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

• Main Authors: Victor A. Alegana, Jim Wright, Carla Pezzulo, Andrew J. Tatem, Peter M. Atkinson
• Format: Article
• Language: English
• Published: BMC 2017-04-01
• Series: BMC Medical Research Methodology
• Subjects: Bayesian hierarchical model; Treatment-seeking behaviour; Item response theory; Markov Chain Monte Carlo
• Online Access: http://link.springer.com/article/10.1186/s12874-017-0346-0

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.