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Geography and Social Distribution of Malaria in Indonesian Papua: a Cross‑Sectional Study Wulung Hanandita* and Gindo Tampubolon

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Geography and Social Distribution of Malaria in Indonesian Papua: a Cross‑Sectional Study Wulung Hanandita* and Gindo Tampubolon Hanandita and Tampubolon Int J Health Geogr (2016) 15:13 DOI 10.1186/s12942-016-0043-y International Journal of Health Geographics RESEARCH Open Access Geography and social distribution of malaria in Indonesian Papua: a cross‑sectional study Wulung Hanandita* and Gindo Tampubolon Abstract Background: 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. Existing studies tend to be descriptive in nature; their inferences are prone to confounding and selection biases. At the same time, there remains limited malaria-cartographic activity in the region. 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/eco= - nomic factors measured at individual and district levels are associated with individual’s probability of contracting the disease. Methods: 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. 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. Robustness is then assessed through sensitivity analyses using alternative hyperpriors. Results: 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. We also find age and gender differentials in malaria prevalence, if only to a small degree. Nine districts are estimated to have higher-than-expected malaria risks; the extent of spatial varia- tion on the island remains notable even after accounting for socio-demographic/economic risk factors. Conclusions: Although we show that malaria is geography-dependent in Indonesian Papua, it is also a disease of poverty. This means that malaria eradication requires not only biological (proximal) interventions but also social (dis- tal) ones. Keywords: Malaria, Map, Papua, Indonesia, Bayesian, Spatial, Multilevel Background parasite incidence (API) greater than 10 % (nationwide Malaria, a mosquito-borne infectious disease that inflicts API < 1%; [7]) and parasite prevalence (PP) as high as devastating health [1, 2] and economic [3–5] costs on 50–75 % (nationwide PP < 1%; [8]). Malaria accounts society, remains a major problem in Indonesian Papua for a considerable proportion (15–34 %) of total hospital [6] (Fig. 1). This region of mixed-parasite endemic- workload in the region [9]; mortality due to severe anae- ity is located in the easternmost part of the Indonesian mia [10] as well as multi-drug resistance with high rate archipelago (Fig. 2) and is classified by the World Health of therapeutic failure (65–95 %) have been documented Organization (WHO) as hyper-endemic area with annual [11, 12]. In 2007, the Ministry of Health of the Republic of Indonesia [13] estimated that the infectious disease was prevalent among one-fifth (22.25 %) of the Papuan *Correspondence: [email protected] population—a figure that is seven times higher than the Cathie Marsh Institute for Social Research (CMIST), University Manchester, Oxford Road, Manchester M13 9PL, UK national average (Fig. 1). Perhaps nothing can highlight © 2016 Hanandita and Tampubolon. This article is distributed under the terms of the Creative Commons Attribution 4.0 Interna- tional License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Com- mons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecom- mons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Hanandita and Tampubolon Int J Health Geogr (2016) 15:13 Page 2 of 15 Nusa Sumatra Jawa−Bali Kalimantan Sulawesi Tenggara Maluku Papua 25 25 20 20 15 15 10 10 5 5 0 0 a a a g va n va a ra a Bali av rta esi esi esi Riau ambi Aceh karta Ja J ka w wesi w wesi apua J law P Bante Ja engga enggar Maluku Lampung BengkuluEast Jaogya Gorontalo T T Y West est Papua Riau Islands rth Sumatr Central W st Kalimantan North Maluku South Sumatr West SumatrNo West SulaNorth Sula Bangka Belitun East Kalimantan South Sula South KalimantanWe Central Sula Central Kalimantan Southeast Su est Nusa W East Nusa Fig. 1 Malaria prevalence in 33 Indonesian provinces in 2007 (%), sorted by island group’s longitude (left to right west to east, low to high preva- = lence; source [13]) the seriousness of this situation better than the fact that intervention that are funded by transfers from central to while malaria prevalence for the whole Indonesian archi- local governments (the Kabupaten/Kota or the district/ pelago decreased from 2.9 % in 2007 [13] to 1.9 % in 2013 municipality). [14], the figure for Papua actually increased to 24 % over This scarcity of malaria-cartographic activity is further the same period. complicated by the