Temporal decomposition of life years lived with disability in India: a growing demographic concern

Abstract

Background: To draw optimal benefits of the demographic dividend, healthy life years of the young adults is a growing concern in India. Rising prevalence of chronic non-communicable diseases among the younger population is responsible for increasing the life years lived with disability among them and for affecting their productivity in turn. This study measures the disability burden in various Indian sub-populations and assesses the contribution of disability to the change in person years lived with a disability during 2001-11. Methods: Data from the Census of India, 2001 and 2011 was used for estimating the age distribution and disability prevalence among males and females. The Sample Registration System was used for age-specific mortality rate to calculate the life table for 15 states in India. Life years Lived with Disability (LLD) were estimated using the Sullivan method. The extension of Arriaga method was used to decompose change in life years lived with disability into Mortality and Disability Effect (ME and DE, respectively). Positive ME explains improvement in life years due to decline in mortality rate and a negative DE explains a decline in disability incidence in 2001-11. Results: At national level, the disability prevalence has increased from 2001 to 2011. The prevalence of disability and the share of LLD to Life Expectancy (LE) is higher for males. High and medium fertility states scored highest on living with disability to LE ratio and measured DE in the decomposition analysis. At the national level, the DE increased in the age groups of 20-35 years. It was higher among the females. The states that are in the advanced stages of demographic transition show a negative DE. Conclusion: The study highlights expansion of DE in prime productive years of life, especially among females, in medium and high fertility states. Decline in skilled employment and productivity can be two major economic adversities due to increasing DE in working ages. Disability among young and working age population needs to be prioritised as most of the Indian states stand at crucial stages of demographic transitions.

Measuring the contributions of elderly and non-elderly age-groups to the disability burden in India: A Decomposition Analysis.

“…A higher disability prevalence and risk difference among educational categories predicts that the impact on disability is much severe among elderly in the selective categories. Study shows non-elderly age-groups have a growing disability burden from a range of conditions that includes communicable diseases, NCDs and injuries (13). Besides, the trend in the incidence of injuries, atrocities in childhood, malnourishment at the early ages, and violence among children and younger age adults are also seeking an attention in the context of India for upcoming decades.…”

“…An improvement in health, as well as disability outcomes among children, has been noticed in recent decades (12). However, an increase in disability among working age-groups in states like Uttar Pradesh, Karnataka, Punjab has also been pointed out in the duration of 2001-11 (13). Disability contributed by those population is substantial to add longer years with disability in the population, and hence, creates a long-term burden on the healthcare system.…”

Unveiling non-communicable disease trends among Indian states: predicting health outcomes with socioeconomic and demographic factors

“…Therefore, the states with a higher share of the older population are more likely to have a diseased population. On the other hand, states with younger populations have also witnessed a paradigm shift in the age at the onset of the disease; rising prevalence of chronic NCDs among the younger population is responsible for increasing the life years lived with disability among them and for affecting their productivity in turn (Banerjee et al, 2019;Mohanty et al, 2021). Bhise et al (2018) studied the NSSO 71st round and found the highest prevalence of NCD in southern states (107/1,000), and the lowest was recorded in the northeast (11/1,000) (Bhise et al, 2018).…”

“…First, the aging population is more vulnerable to NCDs, and increased life expectancy does not necessarily convert into healthy years (LASI Wave 1 Report, 2020). Second, the increasing prevalence of chronic NCDs among younger individuals is accountable for a rise in the years lived with disability among this age group, consequently impacting their productivity (Banerjee et al, 2019;Mishra et al, 2021;Mohanty et al, 2021). The burden of NCDs causes severe socioeconomic impacts in India, whereas the diversity in SED across regions leads to diversity in the prevalence of the diseases, hence broadening the role of SED statistics from explaining health patterns to actively forecasting the distribution within the nation.…”