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In the recent past, spells of extreme heat associated with appreciable mortality have been documented in developed countries, including North America and Europe. However, far fewer research reports are available from developing countries or specific cities in South Asia. In May 2010, Ahmedabad, India, faced a heat wave where the temperatures reached a high of 46.8 °C with an apparent increase in mortality. The purpose of this study is to characterize the heat wave impact and assess the associated excess mortality. We conducted an analysis of all-cause mortality associated with a May 2010 heat wave in Ahmedabad, Gujarat, India, to determine whether extreme heat leads to excess mortality. Counts of all-cause deaths from May 1-31, 2010 were compared with the mean of counts from temporally matched periods in May 2009 and 2011 to calculate excess mortality. Other analyses included a 7-day moving average, mortality rate ratio analysis, and relationship between daily maximum temperature and daily all-cause death counts over the entire year of 2010, using month-wise correlations. The May 2010 heat wave was associated with significant excess all-cause mortality. 4,462 all-cause deaths occurred, comprising an excess of 1,344 all-cause deaths, an estimated 43.1% increase when compared to the reference period (3,118 deaths). In monthly pair-wise comparisons for 2010, we found high correlations between mortality and daily maximum temperature during the locally hottest \"summer\" months of April (r = 0.69, p<0.001), May (r = 0.77, p<0.001), and June (r = 0.39, p<0.05). During a period of more intense heat (May 19-25, 2010), mortality rate ratios were 1.76 [95% CI 1.67-1.83, p<0.001] and 2.12 [95% CI 2.03-2.21] applying reference periods (May 12-18, 2010) from various years. The May 2010 heat wave in Ahmedabad, Gujarat, India had a substantial effect on all-cause excess mortality, even in this city where hot temperatures prevail through much of April-June.

Health effects of heat waves with high baseline temperatures in areas such as India remain a critical research gap. In these regions, extreme temperatures may affect the underlying population’s adaptive capacity; heat wave alerts should be optimized to avoid continuous high alert status and enhance constrained resources, especially under a changing climate. Data from registrars and meteorological departments were collected for four communities in Northwestern India. Propensity Score Matching (PSM) was used to obtain the relative risk of mortality and number of attributable deaths (i.e., absolute risk which incorporates the number of heat wave days) under a variety of heat wave definitions (n = 13) incorporating duration and intensity. Heat waves’ timing in season was also assessed for potential effect modification. Relative risk of heat waves (risk of mortality comparing heat wave days to matched non-heat wave days) varied by heat wave definition and ranged from 1.28 [95% Confidence Interval: 1.11–1.46] in Churu (utilizing the 95th percentile of temperature for at least two consecutive days) to 1.03 [95% CI: 0.87–1.23] in Idar and Himmatnagar (utilizing the 95th percentile of temperature for at least four consecutive days). The data trended towards a higher risk for heat waves later in the season. Some heat wave definitions displayed similar attributable mortalities despite differences in the number of identified heat wave days. These findings provide opportunities to assess the “efficiency” (or number of days versus potential attributable health impacts) associated with alternative heat wave definitions. Findings on both effect modification and trade-offs between number of days identified as “heat wave” versus health effects provide tools for policy makers to determine the most important criteria for defining thresholds to trigger heat wave alerts.

Increasing heat waves-particularly in urban areas where construction is most prevalent, highlight a need for heat exposure assessment of construction workers. This study aims to characterize the effects of heat on construction workers from a site in Gandhinagar. This study involved a mixed methods approach consisting of a cross sectional survey with anthropometric measurements (n = 219) and four focus groups with construction workers, as well as environmental measurements of heat stress exposure at a construction site. Survey data was collected in two seasons i.e., summer and winter months, and heat illness and symptoms were compared between the two time periods. Thematic coding of focus group data was used to identify vulnerability factors and coping mechanisms of the workers. Heat stress, recorded using a wet bulb globe temperature monitor, was compared to international safety standards. The survey findings suggest that heat-related symptoms increased in summer; 59% of all reports in summer were positive for symptoms (from Mild to Severe) as compared to 41% in winter. Focus groups revealed four dominant themes: (1) Non-occupational stressors compound work stressors; (2) workers were particularly attuned to the impact of heat on their health; (3) workers were aware of heat-related preventive measures; and (4) few resources were currently available to protect workers from heat stress. Working conditions often exceed international heat stress safety thresholds. Female workers and new employees might be at increased risk of illness or injury. This study suggests significant health impacts on construction workers from heat stress exposure in the workplace, showed that heat stress levels were higher than those prescribed by international standards and highlights the need for revision of work practices, increased protective measures, and possible development of indigenous work safety standards for heat exposure.

