Diabetes-free life in India: Application of survival function analysis

Abstract

Background:India being a diverse country has an assortment of various socio-economic and demographic structures. These socio-economic and demographic factors often intervene with the diabetes-free survival in any geographical region. The present study explores diabetes-free survival patterns of adult respondents with respect to various socio-economic and demographic factors in eighteen states of India with better demographic indicators. Material and Methods:The study utilizes data from the fourth round of the District Level Household and Facility Survey (DLHS) undertaken during 2012–2013. The study computes the survival functions by five year age groups followed by Peto and Pike's test for assessing the significant differences in the survival pattern between the various sub-groups in 18 states of India. Results:The study points out that the diabetes-free survival of a respondent is affected by factors such as the place of residence, sex, social group, the standard of living (wealth) index, and region of residence. Conclusions:Timely detection, screening, and treatment of non-communicable diseases, as well as palliative care, are recommended to the community for diabetes prevention. A healthy lifestyle including healthy diet, physical activity on regular basis, and avoiding tobacco needs to be promoted.

Exploring the Linkages Between Non-Communicable Disease Multimorbidity, Health Care Utilization and Expenditure Among Aboriginal Older Adult Population in India

“…India is a large and diverse country ( 3 ); which when combined with the rigid social stratification, it acts as the root cause for the social and economic inequalities, which in turn, reciprocates in the form of disparities in opportunities and political and economic decision-making ( 2 ). The Aboriginal population, which comprises around 750 tribes, and account for 8.6 percent of the Indian population, is considered the most vulnerable sub-group in the country ( 2 ).…”

Gender disparities in the burden of non-communicable diseases in India: Evidence from the cross-sectional study

“…7,10,11 This predisposition of the Indian population could be linked to the genetic makeup of the population. 12 Evidence generated from existing literature, suggests that, men have higher rates of mortality, while, women report higher prevalence of morbidity as compared to that of men. 4,13 This situation can be explained by utilizing two broad theories, namely, the biological and the psychosocial.…”

Risk of diabetes and expected years in life without diabetes among adults from an urban community in India: findings from a retrospective cohort

“…Another study conducted in India, employing the life table methodology, revealed that the likelihood of maintaining a diabetes-free life diminishes with age. Specifically, only 30% of the overall participants remained free of diabetes beyond the age of 77 [ 23 ].…”

“…Consistent with our findings, several previous studies have also noted an elevated diabetes risk among males [ 10 – 18 ]. An Indian study specifically highlighted that the likelihood of remaining diabetes-free is notably lower for men compared to women, particularly for urban residents and those belonging to higher socioeconomic classes [ 23 ]. This investigation utilized data from the India Human Development Survey (IHDS) and discovered that only 26% of urban males and 33% of urban females remained diabetes-free until the age of 73 in India [ 23 ].…”

“…Hence, some studies diverge by reporting the age at diabetes detection rather than onset, introducing variations [ 22 ]. Also, numerous studies have employed the life table approach to explore the transition from non-diabetic states to diabetes, assessing the proportion of remaining life with and without diabetes [ 21 , 23 , 24 ]. However, these studies couldn’t estimate diabetes onset and progression patterns in real-time cohorts with continuous follow-up and clinical diagnosis using laboratory tests, as many longitudinal studies in India lack consistent follow-up and were primarily conducted cross-sectionally with an interval of eight to ten years [ 12 , 25 – 27 ].…”