Managing the Health Effects of Temperature in Response to Climate Change: Challenges Ahead

Background: Although many studies have shown that high temperatures are associated with an increased risk of mortality and morbidity, there has been little research on managing the process of planned adaptation to alleviate the health effects of heat events and climate change. In particular, economic evaluation of public health adaptation strategies has been largely absent from both the scientific literature and public policy discussion.Objectives: We examined how public health organizations should implement adaptation strategies and, second, how to improve the evidence base required to make an economic case for policies that will protect the public’s health from heat events and climate change.Discussion: Public health adaptation strategies to cope with heat events and climate change fall into two categories: reducing the heat exposure and managing the health risks. Strategies require a range of actions, including timely public health and medical advice, improvements to housing and urban planning, early warning systems, and assurance that health care and social systems are ready to act. Some of these actions are costly, and given scarce financial resources the implementation should be based on the cost-effectiveness analysis. Therefore, research is required not only on the temperature-related health costs, but also on the costs and benefits of adaptation options. The scientific community must ensure that the health co-benefits of climate change policies are recognized, understood, and quantified.Conclusions: The integration of climate change adaptation into current public health practice is needed to ensure the adaptation strategies increase future resilience. The economic evaluation of temperature-related health costs and public health adaptation strategies are particularly important for policy decisions.     

Time Series Analysis of Cholera in Matlab,Bangladesh, during 1988-2001

The study examined the impact of in-situ climatic and marine environmental variability on cholera incidence in an endemic area of Bangladesh and developed a forecasting model for understanding the magnitude of incidence. Diarrhoea surveillance data collected between 1988 and 2001were obtained from a field research site in Matlab, Bangladesh. Cholera cases were defined as Vibrio cholerae O1 isolated from faecal specimens of patients who sought care at treatment centres serving the Matlab population. Cholera incidence for 168 months was correlated with remotely-sensed sea-surface temperature (SST) and in-situ environmental data, including rainfall and ambient temperature. A seasonal autoregressive integrated moving average (SARIMA) model was used for determining the impact of climatic and environmental variability on cholera incidence and evaluating the ability of the model to forecast the magnitude of cholera. There were 4,157 cholera cases during the study period, with an average of 1.4 cases per 1,000 people. Since monthly cholera cases varied significantly by month, it was necessary to stabilize the variance of cholera incidence by computing the natural logarithm to conduct the analysis. The SARIMA model shows temporal clustering of cholera at one- and 12-month lags. There was a 6% increase in cholera incidence with a minimum temperature increase of one degree celsius in the current month. For increase of SST by one degree celsius, there was a 25% increase in the cholera incidence at currrent month and 18% increase in the cholera incidence at two months. Rainfall did not influenc to cause variation in cholera incidence during the study period. The model forecast the fluctuation of cholera incidence in Matlab reasonably well (Root mean square error, RMSE: 0.108). Thus, the ambient and sea-surface temperature-based model could be used in forecasting cholera outbreaks in Matlab.Key words: Cholera, Climate change, Time series analysis, Matlab, Bangladesh     

Dynamics of cholera outbreaks in Great Lakes region of Africa, 1978-2008

Cholera outbreaks have occurred in Burundi, Rwanda, Democratic Republic of Congo, Tanzania, Uganda, and Kenya almost every year since 1977-1978, when the disease emerged in these countries. We used a multiscale, geographic information system-based approach to assess the link between cholera outbreaks, climate, and environmental variables. We performed time-series analyses and field investigations in the main affected areas. Results showed that cholera greatly increased during El Nino warm events (abnormally warm El Ninos) but decreased or remained stable between these events. Most epidemics occurred in a few hotspots in lakeside areas, where the weekly incidence of cholera varied by season, rainfall, fluctuations of plankton, and fishing activities. During lull periods, persistence of cholera was explained by outbreak dynamics, which suggested a metapopulation pattern, and by endemic foci around the lakes. These links between cholera outbreaks, climate, and lake environments need additional, multidisciplinary study.     

Heat-related mortality risk model for climate change impact projection

Objectives

We previously developed a model for projection of heat-related mortality attributable to climate change. The objective of this paper is to improve the fit and precision of and examine the robustness of the model.

