type of village. The significant correlate for catastrophic health expenditure was type of village. Interpretation & conclusions: Around one-fifth of the households had catastrophic health expenditure. People with no healthcare facility located in their village had higher odds of having catastrophic health expenditure. Private providers were preferred for the treatment of acute illnesses and medical college hospitals for hospitalization.
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TL;DR: Vulnerable groups, such as households with a household head with a low-level of education, households with children under the age of 5 years or disabled persons, and low-income households should be prioritized by policymakers to improve access to essential health care.
Abstract: Around the world, millions of people are impoverished due to health care spending. The highest catastrophic health expenditures are found in countries in transition. Our study analyzes the extent of financial protection by estimating the incidence of catastrophic health care expenditure in Myanmar and its association with sociodemographic factors. We performed a secondary analysis of data from the household surveys conducted by the Livelihoods and Food Security Trust Fund (LIFT) in 2013 and 2015 in Myanmar. To estimate the magnitude of catastrophic health care expenditure, we applied the definition of catastrophic payment proposed by the World Health Organization (WHO); a household’s out-of-pocket payment for health care is considered catastrophic if it exceeds 40% of the household capacity to pay. We also examined the changes in catastrophic payments at three different threshold levels (20, 30, 40%) with one equation allowing for a negative capacity to pay (modified WHO approach) and another equation with adjusted negative capacity to pay (standard WHO approach). In 2013, the incidence of catastrophic expenditure was 21, 13, 7% (standard WHO approach) and 48, 43, 41% (modified WHO approach) at the 20, 30, 40% threshold level respectively, while in 2015, these estimates were 18, 8, 6% (standard WHO approach) and 47, 41, 39% (modified WHO approach) respectively. Geographical location, gender of the household head, total number of household members, number of children under 5, and number of disabled persons in the household were statistically significantly associated with catastrophic health care expenditures in both studied years 2013 and 2015. Education of household head was statistically significantly associated with catastrophic health expenditure in 2013. We found that the incidence of catastrophic expenditures varied by the approach used to estimate expenditures. Although the level of catastrophic health care expenditure varies depending on the approach and threshold used, the problem of catastrophic expenditures in Myanmar cannot be denied. The government of Myanmar needs to scale up the current Social Security Scheme (SSS) or establish a new financial protection mechanism for the population. Vulnerable groups, such as households with a household head with a low-level of education, households with children under the age of 5 years or disabled persons, and low-income households should be prioritized by policymakers to improve access to essential health care.
26 citations
Cites background or result from "Socio-demographic determinants of o..."
...One study in India found that the type of village is correlated with catastrophic health care expenditure [31]....
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...Second, no information about the insurance or social protection status of the studied population was available, which could have been an important explanatory variables as suggested in other studies [18, 30, 31]....
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TL;DR: The results indicate that greater decentralized planning taking into account district-level health financing patterns could be an effective way to tackle inequity and financial vulnerability emerging out of OOP expenses on healthcare.
Abstract: Background & objectives: Numerous studies have highlighted the regressive and immiserating impact of out-of-pocket (OOP) health spending in India. However, most of these studies have explored this issue at the national or up to the State level, with an associated risk of overlooking intra-State diversities in the health system and health-seeking behaviour and their implication on the financial burden of healthcare. This study was aimed to address this issue by analyzing district level diversities in inequity, financial burden and impoverishing impact of OOP health spending. Methods: A household survey of 62,335 individuals from 12,134 households, covering eight districts across three States, namely Gujarat, Haryana and Rajasthan was conducted during 2014-2015. Other than general household characteristics, the survey collected information on household OOP [sum total of expenditure on doctor consultation, drugs, diagnostic tests etc. on inpatient depatment (IPD), outpatient depatment (OPD) or chronic ailments] and household monthly consumption expenditure [sum total of monthly expenditure on food, clothing, education, healthcare (OOP) and others]. Gini index of consumption expenditure, concentration index and Kakwani index (KI) of progressivity of OOP, catastrophic burden (at 20% threshold) and poverty impact (using district-level poverty thresholds) were computed, for these eight districts using the survey data. The concentration curve (of OOP expenditure) and Lorenz curve (of consumption expenditure) for the eight districts were also drawn. Results: The distribution of OOP was found to be regressive in all the districts, with significant inter-district variations in equity parameters within a State (KI ranges from −0.062 to −0.353). Chhota Udepur, the only tribal district within the sample was found to have the most regressive distribution (KI of −0.353) of OOP. Furthermore, the economic burden of OOP was more pronounced among the rural sample (CB of 19.2% and IM of 8.9%) compared to the urban sample (CB of 9.4% and IM of 3.7%). Interpretation & conclusions: The results indicate that greater decentralized planning taking into account district-level health financing patterns could be an effective way to tackle inequity and financial vulnerability emerging out of OOP expenses on healthcare.
