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Indoor residential environments will be an increasing source of dangerous heat exposure as summer temperatures rise. Although air conditioning is a recognized preventative measure, many vulnerable residents either lack air conditioning or cannot always afford the electricity. Understanding the relative influence of different building characteristics and percentages of tree canopy coverage on indoor air temperatures during extreme heat events can help prioritize intervention strategies. We measured indoor and outdoor temperatures at 140 homes in Detroit (MI), Atlanta (GA), and Phoenix (AZ) and surveyed residents to determine the presence of a working central air-conditioning system and their ability to afford utilities. For each home, we collected information on construction year, interior size, single versus multifamily structure, façade type, and percentage of tree canopy. After air conditioning, the importance of building characteristics versus tree canopy measures varied by city. In Detroit, masonry façades worsened the influence of outdoor temperatures, whereas increased tree canopy coverage moderated the influence. In Atlanta, building characteristics were not significant, but tree canopy moderated indoor temperatures in late afternoon. In Phoenix, some building characteristics moderated the influence of outdoor temperatures in late afternoon. In Detroit, only 35% of respondents had and used central air conditioning as needed compared with 57% in Atlanta and 95% in Phoenix. The influence of tree canopy and building characteristics on indoor temperatures varied by city depending on its background climate. We recommend adding questions about air conditioning and utility poverty to the U.S. Census. For rental properties, municipalities should establish and enforce maximum indoor temperature thresholds. States should include cooling in their Low Income Home Energy Assistance (LIHEAP) programs. Public cooling centers need to anticipate electricity outages. Urban planners must ensure efforts to increase mechanical cooling are powered by renewable energy sources.

Extreme weather and climate events, such as heat waves, cyclones, and floods, are an expression of climate variability. These events and events influenced by climate change, such as wildfires, continue to cause significant human morbidity and mortality and adversely affect mental health and well-being. Although adverse health impacts from extreme events declined over the past few decades, climate change and more people moving into harm's way could alter this trend. Long-term changes to Earth's energy balance are increasing the frequency and intensity of many extreme events and the probability of compound events, with trends projected to accelerate under certain greenhouse gas emissions scenarios. While most of these events cannot be completely avoided, many of the health risks could be prevented through building climate-resilient health systems with improved risk reduction, preparation, response, and recovery. Conducting vulnerability and adaptation assessments and developing health system adaptation plans can identify priority actions to effectively reduce risks, such as disaster risk management and more resilient infrastructure. The risks are urgent, so action is needed now.

Background: Multiple methods are employed for modelling adaptation when projecting the impact of climate change on heat-related mortality. The sensitivity of impacts to each is unknown because they have never been systematically compared. In addition, little is known on the relative sensitivity of impacts to “adaptation uncertainty” (i.e. the inclusion/exclusion of adaptation modelling), relative to using multiple climate models and emissions scenarios. Objectives: (1) Compare the range in projected impacts that arises from using different adaptation modelling methods; (2) compare the range in impacts that arises from adaptation uncertainty to ranges from using multiple climate models and emissions scenarios; (3) recommend modelling method(s) to use in future impact assessments. Methods: We estimated impacts for 2070-2099, for 14 European cities, applying six different methods for modelling adaptation; also with climate projections from five climate models, run under two emissions scenarios to explore the relative effects of climate modelling and emissions uncertainty. Results: The range of the difference (%) in impacts between including and excluding adaptation, irrespective of climate modelling and emissions uncertainty, can be as low as 28% with one method and up to 103% with another (mean across 14 cities). In 13 of 14 cities the ranges in projected impacts due to adaptation uncertainty are larger than those associated with climate modelling and emissions uncertainty. Conclusions: Researchers should carefully consider how to model adaptation because it is a source of uncertainty that can be greater than the uncertainty in emissions and climate modelling. We recommend absolute threshold shifts and reductions in slope.

Synoptic climatology has a long history of research where weather data are aggregated and composited to gain a better understanding of atmospheric effects on non-atmospheric variables. This has resulted in an applied scientific discipline that yields methods and tools designed for applications across disciplinary boundaries. The spatial synoptic classification (SSC) is an example of such a tool that helps researcher bridge methodological gaps between disciplines, especially those studying weather effects on human health. The SSC has been applied in several multi-discipline projects, and it appears that there is ample opportunity for growth into new topical areas. Likewise, there is opportunity for the SSC network to be expanded across the globe, especially into mid-latitude locations in the southern hemisphere. There is some question of the utility of the SSC in tropical locations, but such decisions must be based on the actual weather data from individual locations. Despite all of the strengths and potential uses of the SSC, there are some research problems, some locations, and some datasets for which it is not suitable. Nevertheless, the success of the SSC as a cross-disciplinary method is noteworthy because it has become a catalyst for collaboration.

