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Staphylococcus aureus ( S . aureus ) is known to cause human infections and since the late 1990s, community-onset antibiotic resistant infections (methicillin resistant S . aureus (MRSA)) continue to cause significant infections in the United States. Skin and soft tissue infections (SSTIs) still account for the majority of these in the outpatient setting. Machine learning can predict the location-based risks for community-level S . aureus infections. Multi-year (2002–2016) electronic health records of children <19 years old with S . aureus infections were queried for patient level data for demographic, clinical, and laboratory information. Area level data (Block group) was abstracted from U.S. Census data. A machine learning ecological niche model, maximum entropy (MaxEnt), was applied to assess model performance of specific place-based factors (determined a priori) associated with S . aureus infections; analyses were structured to compare methicillin resistant (MRSA) against methicillin sensitive S . aureus (MSSA) infections. Differences in rates of MRSA and MSSA infections were determined by comparing those which occurred in the early phase (2002–2005) and those in the later phase (2006–2016). Multi-level modeling was applied to identify risks factors for S . aureus infections. Among 16,124 unique patients with community-onset MRSA and MSSA, majority occurred in the most densely populated neighborhoods of Atlanta’s metropolitan area. MaxEnt model performance showed the training AUC ranged from 0.771 to 0.824, while the testing AUC ranged from 0.769 to 0.839. Population density was the area variable which contributed the most in predicting S . aureus disease (stratified by CO-MRSA and CO-MSSA) across early and late periods. Race contributed more to CO-MRSA prediction models during the early and late periods than for CO-MSSA. Machine learning accurately predicts which densely populated areas are at highest and lowest risk for community-onset S . aureus infections over a 14-year time span.

Background Community- associated methicillin resistant Staphylococcus aureus (CA-MRSA) cause serious infections and rates continue to rise worldwide. Use of geocoded electronic health record (EHR) data to prevent spread of disease is limited in health service research. We demonstrate how geocoded EHR and spatial analyses can be used to identify risks for CA-MRSA in children, which are tied to place-based determinants and would not be uncovered using traditional EHR data analyses. Methods An epidemiology study was conducted on children from January 1, 2002 through December 31, 2010 who were treated for Staphylococcus aureus infections. A generalized estimated equations (GEE) model was developed and crude and adjusted odds ratios were based on S. aureus risks. We measured the risk of S. aureus as standardized incidence ratios (SIR) calculated within aggregated US 2010 Census tracts called spatially adaptive filters, and then created maps that differentiate the geographic patterns of antibiotic resistant and non-resistant forms of S. aureus . Results CA-MRSA rates increased at higher rates compared to non-resistant forms, p  = 0.01. Children with no or public health insurance had higher odds of CA-MRSA infection. Black children were almost 1.5 times as likely as white children to have CA-MRSA infections (aOR 95% CI 1.44,1.75, p  < 0.0001); this finding persisted at the block group level ( p  < 0.001) along with household crowding (p < 0.001). The youngest category of age (< 4 years) also had increased risk for CA-MRSA (aOR 1.65, 95%CI 1.48, 1.83, p < 0.0001). CA-MRSA encompasses larger areas with higher SIRs compared to non-resistant forms and were found in block groups with higher proportion of blacks ( r  = 0.517, p < 0.001), younger age ( r  = 0.137, p < 0.001), and crowding ( r  = 0.320, p < 0.001). Conclusions In the Atlanta MSA, the risk for CA-MRSA is associated with neighborhood-level measures of racial composition, household crowding, and age of children. Neighborhoods which have higher proportion of blacks, household crowding, and children < 4 years of age are at greatest risk. Understanding spatial relationship at a community level and how it relates to risks for antibiotic resistant infections is important to combat the growing numbers and spread of such infections like CA-MRSA.

