Progress of Inequality in Age at Death in India: Role of Adult Mortality

Trends in adult mortality rates in India, 1970 to 2018: age-period-cohort analysis

High premature adult deaths in developing countries are gaining attention, as recent studies show their increasing impact on overall mortality rates. This paper has twofold objectives: firstly, it investigates the long-term trends and patterns of adult mortality between 1970 and 2018 in India. Secondly, it attempts to detect age, period, and cohort (APC) effects on adult mortality decline over time. Data on age-specific mortality rates and disease-adjusted life years for adult age groups (15-59 years) were collected from the Sample Registration System and the Global Burden of Disease study, respectively. The trends in age-standardized mortality rates were presented graphically, and critical change points were highlighted using a change-point analysis. The intrinsic estimator model was applied to estimate the independent effects of APC on adult mortality. The findings revealed that adult mortality declined between 1970 and 2018 with multiple critical change points. The APC effects showed a notable decline in adult mortality during 2005-2018 and for the recent birth cohorts, 1980-2004. However, the rate of mortality declined slowly over time. Results also indicated that mortality started increasing from mid-adult ages and peaked in older adult ages due to the age effects and provided evidence of a rise in adult life loss due to non-communicable diseases in recent years. Overall, the study underscores the importance of implementing health policies aimed at reducing life loss in the most economically active ages that can have long-term negative implications for the country's economic growth.

Automated Screening of Hip X-rays for Osteoporosis by Singh's Index Using Machine Learning Algorithms

INTRODUCTION

Osteoporosis is a significant and growing global public health problem, projected to increase in the next decade. The Singh Index (SI) is a simple, semi-quantitative evaluation tool for diagnosing osteoporosis with plain hip radiographs based on the visibility of the trabecular pattern in the proximal femur. This work aims to develop an automated tool to diagnose osteoporosis using SI of hip radiograph images with the help of machine learning algorithms.

METHODS

We used 830 hip X-ray images collected from Indian men and women aged between 20 and 70 which were annotated and labeled for appropriate SI. We employed three state-of-the-art machine learning algorithms-Vision Transformer (ViT), MobileNet-V3, and a Stacked Convolutional Neural Network (CNN)-for image pre-processing, feature extraction, and automation. Each algorithm was evaluated and compared for accuracy, precision, recall, and generalization capabilities to diagnose osteoporosis.

RESULTS

The ViT model achieved an overall accuracy of 62.6% with macro-averages of 0.672, 0.597, and 0.622 for precision, recall, and F1 score, respectively. MobileNet-V3 presented a more encouraging accuracy of 69.6% with macro-averages for precision, recall, and F1 score of 0.845, 0.636, and 0.652, respectively. The stacked CNN model demonstrated the strongest performance, achieving an accuracy of 93.6% with well-balanced precision, recall, and F1-score metrics.

CONCLUSION

The superior accuracy, precision-recall balance, and high F1-scores of the stacked CNN model make it the most reliable tool for screening radiographs and diagnosing osteoporosis using the SI.

Subnational estimates of life expectancy at birth in India: evidence from NFHS and SRS data

BACKGROUND

Mortality estimates at the subnational level are of urgent need in India for the formulation of policies and programmes at the district level. This is the first-ever study which used survey data for the estimation of life expectancy at birth ([Formula: see text]) for the 640 districts from NFHS-4 (2015-16) and 707 districts from NFHS-5 (2019-21) for the total, male and female population in India.

METHODS

This study calculated annual age-specific mortality rates from NFHS-4 and NFHS-5 for India and all 36 states for the total, male and female population. This paper constructed the abridged life tables and estimated life expectancy at birth [Formula: see text] and further estimated the model parameters for all 36 states. This study linked state-specific parameters to the respective districts for the estimation of life expectancy at birth [Formula: see text]for 640 districts from NFHS-4 and 707 districts from NFHS-5 for the total, male and female population in India.

RESULTS

Findings at the state level showed that there were similarities between the estimated and calculated [Formula: see text] in most of the states. The results of this article observed that the highest [Formula: see text] varies in the ranges of 70 to 90 years among the districts of the southern region. [Formula: see text] falls below 70 years among most of the central and eastern region districts. In the northern region districts [Formula: see text] lies in the range of 70 years to 75 years. The estimates of life expectancy at birth [Formula: see text] shows the noticeable variations at the state and district levels for the person, male, and female populations from the NFHS (2015-16) and NFHS (2019-21). In the absence of age-specific mortality data at the district level in India, this study used the indirect estimation method of relating state-specific model parameters with the IMR of their respective districts and estimated [Formula: see text] across the 640 districts from NFHS-4 (2015-16) and 707 districts from NFHS-5 (2019-21). The findings of this study have similarities with the state-level estimations of [Formula: see text] from both data sources of SRS and NFHS and found the highest [Formula: see text] in the southern region and the lowest [Formula: see text] in the eastern and central region districts.

CONCLUSIONS

In the lack of [Formula: see text] estimates at the district level in India, this study could be beneficial in providing timely life expectancy estimates from the survey data. The findings clearly shows variations in the district level [Formula: see text]. The districts from the southern region show the highest [Formula: see text] and districts from the central and eastern region has lower [Formula: see text]. Females have higher [Formula: see text] as compared to the male population in most of the districts in India.

