Spatiotemporal Modeling for Fine-Scale Maps of Regional Malaria Endemicity and Its Implications for Transitional Complexities in a Routine Surveillance Network in Western Cambodia.

Suguru Okami,Naohiko Kohtake

doi:10.3389/fpubh.2017.00262

Abstract

Due to the associated and substantial efforts of many stakeholders involved in malaria containment, the disease burden of malaria has dramatically decreased in many malaria-endemic countries in recent years. Some decades after the past efforts of the global malaria eradication program, malaria elimination has again featured on the global health agenda. While risk distribution modeling and a mapping approach are effective tools to assist with the efficient allocation of limited health-care resources, these methods need some adjustment and reexamination in accordance with changes occurring in relation to malaria elimination. Limited available data, fine-scale data inaccessibility (for example, household or individual case data), and the lack of reliable data due to inefficiencies within the routine surveillance system, make it difficult to create reliable risk maps for decision-makers or health-care practitioners in the field. Furthermore, the risk of malaria may dynamically change due to various factors such as the progress of containment interventions and environmental changes. To address the complex and dynamic nature of situations in low-to-moderate malaria transmission settings, we built a spatiotemporal model of a standardized morbidity ratio (SMR) of malaria incidence, calculated through annual parasite incidence, using routinely reported surveillance data in combination with environmental indices such as remote sensing data, and the non-environmental regional containment status, to create fine-scale risk maps. A hierarchical Bayesian frame was employed to fit the transitioning malaria risk data onto the map. The model was set to estimate the SMRs of every study location at specific time intervals within its uncertainty range. Using the spatial interpolation of estimated SMRs at village level, we created fine-scale maps of two provinces in western Cambodia at specific time intervals. The maps presented different patterns of malaria risk distribution at specific time intervals. Moreover, the visualized weights estimated using the risk model, and the structure of the routine surveillance network, represent the transitional complexities emerging from ever-changing regional endemic situations.

Highlights

For many years, malaria has remained an important global health threat that still results in hundreds of thousands of deaths every year [1]
This model was calculated using annual parasite incidence (API) rates derived from routinely reported surveillance data, in combination with environmental indices such as remote sensing data, and the non-environmental regional containment status, to create fine-scale risk maps of two provinces, Pailin and Preah Vihear, in western Cambodia, at specific time intervals
Between 2010 and 2013, 329,830 malaria cases were reported in the malaria surveillance system

Summary

Introduction

Malaria has remained an important global health threat that still results in hundreds of thousands of deaths every year [1]. Migrations of asymptomatic patients have made it difficult to detect remaining transmission risk factors, and to protect people in malaria-free areas from the reintroduction of malaria [16] To address these issues, several pilot studies, such as focused screening and treatment [17], community-based surveillance [18], and mass drug administration [19] have proven effective, but require intensive health-care resources and field practitioner engagement. To address the complex and dynamic nature of situations within low-to-moderate malaria transmission settings, we built a spatiotemporal model of SMR of malaria incidence This model was calculated using API rates derived from routinely reported surveillance data, in combination with environmental indices such as remote sensing data, and the non-environmental regional containment status, to create fine-scale risk maps of two provinces, Pailin and Preah Vihear, in western Cambodia, at specific time intervals. The aim of this analysis is to help understand the transitional complexities existing in the system, in support of better informed decision-making for more efficient resource allocation and intervention planning, through the consideration of spatiotemporal descriptions of regional malaria endemicity

Methods

Results

Discussion

Conclusion