Latent pathway-based Bayesian models to identify intervenable factors of racial disparities in breast cancer stage at diagnosis.

PURPOSE: We built Bayesian Network (BN) models to explain roles of different patient-specific factors affecting racial differences in breast cancer stage at diagnosis, and to identify healthcare related factors that can be intervened to reduce racial health disparities. METHODS: We studied women age 67-74 with initial diagnosis of breast cancer during 2006-2014 in the National Cancer Institute's SEER-Medicare dataset. Our models included four measured variables (tumor grade, hormone receptor status, screening utilization and biopsy delay) expressed through two latent pathways-a tumor biology path, and health-care access/utilization path. We used various Bayesian model assessment tools to evaluate these two latent pathways as well as each of the four measured variables in explaining racial disparities in stage-at-diagnosis. RESULTS: Among 3,010 Black non-Hispanic (NH) and 30,310 White NH breast cancer patients, respectively 70.2% vs 76.9% were initially diagnosed at local stage, 25.3% vs 20.3% with regional stage, and 4.56% vs 2.80% with distant stage-at-diagnosis. Overall, BN performed approximately 4.7 times better than Classification And Regression Tree (CART) (Breiman L, Friedman JH, Stone CJ, Olshen RA. Classification and regression trees. CRC press; 1984) in predicting stage-at-diagnosis. The utilization of screening mammography is the most prominent contributor to the accuracy of the BN model. Hormone receptor (HR) status and tumor grade are useful for explaining racial disparity in stage-at diagnosis, while log-delay in biopsy impeded good prediction. CONCLUSIONS: Mammography utilization had a significant effect on racial differences in breast cancer stage-at-diagnosis, while tumor biology factors had less impact. Biopsy delay also aided in predicting local and regional stages-at-diagnosis for Black NH women but not for white NH women.

Machine learning to predict risk for community-onset Staphylococcus aureus infections in children living in southeastern United States.

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.

Interpersonal Primary Care Continuity for Chronic Conditions Is Associated with Fewer Hospitalizations and Emergency Department Visits Among Medicaid Enrollees.

BACKGROUND: Interpersonal primary care continuity or chronic condition continuity (CCC) is associated with improved health outcomes. Ambulatory care-sensitive conditions (ACSC) are best managed in a primary care setting, and chronic ACSC (CACSC) require management over time. However, current measures do not measure continuity for specific conditions or the impact of continuity for chronic conditions on health outcomes. The purpose of this study was to design a novel measure of CCC for CACSC in primary care and determine its association with health care utilization. METHODS: We conducted a cross-sectional analysis of continuously enrolled, nondual eligible adult Medicaid enrollees with a diagnosis of a CACSC using 2009 Medicaid Analytic eXtract files from 26 states. We conducted adjusted and unadjusted logistic regression models of the relationship between patient continuity status and emergency department (ED) visits and hospitalizations. Models were adjusted for age, sex, race/ethnicity, comorbidity, and rurality. We defined CCC for CACSC as at least 2 outpatient visits with any primary care physician for a CACSC in the year, and (2) more than 50% of outpatient CACSC visits with a single PCP. RESULTS: There were 2,674,587 enrollees with CACSC and 36.3% had CCC for CACSC visits. In fully adjusted models, enrollees with CCC were 28% less likely to have ED visits compared with those without CCC (aOR = 0.71, 95% CI = 0.71 - 0.72) and were 67% less likely to have hospitalization than those without CCC (aOR = 0.33, 95% CI = 0.32-0.33). CONCLUSIONS: CCC for CACSCs was associated with fewer ED visits and hospitalizations in a nationally representative sample of Medicaid enrollees.

Space-time trends of community-onset Staphylococcus aureus infections in children: a group-based trajectory modeling approach.

