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Treatment-seeking behaviour in low- and middle-income countries estimated using a Bayesian model

Treatment-seeking behaviour in low- and middle-income countries estimated using a Bayesian model
Treatment-seeking behaviour in low- and middle-income countries estimated using a Bayesian model
Background: Seeking treatment in formal healthcare for uncomplicated infections is vital to combating disease in low- and middle-income countries (LMICs). Healthcare treatment-seeking behaviour varies within and between communities and is modified by socio-economic, demographic, and physical factors. As a result, it remains a challenge to quantify healthcare treatment-seeking behaviour using a metric that is comparable across communities. Here, we present an application for transforming individual categorical responses (actions related to fever) to a continuous probabilistic estimate of fever treatment for one country in Sub-Saharan Africa (SSA).
Methods: Using nationally representative household survey data from the 2013 Demographic and Health Survey (DHS) in Namibia, individual-level responses (n = 1138) were linked to theoretical estimates of travel time to the nearest public or private health facility. Bayesian Item Response Theory (IRT) models were fitted via Markov Chain Monte Carlo (MCMC) simulation to estimate parameters related to fever treatment and estimate probability of treatment for children under five years. Different models were implemented to evaluate computational needs and the effect of including predictor variables such as rurality. The mean treatment rates were then estimated at regional level.
Results: Modelling results suggested probability of fever treatment was highest in regions with relatively high incidence of malaria historically. The minimum predicted threshold probability of seeking treatment was 0.3 (model 1: 0.340; 95% CI 0.155–0.597), suggesting that even in populations at large distances from facilities, there was still a 30% chance of an individual seeking treatment for fever. The agreement between correctly predicted probability of treatment at individual level based on a subset of data (n = 247) was high (AUC = 0.978), with a sensitivity of 96.7% and a specificity of 75.3%.
Conclusion: We have shown how individual responses in national surveys can be transformed to probabilistic measures comparable at population level. Our analysis of household survey data on fever suggested a 30% baseline threshold for fever treatment in Namibia. However, this threshold level is likely to vary by country or endemicity. Although our focus was on fever treatment, the methodology outlined can be extended to multiple health seeking behaviours captured in routine national survey data and to other infectious diseases.
1471-2288
Alegana, Victor
f5bd6ab7-459e-4122-984f-2bdb5f906d82
Wright, James
94990ecf-f8dd-4649-84f2-b28bf272e464
Pezzulo, Carla
876a5393-ffbd-479a-9edf-f72a59ca2cb5
Tatem, Andrew
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Atkinson, Peter
96e96579-56fe-424d-a21c-17b6eed13b0b
Alegana, Victor
f5bd6ab7-459e-4122-984f-2bdb5f906d82
Wright, James
94990ecf-f8dd-4649-84f2-b28bf272e464
Pezzulo, Carla
876a5393-ffbd-479a-9edf-f72a59ca2cb5
Tatem, Andrew
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Atkinson, Peter
96e96579-56fe-424d-a21c-17b6eed13b0b

Alegana, Victor, Wright, James, Pezzulo, Carla, Tatem, Andrew and Atkinson, Peter (2017) Treatment-seeking behaviour in low- and middle-income countries estimated using a Bayesian model BMC Medical Research Methodology, 17 (doi:10.1186/s12874-017-0346-0).

Record type: Article

Abstract

Background: Seeking treatment in formal healthcare for uncomplicated infections is vital to combating disease in low- and middle-income countries (LMICs). Healthcare treatment-seeking behaviour varies within and between communities and is modified by socio-economic, demographic, and physical factors. As a result, it remains a challenge to quantify healthcare treatment-seeking behaviour using a metric that is comparable across communities. Here, we present an application for transforming individual categorical responses (actions related to fever) to a continuous probabilistic estimate of fever treatment for one country in Sub-Saharan Africa (SSA).
Methods: Using nationally representative household survey data from the 2013 Demographic and Health Survey (DHS) in Namibia, individual-level responses (n = 1138) were linked to theoretical estimates of travel time to the nearest public or private health facility. Bayesian Item Response Theory (IRT) models were fitted via Markov Chain Monte Carlo (MCMC) simulation to estimate parameters related to fever treatment and estimate probability of treatment for children under five years. Different models were implemented to evaluate computational needs and the effect of including predictor variables such as rurality. The mean treatment rates were then estimated at regional level.
Results: Modelling results suggested probability of fever treatment was highest in regions with relatively high incidence of malaria historically. The minimum predicted threshold probability of seeking treatment was 0.3 (model 1: 0.340; 95% CI 0.155–0.597), suggesting that even in populations at large distances from facilities, there was still a 30% chance of an individual seeking treatment for fever. The agreement between correctly predicted probability of treatment at individual level based on a subset of data (n = 247) was high (AUC = 0.978), with a sensitivity of 96.7% and a specificity of 75.3%.
Conclusion: We have shown how individual responses in national surveys can be transformed to probabilistic measures comparable at population level. Our analysis of household survey data on fever suggested a 30% baseline threshold for fever treatment in Namibia. However, this threshold level is likely to vary by country or endemicity. Although our focus was on fever treatment, the methodology outlined can be extended to multiple health seeking behaviours captured in routine national survey data and to other infectious diseases.

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More information

Accepted/In Press date: 12 April 2017
e-pub ahead of print date: 20 April 2017
Organisations: WorldPop, Geography & Environment, Southampton Marine & Maritime Institute, Population, Health & Wellbeing (PHeW)

Identifiers

Local EPrints ID: 410394
URI: http://eprints.soton.ac.uk/id/eprint/410394
ISSN: 1471-2288
PURE UUID: a0a6b9ac-6041-4cee-b44f-3c6560a82e0c
ORCID for Victor Alegana: ORCID iD orcid.org/0000-0001-5177-9227
ORCID for Andrew Tatem: ORCID iD orcid.org/0000-0002-7270-941X

Catalogue record

Date deposited: 07 Jun 2017 16:31
Last modified: 02 Nov 2017 17:31

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