Projecting Temperature-Attributable Mortality and Hospital Admissions due to Enteric Infections in the Philippines

Abstract

Background: Enteric infections cause significant deaths, and global projection studies suggest that mortality from enteric infections will increase in the future with warmer climate. However, a major limitation of these projection studies is the use of risk estimates derived from nonmortality data to project excess enteric infection mortality associated with temperature because of the lack of studies that used actual deaths. Objective: We quantified the associations of daily temperature with both mortality and hospital admissions due to enteric infections in the Philippines. These associations were applied to projections under various climate and population change scenarios. Methods: We modeled nonlinear temperature associations of mortality and hospital admissions due to enteric infections in 17 administrative regions of the Philippines using a two-stage time-series approach. First, we quantified nonlinear temperature associations of enteric infections by fitting generalized linear models with distributed lag nonlinear models. Second, we combined regional estimates using a meta-regression model. We projected the excess future enteric infections due to nonoptimal temperatures using regional temperature–enteric infection associations under various combinations of climate change scenarios according to representative concentration pathways (RCPs) and population change scenarios according to shared socioeconomic pathways (SSPs) for 2010–2099. Results: Regional estimates for mortality and hospital admissions were significantly heterogeneous and had varying shapes in association with temperature. Generally, mortality risks were greater in high temperatures, whereas hospital admission risks were greater in low temperatures. Temperature-attributable excess deaths in 2090–2099 were projected to increase over 2010–2019 by as little as 1.3% [95% empirical confidence intervals (eCI): , 6.5%] under a low greenhouse gas emission scenario (RCP 2.6) or as much as 25.5% (95% eCI: , 48.2%) under a high greenhouse gas emission scenario (RCP 8.5). A moderate increase was projected for temperature-attributable excess hospital admissions, from 0.02% (95% eCI: , 1.9%) under RCP 2.6 to 5.2% (95% eCI: , 21.8%) under RCP 8.5 in the same period. High temperature-attributable deaths and hospital admissions due to enteric infections may occur under scenarios with high population growth in 2090–2099. Discussion: In the Philippines, futures with hotter temperatures and high population growth may lead to a greater increase in temperature-related excess deaths than hospital admissions due to enteric infections. Our results highlight the need to strengthen existing primary health care interventions for diarrhea and support health adaptation policies to help reduce future enteric infections. https://doi.org/10.1289/EHP9324

Tropical cyclone-specific mortality risks and the periods of concern: A multicountry time-series study

“…For each TC in each location, we defined the TC hit day, t 0 , as the first day with a sustained wind speed ≥34 knots for that TC in that location. We obtained the cumulative rainfall (mm) at t 0 for each location from the ERA-5 reanalysis data, which is created by assimilating historical weather data from numerous platforms (e.g., satellite, ground-based stations, radar, boats, airplanes, buoys) using sophisticated data assimilation models [ 40 ] and has been widely used in previous studies [ 41 – 45 ], as an additional metric of TC exposure besides the maximum sustained windspeed.…”

Heat Exposure among Adult Women in Rural Tamil Nadu, India

“…The data set provides hourly estimates at 2 m above the land surface. ERA-5 data have been increasingly used in health effect studies. ,, The second product is NASA’s GLDAS-2 product, which provides temperature estimates every 3 h at a spatial resolution of 0.25 × 0.25°, a scale coarser than ERA-5 (Figure ). GLDAS generates its estimates by fusing satellite- and ground-based observational data products, using advanced land surface modeling and data assimilation techniques…”

“…Ambient monitoring stations are sparse and even where present may not accurately represent individual exposures, as people frequently move between indoor and outdoor environments, both in the sun and in the shade. Improving exposure assessment for temperature can enhance our understanding of the health effects of heat and cold by reducing potential biases and measurement errors associated with ambient monitors and modeled products, which are commonly used in epidemiological and burden of disease studies. ,, Personal measurements may also highlight opportunities for intervention by identifying high-exposure activities.…”

Heat Exposure Among Adult Women in Rural Tamil Nadu, India

“…ERA-5 data has been increasingly used in health effects studies. 1,13,19,20 The second product is NASA’s GLDAS-2 product 21 , which provides temperature estimates every three hours at a spatial resolution of 0.25×0.25 degree, a scale more coarse than ERA-5 (Figure 1). GLDAS generates its estimates by fusing satellite- and ground-based observational data products, using advanced land surface modeling and data assimilation techniques.…”

“…Improving exposure assessment for temperature can enhance our understanding of the health effects of heat and cold by reducing potential biases and measurement error associated with ambient monitors and modeled products, which are commonly used in epidemiological and burden of disease studies. 1,[11][12][13] Personal measurements may also highlight opportunities for intervention by identifying high-exposure activities.…”