© 2016 John Wiley & Sons Ltd. Synoptic climatology has a long history of research where weather data are aggregated and composited to gain a better understanding of atmospheric effects on non-atmospheric variables. This has resulted in an applied scientific discipline that yields methods and tools designed for applications across disciplinary boundaries. The spatial synoptic classification (SSC) is an example of such a tool that helps researchers bridge methodological gaps between disciplines, especially those studying weather effects on human health. The SSC has been applied in several multi-discipline projects, and it appears that there is ample opportunity for growth into new topical areas. Likewise, there is opportunity for the SSC network to be expanded across the globe, especially into mid-latitude locations in the Southern Hemisphere. There is some question of the utility of the SSC in tropical locations, but such decisions must be based on the actual weather data from individual locations. Despite all of the strengths and potential uses of the SSC, there are some research problems, some locations, and some datasets for which it is not suitable. Nevertheless, the success of the SSC as a cross-disciplinary method is noteworthy because it has become a catalyst for collaboration.

Background A growing body of evidence points to the emission of greenhouse gases from human activity as a key factor in climate change. This in turn affects human health and wellbeing through consequential changes in weather extremes. At present, little is known about the effects of weather on the health of sub-Saharan African populations, as well as the related anticipated effects of climate change partly due to scarcity of good quality data. We aimed to study the association between weather patterns and daily mortality in the Nouna Health and Demographic Surveillance System (HDSS) area during 1999–2009. Methods Meteorological data were obtained from a nearby weather station in the Nouna HDSS area and linked to mortality data on a daily basis. Time series Poisson regression models were established to estimate the association between the lags of weather and daily population-level mortality, adjusting for time trends. The analyses were stratified by age and sex to study differential population susceptibility. Results We found profound associations between higher temperature and daily mortality in the Nouna HDSS, Burkina Faso. The short-term direct heat effect was particularly strong on the under-five child mortality rate. We also found independent coherent effects and strong associations between rainfall events and daily mortality, particularly in elderly populations. Conclusion Mortality patterns in the Nouna HDSS appear to be closely related to weather conditions. Further investigation on cause-specific mortality, as well as on vulnerability and susceptibility is required. Studies on local adaptation and mitigation measures to avoid health impacts from weather and climate change is also needed to reduce negative effects from weather and climate change on population health in rural areas of the sub-Saharan Africa., Introduction Climate and weather variability can have significant health consequences of increased morbidity and mortality. However, today the impact of climate and weather variability, and consequentially, of climate change on population health in sub-Saharan Africa is not well understood. In this study, we assessed the association of daily temperature and precipitation with daily mortality by age and sex groups in Northern Ghana. Methods We analysed daily mortality and weather data from 1995 to 2010. We adopted a time-series Poisson regression approach to examine the short-term association of daily mean temperature and daily mean precipitation with daily mortality. We included time factors and daily lagged weather predictors. The correlation between lagged weather predictors was also considered. Results For all populations, a statistically significant association of mean daily temperature with mortality at lag days 0–1 was observed below and above the 25th (27.48°C) and 75th (30.68°C) percentiles (0.19%; 95% confidence interval CI: 0.05%, 0.21%) and (1.14%; 95% CI: 0.12%, 1.54%), respectively. We also observed a statistically significant association of mean daily temperature above 75th percentile at lag days 2–6 and lag days 7–13 (0.32%; 95% CI: 0.16%, 0.25%) and (0.31% 95% CI: 0.14%, 0.26%), respectively. A 10 mm increase in precipitation was significantly associated with a 1.71% (95% CI: 0.10%, 3.34.9%) increase in mortality for all ages and sex groups at lag days 2–6. Similar results were also observed at lag days 2–6 and 14–27 for males, 2.92% (95% CI: 0.80%, 5.09%) and 2.35% (95% CI: 0.28%, 4.45%). Conclusion Short-term weather variability is strongly associated with mortality in Northern Ghana. The associations appear to differ among different age and sex groups. The elderly and young children were found to be more susceptible to short-term temperature-related mortality. The association of precipitation with mortality is more pronounced at the short-term for all age and sex groups and in the medium short-term among males. Reducing exposure to extreme temperature, particularly among the elderly and young children, should reduce the number of daily deaths attributable to weather-related mortality., Background Many studies have established a link between weather (primarily temperature) and daily mortality in developed countries. However, little is known about this relationship in urban populations in sub-Saharan Africa. Objectives The objective of this study was to describe the relationship between