Comprehensive assessment of age-specific mortality rate and its incremental changes using a composite measure: A sub-national analysis of rural Indian women

Abstract

BackgroundDiverse socio-economic and cultural issues contribute to adverse health outcomes and increased mortality rates among rural Indian women across different age categories. The present study aims to comprehensively assess age-specific mortality rates (ASMR) and their temporal trends using a composite measure at the sub-national level for rural Indian females to capture cross-state differences.Materials and methodsA total of 19 states were included in the study to construct a composite age-specific mortality index for 2011 (base year) and 2018 (reference year) and examine the incremental changes in the index values across these years at the sub-national level in India. Sub-index values were calculated for each component age group and were subsequently used to compute the composite ASMR index using the geometric mean method. Based on the incremental changes, the performance of states was categorized into four different typologies.ResultsImprovement in mortality index scores in the 0–4 years age group was documented for all states. The mortality rates for the 60+ age group were recorded to be high for all states. Kerala emerged as the overall top performer in terms of mortality index scores, while Bihar and Jharkhand were at the bottom of the mortality index table. The overall mortality composite score has shown minor improvement from base year to reference year at all India level.ConclusionAn overall reduction in the mortality rates of rural Indian women has been observed over the years in India. However, in states like Bihar and Jharkhand, mortality is high and has considerable scope for improvement. The success of public health interventions to reduce the under-five mortality rate is evident as the female rural mortality rates have reduced sizably for all states. Nevertheless, there is still sizable scope for reducing mortality rates for other component age groups. Additionally, there is a need to divert attention toward the female geriatric (60+ years) population as the mortality rates are still high.

Construction of healthy aging index from two different datasets

“…Subsequently, we performed confirmatory factor analysis (CFA) on both datasets and successfully confirmed the presence of HAIs in them. There are several different methods that might be used for the construction of an index, for example the UNDP approach ( 14 , 15 ), Principal component analysis ( 16 ) etc. CFA was the most suitable method as it allowed us to test specific hypotheses regarding the underlying factor structure and the model’s fit to the data.…”