https://orcid.org/0000-0001-9169-8261
Figures
Abstract
Background
Non-medical health-related social needs (social needs) are major contributors to worse health outcomes and may have an adverse impact on cardiovascular risk factors and cardiovascular disease. The present study evaluated the effect of a closed-loop community-based pathway in reducing social needs among Black men in a lifestyle change program.
Methods
Black men (n = 70) from a large Midwestern city participated in Black Impact, a 24-week community-based team lifestyle change single-arm pilot trial adapted from the Diabetes Prevention Program and American Heart Association’s (AHA) Check, Change, Control Blood Pressure Self-Management Program, which incorporates AHA’s Life’s Simple 7 (LS7) framework. Participants were screened using the Centers for Medicare and Medicaid Services (CMS) Accountable Health Communities Health-Related Social Needs Screening Tool. Participants with affirmative responses were referred to a community hub pathway to address social needs. The primary outcome for this analysis is change in social needs based on the CMS social needs survey at 12 and 24 weeks using mixed effect logistic regressions with random intercepts for each participant. Change in a LS7 score (range 0–14) from baseline to 12 and 24 weeks was evaluated using a linear mixed-effects model stratified by baseline social needs.
Results
Among 70 participants, the mean age of participants was 52 ±10.5 years. The men were sociodemographically diverse, with annual income ranging from <$20,000 (6%) to ≥$75,000 (23%). Forty-three percent had a college degree or higher level of education, 73% had private insurance, and 84% were employed. At baseline 57% of participants had at least one social need. Over 12 and 24 weeks, this was reduced to 37% (OR 0.33, 95%CI: 0.13, 0.85) and 44% (OR 0.50, 95%CI: 0.21, 1.16), respectively. There was no association of baseline social needs status with baseline LS7 score, and LS7 score improved over 12 and 24 weeks among men with and without social needs, with no evidence of a differential effect.
Conclusions
The Black Impact lifestyle change single-arm pilot program showed that a referral to a closed-loop community-based hub reduced social needs in Black men. We found no association of social needs with baseline or change in LS7 scores. Further evaluation of community-based strategies to advance the attainment of LS7 and address social needs among Black men in larger trials is warranted.
Citation: Joseph JJ, Gray DM II, Williams A, Zhao S, McKoy A, Odei JB, et al. (2023) Addressing non-medical health-related social needs through a community-based lifestyle intervention during the COVID-19 pandemic: The Black Impact program. PLoS ONE 18(3): e0282103. https://doi.org/10.1371/journal.pone.0282103
Editor: Jennifer Annette Campbell, Medical College of Wisconsin, UNITED STATES
Received: May 13, 2022; Accepted: February 6, 2023; Published: March 9, 2023
Copyright: © 2023 Joseph et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Data cannot be shared publicly because of risk of re-identification of participants. Data are available from the authors upon approval from the Institutional Data Access / Ethics Committee (contact via corresponding author email [joseph.117@osu.edu]) or Jacob Stoddard (stoddard.13@osu.edu) for researchers who meet the criteria for access to confidential data. Jacob Stoddard is the Associate Director – IRB Operations and Data Analytics within the Office of Responsible Research Practices at The Ohio State University.
Funding: Black Impact was funded through an Ohio State University Connect and Collaborate grant, focused on bringing faculty and community organizations/members together to address health. Black Impact was supported by Award Number Grant UL1TR002733 from the National Center For Advancing Translational Sciences (JJJ, TSN, DG). Preparation of this manuscript was supported by The Robert Wood Johnson Foundation (Harold Amos Medical Faculty Development Program ID# 76236,JJJ), the National Institute of Diabetes and Digestive and Kidney Diseases (K23DK117041, JJJ) and the National Cancer Institute (K08CA245208, TSN) of the National Institutes of Health. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Non-medical health-related social needs (social needs) are individual social and economic needs such as housing, food, transportation, and protection from violence [1]. Social needs are major contributors to worse health outcomes [2–4] and are estimated to impact up to 50–60% of health outcomes [5]. There is a strong body of evidence supporting social needs as a critical lever toward the achievement of health equity and the need to expand the healthcare sector’s purview beyond the traditional walls of a healthcare system [6]. Interventions to address social needs have been shown to: 1) improve processes (e.g. identification of social needs, referrals, and enrollment in community resources); and 2) lower cost and improve utilization (e.g. improved preventive care utilization, decreased length of stay and hospital readmissions) [7–10]. Evidence of health improvements after addressing social needs is mixed, with some studies showing an improvement in blood pressure, lipids, and fruit/vegetable consumption, while other studies did not show improvement in glycemic measures [7–10]. Emerging data shows that higher intervention dosage (number of contacts between the navigator and patient/participant) may be related to greater success of resource connections, with in-person contact being associated with the highest likelihood of success [11].