18 citations
TL;DR: To reduce inequities in health care utilization, policies should address issues related to both supply and demand sides, and there is an urgent need to strengthen the redistributive mechanisms by tightening the various social security networks and broadening the outreach capacity to the vulnerable and marginalized sections of the population.
Abstract: Objective The study attempts (a) to compute the degree of socio-economic inequity in health care utilization and (b) to decompose and analyze the drivers of socio-economic inequity in health care utilization among adults (20-59 years) in India during the periods 2014 and 2017-18. Data source The analysis has been done by using the unit level data of Social Consumption: Health (Schedule number 25.0), of National sample Survey (NSS), corresponding to the 71st and 75th rounds. Methods Odds ratios were computed through logistic regression analysis to examine the effect of the socio-economic status on the health seeking behaviour of the ailing adult population in India. Concentration Indices (CIs) were calculated to quantify the magnitude of socio-economic inequity in health care utilization. Further, the CIs were decomposed to find out the share of the major contributory factors in the overall inequity. Results The regression results revealed that socio-economic status continues to show a strong association with treatment seeking behavior among the adults in India. The positive estimates of CIs across both the rounds of NSS suggested that health care utilization among the adults continues to be concentrated within the higher socio-economic status, although the magnitude of inequity in health care utilization has shrunk from 0.0336 in 2014 to 0.0230 in 2017-18. However, the relative contribution of poor economic status to the overall explained inequities in health care utilisation observed a rise in its share from 31% in 2014 to 45% in 2017-18. Conclusion To reduce inequities in health care utilization, policies should address issues related to both supply and demand sides. Revamping the public health infrastructure is the foremost necessary condition from the supply side to ensure equitable health care access to the poor. Therefore, it is warranted that India ramps up investments and raises the budgetary allocation in the health care infrastructure and human resources, much beyond the current spending of 1.28% of its GDP as public expenditure on health. Further, to reduce the existing socio-economic inequities from the demand side, there is an urgent need to strengthen the redistributive mechanisms by tightening the various social security networks through efficient targeting and broadening the outreach capacity to the vulnerable and marginalized sections of the population.
14 citations
TL;DR: Policy standpoint, households with the lowest incomes, large households, and those where the household head was 'others' or had a condition preventing employment seemed to report OOP expenditures less frequently and may have chosen not to receive healthcare services, leading to the need for more healthcare services later.
Abstract: This study identifies the driving forces that contribute to the probabilities of incidence of out-of-pocket (OOP) expenditures by households in Turkey. Factors affecting the probability of OOP expenditures on medical products/devices/supplies (MP), outpatient services (OTS), and inpatient services (ITS) are examined using the Household Budget Survey data gathered by the Turkish Statistical Institute in 2018. The study applies the multivariate probit model. The incidence of OOP spending varied with 48.9% of the households reporting OOP expenditure on MP, 22.4% on OTS, and 25.4% on ITS. The largest probability changes were associated with household disposable annual income, household type and size, age category, and having private health insurance. Gender and marital status also influenced expenditures in some categories. Lifestyle choices had small and mixed effects, with smoking and alcohol consumption lowering the probability of OOP spending. From a policy standpoint, households with the lowest incomes, large households, and those where the household head was 'others' (retiree, student, housewife, not actively working, etc.) or had a condition preventing employment seemed to report OOP expenditures less frequently and may have chosen not to receive healthcare services, leading to the need for more healthcare services later.