PURPOSE: Staphylococcus aureus (S. aureus) remains a serious cause of infections in the United States and worldwide. In the United States, methicillin-resistant S. aureus (MRSA) is the leading cause of skin and soft tissue infections. This study identifies 'best' to 'worst' infection trends from 2002 to 2016, using group-based trajectory modeling approach. METHODS: Electronic health records of children living in the southeastern United States with S. aureus infections from 2002 to 2016 were retrospectively studied, by applying a group-based trajectory model to estimate infection trends (low, high, very high), and then assess spatial significance of these trends at the census tract level; we focused on community-onset infections and not those considered healthcare acquired. RESULTS: Three methicillin-susceptible S. aureus (MSSA) infection trends (low, high, very high) and three MRSA trends (low, high, very high) were identified from 2002 to 2016. Among census tracts with community-onset S. aureus cases, 29% of tracts belonged to the best trend (low infection) for both methicillin-resistant S. aureus and methicillin-susceptible S. aureus; higher proportions occurring in the less densely populated areas. Race disparities were seen with the worst methicillin-resistant S. aureus infection trends and were more often in urban areas. CONCLUSIONS: Group-based trajectory modeling identified unique trends of S. aureus infection rates over time and space, giving insight into the associated population characteristics which reflect these trends of community-onset infection.

Rural-Urban Differences in Breast Cancer Stage at Diagnosis.

Purpose: To analyze the extent to which rural-urban differences in breast cancer stage at diagnosis are explained by factors including age, race, tumor grade, receptor status, and insurance status. Methods: Using the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) 18 database, analysis was performed using data from women aged 50-74 diagnosed with breast cancer between the years 2013 and 2016. Patient rurality of residence was coded according to SEER's Rural-Urban Continuum Code 2013: Large Urban (RUCC 1), Small Urban (RUCC 2,3), and Rural (RUCC 4,5,6,7,8,9). Stage at diagnosis was coded according to SEER's Combined Summary Stage 2000 (2004+) criteria: Localized (0,1), Regional (2,3,4,5), and Distant (7). Descriptive statistics were analyzed, and variations were tested for across rural-urban categories using Kruskall-Wallis and Kendall's tau-b tests. Additionally, odds ratios (ORs) and 95% confidence intervals for the three ordinal levels of rural-urban residence were calculated while adjusting for other independent variables using ordinal logistic regression. Results: The rural residence category showed the largest proportion of women diagnosed with distant stage breast cancer. Additionally, we determined that patients with residence in both large and small urban areas had statistically significantly lower odds of higher stage diagnosis compared to rural patients even after controlling for age, race, tumor grade, receptor status, and insurance status. Conclusions: Rural women with breast cancer show small but statistically significant disparities in stage-at-diagnosis. Further research is needed to understand local area variation in these disparities across a wide range of rural communities, and to identify the most effective interventions to eliminate these disparities.

Characteristics Associated with Low-Value Cancer Screening Among Office-Based Physician Visits by Older Adults in the USA.

BACKGROUND: After a certain age, cancer screening may expose older adults to unnecessary harms with limited benefits and represent inefficient use of health care resources. OBJECTIVE: To estimate the frequency of cervical, breast, and colorectal cancer screening among adults older than US Preventive Services Task Force (USPSTF) age thresholds at which screening is no longer considered routine and to identify physician and patient factors associated with low-value cancer screening. DESIGN: Observational study using pooled cross-sectional data (2011-2016) from the National Ambulatory Medical Care Survey, a nationally representative probability sample of US office-based physician visits. PARTICIPANTS: Analyses for cervical and breast cancer screening were limited to visits by women over age 65 (N=37,818) and ages 75 and over (N=19,451), respectively. Analyses for colorectal cancer screening were limited to visits by patients over age 75 (N=31,543). MAIN MEASURES: Cancer screening procedures were coded as low value using USPSTF age thresholds. KEY RESULTS: Between 2011 and 2016, an estimated 509, 507, and 273 thousand potentially low-value Pap smears, mammograms, and colonoscopies/sigmoidoscopies, respectively, were ordered annually. Low-valuecervical cancer screening was less likely to occur for visits with older (vs. younger) patients. Compared to visits by non-HispanicWhite women, low-valuecervical and breast cancer screening was less likely to occur for visits by women whose race/ethnicitywas something other than non-HispanicWhite, non-HispanicBlack, or Hispanic. Obstetrician/gynecologistswere more likely to order low-valuePap smears and mammograms compared to family/generalpractice physicians. CONCLUSIONS: Thousands of cervical, breast, and colorectal cancer screenings at ages beyond routine guideline thresholds occur each year in the USA. Further research is needed to understand whether this pattern represents clinical inertia and resistance to de-adoption of previous screening practices, or whether physicians and/or patients perceive a higher value in these tests than that endorsed by experts writing evidence-based guidelines.

