Individual-Level and Neighborhood-Level Factors Associated with Longitudinal Changes in Cardiometabolic Measures in Participants of a Clinic-Based Care Coordination Program: A Secondary Data Analysis

Background: Identifying individual and neighborhood-level factors associated with worsening cardiometabolic risks despite clinic-based care coordination may help identify candidates for supplementary team-based care. Methods: Secondary data analysis of data from a two-year nurse-led care coordination program cohort of Medicare, Medicaid, dual-eligible adults, Leveraging Information Technology to Guide High Tech, High Touch Care (LIGHT2), from ten Midwestern primary care clinics in the U.S. Outcome Measures: Hemoglobin A1C, low-density-lipoprotein (LDL) cholesterol, and blood pressure. Multivariable generalized linear regression models assessed individual and neighborhood-level factors associated with changes in outcome measures from before to after completion of the LIGHT2 program. Results: 6378 participants had pre-and post-intervention levels reported for at least one outcome measure. In adjusted models, higher pre-intervention cardiometabolic measures were associated with worsening of all cardiometabolic measures. Women had worsening LDL-cholesterol compared with men. Women with pre-intervention HbA1c > 6.8% and systolic blood pressure > 131 mm of Hg had worse post-intervention HbA1c and systolic blood pressure compared with men. Adding individual’s neighborhood-level risks did not change effect sizes significantly. Conclusions: Increased cardiometabolic risks and gender were associated with worsening cardiometabolic outcomes. Understanding unresolved gender-specific needs and preferences of patients with increased cardiometabolic risks may aid in tailoring clinic-community-linked care planning.

[1]  Kevin O. Hwang,et al.  Predicting health-related social needs in Medicaid and Medicare populations using machine learning , 2022, Scientific Reports.

[2]  K. Matthews,et al.  Social Role Stress, Reward, and the American Heart Association Life’s Simple 7 in Midlife Women: The Study of Women’s Health Across the Nation , 2020, Journal of the American Heart Association.

[3]  Min Chen,et al.  Social determinants of health in electronic health records and their impact on analysis and risk prediction: A systematic review , 2020, J. Am. Medical Informatics Assoc..

[4]  Donna M. Zulman,et al.  Patient-Reported Social and Behavioral Determinants of Health and Estimated Risk of Hospitalization in High-Risk Veterans Affairs Patients , 2020, JAMA network open.

[5]  R. Gold,et al.  Comparison of Community-Level and Patient-Level Social Risk Data in a Network of Community Health Centers , 2020, JAMA network open.

[6]  Heterogeneous trends in burden of heart disease mortality by subtypes in the United States, 1999-2018: observational analysis of vital statistics , 2020, BMJ.

[7]  Meghan Reading Turchioe,et al.  Assessing the impact of social determinants of health on predictive models for potentially avoidable 30-day readmission or death , 2020, PloS one.

[8]  P. Johnson,et al.  Psychological Distress and Access to Care Among Midlife Women , 2020, Journal of aging and health.

[9]  S. Oparil,et al.  Report of the National Heart, Lung, and Blood Institute Working Group on Hypertension: Barriers to Translation. , 2020, Hypertension.

[10]  Sonal J. Patil.  Task Sharing Chronic Disease Self-Management Training With Lay Health Coaches to Reduce Health Disparities , 2020, The Annals of Family Medicine.

[11]  Roger L. Brown,et al.  Time Pressure During Primary Care Office Visits: a Prospective Evaluation of Data from the Healthy Work Place Study , 2019, Journal of General Internal Medicine.

[12]  M. Marino,et al.  Variation in Electronic Health Record Documentation of Social Determinants of Health Across a National Network of Community Health Centers. , 2019, American journal of preventive medicine.

[13]  Kiran J. Philip,et al.  Gender Disparities in Health Resource Utilization in Patients with Atherosclerotic Cardiovascular Disease: A Retrospective Cross-Sectional Study , 2019, Advances in Therapy.

[14]  Jessica Y. Breland,et al.  Health Equity and Implementation Science in Heart, Lung, Blood, and Sleep-Related Research: Emerging Themes From the 2018 Saunders-Watkins Leadership Workshop. , 2019, Circulation. Cardiovascular quality and outcomes.

