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Purpose: To investigate changes in breast cancer incidence rates associated with Medicaid expansion in California. Methods: We extracted yearly census tract-level population counts and cases of breast cancer diagnosed among women aged between 20 and 64 years in California during years 2010–2017. Census tracts were classified into low, medium and high groups according to their social vulnerability index (SVI). Using a difference-in-difference (DID) approach with Poisson regression models, we estimated the incidence rate, incidence rate ratio (IRR) during the pre- (2010–2013) and post-expansion periods (2014–2017), and the relative IRR (DID estimates) across three groups of neighborhoods. Results: Prior to the Medicaid expansion, the overall incidence rate was 93.61, 122.03, and 151.12 cases per 100,000 persons among tracts with high, medium, and low-SVI, respectively; and was 96.49, 122.07, and 151.66 cases per 100,000 persons during the post-expansion period, respectively. The IRR between high and low vulnerability neighborhoods was 0.62 and 0.64 in the pre- and post-expansion period, respectively, and the relative IRR was 1.03 (95% CI 1.00 to 1.06, p = 0.026). In addition, significant DID estimate was only found for localized breast cancer (relative IRR = 1.05; 95% CI, 1.01 to 1.09, p = 0.049) between high and low-SVI neighborhoods, not for regional and distant cancer stage. Conclusions: The Medicaid expansion had differential impact on breast cancer incidence across neighborhoods in California, with the most pronounced increase found for localized cancer stage in high-SVI neighborhoods. Significant pre-post change was only found for localized breast cancer between high and low-SVI neighborhoods. [ABSTRACT FROM AUTHOR]

Verifying causal effects of neural circuits is essential for proving a direct circuit-behavior relationship. However, techniques for tagging only active neurons with high spatiotemporal precision remain at the beginning stages. Here we develop the soma-targeted Cal-Light (ST-Cal-Light) which selectively converts somatic calcium rise triggered by action potentials into gene expression. Such modification simultaneously increases the signal-to-noise ratio of reporter gene expression and reduces the light requirement for successful labeling. Because of the enhanced efficacy, the ST-Cal-Light enables the tagging of functionally engaged neurons in various forms of behaviors, including context-dependent fear conditioning, lever-pressing choice behavior, and social interaction behaviors. We also target kainic acid-sensitive neuronal populations in the hippocampus which subsequently suppress seizure symptoms, suggesting ST-Cal-Light's applicability in controlling disease-related neurons. Furthermore, the generation of a conditional ST-Cal-Light knock-in mouse provides an opportunity to tag active neurons in a region- or cell-type specific manner via crossing with other Cre-driver lines. Thus, the versatile ST-Cal-Light system links somatic action potentials to behaviors with high temporal precision, and ultimately allows functional circuit dissection at a single cell resolution. Techniques for tagging active neurons with high spatiotemporal precision are limited. Here the authors report soma-targeted CalLight (ST-Cal-Light) which selectively converts somatic calcium rise triggered by action potentials into gene expression, and generate a conditional ST-Cal-Light knock-in mouse. [ABSTRACT FROM AUTHOR]

