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

Abstract

BackgroundSeeking 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).MethodsUsing 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.ResultsModelling 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%.ConclusionWe 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.

Estimating Malaria Incidence From Routine Health Facility-Based Surveillance Data in Uganda

“…This study contributes to the literature by proposing a novel method to more accurately estimate malaria incidence from HMIS data using improved estimates of the population denominator. Previous work to estimate care-seeking probabilities to apply to incidence denominators has relied on representative, crosssectional surveys that ask individuals about their care seeking behavior (13,14,(27)(28)(29). These surveys are costly and are not conducted regularly, and the questions are often non-speci c in that they do not ask respondents which health facility they attended.…”

National and sub-national variation in patterns of febrile case management in sub-Saharan Africa

“…IRT concepts are common to other health applications such as psychometry and could be applied in future household survey analyses to understand the differential item functioning in population sub-groups with similar latent traits 26 , 27 . We conducted an assessment and generalisation of this methodology to mixed effects regression including gender and socio-economic status 13 , 28 , 29 . While inclusion of more regression effects in previous experiments would have been advantageous, the resulting complexity in modelling negatively impacted model performance and efficiency significantly 13 .…”

“…Therefore, no attempt was made to quantify fever attributable to malaria, for example, as recently estimated here 12 . Bayesian item response theory (IRT) 13 was employed to quantify person-level characteristics, namely the latent ability to seek treatment at the nearest public health facility, adjusting for travel time and residence. These latent characteristics were then used to estimate the probability of seeking treatment for fever in the public sector based on geography.…”

Association Between Specific Indoor Air Pollutants and Pneumonia Episodes in Children Under Five in Abuja, Nigeria: A Case-control Study

“…Alongside pollution levels, a higher proportion of cases were within the lowest socioeconomic group compared to controls. Poverty is known to affect early treatment seeking behavior by care givers predominantly due to barriers to nancial access (Alegana et al, 2017). Treatment seeking behavior is associated with high disease incidence usually in low-income countries (Alegana et al, 2017).…”