Clustering of under-five mortality in Rufiji Health and Demographic Surveillance System in rural Tanzania

BACKGROUND\ud \ud Less than 5 years remain before the 2015 mark when countries will be evaluated on their achievements for the Millennium Development Goals (MDGs). The MDG 4 and 6 call for a reduction of child mortality by two-thirds and combating malaria, HIV/AIDS, TB, and other diseases, respectively. To accelerate the achievement of these goals, focused allocation of resources and high deployment of cost-effective interventions is paramount. The knowledge of spatial and temporal distribution of diseases is important for health authorities to prioritize and allocate resources.\ud \ud METHODS\ud \ud To identify possible significant clusters, we used SatTScan software, and analyzed 2,745 cases of under-five with 134,099 person-years for the period between 1999and 2008. Mortality rates for every year were calculated, likewise a spatial scan statistic was used to test for clusters of total under-five mortalities in both space and time.\ud \ud RESULTS\ud \ud A number of significant clusters from space, time, and space-time analysis were identified in several locations for a period of 10 years in the Rufiji Demographic Surveillance Site (RDSS). These locations show that villages within the clusters have an elevated risk of under-five deaths. The spatial analysis identified three significant clusters. The first cluster had only one village, Kibiti A (p < 0.05, the second cluster involved five villages (Mtawanya, Pagae, Kibiti A, Machepe, and Kibiti B; p < 0.05), the third cluster involved one village, Jaribu Mpakani (p < 0.05). A space-time cluster of 10 villages for the period between 1999 and 2002 with a radius of 14.73 km was discovered with the highest risk (RR 1.6, p < 0.001). The mortality rates were very high for the years 1999-2002 according to the analysis. The death rates were 33.5, 26.4, 24.1, and 24.9, respectively. Total childhood mortality rates calculated for the period of 10 years were 21.0 per 1,000 person-years.\ud \ud CONCLUSION\ud \ud During the 10 years of analysis, mortality seemed to decrease in RDSS. The mortality decline should be taken with caution because the Demographic Surveillance System is not statistically representative of the whole population; therefore, inference should not be made to the general population of Tanzania. The pattern observed could be attributed to demographic and weather characteristics of RDSS. This should provide new insights for further studies and interventions toward reducing under-five mortality

Spatially aggregated clusters and scattered smaller loci of elevated malaria vector density and human infection prevalence in urban Dar es Salaam, Tanzania

Background Malaria transmission, primarily mediated by Anopheles gambiae, persists in Dar es Salaam (DSM) despite high coverage with bed nets, mosquito-proofed housing and larviciding. New or improved vector control strategies are required to eliminate malaria from DSM, but these will only succeed if they are delivered to the minority of locations where residual transmission actually persists. Hotspots of spatially clustered locations with elevated malaria infection prevalence or vector densities were, therefore, mapped across the city in an attempt to provide a basis for targeting supplementary interventions. Methods Two phases of a city-wide population-weighted random sample of cross-sectional household surveys of malaria infections were complemented by two matching phases of geographically overlapping, high-resolution, longitudinal vector density surveys; spanning 2010–2013. Spatial autocorrelations were explored using Moran’s I and hotspots were detected using flexible spatial scan statistics. Results Seven hotspots of spatially clustered elevated vector density and eight of malaria infection prevalence were detected over both phases. Only a third of vectors were collected in hotspots in phase 1 (30 %) and phase 2 (33 %). Malaria prevalence hotspots accounted for only half of malaria infections detected in phase 1 (55 %) and phase 2 (47 %). Three quarters (76 % in phase 1 and 74 % in phase 2) of survey locations with detectable vector populations were outside of hotspots. Similarly, more than half of locations with higher infection prevalence (>10 %) occurred outside of hotspots (51 % in phase 1 and 54 % in phase 2). Vector proliferation hazard (exposure to An. gambiae) and malaria infection risk were only very loosely associated with each other (Odds ratio (OR) [95 % Confidence Interval (CI)] = 1.56 [0.89, 1.78], P = 0.52)). Conclusion Many small, scattered loci of local malaria transmission were haphazardly scattered across the city, so interventions targeting only currently identifiable spatially aggregated hotspots will have limited impact. Routine, spatially comprehensive, longitudinal entomological and parasitological surveillance systems, with sufficient sensitivity and spatial resolution to detect these scattered loci, are required to eliminate transmission from this typical African city. Intervention packages targeted to both loci and hotspots of transmission will need to suppress local vector proliferation, treat infected residents and provide vulnerable residents with supplementary protective measures against exposure

Fine scale mapping of malaria infection clusters by using routinely collected health 1 facility data in urban Dar es Salaam, Tanzania

