High-resolution mapping of reproductive tract infections among women of childbearing age in Bangladesh: a spatial-temporal analysis of the demographic and health survey

Abstract

Background: Reproductive tract infections (RTIs) have become major but silent public health problems devastating women's lives in Bangladesh. Accurately and precisely identifying high-risk areas of RTIs through high-resolution risk maps is meaningful for resource-limited settings.

Methods: We obtained data reported with RTI symptoms by women of childbearing age in the years 2007, 2011 and 2014 from Bangladesh Demographic and Health Survey. High-spatial Environmental, socio-economic and demographic layers were downloaded from different open-access data sources. We applied Bayesian spatial-temporal models to identify important influencing factors and to estimate the infection risk at 5 km spatial resolution across survey years in Bangladesh.

Results: We estimated that in Bangladesh, there were approximate 11.1% (95% Bayesian credible interval, BCI: 10.5-11.7%), 13.9% (95% BCI: 13.3-14.5%) and 13.4% (95% BCI: 12.8-14.0%) of women of childbearing age reported with RTI symptoms in 2007, 2011 and 2014, respectively. The risk of most areas shows an obvious increase from 2007 to 2011, then became stable between 2011 and 2014. High risk areas were identified in the southern coastal areas, the western Rajshahi Division, the middle of Khulna Division, and the southwestern Chittagong Division in 2014. The prevalence of Rajshahi and Nawabganj District were increasing during all the survey years.

Conclusion: The high-resolution risk maps of RTIs we produced can guide the control strategies targeted to priority areas cost-effectively. More than one eighth of women of childbearing age reported symptoms suggesting RTIs and the risk of RTIs varies in different geographical area, urging the government to pay more attention to the worrying situation of female RTIs in the country.

Keywords: Bangladesh; High-resolution map; Reproductive tract infections; Spatial-temporal analysis.

Fig. 1

Observed prevalence and estimated risk maps of RTIs in Bangladesh in the three survey years. a, b and c present the survey locations and observed prevalence in the year 2007, 2011 and 2014, respectively; d, e and f present the model-based estimated risk maps based on the median of the posterior estimated distribution of infection risk for the year 2007, 2011, 2014, respectively

Fig. 2

Estimated uncertainty and the number of women reported with RTI symptoms in Bangladesh. a, b and c present estimated uncertainty based on the standard deviation of the posterior estimated distribution of prevalence, for the year 2007, 2011 and 2014, respectively; (d), (e), (f) present the number of infected women of childbearing age based on the estimated prevalence and gridded population of the year 2007, 2011 and 2014, respectively

Fig. 3

Estimated prevalence and number of women reported with RTI symptoms across Bangladesh at division level. a, b and c show the estimated prevalence in 2007, 2011, 2014, respectively, based on the median of the posterior estimated prevalence; d, e and f show the estimated number of infected women of childbearing age in 2007, 2011, 2014, respectively

Fig. 4

Estimated prevalence and number of women reported with RTI symptoms across Bangladesh at district level. a, b and c show the estimated prevalence in 2007, 2011, 2014, respectively, based on the median of the posterior estimated prevalence; d, e and f show the estimated number of infected women of childbearing age reported with RTI symptoms in 2007, 2011, 2014, respectively

Fig. 5

Temporal changes of RTI risk of women of childbearing age in Bangladesh. a depicts the pixel-level percentage change of RTI risk of women of childbearing age in Bangladesh from 2007 to 2014, calculated as the median estimated prevalence for 2007 minus that for 2014 and divided by that for 2007 in each grid; b presents the district-level temporal trends in the three survey years. The decreasing areas were defined as the districts with prevalence significantly decreased compared with the previous survey year (i.e., median of posterior estimated prevalence was less than the lower bound of 95% BCI of posterior estimated prevalence in the previous survey year). Similar definition was defined for the increasing areas. And the areas with predicted prevalence that did not significantly increase or decrease were considered as the stable areas