Socioeconomic inequalities in low birth weight risk before and during the COVID-19 pandemic in Argentina: A cross-sectional study.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic may have exacerbated existing socioeconomic inequalities in health. In Argentina, public hospitals serve the poorest uninsured segment of the population, while private hospitals serve patients with health insurance. This study aimed to assess whether socioeconomic inequalities in low birth weight (LBW) risk changed during the first wave of the COVID-19 pandemic. METHODS: This multicenter cross-sectional study included 15929 infants. A difference-in-difference (DID) analysis of socioeconomic inequalities between public and private hospitals in LBW risk in a pandemic cohort (March 20 to July 19, 2020) was compared with a prepandemic cohort (March 20 to July 19, 2019) by using medical records obtained from ten hospitals. Infants were categorized by weight as LBW < 2500 g, very low birth weight (VLBW) < 1500 g and extremely low birth weight (ELBW) < 1000 g. Log binomial regression was performed to estimate risk differences with an interaction term representing the DID estimator. Covariate-adjusted models included potential perinatal confounders. FINDINGS: Of the 8437 infants in the prepandemic cohort, 4887 (57•9%) were born in public hospitals. The pandemic cohort comprised 7492 infants, 4402 (58•7%) of whom were born in public hospitals. The DID estimators indicated no differences between public versus private hospitals for LBW risk (-1•8% [95% CI -3•6, 0•0]) and for ELBW risk (-0•1% [95% CI -0•6, 0•3]). Significant differences were found between public versus private hospitals in the DID estimators (-1•2% [95% CI, -2•1, -0•3]) for VLBW risk. The results were comparable in covariate-adjusted models. INTERPRETATION: In this study, we found evidence of decreased disparities between public and private hospitals in VLBW risk. Our findings suggest that measures that prioritize social spending to protect the most vulnerable pregnant women during the pandemic contributed to better birth outcomes. FUNDING: No funding was secured for this study.

Automatic temporal lobe atrophy assessment in prodromal AD: Data from the DESCRIPA study.

BACKGROUND: In the framework of the clinical validation of research tools, this investigation presents a validation study of an automatic medial temporal lobe atrophy measure that is applied to a naturalistic population sampled from memory clinic patients across Europe. METHODS: The procedure was developed on 1.5-T magnetic resonance images from the Alzheimer's Disease Neuroimaging Initiative database, and it was validated on an independent data set coming from the DESCRIPA study. All images underwent an automatic processing procedure to assess tissue atrophy that was targeted at the hippocampal region. For each subject, the procedure returns a classification index. Once provided with the clinical assessment at baseline and follow-up, subjects were grouped into cohorts to assess classification performance. Each cohort was divided into converters (co) and nonconverters (nc) depending on the clinical outcome at follow-up visit. RESULTS: We found the area under the receiver operating characteristic curve (AUC) was 0.81 for all co versus nc subjects, and AUC was 0.90 for subjective memory complaint (SMCnc) versus all co subjects. Furthermore, when training on mild cognitive impairment (MCI-nc/MCI-co), the classification performance generally exceeds that found when training on controls versus Alzheimer's disease (CTRL/AD). CONCLUSIONS: Automatic magnetic resonance imaging analysis may assist clinical classification of subjects in a memory clinic setting even when images are not specifically acquired for automatic analysis.