In the United States, the prevalence of social needs is higher among racial/ethnic minority groups, which impacts heart healthy behaviors. Black, Latino, and Filipino adults in the Kaiser Permanente Northern California integrated primary and specialty health care network were more likely than Whites to be in a lower income category and worry about their financial situation [12]. Cost-related reduced medication use was higher among Black individuals, and cost-related reduced fruit/vegetable consumption was higher among Black and Latino populations [12]. Racial/ethnic disparities in income were observed within similar levels of education [12]. In Black adults in the Jackson Heart Study, lower individual income, neighborhood socioeconomic status, and education were all significantly associated with lower American Heart Association (AHA) Life’s Simple 7 (LS7) scores (LS7 metrics include physical activity, diet, cholesterol, blood pressure, body mass index (BMI), smoking, and glycemia) [13]. In community-dwelling Black men participating in African American Male Wellness Walks, lower annual income (<$20,000 vs. ≥$75,000) and Medicare or no insurance vs. private insurance were associated with worse AHA cardiovascular health [14]. Unfortunately, interventions addressing social needs and cardiovascular risk factors have been limited in all populations, including racial/ethnic minority groups. This gap is troubling because in the United States, Black men have lower attainment of ≥ 5 AHA LS7 metrics compared to women and non-Hispanic White (White) populations [15]. Higher AHA LS7 scores are associated with lower risk of cardiovascular disease, type 2 diabetes (diabetes), cancer, and mortality among all races/ethnicities [16–19]. Thus, addressing social needs as one avenue to improve cardiovascular risk factors is critical given the widening racial disparities in preventable deaths from heart disease and stroke [20,21].
Our research group maintains a community-engaged and community-based focus founded in academic-community-government partnerships to advance health [22–24]. In a systematic review, we found no evidence of previous community-based participatory research (CBPR) approaches focused on LS7 in Black men [22]. Thus, using a CBPR approach with our community partner, The National African American Male Wellness Agency (AAMWA) and community members, we co-designed Black Impact, a 24-week CBPR study which improved LS7 attainment in Black men residing in a large Midwestern city [25]. Black Impact had 3 main components: 1) 24-week physical activity, nutrition and education intervention in Black men [25]; 2) Navigating participants without a primary care provider to establish care with a provider and improve patient activation; and 3) Addressing social needs that present barriers to wellness. The current report evaluates the baseline social needs screening, referral and outcomes and the impact on cardiovascular health scores. The study team hypothesized that: 1) participants’ social needs would improve over the course of the intervention; 2) participants with social needs would have worse cardiovascular health at baseline; and 3) baseline social needs would lower the magnitude of improvement in cardiovascular health scores.
Materials and methods
Study design and recruitment
As has been described previously and is shown in Fig 1 [25], we enrolled Black men from the annual AAMWA walk/health fair with poor or average cardiovascular health (< 4 LS7 metrics in the ideal range). The inclusion criteria included: 1) Black men (self-report); 2) adult age 18 years or older; 3) poor or average cardiovascular health (< 4 LS7 metrics in the ideal range); English speaking; 5) lives in Metropolitan Columbus, Ohio area; 6) no healthcare provider-imposed limitations on physical activity; and 7) participant has no contraindications for a group setting. In February 2020, 100 Black men were enrolled in the pilot study and divided into 6 geographic-based teams by the study team [26]. The sample size was based on the number of participants needed to determine effect sizes for the primary outcome (50–100 participants) [26]. Due to COVID-19, the study was paused prior to initiation. In July 2020, the study began with 74 participants with programming through December 2020 [25]. The Black Impact programming phase was implemented over 24 weeks from July 2020 to December 2020. Twelve- and 24-week biometric health screenings occurred at study sites, and survey data were collected electronically via Research Electronic Data Capture (REDCap). The study was reviewed and approved by The Ohio State University Biomedical Sciences Institutional Review Board (Study ID: 2019H0302) and was retrospectively registered on ClinicalTrials.gov Identifier: NCT04787978 on March 9, 2021. The principal investigators were unaware of the necessity for clinical trial registration of pilot single-arm clinical trials at study commencement and confirm that future trials will be prospectively registered. All participants provided written informed consent.
Intervention
The 24-week community-based lifestyle intervention focused on health education, physical activity and addressing social needs through screening and service coordination aimed to improve cardiovascular health among Black men. This single-arm pilot study was adapted from the Diabetes Prevention Program [27] and AHA Check, Change, Control programs, applying evidence-based strategies and stakeholder feedback [28]. Thus, participants were not randomized, and all received the entire intervention using a single-arm trial design [25,26]. Each participant was assigned to a health coach and grouped into six teams of 8–25 participants based on participant proximity to a central meeting location (e.g., Columbus Recreation and Parks recreation center). The Black Impact physical activity, nutrition, and education intervention has been described previously [25]. The Black Impact intervention was grounded in the social cognitive theory at the individual level, and used a multi-level framework consistent with the socioecological model (individual, interpersonal, organizational, community and policy). Our research team used the PETAL framework for CBPR: 1) prioritize health equity; 2) engage the community; 3) target health disparities; 4) act on the data; and 5) learn and improve [22–24,29]. As part of the community engagement and co-designing of the intervention, addressing social needs was determined to be a key component, consistent with the work of Kangovi and others [1].
