The Planetary Health Alliance
Most pathogens and pollutants collect on the land surface or in infrastructure between strong rainfall episodes, and are then mobilized and delivered via storm runoff to areas of human exposure, such as coastal recreational waters, as a consequence of sewage or water treatment systems overflow. In California, USA, precipitation events are projected to become more extreme, and at the same time decrease in frequency as storm tracks move poleward due to polar-amplified global warming. Precipitation extremes in California are dominated by atmospheric rivers (ARs), which carry more moisture in a warmer climate. Thus, the physical driver of extreme precipitation events in this region is expected to grow stronger with climate change, and pollutant accumulation and runoff-generated exposure to those pollutants are expected to increase, particularly after prolonged dry spells. Extreme precipitation effects on water quality and associated human health impacts have been related to waterborne disease outbreaks in the conterminous USA and associated with beach closings in coastal areas due to the flushing of fecal contaminants through storm drains. In southern California, microbiological contamination of water during winter storms exposes human populations to elevated concentrations of microorganisms in bathing waters at beaches, which could cause gastrointestinal and ear infections, and lead to exposure to pathogens causing life-threatening conditions, e.g. hepatitis A. For this purpose, we use a recently published catalog of ARs, in combination with historical daily precipitation and fecal pollution indicators such as total and fecal coliforms in coastal waters to explore associations between extreme events related to ARs and the variability in coastal water quality in California. These associations will be used to identify mechanisms of fecal pollutant delivery to coastal recreational waters via storm runoff and to track sources of pollution common in the region, such as sewage outfalls and homeless encampments near rivers and streams. Overall, this work aims to quantitatively assess the influence of precipitation regime changes on human health via exposure to recreational coastal waters in California, with the ultimate goal of reducing vulnerability to extreme weather, as well as to delineate measures, such as an early warning system, that improve the response and resilience of human populations and ecosystems to a varying and changing climate.
Background India has made great strides toward improving nutrition over the past several decades despite rapid population growth, though all movement has not been consistently positive, particularly for zinc. A previous examination of food balance sheet data by Wessells and Brown (2012) estimated that the prevalence of zinc deficiency in India has increased from 28% to 31% between 1990 and 2005, despite a global decrease from 20.7% to 19.6% over that same period. This worrisome trend has the potential to be exacerbated by rising anthropogenic carbon dioxide emissions. 550 ppm CO2, a concentration predicted to occur by 2050 on our current trajectory, has been shown to deplete most major grains of 5-11% of their zinc (Myers et al., 2014). This is particularly critical for India, which relies upon grain crops for nearly three-quarters of their dietary zinc.
Methods To explore the cause of these past trends as well as the magnitude of the impact of rising CO2 on future zinc deficiency, we used data from seven nationally representative Indian National Sample Survey rounds of household food consumption between 1983 and 2012, paired with nutritional densities from the Indian Food Composition Tables, to calculate past and current zinc from the diet. We then estimate the future risk of zinc deficiency due to depletion of zinc from crops grown under elevated anthropogenic CO2 (550 ppm) by 2050.
Findings We find that nationwide deficiency has increased from 17.1% (15.3–19.0%; 95% UI) in 1983 to 24.6% (22.3–27.1%) in 2011-12, corresponding to an additional 83 million more people becoming zinc deficient than would have otherwise if 1983 rates had persisted. These increases have been driven by a steady drop in coarse cereal consumption (mainly millet and sorghum) and an insufficient increase in per capita zinc intake to keep pace with the growing requirements of an aging population. Deficiency is concentrated in specific subgroups: urban populations, rice-eating states, and, peculiarly, those in the top income quartile. Future CO2 is likely to continue and enhance this trend by depleting dietary zinc further, potentially increasing zinc deficiency by another 5.5% (3.3–7.8%). This corresponds to an additional 93 million people (56-131M) becoming deficient due to elevated CO2 by 2050.
