Effective refractive error coverage in adults aged 50 years and older: estimates from population-based surveys in 61 countries.

BACKGROUND: In 2021, WHO Member States endorsed a global target of a 40-percentage-point increase in effective refractive error coverage (eREC; with a 6/12 visual acuity threshold) by 2030. This study models global and regional estimates of eREC as a baseline for the WHO initiative. METHODS: The Vision Loss Expert Group analysed data from 565 448 participants of 169 population-based eye surveys conducted since 2000 to calculate eREC (met need/[met need + undermet need + unmet need]). A binary logistic regression model was used to estimate eREC by Global Burden of Disease (GBD) Study super region among adults aged 50 years and older. FINDINGS: In 2021, distance eREC was 79·1% (95% CI 72·4-85·0) in the high-income super region; 62·1% (54·7-68·8) in north Africa and Middle East; 49·5% (45·0-54·0) in central Europe, eastern Europe, and central Asia; 40·0% (31·7-48·2) in southeast Asia, east Asia, and Oceania; 34·5% (29·4-40·0) in Latin America and the Caribbean; 9·0% (6·5-12·0) in south Asia; and 5·7% (3·1-9·0) in sub-Saharan Africa. eREC was higher in men and reduced with increasing age. Global distance eREC increased from 2000 to 2021 by 19·0%. Global near vision eREC for 2021 was 20·5% (95% CI 17·8-24·4). INTERPRETATION: Over the past 20 years, distance eREC has increased in each super region yet the WHO target will require substantial improvements in quantity and quality of refractive services in particular for near vision impairment. FUNDING: WHO, Sightsavers, The Fred Hollows Foundation, Fondation Thea, Brien Holden Vision Institute, Lions Clubs International Foundation.

Economic Evaluation of Mental Health Effects of Flooding Using Bayesian Networks.

The appraisal of appropriate levels of investment for devising flooding mitigation and to support recovery interventions is a complex and challenging task. Evaluation must account for social, political, environmental and other conditions, such as flood state expectations and local priorities. The evaluation method should be able to quickly identify evolving investment needs as the incidence and magnitude of flood events continue to grow. Quantification is essential and must consider multiple direct and indirect effects on flood related outcomes. The method proposed is this study is a Bayesian network, which may be used ex-post for evaluation, but also ex-ante for future assessment, and near real-time for the reallocation of investment into interventions. The particular case we study is the effect of flood interventions upon mental health, which is a gap in current investment analyses. Natural events such as floods expose people to negative mental health disorders including anxiety, distress and post-traumatic stress disorder. Such outcomes can be mitigated or exacerbated not only by state funded interventions, but by individual and community skills and experience. Success is also dampened when vulnerable and previously exposed victims are affected. Current measures evaluate solely the effectiveness of interventions to reduce physical damage to people and assets. This paper contributes a design for a Bayesian network that exposes causal pathways and conditional probabilities between interventions and mental health outcomes as well as providing a tool that can readily indicate the level of investment needed in alternative interventions based on desired mental health outcomes.