IJSHR

International Journal of Science and Healthcare Research

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Research Article

Year: 2023 | Month: July-September | Volume: 8 | Issue: 3 | Pages: 85-96

DOI: https://doi.org/10.52403/ijshr.20230314

Practical Insights for Public Health Decision-Makers: Using Time Series Models with NTD Disease Counts (Selected) for Myanmar, Nepal, and Timor-Leste

GV Fant

Visiting Professor, Public Health (Epidemiology & Biostatistics), Jodhpur School of Public Health (JSPH) Executive Director, Society for Epidemiology at JSPH Poornima University, Rajasthan, INDIA Practicing Public Health Epidemiologist in Northern Virginia, USA

Corresponding Author: Dr GV Fant

ABSTRACT

The effort to provide medical and public health services in order to eliminate (or bring to near-zero) the burden of neglected tropical diseases (NTDs) on global populations is a public health issue that can be aided through managerial epidemiologic practice. The use of epidemiology (including statistical methods) to study the health history of a population, diagnose the health of a community, and/or examine the work of health services can be enacted in a way to better understand the health status of a population and matters related to health service utilization within a population or community. This paper describes the use of time series methods from managerial epidemiology to provide additional insights for public health decision-makers seeking to reduce NTD counts in the WHO South-East Asia Region, specifically, the countries of Myanmar, Nepal, and Timor-Leste. The examination of leprosy and visceral leishmaniasis disease counts for the three countries revealed interesting patterns. ARIMA models were used and included the calculation of the 95% CI around forecasted values that had public health significance for public health decision-makers. An important consideration for these decision-makers is to take steps that will control specific neglected tropical diseases and not allow the diseases to get out of control.

Keywords: Neglected tropical diseases; managerial epidemiology; ARIMA time series; public health decision-makers; public health workforce

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