Interpretation Zinc deficiency is a significant cause of morbidity and mortality globally and is trending in the wrong direction in India. Changes in diet, an aging population, and anthropogenic CO2 emissions are likely to exacerbate this concerning trend into the future.
Climate change will increasingly have widespread effects on human health, including transmission of vector-borne diseases such as schistosomiasis. Schistosoma trematodes have snails as intermediate hosts and transmission is closely linked to climatic factors such as temperature and rainfall. However, it remains difficult to predict the effect of climate change on schistosomiasis. This scoping review aims to collate research findings to understand the complex relationship between climate change and schistosomiasis.
A comprehensive literature search was performed using PubMed and Web of Science databases. In total, 266 results were screened and 28 original research papers were included and analysed in this scoping review.
Most studies (N=25) looked at the effect of temperature, whilst some assessed rainfall (N=8) or drought (N=2). Temperature has a non-linear effect on most stages of the parasite life cycle. There is variation in the effect of temperature between intermediate host snail species. Seven studies used laboratory experiments and fourteen used modelling to predict future distribution. Studies have either predicted a local decrease in schistosomiasis due to climate change (N=7), an increase or range shift (N=9), or that changes would be dependent on location (N=5). This scoping review presents an overview of the current literature, offers an evidence-based conceptual framework for the consequences of climate change on schistosomiasis and aims to identify future research needs.
The effects of global climate change on schistosomiasis are complex and heterogeneous. Some cool, wet areas may experience an increase in cases, whilst hotter areas may become less suitable for transmission due to increased snail and parasite mortality. Overall, transmission in sub-Saharan Africa, where schistosomiasis is most common, is predicted to decrease although areas in the east and south of the continent may experience increases. In China, suitable snail habitats could increase in area due to a north-shifting freezing line. Worldwide, there will be some range shift into non-endemic areas at the edge of transmission and at altitude. Most studies have evaluated temperature alone, whereas changes to precipitation and extreme weather events are also likely to be important drivers of epidemiological change. Furthermore, indirect consequences of climate change such as human migration and shifts in land use should be considered. Climate change will impact global control initiatives and it is essential that programmes are responsive to future variation in disease patterns.
Background: Tungiasis, a parasitic zoonosis of impoverished tropical communities, occurs when a female sand flea (Tunga penetrans or T. trimamillata) burrows into the skin. The disease can cause inflammation, altered gait, secondary infections, and death. Nonsterile sand-flea removal has been hypothesized as a driver of blood-borne illnesses among African children. In Brazil, tungiasis peaks during the dry season and decreases sharply with onset of rains. The mechanism linking precipitation to disease is unknown, but previous studies have hypothesized that humid soils hamper larval development or that rains wash away free-living fleas. Here, we combine human surveys and climate data to investigate the relationship between climate conditions and tungiasis in Madagascar.
Methods: We interviewed 58 households in three rural villages of eastern Madagascar and collected data on weather patterns associated with tungiasis. Focusing on the same region, we used present-day and model climate data based on four IPCC Representative Concentration Scenarios (RCPs) to predict climate and tungiasis prevalence in the years 2050 and 2070.
Findings: Our interviews reveal that rural Malagasy, particularly children and elders, currently experience a high sand-flea burden. All interviewees experienced tungiasis at least once, and 12% were actively infected. Of the households interviewed, 93% remove fleas with shared, unsterilized needles, and 39% reported secondary infections following removal. As a preventative treatment, 52% of respondents rub feet with toxic substances such as kerosene or insecticide. Tungiasis was reported to occur most often in the dry-season months of September to December, particularly on hot, dry days. Our climate results from all four RCPs predict a shortening of the wet season (precipitation >250 mm), while the dry (<80 mm) and very-dry (<50 mm) seasons are predicted to lengthen.
Interpretation: The impact of tungiasis in rural Madagascar is significant and likely to increase with climate change due to a lengthening dry season. We argue that tungiasis is a neglected tropical disease that should be given higher priority for planetary-health research and control, as incidence is likely to increase due to the climate change. Our method of pairing disease seasonality with climate projections can also be expanded to endemic regions globally.