Methods

We obtained daily data for number of deaths and maximum temperature from respective governmental organizations of Japan, Korea, Taiwan, the USA, and European countries. For future projection, we used the Bergen climate model 2 (BCM2) general circulation model, the Special Report on Emissions Scenarios (SRES) A1B socioeconomic scenario, and the mortality projection for the 65+-year-old age group developed by the World Health Organization (WHO). The heat-related excess mortality was defined as follows: The temperature–mortality relation forms a V-shaped curve, and the temperature at which mortality becomes lowest is called the optimum temperature (OT). The difference in mortality between the OT and a temperature beyond the OT is the excess mortality. To develop the model for projection, we used Japanese 47-prefecture data from 1972 to 2008. Using a distributed lag nonlinear model (two-dimensional nonparametric regression of temperature and its lag effect), we included the lag effect of temperature up to 15 days, and created a risk function curve on which the projection is based. As an example, we perform a future projection using the above-mentioned risk function. In the projection, we used 1961–1990 temperature as the baseline, and temperatures in the 2030s and 2050s were projected using the BCM2 global circulation model, SRES A1B scenario, and WHO-provided annual mortality. Here, we used the “counterfactual method” to evaluate the climate change impact; For example, baseline temperature and 2030 mortality were used to determine the baseline excess, and compared with the 2030 excess, for which we used 2030 temperature and 2030 mortality. In terms of adaptation to warmer climate, we assumed 0 % adaptation when the OT as of the current climate is used and 100 % adaptation when the OT as of the future climate is used. The midpoint of the OTs of the two types of adaptation was set to be the OT for 50 % adaptation.

Results

We calculated heat-related excess mortality for 2030 and 2050.

Conclusions

Our new model is considered to be better fit, and more precise and robust compared with the previous model.     

Influence of temperature and rainfall on the evolution of cholera epidemics in Lusaka, Zambia, 2003-2006: analysis of a time series

In this study, we aimed to describe the evolution of three cholera epidemics that occurred in Lusaka, Zambia, between 2003 and 2006 and to analyse the association between the increase in number of cases and climatic factors. A Poisson autoregressive model controlling for seasonality and trend was built to estimate the association between the increase in the weekly number of cases and weekly means of daily maximum temperature and rainfall. All epidemics showed a seasonal trend coinciding with the rainy season (November to March). A 1 degrees C rise in temperature 6 weeks before the onset of the outbreak explained 5.2% [relative risk (RR) 1.05, 95% CI 1.04-1.06] of the increase in the number of cholera cases (2003-2006). In addition, a 50 mm increase in rainfall 3 weeks before explained an increase of 2.5% (RR 1.02, 95% CI 1.01-1.04). The attributable risks were 4.9% for temperature and 2.4% for rainfall. If 6 weeks prior to the beginning of the rainy season an increase in temperature is observed followed by an increase in rainfall 3 weeks later, both exceeding expected levels, an increase in the number of cases of cholera within the following 3 weeks could be expected. Our explicative model could contribute to developing a warning signal to reduce the impact of a presumed cholera epidemic.     

The effect of rainfall on the incidence of cholera in Bangladesh

BACKGROUND: The incidence of cholera in Bangladesh shows clear seasonality, suggesting that weather factors could play a role in its epidemiology. We estimated the effects of rainfall on the incidence of cholera in Dhaka, Bangladesh. METHODS: We examined time-series patterns of the weekly number of hospital visits due to cholera in relation to weekly rainfall from 1996 to 2002. We used Poisson regression models, adjusted for seasonal variation, between-year variation, public holidays, and temperature. The role of river level on the rainfall-cholera relationship was also examined by incorporating river-level terms into the models. RESULTS: The weekly number of cholera cases increased by 14% (95% confidence interval = 10.1%-18.9%) for each 10-mm increase above the threshold of 45 mm for the average rainfall, over lags 0 to 8 weeks. Conversely, the number of cholera cases increased by 24% (10.7%-38.6%) for a 10-mm decrease below the same threshold of average rainfall, over lags 0 to 16 weeks. River level partly explained the association between high rainfall and the number of cholera cases. CONCLUSIONS: The number of cholera cases increased with both high and low rainfall in the weeks preceding hospital visits. These results suggest that factors associated with river level are on the causal pathway between high rainfall and incidence of cholera.     