Examining the relationship between self-reported lifetime cancer diagnosis and nativity: findings from the National Health and Nutrition Examination Survey (NHANES) 2011-2018.

PURPOSE: Cancer incidence in the USA remains higher among certain groups, regions, and communities, and there are variations based on nativity. Research has primarily focused on specific groups and types of cancer. This study expands on previous studies to explore the relationship between country of birth (nativity) and all cancer site incidences among USA and foreign-born residents using a nationally representative sample. METHODS: This is a cross-sectional study of (unweighted n = 22,554; weighted n = 231,175,933) participants between the ages of 20 and 80 from the National Health and Nutrition Examination Survey (NHANES) 2011-2018. Using weighted logistic regressions, we analyzed the impact of nativity on self-reported cancer diagnosis controlling for routine care, smoking status, overweight, race/ethnicity, age, and gender. We ran a partial model, adjusting only for age as a covariate, a full model with all other covariates, and stratified by race/ethnicity. RESULTS: In the partial and full models, our findings indicate that US-born individuals were more likely to report a cancer diagnosis compared to their foreign-born counterparts (OR 2.34, 95% CI [1.93; 2.84], p < 0.01) and (OR 1. 39, 95% CI [1.05; 1.84], p < 0.05), respectively. This significance persisted only among non-Hispanic Blacks when stratified by race. Non-Hispanic Blacks who were US-born were more likely to report a cancer diagnosis compared to their foreign-born counterparts (OR 2.30, 95% [CI 1.31; 4.02], p < 0.05). CONCLUSION: A variety of factors may reflect lower self-reported cancer diagnosis in foreign-born individuals in the USA other than a healthy immigrant advantage. Future studies should consider the factors behind the differences in cancer diagnoses based on nativity status, particularly among non-Hispanic Blacks.

Naïve Bayesian network-based contribution analysis of tumor biology and healthcare factors to racial disparity in breast cancer stage-at-diagnosis.

BACKGROUND: Variation in breast cancer stage at initial diagnosis (including racial disparities) is driven both by tumor biology and healthcare factors. METHODS: We studied women age 67-74 with initial diagnosis of breast cancer from 2006 through 2014 in the SEER-Medicare database. We extracted variables related to tumor biology (histologic grade and hormone receptor status) and healthcare factors (screening mammography [SM] utilization and time delay from mammography to diagnostic biopsy). We used naïve Bayesian networks (NBNs) to illustrate the relationships among patient-specific factors and stage-at-diagnosis for African American (AA) and white patients separately. After identifying and controlling confounders, we conducted counterfactual inference through the NBN, resulting in an unbiased evaluation of the causal effects of individual factors on the expected utility of stage-at-diagnosis. An NBN-based decomposition mechanism was developed to evaluate the contributions of each patient-specific factor to an actual racial disparity in stage-at-diagnosis. 2000 bootstrap samples from our training patients were used to compute the 95% confidence intervals (CIs) of these contributions. RESULTS: Using a causal-effect contribution analysis, the relative contributions of each patient-specific factor to the actual racial disparity in stage-at-diagnosis were as follows: tumor grade, 45.1% (95% CI: 44.5%, 45.8%); hormone receptor status, 5.0% (4.5%, 5.4%); mammography utilization, 23.1% (22.4%, 24.0%); and biopsy delay 26.8% (26.1%, 27.3%). CONCLUSION: The modifiable mechanisms of mammography utilization and biopsy delay drive about 49.9% of racial difference in stage-at-diagnosis, potentially guiding more targeted interventions to eliminate cancer outcome disparities. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13755-021-00165-5.

Racial and ethnic disparities in benefits eligibility and spending among adults on the autism spectrum: A cohort study using the Medicare Medicaid Linked Enrollees Analytic Data Source.

