The World Bank is publishing nine volumes of Disease Control Priorities, 3rd edition (DCP3) between 2015 and 2018. Volume 9, Improving Health and Reducing Poverty, summarises the main messages from all the volumes and contains cross-cutting analyses. This Review draws on all nine volumes to convey conclusions. The analysis in DCP3 is built around 21 essential packages that were developed in the nine volumes. Each essential package addresses the concerns of a major professional community (eg, child health or surgery) and contains a mix of intersectoral policies and health-sector interventions. 71 intersectoral prevention policies were identified in total, 29 of which are priorities for early introduction. Interventions within the health sector were grouped onto five platforms (population based, community level, health centre, first-level hospital, and referral hospital). DCP3 defines a model concept of essential universal health coverage (EUHC) with 218 interventions that provides a starting point for country-specific analysis of priorities. Assuming steady-state implementation by 2030, EUHC in lower-middle-income countries would reduce premature deaths by an estimated 4·2 million per year. Estimated total costs prove substantial: about 9·1% of (current) gross national income (GNI) in low-income countries and 5·2% of GNI in lower-middle-income countries. Financing provision of continuing intervention against chronic conditions accounts for about half of estimated incremental costs. For lower-middle-income countries, the mortality reduction from implementing the EUHC can only reach about half the mortality reduction in non-communicable diseases called for by the Sustainable Development Goals. Full achievement will require increased investment or sustained intersectoral action, and actions by finance ministries to tax smoking and polluting emissions and to reduce or eliminate (often large) subsidies on fossil fuels appear of central importance. DCP3 is intended to be a model starting point for analyses at the country level, but country-specific cost structures, epidemiological needs, and national priorities will generally lead to definitions of EUHC that differ from country to country and from the model in this Review. DCP3 is particularly relevant as achievement of EUHC relies increasingly on greater domestic finance, with global developmental assistance in health focusing more on global public goods. In addition to assessing effects on mortality, DCP3 looked at outcomes of EUHC not encompassed by the disability-adjusted life-year metric and related cost-effectiveness analyses. The other objectives included financial protection (potentially better provided upstream by keeping people out of the hospital rather than downstream by paying their hospital bills for them), stillbirths averted, palliative care, contraception, and child physical and intellectual growth. The first 1000 days after conception are highly important for child development, but the next 7000 days are likewise important and often neglected.

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An overview is presented of the rationale, design, and analysis plan for the WMH-CIDI clinical calibration studies. As no clinical gold standard assessment is available for the DSM-IV disorders assessed in the WMH-CIDI, we adopted the goal of calibration rather than validation; that is, we asked whether WMH-CIDI diagnoses are 'consistent' with diagnoses based on a state-of-the-art clinical research diagnostic interview (SCID; Structured Clinical Interview for DSM-IV) rather than whether they are 'correct'. Consistency is evaluated both at the aggregate level (consistency of WMH-CIDI and SCID prevalence estimates) and at the individual level (consistency of WMH-CIDI and SCID diagnostic classifications). Although conventional statistics (sensitivity, specificity, Cohen's κ) are used to describe diagnostic consistency, an argument is made for considering the area under the receiver operator curve (AUC) to be a more useful general-purpose measure of consistency. In addition, more detailed analyses are used to evaluate consistency on a substantive level. These analyses begin by estimating prediction equations in a clinical calibration subsample, with WMH-CIDI symptom-level data used to predict SCID diagnoses, and using the coefficients from these equations to assign predicted probabilities of SCID diagnoses to each respondent in the remainder of the sample. Substantive analyses then investigate whether estimates of prevalence and associations when based on WMH-CIDI diagnoses are consistent with those based on predicted SCID diagnoses. Multiple imputation is used to adjust estimated standard errors for the imprecision introduced by SCID diagnoses being imputed under a model rather than measured directly. A brief illustration of this approach is presented in comparing the precision of SCID and predicted SCID estimates of prevalence and correlates under varying sample designs.

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