At baseline, participants were screened for social needs using The Center for Medicare and Medicaid (CMS) Accountable Health Communities Health-Related Social Needs Screening Tool by the study team. The screening tool includes 26 questions addressing living situation, food security, transportation, utilities, safety, financial strain, employment, family and community support, education, physical activity, substance abuse, mental health, and disabilities [30,31]. Participants who screened positive for any social needs were referred to the Healthcare Collaborative of Greater Columbus Central (HCGC) Ohio Pathways Hub (Hub) using a secure web interface by Black Impact study staff. Black Impact participants were paired with a community health worker (CHW) from a care coordination agency (CCA) within the HCGC Hub. There were 13 CCAs employing over 30 CHWs participating in the HCGC Hub. CHWs served as partners, advocates, and coaches for their clients and worked to identify health needs and risks. The CHW contacted the study participants and conducted a comprehensive social needs screening assessment which aligned with the CMS screening tool but went into greater depth. Each risk or need was then translated into a pathway, with the CHW guiding participants through the appropriate care pathways, which were tracked in the Care Coordination Systems (CCS) secure data collection platform. CHWs were required to meet face-to-face with each participant monthly as well as have a second contact (phone, text, or email) per month. CHWs continued assisting participants in completing pathways and mitigating risks until participants’ needs were addressed. In the CCS secure data collection platform, referrals from the Black Impact program were flagged into a program designation and pertinent data was aggregated. HCGC reviewed the participants on an ongoing basis via a Checklist, Pathways, Tools (CPT) report and shared progress with Black Impact study staff. The report articulated all comprehensive risk assessments, any care pathways that were opened, completed successfully, or unsuccessfully and the reason why, and any tools utilized to help support client needs. The complete process is shown in Fig 2.
The model to address social needs noted above was derived from the validated Agency for Healthcare Research and Quality (AHRQ) Pathways Community Hub model [32]. This comprehensive, evidence-based approach leverages the known impact of care coordination facilitated by a Community Health Worker (CHW) to complete social needs-related screenings and referrals to address identified needs via coordination of health and social services across multiple community settings [33–36]. Notably, the Pathways Community Hub model facilitates cross-sector integration by supporting data sharing and aligning payment models through reimbursement for completed referrals to community-based organizations (CBOs) [37,38]. Specifically, the HCGC Hub model consists of three features [39]: (1) A regional coordination entity that employs CHWs to assess the medical and social needs of vulnerable patients and connect them to community resources; (2) the CHWs initiate a “care pathway,” a defined action plan that describes how patient needs will be addressed, which is then recorded and tracked in an electronic database (S1 Table); (3) completion of each care pathway is linked to payment from healthcare payers (Medicaid-managed care plans and other community partners) based on specific performance benchmarks. A financial contract is attached to each standardized care pathway; when a care pathway is completed, a CHW must confirm that a measurable outcome (e.g., patient has received food) is obtained in order for the agency to receive payment (Fig 2).
Data collection and measures
Biometric assessments were performed at baseline, 12 weeks, and 24 weeks. Data from participants included self-reported measures (sociodemographic and self-reported health history), survey data collected via REDCap, including the CMS Accountable Health Communities Health-Related Social Needs Screening Tool either onsite at the Recreation and Parks locations or at participant homes [30]. Biometric measurements, including blood pressure (mmHg), fasting cholesterol (mg/dl), fasting glucose (mg/dl), weight (lbs), and BMI were collected onsite at the Recreation and Parks locations and recorded in REDCap at each time point. The sociodemographic data included age, education, race, ethnicity, employment status, insurance status, and annual income. The self-reported health history included hypertension, diabetes, hyperlipidemia, and smoking status (I have never smoked, I currently smoke, I quit smoking > 1 year ago or I quit smoking ≤ 1 year ago), as well as medications for the aforementioned chronic conditions.
The survey data included the Diet History Questionnaire (DHQ) III [40]. The DHQ-III nutrient and food group database is based on a compilation of national 24-hour dietary recall data from the National Health and Nutrition Examination Surveys (NHANES). Prior research has shown the questionnaire is valid and reliable [41–43]. In the current evaluation, we calculated physical activity minutes per week using the validated moderate physical activity 2-question physical activity questionnaire within the CMS screening tool [44].
