Spatial-temporal clustering of malaria using routinely collected health facility data on the Kenyan Coast

Malaria transmission heterogeneity in different eco-epidemiological areas of western Kenya: a region-wide observational and risk classification study for adaptive intervention planning

BACKGROUND

Understanding of malaria ecology is a prerequisite for designing locally adapted control strategies in resource-limited settings. The aim of this study was to utilize the spatial heterogeneity in malaria transmission for the designing of adaptive interventions.

METHODS

Field collections of clinical malaria incidence, asymptomatic Plasmodium infection, and malaria vector data were conducted from 108 randomly selected clusters which covered different landscape settings including irrigated farming, seasonal flooding area, lowland dryland farming, and highlands in western Kenya. Spatial heterogeneity of malaria was analyzed and classified into different eco-epidemiological zones.

RESULTS

There was strong heterogeneity and detected hot/cold spots in clinical malaria incidence, Plasmodium prevalence, and vector abundance. The study area was classified into four zones based on clinical malaria incidence, parasite prevalence, vector density, and altitude. The two irrigated zones have either the highest malaria incidence, parasite prevalence, or the highest malaria vector density; the highlands have the lowest vector density and parasite prevalence; and the dryland and flooding area have the average clinical malaria incidence, parasite prevalence and vector density. Different zones have different vector species, species compositions and predominant species. Both indoor and outdoor transmission may have contributed to the malaria transmission in the area. Anopheles gambiae sensu stricto (s.s.), Anopheles arabiensis, Anopheles funestus s.s., and Anopheles leesoni had similar human blood index and malaria parasite sporozoite rate.

CONCLUSION

The multi-transmission-indicator-based eco-epidemiological zone classifications will be helpful for making decisions on locally adapted malaria interventions.

Mapping the stability of febrile illness hotspots in Punjab from 2012 to 2019- a spatial clustering and regression analysis

INTRODUCTION

Febrile illnesses (FI) represent a typical spectrum of diseases in low-resource settings, either in isolation or with other common symptoms. They contribute substantially to morbidity and mortality in India. The primary objective was to study the burden of FI based on Integrated Disease Surveillance Programme (IDSP) data in Punjab, analyze geospatial and temporal trends and patterns, and identify the potential hotspots for effective intervention.

METHODS

A retrospective ecological study used the district-level IDSP reports between 2012 and 2019. Diseases responsible for FI on a large scale, like Dengue, Chikungunya, Malaria (Plasmodium Falciparum, P. Vivax), Enteric fever, and Pyrexia of Unknown Origin (PUO), were included in the analysis. The digital map of Punjab was obtained from GitHub. Spatial autocorrelation and cluster analysis were done using Moran's I and Getis-Ord G* to determine hotspots of FI using the incidence and crude disease numbers reported under IDSP. Further, negative binomial regression was used to determine the association between Spatio-temporal and population variables per the census 2011. Stable hotspots were depicted using heat maps generated from district-wise yearly data.

RESULTS

PUO was the highest reported FI. We observed a rising trend in the incidence of Dengue, Chikungunya, and Enteric fever, which depicted occasional spikes during the study period. FI expressed significant inter-district variations and clustering during the start of the study period, with more dispersion in the latter part of the study period. P.Vivax malaria depicted stable hotspots in southern districts of Punjab. In contrast, P. Falciparum malaria, Chikungunya, and PUO expressed no spatial patterns. Enteric Fever incidence was high in central and northeastern districts but depicted no stable spatial patterns. Certain districts were common incidence hotspots for multiple diseases. The number of cases in each district has shown over-dispersion for each disease and has little dependence on population, gender, or residence as per regression analysis.

CONCLUSIONS

The study demonstrates that information obtained through IDSP can describe the spatial epidemiology of FI at crude spatial scales and drive concerted efforts against FI by identifying actionable points.

Spatiotemporal dynamics of malaria in Zanzibar, 2015-2020

BACKGROUND

Despite high coverage of malaria interventions, malaria elimination in Zanzibar remains elusive, with the annual number of cases increasing gradually over the last 3 years.

OBJECTIVE

The aims of the study were to (1) assess the spatiotemporal dynamics of malaria in Zanzibar between 2015 and 2020 and (2) identify malaria hotspots that would allow Zanzibar to develop an epidemiological stratification for more effective and granular intervention targeting.

METHODS

In this study, we analysed data routinely collected by Zanzibar's Malaria Case Notification (MCN) system. The system collects sociodemographic and epidemiological data from all malaria cases. Cases are passively detected at health facilities (ie, primary index cases) and through case follow-up and reactive case detection (ie, secondary cases). Analyses were performed to identify the spatial heterogeneity of case reporting at shehia (ward) level during transmission seasons.

RESULTS

From 1 January 2015 to 30 April 2020, the MCN system reported 22 686 index cases. Number of cases reported showed a declining trends from 2015 to 2016, followed by an increase from 2017 to 2020. More than 40% of cases had a travel history outside Zanzibar in the month prior to testing positive for malaria. The proportion of followed up index cases was approximately 70% for all years. Out of 387 shehias, 79 (20.4%) were identified as malaria hotspots in any given year; these hotspots reported 52% of all index cases during the study period. Of the 79 hotspot shehias, 12 were hotspots in more than 4 years, that is, considered temporally stable, reporting 14.5% of all index cases.

CONCLUSIONS

Our findings confirm that the scale-up of malaria interventions has greatly reduced malaria transmission in Zanzibar since 2006. Analyses identified hotspots, some of which were stable across multiple years. Malaria efforts should progress from a universal intervention coverage approach to an approach that is more tailored to a select number of hotspot shehias.