Incidence of Dengue in Ozamiz City, Philippines

Abstract

The incidence of dengue has become a serious health concern in many areas around the world. The increasing number of dengue cases are said to be the result of rapid population growth and climate change. This study assessed the incidence of dengue in Ozamiz City, Philippines. It also determined the relationship of climatic factors on dengue incidence reported in the area. Documentary analysis was done in seven hospitals to identify the incidence of dengue in the city. Pearson's correlation was used to determine the association between climatic factors and dengue incidence. Multiple regression analysis was also used as a statistical model. A total of 1,908 dengue cases were reported from January 2008 to September 2013. Majority of the patients belonged to the age group of 1-10 years. Maximum number of cases was seen in 2010 (398 cases) which was highest in the month of January (215 cases). Out of the total 1,908 cases, 821 (43.03%) were dengue hemorrhagic fever, 781 (40.93%) were dengue fever and 306 (16.04%) were dengue shock syndrome. Among the 51 Barangays in the City, Barangay Aguada has the highest number of 186 cases (9.75%). Of all climatic factors, only relative humidity has a positive correlation with dengue incidence. Vector control and education campaign needs should be assessed as these factors may significantly influence the occurrence of dengue in the city. Strategic plans and measures for dengue prevention need to be implemented targeting young population.

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).…”

“…Eighty-two percent (28/34) of the studies reported associations between exposures and health outcomes, with 27 studies (79%) reporting positive associations [14,15,16,18,20,21,22,24,25,26,27,29,30,31,32,33,34,36,37,38,39,41,42,44,45,47], and six studies (18%) reporting negative associations [16,19,34,36,46,47]. Fifteen studies (44%) reported no or insignificant associations [19,24,25,27,28,33,34,35,37,38,40,41,44,46,47].…”

“…For a study on dengue virus samples, its minimum infection rate was found to be positively associated with relative humidity [19,37,38,44,46]. On the other hand, studies reported insignificant associations ( p > 0.05) between dengue incidence and relative humidity (40%, 6/15) [19,33,35,37,38,41], rainfall (27%, 4/15) [34,35,38,41], air temperature (20%, 3/15) [19,27,35], and minimum sea-level pressure (7%, 1/15) [33].…”

“…Majority of the studies (62%, 21/34) did not report limitations related to climate change and health [18,19,23,24,25,26,27,28,29,30,31,32,33,35,36,37,38,39,40,41,43,46]. With the ones reporting limitations, recurring statements of limitation were related to poor data quality (35%, 12/34) [14,15,16,17,20,21,22,34,42,44,45,47], as studies reported a lack of long daily time-series data [16,34], significant missing values [22,42,44,45], and unavailable geographical or spatial data [15,16].…”