The Cook Partisan Voting Index (PVI) determines how strongly a state leans toward the Democratic or Republican Party in US presidential elections compared to the nation. We set out to determine the correlation between childhood health outcomes and state-level partisanship using PVI. Sixteen measures of childhood health were obtained from several US governmental agencies for 2003–2017. The median PVI for every state was calculated for the same time period. Pearson's rho determined the correlation between PVI and each health outcome. Multiple regression was also conducted, adjusting for educational attainment and percentage of non-White residents. We also compared childhood health in moderately Democratic and Republican states (5–9.9% more Democratic/Republican than the national mean) and, similarly, for extremely Democratic and Republican states (10% or more Democratic/Republican than the national mean), using Wilcoxon tests. For all 16 health measures, the median values in Democratic-leaning states represented better outcomes than Republican-leaning states (9/16 had a beta value for linear regression associated with P < 0.05). When compared to Republican states, the median values in moderately Democratic states represented better outcomes for 14 of 16 health measures (9/14 associated with P < 0.05). Similarly, the median values for extremely Democratic states represented better outcomes with regard to all 16 health measures, when compared to Republican-leaning states (8/16 associated with P < 0.05). Conclusions: Democratic-leaning states displayed superior outcomes for multiple childhood health measures when compared to Republican counterpart states. Future research should investigate the significance of these findings and attempt to determine which state-level policies may have contributed to such disparate health outcomes. What is Known: • In the United States, many health disparities exist among children along racial, economic and geographic lines. • Many US states lean strongly towards either the Democratic or Republican political parties in federal elections. What is New: • Trends for multiple measures of childhood health vary in association with the political partisanship of the state being examined. • Multiple barometers of childhood health are superior in Democratic-leaning states, while no measures are better in Republican-leaning states. [ABSTRACT FROM AUTHOR]

Prediabetes and diabetes mellitus (preDM/DM) have become alarmingly prevalent among youth in recent years. However, simple questionnaire-based screening tools to reliably assess diabetes risk are only available for adults, not youth. As a first step in developing such a tool, we used a large-scale dataset from the National Health and Nutritional Examination Survey (NHANES) to examine the performance of a published pediatric clinical screening guideline in identifying youth with preDM/DM based on American Diabetes Association diagnostic biomarkers. We assessed the agreement between the clinical guideline and biomarker criteria using established evaluation measures (sensitivity, specificity, positive/negative predictive value, F-measure for the positive/negative preDM/DM classes, and Kappa). We also compared the performance of the guideline to those of machine learning (ML) based preDM/DM classifiers derived from the NHANES dataset. Approximately 29% of the 2858 youth in our study population had preDM/DM based on biomarker criteria. The clinical guideline had a sensitivity of 43.1% and specificity of 67.6%, positive/negative predictive values of 35.2%/74.5%, positive/negative F-measures of 38.8%/70.9%, and Kappa of 0.1 (95%CI: 0.06–0.14). The performance of the guideline varied across demographic subgroups. Some ML-based classifiers performed comparably to or better than the screening guideline, especially in identifying preDM/DM youth (p = 5.23 × 10−5).We demonstrated that a recommended pediatric clinical screening guideline did not perform well in identifying preDM/DM status among youth. Additional work is needed to develop a simple yet accurate screener for youth diabetes risk, potentially by using advanced ML methods and a wider range of clinical and behavioral health data. [ABSTRACT FROM AUTHOR]

Background: On April 17, 2020, the State of New York (NY) implemented an Executive Order that requires all people in NY to wear a face mask or covering in public settings where social distancing cannot be maintained. Although the Centers for Disease Control and Prevention recommended face mask use by the general public, there is a lack of evidence on the effect of face mask policies on the spread of COVID-19 at the state level. Objective: To assess the impact of the Executive Order on face mask use on COVID-19 cases and mortality in NY. Design: A comparative interrupted time series analysis was used to assess the impact of the Executive Order in NY with Massachusetts (MA) as a comparison state. Participants: We analyzed data on COVID-19 in NY and MA from March 25 to May 6, 2020. Intervention: The Executive Order on face mask use in NY. Main Measures: Daily numbers of COVID-19 confirmed cases and deaths. Key Results: The average daily number of confirmed cases in NY decreased from 8549 to 5085 after the Executive Order took effect, with a trend change of 341 (95% CI, 187–496) cases per day. The average daily number of deaths decreased from 521 to 384 during the same two time periods, with a trend change of 52 (95% CI, 44–60) deaths per day. Compared to MA, the decreasing trend in NY was significantly greater for both daily numbers of confirmed cases (P = 0.003) and deaths (P < 0.001). Conclusions: The Executive Order on face mask use in NY led to a significant decrease in both daily numbers of COVID-19 confirmed cases and deaths. Findings from this study provide important evidence to support state-level policies that require face mask use by the general public. [ABSTRACT FROM AUTHOR]

