The Income Gradient in Mortality during the Covid-19 Crisis: Evidence from Belgium.

We use population-wide data from linked administrative registers to study the distributional pattern of mortality before and during the first wave of the Covid-19 pandemic in Belgium. Over the March-May 2020 study period, excess mortality is only found among those aged 65 and over. For this group, we find a significant negative income gradient in excess mortality, with excess deaths in the bottom income decile more than twice as high as in the top income decile for both men and women. However, given the high inequality in mortality in normal times, the income gradient in all-cause mortality is only marginally steeper during the peak of the health crisis when expressed in relative terms. Leveraging our individual-level data, we gauge the robustness of our results for other socioeconomic factors and decompose the role of individual vs. local effects. We provide direct evidence that geographic location effects on individual mortality are particularly strong during the first wave of the Covid-19 pandemic, channeling through the local number of Covid infections. This makes inference about the income gradient in excess mortality based on geographic variation misguided. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10888-021-09505-7.

Data-driven prioritization of genetic disorders for global genomic newborn screening programs.

Genomic sequencing is poised to expand newborn screening for treatable childhood-onset disorders. Over 30 international research studies and companies are exploring its use, collectively aiming to screen more than 500,000 infants. A key challenge is determining which genes to include in screening. Among 27 newborn sequencing programs, the number of genes analyzed ranged from 134 to 4,299, with only 74 genes included by over 80% of programs. To understand this variability, we assembled a dataset with 25 characteristics of 4,389 genes included in any program and used a multivariate regression analysis to identify characteristics associated with inclusion across programs. These characteristics included presence on the US Recommended Uniform Screening panel, evidence regarding the natural history of disease, and efficacy of treatment. We then used a machine learning model to generate a ranked list of genes, offering a data-driven approach to the future prioritization of disorders for public health newborn screening efforts.