Spatio-temporal modelling of routine health facility data for malaria risk micro-stratification in mainland Tanzania

As malaria transmission declines, the need to monitor the heterogeneity of malaria risk at finer scales becomes critical to guide community-based targeted interventions. Although routine health facility (HF) data can provide epidemiological evidence at high spatial and temporal resolution, its incomplete nature of information can result in lower administrative units without empirical data. To overcome geographic sparsity of data and its representativeness, geo-spatial models can leverage routine information to predict risk in un-represented areas as well as estimate uncertainty of predictions. Here, a Bayesian spatio-temporal model was applied on malaria test positivity rate (TPR) data for the period 2017-2019 to predict risks at the ward level, the lowest decision-making unit in mainland Tanzania. To quantify the associated uncertainty, the probability of malaria TPR exceeding programmatic threshold was estimated. Results showed a marked spatial heterogeneity in malaria TPR across wards. 17.7 million people resided in areas where malaria TPR was high (≥ 30; 90% certainty) in the North-West and South-East parts of Tanzania. Approximately 11.7 million people lived in areas where malaria TPR was very low (< 5%; 90% certainty). HF data can be used to identify different epidemiological strata and guide malaria interventions at micro-planning units in Tanzania. These data, however, are imperfect in many settings in Africa and often require application of geo-spatial modelling techniques for estimation

Spatio-temporal modelling of routine health facility data for malaria risk micro-stratification in mainland Tanzania

As malaria transmission declines, the need to monitor the heterogeneity of malaria risk at finer scales becomes critical to guide community-based targeted interventions. Although routine health facility (HF) data can provide epidemiological evidence at high spatial and temporal resolution, its incomplete nature of information can result in lower administrative units without empirical data. To overcome geographic sparsity of data and its representativeness, geo-spatial models can leverage routine information to predict risk in un-represented areas as well as estimate uncertainty of predictions. Here, a Bayesian spatio-temporal model was applied on malaria test positivity rate (TPR) data for the period 2017-2019 to predict risks at the ward level, the lowest decision-making unit in mainland Tanzania. To quantify the associated uncertainty, the probability of malaria TPR exceeding programmatic threshold was estimated. Results showed a marked spatial heterogeneity in malaria TPR across wards. 17.7 million people resided in areas where malaria TPR was high (>/= 30; 90% certainty) in the North-West and South-East parts of Tanzania. Approximately 11.7 million people lived in areas where malaria TPR was very low (< 5%; 90% certainty). HF data can be used to identify different epidemiological strata and guide malaria interventions at micro-planning units in Tanzania. These data, however, are imperfect in many settings in Africa and often require application of geo-spatial modelling techniques for estimation

The use of routine health facility data for micro-stratification of malaria risk in mainland Tanzania

Background Current efforts to estimate the spatially diverse malaria burden in malaria-endemic countries largely involve the use of epidemiological modelling methods for describing temporal and spatial heterogeneity using sparse interpolated prevalence data from periodic cross-sectional surveys. However, more malaria-endemic countries are beginning to consider local routine data for this purpose. Nevertheless, routine information from health facilities (HFs) remains widely under-utilized despite improved data quality, including increased access to diagnostic testing and the adoption of the electronic District Health Information System (DHIS2). This paper describes the process undertaken in mainland Tanzania using routine data to develop a high-resolution, micro-stratification risk map to guide future malaria control efforts. Methods Combinations of various routine malariometric indicators collected from 7098 HFs were assembled across 3065 wards of mainland Tanzania for the period 2017–2019. The reported council-level prevalence classification in school children aged 5–16 years (PfPR5–16) was used as a benchmark to define four malaria risk groups. These groups were subsequently used to derive cut-offs for the routine indicators by minimizing misclassifications and maximizing overall agreement. The derived-cutoffs were converted into numbered scores and summed across the three indicators to allocate wards into their overall risk stratum. Results Of 3065 wards, 353 were assigned to the very low strata (10.5% of the total ward population), 717 to the low strata (28.6% of the population), 525 to the moderate strata (16.2% of the population), and 1470 to the high strata (39.8% of the population). The resulting micro-stratification revealed malaria risk heterogeneity within 80 councils and identified wards that would benefit from community-level focal interventions, such as community-case management, indoor residual spraying and larviciding. Conclusion The micro-stratification approach employed is simple and pragmatic, with potential to be easily adopted by the malaria programme in Tanzania. It makes use of available routine data that are rich in spatial resolution and that can be readily accessed allowing for a stratification of malaria risk below the council level. Such a framework is optimal for supporting evidence-based, decentralized malaria control planning, thereby improving the effectiveness and allocation efficiency of malaria control interventions

The use of routine health facility data for micro-stratification of malaria risk in mainland Tanzania

