Regional Differences in Intervention Coverage and Health System Strength in Tanzania.

Assessments of subnational progress and performance coverage within countries should be an integral part of health sector reviews, using recent data from multiple sources on health system strength and coverage. As part of the midterm review of the national health sector strategic plan of Tanzania mainland, summary measures of health system strength and coverage of interventions were developed for all 21 regions, focusing on the priority indicators of the national plan. Household surveys, health facility data and administrative databases were used to compute the regional scores. Regional Millennium Development Goal (MDG) intervention coverage, based on 19 indicators, ranged from 47% in Shinyanga in the northwest to 71% in Dar es Salaam region. Regions in the eastern half of the country have higher coverage than in the western half of mainland. The MDG coverage score is strongly positively correlated with health systems strength (r = 0.84). Controlling for socioeconomic status in a multivariate analysis has no impact on the association between the MDG coverage score and health system strength. During 1991-2010 intervention coverage improved considerably in all regions, but the absolute gap between the regions did not change during the past two decades, with a gap of 22% between the top and bottom three regions. The assessment of regional progress and performance in 21 regions of mainland Tanzania showed considerable inequalities in coverage and health system strength and allowed the identification of high and low-performing regions. Using summary measures derived from administrative, health facility and survey data, a subnational picture of progress and performance can be obtained for use in regular health sector reviews

Sub-national stratification of malaria risk in mainland Tanzania: a simplified assembly of survey and routine data

Recent malaria control efforts in mainland Tanzania have led to progressive changes in the prevalence of malaria infection in children, from 18.1% (2008) to 7.3% (2017). As the landscape of malaria transmission changes, a sub-national stratification becomes crucial for optimized cost-effective implementation of interventions. This paper describes the processes, data and outputs of the approach used to produce a simplified, pragmatic malaria risk stratification of 184 councils in mainland Tanzania.; Assemblies of annual parasite incidence and fever test positivity rate for the period 2016-2017 as well as confirmed malaria incidence and malaria positivity in pregnant women for the period 2015-2017 were obtained from routine district health information software. In addition, parasite prevalence in school children (PfPR; 5to16; ) were obtained from the two latest biennial council representative school malaria parasitaemia surveys, 2014-2015 and 2017. The PfPR; 5to16; served as a guide to set appropriate cut-offs for the other indicators. For each indicator, the maximum value from the past 3 years was used to allocate councils to one of four risk groups: very low (< 1%PfPR; 5to16; ), low (1- < 5%PfPR; 5to16; ), moderate (5- < 30%PfPR; 5to16; ) and high (≥ 30%PfPR; 5to16; ). Scores were assigned to each risk group per indicator per council and the total score was used to determine the overall risk strata of all councils.; Out of 184 councils, 28 were in the very low stratum (12% of the population), 34 in the low stratum (28% of population), 49 in the moderate stratum (23% of population) and 73 in the high stratum (37% of population). Geographically, most of the councils in the low and very low strata were situated in the central corridor running from the north-east to south-west parts of the country, whilst the areas in the moderate to high strata were situated in the north-west and south-east regions.; A stratification approach based on multiple routine and survey malaria information was developed. This pragmatic approach can be rapidly reproduced without the use of sophisticated statistical methods, hence, lies within the scope of national malaria programmes across Africa

Perceived Usefulness, Competency, and Associated Factors in Using District Health Information System Data Among District Health Managers in Tanzania: Cross-sectional Study

