The Importance of Socio-Economic Versus Environmental Risk Factors for Reported Dengue Cases in Java, Indonesia

Background: Dengue is a major mosquito-borne viral disease and an important public health problem. Identifying which factors are important determinants in the risk of dengue infection is critical in supporting and guiding preventive measures. In South-East Asia, half of all reported fatal infections are recorded in Indonesia, yet little is known about the epidemiology of dengue in this country. Methodology/Principal findings: Hospital-reported dengue cases in Banyumas regency, Central Java were examined to build Bayesian spatial and spatio-temporal models assessing the influence of climatic, demographic and socio-economic factors on the risk of dengue infection. A socio-economic factor linking employment type and economic status was the most influential on the risk of dengue infection in the Regency. Other factors such as access to healthcare facilities and night-time temperature were also found to be associated with higher risk of reported dengue infection but had limited explanatory power. Conclusions/Significance: Our data suggest that dengue infections are triggered by indoor transmission events linked to socio-economic factors (employment type, economic status). Preventive measures in this area should therefore target also specific environments such as schools and work areas to attempt and reduce dengue burden in this community. Although our analysis did not account for factors such as variations in immunity which need further investigation, this study can advise preventive measures in areas with similar patterns of reported dengue cases and environmen

Dengue in Java, Indonesia: relevance of mosquito indices as risk predictors

Background: No vaccine is currently available for dengue virus (DENV), therefore control programmes usually focus on managing mosquito vector populations. Entomological surveys provide the most common means of characterising vector populations and predicting the risk of local dengue virus transmission. Despite Indonesia being a country strongly affected by DENV, only limited information is available on the local factors affecting DENV transmission and the suitability of available survey methods for assessing risk. Methodology/principal findings: We conducted entomological surveys in the Banyumas Regency (Central Java) where dengue cases occur on an annual basis. Four villages were sampled during the dry and rainy seasons: two villages where dengue was endemic, one where dengue cases occurred sporadically and one which was dengue-free. In addition to data for conventional larvae indices, we collected data on pupae indices, and collected adult mosquitoes for species identification in order to determine mosquito species composition and population density. Traditionally used larval indices (House indices, Container indices and Breteau indices) were found to be inadequate as indicators for DENV transmission risk. In contrast, species composition of adult mosquitoes revealed that competent vector species were dominant in dengue endemic and sporadic villages. Conclusions/significance: Our data suggested that the utility of traditional larvae indices, which continue to be used in many dengue endemic countries, should be re-evaluated locally. The results highlight the need for validation of risk indicators and control strategies across DENV affected areas here and perhaps elsewhere in SE Asia

Comparison between observed and predicted SMR for both models.

<p><b>(A)</b> Goodness-of-fit for the spatial-only model, <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0004964#pntd.0004964.e001" target="_blank">Model 1</a>. <b>(B)</b> Goodness-of-fit for the spatio-temporal model, <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0004964#pntd.0004964.e002" target="_blank">Model 2</a>. Goodness-of-fit was computed by comparing the observed standardised morbidity ratio (SMR, i.e. observed-to-expected cases) for each village of the study area with those computed from the spatial-only and spatio-temporal models. Solid diagonal line indicates where values should lay for a perfect correspondence between predictions and observations. Inferences from <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0004964#pntd.0004964.e002" target="_blank">Model 2</a> were averaged at village level to provide a proper comparison with <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0004964#pntd.0004964.e001" target="_blank">Model 1</a>.</p

Dengue risk factors.

<p><b>(A)</b> Total number of inhabitant in villages, as recorded in the Indonesian 2010 census. <b>(B)</b> Proportion of urban area in the village (%). <b>(C)</b> Changes in the proportion of urban area in the village (% per 10 years), as recorded by the difference in proportion of urban area between 2000 and 2010. <b>(D)</b> First socio-economic axis (PCA1), informing on the structure in employment type and education. <b>(E)</b> Second socio-economic axis (PCA2), informing on the age structure in each village. <b>(F)</b> Straight-line distance to the nearest hospital within the Regency (in km). <b>(G)</b> Mean average daytime temperature (°C), as recorded between March 2000 and December 2013. <b>(H)</b> Mean precipitation (mm per day), as recorded between January 1990 and December 2000. <b>(I)</b> Mean vegetation index (EVI), as recorded between February 2000 and December 2013.</p

Model 2, spatio-temporal model.

<p>Posterior estimates of the final spatio-temporal model (<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0004964#pntd.0004964.e002" target="_blank">Model 2</a>) for the risk of dengue infections in the Regency of Banyumas, Central Java, Indonesia.</p

Model 1, spatial-only model.

<p>Posterior estimates of the final spatial-only model (<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0004964#pntd.0004964.e001" target="_blank">Model 1</a>) for the risk of dengue infections in the Regency of Banyumas Regency, Central Java, Indonesia.</p

Adjusted village-level risk of dengue for the period 2000–2013.

<p><b>(A)</b> Map of the spatial pattern of the unexplained risk for dengue infection, as identified in <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0004964#pntd.0004964.e001" target="_blank">Model 1</a>. The risk ratio takes the value one if no deviation exists between the model’s inferences based on the included, known risk factors and observations. Values of less than 1 (white and lightest shade of green) indicate villages that have a lesser risk of infection than predicted, whereas the darker shade of green indicates villages for which the model did not account for all the risk of infection. <b>(B)</b> Distribution of the significant village-specific posterior probability of the spatial random effect for <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0004964#pntd.0004964.e001" target="_blank">Model 1</a>. <b>(C)</b> Distribution of the significant village-specific posterior probability of the spatial random effect for <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0004964#pntd.0004964.e002" target="_blank">Model 2</a>. Villages in dark red in <b>(B)</b> and <b>(C)</b> show villages where posterior probabilities p(exp(<i>σ</i>_<i>i</i>) > 1|y) > 0.8, indicating an excess risk of dengue with relatively small level of associated uncertainty.</p

Number of mosquito species within the larvae identified in each of the four villages in dry and rainy season.

<p>Each bar represents the mosquito species as indicated in the legend.</p