Background: The Climate Agreement signed in Paris aims to limit rises in global-mean temperature to below 2°C. It also aims to pursue efforts to limit temperature rises to 1.5°C in comparison to preindustrial levels. Although there are benefits for human health in limiting global warming to 1.5°C, the magnitude with which those societal benefits will be accrued remains fundamentally unknown. Crucial to public health preparedness and response is understanding and quantifying the impacts at different levels of warming. Dengue is a rapidly spreading vector-borne viral disease that is endemic to over 100 countries. It is estimated to cause ~390 million cases each year, 54 million of which occur in Latin America and the Caribbean.
Method: Monthly counts of laboratory confirmed dengue cases were obtained from the Mexican, Brazilian and Colombian Ministries of Health for the period January 2001 to December 2012. Generalized additive mixed model were used to model associations with current weather. A multi-GCM, multi-scenario approach was then used to investigate and quantify the risks avoided by limiting global mean temperature to 1.5°C above pre-industrial compared to those occurring at 2°C or 3.7°C. Using the ClimGen pattern-scaling tool, we generated climate projections from five CMIP5 climate models by scaling their simulated patterns of climate change by time-series of global temperature rise obtained using the IMAGE modeling framework which identifies socioeconomic pathways and projects the climatic implications of different climate and energy policy scenarios.
Findings and interpretation: Policies to limit global warming to 1.5°C could reduce dengue cases by up to 5.2 (1.7–13.8) million cases per year by the end of the century compared with a no-policy scenario that warms by 3.7°C. This number is comparable to ~50% of the estimated burden for the region in 2010. Furthermore, we found that by limiting global warming we can limit the expansion of the disease towards areas where incidence is currently low. Our results demonstrate that although future climate change may amplify dengue transmission in the region, some impacts could be avoided by constraining the level of warming.
Background: Vector-borne diseases, such as dengue, Zika and malaria, are highly sensitive to environmental changes, including variations in climate and land-surface characteristics. For example, changes in temperature and moisture impact developmental rates and survival of both the vector and pathogen, and the availability of vector breeding sites. Vector-borne disease emergence and spread is also exacerbated by anthropogenic activities, such as deforestation, mining, urbanisation and human mobility, which alter the natural habitats of vectors and increase vector-host interactions.
Innovative epidemiological modelling tools can help understand how environmental conditions interact with socio-economic risk factors to determine the risk of disease transmission and spread. In recent years, disease modelling has benefited from computational advances in fitting complex mathematical models and the increasing availability of environmental, socio-economic and disease surveillance datasets. At the same time, the ability to understand and model the climate system has steadily improved. Climate forecasts at sub-seasonal to seasonal time scales tend to be more skilful during El Niño-Southern Oscillation events in certain regions of the tropics. Thus, climate forecasts provide an opportunity to anticipate potential outbreaks of vector-borne diseases from several months to a year in advance.
Method: A Bayesian spatio-temporal model framework, which quantifies the extent to which environmental and socio-economic indicators can explain variations in disease risk, is presented. The framework is designed to disentangle the impacts of climate from other risk factors, using multi-source data and random effects, which account for unknown and unmeasured sources of spatial, seasonal and inter-annual variation. The model provides probabilistic predictions of monthly dengue incidence and the probability of exceeding outbreak thresholds, which are determined in consultation with public health stakeholders.
Findings: This disease model framework, combined with seasonal climate forecasts, has been successfully applied to produce real-time probabilistic dengue early warnings ahead of a mass gathering event in Brazil and following a major El Niño event in southern coastal Ecuador. Forecasts from the new model framework performed better than benchmark models, based on historical seasonal dengue averages.
Interpretation: This flexible model framework can be adapted to predict any climate-sensitive disease at various spatio-temporal scales and in diverse ecological settings. Incorporating sub-seasonal and seasonal climate forecasts in disease prediction models could support public health decision-makers in targeting timely disease control and prevention strategies months in advance, to mitigate the risk of imminent disease epidemics and emerging disease threats.