Biometric screenings were performed by trained healthcare staff, including nurses and physicians. Blood pressure was checked via an automated oscillometric sphygmomanometer (Omron 5 series) with two measurements performed after the participants were seated for 5 minutes and averaged. Weight was measured using a zeroed and calibrated Omron Body Composition Monitor and Scale (Model: HBF-514C). Height was measured via a tape measurer. BMI was calculated by multiplying weight (lbs) by 703 and then dividing by height squared (inch2). Blood total cholesterol and glucose were measured in the fasting state using the Cardio Check Silver® (Polymer Technology, Inc., Heath, OH, USA) device. All participants received individual results at baseline, 12, and 24 weeks.
Social needs outcome.
The main outcome in this analysis was change in social needs at 12 and 24-weeks compared to baseline. Social needs were coded as a “1” if any social need was identified on the CMS Accountable Health Communities Health-Related Social Needs Screening Tool [30,31] and “0” if none were identified. The social needs in the analysis included: 1) unstable or unsafe living situation; 2) food insecurity; 3) lack of transportation; 4) challenges with utilities; 5) physical safety; 6) financial strain; and 7) employment. The safety score was calculated from the 4 question HITS short domestic violence screening tool (a component of the CMS screening tool) with Likert scale answers from “Never” to “Frequently” with scores ranging from 1 (Never) to 5 (Frequently) [45]. A score of 11 or more when the numerical values for answers to questions 7–10 were added showed that the person might not be safe and was coded as a “1,” as has been validated in women and men [45,46].
Cardiovascular health outcomes.
The secondary outcome measure was change in LS7 cardiovascular health score (range 0–14) by baseline social needs. The LS7 cardiovascular health score was summed based on the individual LS7 metrics (glucose, cholesterol, blood pressure, BMI, physical activity, diet and smoking) categories of poor (0 points), intermediate (1) and ideal (2) cardiovascular health at baseline, 12 and 24 weeks (S2 Table), based on the AHA guidelines [47], as has been done previously [17,18,25]. Additionally, we developed a score using 6 components of the LS7 cardiovascular health score excluding diet (range 0–12) and 5 components excluding diet and physical activity (range 0–10). The 5 and 6 component scores were used in sensitivity analyses to confirm the robustness of the findings given that diet and physical activity were self-reported.
Statistical analysis
Descriptive statistics were performed for all variables, including mean (standard deviation [SD]) for continuous variables and frequencies and percentages for categorical variables. These characteristics have been compared between participants with and without social needs using two-sample t-test for continuous variables and chi-squared or fisher’s exact test for categorical variables. Mixed effect logistic regressions (generalized mixed models) with random intercepts for each participant were used to examine the change of social need from baseline to 24 weeks.
Odds ratios (ORs) and corresponding 95% confidence intervals (CIs) were reported. The models were sequentially adjusted for: 1) age and; 2) age and education. Additionally, change in LS7 cardiovascular health was calculated using linear mixed models with random intercepts for each participant stratified by baseline social needs status. Sensitivity analyses were performed where each instance of a social need from the first 12 questions of the CMS survey were counted at each time point and listed in S3 Table. If a participant had one of those listed social needs at a time point, they were considered to have a social need at that time point. A separate data set was created that included only those men who had a social need at any time point (n = 44). A spaghetti plot showing change in social needs among these participants per time point is presented in Fig 3. Statistical significance for all analyses was defined as two-sided alpha < 0.05. Statistical analyses were performed using SAS 9.4 (SAS Institute, Inc.; Cary, North Carolina, USA) and R version 3.4.3 (R Foundation for Statistical Computing, Vienna, Austria).
Lines represent the 44 participants that had social need(s) at some point during the study. Four had no social needs at baseline, then gained at least one social need as the study progressed. Forty had a social need at baseline. Thirteen of the 40 had no social needs by week 12, and one participant took until week 24 to resolve his social need(s). Six of the 13 that had social needs addressed by week 12 had regained social need(s) by week 24. The lines in the plot have random jitter added to allow individual participants to be distinguished.
Results
Seventy-four Black men participated in the intervention, and 70 are included in the current analysis (Fig 1). Baseline demographic characteristics of Black Impact participants are shown in Table 1. The mean age of participants was 52.0 years (SD 10.5). All participants had a high school degree or equivalent and 43% had a college degree or higher level of education. The majority of participants were employed with private insurance (84.3% and 72.9%, respectively). The income of participants was heterogeneous, ranging from <$20,000 (5.7%) to ≥$75,000 (22.9%). LS7 cardiovascular health scores had a high proportion of participants in the poor range for blood pressure (47.1%), glucose (28.6%), body mass index (54.3%), and diet (40.0%). At baseline there was no difference in cardiovascular health scores stratified by social needs status (identified social need vs. not) using 5 (excluding diet and physical activity), 6 (excluding diet), or all 7 components of the cardiovascular health scores (Life’s Simple 7).
Forty out of the 70 men (57.1%) had an identified social need. Thirty-one of the 40 men were interested and referred to the HCGC Hub. Eight of the thirty-one men were enrolled into pathways including education, social services, medical referral, behavioral health, employment, and medical home pathways.