Climate influence on dengue epidemics in Puerto Rico

The variability of the insect-borne disease dengue in Puerto Rico was studied in relation to climatic variables in the period 1979-2005. Annual and monthly reported dengue cases were compared with precipitation and temperature data. Results show that the incidence of dengue in Puerto Rico was relatively constant over time despite global warming, possibly due to the offsetting effects of declining rainfall, improving health care and little change in population. Seasonal fluctuations of dengue were driven by rainfall increases from May to November. Year-to-year variability in dengue cases was positively related to temperature, but only weakly associated with local rainfall and an index of El Nino Southern Oscillation (ENSO). Climatic conditions were mapped with respect to dengue cases and patterns in high and low years were compared. During epidemics, a low pressure system east of Florida draws warm humid air over the northwestern Caribbean. Long-term trends in past observed and future projected rainfall and temperatures were studied. Rainfall has declined slowly, but temperatures in the Caribbean are rising with the influence of global warming. Thus, dengue may increase in the future, and it will be necessary to anticipate dengue epidemics using climate forecasts, to reduce adverse health impacts.     

Prediction of epidemic cholera due to Vibrio cholerae O1 in children younger than 10 years using climate data in Bangladesh

To determine if a prediction of epidemic cholera using climate data can be made, we performed autoregression analysis using the data recorded in Dhaka City, Bangladesh over a 20-year period (1983-2002) comparing the number of children aged <10 years who were infected with Vibrio cholerae O1 to the maximum and minimum temperatures and rainfall. We formulated a simple autoregression model that predicts the monthly number of patients using earlier climate variables. The monthly number of patients predicted by this model agreed well with the actual monthly number of patients where the Pearson's correlation coefficient was 0.95. Arbitrarily defined, 39.4% of the predicted numbers during the study period were within 0.8-1.2 times the observed numbers. This prediction model uses the climate data recorded 2-4 months before. Therefore, our approach may be a good basis for establishing a practical early warning system for epidemic cholera.     

Climate change and hygienic assessment of weather conditions in Omsk and the Omsk Region

The paper deals with trends in climate change in the Omsk Region: the increases in average annual air temperatures and rainfall, which are attended by the higher number of abnormal weather events, as shown by the data of the Omsk Regional Board, Russian Federal Service for Hydrometeorology and Environmental Monitoring. There is information on weather severity in 2008: there was mild weather in spring and severe weather in winter, in January in particular. A survey of physicians has revealed that medical workers are concerned about climate problems and global warming and ascertained weather events mostly affecting the population's health. People worry most frequently about a drastic temperature drop or rise (as high as 71%), atmospheric pressure change (53%), and "when it is too hot in summer (47%).     

Climate variability and change and their potential health effects in small island states: information for adaptation planning in the health sector

Small island states are likely the countries most vulnerable to climate variability and longterm climate change. Climate models suggest that small island states will experience warmer temperatures and changes in rainfall, soil moisture budgets, prevailing winds (speed and direction), and patterns of wave action. El Ni?o events likely will strengthen shortterm and interannual climate variations. In addition, global mean sea level is projected to increase by 0.09-0.88 m by 2100, with variable effects on regional and local sea level. To better understand the potential human health consequences of these projected changes, a series of workshops and a conference organized by the World Health Organization, in partnership with the World Meteorological Organization and the United Nations Environment Programme, addressed the following issues: the current distribution and burden of climate-sensitive diseases in small island states, the potential future health impacts of climate variability and change, the interventions currently used to reduce the burden of climate-sensitive diseases, additional interventions that are needed to adapt to current and future health impacts, and the health implications of climate variability and change in other sectors. Information on these issues is synthesized and key recommendations are identified for improving the capacity of the health sector to anticipate and prepare for climate variability and change in small island states.     