BACKGROUND: Research on children and youth on the autism spectrum reveal racial and ethnic disparities in access to healthcare and utilization, but there is less research to understand how disparities persist as autistic adults age. We need to understand racial-ethnic inequities in obtaining eligibility for Medicare and/or Medicaid coverage, as well as inequities in spending for autistic enrollees under these public programs. METHODS: We conducted a cross-sectional cohort study of U.S. publicly-insured adults on the autism spectrum using 2012 Medicare-Medicaid Linked Enrollee Analytic Data Source (n = 172,071). We evaluated differences in race-ethnicity by eligibility (Medicare-only, Medicaid-only, Dual-Eligible) and spending. FINDINGS: The majority of white adults (49.87%) were full-dual eligible for both Medicare and Medicaid. In contrast, only 37.53% of Black, 34.65% Asian/Pacific Islander, and 35.94% of Hispanic beneficiaries were full-dual eligible for Medicare and Medicare, with most only eligible for state-funded Medicaid. Adjusted logistic models controlling for gender, intellectual disability status, costly chronic condition, rural status, county median income, and geographic region of residence revealed that Black beneficiaries were significantly less likely than white beneficiaries to be dual-eligible across all ages. Across these three beneficiary types, total spending exceeded $10 billion. Annual total expenditures median expenditures for full-dual and Medicaid-only eligible beneficiaries were higher among white beneficiaries as compared with Black beneficiaries. CONCLUSIONS: Public health insurance in the U.S. including Medicare and Medicaid aim to reduce inequities in access to healthcare that might exist due to disability, income, or old age. In contrast to these ideals, our study reveals that racial-ethnic minority autistic adults who were eligible for public insurance across all U.S. states in 2012 experience disparities in eligibility for specific programs and spending. We call for further evaluation of system supports that promote clear pathways to disability and public health insurance among those with lifelong developmental disabilities.

Traversing Traditions: Prenatal Care and Birthing Practice Preferences Among Black Women in North Florida.

Objective: Our goal was to explore prenatal practices and birthing experiences among Black women living in an urban North Florida community. Design: Non-random qualitative study. Setting: Private spaces at a convenient location selected by the participant. Participants: Eleven Black women, aged 25-36 years, who were either pregnant or had given birth at least once in the past five years in North Florida. Methods: Semi-structured interviews were completed in July 2017, followed by thematic analysis of interview transcripts. Results: Four main themes emerged: a) decision-making strategies for employing alternative childbirth preparation (ie, midwives, birthing centers, and doulas); b) having access to formal community resources to support their desired approaches to perinatal care; c) seeking advice from women with similar perspectives on birthing and parenting; and d) being confident in one's decisions. Despite seeking to incorporate "alternative" methods into their birthing plans, the majority of our participants ultimately delivered in-hospital. Conclusions: Preliminary results suggest that culturally relevant and patient-centered decision-making might enhance Black women's perinatal experience although further research is needed to see if these findings are generalizable to a heterogenous US Black population. Implications for childbirth educators and health care professionals include: 1) recognizing the importance of racially and professionally diverse staffing in obstetric care practices; 2) empowering patients to communicate and achieve their childbirth desires; 3) ensuring an environment that is not only free of discrimination and disrespect, but that embodies respect (as perceived by patients of varied racial backgrounds) and cultural competence; and, 4) providing access to education and care outside of traditional work hours.

Racial Disparities in Healthcare Utilization Among Individuals with Cardiometabolic Risk Factors and Comorbid Anxiety Disorder.

OBJECTIVE: This study addresses racial/ethnic differences in adverse health care utilization among individuals with comorbid anxiety disorder and cardiometabolic syndrome (CMetS) risk factors. METHODS: Utilizing 2011-2015 Medical Expenditure Panel Survey (MEPS) data, logistic regression models were estimated to determine the likelihood of receiving CMetS-related medical treatment in the emergency department (ED) or via inpatient services and to determine if the likelihood is associated with race/ethnicity. Adjusted models controlled for age, sex, and insurance type. RESULTS: Significant racial-ethnic differences were observed for utilization (any emergency department and/or inpatient visit). The odds of non-Hispanic Black respondents reporting emergency department and/or inpatient utilization was 2.39 (p < 0.05) times the odds of non-Hispanic White respondents. CONCLUSION: Racial-ethnic variation in adverse healthcare utilization suggests an opportunity to improve care and outcomes for persons diagnosed with comorbid anxiety disorder and cardiometabolic syndrome. Integrated interventions could simultaneously improve mental health and facilitate CMetS disease self-management.

