Involving 987,205 individuals from 258,366 households in 440 districts, Riskesdas is the largest public health study ever conducted by the Ministry of Health of the Republic of Indonesia. We analyse data drawn from the National Basic Health Research ( Riset Kesehatan Dasar, Riskesdas) 2007. The combination of geographic features, climate conditions and extreme poverty provides a suitable environment for malaria transmission, both biologically and socially. 4, Hanandita and Tampubolon estimate that approximately a quarter of Papuan adults aged 18 and older were multidimensionally poor in 2013 collectively, they were subjected to about 10 % of the total deprivation (in terms of income, illness episodes, morbidity, schooling and literacy) potentially experienced by all adult Indonesian that year. Despite hosting one of the planet’s largest gold mining operations (the Grassberg mine in Mimika district), Papuan society is plagued by poverty and under-development. As many as 70–75 % of Papuans live in rural areas. 2) and is classified by the World Health Organization (WHO) as hyper-endemic area with annual parasite incidence (API) greater than 10 % (nationwide \(\) (22 % of the country’s land area), with a population density of just 9 persons per square kilometre (the lowest in Indonesia). This region of mixed-parasite endemicity is located in the easternmost part of the Indonesian archipelago (Fig. Malaria, a mosquito-borne infectious disease that inflicts devastating health and economic costs on society, remains a major problem in Indonesian Papua (Fig. This means that malaria eradication requires not only biological (proximal) interventions but also social (distal) ones. ConclusionsĪlthough we show that malaria is geography-dependent in Indonesian Papua, it is also a disease of poverty. Nine districts are estimated to have higher-than-expected malaria risks the extent of spatial variation on the island remains notable even after accounting for socio-demographic/economic risk factors. We also find age and gender differentials in malaria prevalence, if only to a small degree. We find that rural Papuans as well as those who live in poor, densely forested, lowland districts are at a higher risk of infection than their counterparts. Robustness is then assessed through sensitivity analyses using alternative hyperpriors. Both individual and contextual characteristics are included as predictors in the model a normal conditional autoregressive prior and an exchangeable one are assigned to the random effects. We adopt a Bayesian hierarchical logistic regression model that accommodates not only the nesting of individuals within the island’s 27 administrative units but also the spatial autocorrelation among these locations. Analysing a subset (N = 22,643) of the National Basic Health Research 2007 dataset (N = 987,205), this paper aims to quantify the district-specific risk of malaria in Papua and to understand how socio-demographic/economic factors measured at individual and district levels are associated with individual’s probability of contracting the disease. At the same time, there remains limited malaria-cartographic activity in the region. Existing studies tend to be descriptive in nature their inferences are prone to confounding and selection biases.
Despite being one of the world’s most affected regions, only little is known about the social and spatial distributions of malaria in Indonesian Papua.
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