The longitudinal change in social needs is shown in Table 2. The 57.1% of participants with an identified social need at baseline decreased to 36.6% and 44.2% at 12 and 24 weeks, respectively. The odds of having a social need at week 12 and 24 were 67% lower (OR 0.33, 95% CI: 0.13, 0.85) and 50% lower (OR 0.50, 95% CI: 0.21, 1.16) than baseline. Given the study occurred during the COVID-19 pandemic we also evaluated social needs using 5 of the 7 social needs components excluding financial strain or employment. The results were similar with significant reductions in social needs at 12 weeks (p = 0.035) and a trend at 24 weeks (p = 0.13). Among the 7 individual social needs the majority showed trends in the direction of improvement except for employment with a numerically higher proportion (non-significant) of participants indicating a desire for “help finding work” or “help keeping work”.
Table 3 shows the longitudinal change of cardiovascular health stratified by baseline social needs. Cardiovascular health scores improved by 0.94 points (p = 0.013) and 0.87 points (p = 0.022) in the group without and with social needs, respectively. No differential effect by baseline social needs status existed (interaction p-value for the interaction effect between social needs and time was p = 0.895).
In sensitivity analyses among participants with social needs, among the 40 participants with social needs at baseline 13 had the social need resolved by week 12, but 4 of the 13 noted a new social need by week 24 (Fig 3).
Discussion
In this novel, 24-week CBPR lifestyle intervention that addressed social needs through screening and service coordination in Black men, a majority of men had social needs at baseline and were interested in addressing them through a referral program. Reductions in social needs were seen at 12 and 24 weeks. Social needs status at baseline was not associated with baseline cardiovascular health scores nor change in cardiovascular health scores at 12 and 24 weeks. To our knowledge this is the first study focused on addressing a complete suite of cardiovascular risk factors through a program that includes a lifestyle intervention, along with assessing and addressing social needs in Black men.
Addressing social needs in a comprehensive intervention
The referrals through the HCGC Hub model facilitated cross-sector integration between the Black Impact program and CHWs to address social needs [37,38]. The model also supported data sharing and aligned payment models through reimbursement for completed referrals to CBOs. Previous programs have addressed social needs through social workers, navigators, CHWs, advocates or referral-based programs in healthcare systems and other settings [7,48,49]. Programming solely focusing on addressing social needs without other components has been systematically reviewed [7]. The majority of interventions reported successfully identifying unmet social needs and referring to clinic and community resources [7]. There was wide heterogeneity in the uptake of the referrals, but generally studies show improvement in social needs [7]. Additionally, previous analyses of Pathways Community Hub models have shown that counties with broad networks of community-based services focused on care coordination to address the social needs of older adults had lower readmission rates and less avoidable nursing home care [50]. Likewise, a population-level analysis of cross-sector collaborations demonstrated lower mortality associated with cardiovascular disease, diabetes, and influenza [51]. The Black Impact study is the first study to our knowledge to address social needs as part of a comprehensive, community-based intervention founded in improving cardiovascular health in Black men.
Social needs and life’s simple 7
Limited data exists on the impact of social needs on LS7. The study team hypothesized that participants with social needs would have worse cardiovascular health per the AHA LS7 measures at baseline. The hypothesis was not supported by our findings. The majority of the extant literature examines the relationship between socioeconomic status and LS7. In Black adults in the Jackson Heart Study, higher individual income, neighborhood socioeconomic status and education were associated with higher LS7 cardiovascular health scores, although sex-stratified findings were not reported and two-thirds of Jackson Heart Study participants are women [13]. Thus, it is unclear if these findings are consistent in Black men. In Black men participating in US AAMWA Walks there was no association of higher levels of education or employment status with six components (excluding diet) of the LS7 cardiovascular health scores, but were positive associations with income and insurance [14]. Caleyahetta et al. showed that a higher cumulative risk score summing four socioeconomic status measures (low family income, low education level, minority race, and single-living status) was associated with lower attainment of LS7 [52].
In terms of specific social needs, there are studies evaluating the relationship of social needs, most commonly housing, food insecurity, and financial stress with cardiovascular risk factors or chronic disease [53–55]. There are limited studies evaluating social needs and LS7. In a weight loss study in Louisiana (two-thirds Black participants), the mean LS7 total score was not significantly different by food security status at baseline [56]. Additionally, there was no association of food insecurity with adiposity in men [56]. Discordant with our study, food insecurity was associated with lower prevalence of “good” (LS7 ideal and intermediate vs. poor) cardiovascular health among majority White (85%) individuals in Wisconsin [57]. Contrary to the authors hypotheses, food insecure individuals were more likely to have ideal blood pressure and total cholesterol [57]. Among middle age and older female health professional majority (95%) White women, the number of financial stressors was associated with lower ideal cardiovascular health [58]. Lack of housing has been associated with higher cardiovascular disease risk, but there are no studies assessing housing security and/or quality and LS7, as most large cardiovascular cohorts and national data sources do not include individuals with significant housing insecurity or homelessness [59]. Thus, the findings of Black Impact showing no association of social needs and Life’s Simple 7 at baseline are an important contribution to the limited extant literature. The lack of association of social needs with LS7 in Black Impact may be due to multiple factors as previously noted by Azap et al. including “John Henryism”, allostatic load from multiple stresses including racism and discrimination, discrimination and bias in the healthcare setting leading to medical mistrust, and inequities in wealth, such that those without social needs, may still have difficulty attaining high levels of cardiovascular health [14,29]. Further studies addressing the role of social needs in LS7 cardiovascular health are a critical area of inquiry to discern the role and mechanisms of social needs in cardiovascular risk factors particularly among racial/ethnic minority sex groups.
