RESUMEN
East Africa is highly affected by neglected tropical diseases (NTDs), which are projected to be exacerbated by climate change. Consequently, understanding what research has been conducted and what knowledge gaps remain regarding NTDs and climate change is crucial to informing public health interventions and climate change adaptation. We conducted a systematic scoping review to describe the extent, range, and nature of publications examining relationships between NTDs and climatic factors in East Africa. We collated all relevant English and French publications indexed in PubMed®, Web of Science™ Core Collection, and CAB Direct© databases published prior to 2019. Ninety-six publications were included for review. Kenya, Tanzania, and Ethiopia had high rates of publication, whereas countries in the Western Indian Ocean region were underrepresented. Most publications focused on schistosomiasis (n = 28, 29.2%), soil-transmitted helminthiases (n = 16, 16.7%), or human African trypanosomiasis (n = 14, 14.6%). Precipitation (n = 91, 94.8%) and temperature (n = 54, 56.3%) were frequently investigated climatic factors, whereas consideration of droughts (n = 10, 10.4%) and floods (n = 4, 4.2%) was not prominent. Publications reporting on associations between NTDs and changing climate were increasing over time. There was a decrease in the reporting of Indigenous identity and age factors over time. Overall, there were substantial knowledge gaps for several countries and for many NTDs. To better understand NTDs in the context of a changing climate, it would be helpful to increase research on underrepresented diseases and regions, consider demographic and social factors in research, and characterize how these factors modify the effects of climatic variables on NTDs in East Africa.
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Cambio Climático , Enfermedades Desatendidas/epidemiología , Clima Tropical , África Oriental/epidemiología , Bases de Datos Factuales , HumanosRESUMEN
Background: Season and weather are associated with many health outcomes, which can influence hospital admission rates. We examined associations between hospital admissions (all diagnoses) and local meteorological parameters in Southwestern Uganda, with the aim of supporting hospital planning and preparedness in the context of climate change. Methods: Hospital admissions data and meteorological data were collected from Bwindi Community Hospital and a satellite database of weather conditions, respectively (2011 to 2014). Descriptive statistics were used to describe admission patterns. A mixed-effects Poisson regression model was fitted to investigate associations between hospital admissions and season, precipitation, and temperature. Results: Admission counts were highest for acute respiratory infections, malaria, and acute gastrointestinal illness, which are climate-sensitive diseases. Hospital admissions were 1.16 (95% CI: 1.04, 1.31; p = 0.008) times higher during extreme high temperatures (i.e., >95th percentile) on the day of admission. Hospital admissions association with season depended on year; admissions were higher in the dry season than the rainy season every year, except for 2014. Discussion: Effective adaptation strategy characteristics include being low-cost and quick and practical to implement at local scales. Herein, we illustrate how analyzing hospital data alongside meteorological parameters may inform climate-health planning in low-resource contexts.
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Cambio Climático , Planificación en Desastres/métodos , Necesidades y Demandas de Servicios de Salud , Hospitalización/estadística & datos numéricos , Tiempo (Meteorología) , Adolescente , Adulto , Anciano , Anciano de 80 o más Años , Niño , Preescolar , Femenino , Humanos , Lactante , Recién Nacido , Masculino , Persona de Mediana Edad , Modelos Teóricos , Distribución de Poisson , Estaciones del Año , Uganda , Adulto JovenRESUMEN
BACKGROUND: Acute respiratory infections (ARI) are a leading cause of morbidity and mortality globally, and are often linked to seasonal and/or meteorological conditions. Globally, Indigenous peoples may experience a different burden of ARI compared to non-Indigenous peoples. This protocol outlines our process for conducting a systematic review to investigate whether associations between ARI and seasonal or meteorological parameters differ between Indigenous and non-Indigenous groups residing in the same geographical region. METHODOLOGY: A search string will be used to search PubMed®, CAB Abstracts/CAB Direct©, and Science Citation Index® aggregator databases. Articles will be screened using inclusion/exclusion criteria applied first at the title and abstract level, and then at the full article level by two independent reviewers. Articles maintained after full article screening will undergo risk of bias assessment and data will be extracted. Heterogeneity tests, meta-analysis, and forest and funnel plots will be used to synthesize the results of eligible studies. DISCUSSION AND REGISTRATION: This protocol paper describes our systematic review methods to identify and analyze relevant ARI, season, and meteorological literature with robust reporting. The results are intended to improve our understanding of potential associations between seasonal and meteorological parameters and ARI and, if identified, whether this association varies by place, population, or other characteristics. The protocol is registered in the PROSPERO database (#38051).
