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

Countdown to 2030 : tracking progress towards universal coverage for reproductive, maternal, newborn, and child health

Building upon the successes of Countdown to 2015, Countdown to 2030 aims to support the monitoring and measurement of women's, children's, and adolescents' health in the 81 countries that account for 95% of maternal and 90% of all child deaths worldwide. To achieve the Sustainable Development Goals by 2030, the rate of decline in prevalence of maternal and child mortality, stillbirths, and stunting among children younger than 5 years of age needs to accelerate considerably compared with progress since 2000. Such accelerations are only possible with a rapid scale-up of effective interventions to all population groups within countries (particularly in countries with the highest mortality and in those affected by conflict), supported by improvements in underlying socioeconomic conditions, including women's empowerment. Three main conclusions emerge from our analysis of intervention coverage, equity, and drivers of reproductive, maternal, newborn, and child health (RMNCH) in the 81 Countdown countries. First, even though strong progress was made in the coverage of many essential RMNCH interventions during the past decade, many countries are still a long way from universal coverage for most essential interventions. Furthermore, a growing body of evidence suggests that available services in many countries are of poor quality, limiting the potential effect on RMNCH outcomes. Second, within-country inequalities in intervention coverage are reducing in most countries (and are now almost non-existent in a few countries), but the pace is too slow. Third, health-sector (eg, weak country health systems) and non-health-sector drivers (eg, conflict settings) are major impediments to delivering high-quality services to all populations. Although more data for RMNCH interventions are available now, major data gaps still preclude the use of evidence to drive decision making and accountability. Countdown to 2030 is investing in improvements in measurement in several areas, such as quality of care and effective coverage, nutrition programmes, adolescent health, early childhood development, and evidence for conflict settings, and is prioritising its regional networks to enhance local analytic capacity and evidence for RMNCH

A call for standardised age-disaggregated health data.

The 2030 Sustainable Development Goals agenda calls for health data to be disaggregated by age. However, age groupings used to record and report health data vary greatly, hindering the harmonisation, comparability, and usefulness of these data, within and across countries. This variability has become especially evident during the COVID-19 pandemic, when there was an urgent need for rapid cross-country analyses of epidemiological patterns by age to direct public health action, but such analyses were limited by the lack of standard age categories. In this Personal View, we propose a recommended set of age groupings to address this issue. These groupings are informed by age-specific patterns of morbidity, mortality, and health risks, and by opportunities for prevention and disease intervention. We recommend age groupings of 5 years for all health data, except for those younger than 5 years, during which time there are rapid biological and physiological changes that justify a finer disaggregation. Although the focus of this Personal View is on the standardisation of the analysis and display of age groups, we also outline the challenges faced in collecting data on exact age, especially for health facilities and surveillance data. The proposed age disaggregation should facilitate targeted, age-specific policies and actions for health care and disease management

Monitoring universal health coverage within the Sustainable Development Goals: development and baseline data for an index of essential health services

Background: Achieving universal health coverage, including quality essential service coverage and financial protection for all, is target 3.8 of the Sustainable Development Goals (SDG). As a result, an index of essential health service coverage indicators was selected by the UN as SDG indicator 3.8.1. We have developed an index for measuring SDG 3.8.1, describe methods for compiling the index, and report baseline results for 2015. Methods: 16 tracer indicators were selected for the index, which included four from within each of the categories of reproductive, maternal, newborn, and child health; infectious disease; non-communicable diseases; and service capacity and access. Indicator data for 183 countries were taken from UN agency estimates or databases, supplemented with submissions from national focal points during a WHO country consultation. The index was computed using geometric means, and a subset of tracer indicators were used to summarise inequalities. Findings: On average, countries had primary data since 2010 for 72% of the final set of indicators. The median national value for the service coverage index was 65 out of 100 (range 22–86). The index was highly correlated with other summary measures of health, and after controlling for gross national income and mean years of adult education, was associated with 21 additional years of life expectancy over the observed range of country values. Across 52 countries with sufficient data, coverage was 1% to 66% lower among the poorest quintile as compared with the national population. Sensitivity analyses suggested ranks implied by the index are fairly stable across alternative calculation methods. Interpretation: Service coverage within universal health coverage can be measured with an index of tracer indicators. Our universal health coverage service coverage index is simple to compute by use of available country data and can be refined to incorporate relevant indicators as they become available through SDG monitoring. Funding: Ministry of Health, Japan, and the Rockefeller Foundation

Socioeconomic inequality in disability among adults: a multicountry study using the World Health Survey.

Objectives: We compared national prevalence and wealth-related inequality in disability across a large number of countries from all income groups. Methods: Data on 218 737 respondents participating in the World Health Survey 2002–2004 were analyzed. A composite disability score (0–100) identified respondents who experienced significant disability in physical, mental, and social functioning irrespective of their underlying health condition. Disabled persons had disability composite scores above 40. Wealth was evaluated using an index of economic status in households based on ownership of selected assets. Socioeconomic inequalities were measured using the slope index of inequality and the relative index of inequality. Results: Median age-standardized disability prevalence was higher in the low- and lower middle-income countries. In all the study countries, disability was more prevalent in the poorest than in the richest wealth quintiles. Pro-rich inequality was statistically significant in 43 of 49 countries, with disability prevalence higher among populations with lower wealth. Median relative inequality was higher in the high- and upper middle-income countries. Conclusions: Integrating equity components into the monitoring of disability trends would help ensure that interventions reach and benefit populations with greatest need

Estimating family planning coverage from contraceptive prevalence using national household surveys

Background: Contraception is one of the most important health interventions currently available and yet, many women and couples still do not have reliable access to modern contraceptives. The best indicator for monitoring family planning is the proportion of women using contraception among those who need it. This indicator is frequently called demand for family planning satisfied and we argue that it should be called family planning coverage (FPC). This indicator is complex to calculate and requires a considerable number of questions to be included in a household survey. Objectives: We propose a model that can predict FPC from a much simpler indicator – contraceptive use prevalence – for situations where it cannot be derived directly. Design: Using 197 Multiple Indicator Cluster Surveys and Demographic and Health Surveys from 82 countries, we explored least-squares regression models that could be used to predict FPC. Non-linearity was expected in this situation and we used a fractional polynomial approach to find the best fitting model. We also explored the effect of calendar time and of wealth on the models explored. Results: Given the high correlation between the variables involved in FPC, we managed to derive a relatively simple model that depends only on contraceptive use prevalence but explains 95% of the variability of the outcome, with high precision for the estimated regression line. We also show that the relationship between the two variables has not changed with time. A concordance analysis showed agreement between observed and fitted results within a range of ±9 percentage points. Conclusions: We show that it is possible to obtain fairly good estimates of FPC using only contraceptive prevalence as a predictor, a strategy that is useful in situations where it is not possible to estimate FPC directly