IntroductionAir pollution presents a major public health threat to India, affecting more than three quarters of the country’s population. In the current project, GEOHealth Health Effects of Selected Environmental Exposomes Across the Life CourSe–India, we aim to study the effect of environmental exposomes—fine particulate matter (PM2.5), nitrogen dioxide (NO2), ozone (O3) and extremes of temperature—on multiple health outcomes using a modified life course approach. The associated training grant aims to build capacity in India to address the unique environmental health problems.Methods and analysisThe project aims to (A) Develop exposure assessments in seven cities, namely Delhi, Chennai, Sonipat, Vizag, Pune, Hyderabad and Bikaner, for: (1) A fine-scale spatiotemporal model for multiple pollutants (PM2.5, NO2, O3, temperature); (2) Combined ground monitoring and modelling for major chemical species of ambient PM2.5 at seven cities; and (3) Personal exposure assessment in a subsample from the six cities, except Pune, and (B) Conduct health association studies covering a range of chronic non-communicable diseases and their risk factors leveraging a unique approach using interdigitating cohorts. We have assembled existing pregnancy, child, adolescent, adult and older adult cohorts across India to explore health effects of exposomes using causal analyses. We propose to use Bayesian kernel machine regression to assess the effects of mixtures of all pollutants including species of PM2.5 on health while accounting for potential non-linearities and interactions between exposures. This builds on earlier work that constructed a fine spatiotemporal model for PM2.5 exposure to study health outcomes in two Indian cities.Ethics and disseminationEthical clearance for conduct of the study was obtained from the Institutional Ethics Committee (IEC) of the Centre for Chronic Disease Control, and all the participating institutes and organisations. National-level permission was provided by the Indian Council of Medical Research. The research findings will be disseminated through peer-reviewed publications, policy briefs, print and social media, and communicating with the participating communities and stakeholders. Training of Indian scientists will build the capacity to undertake research on selected adverse environmental exposures on population health in India.

Recurrent heat waves, already a concern in rapidly growing and urbanizing South Asia, will very likely worsen in a warming world. Coordinated adaptation efforts can reduce heat’s adverse health impacts, however. To address this concern in Ahmedabad (Gujarat, India), a coalition has been formed to develop an evidence-based heat preparedness plan and early warning system. This paper describes the group and initial steps in the plan’s development and implementation. Evidence accumulation included extensive literature review, analysis of local temperature and mortality data, surveys with heat-vulnerable populations, focus groups with health care professionals, and expert consultation. The findings and recommendations were encapsulated in policy briefs for key government agencies, health care professionals, outdoor workers, and slum communities, and synthesized in the heat preparedness plan. A 7-day probabilistic weather forecast was also developed and is used to trigger the plan in advance of dangerous heat waves. The pilot plan was implemented in 2013, and public outreach was done through training workshops, hoardings/billboards, pamphlets, and print advertisements. Evaluation activities and continuous improvement efforts are ongoing, along with plans to explore the program’s scalability to other Indian cities, as Ahmedabad is the first South Asian city to address heat-health threats comprehensively.

Abstract High-resolution assessment of historical levels is essential for assessing the health effects of ambient air pollution in the large Indian population. The diversity of geography, weather patterns, and progressive urbanization, combined with a sparse ground monitoring network makes it challenging to accurately capture the spatiotemporal patterns of ambient fine particulate matter (PM2.5) pollution in India. We developed a model for daily average ambient PM2.5 between 2008 and 2020 based on monitoring data, meteorology, land use, satellite observations, and emissions inventories. Daily average predictions at each 1 km × 1 km grid from each learner were ensembled using a Gaussian process regression with anisotropic smoothing over spatial coordinates, and regression calibration was used to account for exposure error. Cross-validating by leaving monitors out, the ensemble model had an R2 of 0.86 at the daily level in the validation data and outperformed each component learner (by 5–18%). Annual average levels in different zones ranged between 39.7 μg/m3 (interquartile range: 29.8–46.8) in 2008 and 30.4 μg/m3 (interquartile range: 22.7–37.2) in 2020, with a cross-validated (CV)-R2 of 0.94 at the annual level. Overall mean absolute daily errors (MAE) across the 13 years were between 14.4 and 25.4 μg/m3. We obtained high spatial accuracy with spatial R2 greater than 90% and spatial MAE ranging between 7.3–16.5 μg/m3 with relatively better performance in urban areas at low and moderate elevation. We have developed an important validated resource for studying PM2.5 at a very fine spatiotemporal resolution, which allows us to study the health effects of PM2.5 across India and to identify areas with exceedingly high levels.