BACKGROUND: Current efforts to estimate the spatially diverse malaria burden in malaria-endemic countries largely involve the use of epidemiological modelling methods for describing temporal and spatial heterogeneity using sparse interpolated prevalence data from periodic cross-sectional surveys. However, more malaria-endemic countries are beginning to consider local routine data for this purpose. Nevertheless, routine information from health facilities (HFs) remains widely under-utilized despite improved data quality, including increased access to diagnostic testing and the adoption of the electronic District Health Information System (DHIS2). This paper describes the process undertaken in mainland Tanzania using routine data to develop a high-resolution, micro-stratification risk map to guide future malaria control efforts. METHODS: Combinations of various routine malariometric indicators collected from 7098 HFs were assembled across 3065 wards of mainland Tanzania for the period 2017-2019. The reported council-level prevalence classification in school children aged 5-16 years (PfPR(5-16)) was used as a benchmark to define four malaria risk groups. These groups were subsequently used to derive cut-offs for the routine indicators by minimizing misclassifications and maximizing overall agreement. The derived-cutoffs were converted into numbered scores and summed across the three indicators to allocate wards into their overall risk stratum. RESULTS: Of 3065 wards, 353 were assigned to the very low strata (10.5% of the total ward population), 717 to the low strata (28.6% of the population), 525 to the moderate strata (16.2% of the population), and 1470 to the high strata (39.8% of the population). The resulting micro-stratification revealed malaria risk heterogeneity within 80 councils and identified wards that would benefit from community-level focal interventions, such as community-case management, indoor residual spraying and larviciding. CONCLUSION: The micro-stratification approach employed is simple and pragmatic, with potential to be easily adopted by the malaria programme in Tanzania. It makes use of available routine data that are rich in spatial resolution and that can be readily accessed allowing for a stratification of malaria risk below the council level. Such a framework is optimal for supporting evidence-based, decentralized malaria control planning, thereby improving the effectiveness and allocation efficiency of malaria control interventions

Prevalence and correlates of partner violence among adolescent girls and young women: Evidence from baseline data of a cluster randomised trial in Tanzania.

BACKGROUND: Little has been documented about partner violence among adolescent girls and young women (AGYW) who are out of school, a factor associated with HIV acquisition. To understand areas for prioritising HIV prevention intervention efforts, we explored the prevalence and correlates of partner violence among out of school AGYW in Shinyanga, Tanzania. METHODS: A cross-sectional analysis of data from AGYW aged 15-23 years recruited in a cluster randomised trial conducted between October and December 2017 was used to examine correlates of partner violence. Data were collected through an Audio Computer-Assisted Self-interview. Multivariate logistic regression analysis was used to evaluate the association. RESULTS: 2276 (75.5%) AGYW were sexually active. Of these, 816 (35.9%) reported having experienced violence from partners in the last six months. After adjusting for other covariates, being formerly married (AOR = 1.55, 95% CI:1.02, 2.37), having children (AOR = 1.79, 95% CI:1.47, 2.16), anxiety and depression symptoms (AOR = 3.27, 95%CI: 2.15, 4.96), having engaged in sex work in the past six months (AOR = 1.92, 95% CI: 1.45, 2.53) and economic deprivation (AOR = 1.61, 95% CI: 1.34,1.92) were significantly associated with partner violence. CONCLUSIONS: Almost one in three sexually active AGYW had experienced partner violence in the 6 months preceding the survey. The findings underscore the need for future research to focus on understanding the reasons and dynamics underlying high level of partner violence among AGYW. Furthermore, there is a need for implementing intervention programs that aim to reduce economic deprivation among AGYWs and address social norms and structures perpetuating violence against AGYW. TRIAL REGISTRATION: ClinicalTrials.gov-ID NCT03597243

Land resources inventory and suitability assessment for the production of the major crops in the eastern part of Morogoro rural district, Tanzania

