A geostatistical analysis of the association between armed conflicts and Plasmodium falciparum malaria in Africa, 1997–2010

Abstract

BackgroundThe absence of conflict in a country has been cited as a crucial factor affecting the operational feasibility of achieving malaria control and elimination, yet mixed evidence exists on the influence that conflicts have had on malaria transmission. Over the past two decades, Africa has seen substantial numbers of armed conflicts of varying length and scale, creating conditions that can disrupt control efforts and impact malaria transmission. However, very few studies have quantitatively assessed the associations between conflicts and malaria transmission, particularly in a consistent way across multiple countries.MethodsIn this analysis an explicit geostatistical, autoregressive, mixed model is employed to quantitatively assess the association between conflicts and variations in Plasmodium falciparum parasite prevalence across a 13-year period in sub-Saharan Africa.ResultsAnalyses of geolocated, malaria prevalence survey variations against armed conflict data in general showed a wide, but short-lived impact of conflict events geographically. The number of countries with decreased P. falciparum parasite prevalence (17) is larger than the number of countries with increased transmission (12), and notably, some of the countries with the highest transmission pre-conflict were still found with lower transmission post-conflict. For four countries, there were no significant changes in parasite prevalence. Finally, distance from conflicts, duration of conflicts, violence of conflict, and number of conflicts were significant components in the model explaining the changes in P. falciparum parasite rate.ConclusionsThe results suggest that the maintenance of intervention coverage and provision of healthcare in conflict situations to protect vulnerable populations can maintain gains in even the most difficult of circumstances, and that conflict does not represent a substantial barrier to elimination goals.

Bayesian spatio-temporal modeling of mortality in relation to malaria incidence in Western Kenya

“…The rise in all-cause/malaria-specific mortality and SPR could be attributed to these clashes, which disrupted health services, interfered with the supply and provision of antimalarial drugs and led to a surge in malaria among other infectious diseases. Our findings echo what has been observed in other conflict areas in Africa and demonstrated the impact of conflicts on malaria burden [ 31 ]. Malaria as a proportion of all deaths was most affected since its increase in 2008–2009 coincided with a drop in HIV/AIDS, acute respiratory infections and cardio-vascular diseases among others showing that malaria can easily bounce back more forcefully whenever control efforts are interrupted.…”

Estimating indirect mortality impacts of armed conflict in civilian populations: panel regression analyses of 193 countries, 1990–2017

“…Previous cross-national research on conflict and health has methodological limitations including omitting conflicts that occur between non-state groups [5][6][7][8][9][10][11][12][13][14][15][16], conceptualising the wide spectrum of conflict types and intensities as a simple binary explanatory variable [11][12][13][14][15][17][18][19][20], and assessing only immediate or short-term impacts [5][6][7][8][9][10][11][12][13][14][15][16][18][19][20][21][22][23]. Statistical models often lack grounding in a theoretical framework and therefore omit important confounding variables that may explain the association between armed conflict and health [6,14,[16][17][18]22]. Models are also prone to a bias in cases where the number of battle-related deaths determines both the armed conflict explanatory variable and health outcome…”

“…Models are also prone to a bias in cases where the number of battle-related deaths determines both the armed conflict explanatory variable and health outcome [ 6 – 13 , 16 , 24 , 25 ]. Studies frequently restrict their analyses to an examination of communicable diseases [ 5 , 6 , 15 , 17 , 18 , 22 ] and sub-Saharan Africa [ 5 , 14 , 17 , 19 , 21 – 25 ] which limits generalisability to other conditions and settings.…”

What drives mortality among HIV patients in a conflict setting? A prospective cohort study in the Central African Republic

“…The relationship between conflict and the transmission of infectious disease is more complex and contested in the literature. Conflict in SSA between 1997 and 2010 did not appear to result in higher transmission rates for falciparum malaria and the pre-conflict trend was generally maintained [10]. For HIV, a number of multi-site studies in SSA have argued there is no evidence that infection incidence increases during conflict when compared with pre-conflict rates [11–13].…”