Social needs interventions and life’s simple 7
The authors hypothesized that even with addressing social needs as part of the Black Impact intervention, participants with baseline social needs would have less improvements in cardiovascular health scores. Importantly, in the study there was no difference in improvement in cardiovascular health scores across 12 and 24 weeks, suggesting that improvements in social needs and addressing cardiovascular health through physical activity and health education in a community-setting may be a potential strategy to advance cardiovascular health irrespective of social needs.
There are limited studies to which to compare the Black Impact study, due to the novel nature of the community-based participatory research intervention in Black men. Interventions addressing social needs and cardiovascular risk factors have mostly focused on the healthcare setting. Overall, studies reporting health, utilization, or cost outcomes report mixed results [7]. A prominent study by Berkowitz, et al. used advocates in the healthcare setting to help individuals obtain resources across multiple social needs and showed reductions in blood pressure and cholesterol but not glycemia in individuals engaged in healthcare with cardiometabolic diseases [48]. Kangovi et al. studied goal setting vs. goal setting and a CHW to improve glycemia, blood pressure, obesity, or smoking in 302 individuals who were predominantly Black (95%) and female (75%) over 6-months [60]. While none of the individual categories were significantly different for goal setting vs. goal setting/CHW, there was a trend (p = 0.08) towards overall greater improvements in the outcomes (A1c, systolic blood pressure, BMI, or smoking) with the addition of the CHW [60]. The study team performed a follow-up study in 592 individuals with similar characteristics over 9 months and found no difference in changes in self-rated physical health, mental health nor a combination of A1c, systolic blood pressure, BMI, or smoking (p = 0.21) [61].
In a systematic review by Gottlieb et al, the authors found 81 studies where social needs were addressed as part of comprehensive interventions [7]. Only three of these studies had components of LS7 as outcomes and none had all 7 factors. Watt et al, performed a primary care based early childhood intervention in low-income Hispanic pregnant women. The program provided vouchers for fruits and vegetables from the local farmers’ market, nutrition classes, cooking classes, and lactation counseling. Women in the intervention compared to control had significant improvements in diet, exercise, and depression (p≤0.05) [62]. Loskutova et al, examined telephone-based nonprofessional patient navigation to promote linkages between the primary care provider and community programs in 179 patients with or at risk for diabetes. Two patient navigators provided services over the phone, including assessment of needs, barriers and limitations, motivational interviewing and a suggestion of 2 to 3 community programs with an average of 6 calls per patient. In pre-post analyses they showed a reduction in hemoglobin A1c (7.8% vs 7.2%, P = 0.001) in those with diabetes and improvement in patient self-efficacy. They found no change in fasting glucose, BMI, total cholesterol, low-density lipoprotein, high-density lipoprotein, or triglycerides [63]. At Intermountain Healthcare, a generalist model of chronic disease management was formulated, with care managers located within multi-payer primary care clinics collaborating with physicians, patients, and other members of a primary care team to improve patient outcomes. In patients with diabetes, they found a greater reduction in A1c compared to controls over 1 year (8.0% to 7.4% [intervention] compared to 7.7% to 7.5% [control, p<0.001]) [64].
In comparison, the nearly 1-point increase in cardiovascular health score from baseline to 24-weeks in Black Impact was a large improvement in cardiovascular health, considering a 1-point higher cardiovascular health score is associated with an 18% and 19% lower odds of stroke and myocardial infarction, respectively and an 11% and 19% lower risk of all-cause and cardiovascular mortality [25]. Additionally, there were improvements in individual components including body mass index, systolic blood pressure, fasting glucose, total cholesterol and dietary intake [25].
