Artificial Intelligence (AI) Reveals Ethnic Disparities in Cataract Detection and Treatment

INTRODUCTION

The aim of this work is to identify patients at risk of limited access to healthcare through artificial intelligence using a name-ethnicity classifier (NEC) analyzing the clinical stage of cataract at diagnosis and preoperative visual acuity.

METHODS

This retrospective, cross-sectional study includes patients seen in the cataract clinic of a tertiary care hospital between September 2017 and February 2020 with subsequent cataract surgery in at least one eye. We analyzed 4971 patients and 8542 eyes undergoing surgery.

RESULTS

The NEC identified 360 patients with names classified as 'non-German' compared to 4611 classified as 'German'. Advanced cataract (7 vs. 5%; p = 0.025) was significantly associated with group 'non-German'. Mean best-corrected visual acuity in group 'non-German' was 0.464 ± 0.406 (LogMAR), and in group 'German' was 0.420 ± 0.334 (p = 0.009). This difference remained significant after exclusion of patients with non-lenticular ocular comorbidities. Surgical time and intraoperative complications did not differ between the groups. Retrobulbar or general anesthesia was chosen significantly more frequently over topical anesthesia in group 'non-German' compared to group 'German' (24 vs. 18% respectively; p < 0.001).

CONCLUSIONS

This study shows that artificial intelligence is able to uncover health disparities between people with German compared to non-German names using NECs. Patients with non-German names, possibly facing various social barriers to healthcare access such as language barriers, have more advanced cataracts and worse visual acuity upon presentation. Artificial intelligence may prove useful for healthcare providers to discover and counteract such inequalities and establish tailored preventive measures to decrease morbidity in vulnerable population subgroups.

Immigrant assimilation in health care utilisation in Spain

Abundant evidence has tracked the labour market and health assimilation of immigrants, including static analyses of differences in how foreign-born and native-born residents consume health care services. However, we know much less about how migrants' patterns of healthcare usage evolve with time of residence, especially in countries providing universal or quasi-universal coverage. We investigate this process in Spain by combining all the available waves of the local health survey, which allows us to separately identify period, cohort, and assimilation effects. We find robust evidence of migrant assimilation in health care use, specifically in visits to general practitioners and emergency care and among foreign-born women. The differential effects of ageing on health care use between foreign-born and native-born populations contributes to the convergence of utilisation patterns in most health services after 15 years in Spain. Substantial heterogeneity by the time of arrival and by region of origin both suggest that studies modelling future welfare state finances would benefit from a more thorough assessment of migration.

Context, health and migration: a systematic review of natural experiments

Background

Migration health research pays little attention to the places into which people migrate. Studies on health effects of contextual factors are often limited because of the ability of individuals to self-select their environment, but natural experiments may allow for the causal effect of contexts to be examined. The objective was to synthesise the evidence on contextual health effects from natural experiments among migrant groups.

Methods

We performed a systematic review of natural experiments among migrant populations in PubMed/MEDLINE, The Cochrane Library, Web of Science, CINAHL and Google Scholar for literature published until 13 October 2022. 5870 articles were screened in duplicate using the following inclusion criteria: quantitative natural experiment design, migrant population, context factor as treatment variable and health or healthcare outcome variable. Synthesis without meta-analysis was performed following quality appraisal using the EPHPP tool for quantitative studies and data extraction (PROSPERO: CRD42020169236).

Findings

The 46 included articles provide evidence for negative effects of neighbourhood disadvantage on physical health and mortality, while finding mixed effects on mental health. Articles comparing migrants with those that stayed behind demonstrate detrimental effects of migration and adverse post-migratory contexts on physical health and mortality, while demonstrating favourable effects for mental and child health. Natural experiments of policy environments indicate the negative impacts of restrictive migration and social policies on healthcare utilization, mental health and mortality, as well as the positive health effects when restrictions are lifted.

Interpretation

Natural experiments complement observational studies and provide robust evidence to advocate for more inclusive migration, health and social policies as well as neighbourhood improvement programmes. In order to strengthen the methodological approach, future research utilising natural experiments should be more explicit in the mechanisms underlying the experiment and provide details on potential causal mechanisms for the observed effects.

Funding

German Science Foundation (FOR: 2928/GZ: BO5233/1-1).