Quantifying the effects of the COVID-19 pandemic on gender equality on health, social, and economic indicators: a comprehensive review of data from March, 2020, to September, 2021.

BACKGROUND: Gender is emerging as a significant factor in the social, economic, and health effects of COVID-19. However, most existing studies have focused on its direct impact on health. Here, we aimed to explore the indirect effects of COVID-19 on gender disparities globally. METHODS: We reviewed publicly available datasets with information on indicators related to vaccine hesitancy and uptake, health care services, economic and work-related concerns, education, and safety at home and in the community. We used mixed effects regression, Gaussian process regression, and bootstrapping to synthesise all data sources. We accounted for uncertainty in the underlying data and modelling process. We then used mixed effects logistic regression to explore gender gaps globally and by region. FINDINGS: Between March, 2020, and September, 2021, women were more likely to report employment loss (26·0% [95% uncertainty interval 23·8-28·8, by September, 2021) than men (20·4% [18·2-22·9], by September, 2021), as well as forgoing work to care for others (ratio of women to men: 1·8 by March, 2020, and 2·4 by September, 2021). Women and girls were 1·21 times (1·20-1·21) more likely than men and boys to report dropping out of school for reasons other than school closures. Women were also 1·23 (1·22-1·23) times more likely than men to report that gender-based violence had increased during the pandemic. By September 2021, women and men did not differ significantly in vaccine hesitancy or uptake. INTERPRETATION: The most significant gender gaps identified in our study show intensified levels of pre-existing widespread inequalities between women and men during the COVID-19 pandemic. Political and social leaders should prioritise policies that enable and encourage women to participate in the labour force and continue their education, thereby equipping and enabling them with greater ability to overcome the barriers they face. FUNDING: The Bill & Melinda Gates Foundation.

US Health Care Spending by Race and Ethnicity, 2002-2016.

Importance: Measuring health care spending by race and ethnicity is important for understanding patterns in utilization and treatment. Objective: To estimate, identify, and account for differences in health care spending by race and ethnicity from 2002 through 2016 in the US. Design, Setting, and Participants: This exploratory study included data from 7.3 million health system visits, admissions, or prescriptions captured in the Medical Expenditure Panel Survey (2002-2016) and the Medicare Current Beneficiary Survey (2002-2012), which were combined with the insured population and notified case estimates from the National Health Interview Survey (2002; 2016) and health care spending estimates from the Disease Expenditure project (1996-2016). Exposure: Six mutually exclusive self-reported race and ethnicity groups. Main Outcomes and Measures: Total and age-standardized health care spending per person by race and ethnicity for each year from 2002 through 2016 by type of care. Health care spending per notified case by race and ethnicity for key diseases in 2016. Differences in health care spending across race and ethnicity groups were decomposed into differences in utilization rate vs differences in price and intensity of care. Results: In 2016, an estimated $2.4 trillion (95% uncertainty interval [UI], $2.4 trillion-$2.4 trillion) was spent on health care across the 6 types of care included in this study. The estimated age-standardized total health care spending per person in 2016 was $7649 (95% UI, $6129-$8814) for American Indian and Alaska Native (non-Hispanic) individuals; $4692 (95% UI, $4068-$5202) for Asian, Native Hawaiian, and Pacific Islander (non-Hispanic) individuals; $7361 (95% UI, $6917-$7797) for Black (non-Hispanic) individuals; $6025 (95% UI, $5703-$6373) for Hispanic individuals; $9276 (95% UI, $8066-$10 601) for individuals categorized as multiple races (non-Hispanic); and $8141 (95% UI, $8038-$8258) for White (non-Hispanic) individuals, who accounted for an estimated 72% (95% UI, 71%-73%) of health care spending. After adjusting for population size and age, White individuals received an estimated 15% (95% UI, 13%-17%; P < .001) more spending on ambulatory care than the all-population mean. Black (non-Hispanic) individuals received an estimated 26% (95% UI, 19%-32%; P < .001) less spending than the all-population mean on ambulatory care but received 19% (95% UI, 3%-32%; P = .02) more on inpatient and 12% (95% UI, 4%-24%; P = .04) more on emergency department care. Hispanic individuals received an estimated 33% (95% UI, 26%-37%; P < .001) less spending per person on ambulatory care than the all-population mean. Asian, Native Hawaiian, and Pacific Islander (non-Hispanic) individuals received less spending than the all-population mean on all types of care except dental (all P < .001), while American Indian and Alaska Native (non-Hispanic) individuals had more spending on emergency department care than the all-population mean (estimated 90% more; 95% UI, 11%-165%; P = .04), and multiple-race (non-Hispanic) individuals had more spending on emergency department care than the all-population mean (estimated 40% more; 95% UI, 19%-63%; P = .006). All 18 of the statistically significant race and ethnicity spending differences by type of care corresponded with differences in utilization. These differences persisted when controlling for underlying disease burden. Conclusions and Relevance: In the US from 2002 through 2016, health care spending varied by race and ethnicity across different types of care even after adjusting for age and health conditions. Further research is needed to determine current health care spending by race and ethnicity, including spending related to the COVID-19 pandemic.