Trends in water level and flooding in Dhaka, Bangladesh and their impact on mortality

Dhaka city water logging hazards: area identification and vulnerability assessment through GIS-remote sensing techniques

Water logging is one of the most detrimental phenomena continuing to burden Dhaka dwellers. This study aims to spatio-temporarily identify the water logging hazard zones within Dhaka Metropolitan area and assess the extent of their water logging susceptibility based on informal settlements, built-up areas, and demographical characteristics. The study utilizes integrated geographic information system (GIS)-remote sensing (RS) methods, using the Normalized Difference Vegetation Water and Moisture Index, distance buffer zone from drainage streams, and built-up distributions to identify waterlogged zones with a temporal extent, incorporating social and infrastructural attributes to evaluate water logging effects. These indicators were integrated into an overlay GIS method to measure the vulnerability level across Dhaka city areas. The findings reveal that south and south-western parts of Dhaka were more susceptible to water logging hazards. Almost 35% of Dhaka belongs to the high/very highly vulnerable zone. Greater number of slum households were found within high to very high water logging vulnerable zones and approximately 70% of them are poorly structured. The built-up areas were observed to be increased toward the northern part of Dhaka and were exposed to severe water logging issues. The overall findings reveal the spatio-temporal distribution of the water logging vulnerabilities across the city as well as its impact on the social indicators. An integrated approach is necessary for future development plans to mitigate the risk of water logging.

Considering spatial heterogeneity in the distributed lag non-linear model when analyzing spatiotemporal data

The distributed lag non-linear (DLNM) model has been frequently used in time series environmental health research. However, its functionality for assessing spatial heterogeneity is still restricted, especially in analyzing spatiotemporal data. This study proposed a solution to take a spatial function into account in the DLNM, and compared the influence with and without considering spatial heterogeneity in a case study. This research applied the DLNM to investigate non-linear lag effect up to 7 days in a case study about the spatiotemporal impact of fine particulate matter (PM_2.5) on preschool children's acute respiratory infection in 41 districts of northern Taiwan during 2005 to 2007. We applied two spatiotemporal methods to impute missing air pollutant data, and included the Markov random fields to analyze district boundary data in the DLNM. When analyzing the original data without a spatial function, the overall PM_2.5 effect accumulated from all lag-specific effects had a slight variation at smaller PM_2.5 measurements, but eventually decreased to relative risk significantly <1 when PM_2.5 increased. While analyzing spatiotemporal imputed data without a spatial function, the overall PM_2.5 effect did not decrease but increased in monotone as PM_2.5 increased over 20 μg/m³. After adding a spatial function in the DLNM, spatiotemporal imputed data conducted similar results compared with the overall effect from the original data. Moreover, the spatial function showed a clear and uneven pattern in Taipei, revealing that preschool children living in 31 districts of Taipei were vulnerable to acute respiratory infection. Our findings suggest the necessity of including a spatial function in the DLNM to make a spatiotemporal analysis available and to conduct more reliable and explainable research. This study also revealed the analytical impact if spatial heterogeneity is ignored.

Is precipitation a predictor of mortality in Bangladesh? A multi-stratified analysis in a South Asian monsoon climate

While numerous studies have assessed the association between temperature and mortality in various locations, few have addressed the relationship between precipitation and mortality. Given the high amounts of rainfall in many tropical monsoon areas and the often seasonally pronounced differences, there might be a potentially strong impact on health outcomes and death. In this study, we investigated the association between precipitation and daily death counts in Bangladesh from 2003 to 2007 using regression models with a quasipoisson distribution adjusting for long-term time and seasonal trends, day of the month, age and perceived temperature. Effects were assessed for all ages, the elderly and by gender. During the dry season a sharp increase in death risk was found at very high precipitation amounts which are most likely to be cyclone-related. This cyclone effect was most pronounced for females at the immediate day with an increase of 18.7% (3.8-35.6%) in non-external cause mortality per mm precipitation above 5mm. At longer lags we found a negative association between precipitation and mortality indicating some kind of dry effect which was more pronounced for the elderly with a mortality increase of 4.4% (2.6-6.2%) per mm decrease in precipitation. During the rainy season, we observed a protective effect of rainfall which was strongest during periods of seasonally high equivalent temperatures with a decrease in mortality of 4.0% (2.3-5.6%) per mm increase in precipitation on the immediate day. The observed associations between precipitation and mortality differed by season, age and gender. Generally, a strong short-term increase in mortality was associated with cyclonic activity during the dry season, while ongoing low rainfall seemed to have an adverse impact at higher lags. During the rainy season, precipitation seemed to mitigate heat effects.