The impact of heat islands on mortality in Paris during the August 2003 heat wave

Background: Heat waves have a drastic impact on urban populations, which could increase with climate change.Objectives: We evaluated new indicators of elderly people’s exposure to heat in Paris, from a public health prevention perspective, using satellite thermal images.Methods: We used a time series of 61 images from the satellites of the National Oceanic and Atmospheric Administration’s (NOAA) Advanced Very High Resolution Radiometer (AVHRR) taken from 1 to 13 August 2003 to produce thermal indicators of minimum, maximum, and mean surface temperatures and diurnal temperature amplitude, with different lags between the meteorological data and the health impact. Health data came from a case–control study involving 241 people ≥ 65 years of age who died in the city of Paris or the nearby suburban area of Val-de-Marne during the August 2003 heat wave, and 241 controls who were matched to cases on age, sex, and residential zone. For each person, we integrated the thermal indicators in a conditional logistic regression model, adjusted for age and other potential confounders. We computed odds ratios (ORs) comparing the 90th and 50th percentiles of the temperature differences between cases and controls for various indicators.Results: Mortality risk was significantly associated with exposure for two indicators: minimum temperatures averaged for 1–13 August [for a 0.41°C increase, OR = 2.17; 95% confidence interval (CI): 1.14, 4.16] and minimum temperature averaged on the day of death and the 6 preceding days (for a 0.51°C increase: OR = 2.24; 95% CI: 1.03, 4.87).Conclusions: Our results support the influence of night temperatures on the health impact of heat waves in urban areas. Urban heat exposure indicators based on satellite imagery have the potential to identify areas with higher risk of death, which could inform intervention decisions by key stakeholders.     

Climate Change and Levels of Violence in Socially Disadvantaged Neighborhood Groups

The current study examines the link between climate change and neighborhood levels of violence using 20 years of monthly climatic and crime data from St. Louis, MO, USA. St. Louis census tracts are aggregated in neighborhood groups of similar levels of social disadvantage, after which each group is subjected to time series analysis. Findings suggest that neighborhoods with higher levels of social disadvantage are very likely to experience higher levels of violence as a result of anomalously warm temperatures. The 20 % of most disadvantaged neighborhoods in St. Louis, MO, USA are predicted to experience over half of the climate change-related increase in cases of violence. These results provide further evidence that the health impacts of climate change are proportionally higher among populations that are already at high risk and underscore the need to comprehensively address climate change.     

Six climate change-related events in the United States accounted for about $14 billion in lost lives and health costs

The future health costs associated with predicted climate change-related events such as hurricanes, heat waves, and floods are projected to be enormous. This article estimates the health costs associated with six climate change-related events that struck the United States between 2000 and 2009. The six case studies came from categories of climate change-related events projected to worsen with continued global warming-ozone pollution, heat waves, hurricanes, infectious disease outbreaks, river flooding, and wildfires. We estimate that the health costs exceeded $14?billion, with 95?percent due to the value of lives lost prematurely. Actual health care costs were an estimated $740?million. This reflects more than 760,000 encounters with the health care system. Our analysis provides scientists and policy makers with a methodology to use in estimating future health costs related to climate change and highlights the growing need for public health preparedness.     

Spatial and temporal variation in the association between temperature and salmonellosis in NZ

Objective: Modelling the relationship between weather, climate and infectious diseases can help identify high‐risk periods and provide understanding of the determinants of longer‐term trends. We provide a detailed examination of the non‐linear and delayed association between temperature and salmonellosis in three New Zealand cities (Auckland, Wellington and Christchurch). Methods: Salmonella notifications were geocoded to the city of residence for the reported case. City‐specific associations between weekly maximum temperature and the onset date for reported salmonella infections (1997–2007) were modelled using non‐linear distributed lag models, while controlling for season and long‐term trends. Results: Relatively high temperatures were positively associated with infection risk in Auckland (n=3,073) and Christchurch (n=880), although the former showed evidence of a more immediate relationship with exposure to high temperatures. There was no significant association between temperature and salmonellosis risk in Wellington. Conclusions: Projected increases in temperature with climate change may have localised health impacts, suggesting that preventative measures will need to be region‐specific. This evidence contributes to the increasing concern over the public health impacts of climate change.     

