Determining the characteristics of genetic disorders that predict inclusion in newborn genomic sequencing programs.

Over 30 international research studies and commercial laboratories are exploring the use of genomic sequencing to screen apparently healthy newborns for genetic disorders. These programs have individualized processes for determining which genes and genetic disorders are queried and reported in newborns. We compared lists of genes from 26 research and commercial newborn screening programs and found substantial heterogeneity among the genes included. A total of 1,750 genes were included in at least one newborn genome sequencing program, but only 74 genes were included on >80% of gene lists, 16 of which are not associated with conditions on the Recommended Uniform Screening Panel. We used a linear regression model to explore factors related to the inclusion of individual genes across programs, finding that a high evidence base as well as treatment efficacy were two of the most important factors for inclusion. We applied a machine learning model to predict how suitable a gene is for newborn sequencing. As knowledge about and treatments for genetic disorders expand, this model provides a dynamic tool to reassess genes for newborn screening implementation. This study highlights the complex landscape of gene list curation among genomic newborn screening programs and proposes an empirical path forward for determining the genes and disorders of highest priority for newborn screening programs.

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.