There is growing evidence on the effect of face mask use in controlling the spread of COVID-19. However, few studies have examined the effect of local face mask policies on the pandemic. In this study, we developed a dynamic compartmental model of COVID-19 transmission in New York City (NYC), which was the epicenter of the COVID-19 pandemic in the USA. We used data on daily and cumulative COVID-19 infections and deaths from the NYC Department of Health and Mental Hygiene to calibrate and validate our model. We then used the model to assess the effect of the executive order on face mask use on infections and deaths due to COVID-19 in NYC. Our results showed that the executive order on face mask use was estimated to avert 99,517 (95% CIs 72,723–126,312) COVID-19 infections and 7978 (5692–10,265) deaths in NYC. If the executive order was implemented 1 week earlier (on April 10), the averted infections and deaths would be 111,475 (81,593–141,356) and 9017 (6446–11,589), respectively. If the executive order was implemented 2 weeks earlier (on April 3 when the Centers for Disease Control and Prevention recommended face mask use), the averted infections and deaths would be 128,598 (94,373–162,824) and 10,515 (7540–13,489), respectively. Our study provides public health practitioners and policymakers with evidence on the importance of implementing face mask policies in local areas as early as possible to control the spread of COVID-19 and reduce mortality. [ABSTRACT FROM AUTHOR]

Stroke exerts a massive burden on the US health and economy. Place-based evidence is increasingly recognized as a critical part of stroke management, but identifying the key determinants of neighborhood stroke prevalence and the underlying effect mechanisms is a topic that has been treated sparingly in the literature. We aim to fill in the research gaps with a study focusing on urban health. We develop and apply analytical approaches to address two challenges. First, domain expertise on drivers of neighborhood-level stroke outcomes is limited. Second, commonly used linear regression methods may provide incomplete and biased conclusions. We created a new neighborhood health data set at census tract level by pooling information from multiple sources. We developed and applied a machine learning–based quantile regression method to uncover crucial neighborhood characteristics for neighborhood stroke outcomes among vulnerable neighborhoods burdened with high prevalence of stroke. Neighborhoods with a larger share of non-Hispanic blacks, older adults, or people with insufficient sleep tended to have a higher prevalence of stroke, whereas neighborhoods with a higher socio-economic status in terms of income and education had a lower prevalence of stroke. The effects of five major determinants varied geographically and were significantly stronger among neighborhoods with high prevalence of stroke. Highly flexible machine learning identifies true drivers of neighborhood cardiovascular health outcomes from wide-ranging information in an agnostic and reproducible way. The identified major determinants and the effect mechanisms can provide important avenues for prioritizing and allocating resources to develop optimal community-level interventions for stroke prevention. [ABSTRACT FROM AUTHOR]

Several population health big data projects have been initiated in the USA recently. These include the County Health Rankings & Roadmaps (CHR) initiated in 2010, the 500 Cities Project initiated in 2016, and the City Health Dashboard project initiated in 2017. Such projects provide data on a range of factors that determine health—such as socioeconomic factors, behavioral factors, health care access, and environmental factors—either at the county or city level. They provided state-of-the-art data visualization and interaction tools so that clinicians, public health practitioners, and policymakers can easily understand population health data at the local level. However, these recent initiatives were all built from data collected using long-standing and extant public health surveillance systems from organizations such as the Centers for Disease Control and Prevention and the U.S. Census Bureau. This resulted in a large extent of similarity among different datasets and a potential waste of resources. This perspective article aims to elaborate on the diminishing returns of creating more population health datasets and propose potential ways to integrate with clinical care and research, driving insights bidirectionally, and utilizing advanced analytical tools to improve value in population health big data. [ABSTRACT FROM AUTHOR]