Excessive Heat and Respiratory Hospitalizations in New York State: Estimating Current and Future Public Health Burden Related to Climate Change

Background: Although many climate-sensitive environmental exposures are related to mortality and morbidity, there is a paucity of estimates of the public health burden attributable to climate change.Objective: We estimated the excess current and future public health impacts related to respiratory hospitalizations attributable to extreme heat in summer in New York State (NYS) overall, its geographic regions, and across different demographic strata.Methods: On the basis of threshold temperature and percent risk changes identified from our study in NYS, we estimated recent and future attributable risks related to extreme heat due to climate change using the global climate model with various climate scenarios. We estimated effects of extreme high apparent temperature in summer on respiratory admissions, days hospitalized, direct hospitalization costs, and lost productivity from days hospitalized after adjusting for inflation.Results: The estimated respiratory disease burden attributable to extreme heat at baseline (1991–2004) in NYS was 100 hospital admissions, US$644,069 in direct hospitalization costs, and 616 days of hospitalization per year. Projections for 2080–2099 based on three different climate scenarios ranged from 206–607 excess hospital admissions, US$26–$76 million in hospitalization costs, and 1,299–3,744 days of hospitalization per year. Estimated impacts varied by geographic region and population demographics.Conclusions: We estimated that excess respiratory admissions in NYS due to excessive heat would be 2 to 6 times higher in 2080–2099 than in 1991–2004. When combined with other heat-associated diseases and mortality, the potential public health burden associated with global warming could be substantial.     

Risk adjustment for high utilizers of public mental health care

BACKGROUND: Publicly funded mental health systems are increasingly implementing managed care systems, such as capitation, to control costs. Capitated contracts may increase the risk for disenrollment or adverse outcomes among high cost clients with severe mental illness. Risk-adjusted payments to providers are likely to reduce providers' incentives to avoid or under-treat these people. However, most research has focused on Medicare and private populations, and risk adjustment for individuals who are publicly funded and severely mentally ill has received far less attention. AIMS OF THE STUDY: Risk adjustment models for this population can be used to improve contracting for mental health care. Our objective is to develop risk adjustment models for individuals with severe mental illness and assess their performance in predicting future costs. We apply the risk adjustment model to predict costs for the first year of a pilot capitation program for the severely mentally ill that was not risk adjusted. We assess whether risk adjustment could have reduced disenrollment from this program. METHODS: This analysis uses longitudinal administrative data from the County of Los Angeles Department of Mental Health for the fiscal years 1991 to 1994. The sample consists of 1956 clients who have high costs and are severely mentally ill. We estimate several modified two part models of 1993 cost that use 1992 client-based variables such as demographics, living conditions, diagnoses and mental health costs (for 1992 and 1991) to explain the variation in mental health and substance abuse costs. RESULTS: We find that the model that incorporates demographic characteristics, diagnostic information and cost data from two previous years explains about 16 percent of the in-sample variation and 10 percent of the out-of-sample variation in costs. A model that excludes prior cost covariates explains only 5 percent of the variation in costs. Despite the relatively low predictive power, we find some evidence that the disenrollment from the pilot capitation initiative input have been reduced if risk adjustment had been used to set capitation rates. DISCUSSION: The evidence suggests that even though risk adjustment techniques have room to improve, they are still likely to be useful for reducing risk selection in capitation programs. Blended payment schemes that combine risk adjustment with risk corridors or partial fee-for-service payments should be explored. IMPLICATIONS FOR HEALTH CARE PROVISION, USE, AND POLICY: Our results suggest that risk adjustment methods, as developed to data, do not have the requisite predictive power to be used as the sole approach to adjusting capitation rates. Risk adjustment is informative and useful; however, payments to providers should not be fully capitated, and may need to involve some degree of risk sharing between providers and public mental health agencies. A blended contract design may further reduce incentives for risk selection by incorporating a partly risk-adjusted capitation payment, without relying completely on the accuracy of risk adjustment models. IMPLICATIONS FOR FURTHER RESEARCH: Risk adjustment models estimated using data sets containing better predictors of rehospitalization and more precise clinical information are likely to have higher predictive power. Further research should also focus on the effect of combination contract designs.     

Climate change and vector-borne diseases: an economic impact analysis of malaria in Africa

A semi-parametric econometric model is used to study the relationship between malaria cases and climatic factors in 25 African countries. Results show that a marginal change in temperature and precipitation levels would lead to a significant change in the number of malaria cases for most countries by the end of the century. Consistent with the existing biophysical malaria model results, the projected effects of climate change are mixed. Our model projects that some countries will see an increase in malaria cases but others will see a decrease. We estimate projected malaria inpatient and outpatient treatment costs as a proportion of annual 2000 health expenditures per 1,000 people. We found that even under minimal climate change scenario, some countries may see their inpatient treatment cost of malaria increase more than 20%.     