Most major diseases have important social determinants. In this context, classification of disease based on etiologic or anatomic criteria may be neither mutually exclusive nor optimal. Units of analysis comprised large metropolitan central and fringe metropolitan counties with reliable mortality rates--(n = 416). Participants included infants and adults ages 25 to 64 years with selected causes of death (1999 to 2006). Exposures included that residential segregation and race-specific social deprivation variables. Main outcome measures were obtained via principal components analyses with an orthogonal rotation to identify a common factor. To discern whether the common factor was socially mediated, negative binomial multiple regression models were developed for which the dependent variable was the common factor. Results showed that infant deaths, mortality from assault, and malignant neoplasm of the trachea, bronchus and lung formed a common factor for race-gender groups (black/white and men/women). Regression analyses showed statistically significant, positive associations between low socio-economic status for all race-gender groups and this common factor. Between 1999 and 2006, deaths classified as \"assault\" and \"lung cancer\", as well as \"infant mortality\" formed a socially mediated factor detectable in population but not individual data. Despite limitations related to death certificate data, the results contribute important information to the formulation of several hypotheses: (a) disease classifications based on anatomic or etiologic criteria fail to account for social determinants; (b) social forces produce demographically and possibly geographically distinct population-based disease constellations; and (c) the individual components of population-based disease constellations (e.g., lung cancer) are phenotypically comparable from one population to another but genotypically different, in part, because of socially mediated epigenetic variations. Additional research may produce new taxonomies that unify social determinants with anatomic and/or etiologic determinants. This may lead to improved medical management of individuals and populations.

Background: Patients with comorbid medical and mental conditions are at risk for poor quality of care. With the anticipated expansion of Medicaid under health reform, it is particularly important to develop national estimates of the magnitude and correlates of quality deficits related to mental comorbidity among Medicaid enrollees. Methods: For all 657,628 fee-for-service Medicaid enrollees with diabetes during 2003 to 2004, the study compared Healthcare Effectiveness Data and Information Set (HEDIS) diabetes performance measures (hemoglobin A1C, eye examinations, low density lipoproteins screening, and treatment for nephropathy) and admissions for ambulatory care-sensitive conditions (ACSCs) between persons with and without mental comorbidity. Nested hierarchical models included individual, county, and state-level measures. Results: A total of 17.8% of the diabetic sample had a comorbid mental condition. In adjusted models, presence of a mental condition was associated with a 0.83 (0.82-0.85) odds of obtaining 2 or more HEDIS indicators, and a 1.32 (1.29-1.34) increase in odds of one or more ACSC hospitalization. Among those with diabetes and mental comorbidities, living in a county with a shortage of primary care physicians was associated with reduced performance on HEDIS measures; living in a state with higher Medicaid reimbursement fees and department of mental health expenses per client were associated both with higher quality on HEDIS measures and lower (better) rates of ACSC hospitalizations. Conclusions: Among persons with diabetes treated in the Medicaid system, mental comorbidity is an important risk factor for both underuse and overuse of medical care. Modifiable county and state-level factors may mitigate these quality deficits.

African Americans have higher rates of cardiovascular disease (CVD) and poorer outcomes compared to others. The American Diabetes Association and the National Diabetes Education Program have promoted use of the ABC approach (glycated hemoglobin A 1c , blood pressure, cholesterol) for identifying and controlling the leading indicators of CVD risk. In the present study, researchers added a D factor, for depression, because this disorder is common and also predictive of CVD risk and of control of diabetes. Particularly among low-income African Americans, depression is frequently not targeted or treated. The current study tests the effectiveness of recruiting African Americans in churches and training community health workers (CHWs) to educate their peers about CVD and risk reduction. For the intervention group, CHWs participated in a 16-hour training session and delivered a 6-week tailored educational program with counseling sessions and demonstrations. The control group received a weekly lecture by clinical experts. The CHW active-learning intervention was more effective than lectures by clinical experts in increasing the knowledge of CVD risk. The only significant difference in clinical measures reflected a worsening of HbA 1c levels in the control group; the CHW intervention group showed a slight improvement. Participants also learned self-management skills, such as taking blood pressure, measuring glucose, and reading labels. Nevertheless, more longitudinal research and a larger sample size are needed to confirm the impact of CHWs in community settings to change factors associated with CVD risk.

There is consensus that all adults over 50 years of age, regardless of gender, race, or ethnicity, should receive a physician recommendation for colorectal cancer (CRC) screening. Disparities in CRC screening result in poorer health outcomes for Blacks than for Whites. The purpose of this study was to determine whether there are Black-White differences in receiving a physician recommendation for CRC screening and reasons for undergoing screening. With 12,729 U.S. adults ages 50 to 74 included in the analysis, Whites were more likely than Blacks to report receiving a physician recommendation for CRC screening. Based on age-adjusted odds ratio, one out of three Blacks were less likely to report receiving a CRC screening recommendation from their physician (OR=0.68, 95% CI 0.57,0.81). This association persisted after adjusting for socioeconomic and other health-related factors (OR=0.61; 95% CI 0.53,0.71). This study suggests that additional steps need to be taken to reduce cancer health disparities.

