One of every 3 persons is expected to be diagnosed as having cancer in his or her lifetime,^1,2 with cancer ranking as the second leading cause of death in the United States.³ Although cancer-related mortality has declined over the past few decades, substantial racial/ethnic,^4,5 rural,^4,6 educational attainment,^5,7 and socioeconomic^7,8 disparities remain.⁹ Breast, cervical, and colorectal cancers are among the most frequently diagnosed cancers in the United States, and breast cancer and colorectal cancer are the second and fourth most prevalent causes of cancer-related deaths.^10,11 The morbidity and mortality associated with these cancers can be reduced with timely guideline-recommended screening, diagnosis, and treatment.^12-14 Therefore, lower rates of preventive screening for cancer may contribute to and exacerbate the disparities in cancer-related health outcomes in minority,^5,7 rural,⁶ and low-income^7,8 individuals.⁸ As such, it is important to improve our understanding of contemporary cancer screening disparities beyond the individual patient and potentially identify opportunities for population-focused and area-focused interventions.⁶

We aimed to characterize metabolites during tuberculosis (TB) disease and identify new pathophysiologic pathways involved in infection as well as biomarkers of TB onset, progression and resolution. Such data may inform development of new anti-tuberculosis drugs. Plasma samples from adults with newly diagnosed pulmonary TB disease and their matched, asymptomatic, sputum culture-negative household contacts were analyzed using liquid chromatography high-resolution mass spectrometry (LC-MS) to identify metabolites. Statistical and bioinformatics methods were used to select accurate mass/charge (m/z) ions that were significantly different between the two groups at a false discovery rate (FDR) of q<0.05. Two-way hierarchical cluster analysis (HCA) was used to identify clusters of ions contributing to separation of cases and controls, and metabolomics databases were used to match these ions to known metabolites. Identity of specific D-series resolvins, glutamate and Mycobacterium tuberculosis (Mtb)-derived trehalose-6-mycolate was confirmed using LC-MS/MS analysis. Over 23,000 metabolites were detected in untargeted metabolomic analysis and 61 metabolites were significantly different between the two groups. HCA revealed 8 metabolite clusters containing metabolites largely upregulated in patients with TB disease, including anti-TB drugs, glutamate, choline derivatives, Mycobacterium tuberculosis-derived cell wall glycolipids (trehalose-6-mycolate and phosphatidylinositol) and pro-resolving lipid mediators of inflammation, known to stimulate resolution, efferocytosis and microbial killing. The resolvins were confirmed to be RvD1, aspirin-triggered RvD1, and RvD2. This study shows that high-resolution metabolomic analysis can differentiate patients with active TB disease from their asymptomatic household contacts. Specific metabolites upregulated in the plasma of patients with active TB disease, including Mtb-derived glycolipids and resolvins, have potential as biomarkers and may reveal pathways involved in TB disease pathogenesis and resolution.

In the US, 34.1 million people, or 13% of the population, are living with diabetes.¹ Diabetes and its complications are associated with poor health,² shortened life expectancy,^3,4 and impaired quality of life.^5,6 In 2017 alone, $237 billion was spent in the US on direct medical costs related to this disease.² The burden of diabetes and its complications is disproportionately larger for racial and ethnic minority groups, low-income individuals, and rural residents.^7-9 Factors in high rates of diabetes complications in these underserved populations include suboptimal control of hyperglycemia and other cardiovascular disease risk factors,¹⁰ including hypertension, dyslipidemia, and tobacco smoking.^8,11 To improve the quality of diabetes care, health systems and payers rely on publicly reported metrics to evaluate care delivery, identify opportunities for improvement, and support pay-for-performance reimbursement. However, achieving equity in diabetes care also requires these metrics to be leveraged to identify populations and regions with lagging health outcomes. When such data are available in real time, quality metrics can generate actionable information for clinicians, health systems, public health agencies, and payers on subgroups of patients in need of additional support and targeted interventions.

Background: As the year 2020 approaches, there is a need to evaluate progress toward the United States government’s Healthy People 2020 (HP2020) health information technology and communication objectives to establish baselines upon which Healthy People 2030 objectives can be based. Objective: The aim of this study was to use the National Cancer Institute’s (NCI) Health Information National Trends Survey (HINTS) to benchmark progress toward HP2020 goals related to increasing internet access using broadband, and to assess the state of the digital divide for various sociodemographic groups. Methods: We merged and analyzed data from 8 administrations of HINTS (2003-2017). Descriptive statistics were generated, and predicted marginals were calculated using interaction terms between survey year and selected sociodemographic variables of interest, including age, sex, race and ethnicity, income, education, and geography (rural versus urban), to test for differential change over time. Results: The number of users having access to the internet increased between 2003 and 2014 (63.15% [3982/6358] to 83.41% [2802/3629]); it remained relatively steady from 2014 to 2017 (81.15% [2533/3283]). Broadband access increased between 2003 and 2011 (from 32.83% [1031/3352] to 77.87% [3375/4405]), but has been declining since (55.93% [1364/2487] in 2017). Access via cellular network increased between 2008 and 2017 (from 6.86% [240/4405] to 65.43% [1436/2489]). Statistically significant disparities in overall internet access were noted in the predicted marginals for age, sex, race and ethnicity, income, and education; for age, sex, income, and geography for broadband access; and for age and sex for cellular network. Conclusions: The targets set forth in HP2020 were met for overall internet access and for internet access via cellular network; however, the target was not met for internet access via broadband. Furthermore, although the digital divide persisted by sociodemographic characteristics, the magnitude of many disparities in access decreased over time.