Enteric infections are a major cause of morbidity and mortality, particularly among children less than 5 years of age. Climate change projections for Rwanda predict increases in average temperature, along with increases in total precipitation and frequency of heavy events. Previous research in Rwanda has found that heavy rainfall events increase the risk of contaminated drinking water, a substantial contributor to diarrhea in this setting. Relatively little is known about the impact of precipitation on more severe clinic-diagnosed infections in rural, low-income settings such as Rwanda.
In the context of a randomized controlled trial to assess the impact of a national environmental health campaign in Rwanda, we collected data from all government health facilities in Rusizi District, Western Province for calendar year 2015. Patient data for children under 5 from all 150 study villages were extracted from paper-based registers. Gridded daily precipitation data were downloaded from Climate Hazards Group InfraRed Precipitation with Station data and NASA’s Tropical Rainfall Measurement Mission, supplemented by local weather station data. Using both time-series and case-crossover designs, we examined the effect of extreme precipitation (95th percentile) on visits for enteric symptoms (i.e, diarrhea, gastroenteritis, vomiting controlling for intervention status and village distance to primary health facility. We also investigated the association between temperature and humidity for enteric symptoms, as well as respiratory symptoms, diagnosed pneumonia, and diagnosed malaria.
Data were extracted from 50 facilities, with a study catchment area of approximately 13230 children under 5. Preliminary results indicate there were 3736 cases of clinic-reported enteric symptoms in 2015, with an estimated annual incidence of 282 cases per 1000. Incidence was lowest in dry season months and increased in both rainy season periods. This trend was evident for all children and those less than 2 years of age, an age group with high rotavirus vaccination rates. Results of extreme precipitation impacts will be presented.
Clinic data from low-income settings is a rich and under-utilized resource which could have broad applications for improving planetary health at household, community, and regional levels. They add objectivity over self-reported conditions and capture the more serious outcomes that impact health care systems. Our results are consistent with others showing increased risk from rainfall events, likely due to flushed contaminants and water supply vulnerabilities. Understanding the impacts and mechanisms of precipitation on infectious diseases can help inform the design of locally-relevant intervention and adaptation strategies.
Background: Globally, pollen allergy is a major public health problem, but a fundamental unknown is the likely impact of climate change. To our knowledge, this is the first study to quantify the consequences of climate change upon pollen allergy in humans. The study focussed upon the annual herbaceous plant common ragweed (Ambrosia artemisiifolia) in Europe. Ragweed is highly invasive; it thrives on disturbed land, with each plant producing ≤ 62,000 seeds per year. Ragweed is particularly harmful for public health because each plant produces a large amount of pollen (≤ 1 billion grains a year).
Method: A process-based model estimated the change in ragweed’s range under climate change. A second model simulated current and future ragweed pollen levels. These findings were translated into 2 different measures of health burdens. Changes in pollen sensitisation rates were estimated using a dose–response curve generated from a systematic review and from current and future population data. Changes in allergy symptoms were modelled by first obtaining dose-response relationships between pollen and symptoms from a cohort of sensitised individuals. These relationships were applied to future pollen loads. Future pollen models considered two different suites of regional climate/pollen models, two greenhouse gas emissions scenarios [RCPs 4.5 and 8.5], and three different plant invasion scenarios. A second set of health impacts were obtained.
Findings and interpretation: Sensitization to ragweed will more than double in Europe, from 33 to 77 million people, by 2041–2060. According to our projections, sensitization will increase in countries with an existing ragweed problem (e.g., Hungary, the Balkans), but the greatest proportional increases will occur where sensitization is uncommon (e.g., Germany, Poland, France). Higher pollen concentrations and a longer pollen season may also increase the severity of symptoms. Our quantitative estimates indicate that ragweed pollen allergy will become a common health problem across Europe, expanding into areas where it is currently uncommon. Control of ragweed spread may be an important adaptation strategy in response to climate change.