A Spatial Analysis of Health Disparities Associated with Antibiotic Resistant Infections in Children Living in Atlanta (2002-2010).

BACKGROUND: Antibiotic resistant bacteria like community-onset methicillin resistant Staphylococcus aureus (CO-MRSA) have continued to cause infections in children at alarming rates and are associated with health disparities. Geospatial analyses of individual and area level data can enhance disease surveillance and identify socio-demographic and geographic indicators to explain CO-MRSA disease transmission patterns and risks. METHODS: A case control epidemiology approach was undertaken to compare children with CO-MRSA to a noninfectious condition (unintentional traumatic brain injury (uTBI)). In order to better understand the impact of place based risks in developing these types of infections, data from electronic health records (EHR) were obtained from CO-MRSA cases and compared to EHR data from controls (uTBI). US Census data was used to determine area level data. Multi-level statistical models were performed using risk factors determined a priori and geospatial analyses were conducted and mapped. RESULTS: From 2002-2010, 4,613 with CO-MRSA and 34,758 with uTBI were seen from two pediatric hospitals in Atlanta, Georgia. Hispanic children had reduced odds of infection; females and public health insurance were more likely to have CO-MRSA. Spatial analyses indicate significant 'hot spots' for CO-MRSA and the overall spatial cluster locations, differed between CO-MRSA cases and uTBI controls. CONCLUSIONS: Differences exist in race, age, and type of health insurance between CO-MRSA cases compared to noninfectious control group. Geographic clustering of cases is distinct from controls, suggesting placed based factors impact risk for CO-MRSA infection.

Correction to: Geographic surveillance of community associated MRSA infections in children using electronic health record data.

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Identifying County-Level All-Cause Mortality Rate Trajectories and Their Spatial Distribution Across the United States.

INTRODUCTION: All-cause mortality in the United States declined from 1935 through 2014, with a recent uptick in 2015. This national trend is composed of disparate local trends. We identified distinct groups of all-cause mortality rate trajectories by grouping US counties with similar temporal trajectories. METHODS: We used all-cause mortality rates in all US counties for 1999 through 2016 and estimated discrete mixture models by using county level mortality rates. Proc Traj in SAS was used to detect how county trajectories clustered into groups on the basis of similar intercepts, slopes, and higher order terms. Models with increasing numbers of groups were assessed on the basis of model fit. We created county-level maps of mortality trajectory groups by using ArcGIS. RESULTS: Eight unique trajectory groups were detected among 3,091 counties. The average mortality rate in the most favorable trajectory group declined 29.4%, from 592.3 deaths per 100,000 in 1999 to 418.2 in 2016. The least favorable mortality trajectory group declined 3.4% over the period, from 1,280.3 deaths per 100,000 to 1,236.9. We saw significant differences in the demographic and socioeconomic profiles and geographic patterns across the trajectory categories, with favorable mortality trajectories in the Northeast, Midwest, and on the West Coast and unfavorable trajectories concentrated in the Southeast. CONCLUSIONS: County-level disparities in all-cause mortality rates widened over the past 18 years. Further investigation of the determinants of the trajectory groupings and the geographic outliers identified by our research could inform interventions to achieve equitable distribution of county mortality rates.

Geographic surveillance of community associated MRSA infections in children using electronic health record data.

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.

Pockets of progress amidst persistent racial disparities in low birthweight rates.