Land resources inventory and suitability assessment of the eastern part of Morogoro Rural District was carried out to assess the potentials and constraints of the various land units for the production of smallholder low input rainfed maize, rice, citrus and sesame production. The study covered Divisions, i.e, Mkuyuni, Matombo, Mvuha and Dutumi with a total area of 503,101 ha. The centre of the area is Matombo, which is about 60 km south-east of Morogoro Municipality. The area is located between latitudes 6 0 45S and 7 0 30S and between longitudes 37 0 40E and 38 0 15E. It is bounded by Uluguru mountain ranges in the southwest, TAZARA railway in the east and Dar es Salaam-Mwanza/Kigoma central line in the north. The study area has sub-humid tropical climate (Sharma, 1987). The mean annual rainfall ranges from 900 mm at Mikula to 2600 mm at Kibungo. The area experiences bimodal rainfall characterized by two rainfall peaks in a year. The short and lighter rains usually last from October to January with its peak in December; and the long rains from February to May with their peak in April. The distribution pattern of both long and short rains is irregular and therefore rainfall in the study area is to some extent unreliable. There is considerable temperature variability in the study area with the mean annual temperature ranging from 21.3 0 C to 25.9 0 C. The mean monthly maximum temperature ranges from 23.5 0 C to 28.1 0 C (December) and the mean monthly minimum temperature ranges from 18.1 0 C to 22 0 C (July). The longest length of growing period in the mountain landscape at an altitude ranging between 800 to 2,400 m asl. is 300 days. The piedmonts and the plains at an altitude of 400 and 800 m asl have length of growing period of 240 days. In the valleys and plains below 400 m asl the length of growing period ranges between 180 to 240 days. The rocks of the study area are predominantly metasediments of Precambrian age belonging to the Usagaran system of the Mozambican belt. In the mountain landscape the major rocks are hornblende pyroxene and biotite granulites and crystalline limestones. The colluvial materials in the piedmonts are overlying gneisses, sandstones, shales and gabbroic rocks. Peneplains are formed from Karroo sediments, gneisses and gravels while valleys have a mixture of alluvial and colluvial materials derived from sandstones, shales, calcareous sediments and gneisses. Four major landforms were identified, namely mountains, piedmonts, peneplains and valleys. The mountains (800-2,500 m asl) are comprised of strongly and moderately dissected ridges having complex steep slopes with deep V-shaped valleys while piedmonts at an elevation of 400-600 m asl are moderately dissected with gentle to moderate slopes. The peneplains (200-600 m asl) have undulating to rolling topography with some isolated hills. The valleys (<200 m asl) form the low lying landscape and are comprised of watershed valleys, floodplains and backswamps.iii In the study area there are three major types of forests namely, mountain rain forest, tropical rain forest and miombo woodland. The mountains are dominated by rain forest while miombo woodlands are typical for the peneplains and plains. The main tree species are Brachystegia spp., Isoberlina spp., and Acacia spp. Other vegetation types include bushed shrubland and grassland. The current major land use types include smallholder rainfed and irrigated farming. Cultivation of maize, rice, beans and cassava as staple foods is a common practice. Other crops grown are citrus and oil crops. Landforms, parent materials and climate have influenced soil characteristics and their distribution in the study area. Human activities particularly deforestation, cultivation without proper land management practices and annual bush and forest fires have also influenced the soils. The soils of the strongly dissected mountains on pyroxene granulites and crystalline limestones and dolomites are complex of rock outcrops, boulders and stones and moderately deep to very deep well drained dusky red clays, reddish brown sandy clay loams, brown sandy clays and brown clays. The dissected piedmont slopes with colluvial materials have complex of deep to very deep, well to imperfectly drained, dark reddish brown clays and brown sandy clay loams. Those of the peneplains on gneisses and various undifferentiated Karroo sediments are deep, imperfectly to well drained, dark grey clay loams, brown sandy clays and brown sand loams. The valleys with alluvial-colluvial materials of diverse origin have complex soils which are deep to very deep, moderately well drained to imperfectly drained, stratified, greyish brown sand clays, dark grey clays, dark grey clay loams and loamy sands. The soils of the eastern Morogoro Rural District are strongly acid to alkaline with low organic matter contents and poor soil fertility coupled with low supply of major plant nutrients. These soils classify into eight major soil types namely: Lixisols, Leptosols, Luvisols, Acrisols, Cmbisols, Phaeozems, Fluvisols and Regosols. Four major land utilisation types (LUTs) namely smallholder low input rainfed maize, rice, citrus and sesame were studied in the study area. Potential suitability of the lands for the studied LUTs indicates that none of the land mapping units was rated as highly suitable for any of the four LUTs. Among the four LUTs, citrus was more suited to the area for it can be grown on about 90% of the area. The area has enough rainfall to supply moisture, which is among the critical factors for citrus production. Sesame ranks the second (about 78% of the total area). Maize, although most popular in the area, can be grown profitably on only 57% of the total area. Poor soil fertility and severe soil erosion pose serious constraints to agricultural production in these areas. From this study it is apparent that most of the soils are likely to respond to mineral and organic fertilisers. Therefore, it is strongly recommended that research to determine rates and types of mineral and organiciv fertilisers should be carried out. The economics and social implications of both types of fertilisers should be investigated. Appropriate agro-forestry farming systems such as alley cropping are recommended in order to protect the lands from further erosion as well as to improve soil fertility. Cultivation of citrus and sesame is highly recommended as potential LUTs for the lands of eastern part of Morogoro Rural District. However, socio-economic implications for these LUTs ought to be investigated. Further research on land evaluation for mixed/intercropping smallholder farming is highly recommended. Multidisplinary approaches towards land evaluation studies at district level should be emphasise