Constructing and validating a transferable epidemic risk index in data scarce environments using open data: A case study for dengue in the Philippines

Consequences of geographical accessibility to post-exposure treatment for rabies and snakebite in Africa: a mini review

Introduction

Rabies and snakebite envenoming are two zoonotic neglected tropical diseases (NTDs) transmitted to humans by animal bites, causing each year around 179,000 deaths and are most prevalent in Asia and Africa. Improving geographical accessibility to treatment is crucial in reducing the time from bite to treatment. This mini review aims to identify and synthesize recent studies on the consequences of distance and travel time on the victims of these diseases in African countries, in order to discuss potential joint approaches for health system strengthening targeting both diseases.

Methods

A literature review was conducted separately for each disease using Pubmed, Google Scholar, and snowball searching. Eligible studies, published between 2017 and 2022, had to discuss any aspect linked to geographical accessibility to treatments for either disease in Africa.

Results

Twenty-two articles (8 on snakebite and 14 on rabies) were eligible for data extraction. No study targeted both diseases. Identified consequences of low accessibility to treatment were classified into 6 categories: (1) Delay to treatment; (2) Outcome; (3) Financial impacts; (4) Under-reporting; (5) Compliance to treatment, and (6) Visits to traditional healers.

Discussion and conclusion

Geographical access to treatment significantly influences the burden of rabies and snakebite in Africa. In line with WHO's call for integrating approaches among NTDs, there are opportunities to model disease hotspots, assess population coverage, and optimize geographic access to care for both diseases, possibly jointly. This could enhance the management of these NTDs and contribute to achieving the global snakebite and rabies roadmaps by 2030.

Analysing the outbreaks of leptospirosis after floods in Kerala, India

A growing number of studies have linked the incidence of leptospirosis with the occurrence of flood events. Nevertheless, the interaction between flood and leptospirosis has not been extensively studied to understand the influence of flood attributes in inducing new cases. This study reviews leptospirosis cases in relation to multiple flood occurrences in Kerala, India. Leptospirosis data were obtained for three years: 2017 (non-flood year) and two years with flooding-2018 (heavy flooding) and 2019 (moderate flooding). We considered the severity of flood events using the discharge, duration and extent of each flooding event and compared them with the leptospirosis cases. The distribution of cases regarding flood discharge and duration was assessed through descriptive and spatiotemporal analyses, respectively. Furthermore, cluster analyses and spatial regression were completed to ascertain the relationship between flood extent and the postflood cases. This study found that postflood cases of leptospirosis can be associated with flood events in space and time. The total cases in both 2018 and 2019 increased in the post-flood phase, with the increase in 2018 being more evident. Unlike the 2019 flood, the flood of 2018 is a significant spatial indicator for postflood cases. Our study shows that flooding leads to an increase in leptospirosis cases, and there is stronger evidence for increased leptospirosis cases after a heavy flood event than after a moderate flooding event. Flood duration may be the most important factor in determining the increase in leptospirosis infections.

Differences between gridded population data impact measures of geographic access to healthcare in sub-Saharan Africa

Background

Access to healthcare is imperative to health equity and well-being. Geographic access to healthcare can be modeled using spatial datasets on local context, together with the distribution of existing health facilities and populations. Several population datasets are currently available, but their impact on accessibility analyses is unknown. In this study, we model the geographic accessibility of public health facilities at 100-meter resolution in sub-Saharan Africa and evaluate six of the most popular gridded population datasets for their impact on coverage statistics at different administrative levels.

Methods

Travel time to nearest health facilities was calculated by overlaying health facility coordinates on top of a friction raster accounting for roads, landcover, and physical barriers. We then intersected six different gridded population datasets with our travel time estimates to determine accessibility coverages within various travel time thresholds (i.e., 30, 60, 90, 120, 150, and 180-min).

Results

Here we show that differences in accessibility coverage can exceed 70% at the sub-national level, based on a one-hour travel time threshold. The differences are most notable in large and sparsely populated administrative units and dramatically shape patterns of healthcare accessibility at national and sub-national levels.

Conclusions

The results of this study show how valuable and critical a comparative analysis between population datasets is for the derivation of coverage statistics that inform local policies and monitor global targets. Large differences exist between the datasets and the results underscore an essential source of uncertainty in accessibility analyses that should be systematically assessed.