Racial disparities persist in adverse perinatal outcomes such as preterm birth, low birthweight (LBW), and infant mortality across the U.S. Although pervasive, these disparities are not universal. Some communities have experienced significant improvements in black (or African American) birth outcomes, both in absolute rates and in rate ratios relative to whites. This study assessed county-level progress on trends in black and white LBW rates as an indicator of progress toward more equal birth outcomes for black infants. County-level LBW data were obtained from the 2003 to 2013 U.S. Natality files. Black LBW rates, black-white rate ratios and percent differences over time were calculated. Trend lines were first assessed for significant differences in slope (i.e., converging, diverging, or parallel trend lines). For counties with parallel trend lines, intercepts were tested for statistically significant differences (sustained equality vs. persistent disparities). To assess progress, black LBW rates were compared to white LBW rates, and the trend lines were tested for significant decline. Each county's progress toward black-white equality was ultimately categorized into five possible trend patterns (n = 408): (1) converging LBW rates with reductions in the black LBW rate (decreasing disparities, n = 4, 1%); (2) converging LBW rates due to worsening white LBW rates (n = 5, 1%); (3) diverging LBW rates (increasing disparities, n = 9, 2%); (4) parallel LBW rates (persistent disparities, n = 373, 91%); and (5) overlapping trend lines (sustained equality, n = 18, 4%). Only four counties demonstrated improvement toward equality with decreasing black LBW rates. There is significant county-level variation in progress toward racial equality in adverse birth outcomes such as low birthweight. Still, some communities are demonstrating that more equitable outcomes are possible. Further research is needed in these positive exemplar communities to identify what works in accelerating progress toward more equal birth outcomes.

Altitude and Variable Effects on Infant Mortality in the United States.

Levine, Robert S., Jason L. Salemi, Maria C. Mejia de Grubb, Sarah K. Wood, Lisa Gittner, Hafiz Khan, Michael A. Langston, Baqar A. Husaini, George Rust, and Charles H. Hennekens. Altitude and variable effects on infant mortality in the United States. High Alt Med Biol. 19:265-271, 2018. AIMS: To explore whether altitude has different effects on infant mortality from newborn respiratory distress, nontraumatic intracranial hemorrhage, and necrotizing enterocolitis. RESULTS: Infants born in the US Mountain Census Division (AR, CO, ID, NV, NM, UT, and WY) had lower mortality from newborn respiratory distress (p < 0.001, mortality rate ratios [MRR] = 0.5 for non-Hispanic blacks and non-Hispanic whites and 0.6 for Hispanic whites) relative to infants born elsewhere in the United States, while Mountain Division non-Hispanic white infants had significantly higher mortality from nontraumatic intracranial hemorrhage (MRR = 1.3 [1.1, 1.6] p < 0.001). After adjustment for state average birth weight, gestational age, and income inequality, a statistically significant, inverse association remained between state average altitude and non-Hispanic white infant mortality from newborn respiratory distress. County altitude (3058 counties in 9 categories from ≤0 to ≥7000 feet) was negatively correlated with newborn respiratory distress (r = -0.91, p < 0.001) and necrotizing enterocolitis (r = -0.81, p = 0.006) at ≤0 to ≥7000 feet and positively correlated with nontraumatic intracranial hemorrhage at ≤0 to 6000-6999 feet (r = 0.78, p = 0.02). CONCLUSIONS: These data show variable cause-specific effects of altitude on infant mortality. Analytic epidemiologic research is needed to confirm or refute the hypotheses generated by these descriptive data.

A Practical Risk Stratification Approach for Implementing a Primary Care Chronic Disease Management Program in an Underserved Community.

The use of value metrics is often dependent on payer-initiated health care management incentives. There is a need for practices to define and manage their own patient panels regardless of payer to participate effectively in population health management. A key step is to define a panel of primary care patients with high comorbidity profiles. Our sample included all patients seen in an urban academic family medicine clinic over a two-year period. The simplified risk stratification was built using internal electronic health record and billing system data based on ICD-9 codes. There were 347 patients classified as high-risk out of the 5,364 patient panel. Average age was 59 years (SD 15). Hypertension (90%), hyperlipidemia (62%), and depression (55%) were the most common conditions among high-risk patients. Simplified risk stratification provides a feasible option for our team to understand and respond to the nuances of population health in our underserved community.