Resource requirements for essential universal health coverage: a modelling study based on findings from Disease Control Priorities, 3rd edition
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Disease Control Priorities, 3rd edition (DCP3), published two model health benefits packages (HBPs). This study estimates the overall costs and individual component costs of these packages in low-income countries (LICs) and lower-middle-income countries (lower-MICs). METHODS: This study reports on our Disease Control Priorities Cost Model (DCP-CM), developed as part of the DCP3 project to determine the overall costs of the 218 health sector interventions recommended in the model HBP termed essential universal health coverage (EUHC). Model inputs included data on intervention unit costs, demographic and epidemiological data to quantify the populations in need of specific interventions, baseline coverage indicators, and estimates of required health system costs to support direct service delivery. The DCP-CM was informed primarily by published estimates of economic costs of interventions measured from the health system perspective. We estimated counterfactual annual costs for the year 2015. We disaggregated costs according to intervention characteristics (delivery platform, delivery timing, and health system objective) and did one-way and probabilistic sensitivity analyses with determination of 95% credible intervals (Crls). FINDINGS: At 80% population coverage, the annual cost of EUHC would be US$79 (95% Crl 60-110) per capita (in 2016 US dollars) in LICs and US$130 (100-180) per capita in lower-MICs. As a share of 2015 gross national income (GNI), additional investments would require 8·0% (95% Crl 5·7-11·3) in LICs and 4·2% (2·9-5·9) in lower-MICs. A highest priority subpackage comprising 115 of the EUHC interventions would cost approximately half of these amounts (3·7% [2·6-5·3] of 2015 GNI in LICs and 2·0% [1·4-2·8] in lower-MICs). Mortality-reducing interventions would require around two-thirds of the overall package costs, with interventions to reduce mortality at age 5-69 years from non-communicable disease and injury comprising the highest share of total EUHC costs in both income groups (37·6% [37·2-37·9] in LICs and 43·0% [42·6-43·4] in lower-MICs). Interventions addressing chronic health conditions (requiring 45·5% [44·8-46·4] 2015 GNI for LICs and lower-MICs combined) and interventions delivered in health centres (requiring 49·8% [49·5-50·2] 2015 GNI for LICs and lower-MICs combined) would each comprise the plurality of costs. INTERPRETATION: Implementation of EUHC would require costly investment, especially in LICs. DCP-CM is available as an online tool that can inform local HBP deliberation and support efficient investment in UHC, especially as countries pivot towards non-communicable disease and injury care. FUNDING: Bill & Melinda Gates Foundation, Trond Mohn Foundation, and Norwegian Agency for Development Cooperation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it