daily weather and mortality in Nairobi, Kenya, and to evaluate this relationship with regard to cause of death, age, and sex. Methods We utilized mortality data from the Nairobi Urban Health and Demographic Surveillance System and applied time-series models to study the relationship between daily weather and mortality for a population of approximately 60,000 during the period 2003–2008. We used a distributed lag approach to model the delayed effect of weather on mortality, stratified by cause of death, age, and sex. Results Increasing temperatures (above 75th percentile) were significantly associated with mortality in children and non-communicable disease (NCD) deaths. We found all-cause mortality of shorter lag of same day and previous day to increase by 3.0% for a 1 degree decrease from the 25th percentile of 18°C (not statistically significant). Mortality among people aged 50+ and children aged below 5 years appeared most susceptible to cold compared to other age groups. Rainfall, in the lag period of 0–29 days, increased all-cause mortality in general, but was found strongest related to mortality among females. Low temperatures were associated with deaths due to acute infections, whereas rainfall was associated with all-cause pneumonia and NCD deaths. Conclusions Increases in mortality were associated with both hot and cold weather as well as rainfall in Nairobi, but the relationship differed with regard to age, sex, and cause of death. Our findings indicate that weather-related mortality is a public health concern for the population in the informal settlements of Nairobi, Kenya, especially if current trends in climate change continue., Background Weather and climate changes are associated with a number of immediate and long-term impacts on human health that occur directly or indirectly, through mediating variables. Few studies to date have established the empirical relationship between monthly weather and mortality in sub-Saharan Africa. Objectives The objectives of this study were to assess the association between monthly weather (temperature and rainfall) on all-cause mortality by age in Rufiji, Tanzania, and to determine the differential susceptibility by age groups. Methods We used mortality data from Rufiji Health and Demographic Surveillance System (RHDSS) for the period 1999 to 2010. Time-series Poisson regression models were used to estimate the association between monthly weather and mortality adjusted for long-term trends. We used a distributed lag model to estimate the delayed association of monthly weather on mortality. We stratified the analyses per age group to assess susceptibility. Results In general, rainfall was found to have a stronger association in the age group 0–4 years (RR=1.001, 95% CI=0.961–1.041) in both short and long lag times, with an overall increase of 1.4% in mortality risk for a 10 mm rise in rainfall. On the other hand, monthly average temperature had a stronger association with death in all ages while mortality increased with falling monthly temperature. The association per age group was estimated as: age group 0–4 (RR=0.934, 95% CI=0.894–0.974), age group 5–59 (RR=0.956, 95% CI=0.928–0.985) and age group over 60 (RR=0.946, 95% CI=0.912–0.979). The age group 5–59 experienced more delayed lag associations. This suggests that children and older adults are most sensitive to weather related mortality. Conclusion These results suggest that an early alert system based on monthly weather information may be useful for disease control management, to reduce and prevent fatal effects related to weather and monthly weather., Background Research in mainly developed countries has shown that some changes in weather are associated with increased mortality. However, due to the lack of accessible data, few studies have examined such effects of weather on mortality, particularly in rural regions in developing countries. Objective In this study, we aimed to investigate the relationship between temperature and rainfall with daily mortality in rural India. Design Daily mortality data were obtained from the Health and Demographic Surveillance System (HDSS) in Vadu, India. Daily mean temperature and rainfall data were obtained from a regional meteorological center, India Meteorological Department (IMD), Pune. A Poisson regression model was established over the study period (January 2003–May 2010) to assess the short-term relationship between weather variables and total mortality, adjusting for time trends and stratifying by both age and sex. Result Mortality was found to be significantly associated with daily ambient temperatures and rainfall, after controlling for seasonality and long-term time trends. Children aged 5 years or below appear particularly susceptible to the effects of warm and cold temperatures and heavy rainfall. The population aged 20–59 years appeared to face increased mortality on hot days. Most age groups were found to have increased mortality rates 7–13 days after rainfall events. This association was particularly evident in women. Conclusion We found the level of mortality in Vadu HDSS in rural India to be highly affected by both high and low temperatures and rainfall events, with time lags of up to 2 weeks. These results suggest that weather-related mortality may be a public health problem in rural India today. Furthermore, as changes in local climate occur, adaptation measures should be considered to mitigate the potentially negative impacts on public health in these rural communities., Introduction The association of weather and mortality have not been widely studied in subtropical monsoon regions, particularly in Bangladesh. This study aims to assess