Strengths/Limitations
The strengths of our study include: 1) a focus on an understudied population with large disparities in cardiovascular health and mortality; 2) community engagement framework for CBPR that recognized the importance of addressing social needs in Black Impact; 3) collaborations across a number of organizations to screen and address social needs through the evidenced-based Pathways Community Hub model; and 4) the use of trained health professionals using evidence-based approaches for biometric data collection. Despite these strengths, the study should be considered in light of some limitations. First, the study was not randomized due to: 1) no previous test of intervention feasibility and acceptability; and 2) concerns raised from community members in regards to not receiving a potentially beneficial intervention (albeit, novel and not previously tested). A second limitation is the lack of a control group. Our findings may be influenced by regression toward the mean, but this is unlikely given the difficulty in addressing social needs without a supportive system. The effect estimates generated from the study are being used to plan a powered, randomized, wait-list controlled intervention. Third, even with our sociodemographically diverse cohort, the Black Impact participants may not be representative of other populations of Black men. Fourth, only 8 of the 31 men referred to the HGCG Hub were enrolled in a pathway. For the 23 non-enrolled men they were given paper resources but were not interested in being enrolled in a pathway. In future iterations of Black Impact, the study team will consider performing further education with participants regarding the importance of addressing social needs and the benefit of enrolling in pathways. Additionally, we have discussed having the CHW come to the Black Impact study site to meet with participants and build relationships. Increased enrollment in pathways may lead to even greater reductions in social needs over the course of the intervention. Lastly, our study was performed during the COVID-19 pandemic and participants may have different social needs in a non-pandemic setting. Notably, the lack of worse cardiovascular health at baseline among participants with vs. without social needs may have been due to impacts of the COVID-19 pandemic increasing social needs and worsening cardiovascular health due to pandemic restrictions (e.g. closure of gyms, food shortages, etc.) and difficulty accessing preventive medical care.
Conclusion
To our knowledge this is the first study to show improvements in social needs as a component of a comprehensive lifestyle intervention in Black men. As part of the community engagement and co-design of the intervention using the PETAL framework for CBPR, addressing social needs was determined to be a key component. There was no evidence of baseline differences in LS7 by social needs, and the group with baseline social needs had similar improvements in cardiovascular health. In future iterations of Black Impact, it will be important to include an arm of the study that does not address social needs to determine the necessity of addressing social needs for LS7 improvement. Further research to evaluate how the dose and timing of addressing social needs may impact physical, mental health and quality of life outcomes is also warranted. Addressing social needs in Black men is an attainable goal through a multi-component intervention and may help individuals with social needs improve cardiovascular health.
Supporting information
S1 Table. Core pathways available through the central ohio pathways hub model.
https://doi.org/10.1371/journal.pone.0282103.s002
(DOCX)
S2 Table. American heart association definitions of poor, intermediate, and ideal cardiovascular healtha.
a Adapted from The American Heart Association’s Strategic Planning Task Force and Statistical Committee 2020 Guidelines [47]. b Adapted from The American Heart Association’s Strategic Planning Task Force and Statistical Committee 2020 Guidelines: Fruits and vegetables ≥4·5 cups/day, fish ≥two 3.5 ounce servings per week (non-fried), fiber-rich whole grains ≥ three 1 ounce-equivalent servings/day, sodium <1500 mg/day, and sugar-sweetened beverages ≤ 1884 kJ (36 ounces)/week [47].
https://doi.org/10.1371/journal.pone.0282103.s003
(DOCX)
S3 Table. Counts for individual social needs over time.
Each instance of each social need from the first 12 questions of the CMS survey were counted at each time point and listed here.
https://doi.org/10.1371/journal.pone.0282103.s004
(DOCX)
Acknowledgments
The authors wish to thank the staff and participants of Black Impact. The authors wish to thank all of the partners that made this project possible including Allan Sommer, ACSM-CPT, the American Cancer Society, Cardinal Health, Central Ohio American Heart Association, Cigna Foundation, City of Columbus Recreation and Parks and Public Health, Franklin County Public Health, Healthcare Collaborative of Greater Columbus, Molina Healthcare, OhioHealth, The National African American Male Wellness Agency, The Ohio State University Center for Clinical and Translational Science, The Ohio State University Comprehensive Cancer Center Mobile Education Kitchen, The Ohio State University Wexner Medical Center and Quinn Capers IV, MD.
References
- 1.
Board on Population Health and Public Health Practice, Health and Medicine Division, National Academies of Sciences, Engineering, and Medicine. Investing in Interventions That Address Non-Medical, Health-Related Social Needs: Proceedings of a Workshop. Martinez RM, Alper , editors. Washington, D.C.: National Academies Press; 2019. p. 25544. https://doi.org/10.17226/25544
- 2.
National Academy of Medicine, Dzau VJ, McClellan M, Duke University, Burke S, Harvard Kennedy School, et al. Vital Directions for Health and Health Care: Priorities from a National Academy of Medicine Initiative. NAM Perspectives. 2017;7. https://doi.org/10.31478/201703e
- 3.
HPOE.org—Social Determinants of Health Series: Housing. [cited 1 Jun 2021]. Available: http://www.hpoe.org/resources/ahahret-guides/3063.