Objectives. We sought to describe Black–White differences in HIV disease mortality before and after the introduction of highly active antiretroviral treatment (HAART). Methods. Black–White mortality from HIV is described for the nation as a whole. We performed regression analyses to predict county-level mortality for Black men aged 25–84 years and the corresponding Black:White male mortality ratios (disparities) in 140 counties with reliable Black mortality for 1999–2002. Results. National Black–White disparities widened significantly after the introduction of HAART, especially among women and the elderly. In county regression analyses, contextual socioeconomic status (SES) was not a significant predictor of Black:White mortality rate ratio after we controlled for percentage of the population who were Black and percentage of the population who were Hispanic, and neither contextual SES nor race/ethnicity were significant predictors after we controlled for pre-HAART mortality. Contextual SES, race, and pre-HAART mortality were all significant and independent predictors of mortality among Black men. Conclusions. Although nearly all segments of the Black population experienced widened post-HAART disparities, disparities were not inevitable and tended to reflect pre-HAART levels. Public health policymakers should consider the hypothesis of unequal diffusion of the HAART innovation, with place effects rendering some communities more vulnerable than others to this potential problem.

Objectives. We explored whether the introduction of 3 lifesaving innovations introduced between 1989 and 1996 increased, decreased, or had no effect on disparities in Black–White mortality in the United States through 2006. Methods. Centers for Disease Control and Prevention data were used to assess disease-, age-, gender-, and race-specific changes in mortality after the introduction of highly active anti-retroviral therapy (HAART) for treatment of HIV, surfactants for neonatal respiratory distress syndrome, and Medicare reimbursement of mammography screening for breast cancer. Results. Disparities in Black–White mortality from HIV significantly increased after the introduction of HAART, surfactant therapy, and reimbursement for screening mammography. Between 1989 and 2006, these circumstances may have accounted for an estimated 22 441 potentially avoidable deaths among Blacks. Conclusions. These descriptive data contribute to the formulation of the hypothesis that federal laws promote increased disparities in Black–White mortality by inadvertently favoring Whites with respect to access to lifesaving innovations. Failure of legislation to address known social factors is a plausible explanation, at least in part, for the observed findings. Further research is necessary to test this hypothesis, including analytic epidemiological studies designed a priori to do so.

Staphylococcus aureus (S. aureus) is known to cause human infections and since the late 1990s, community-onset antibiotic resistant infections (methicillin resistant S. aureus (MRSA)) continue to cause significant infections in the United States. Skin and soft tissue infections (SSTIs) still account for the majority of these in the outpatient setting. Machine learning can predict the location-based risks for community-level S. aureus infections. Multi-year (2002–2016) electronic health records of children <19 years old with S. aureus infections were queried for patient level data for demographic, clinical, and laboratory information. Area level data (Block group) was abstracted from U.S. Census data. A machine learning ecological niche model, maximum entropy (MaxEnt), was applied to assess model performance of specific place-based factors (determined a priori) associated with S. aureus infections; analyses were structured to compare methicillin resistant (MRSA) against methicillin sensitive S. aureus (MSSA) infections. Differences in rates of MRSA and MSSA infections were determined by comparing those which occurred in the early phase (2002–2005) and those in the later phase (2006–2016). Multi-level modeling was applied to identify risks factors for S. aureus infections. Among 16,124 unique patients with community-onset MRSA and MSSA, majority occurred in the most densely populated neighborhoods of Atlanta’s metropolitan area. MaxEnt model performance showed the training AUC ranged from 0.771 to 0.824, while the testing AUC ranged from 0.769 to 0.839. Population density was the area variable which contributed the most in predicting S. aureus disease (stratified by CO-MRSA and CO-MSSA) across early and late periods. Race contributed more to CO-MRSA prediction models during the early and late periods than for CO-MSSA. Machine learning accurately predicts which densely populated areas are at highest and lowest risk for community-onset S. aureus infections over a 14-year time span.