Predicting COVID-19 and respiratory illness: results of the 2022-2023 Armed Forces Health Surveillance Division forecasting challenge.

Since 2019, the Integrated Biosurveillance Branch of the Armed Forces Health Surveillance Division has conducted an annual forecasting challenge during influenza season to predict short-term respiratory disease activity among Military Health System beneficiaries. Weekly case and encounter observed data were used to generate 1- through 4-week advanced forecasts of disease activity. To create unified combinations of model inputs for evaluation across multiple spatial resolutions, 8 individual models were used to calculate 3 ensemble models. Forecast accuracy compared to the observed activity for each model was evaluated by calculating a weighted interval score. Weekly 1- through 4-week ahead forecasts for each ensemble model were generally higher than observed data, especially during periods of peak activity, with peaks in forecasted activity occurring later than observed peaks. The larger the forecasting horizon, the more pronounced the gap between forecasted peak and observed peak. The results showed that several models accurately predicted COVID-19 cases and respiratory encounters with enough lead time for public health response by senior leaders.

Nativity as a Determinant of Health Disparities Among Children.

Nativity is not often considered in the study of health disparities. We conducted a cross-sectional, parent-reported survey of demographics, socioeconomic characteristics, healthcare access, and health conditions in New York City schoolchildren (n = 9029). US-born children with US-born parents (US/US) had higher socioeconomic status, better access to healthcare, and reported higher rates of disease diagnoses compared to US-born children with immigrant parents and to immigrant children. Dental cavities were the only condition in which US/US children reported lower prevalence. US/US children had the best healthcare access, most favorable parent-reported health status and highest rate of satisfaction with healthcare. The magnitude of racial/ethnic disparities varied based on nativity of the children being compared. Factors such as the healthy immigrant effect and differential diagnosis rates may explain the results. In conclusion, nativity influences disease burdens and should be considered in health disparities studies.

Determinants of breast cancer treatment delay differ for African American and White women.

BACKGROUND: Timeliness of care may contribute to racial disparities in breast cancer mortality. African American women experience greater treatment delay than White women in most, but not all studies. Understanding these disparities is challenging as many studies lack patient-reported data and use administrative data sources that collect limited types of information. We used interview and medical record data from the Carolina Breast Cancer Study (CBCS) to identify determinants of delay and assess whether disparities exist between White and African American women (n = 601). METHODS: The CBCS is a population-based study of North Carolina women. We investigated the association of demographic and socioeconomic characteristics, healthcare access, clinical factors, and measures of emotional and functional well-being with treatment delay. The association of race and selected characteristics with delays of more than 30 days was assessed using logistic regression. RESULTS: Household size, losing a job due to one's diagnosis, and immediate reconstruction were associated with delay in the overall population and among White women. Immediate reconstruction and treatment type were associated with delay among African American women. Racial disparities in treatment delay were not evident in the overall population. In the adjusted models, African American women experienced greater delay than White women for younger age groups: OR, 3.34; 95% confidence interval (CI), 1.07-10.38 for ages 20 to 39 years, and OR, 3.40; 95% CI, 1.76-6.54 for ages 40 to 49 years. CONCLUSIONS: Determinants of treatment delay vary by race. Racial disparities in treatment delay exist among women younger than 50 years. IMPACT: Specific populations need to be targeted when identifying and addressing determinants of treatment delay.