Dengue fever-pdf file

A recent study demonstrates lowland area of north-western Java island. It covers an area of Based on the census report, the population amounts to Lestari et al. To date, a tetravalent approximately 9. Jakarta has a tropical climate Biswal et al. However, so far, DF control is primarily with a mean temperature of Despite its importance, information on the variation in the distribution of Data acquisition risk of DF and socio-ecological characteristics affecting its trans- Confirmed DF cases for the period of January to mission spatially and temporally at the village level within the area December were obtained from the disease information sys- covered by Jakarta are lacking.

The would provide evidence for allowing better intervention strategies data contain information on age, gender, date of hospital admission in supporting existing DF control efforts. In Indonesia, DF is classified ly A large number of factors may affect the variation in DF inci- as a notifiable disease since All health facilities are required on dence at finer scales, e.

Population data and population density nomic status Schmidt et al. In addition, climatic factors have been known to have a strong effect on the distribution DF trans- The village-level socio-ecological data were extracted from us mission Astuti et al.

For each village, data for the num- have either a positive or negative effect by rainfall producing water ber of households in slum, drinking water source, main occupation, number of doctors and doctor per people were collected.

Yet, extreme rainfall and Additionally, road network density data per km2 for each county ci flooding could flush and diminish the immature mosquitoes and were calculated based on road length divided by areal space. Further, humidi- road network density was used as a proxy for connectivity which er ty together with temperature within suitable margins contribute to reflects the movement of people, which is known to be a factor associated with dengue distribution in urban areas Qi et al.

Monthly climate data, including the mean temper- important function on mosquito survival and their ability to host ature Tmean , the minimum temperature Tmin , the maximum tem- om and transmit the dengue virus Morin et al. Spatial analyses allow the identification of disease clusters, jamstec.

This can be useful in supporting epidemiolog- ical surveillance and in providing better visualization and generat- Data analysis ing hypotheses, which could facilitate more thorough control strategies Fletcher-Lartey and Caprarelli, Approach to seasonal variation The present study aimed to: i examine the relationship To explore seasonal patterns and trends with respect to dengue between DF incidence and climate variability during the period incidence, we expressed the weekly incidence of dengue Yt as ; ii identify spatial clusters with the highest risk of DF; trend Tt , cyclical component Ct , seasonal component St and and iii attempt to profile socio-ecological characteristics of high- error or residual component Et by employing a multiplicative, risk clusters of DF in Jakarta, since better information would help seasonal decomposition analysis using SPSS version 21 IBM local health authorities to better design and implement dengue con- Corp.

This analysis decomposes the time- trol strategies in the city of Jakarta. This analysis was done using the rpart packages of R Therneau et Spatial analysis of the incidence of dengue al. Nine socio-demographic factors at the village level, i. We did not include villages in Kepulauan Seribu tion; proportion of households in the slum area; population densi- Thousand Islands in the study as the number of DF cases there ty; road density; total number of doctors; and doctors per were low.

Village-level polygons shapefile from two classes HH and LL based on the clustering analysis. Results ly Incidence mapping on Crude incidence of dengue at village-level per 10, people Descriptive statistics was calculated and mapped. The village-level incidence maps of dengue were created by ArcGIS A total of , DF cases were reported in Jakarta in the period The proportion of DF cases among those aged e Clustering assessment 24 years or more at the time of infection was 94, Furthermore, local indicator spatial asso- ciation analysis was performed to locate high-high HH risk clusters, However, the annual incidence of DF fluctuated over the study er low-low LL risk cluster and outliers designated as high-low HL years with the highest incidence observed in A HH cluster con- people Table 1.

All of the above spatial clustering analyses The variability of monthly incidence was evident Figure 1. Further, the boxplot chart shows that the monthly mean incidence github. Profiling and comparisons on Seasonal decomposition confirmed the fluctuated trend and strong Demographical and socio-ecological characteristics of HH and seasonality in the DF incidence Figure 3.

N Table 1. Summary statistics of notified dengue cases in Jakarta, Indonesia Year Age year total group no. Incidence of dengue fever in Jakarta, Indonesia Figure 2. Monthly distribution of the incidence of dengue fever Decomposition of the dengue fever incidence in Jakarta, Indonesia. Based on CART analysis, age The analysis also identified 38 LL Table 2.

Annual spatial clustering estimated by Moran analysis. The map shown in Figure 6 illustrates the annual dynamic of the 0. Hotspots were iden- 0.

Meanwhile, it was evident that some on hotspots emerged in the northwest and south Jakarta in the period 0. The population at risk e 0. Compared to ci 0. Cumulative crude incidence per 10, population of incidence and climatic variables. On the other hand, the dence and climate variables based on a year dataset. We found strong DF seasonality was obvious and this can be explained by its significant changes in the trends of the spatial DF incidence pattern significant correlation with the seven climatic factors as evidenced and identified 22 hotspots across the city, some of which persisted in the present study.

Our analysis suggests that the temporal pattern over the whole study period. It was also noted that the DF high-risk of DF incidence is positively correlated with the monthly variabil- clusters primarily included individuals over 24 years of age living ity of rainfall and humidity but negatively influenced by the mini- in better, less populated surroundings. Our analysis showed that mum temperature and DMI. This finding is consistent with studies age and occupation were the main contributors to the clustering.

However, this association We also attempted to further investigate the influence of the ly on Table 3. Descriptive statistics of dengue fever clusters as identified by local indicator spatial association in Jakarta, Indonesia Sociodemographic and ecological characteristics of dengue fever clusters in Jakarta, Indonesia Although we found no significant correlation between DF inci- Table 5. The positive correlation m observed between DMI and the DF incidence might have triggered Variable Overall an increase in DF incidence in and after a months om Mean age in years A combina- Population density 9.

Sum of doctor 0. We did not include meteorological factors e. Central Jakarta is a highly fall, temperature and humidity in the comparative analysis since populated area and it is the center of economic and business activ- the meteorological variation at the village level was not evident. Moreover, population-based studies are required to and Susanna, The results in eastern Jakarta, on the other better understand the variation in risk factors at the various levels hand, could have primarily been caused by poor sanitation.

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