MORBIDITY PATTERN OF HOSPITALIZATION AND ASSOCIATED OUT OF POCKET EXPENDITURE: EVIDENCE FROM NSSO (2017-2018)

In 2018, according to the National Sample Survey Report, the number of cases of hospitalization per 1000 persons in 365 days was 29 in India (26 per 1000 in rural and 34 per 1000 in urban areas). Between 2004 and 2014, for example, the average medical expenditure per hospitalization for urban patients increased by about 176%, and for rural patients, it jumped by a little over 160%. Most of these hospitalizations are for infections, but a significant number also for treatment for cancer and blood-related diseases. The increase in access to healthcare has also brought with it a massive spike in costs. India is rapidly undergoing an epidemiological transition with a sudden change in the disease profile of its population. This study aimed to analyze hospitalization due to different factors like age and morbidity and its effect on health care utilization from nationally representative data from 2018 among the total population of India. 75th round of National Sample Survey Organisation (NSSO) conducted in July 2017- June 2018 has been used to examine what are the determinant factors that affect the hospitalization and mean monthly disease-specific expenditure in the different age group populations in India. We have used cross-tabulation to understand the association between morbidity patterns and healthcare utilization with other socio-demographic variables. A set of logistic regression analyses was carried out to understand the role of age patterns on hospitalization. A log-linear regression model was used to understand the significant predictors of out-of-pocket expenditure (OOPE).

Understanding socio-economic inequalities in the prevalence of asthma in India: an evidence from national sample survey 2017–18

Background Today, over 300 million people reside with asthma worldwide and India alone is home for 6% of children and 2% of adults suffering from this chronic disease. A common notion of disparity persists in terms of health outcomes across the poor and better-off section of the society. Thus, there is a need to explore socio-economic inequality in the contribution of various factors associated with asthma prevalence in India. Methods Data for the study were carved out from the 75th round of National Sample Survey (NSS), collected by the National Sample Survey Organization (NSSO) during 2017–18. The sample size for this study was 555,289 individuals, for which data was used for the analysis. Descriptive statistics were used to show the distribution of the study population. Further, bivariate and multivariate analysis was performed to identify the factors associated with Asthma prevalence. The concentration index was used to measure the inequality. Further, we used decomposition analysis to find the contribution of factors responsible for socio-economic status-related inequality in asthma prevalence. Results The prevalence of asthma was 2 per 1000 in the whole population; however, the prevalence differs by age groups in a significant manner. Age, sex, educational status, place of residence, cooking fuel, source of drinking water, household size and garbage disposal facility were significantly associated with asthma prevalence in India. It was found that asthma was more concentrated among individuals from higher socioeconomic status (concentration index: 0.15; p < 0.05). While exploring socio-economic inequality for asthma, richest wealth status (53.9%) was the most significant contributor in explaining the majority of the inequality followed by the urban place of residence (37.9%) and individual from age group 45–65 years (33.3%). Additionally, individual aged 65 years and above (27.9%) and household size less than four members (14.7%) contributed in explaining socio-economic inequality for asthma. Conclusion Due to the heterogeneous nature of asthma, associations between different socio-economic indicators and asthma can be complex and may point in different directions. Hence, considering the concentration of asthma prevalence in vulnerable populations and its long-term effect on general health, a comprehensive programme to tackle chronic respiratory diseases and asthma, in particular, is urgently needed.

Determinants of hand-hygiene practices in India: reflections from the 76th round National Sample Survey, 2018

This paper studies the differences and determinants of handwashing practices in India and identifies sections of the population with poor handwashing practices who are relatively more vulnerable during the COVID-19 pandemic. We have used the data from the recent National Sample Survey (NSS, 76th round) for India (2018). Bivariate and logistic regression analyses have been performed to predict the determinants of handwashing practices across states and socio-economic groups. Levels of education of the household head, Usual Monthly Per Capita Consumption Expenditure (UMPCE) of the household, access to water (other than drinking water) resources and sanitation facilities, and the availability of water with soap in and around latrine are major socio-economic and demographic factors that impact handwashing practices. Higher access to principal sources of water for drinking and other purposes, access to bathroom and latrine with soap, and the availability of water in or around latrine increase the likelihood of handwashing among the people. Universal handwashing across different sections of population will be effective to prevent further infection. The available data help us to identify the vulnerable sections of the population which are towards the lower end of the handwashing compliance spectrum. The policymakers can outline specific planning and strategy implementation for them.

Differentials and predictors of hospitalization among the elderly people in India: Evidence from 75th round of National Sample Survey (2017-18)

IntroductionThere are limited evidences on the determinants of hospitalization and its causes in India. We examined the differential in the hospitalization rates and its socio-economic determinants. We also examined the causes of diseases in hospitalization among the elderly (≥60 years) in India.MethodsWe used data from 75th round of the National Sample Survey Organizations (NSSO), collected from July 2017 to June 2018. The elderly samples in this survey are 42759, where 11070 were hospitalized, and 31,689 were not hospitalized in the last year or 365 days. We estimated hospitalization rates and carried out binary logistic regression analysis to examine the associations of hospitalization with the background variables. The cause of diseases in hospitalizations were also calculated.ResultsHospitalization rate was lower among female elderly compared to male elderly. Elderly who belongs to middle-old aged groups, non-married, North-Eastern region, Southern region, general caste, health insurance, partially & fully economically dependent elderly have a higher chance of being hospitalized. About 38% elderly were hospitalized due to communicable diseases (CDs), 52% due to non-communicable diseases (NCDs) and 10% due to Injuries & others. Nearly 40% elderly were hospitalized in public hospitals due to CDs, while 52% were hospitalized in private hospitals due to NCDs and 11% due to Injuries & others.ConclusionsRaising awareness, promoting a healthy lifestyle, and improving the quality of good healthcare provisions at the primary level is necessary. Early screening and early treatment for NCDs are needed, which is non-existent in almost all parts of India.

Is Distance to Secondary School a Barrier to Secondary and Higher Education in India?

This article has examined the implications of distance to secondary school on the achievement of secondary and higher education in India. Using the 71st round of National Sample Survey Office (NSSO) data, the article found that distance to secondary school beyond 2–3 km reduces the chances of getting secondary and higher education. For female members, secondary schools located beyond 2–3 km become a barrier to secondary and higher education; however, the distance beyond 5 km matters for male members. Economically better-off households and larger households have higher chances of completing secondary and higher education. Scheduled tribe households and households with casual workers have fewer chances of getting secondary or higher education. The households living in states with better transport facilities to the secondary schools have higher chances of getting secondary and higher education.