A major goal in human genetics is to understand the role of common genetic variants in susceptibility to common diseases. This will require characterizing the nature of gene variation in human populations, assembling an extensive catalogue of single-nucleotide polymorphisms (SNPs) in candidate genes and performing association studies for particular diseases. At present, our knowledge of human gene variation remains rudimentary. Here we describe a systematic survey of SNPs in the coding regions of human genes. We identified SNPs in 106 genes relevant to cardiovascular disease, endocrinology and neuropsychiatry by screening an average of 114 independent alleles using 2 independent screening methods. To ensure high accuracy, all reported SNPs were confirmed by DNA sequencing. We identified 560 SNPs, including 392 coding-region SNPs (cSNPs) divided roughly equally between those causing synonymous and non-synonymous changes. We observed different rates of polymorphism among classes of sites within genes (non-coding, degenerate and non-degenerate) as well as between genes. The cSNPs most likely to influence disease, those that alter the amino acid sequence of the encoded protein, are found at a lower rate and with lower allele frequencies than silent substitutions. This likely reflects selection acting against deleterious alleles during human evolution. The lower allele frequency of missense cSNPs has implications for the compilation of a comprehensive catalogue, as well as for the subsequent application to disease association.

BackgroundThe overall prevalence of complementary medicine (CM) use among adults in the United States with diabetes has been examined both in representative national samples and in more restricted populations. However, none of these earlier studies attempted to identify predictors of CM use to treat diabetes among the populations sampled, nor looked for a relationship between CM use and diabetes severity.MethodsCombining data from the 2002 and 2007 National Health Interview Survey (NHIS), we constructed a nationally representative sample of 3,978 U.S. adults aged ≥18 years with self-reported diabetes. Both the 2002 and 2007 NHIS contained extensive questions on the use of CM. We used logistic regression to examine the association between diabetes severity and overall CM use, as well as the use of specific categories of CM.ResultsIn adults with type-2 diabetes, 30.9% used CM for any reason, but only 3.4% used CM to treat or manage their type-2 diabetes versus 7.1% of those with type-1 diabetes. Among those using CM to treat/manage their type-2 diabetes, 77% used both CM and conventional prescription medicine for their diabetes. The most prevalent types of CM therapies used were diet-based interventions (35.19%, S.E. 5.11) and non-vitamin/non-mineral dietary supplements (33.74%, S.E. 5.07). After controlling for sociodemographic factors, we found that, based on a count of measures of diabetes severity, persons with the most severe diabetes had nearly twice the odds of using CM as those with less severe disease (OR=1.9, 95%CI 1.2-3.01). Persons who had diabetes 10 years or more (OR=1.66, 95%CI 1.04-3.66) and those that had a functional limitation resulting from their diabetes (OR=1.74, 95%CI 1.09-2.8) had greater odds of using CM than those not reporting these measures. No significant associations were observed between overall CM use and other individual measures of diabetes severity: use of diabetic medications, weak or failing kidneys, coronary heart disease, or severe vision problems.ConclusionsOur results demonstrate that individuals with more severe diabetes are more likely to use CM independent of sociodemographic factors. Further studies are essential to determine if CM therapies actually improve clinical outcomes when used to treat/manage diabetes.

Containing coronavirus disease 2019 (COVID-19) through case investigation and contact tracing is a crucial strategy for governmental public health agencies to control the spread of COVID-19 infection in the United States. Because of the recency of the pandemic, few examples of COVID-19 contact-tracing models have been shared among local, state, and federal public health officials to date. This case study of the Anne Arundel County Department of Health (Maryland) illustrates one model of contact-tracing activity developed early in the outbreak. We describe the contact-tracing effort’s place within the broader county health agency Incident Command System, as well as the capabilities needed, team composition, special considerations, and major lessons learned by county health officials. Other local, state, tribal, territorial, and federal health officials and policy makers can use this case study to innovate, iterate, and further refine contact-tracing efforts to prevent the spread of COVID-19 infection and support community members in isolation or quarantine.

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