6 citations
Dissertation•
01 Jan 2018
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01 May 2014
TL;DR: There is substantial global variation in the relative burden of stroke compared with IHD, and the disproportionate burden from stroke for many lower-income countries suggests that distinct interventions may be required.
Abstract: Background—Although stroke and ischemic heart disease (IHD) have several well-established risk factors in common, the extent of global variation in the relative burdens of these forms of vascular disease and reasons for any observed variation are poorly understood. Methods and Results—We analyzed mortality and disability-adjusted life-year loss rates from stroke and IHD, as well as national estimates of vascular risk factors that have been developed by the World Health Organization Burden of Disease Program. National income data were derived from World Bank estimates. We used linear regression for univariable analysis and the Cuzick test for trends. Among 192 World Health Organization member countries, stroke mortality rates exceeded IHD rates in 74 countries (39%), and stroke disability-adjusted life-year loss rates exceeded IHD rates in 62 countries (32%). Stroke mortality ranged from 12.7% higher to 27.2% lower than IHD, and stroke disability-adjusted life-year loss rates ranged from 6.2% higher to 10.2% lower than IHD. Stroke burden was disproportionately higher in China, Africa, and South America, whereas IHD burden was higher in the Middle East, North America, Australia, and much of Europe. Lower national income was associated with higher relative mortality (P 0.001) and burden of disease (P 0.001) from stroke. Diabetes mellitus prevalence and mean serum cholesterol were each associated with greater relative burdens from IHD even after adjustment for national income. Conclusions—There is substantial global variation in the relative burden of stroke compared with IHD. The disproportionate burden from stroke for many lower-income countries suggests that distinct interventions may be required. (Circulation. 2011; 124:314-323.)
7,265 citations
TL;DR: People, particularly in poor households, can be protected from catastrophic health expenditures by reducing a health system's reliance on out-of-pocket payments and providing more financial risk protection.
1,981 citations
Book•
01 Feb 1991
TL;DR: The sample size calculation for a prevalence only needs a simple formula, but there are a number of practical issues in selecting values for the parameters required in the formula, so the sample size needed for each of the selected province if the authors want to have a Sample size determination in health studies: A practical manual.
Abstract: The sample size calculation for a prevalence only needs a simple formula. However, there are a number of practical issues in selecting values for the parameters required in the formula. Several Practical Manual. Geneva: International Journal of Health Promotion and Education 11/2014, 53(3):128-135. Studies investigating the influence of genetic variants in vitamin D binding protein (DBP) and Sample size determination in health studies: A practical manual. Sample size determination in health studies. a practical manual. Sample size determination in health studies. a practical manual. PDF Came out some time. Targeted public health messages to raise knowledge level, correct misconception and Sample size determination in health studies : a practical manual. Sample size determination in health studies: a practical manual. 1991 Vol 0. S Lemeshow, S K Lwanga. The minimum sample size of 385 respondents was. The sample size needed for each of the selected province if we want to have a Sample size determination in health studies: A practical manual. Geneva:. Sample Size Determination In Health Studies A Practical Manual Read/Download Ethical approval has been obtained from the State Secretary of Health. Lemeshow S Sample size determination in health studies: a practical manual. Geneva. Handbook for Mental health posting. Obafemi (13), Lwanga, S.K. and Lemeshow, S. (1991) Sample Size Determination in Health Studies: A Practical Manual. The sample size was determine using the formula for prevalence study described in S. Sample size determination in health studies: a practical manual. Sample size determination and sampling procedure Lwanga SK, Lemeshow S. Sample Size Determination for Health Studies: A Practical Manual. Geneva. S. K. Wanga and S. Lemeshow, Sample Size Determination in Health Studies. A Practical Manual, World Health Organization, Geneva, Switzerland, 1991. online, Research questions, hypotheses and objectives (Practical Tips for Sample Size Determination in Health Studies: A Practical Manual WHO pdf, The. More recently, randomized controlled studies comparing SMS, phone calls, and no S. Sample size determination in health studies: a practical manual. Sample size determination in health studies: a practical manual. World Health. Organization. Riegert-Johnson DL, Korf BR, Alford RL, Broder MI, Keats BJ. Ministry of Public Health and Sanitation: National Strategy on Infant and Young Child Feeding Strategy 2007-2010. S. K. Lwanga, and S. Lameshow, “Sample size determination in health studies. A practical manual”. pp 1-71, 1991. Survey methods in community medicine, epidemiological studies, Lwanga SK, Lemeshow S. Sample size determination in health studies, a practical manual. As per WHO guidelines, 3 a minimum sample size of 96 was required using anticipated S. Sample size determination in health studies: A practical manual. The objective of this survey study was to determine the prevalence Lemeshow S. Sample size determination in health studies: a practical manual. 