Keywords: Alzheimer's disease; ABCA7; Gene expression; rs4147929 variant; eQTLs analysis EN Alzheimer's disease ABCA7 Gene expression rs4147929 variant eQTLs analysis 937 940 4 04/25/20 20200501 NES 200501 Electronic supplementary material The online version of this article (10.1007/s00401-020-02135-9) contains supplementary material, which is available to authorized users. The second is from the Mayo Clinic consisting of 773 brain tissue samples (197 AD cerebellar samples, 202 AD temporal cortex samples, 177 non-AD cerebellar samples, 197 non-AD temporal cortex samples) [[16]]. Alzheimer's disease, ABCA7, Gene expression, rs4147929 variant, eQTLs analysis. [Extracted from the article]

Purpose: Although exposure to polycyclic aromatic hydrocarbons (PAHs) is common in both environmental and occupational settings, few studies have compared PAH exposure among people with different professions. The purpose of this study was to investigate the variations in recent PAH exposure among different occupational groups over time using national representative samples. Method: The study population consisted of 4162 participants from the 2001 to 2008 National Health and Nutrition Examination Survey, who had both urinary PAH metabolites and occupational information. Four corresponding monohydroxy-PAH urine metabolites: naphthalene (NAP), fluorene (FLUO), phenanthrene (PHEN), and pyrene (PYR) among seven broad occupational groups were analyzed using weighted linear regression models, adjusting for creatinine levels, sociodemographic factors, smoking status, and sampling season. Results: The overall geometric mean concentrations of NAP, FLUO, PHEN, and PYR were 6927, 477, 335, and 87 ng/L, respectively. All four PAH metabolites were elevated in the 'extractive, construction, and repair (ECR)' group, with 21-42 % higher concentrations than those in the reference group of 'management.' Similar trends were seen in the 'operators, fabricators, and laborers (OFL)' group for FLUO, PHEN, and PYR. In addition, both 'service' and 'support' groups had elevated FLUO. Significant ( p < 0.001) upward temporal trends were seen in NAP and PYR, with an approximately 6-17 % annual increase, and FLUO and PHEN remained relatively stable. Race and socioeconomic status show independent effects on PAH exposure. Conclusions: Heterogeneous distributions of urinary PAH metabolites among people with different job categories exist at the population level. The upward temporal trends in NAP and PYR warrant reduction in PAH exposure, especially among those with OFL and ECR occupations. [ABSTRACT FROM AUTHOR]

Methylenetetrahydrofolate reductase ( MTHFR) is an important enzyme involved in folate metabolism, which is essential for DNA synthesis and methylation. Genetic variations in the MTHFR gene seem to contribute to a decreased activity of MTHFR, ultimately confer increased susceptibility to cancer. As the most extensively studied polymorphism, MTHFR C677T polymorphism was shown to contribute to cancer susceptibility but the results were inconsistent. The authors performed a meta-analysis including 134 studies (46,207 cases and 69,160 controls) to address the issue. Odds ratios (ORs) with corresponding 95 % confidence intervals (CIs) were used to assess the association. Overall, a significant elevated risk of cancer was associated with the MTHFR C677T polymorphism in T-allele versus C-allele comparison (OR = 1.06, 95 % CI 1.02-1.11, P < 0.001), homozygote model (OR = 1.08, 95 % CI 1.01-1.17, P < 0.001) and dominant model (OR = 1.05, 95 % CI 1.00-1.10, P < 0.001). In the stratified analyses, significantly increased cancer risks were indicated among Asians in all genetic models except for heterozygote model. Further analysis revealed that C677T was significantly associated with an increased risk of esophageal and stomach cancer. This meta-analysis supports an association between the MTHFR C677T polymorphism and increased risk of esophageal and stomach cancer, especially among Asians. Additionally, more high-quality studies and that the covariates responsible for heterogeneity should be controlled to obtain a more conclusive response about the function of MTHFR C677T in cancer. [ABSTRACT FROM AUTHOR]