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Emigrantes e Inmigrantes/estadística & datos numéricos , Grupos de Población/estadística & datos numéricos , Infecciones del Sistema Respiratorio/etiología , Estaciones del Año , Tiempo (Meteorología) , Enfermedad Aguda , Humanos , Infecciones del Sistema Respiratorio/epidemiología , Revisiones Sistemáticas como AsuntoRESUMEN
Climate change has increased the occurrence of heat waves, causing heat stress among humans and livestock, with potentially fatal consequences. Heat stress maps provide information about related health risks and insight for control strategies. Weather data were collected throughout Southern Ontario, and the heat stress index (HSI) was estimated for 2010-2012. Geostatistical kriging was applied to map heat stress, heat waves, and control periods. Average HSI for each period ranged from 55 to 78 during control periods, and from 65 to 84 during heat waves, surpassing levels where morbidity is known to increase substantially. Heat stress followed a temporally consistent geographic pattern. HSI maps indicate high-risk areas for heat-related illness and indicate areas where agriculture and human health may be at increased risk in future.
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Bovinos , Trastornos de Estrés por Calor/epidemiología , Calor , Población Rural , Animales , Ontario/epidemiología , Análisis EspacialRESUMEN
BACKGROUND: Heat stress is a physiological response to extreme environmental heat such as heat waves. Heat stress can result in mortality in dairy cows when extreme heat is both rapidly changing and has a long duration. As a result of climate change, heat waves, which are defined as 3 days of temperatures of 32 °C or above, are an increasingly frequent extreme weather phenomenon in Southern Ontario. Heat waves are increasing the risk for on-farm dairy cow mortality in Southern Ontario. Heat stress indices (HSIs) are generally based on temperature and humidity and provide a relative measure of discomfort which can be used to predict increased risk of on-farm dairy cow mortality. In what follows, the heat stress distribution was described over space and presented with maps. Similarly, on-farm mortality was described and mapped. The goal of this study was to demonstrate that heat waves and related HSI increases during 2010-2012 were associated with increased on-farm dairy cow mortality in Southern Ontario. Mortality records and farm locations for all farms registered in the CanWest Dairy Herd Improvement Program in Southern Ontario were retrieved for 3 heat waves and 6 three-day control periods from 2010 to 2012. A random sample of controls (2:1) was taken from the data set to create a risk-based hybrid design. On-farm heat stress was estimated using data from 37 weather stations and subsequently interpolated across Southern Ontario by geostatistical kriging. A Poisson regression model was applied to assess the on-farm mortality in relation to varying levels of the HSI. RESULTS: For every one unit increase in HSI the on-farm mortality rate across Southern Ontario increases by 1.03 times (CI95% (IRR) = (1.025,1.035); p = ≤ 0.001). With a typical 8.6 unit increase in HSI from a control period to a heat wave, mortality rates are predicted to increase by 1.27 times. CONCLUSIONS: Southern Ontario was affected by heat waves, as demonstrated by high levels of heat stress and increased on-farm mortality. Farmers should be aware of these risks, and informed of appropriate methods to mitigate such risks.
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Enfermedades de los Bovinos/mortalidad , Trastornos de Estrés por Calor/veterinaria , Animales , Bovinos , Enfermedades de los Bovinos/epidemiología , Trastornos de Estrés por Calor/epidemiología , Trastornos de Estrés por Calor/mortalidad , Ontario/epidemiología , Estudios RetrospectivosRESUMEN
BACKGROUND: In Southern Ontario, climate change may have given rise to an increasing occurrence of heat waves since the year 2000, which can cause heat stress to the general public, and potentially have detrimental health consequences. Heat waves are defined as three consecutive days with temperatures of 32 °C and above. Heat stress is the level of discomfort. A variety of heat stress indices have been proposed to measure heat stress (e.g., the heat stress index (HSI)), and has been shown to predict increases in morbidity and/or mortality rates in humans and other species. Maps visualizing the distribution of heat stress can provide information about related health risks and insight for control strategies. Information to inform heat wave preparedness models in Ontario was previously only available for major metropolitan areas. METHODS: Hospitals in communities of fewer than 100,000 individuals were recruited for a pilot study by telephone. The number of people visiting the emergency room or 24-hour urgent care service was collected for a total of 27 days, covering three heat waves and six 3-day control periods from 2010-2012. The heat stress index was spatially predicted using data from 37 weather stations across Southern Ontario by geostatistical kriging. Poisson regression modeling was applied to determine the rate of increased number of emergency room visits in rural hospitals with respect to the HSI. RESULTS: During a heat wave, the average rate of emergency room visits was 1.11 times higher than during a control period (IRR = 1.11, CI95% (IRR) = (1.07,1.15), p ≤ 0.001). In a univariable model, HSI was not a significant predictor of emergency room visits, but when accounting for the confounding effect of a spatial trend polynomial in the hospital location coordinates, a one unit increase in HSI predicted an increase in daily emergency rooms visits by 0.4% (IRR = 1.004, CI95%(IRR) = (1.0005,1.007), p = 0.024) across the region. One high-risk cluster and no low risk clusters were identified in the southwestern portion of the study area by the spatial scan statistic during heat waves. The high-risk cluster is located in a region with high levels of heat stress during heat waves. CONCLUSIONS: This finding will aid hospitals and rural public health units in preventing and preparing for emergencies of foreseeable heat waves. Future research is needed to assess the relation between heat stress and individual characteristics and demographics of rural communities in Ontario.