BackgroundTanzania introduced District Health Information Software (version 2; DHIS2) in 2013 to support existing health management information systems and to improve data quality and use. However, to achieve these objectives, it is imperative to build human resource capabilities to address the challenges of new technologies, especially in resource-constrained countries. ObjectiveThis study aimed to determine the perceived usefulness, competency, and associated factors in using DHIS2 data among district health managers (DHMs) in Tanzania. MethodsThis descriptive cross-sectional study used a quantitative approach, which involved using a self-administered web-based questionnaire. This study was conducted between April and September 2019. We included all core and co-opted members of the council or district health management teams (DHMTs) from all 186 districts in the country. Frequency and bivariate analyses were conducted, and the differences among categories were measured by using a chi-square test. P values of <.05 were considered significant. ResultsA total of 2667 (77.96%) of the expected 3421 DHMs responded, of which 2598 (97.41%) consented and completed the questionnaires. Overall, the DHMs were satisfied with DHIS2 (2074/2596, 79.83%) because of workload reduction (2123/2598, 81.72%), the ease of learning (1953/2598, 75.17%), and enhanced data use (2239/2598, 86.18%). Although only half of the managers had user accounts (1380/2598, 53.12%) and were trained on DHIS2 data analysis (1237/2598, 47.61%), most claimed to have average to advanced skills in data validation (1774/2598, 68.28%), data visualization (1563/2598, 60.16%), and DHIS2 data use (1321/2598, 50.85%). The biggest challenges facing DHMs included the use of a paper-based system as the primary data source (1890/2598, 72.75%) and slow internet speed (1552/2598, 59.74%). Core members were more confident in using DHIS2 compared with other members (P=.004), whereas program coordinators were found to receive more training on data analysis and use (P=.001) and were more confident in using DHIS2 data compared with other DHMT members (P=.001). ConclusionsThis study showed that DHMs have appreciable competencies in using the DHIS2 and its data. However, their skill levels have not been commensurate with the duration of DHIS2 use. This study recommends improvements in the access to and use of DHIS2 data. More training on data use is required and should involve using cost-effective approaches to include both the core and noncore members of the DHMTs. Moreover, enhancing the culture and capacity of data use will ensure the better management and accountability of health system performance

Midterm review of national health plans: an example from the United Republic of Tanzania

In the health sector, planning and resource allocation at country level are mainly guided by national plans. For each such plan, a midterm review of progress is important for policy-makers since the review can inform the second half of the plan’s implementation and provide a situation analysis on which the subsequent plan can be based. The review should include a comprehensive analysis using recent data – from surveys, facility and administrative databases – and global health estimates. Any midterm analysis of progress is best conducted by a team comprising representatives of government agencies, independent national institutions and global health organizations. Here we present an example of such a review, done in 2013 in the United Republic of Tanzania. Compared to similar countries, the results of this midterm review showed good progress in all health indicators except skilled birth attendance

Indicators included in the Millennium Development Goals coverage score, with weighting.

<p>HMIS = health management information system (facility reports); THMIS = Tanzania Health and Malaria Indicator Survey; NACP = National AIDS Control Programme; NTPLCP = National Tuberculosis and Leprosy Control Programme</p><p>Indicators included in the Millennium Development Goals coverage score, with weighting.</p

Relative ranking of regions by health MDG coverage score, Tanzania mainland, 2012.

<p>Relative ranking of regions by health MDG coverage score, Tanzania mainland, 2012.</p

Relative ranking of regions by level of socioeconomic development, Tanzania mainland, 2012.

<p>Relative ranking of regions by level of socioeconomic development, Tanzania mainland, 2012.</p

Regional trends in the Countdown maternal, newborn and child health coverage score (%), based on demographic and health survey data 1991–2010, with average annual rate of relative change of the coverage rate during the whole period (1991–2010) and between the last two surveys (2004–2010).

<p>Regional trends in the Countdown maternal, newborn and child health coverage score (%), based on demographic and health survey data 1991–2010, with average annual rate of relative change of the coverage rate during the whole period (1991–2010) and between the last two surveys (2004–2010).</p

Health MDG coverage score by health system strength score, Tanzania regions.

<p>Health MDG coverage score by health system strength score, Tanzania regions.</p

Inequalities in the Countdown MNCH coverage score (see text), based on survey data 1991–2010, national trend with three best and three poorest regions according to the 2010 survey.

<p>Inequalities in the Countdown MNCH coverage score (see text), based on survey data 1991–2010, national trend with three best and three poorest regions according to the 2010 survey.</p