The air pollution tradeoff in India: Saving more lives versus reducing the inequality of exposure

Chronic exposure to ambient fine particulate matter (PM2.5) represents one of the largest global public health risks, leading to millions of premature deaths annually. For a country facing high and spatially variable exposures, prioritizing where to reduce PM2.5 concentrations leads to an inherent tradeoff between saving the most lives and reducing inequality of exposure. This tradeoff results from the shape of the concentration-response function between exposure to PM2.5 and mortality, which indicates that the additional lives saved per unit reduction in PM2.5 declines as concentrations increase. We estimate this concentration-response function for urban areas of India, finding that a 10 unit reduction in PM2.5 in already-clean locations will reduce the mortality rate substantially (4.2% for a reduction from 30 to 20 µgm-3), while a 10 unit reduction in the dirtiest locations will reduce mortality only modestly (1.2% for a reduction from 90 to 80 µgm-3). We explore the implications of this PM2.5/mortality relationship by considering a thought experiment. If India had a fixed amount of resources to devote to PM2.5 concentration reductions across urban areas, what is the lives saved/inequality of exposure tradeoff from three different methods of employing those resources? Across our three scenarios—1) which reduces exposures for the dirtiest districts, 2) which reduces exposures everywhere equally, and 3) which reduces exposures to save the most lives—scenario 1 saves 18,000 lives per year while reducing the inequality of exposure by 65%, while scenario 3 saves 126,000 lives per year, but increases inequality by 19%.

Impact of Behavioral Risk Factors on Mortality Risk in Older Korean Women

Background: To investigate the impact of lifestyle risk factors on all-cause and cardiovascular disease (CVD) mortality in Korean women aged 60 yr and older. Methods: Data (n = 3,034) obtained from the Korean longitudinal study of aging were analyzed. Exposures included lifestyle risk factors, such as smoking, alcohol abuse, underweight/obesity, physical inactivity, and unintentional weight loss. Primary outcomes were premature deaths from specific and all-causes. Results: During 9.6±2.0 yr of follow-up, there were 628 cases (20.7%) of death from all causes, of which 137 cases (4.5%) were from CVD. Compared to zero risk factor (hazard ratio, HR=1), crude HR of all-cause mortality was 2.277 (95% confidence interval, CI, 1.712 ~ 3.030, P < 0.001) for one risk factor, 2.977 (95% CI, 2.124 ~ 4.003, P < 0.001) for two risk factors, and 5.154 (95% CI, 3.515 ~ 7.557, P < 0.001) for three or more risk factors. Compared to zero risk factor (HR=1), crude HR of CVD mortality was 2.035 (95% CI, 1.422 ~ 2.913, P < 0.001) for one risk factor, 2.468 (95% CI, 1.708 ~ 3.567, P < 0.001) for two risk factor, and 4.484 (95% CI, 2.830 ~ 7.102, P < 0.001) for three or more risk factors. Adjusted HRs of all-cause (P = 0.016) and CVD (P = 0.050) for three or more risk factors only remained significant for three or more risk factors. Conclusion: The current findings showed that individual and combined lifestyle risk factors were significantly associated with increased risks of all-cause and CVD mortality in older Korean women.

Machine Learning-Based Approach for Predictive Analytics in Healthcare

In this internet era, due to digitization in every application, a huge amount of data is produced digitally from the healthcare sectors. As per the World Health Organization (WHO), the mortality rate due to the various chronic diseases is increasing each day. Every year these diseases are taking lives of at least 50 million people globally, which includes even premature deaths. These days, machine learning (ML)-based predictive analytics are turning out as effective tools in the healthcare sectors. These techniques can extract meaningful insights from the medical data to analyze the future trend. By predicting the risk of diseases at the preliminary stage, the mortality rate can be reduced, and at the same time, the expensive healthcare cost can be eliminated. The chapter aims to briefly provide the domain knowledge on chronic diseases, the biological correlation between theses disease, and more importantly, to explain the application of ML algorithm-based predictive analytics in the healthcare sectors for the early prediction of chronic diseases.

