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.

ROP Screening Tool Assessment and Validation in a Third-Level Hospital in Argentina: A Pilot Study.

PURPOSE: To evaluate whether a mathematical tool that predicts severe retinopathy of prematurity (ROP) using clinical parameters at 6 weeks of life (ROPScore calculator smartphone application; PABEX Corporation) can be useful to predict severe ROP in a population of premature infants in Argentina. METHODS: In this retrospective study, data from the clinical records of all premature infants examined between 2012 and 2018 in the ophthalmology department of a public third-level hospital in Córdoba, Argentina, were obtained. ROPScore screening was applied using a Microsoft Excel spreadsheet (Microsoft Corporation). The sensitivity, specificity, and positive (PPV) and negative (NPV) predictive values of the algorithm were analyzed. RESULTS: Between 2012 and 2018, a total of 2,894 pre-term infants were examined and 411 met the inclusion criteria, of whom 34% (n = 139) presented some form of ROP and 6% (n = 25) developed severe forms that required treatment. The sensitivity of the algorithm for any ROP and severe ROP was 100%. The PPV and NPV were 35.64% and 100%, respectively, for any ROP and 9.88% and 100% for severe ROP. CONCLUSIONS: One-time only calculation of the ROPScore algorithm could identify severe cases after validation, reducing the number of screened infants by 38% in infants with a birth weight of 1,500 g or less or a gestational age of 32 weeks or younger. [J Pediatr Ophthalmol Strabismus. 2021;58(1):55-61.].