Few prospective studies have examined the frequency, predictors and long-term outcomes of spontaneous hemorrhagic transformation (HT) in patients with ischemic stroke not receiving thrombolytic treatment. We prospectively enrolled a consecutive cohort of 407 patients with ischemic stroke who were admitted within one month of stroke onset. In patients who developed spontaneous HT, the area of the infarct and HT were examined by computed tomography (CT) or magnetic resonance imaging (MRI). Univariate analysis was used to correlate clinical characteristics with appearance of HT, then multivariate logistical regression was used to identify independent predictors of spontaneous HT and factors that predict 3-month prognosis of ischemic stroke. Spontaneous HT was observed in 50 patients (12.3 %), comprising 33 cases (66 %) of hemorrhagic infarction, 17 (34 %) of parenchymal hematoma, 32 (64 %) of non-symptomatic HT, and 18 (36 %) of symptomatic HT. In 40 % of HT cases, the condition was detected by CT or MRI within 4-7 days of symptom onset. Multivariate logistic regression identified atrial fibrillation (OR 4.88, 95 % CI 1.83-13.00, P = 0.002) and infarct area (OR 4.48, 95 % CI 1.85-10.85, P = 0.001) as independent predictors of HT in ischemic stroke. Multivariate analysis also found that spontaneous HT was not independently associated with a worse 3-month prognosis for ischemic stroke (OR 1.59, 95 % CI 0.38-6.69, P = 0.527). Spontaneous HT occurred in 12.3 % of our patients with ischemic stroke, and atrial fibrillation and large infarct area were independent predictors. Spontaneous HT was not an independent predictor of a worse 3-month prognosis for ischemic stroke. [ABSTRACT FROM AUTHOR]

Single-molecule studies can overcome the complications of asynchrony and ensemble-averaging in bulk-phase measurements, provide mechanistic insights into molecular activities, and reveal interesting variations between individual molecules. The application of these techniques to the RecBCD helicase of Escherichia coli has resolved some long-standing discrepancies, and has provided otherwise unattainable mechanistic insights into its enzymatic behaviour. Enigmatically, the DNA unwinding rates of individual enzyme molecules are seen to vary considerably, but the origin of this heterogeneity remains unknown. Here we investigate the physical basis for this behaviour. Although any individual RecBCD molecule unwound DNA at a constant rate for an average of approximately 30,000 steps, we discover that transiently halting a single enzyme-DNA complex by depleting Mg2+-ATP could change the subsequent rates of DNA unwinding by that enzyme after reintroduction to ligand. The proportion of molecules that changed rate increased exponentially with the duration of the interruption, with a half-life of approximately 1 second, suggesting that a conformational change occurred during the time that the molecule was arrested. The velocity after pausing an individual molecule was any velocity found in the starting distribution of the ensemble. We suggest that substrate binding stabilizes the enzyme in one of many equilibrium conformational sub-states that determine the rate-limiting translocation behaviour of each RecBCD molecule. Each stabilized sub-state can persist for the duration (approximately 1 minute) of processive unwinding of a DNA molecule, comprising tens of thousands of catalytic steps, each of which is much faster than the time needed for the conformational change required to alter kinetic behaviour. This ligand-dependent stabilization of rate-defining conformational sub-states results in seemingly static molecule-to-molecule variation in RecBCD helicase activity, but in fact reflects one microstate from the equilibrium ensemble that a single molecule manifests during an individual processive translocation event. [ABSTRACT FROM AUTHOR]

References 1 Oronce CI, Scannell CA, Kawachi I, Tsugawa Y. Association between state-level income inequality and COVID-19 cases and mortality in the USA. Dear Editors, We read with great interest the article titled "Association Between State-Level Income Inequality and COVID-19 Cases and Mortality in the USA."[1] We commend the authors for their efforts in exploring the role of income inequality in determining the burden of COVID-19 across different states in the United States (US). Compared to state-level analysis, county-level analysis data could provide more fine-grained findings; income inequality and COVID-19 cases and mortality are available at the county level, as are most of the covariates. [Extracted from the article]