High-resolution assessment of historical levels is essential for assessing the health effects of ambient air pollution in the large Indian population. The diversity of geography, weather patterns, and progressive urbanization, combined with a sparse ground monitoring network makes it challenging to accurately capture the spatiotemporal patterns of ambient fine particulate matter (PM ) pollution in India. We developed a model for daily average ambient PM between 2008 and 2020 based on monitoring data, meteorology, land use, satellite observations, and emissions inventories. Daily average predictions at each 1 km × 1 km grid from each learner were ensembled using a Gaussian process regression with anisotropic smoothing over spatial coordinates, and regression calibration was used to account for exposure error. Cross-validating by leaving monitors out, the ensemble model had an of 0.86 at the daily level in the validation data and outperformed each component learner (by 5-18%). Annual average levels in different zones ranged between 39.7 μg/m (interquartile range: 29.8-46.8) in 2008 and 30.4 μg/m (interquartile range: 22.7-37.2) in 2020, with a cross-validated (CV)- of 0.94 at the annual level. Overall mean absolute daily errors (MAE) across the 13 years were between 14.4 and 25.4 μg/m . We obtained high spatial accuracy with spatial greater than 90% and spatial MAE ranging between 7.3-16.5 μg/m with relatively better performance in urban areas at low and moderate elevation. We have developed an important validated resource for studying PM at a very fine spatiotemporal resolution, which allows us to study the health effects of PM across India and to identify areas with exceedingly high levels.

High-resolution assessment of historical levels is essential for assessing the health effects of ambient air pollution in the large Indian population. The diversity of geography, weather patterns, and progressive urbanization, combined with a sparse ground monitoring network makes it challenging to accurately capture the spatiotemporal patterns of ambient fine particulate matter ([PM.sub.2.5]) pollution in India. We developed a model for daily average ambient pM25 between 2008 and 2020 based on monitoring data, meteorology, land use, satellite observations, and emissions inventories. Daily average predictions at each 1 km x 1 km grid from each learner were ensembled using a Gaussian process regression with anisotropic smoothing over spatial coordinates, and regression calibration was used to account for exposure error. Cross-validating by leaving monitors out, the ensemble model had an [R.sup.2] of 0.86 at the daily level in the validation data and outperformed each component learner (by 5-18%). Annual average levels in different zones ranged between 39.7 [micro]g/[m.sup.3] (interquartile range: 29.8-46.8) in 2008 and 30.4 [micro]g/[m.sup.3] (interquartile range: 22.7-37.2) in 2020, with a cross-validated (CV)-[R.sup.2] of 0.94 at the annual level. Overall mean absolute daily errors (MAE) across the 13 years were between 14.4 and 25.4 [micro]g/[m.sup.3]. We obtained high spatial accuracy with spatial [R.sup.2] greater than 90% and spatial MAE ranging between 7.3-16.5 [micro]g/[m.sup.3] with relatively better performance in urban areas at low and moderate elevation. We have developed an important validated resource for studying [PM.sub.2.5] at a very fine spatiotemporal resolution, which allows us to study the health effects of [PM.sub.2.5] across India and to identify areas with exceedingly high levels.

Small-scale local data resources may serve to provide a highly resolved estimate of health effects, which can be spatially heterogeneous in highly populated urban centers in developing countries. We aim to highlight the challenges and opportunities of health data registries in a developing world context. In western India, government-collected daily mortality registry data were obtained from five cities, along with daily hospital admissions data from three government hospitals in Ahmedabad. In Nepal, individual-level data on hospital admissions were collected from six major hospitals in Kathmandu Valley. Our process illustrates many challenges for researchers, governments, and record keepers inherent to data collection in developing countries: creating and maintaining a centralized record-keeping system; standardizing the data collected; obtaining data from some local agencies; assuring data completeness and availability of back-ups to the datasets; as well as translating, cleaning, and comparing data within and across localities. We suggest that these “small-data” resources may better serve the analysis of health outcomes than exposure-response functions extrapolated from data collected in other areas of the world.