Repeated mosquito net distributions, improved treatment, and trends in malaria cases in sentinel health facilities in Papua New Guinea

Abstract

Background: Long-lasting insecticidal nets (LLIN), improved diagnosis and artemisinin-based combination therapy (ACT) have reduced malaria prevalence in Papua New Guinea since 2008. Yet, national incidence trends are inconclusive due to confounding effects of the scale-up of rapid diagnostic tests, and inconsistencies in routine reporting. Methods: Malaria trends and their association with LLIN and ACT roll-out between 2010 and 2014 in seven sentinel health facilities were analysed. The analysis included 35,329 fever patients. Intervention effects were estimated using regression models. Results: Malaria incidence initially ranged from 20 to 115/1000 population; subsequent trends varied by site. Overall, LLIN distributions had a cumulative effect, reducing the number of malaria cases with each round (incidence rate ratio ranging from 0.12 to 0.53 in five sites). No significant reduction was associated with ACT introduction. Plasmodium falciparum remained the dominant parasite in all sentinel health facilities. Resurgence occurred in one site in which a shift to early and outdoor biting of anophelines had previously been documented. Conclusions: LLINs, but not ACT, were associated with reductions of malaria cases in a range of settings, but sustainability of the gains appear to depend on local factors. Malaria programmes covering diverse transmission settings such as Papua New Guinea must consider local heterogeneity when choosing interventions and ensure continuous monitoring of trends.

The epidemiology of Plasmodium falciparum and Plasmodium vivax in East Sepik Province, Papua New Guinea, pre- and post-implementation of national malaria control efforts

“…This effect has, however, not been well quantified and impact of LLINs varies with the coverage rate. In addition, the required coverage might be different for different areas, depending on local factors such as the anopheline density, species composition and both vector and human behaviour [47].…”

Spatial prediction of malaria prevalence in Papua New Guinea: a comparison of Bayesian decision network and multivariate regression modelling approaches for improved accuracy in prevalence prediction

“…The epidemiology of malaria varies considerably across the country and small-area spatial variation in malaria prevalence also exists [ 7 , 8 ], attributed to a range of factors including varied uptake of interventions such as LLINs, as well as human behaviour and vector ecology [ 9 , 10 ]. Environmental and climate factors associated with mosquito breeding sites and different vector dynamics, particularly between low-lying coastal areas and the highlands, also contribute to the variation in spatial patterns of malaria transmission [ 11 , 12 ].…”

“…In these areas, which are prone to seasonal epidemics or outbreaks and where populations lack acquired immunity, morbidity and mortality can be more severe [ 13 ]. The spatial distribution of both P. falciparum and P. vivax spans the entire country, however in terms of relative contribution to disease, P. falciparum is responsible for a greater proportion of infections in most settings [ 9 ]. There has been heterogeneity in the decline of P. falciparum and P. vivax prevalence in PNG, however, with a slower observed reduction in P. vivax due to transmission attributed to hypnozoite reservoirs of the P. vivax parasite [ 14 ].…”

The epidemiology of P. falciparum and P. vivax in East Sepik Province, Papua New Guinea, pre- and post-implementation of national malaria control efforts

“…This effect has, however, not been well quantified and impact of LLINs varies with the coverage rate. In addition, the required coverage might be different for different areas, depending on local factors such as the anopheline density, species composition and both vector and human behaviour (47). In future studies, investigating the complex interplay of these different parasite, vector and human factors at the village level would facilitate a better understanding of the impact of bednets and other communitylevel interventions.…”