SUMMARYOut-of-pocket (OOP) payments are the principal means of financing health care throughout much of Asia. We estimate the magnitude and distribution of OOP payments for health care in fourteen countries and territories accounting for 81% of the Asian population. We focus on payments that are catastrophic, in the sense of severely disrupting household living standards, and approximate such payments by those absorbing a large fraction of household resources. Bangladesh, China, India, Nepal and Vietnam rely most heavily on OOP financing and have the highest incidence of catastrophic payments. Sri Lanka, Thailand and Malaysia stand out as low to middle income countries that have constrained both the OOP share of health financing and the catastrophic impact of direct payments. In most low/middle-income countries, the better-off are more likely to spend a large fraction of total household resources on health care. This may reflect the inability of the poorest of the poor to divert resources from other basic needs and possibly the protection of the poor from user charges offered in some countries. But in China, Kyrgyz and Vietnam, where there are no exemptions of the poor from charges, they are as, or even more, likely to incur catastrophic payments.

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Out-of-pocket (OOP) expenditure on health care has significant implications for poverty in many developing countries. This paper aims to assess the differential impact of OOP expenditure and its components, such as expenditure on inpatient care, outpatient care and on drugs, across different income quintiles, between developed and less developed regions in India. It also attempts to measure poverty at disaggregated rural-urban and state levels. Based on Consumer Expenditure Survey (CES) data from the National Sample Survey (NSS), conducted in 1999-2000, the share of households' expenditure on health services and drugs was calculated. The number of individuals below the state-specific rural and urban poverty line in 17 major states, with and without netting out OOP expenditure, was determined. This also enabled the calculation of the poverty gap or poverty deepening in each region. Estimates show that OOP expenditure is about 5% of total household expenditure (ranging from about 2% in Assam to almost 7% in Kerala) with a higher proportion being recorded in rural areas and affluent states. Purchase of drugs constitutes 70% of the total OOP expenditure. Approximately 32.5 million persons fell below the poverty line in 1999-2000 through OOP payments, implying that the overall poverty increase after accounting for OOP expenditure is 3.2% (as against a rise of 2.2% shown in earlier literature). Also, the poverty headcount increase and poverty deepening is much higher in poorer states and rural areas compared with affluent states and urban areas, except in the case of Maharashtra. High OOP payment share in total health expenditures did not always imply a high poverty headcount; state-specific economic and social factors played a role. The paper argues for better methods of capturing drugs expenditure in household surveys and recommends that special attention be paid to expenditures on drugs, in particular for the poor. Targeted policies in just five poor states to reduce OOP expenditure could help to prevent almost 60% of the poverty headcount increase through OOP payments.

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The article compares the incidence of public healthcare across 11 Asian countries and provinces, testing the dominance of healthcare concentration curves against an equal distribution and Lorenz curves and across countries. The analysis reveals that the distribution of public healthcare is prorich in most developing countries. That distribution is avoidable, but a propoor incidence is easier to realize at higher national incomes. The experiences of Malaysia, Sri Lanka, and Thailand suggest that increasing the incidence of propoor healthcare requires limiting the use of user fees, or protecting the poor effectively from them, and building a wide network of health facilities. Economic growth may not only relax the government budget constraint on propoor policies but also increase propoor incidence indirectly by raising richer individuals' demand for private sector alternatives. JEL Codes: H22, H42, H51.

Noncommunicable diseases (NCDs) have become a major public health problem in India accounting for 62% of the total burden of foregone DALYs and 53% of total deaths. In this paper, we review the social and economic impact of NCDs in India. We outline this impact at household, health system and the macroeconomic level. Cardiovascular diseases (CVDs) figure at the top among the leading ten causes of adult (25–69 years) deaths in India. The effects of NCDs are inequitable with evidence of reversal in social gradient of risk factors and greater financial implications for the poorer households in India. Out-of-pocket expenditure associated with the acute and long-term effects of NCDs is high resulting in catastrophic health expenditure for the households. Study in India showed that about 25% of families with a member with CVD and 50% with cancer experience catastrophic expenditure and 10% and 25%, respectively, are driven to poverty. The odds of incurring catastrophic hospitalization expenditure were nearly 160% higher with cancer than the odds of incurring catastrophic spending when hospitalization was due to a communicable disease. These high numbers also pose significant challenge for the health system for providing treatment, care and support. The proportion of hospitalizations and outpatient consultations as a result of NCDs rose from 32% to 40% and 22% to 35%, respectively, within a decade from 1995 to 2004. In macroeconomic term, most of the estimates suggest that the NCDs in India account for an economic burden in the range of 5–10% of GDP, which is significant and slowing down GDP thus hampering development. While India is simultaneously experiencing several disease burdens due to old and new infections, nutritional deficiencies, chronic diseases, and injuries, individual interventions for clinical care are unlikely to be affordable on a large scale. While it is clear that “treating our way out” of the NCDs may not be the efficient way, it has to be strongly supplemented with population-based services aimed at health promotion and action on social determinants of health along with individual services. Since health sector alone cannot deal with the “chronic emergency” of NCDs, a multi-sectoral action addressing the social determinants and strengthening of health systems for universal coverage to population and individual services is required.

Comparison of two major studies conducted by National family health survey (NFHS-2) in 1998-1999 and NFHS-3 in 2005-2006 shows that prevalence of obesity among Indian women has elevated from 10.6% to 12.6% (increased by 24.52%). The prevalence is more profound in the women of age between 40-49 years (23.7%), residing in cities (23.5%), having high qualification (23.8%), belonging to Sikh community (31.6%) and households in the highest wealth quintile (30.5%). Highest percentage of obese women is found in Punjab (29.9%). Although this number seems small in the international perspective, it is significant because of the sheer size of population in India. While the problem of under-nutrition still exists in many parts of India, the additional burden of obesity due to increasing sedentary lifestyle, junk food habits in some urban and economically sound areas is really alarming. Prevention and control of this serious problem through awareness programmes to adopt diversified nutritional food and healthy lifestyle are strongly recommended.

Background Coronavirus disease-2019 (COVID-19) is now becoming a global threat. Studies reported dyslipidemia in patients with COVID-19. Herein, we conducted a systematic review and meta-analysis of published articles to evaluate the association of lipid profile with the severity and mortality in COVID-19 patients. Methods PubMed/Medline, Europe PMC, and Google Scholar were searched for studies published between January 1, 2020 and January 13, 2021. Random or Fixed effects models were used to calculate the mean difference (MD) and 95% confidence intervals (CIs). Statistical heterogeneity was assessed using Cochran’s Q test and I 2 statistics. Results This meta-analysis included 19 studies. Of which, 12 studies were categorized by severity, 04 studies by mortality, and 03 studies by both severity and mortality. Our findings revealed significantly decreased levels of total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) in the severe group when compared with the non-severe group in a random effect model. Similarly, random effect model results demonstrated significantly lower levels of HDL-C and LDL-C in the non-survivor group when compared with the survivor group. The level of TC was also found to be decreased in the non-survivor group when compared to the survivor group in a fixed-effect model. Conclusion In conclusion, lipid profile is associated with both the severity and mortality in COVID-19 patients. Hence, a lipid profile may be used for assessing the severity and prognosis of COVID-19. PROSPERO registration Number CRD42021216316 .