Monitoring universal health coverage within the Sustainable Development Goals: development and baseline data for an index of essential health services
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".