Diarrhea Patterns and Climate: A Spatiotemporal Bayesian Hierarchical Analysis of Diarrheal Disease in Afghanistan

Abstract

Subject to a high burden of diarrheal diseases, Afghanistan is also susceptible to climate change. This study investigated the spatiotemporal distribution of diarrheal disease in the country and how associated it is with climate variables. Using monthly aggregated new cases of acute diarrhea reported between 2010 and 2016 and monthly averaged climate data at the district level, we fitted a hierarchical Bayesian spatiotemporal statistical model. We found aridity and mean daily temperature were positively associated with diarrhea incidence; every 1°C increase in mean daily temperature and 0.01-unit change in the aridity index were associated with a 0.70% (CI: 0.67%, 0.73%) increase and a 4.79% (CI: 4.30%, 5.26%) increase in the risk of diarrhea, respectively. Average annual temperature, on the other hand, was negatively associated, with a 3.7% (CI: 3.74%, 3.68) decrease in risk for every degree Celsius increase in annual average temperature. Temporally, most districts exhibited similar seasonal trends, with incidence peaking in summer, except for the eastern region where differences in climate patterns and population density may be associated with high rates of diarrhea throughout the year. The results from this study highlight the significant role of climate in shaping diarrheal patterns in Afghanistan, allowing policymakers to account for potential impacts of climate change in their public health assessments.

Diarrhea in Under Five Year-old Children in Nepal: A Spatiotemporal Analysis Based on Demographic and Health Survey Data

“…Such a modeling approach is one of the most rigorous methods to analyse the space and temporal patterns of disease risk and the corresponding influencing factors [26]. A series of models have been applied to understand the spatial and temporal variations of diarrhea in many regions using diarrhea survey data [27][28][29]. However, to our knowledge, there hasn't been any study exploring the spatiotemporal patterns of diarrheal risk in Nepal.…”

Estimating The Seasonally Varying Effect of Climatic Factors On The District-Level Incidence of Acute Watery Diarrhea Among Under-Five Children of Iran, 2014-2018: A Bayesian Hierarchical Spatiotemporal Model

“…Assessing the risk of U5AWD, a great number of determinants have been looked over in literature consisting: cumulative rainfall [6][7][8][9][10][11][12][13][14][15][16][17][18], temperature [6,8,10,[12][13][14][15][16][17][18][19][20][21][22][23][24], wind speed [12,13,17], elevation [25], traveling history [26], drinking water source [9][10][11][27][28][29][30][31][32][33][34][35], disposal system [11,29,31,[36][37]…”

Impact of Varying Community Networks on Disease Invasion

“…As disease vectors (e.g., mosquitoes and ticks) can also be carried via human/goods transport, the outbreak and spread of vector-borne diseases such as dengue, Lyme disease, malaria, West Nile virus, yellow fever, and Zika virus have exhibited strong spatio-temporal patterns [15,22,26,37,40,41,42,47] (also see the recent special issues [31,39]); this is partly due to the interplay between disease epidemiology and vector ecology. Spatio-temporal patterns have also been observed for many waterborne diseases caused by pathogenic micro-organisms such as bacteria and protozoa that are transmitted in water/river networks [3,20,33,38,45,46]. One of the main scientific challenges is to determine the connection between disease risk and the change of network structures (as a consequence of human behavior and/or environmental uncertainty).…”