Exposure to Natural Cold and Heat: Hypothermia and Hyperthermia Medicare Claims,United States, 2004–2005

Objectives. We measured the burden of hypothermia- and hyperthermia-related health care visits, identified risk factors, and determined the health care costs associated with environmental heat or cold exposure among Medicare beneficiaries.Methods. We obtained Medicare fee-for-service claims data of inpatient and outpatient health care visits for hypothermia and hyperthermia from 2004 to 2005. We examined the distribution and differences of visits by age, sex, race, geographic regions, and direct costs. We estimated rate ratios to determine risk factors.Results. Hyperthermia-related visits (n = 10 007) were more frequent than hypothermia-related visits (n = 8761) for both years. However, hypothermia-related visits resulted in more deaths (359 vs 42), higher mortality rates (0.50 per 100 000 vs 0.06 per 100 000), higher inpatient rates (5.29 per 100 000 vs 1.76 per 100 000), longer hospital stays (median days = 4 vs 2), and higher total health care costs ($98 million vs $36 million).Conclusions. This study highlighted the magnitude of these preventable conditions among older adults and disabled persons and the burden on the Medicare system. These results can help target public education and preparedness activities for extreme weather events.Older adults (≥ 65 years) and persons with chronic diseases are at risk for heat- and cold-related mortality and morbidity during extreme ambient temperatures.1–3 Even slight changes in temperature can adversely affect these populations because of their weakened physiological adaptability and socioeconomic factors.1,4 As the growing evidence of global climate change supports anticipated increases in the intensity and frequency of heat waves and extreme cold events, older adults and those with chronic diseases will be at an increased risk for hyperthermia and hypothermia.1,5The US Census Bureau projects that the number of older adults will rapidly increase during the 2010 to 2030 period. Accordingly, it is projected that by 2030, the older population will be 2 times greater than in 2000, growing from 35 million to 72 million, or nearly 20% of the total US population.6Little has been published on national incidence rates, risk factors, and associated health care costs of heat- and cold-related mortality and morbidity among older adults. The purpose of this study was therefore to measure the burden of hypothermia- and hyperthermia-related health care visits, identify risk factors, and estimate the direct health care costs associated with environmental exposure to heat or cold among Medicare beneficiaries. Medicare is the nation’s largest health insurance program and covers nearly 40 million Americans aged 65 years and older and persons with eligible disabilities (e.g., persons with a debilitating physical or mental disease or end-stage renal disease).7Findings from this study can guide preparedness planning to better target public health messaging to older Americans, disabled persons, their caregivers, and health care professionals to reduce the health effects of hot and cold weather events. In addition, we anticipate that organizations such as the Healthy Aging Research Network and the Healthy Aging Program at the Centers for Disease Control and Prevention (CDC) can use these findings to support their efforts to promote and protect the health of older adults during extreme weather events.     

Current and future costs of obesity in Sweden

BackgroundObesity is a growing health issue. This study estimated the costs of obesity among people aged 25–84 years in Sweden using disease and non-disease specific attributable fractions from published data. A prognosis of costs of obesity in 2030 is presented.Methods and materialsDiseases related to obesity and their respective risks and population attributable fraction were retrieved by literature review. Longitudinal data on age and sex related prevalence of obesity was used to construct three scenarios for costs of obesity in 2030.ResultsNearly 4% of all deaths among people 25–84 years in 2016 (n = 3,400) were attributed to obesity. Obesity cost EUR 2.7 billion in 2016, or EUR 377 per inhabitant aged ≥25 years. Non-health care costs were dominant and represented 80% of total societal costs. Main drivers were premature mortality (28%) and permanent sick leave (37%). If the proportion of obese remain at 2016 level, costs will increase 9% by 2030, but with continued linear growth, costs will increase by 66%.ConclusionsThe responsibility, costs and treatment fall on several actors with a considerable burden falling on the individual and the society at large. New health promoting interventions and policy programs are needed and must be evaluated in terms of resource use and expected return.