Electronic waste management is a global rising concern that is primarily being handled by informal recycling practices. These release a mix of potentially hazardous chemicals, which is an important public health concern. These chemicals include polybrominated diphenyl ethers (PBDEs), used as flame retardants in electronic parts, which are persistent in nature and show bioaccumulative characteristics. Although PBDEs are suspected endocrine disruptors, particularly targeting thyroid and reproductive hormone functions, the relationship of PBDEs with these health effects are not well established. We used the Navigation Guide methodology to conduct a systematic review of studies in populations exposed to e-waste to better understand the relationships of these persistent flame retardants with hormonal and reproductive health. We assessed nineteen studies that fit our pre-determined inclusion criteria for risk of bias, indirectness, inconsistency, imprecision, and other criteria that helped rate the overall evidence for its quality and strength of evidence. The studies suggest PBDEs may have an adverse effect on thyroid hormones, reproductive hormones, semen quality, and neonatal health. However, more research is required to establish a relationship of these effects in the e-waste-exposed population. We identified the limitations of the data available and made recommendations for future scientific work.

Morbidity statistics can be reported as grouped data for health services rather than for individual residence area, especially in low-middle income countries. Although such reports can support some evidence-based decisions, these are of limited use if the geographical distribution of morbidity cannot be identified. This study estimates the spatial rate of Acute respiratory infections (ARI) in census districts in Cúcuta -Colombia, using an analysis of the spatial distribution of health services providers. The spatial scope (geographical area of influence) of each health service was established from their spatial distribution and the population covered. Three levels of spatial aggregation were established considering the spatial scope of primary, intermediate and tertiary health services providers. The ARI cases per census district were then calculated and mapped using the distribution of cases per health services provider and the proportion of population per district in each level respectively. Hotspots of risk were identified using the Local Moran’s I statistic. There were 98 health services providers that attended 8994, 18,450 and 91,025 ARI cases in spatial levels 1, 2 and 3, respectively. Higher spatial rates of ARI were found in districts in central south; northwest and northeast; and southwest Cúcuta with hotspots of risk found in central and central south and west and northwest Cucuta. The method used allowed overcoming the limitations of health data lacking area of residence information to implementing epidemiological analyses to identify at risk communities. This methodology can be used in socioeconomic contexts where geographic identifiers are not attached to health statistics.

Effective analysis to support decision-making in public health requires adequate data that can be linked to the sociodemographic characteristics and distribution of communities that would be recipients of health programs. Although official statistics are an important source of data to support decisions on public health strategies, health statistics are of limited use if they do not include categorisations related to the spatial distribution of the population. This study introduces a method to use the frequency of disease reported by health care providers (HCP) to calculate disease rates in geographical areas in urban settings. Specifically, this study uses statistics on acute respiratory infections (ARI) to calculate the rate of these diseases at the census district level in Cucuta, a major city in Colombia. A Geographic Information System was used to establish the geographical area of influence (spatial scope) of each HCP, according to the distribution of the basic census geographical areas; the Urban Sections (USEC), in Cucuta. Three levels of increasing spatial aggregation were established considering the characteristics of the population receiving health care in primary, intermediate and tertiary public health services to establish the spatial scope of each HCP. The cases of ARI per USEC were calculated according to the proportion of the population of each USEC in each of the three levels. The spatial rate of ARI per USEC and the hotspots of higher risk of ARI were calculated using an Empirical Bayes method using a geostatistical software. There were 97 HCP, of which, 31 provided health services in USEC in level 1; 20 provided health services in USEC in level 2; and 47 provided health services in USEC in level 3. A higher spatial rate of ARI was found in USEC in central south; central west; north and northwest; northeast; central east; and central regions, compared to the whole of Cucuta. Hotspots of higher risk were identified in two clusters of USEC in central south and west Cucuta and three isolated USEC in central and northwest Cucuta. Health indicators at the census district geographical level could be calculated using basic statistics that do not include geographic identifiers by considering the characteristics of the HCP and the population receiving their services. The methodology of this study can be applied to other socioeconomic contexts where geographic identifiers are not attached to public health statistics to create better public health indicators and support potential health promotion and disease prevention strategies.