Impact of COVID-19 on subnational variations in life expectancy and life disparity at birth in India: evidence from NFHS and SRS data

BACKGROUND

Measuring life expectancy and life disparity can assist in comprehending how the COVID-19 pandemic has affected the mortality estimates in the Indian population. The present study aims to study the life expectancy and life disparity at birth at the national and subnational levels before and during the COVID-19 pandemic using the NFHS and SRS data.

METHODS

The measures Life expectancy at birth ([Formula: see text]) and Life disparity at birth ([Formula: see text]) were computed for the non-pandemic and pandemic years from NFHS (2015-16), SRS (2015) and NFHS (2019-21), SRS (2020) respectively at the national and Subnational level in India. Using NFHS data for the 36 states and SRS data for the 22 states, the study calculates [Formula: see text] and [Formula: see text] by total, male and female population.

RESULTS

The [Formula: see text] for male and female decline from 64.3 years and 69.2 years in 2015-16 to 62.9 years and 68.9 years in 2019-21. The [Formula: see text] shows a drop of approximately 1.4 years for males and 0.3 years for females in the pandemic year 2019-21 when compared to the non-pandemic year 2015-16. At the subnational level [Formula: see text] shows a decline for 22 states in person, 23 states in males and 21 states in females in the pandemic year 2019-21 as compared to the non-pandemic years 2015-16. The [Formula: see text] shows a increase for 21 states in person, 24 states in females and 17 states in males in the pandemic year than non-pandemic year. The findings shows a significant losses in [Formula: see text] and gains in [Formula: see text] for males than females in the pandemic year as compared to the non-pandemic year at the subnational level in India.

CONCLUSIONS

COVID-19 pandemic has decreased [Formula: see text] and increased [Formula: see text] in the pandemic year 2019-21 at the national and subnational level in India. COVID-19 had a significant impact on the age pattern of mortality for many states and male, female population and delayed the mortality transition in India.

The role of age inequalities in cause of death in the slow pace of epidemiological transition in India

In developed countries, low disparity in lifespan contributed by the reduction in the burden of noncommunicable diseases (NCDs) is the key to advances in epidemiological transition. Contrarily, India passing through a phase of the dual burden of CDs and NCDs shows a heavy burden of NCDs responsible for the high disparity in lifespan. The Gini coefficient was decomposed for examining the contribution of 22 causes of death and their repercussions for inequality in age at death for 30 years between 1990-1994 and 2015-2019, using Global Burden of Disease data. The outcomes of the study reveal that India's epidemiological transition has been just modest on account of high inequality in mortality by NCDs emplaced in the middle through old age despite a consistent mortality decline at infant through old age for communicable diseases (CDs). The structural changes in causes of death structure is shaped by CDs rather than NCDs, but overall bolstered by the adult mortality decline, especially in women. However, the process is restrained by the small contribution of the middle age group and a benign contribution of old mortality decline owing to the low threshold age. India needs to target health interventions in seeking significant mortality decline in the middle age group of 50-69 years that is warranted for epidemiological transition apace as evident in the developed nations.

Impact of COVID-19 on life expectancy at birth in India: a decomposition analysis

BACKGROUND

Quantifying excess deaths and their impact on life expectancy at birth (e0) provide a more comprehensive understanding of the burden of coronavirus disease of 2019 (COVID-19) on mortality. The study aims to comprehend the repercussions of the burden of COVID-19 disease on the life expectancy at birth and inequality in age at death in India.

METHODS

The mortality schedule of COVID-19 disease in the pandemic year 2020 was considered one of the causes of death in the category of other infectious diseases in addition to other 21 causes of death in the non-pandemic year 2019 in the Global Burden of Disease (GBD) data. The measures e0 and Gini coefficient at age zero (G0) and then sex differences in e0 and G0 over time were analysed by assessing the age-specific contributions based on the application of decomposition analyses in the entire period of 2010-2020.

RESULTS

The e0 for men and women decline from 69.5 and 72.0 years in 2019 to 67.5 and 69.8 years, respectively, in 2020. The e0 shows a drop of approximately 2.0 years in 2020 when compared to 2019. The sex differences in e0 and G0 are negatively skewed towards men. The trends in e0 and G0 value reveal that its value in 2020 is comparable to that in the early 2010s. The age group of 35-79 years showed a remarkable negative contribution to Δe0 and ΔG0. By causes of death, the COVID-19 disease has contributed - 1.5 and - 9.5%, respectively, whereas cardiovascular diseases contributed the largest value of was 44.6 and 45.9%, respectively, to sex differences in e0 and G0 in 2020. The outcomes reveal a significant impact of excess deaths caused by the COVID-19 disease on mortality patterns.

CONCLUSIONS

The COVID-19 pandemic has negative repercussions on e0 and G0 in the pandemic year 2020. It has severely affected the distribution of age at death in India, resulting in widening the sex differences in e0 and G0. The COVID-19 disease demonstrates its potential to cancel the gains of six to eight years in e0 and five years in G0 and has slowed the mortality transition in India.