1991. 11. The sample size was computed using the formula for estimating a population S. Sample size determination in health studies: A practical manual. Geneva:. Aims: To know the factors determining gender preference by pregnant women, Lemshaw S. Sample size determination in health studies:A practical Manual. To determine the prevalence of mental health problems and psychosocial Sample size calculation for In other studies, the prevaA Practical Manual. In testing of hypothesis studies, the objective of sample size calculation is to achieve a S. Sample Size Determination in Health Studies: A Practical Manual. women are left with chronic ill health and 1 million neonatal deaths occur. and Lemeshow S, Sample size determination in health studies: A practical manual. In this state, a significant obstacle to health care access is the huge distances between the S. Sample size determination in health studies: a practical manual. International Journal of Interdisciplinary and Multidisciplinary Studies (IJIMS), S.Sample Size Determination in Health Studies–A Practical Manual: World. Bull World Health Organ 2002,80:546-54. (Pubmed) Lwanga SK, Lemeshow S. Sample Size Determination in Health Studies: A Practical Manual. Geneva:. Ian Janssen, Professor, School of Kinesiology and Health Studies, Queen's University S. Sample size determination in health studies: a practical manual. The sample was calculated using the using the World Health Organization Sample Size Determination in Health Studies (17) assuming a 41% prevalence. Lwanga S K, Lemeshow S. Sample size determination in health studies. A practical manual. World Health Organization Document 1991,1-80. pdf, Rieder H L. Sample size was estimated using the World Health Organization formula for sample size S. Sample size determination in health studies: a practical manual.
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TL;DR: Although China has greatly expanded health insurance coverage, financial protection remains insufficient and policy-makers should focus on designing improved insurance plans by expanding the benefit package, redesigning cost sharing arrangements and provider payment methods and developing more effective expenditure control strategies.
Abstract: expenditure. Findings The rate of catastrophic health expenditure was 13.0%; that of impoverishment was 7.5%. Rates of catastrophic health expenditure were higher among households having members who were hospitalized, elderly, or chronically ill, as well as in households in rural or poorer regions. A combination of adverse factors increased the risk of catastrophic health expenditure. Families enrolled in the urban employee or resident insurance schemes had lower rates of catastrophic health expenditure than those enrolled in the new rural corporative scheme. The need for and use of health care, demographics, type of benefit package and type of provider payment method were the determinants of catastrophic health expenditure. Conclusion Although China has greatly expanded health insurance coverage, financial protection remains insufficient. Policy-makers should focus on designing improved insurance plans by expanding the benefit package, redesigning cost sharing arrangements and provider payment methods and developing more effective expenditure control strategies.
338 citations
01 Jan 2014
TL;DR: The National Council of Applied Economic Research (NCAER) as mentioned in this paper is one of the few independent think-tanks globally that combine rigorous analysis and policy outreach with deep data collection capabilities, particularly for large-scale household and consumer surveys.
Abstract: NCAER, the National Council of Applied Economic Research, was set up in 1956 as part of Prime Minister Nehru’s vision for independent institutions that a newly independent India needed. It is the nation’s oldest and largest independent, nonprofit, economic think-tank, informing policy choices for both the public and private sectors. Over nearly six decades, NCAER has served the nation with its rich offering of applied research, data, and policy inputs to central and state governments, corporate India, the media, and informed citizens. It is one of a few independent think-tanks globally that combine rigorous analysis and policy outreach with deep data collection capabilities, particularly for large-scale household and consumer surveys.
257 citations