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Abhijeet Chavan

Jr. Economist intern

Caste differences in hypertension among women in India: diminishing health returns to socioeconomic status for lower caste groups.

Published on: December 20, 2022

Original author: Uddin, J., Acharya, S., Valles, J. et al. (2020) (DOI: 10.1007/s40615-020-00723-9)

The caste system is a relatively rigid system of social hierarchy in India. Caste membership defines one’s access to resources and life opportunities. A growing body of research suggests that lower caste groups have an excess burden of morbidity and mortality in India. Hypertension is a serious risk factor for many non-communicable diseases and has become a major global public health concern. Methodology They have used de-identified data from the National Family Health Survey (NFHS) 2015–2016, India. The NFHS is a nationally representative population-based household survey. The NFHS follows a two-stage probability sampling strategy. The first stage of the sampling strategy includes the selection of villages as the primary sampling unit (PSU) in rural areas and census enumeration blocks (CEB) in urban areas. In the second stage, using systematic random sampling, 22 households were selected from each PSU and CEB, resulting in a total of 628,900 households, of which 601,509 households were finally selected for interviews. In the selected households, 723,875 women aged 15–49 years were found to be eligible for an interview using the women’s questionnaire. Of the eligible women respondents, 699,686 took part in the interviews with a response rate of 97%. After deleting cases with missing information on any of the variables included in the analysis, the analytic sample consisted of 648,064 women aged 15–49 years. The survey used standardized protocols and field-friendly technologies to examine blood pressure (BP). Using the Omron blood pressure monitor, BP was measured three times at five-minute intervals. Finally, hypertension was defined as having an average systolic BP of ≥ 140 mmHg, and/or diastolic BP ≥ 90 mmHg, and/or self-reported use of any antihypertensive medication.​ Social caste was based on the respondent’s self-reports of whether they belong to one of the following categories: scheduled caste, scheduled tribe, other backward castes, and other castes. SES indicators include education and the wealth index. The analysis controls for several sociodemographic factors, health behaviors, and health conditions were analyzed. The analysis included 648,064 Indian women aged 15– 49 years. They used logistic regression analysis to regress a binary outcome of hypertension on the caste categories controlling the covariates. Results & discussion The prevalence of hypertension was highest in the non-caste (13.34%) group followed by the upper caste (12.70%) and scheduled tribe (11.33%). Other backward classes (10.47%) and scheduled caste (10.45%) categories had a similarly low level of hypertension prevalence. The study found that compared to the upper caste, both scheduled tribe and non-caste women had higher odds of hypertension. However, other backward-class women had lower odds of hypertension. Examining the SES indicators in model 2, they found that increasing education was associated with decreased odds of hypertension. Discussion This study evaluated the caste differences associated with hypertension in India using a nationally representative data set. The analysis revealed population-level caste differences in the prevalence of hypertension and the extent to which two important SES indicators, education, and wealth, condition the caste differences in hypertension among reproductive-age group women. The unique contribution of this study is highlighting caste membership, as an important factor for social stratification in health, which also intersects in complex ways with SES in patterning the population-level disparities associated with hypertension in India. Impact of research These findings provide evidence of differential returns to SES and have implications in understanding the causes of SES patterning in health among the disadvantaged caste groups in India.

Caste, religion, and mental health in India.

Published on: September 21, 2022

Original author: Gupta, A., Coffey, D. et al. (2020) (DOI: 10.1007/s11113-020-09585-9)

A large, multi-disciplinary literature in social epidemiology, public health, and medical sociology has been concerned with documenting and understanding disparities in health by race, ethnicity, gender, socioeconomic status, and caste. This paper investigates population-level disparities in self-reported anxiety and depression among adults in India, by caste and religion. Although prior research has shown that, in India, caste and religion play an important role in many other health outcomes, including infant mortality, child height, and the use of health services to the knowledge of the researcher, no prior study has investigated disparities in mental health by social group using population-representative data in India. They document and analyze gaps in these self-reported mental health indicators between higher caste Hindus, the dominant social group, and scheduled caste (sometimes called “untouchables” or Dalits), a marginalized group that comprises about 17% of India’s population. They also study gaps in self-reported mental health between higher caste Hindus and Muslims. Muslims are a religious minority who constitute about 14% of India’s population. Their questions are: (a) Are there disparities in self-reported mental health indicators between Muslims and higher caste Hindus, and between Scheduled Castes and higher Caste Hindus? (b) What is the magnitude of these disparities? (c) To what extent can they be explained by disparities in socioeconomic status? This research builds on prior work which documents and interprets health disparities among people from advantaged and marginalized social groups. A rich literature from the USA considers the extent to which health disparities between people of different races can be explained by differences in economic status and education (Williams et al. 2010; Geruso 2012; Do et al. 2012). Two observations about this framework are particularly relevant to our analysis. The first is that the causality between socioeconomic variables and health can run in both directions: poor health can also cause low socioeconomic status (Deaton 2003). And others have pointed out, that even if differences in socioeconomic status do explain a particular racial or ethnic gap in health, this does not mean that discrimination is not an ultimate cause of the disparity: discrimination often causes differences in economic and educational outcomes, as well (Pager et al. 2009). Methodology They analyzed the World Health Organization’s SAGE (Survey of Global Ageing and Adult Health) data collected in India from 2007–2008. SAGE surveys adult health, with a focus on older adults, and is unique among large-scale population surveys in India because it asks questions about mental health as well as physical health. SAGE is representative of the adult population aged 18 and above in six states: Assam, Karnataka, Maharashtra, Rajasthan, Uttar Pradesh, and West Bengal. These states were selected from among Indian states with more than five million populations based on their geographic region and level of economic and human development (World Health Organization 2013). SAGE collected detailed demographic data for each respondent, including sex, age, caste, and religion. It also collected information on whether respondents belonged to one of the Scheduled Tribes recognized by the Indian government’s affirmative action programs. They used responses to two general, simply-worded questions about self-reported mental health as their outcomes of interest. SAGE asked respondents the following questions: ​(a) Overall in the last 30 days, how much of a problem did you have with feeling sad, low, or depressed? None, mild, moderate, severe, or extreme? (b) Overall in the last 30 days, how much of a problem did you have with worry or anxiety? None, mild, moderate, severe, or extreme? These questions are common to most mental health screening tools (Diener et al. 1999). In additional analyses, we also test the robustness of our results using additional mental health-related outcomes in the WHO-SAGE. The independent variables used in their analysis are age, sex of the respondent, household assets owned, education, per capita log expenditure, rural residence, and state. They used a non-parametric reweighting standardization technique to generate counterfactual distributions of mental health outcomes among Scheduled Castes and Muslims. Secondly, parametric ordered logistic regression was used to show that Scheduled Castes and Muslims have worse mental health after controlling for SES differences. Results & discussion This study will contribute to broader efforts to draw attention to the need for research and policies to address social inequality in India. Further, it will study the relationship between discrimination, prejudice, and mental health in the context of social inequality and material disadvantage in India and would inform the social policy in India. Conclusion Their research provides the first population-level evidence that Scheduled Castes and Muslims have worse self-reported mental health than higher-caste Hindus. In most cases, these gaps remain even after accounting for the fact that Scheduled Castes and Muslims have less education and own fewer assets. Additional data are needed to better understand the mechanisms and processes that generate the gaps that are documented.

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