Estimation of national and subnational all-cause mortality indicators in Nepal, 2017

Abstract

Background Despite the civil registration and vital statistics (CRVS) system in Nepal operating for several decades, it has not been used to produce routine mortality statistics. Instead, mortality statistics rely on irregular surveys and censuses that primarily focus on child mortality. To fill this knowledge gap, this study estimates levels and subnational differentials in mortality across all ages in Nepal, primarily using CRVS data adjusted for incompleteness. Methods We analyzed death registration data (offline or paper-based) and CRVS survey reported death data, estimating the true crude death rate (CDR) and number of deaths by sex and year for each province and ecological belt. The estimated true number of deaths for 2017 was used with an extension of the empirical completeness method to estimate the adult mortality (45q15) and life expectancy at birth by sex and subnational level. Plausibility of subnational mortality estimates was assessed against poverty head count rates. Results Adult mortality in Nepal for 2017 is estimated to be 159 per 1000 for males and 116 for females, while life expectancy was estimated as 69.7 years for males and 73.9 years for females. Subnationally, male adult mortality ranges from 129 per 1000 in Madhesh to 224 in Karnali and female adult mortality from 89 per 1000 in Province 1 to 159 in Sudurpashchim. Similarly, male life expectancy is between 64.9 years in Karnali and 71.8 years in Madhesh and female male life expectancy between 69.6 years in Sudurpashchim and 77.0 years in Province 1. Mountain ecological belt and Sudurpashchim and Karnali provinces have high mortality and high poverty levels, whereas Terai and Hill ecological belts and Province 1, Madhesh, and Bagmati and Gandaki provinces have low mortality and poverty levels. Conclusions This is the first use of CRVS system data in Nepal to estimate national and subnational mortality levels and differentials. The national results are plausible when compared with Global Burden of Disease and United Nations World Population Prospects estimates. Understanding of the reasons for inequalities in mortality in Nepal should focus on improving cause of death data and further strengthening CRVS data.

Life expectancy, long-term care demand and dynamic financing mechanism simulation: an empirical study of Zhejiang Pilot, China

“…Model life tables methods are widely used in simulation of mortality for their effectiveness and accessibility to overcome the limited mortality information in developing countries [ 48 , 49 ]. Two-parameters log-quadratic model considering the child and adult mortality overcomes the shortage of Coale-Demeny and UN model life tables, among those model life tables methods [ 50 ].…”