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.

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

BACKGROUND: 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. METHOD: 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. FINDINGS: 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. INTERPRETATION: 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.

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.

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.