[15]  P. Johnson,et al.  Health Care Disparities Among U.S. Women of Reproductive Age by Level of Psychological Distress. , 2019, Journal of women's health.

[16]  L. Fagnan,et al.  The Role of Health Extension in Practice Transformation and Community Health Improvement: Lessons From 5 Case Studies , 2019, The Annals of Family Medicine.

[17]  Ana H. Jackson,et al.  Clinician Experiences and Attitudes Regarding Screening for Social Determinants of Health in a Large Integrated Health System , 2019, Medical care.

[18]  Tiffany M. Powell-Wiley,et al.  Neighborhood Social Environment and Cardiovascular Disease Risk , 2019, Current Cardiovascular Risk Reports.

[19]  S. Straus,et al.  Effectiveness of interventions for managing multiple high-burden chronic diseases in older adults: a systematic review and meta-analysis , 2018, Canadian Medical Association Journal.

[20]  Michael N. Cantor,et al.  FACETS: using open data to measure community social determinants of health , 2018, J. Am. Medical Informatics Assoc..

[21]  Francesca N. Delling,et al.  Heart Disease and Stroke Statistics—2018 Update: A Report From the American Heart Association , 2018, Circulation.

[22]  Karriem S. Watson,et al.  Reducing Cardiovascular Disparities Through Community-Engaged Implementation Research: A National Heart, Lung, and Blood Institute Workshop Report , 2018, Circulation research.

[23]  Timothy J. Cunningham,et al.  Vital Signs: Racial Disparities in Age-Specific Mortality Among Blacks or African Americans — United States, 1999–2015 , 2017, MMWR. Morbidity and mortality weekly report.

[24]  Taneya Y. Koonce,et al.  Institute of Medicine Measures of Social and Behavioral Determinants of Health: A Feasibility Study. , 2017, American journal of preventive medicine.

[25]  Thao M. Doan,et al.  Integrating Social Determinants of Health With Treatment and Prevention: A New Tool to Assess Local Area Deprivation , 2016, Preventing chronic disease.

[26]  E. Bradley,et al.  Leveraging the Social Determinants of Health: What Works? , 2016, PloS one.

[27]  L. Popejoy,et al.  Monitoring Resource Utilization in a Health Care Coordination Program , 2015, Professional case management.

[28]  M. R. Umstattd Meyer,et al.  Built Environments and Active Living in Rural and Remote Areas: a Review of the Literature , 2015, Current Obesity Reports.

[29]  N. Adler,et al.  Patients in context--EHR capture of social and behavioral determinants of health. , 2015, The New England journal of medicine.

[30]  E. Taveras,et al.  Clinic-community linkages for high-value care. , 2014, The New England journal of medicine.

[31]  Anilkrishna B. Thota,et al.  Team-based care and improved blood pressure control: a community guide systematic review. , 2014, American journal of preventive medicine.

[32]  Behavioral Domains,et al.  COMMITTEE ON THE RECOMMENDED SOCIAL AND BEHAVIORAL DOMAINS AND MEASURES FOR ELECTRONIC HEALTH RECORDS , 2014 .

[33]  I. Kawachi,et al.  Living arrangement and coronary heart disease: the JPHC study , 2008, Heart.

[34]  S. Pocock,et al.  The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. , 2007, Preventive medicine.

[35]  David Freshwater,et al.  The production of social capital in US counties , 2006 .

[36]  C. Caspersen,et al.  The effectiveness of disease and case management for people with diabetes. A systematic review. , 2002, American journal of preventive medicine.

[37]  Jacob Cohen,et al.  A power primer. , 1992, Psychological bulletin.

[38]  M. Brucker.  Social Determinants of Health. , 2017, Nursing for women's health.

[39]  Mohammad Khalilia,et al.  Identifying Patients at Risk of High Healthcare Utilization , 2016, AMIA.

[40]  Tim Evans,et al.  Applying an equity lens to interventions: using PROGRESS ensures consideration of socially stratifying factors to illuminate inequities in health. , 2014, Journal of clinical epidemiology.

[41]  M. Joshi,et al.  Care Coordination , 2018, Encyclopedia of Gerontology and Population Aging.