Tobacco consumption patterns amongst recruits at a training centre

Background: Approximately 3 million premature deaths occur every year due to tobacco. Gaps were observed in the scientific knowledge about tobacco consumption practices among armed forces personnel in our country. Keeping in view the paucity of studies in this field workers chose to undertake the present study.Methods: A cross-sectional study was conducted among service personnel across a training centre to determine prevalence of tobacco consumption. The sample size was calculated to estimate 95% confidence interval for prevalence of tobacco consumption with 5% absolute precision. The minimum sample size was calculated to be 250, however, 285 personnel were included in the study. “Personal interview technique” was used for data and standard statistical methods were utilized for data analysis.Results: The mean age (standard deviation) of the study subjects was 20.80 years (1.23). The overall prevalence of currently using tobacco in any form was 9.82%, mean (SD) of number of years of tobacco use was 2.33 years (1.27). Mean (SD) of number of cigarettes/bidis smoked per day was 7.52 (6.71). Average amount spent on tobacco consumption per month was Rs310.95 (2.42% of monthly salary). The commonest reasons for smoking is peer pressure, to relax and feel like hero.Conclusions: Our study has helped to fill in the existing gaps in the scientific knowledge about tobacco consumption practices among armed forces personnel in our country.

Screening Approach for Short-Term PM2.5 Health Co-Benefits: A Case Study from 15 Metropolitan Cities around the World during the COVID-19 Pandemic

Fifteen cities across the world have been selected to investigate the public health co-benefits of PM2.5 reduction, during a period when various non-pharmaceutical interventions (NPIs) were adopted in the COVID-19 pandemic. Through applying a public health model, AirQ+, substantial spatial variations of global public health co-benefits were identified. Differences in seasonal air quality and population baselines were key underlying factors. For cities in North America, NPIs were introduced during the low pollution season, generating no co-benefits. On the other hand, tremendous health co-benefits were observed for cities in India and China, due to the high PM2.5 background with a large population. Among all, New Delhi has received the largest co-benefits, which saved over 14,700 premature deaths. As the pollution level (i.e., 45 μg m−3) with NPIs still exceeded the air quality standard, more rigorous emission controls are urgently needed to protect the public′s health in India. At last, a novel and practical tool for co-benefit screening was developed using data from one of the global measurement networks (i.e., IQAir).

Reduced health burden and economic benefits of cleaner fuel usage from household energy consumption across rural and urban China

Energy consumption in the residential sector is increasing rapidly in China. This study applies an integrated assessment model to investigate the adverse impacts of household energy consumption by various fuel types across rural and urban areas on age- and sex- specific premature deaths associated with PM2.5 pollution at provincial levels for 2015. We further estimate the economic and health co-benefits of a switch from solid fuels to electricity within households. We find that energy consumed by Chinese urban households was nearly 1.8 times than that of rural households. However, premature deaths due to household energy usage was 1.1 times higher in rural areas compared to urban areas due to direct use of coal for heating in rural households. The majority of household consumption-related premature deaths are predominately in the Southern area of China due to the population size and aging population. By replacing coal and biomass with electricity, this paper estimates economic benefits equal to 0.09% (95% CI: 0.08%-0.1%) GDP for rural areas and 0.006% (0.005%-0.007%) of GDP for urban areas of China. The results suggest that mitigation measures such as the promotion and subsidization of cleaner fuels, modern stove within rural households would yield these potential significant economic benefits.

Prevalence and inequality in persistent undiagnosed, untreated, and uncontrolled hypertension: Evidence from a cohort of older Mexicans

Hypertension is the leading risk factor for cardiovascular diseases (CVDs) and substantial gaps in diagnosis, treatment and control signal failure to avert premature deaths. Our aim was to estimate the prevalence and assess the socioeconomic distribution of hypertension that remained undiagnosed, untreated, and uncontrolled for at least five years among older Mexicans and to estimate rates of transition from those states to diagnosis, treatment and control. We used data from a cohort of Mexicans aged 50+ in two waves of the WHO Study on Global AGEing and adult health (SAGE) collected in 2009 and 2014. Blood pressure was measured, hypertension diagnosis and treatment self-reported. We estimated prevalence and transition rates over five years and calculated concentration indices to identify socioeconomic inequalities using a wealth index. Using probit models, we identify characteristics of those facing the greatest barriers in receiving hypertension care. More than 60 percent of individuals with full item response (N = 945) were classified as hypertensive. Over one third of those undiagnosed continued to be in that state five years later. More than two fifths of those initially untreated remained so, and over three fifths of those initially uncontrolled failed to achieve continued blood pressure control. While being classified as hypertensive was more concentrated among the rich, missing diagnosis, treatment and control were more prevalent among the poor. Men, singles, rural dwellers, uninsured, and those with overweight were more likely to have persistent undiagnosed, untreated, and uncontrolled hypertension. There is room for improvement in both hypertension diagnosis and treatment in Mexico. Clinical and public health attention is required, even for those who initially had their hypertension controlled. To ensure more equitable hypertension care and effectively prevent premature deaths, increased diagnosis and long-term treatment efforts should especially be directed towards men, singles, uninsured, and those with overweight.