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Weather, climate, and climate change are affecting human health, with scientific evidence increasing substantially over the past two decades, but with very limited research from low- and middle-income countries. The health effects of climate change occur mainly because of the consequences of rising temperatures, rising sea levels, and an increase in extreme weather events. These exposures interact with demographic, socio-economic, and environmental factors, as well as access to and the quality of health care, to affect the magnitude and pattern of risks. Health risks are unevenly distributed around the world, and within countries and across population groups. Existing health challenges and inequalities are likely to be exacerbated by climate change. This narrative review provides an overview of the health impacts of weather, climate, and climate change, particularly on vulnerable regions and populations in sub-Saharan Africa and South Asia, and discusses the importance of protecting human health in a changing climate; such measures are critical to reducing poverty and inequality at all scales. Three case summaries from the INDEPTH Health and Demographic Surveillance Systems highlight examples of research that quantified associations between weather and health outcomes. These and comparable surveillance systems can provide critical knowledge to increase resilience and decrease inequalities in an increasingly warming world.

Background: Sri Lanka is vulnerable to floods and other hydro-meteorological disasters. Climate change is projected to increase the intensity of these events., Objective: This study aimed to assess the flood preparedness in healthcare facilities in Eastern Province., Design: This was a cross-sectional, descriptive, mixed methods study conducted in Trincomalee District. Surveys were conducted in 31 government healthcare facilities, using a pre-tested, structured questionnaire covering the last 5 years. Seven in-depth interviews were conducted with randomly selected Medical Officers in Charge or their equivalent, and 3 interviews were conducted with Medical Offices of Health., Results: Two general hospitals, 3 base hospitals, 11 divisional hospitals, and 15 primary care units were included. Six respondents (19.4%) reported flooding in their facility, and 19 (61.3%) reported flooding in their catchment area. For the health workforce, 77.4% of respondents reported not enough staff to perform normal service delivery during disasters, and 25.5% reported staff absenteeism due to flooding. Several respondents expressed a desire for more disaster-specific and general clinical training opportunities for themselves and their staff. Most respondents (80.7%) reported no delays in supply procurement during weather emergencies, but 61.3% reported insufficient supplies to maintain normal service delivery during disasters. Four facilities (12.9%) had disaster preparedness plans, and 4 (12.9%) had any staff trained on disaster preparedness or management within the last year. One quarter (25.8%) of respondents had received any written guidance on disaster preparedness from the regional, provincial, or national level in the last year., Conclusions: While there is a strong health system operating in Sri Lanka, improvements are needed in localized and appropriate disaster-related training, resources for continuing clinical education, and investments in workforce to strengthen flood and other disaster resilience within the government healthcare system in the study district.

Background: The purpose of the study is to determine the epidemiology of road traffic collisions (RTCs) in Trinidad and Tobago by characterizing RTCs in terms of number of collisions, fatalities, victim profiles, and locations for the purpose of informing accident prevention programs. Previous studies of RTCs in Trinidad and Tobago were primarily concerned with patterns of drivers use of seat belts, road collisions as a cause of mortality in young men, and the economic burden of road collisions. Attempts were made to model road fatalities, but limited epidemiological data meant that it was difficult to determine trends or develop models., Methods: This study determined the epidemiology of RTCs in Trinidad and Tobago over the period 2000-2011 using data collected by the Trinidad and Tobago Road Traffic Branch of the Police Service and secondary data from the Central Statistical Office. Data were analyzed using Excel, SPSS, and R statistical packages., Results: Fatalities were greater among men (80%) than among women (20%) and were highest on two major freeways in Trinidad [the Churchill-Roosevelt Highway and the Sir Solomon Hochoy Highway]. Most collisions occurred during the night among individuals between the ages of 15 and 44 years. Fatalities among drivers steadily increased over the study period and overtook fatalities among pedestrians, who were the group most affected in 2000. Most fatalities occurred at weekends., Conclusions: These patterns can inform (i) education programs and (ii) road and traffic control measures., Competing Interests: and funding The authors have not received any funding or benefits from industry or elsewhere to conduct this study.

Mathematical and statistical models are needed to understand the extent to which weather, climate variability, and climate change are affecting current and may affect future health burdens in the context of other risk factors and a range of possible development pathways, and the temporal and spatial patterns of any changes. Such understanding is needed to guide the design and the implementation of adaptation and mitigation measures. Because each model projection captures only a narrow range of possible futures, and because models serve different purposes, multiple models are needed for each health outcome ('horses for courses'). Multiple modeling results can be used to bracket the ranges of when, where, and with what intensity negative health consequences could arise. This commentary explores some climate change and health modeling issues, particularly modeling exposure-response relationships, developing early warning systems, projecting health risks over coming decades, and modeling to inform decision-making. Research needs are also suggested.