Mortality-based indicators for measuring health system performance and population health in high-income countries: a systematic review
Bibliographic record
Abstract
Abstract Objectives Mortality-based indicators are commonly used as measures of population health but less frequently applied to measuring health system performance. This systematic review aimed to identify and describe mortality indicators relevant to the measurement of population health and health system performance in high-income countries. Methods We searched peer-reviewed databases (MEDLINE, Embase, CINAHL, Health Business Elite, Health Policy Reference Center, and POPLINE) and grey literature (government agencies, professional associations, and international/non-governmental health organizations). The search was limited to indicators identified for use in high-income countries. We extracted information on indicator characteristics and alignment with dimensions of effectiveness, efficiency, and equity. We assessed the applicability of the indicator to the context of population health, health system, or both settings, alignment with SMART criteria (specific, measurable, actionable, relevant, and timely), and potential for analyses of population subgroups. Health system was defined as encompassing both medical care and public health services and activities. Results We extracted 385 mortality-based indicators from 240 sources. Indicators were organized into six major domains: all-cause mortality (n = 12), premature mortality (n = 92), life expectancy (n = 23), cause-specific mortality (n = 127), infant, child, and adolescent mortality (n = 50), and hospital-related mortality (n = 81). The majority of indicators (86%) could be applied to measuring health system performance. Premature mortality indicators showed the most potential to measuring both population health and health system performance. Conclusions This review compiled a wide range of mortality indicators relevant to measuring health system performance in high-income countries. Indicators of premature mortality were most relevant to measuring both population health and health system performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".