fact that, unlike in Africa, the social Defeating malaria is certainly a high priority for Indo- and environmental determinants of malaria in Papua nesian policy makers; they have not only set the year have not yet been thoroughly examined. Existing knowl- 2030 as the deadline for malaria elimination in the coun- edge—that the risk of contracting the disease seems to try [15] but have also entrusted local Papuan administra- be higher among non-native Papuans [12, 21], children tors with the responsibility for preventing and combating and young adults [10], as well as rural [21] and lowland endemic diseases through the enactment of the 2001 dwellers [10]—was in fact elicited from simple descrip- Papua Special Autonomy Law No. 21 [16]. Notwith- tive or bivariate analyses performed on small community standing these political commitments, challenges to dis- or facility samples that are prone to both confounding ease control in Papua remain. Principal among them is and selection biases. So, although Papua is reputed to that the spatial distribution of malaria, which is vital for be one of the most malaria-ridden regions in the world guiding efficient and equitable allocation of the limited [22], to date, only little is known about the social and spa- resources available for intervention, is still understudied. tial aspects of the disease. Without precise knowledge To date, the only risk map available for the region is the of where in Papua malaria strikes and which population one produced by the Malaria Atlas Project (MAP), which, subgroup it hits the hardest, it is likely to be difficult for while informative, was unfortunately based on commu- Indonesian policy makers to meet the 2030 elimination nity blood surveys carried out in non-randomly selected target on time. locations [17, 18]. Moreover, because the risk estimate in Analysing large population data (N = 22,643) from the existing malaria maps is presented as a continuous the National Basic Health Research 2007 (Riset Keseha- surface obtained from geostatistical models that are blind tan Dasar; [13]), this study aims to address these gaps. to political boundaries, there is no straightforward way to Through the application of a Bayesian hierarchical mod- obtain a single summary [19] for each local administra- elling technique that accounts for both the nesting of tive unit in Papua. Policy makers in now-decentralised individuals within districts (vertical dependence) and Indonesia [20] are therefore deprived of an intuitive tool the spatial autocorrelation among these areas (horizontal for prioritising development projects or other forms of dependence; see Fig. 3), this paper seeks (1) to quantify Hanandita and Tampubolon Int J Health Geogr (2016) 15:13 Page 3 of 15 Fig. 2 Setting of the study the district-specific risk of malaria in Papua and (2) to randomly sampled population data from Indonesia’s larg- understand how socio-demographic/economic factors est public health study, this paper avoids the problem of measured at individual and district levels are associated confounding and selection biases that beset earlier stud- with an individual’s probability of contracting the dis- ies mentioned above. Second, through its spatial analy- ease. The novelty of this paper is threefold. First, in using sis of irregular lattice data, this study is able to deliver a Hanandita and Tampubolon Int J Health Geogr (2016) 15:13 Page 4 of 15 District 3 District 4 District 5 District 6 Fig. 3 Illustration of hierarchical and spatial dependence single risk summary for each district and municipality in of the country’s land area), with a population density of Papua, which is the lowest autonomous administrative just 9 persons per square kilometre (the lowest in Indo- unit in the Indonesian political system. Finally, the pre- nesia). As many as 70–75 % of Papuans live in rural areas sent study is also distinguished from others in its multi- [27]. Despite hosting one of the planet’s largest gold min- level analysis of individual and contextual determinants ing operations (the Grassberg mine in Mimika district), of malaria, avoiding ecological fallacy [23, 24]. Papuan society is plagued by poverty and under-devel- The remainder of this paper is structured as follows. opment. As shown in Fig. 4, Hanandita and Tampubo- The next section describes the study site, data, meas- lon [28] estimate that approximately a quarter of Papuan ures and modelling techniques. Third section presents adults aged 18 and older were multidimensionally poor the results. Fourth section discusses the findings. Finally, in 2013; collectively, they were subjected to about 10 % of fifth section concludes.
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