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.

Impact of change in household environment condition on morbidity in India: Evidence from longitudinal data

Background

Household environment condition is an important predictor of morbidity of the household members. Without forming a healthy household environment, creating a healthy population is not possible. In this background, this study assesses the impact of change in household environment conditions on morbidity.

Methods

For the empirical analysis purpose of this study, we used two waves of longitudinal data from India Human Development Survey (2004–05, 2011–12). This study is based on 34131 re-contacted households in 2011–12 from the base year 2005. The bivariate and ANOVA tests were performed to assess any short-term morbidity (diarrhoea, fever and cough) with respect to change in household environment condition from 2005 to 2011. The multivariate linear regression was performed to assess the impact of change in household environment conditions on morbidity. The multinomial logistic regression was used to assess the impact of change in household environment condition on change in morbidity.

Results

The results from multivariate linear regression have shown that the share of household members fell sick due to any short-term morbidity (ASM) was significantly lower (β = –0.060, P<0.001) among the households who lived in clean environment condition in both the periods, 2004–05 and in 2011–12 as compared to those who were living in poor environment condition in both periods net of other socio-economic characteristics of the households. The share of household members fell sick due to any short-term morbidity has significantly declined (β = –0.051, P<0.001) among the household whose household environment condition has changed from poor in 2004–05 to clean environment in 2011–12 as compared to the households who have lived in poor environment condition in both periods in 2004–05 and 2011–12. The results of adjusted percentage from multinomial logistic regression have shown that the household members who fell sick with ASM was remained higher (4.9%; P<0.05) among the households whose environment condition was remained poor in both years in 2005 and 2011 as compared to the other households (2.7%) who remained in the better-off condition in both years in 2005 and 2011.

Conclusion

Considering the findings of the study, we suggest that ongoing government flagships programmes such as Swacch Bharat Mission (Clean India Mission), Pradhan Mantri Ujjwala Yojana (Prime Minister Clean Energy Scheme) and Pradhan Mantri Awas Yojana (Prime Minister Housing Scheme), and Jal Jeevan Mission (Improved Source of Drinking Water Scheme) should work in tandem to improve household environment conditions.

Population attributable risk fraction for selected chronic diseases in India

BACKGROUND

India's current health transition stage poses a critical challenge of dealing with the unfinished agenda of communicable diseases and the steadily rising burden of noncommunicable diseases. A significant burden of chronic diseases in India is attributable to household and individual level health risk factors coupled with socioeconomic conditions. From this perspective, this article made a first time effort to assess disease burden attributable to health risk factors using cross-sectional population health survey data.

METHODS

Population attributable fractions (PAF) were estimated for a cluster of health risk factors that include unsafe water, lack of sanitation, exposure to cooking smoke, tobacco and alcohol use, physical inactivity, and socioeconomic conditions on a set of widely prevalent chronic diseases such as tuberculosis, malaria, diarrhea, diabetes, angina, and asthma. Data from the 2003 World Health Survey was used.

RESULTS

The analysis revealed evidence of a significant contribution of health risk factors to India's escalating chronic disease burden. The contribution of health risk factors toward chronic disease burden varied by residence.

CONCLUSION

Results suggest that promotional health care based policies to deal with health risks should be a major priority in policy agenda to combat with the challenge of emerging noncommunicable disease coupled with the persistent burden of communicable diseases. Disease burden in India could be halved by effectively modifying exposure to the risk factors through promotional health care.