This study investigated whether passively collected routine health facility data can be used for mapping spatial heterogeneities in malaria transmission at the level of local government housing cluster administrative units in Dar es Salaam, Tanzania. From June 2012 to Jan 2013, residential locations of patients tested for malaria at a public health facility were traced based on their local leaders’ names and geo-referencing the point locations of these leaders’ houses. Geographic information systems (GIS) were used to visualise the spatial distribution of malaria infection rates. Spatial scan statistics were deployed to detect spatial clustering of high infection rates. Among 2,407 patients tested for malaria, 46.6% (1,121) could be traced to their 411 different residential housing clusters. One small spatially aggregated cluster of neighbourhoods with high prevalence was identified. While the home residence housing cluster leader was unambiguously identified for 73.8% (240/325) of malaria-positive patients, only 42.3% (881/2,082) of those with negative test results were successfully traced. It was concluded that recording simple points of reference during routine health facility visits can be used for mapping malaria infection burden on very fine geographic scales, potentially offering a feasible approach to rational geographic targeting of malaria control interventions. However, in order to tap the full potential of this approach, it would be necessary to optimise patient tracing success and eliminate biases by blinding personnel to test results

Topographic mapping of the interfaces between human and aquatic mosquito habitats to enable barrier targeting of interventions against malaria vectors.

Geophysical topographic metrics of local water accumulation potential are freely available and have long been known as high-resolution predictors of where aquatic habitats for immature mosquitoes are most abundant, resulting in elevated densities of adult malaria vectors and human infection burden. Using existing entomological and epidemiological survey data, here we illustrate how topography can also be used to map out the interfaces between wet, unoccupied valleys and dry, densely populated uplands, where malaria vector densities and infection risk are focally exacerbated. These topographically identifiable geophysical boundaries experience disproportionately high vector densities and malaria transmission risk, because this is where mosquitoes first encounter humans when they search for blood after emerging or ovipositing in the valleys. Geophysical topographic indicators accounted for 67% of variance for vector density but for only 43% for infection prevalence, so they could enable very selective targeting of interventions against the former but not the latter (targeting ratios of 5.7 versus 1.5 to 1, respectively). So, in addition to being useful for targeting larval source management to wet valleys, geophysical topographic indicators may also be used to selectively target adult mosquitoes with insecticidal residual sprays, fencing, vapour emanators or space sprays to barrier areas along their fringes

The epidemiology of residual Plasmodium falciparum malaria transmission and infection burden in an African city with high coverage of multiple vector control measures.

BACKGROUND In the Tanzanian city of Dar es Salaam, high coverage of long-lasting insecticidal nets (LLINs), larvicide application (LA) and mosquito-proofed housing, was complemented with improved access to artemisinin-based combination therapy and rapid diagnostic tests by the end of 2012. METHODS Three rounds of city-wide, cluster-sampled cross-sectional surveys of malaria parasite infection status, spanning 2010 to 2012, were complemented by two series of high-resolution, longitudinal surveys of vector density. RESULTS Larvicide application using a granule formulation of Bacillus thuringiensis var. israelensis (Bti) had no effect upon either vector density (P = 0.820) or infection prevalence (P = 0.325) when managed by a private-sector contractor. Infection prevalence rebounded back to 13.8 % in 2010, compared with <2 % at the end of a previous Bti LA evaluation in 2008. Following transition to management by the Ministry of Health and Social Welfare (MoHSW), LA consistently reduced vector densities, first using the same Bti granule in early 2011 [odds ratio (OR) (95 % confidence interval (CI)) = 0.31 (0.14, 0.71), P = 0.0053] and then a pre-diluted aqueous suspension formulation from mid 2011 onwards [OR (95 % CI) = 0.15 (0.07, 0.30), P ≪ 0.000001]. While LA by MoHSW with the granule formulation was associated with reduced infection prevalence [OR (95 % CI) = 0.26 (0.12, 0.56), P = 0.00040], subsequent liquid suspension use, following a mass distribution to achieve universal coverage of LLINs that reduced vector density [OR (95 % CI) = 0.72 (0.51, 1.01), P = 0.057] and prevalence [OR (95 % CI) = 0.80 (0.69, 0.91), P = 0.0013], was not associated with further prevalence reduction (P = 0.836). Sleeping inside houses with complete window screens only reduced infection risk [OR (95 % CI) = 0.71 (0.62, 0.82), P = 0.0000036] if the evenings and mornings were also spent indoors. Furthermore, infection risk was only associated with local vector density [OR (95 % CI) = 6.99 (1.12, 43.7) at one vector mosquito per trap per night, P = 0.037] among the minority (14 %) of households lacking screening. Despite attenuation of malaria transmission and immunity, 88 % of infected residents experienced no recent fever, only 0.4 % of these afebrile cases had been treated for malaria, and prevalence remained high (9.9 %) at the end of the study. CONCLUSIONS While existing vector control interventions have dramatically attenuated malaria transmission in Dar es Salaam, further scale-up and additional measures to protect against mosquito bites outdoors are desirable. Accelerated elimination of chronic human infections persisting at high prevalence will require active, population-wide campaigns with curative drugs