How Does Ambient Air Temperature Affect Diabetes Mortality in Tropical Cities?

Abstract

Diabetes is well-known as one of the many chronic diseases that affect different age groups. Currently, most studies that evaluated the effects of temperature on diabetes mortality focused on temperate and subtropical settings, but no study has been conducted to assess the relationship in a tropical setting. We conducted the first multi-city study carried out in tropical cities, which evaluated the temperature–diabetes relationship. We collected daily diabetes mortality (ICD E10–E14) of four Philippine cities from 2006 to 2011. Same period meteorological data were obtained from the National Oceanic and Atmospheric Administration. We used a generalized additive model coupled with a distributed lag non-linear model (DLNM) in determining the relative risks. Results showed that both low and high temperatures pose greater risks among diabetics. Likewise, the study was able to observe the: (1) high risk brought about by low temperature, aside from the largely observed high risks by high temperature; and (2) protective effects in low temperature percentile. These results provide significant policy implications with strategies related to diabetes risk groups in relation to health service and care strategies.

Scoping Review of Climate Change and Health Research in the Philippines: A Complementary Tool in Research Agenda-Setting

“…A total of 40 articles and grey literature were found eligible for this review. Only 34 quantitative studies were presented here [14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47].…”

“…Among the time-series studies, six (22%) simply visualized patterns from time-series plots [28,31,39,40,43,47], while the rest used a variety of statistical models. Eight (30%) used general linear models (i.e., simple and multiple linear regressions) [27,32,34,35,38,41,44,45], eight (30%) used generalized linear models (e.g., quasi-poisson/poisson models and distributed lag nonlinear models) [15,19,22,25,33,36,42], two (7%) used autoregressive models (i.e., autoregressive integrated moving average models and seasonal autoregressive integrated moving average models) [37,44], two (7%) used wavelet analysis [16,20], and four (15%) used other kinds of models (i.e., general additive model [14,15], spectral analysis [18], dynamic linear model [16], and transfer entropy [46]). The temporal resolutions used in the time-series studies were daily (19%, 5/27), weekly (15%, 4/27), monthly (63%, 17/27), and annual (7%, 2/27).…”

“…The temporal resolutions used in the time-series studies were daily (19%, 5/27), weekly (15%, 4/27), monthly (63%, 17/27), and annual (7%, 2/27). Only 33% of these studies (9/27) considered temporal lags of exposure to health outcomes [14,15,18,22,25,36,37,42,44]. All analyzed short-term associations of disease patterns were on preceding and concurrent exposure, but only seven studies controlled temporal trends and seasonal patterns [14,15,22,25,36,37,42].…”

Meteorological factors and the incidence of mumps in Fujian Province, China, 2005–2013: Non-linear effects

“…Non-linear models have been widely used in assessing the relationships between meteorological factors and health outcomes (Peng et al, 2017;Wang et al, 2017). Meanwhile, most studies found the lagged effects of meteorological factors (Antunes et al, 2017;Seposo et al, 2017). Therefore, some researchers adopted distributed lag non-linear model (DLNM) to assess the non-linear effects of meteorological factors and the lagged effects simultaneously (Gasparrini, 2014).…”

Short-term association between ambient temperature and acute myocardial infarction hospitalizations for diabetes mellitus patients: A time series study

“…A worldwide meta-regression study found a positive association between glucose intolerance and outdoor temperature [ 9 ]. Studies from the United States [ 2 – 4 ] and Sydney, Australia [ 5 ] found positive associations between temperature and DM-related complications and mortality, while studies from the Philippines [ 6 ] and China [ 7 , 8 ] found increased DM mortality at both high and low temperatures. Climate change, which is leading to higher average global temperatures [ 10 ], and the rising prevalence of DM have been suggested to pose a “dual threat” in increasing the disease burden [ 11 ].…”