the association of weather and mortality (measured with temperature and rainfall), adjusting for time trend and seasonal patterns in Abhoynagar, Bangladesh. Material and methods A sample vital registration system (SVRS) was set up in 1982 to facilitate operational research in family planning and maternal and child health. SVRS provided data on death counts and population from 1983–2009. The Bangladesh Meteorological Department provided data on daily temperature and rainfall for the same period. Time series Poisson regression with cubic spline functions was used, allowing for over-dispersion, including lagged weather parameters, and adjusting for time trends and seasonal patterns. Analysis was carried out using R statistical software. Results Both weekly mean temperature and rainfall showed strong seasonal patterns. After adjusting for seasonal pattern and time trend, weekly mean temperatures (lag 0) below the 25th percentile and between the 25th and 75th percentiles were associated with increased mortality risk, particularly in females and adults aged 20–59 years by 2.3–2.4% for every 1°C decrease. Temperature above the 75th percentile did not increase the risk. Every 1 mm increase in rainfall up to 14 mm of weekly average rainfall over lag 0–4 weeks was associated with decreased mortality risks. Rainfall above 14 mm was associated with increased mortality risk. Conclusion The relationships between temperature, rainfall and mortality reveal the importance of understanding the current factors contributing to adaptation and acclimatization, and how these can be enhanced to reduce negative impacts from weather., Introduction While the association of weather and mortality has been well documented for moderate climate zones, little is known about sub-tropical zones, particularly Bangladesh. This study aims to assess the short-term relationship of temperature and rainfall on daily mortality after controlling for seasonality and time-trends. The study used data from Matlab, Bangladesh, where a rigorous health and demographic surveillance system (HDSS) has been operational since 1966. Material and methods Matlab HDSS data on mortality and population for the period 1983–2009 were used. Weather data for the same period were obtained from a nearby government weather station. Time series Poisson regression with cubic spline functions was applied allowing for lagged effects of weather and extreme weather events on mortality, and controlling for time trends and seasonal patterns. Analysis was carried out using R statistical software. Results Both temperature and rainfall showed strong seasonal patterns, explaining a significant part of mortality in all age groups. After adjusting for seasonality and trend, mortality and temperature show a U-shaped pattern; below a temperature of around 29°C, a decrease in temperature resulted in an increase in mortality, whereas above 29°C, increased temperature resulted in increased mortality. The strongest negative mortality temperature association was observed in the elderly (5.4% increase with every 1°C decrease in temperature at temperatures below 23°C), and the opposite trend was observed in the age groups 1–4 and 5–19 years old. At aggregate level, the rainfall–mortality association is statistically weak. However in the age group 5–19, a 0.6% increase in mortality per 1 mm additional rainfall was found, at rainfall levels over 100 mm per day. Multivariate analysis showed high mortality risks for women aged 20–59 years of age during cyclone episodes. Discussion Weather and extreme weather were associated with mortality with differential impacts in age and sex sub-groups. Further studies should investigate these findings more closely and develop policy recommendations targeted at improving public health and protecting population groups susceptible to environmental stressors., Background Climate and weather affect human health directly and indirectly. There is a renewed interest in various aspects of environmental health as our understanding of ongoing climate change improves. In particular, today, the health effects in low- and middle-income countries (LMICs) are not well understood. Many computer models predict some of the biggest changes in places where people are equipped with minimal resources to combat the effects of a changing environment, particularly with regard to human health. Objective This article documents the observed and projected climate profiles of select sites within the International Network for the Demographic Evaluation of Populations and Their Health (INDEPTH) network of Health and Demographic Surveillance System sites in Africa and Asia to support the integration of climate research with health practice and policy. Design The climatology of four meteorological stations representative of a suite of INDEPTH Health and Demographic Surveillance Systems (HDSSs) was assessed using daily data of 10 years. Historical and future trends were analyzed using reanalysis products and global climate model projections. Results The climate characteristics of the HDSS sites investigated suggest vulnerability to different environmental stressors, and the changes expected over the next century are far greater in magnitude than those observed at many of the INDEPTH member sites. Conclusions The magnitude of potential future climate changes in the LMICs highlights the need for improvements in collaborative climate–health research in these countries. Climate data resources are available to support such research efforts. The INDEPTH studies presented in this supplement are the first attempt to assess and document associations of climatic factors with mortality at the HDSSs.