- 4. Kaiser ML, Cafer A. Understanding High Incidence of Severe Obesity and Very Low Food Security in Food Pantry Clients: Implications For Social Work. Social Work in Public Health. 2018;33: 125–139. pmid:29297775
- 5. Hill-Briggs F, Adler NE, Berkowitz SA, Chin MH, Gary-Webb TL, Navas-Acien A, et al. Social determinants of health and diabetes: a scientific review. Diabetes Care. 2021;44: 258–279.
- 6. Fichtenberg C, Delva J, Minyard K, Gottlieb LM. Health And Human Services Integration: Generating Sustained Health And Equity Improvements: An overview of collaborations, partnerships, and other integration efforts between health care and social services organizations. Health Affairs. 2020;39: 567–573.
- 7. Gottlieb LM, Wing H, Adler NE. A Systematic Review of Interventions on Patients’ Social and Economic Needs. American Journal of Preventive Medicine. 2017;53: 719–729. pmid:28688725
- 8. Ruiz Escobar E, Pathak S, Blanchard CM. Screening and Referral Care Delivery Services and Unmet Health-Related Social Needs: A Systematic Review. Prev Chronic Dis. 2021;18: 200569. pmid:34387188
- 9. Albertson EM, Chuang E, O’Masta B, Miake-Lye I, Haley LA, Pourat N. Systematic Review of Care Coordination Interventions Linking Health and Social Services for High-Utilizing Patient Populations. Population Health Management. 2022;25: 73–85. pmid:34134511
- 10. Yan AF, Chen Z, Wang Y, Campbell JA, Xue Q-L, Williams MY, et al. Effectiveness of Social Needs Screening and Interventions in Clinical Settings on Utilization, Cost, and Clinical Outcomes: A Systematic Review. Health Equity. 2022;6: 454–475. pmid:35801145
- 11. Manian N, Wagner CA, Placzek H, Darby BA, Kaiser TJ, Rog DJ. Relationship between intervention dosage and success of resource connections in a social needs intervention. Public Health. 2020;185: 324–331. pmid:32726729
- 12. Gordon NP, Banegas MP, Tucker-Seeley RD. Racial-ethnic differences in prevalence of social determinants of health and social risks among middle-aged and older adults in a Northern California health plan. Roy B, editor. PLoS ONE. 2020;15: e0240822. pmid:33147232
- 13. Foraker RE, Bush C, Greiner MA, Sims M, Henderson K, Smith S, et al. Distribution of Cardiovascular Health by Individual- and Neighborhood-Level Socioeconomic Status: Findings From the Jackson Heart Study. gh. 2019;14: 241. pmid:31196828
- 14. Azap RA, Nolan TS, Gray DM, Lawson K, Gregory J, Capers Q, et al. Association of Socioeconomic Status With Ideal Cardiovascular Health in Black Men. J Am Heart Assoc. 2021;10: e020184. pmid:34816728
- 15. Virani SS, Alonso A, Benjamin EJ, Bittencourt MS, Callaway CW, Carson AP, et al. Heart Disease and Stroke Statistics—2020 Update: A Report From the American Heart Association. Circulation. 2020;141. pmid:31992061
- 16. Dong C, Rundek T, Wright CB, Anwar Z, Elkind MSV, Sacco RL. Ideal cardiovascular health predicts lower risks of myocardial infarction, stroke, and vascular death across whites, blacks, and hispanics: the northern Manhattan study. Circulation. 2012;125: 2975–2984. pmid:22619283
- 17. Joseph JJ, Echouffo-Tcheugui JB, Carnethon MR, Bertoni AG, Shay CM, Ahmed HM, et al. The association of ideal cardiovascular health with incident type 2 diabetes mellitus: the Multi-Ethnic Study of Atherosclerosis. Diabetologia. 2016;59: 1893–1903. pmid:27272340
- 18. Joseph JJ, Bennett A, Echouffo Tcheugui JB, Effoe VS, Odei JB, Hidalgo B, et al. Ideal cardiovascular health, glycaemic status and incident type 2 diabetes mellitus: the REasons for Geographic and Racial Differences in Stroke (REGARDS) study. Diabetologia. 2019;62: 426–437. pmid:30643923
- 19. Rasmussen-Torvik LJ, Shay CM, Abramson JG, Friedrich CA, Nettleton JA, Prizment AE, et al. Ideal Cardiovascular Health Is Inversely Associated With Incident Cancer: The Atherosclerosis Risk in Communities Study. Circulation. 2013;127: 1270–1275. pmid:23509058
- 20. Khan MS, Kumar P, Sreenivasan J, Khan SU, Michos ED, Yancy CW, et al. Preventable Deaths From Heart Disease and Stroke Among Racial and Ethnic Minorities in the United States. Circ: Cardiovascular Quality and Outcomes. 2021 [cited 2 Jul 2021]. pmid:34176295
- 21. Van Dyke M, Greer S, Odom E, Schieb L, Vaughan A, Kramer M, et al. Heart Disease Death Rates Among Blacks and Whites Aged ≥35 Years—United States, 1968–2015. MMWR Surveill Summ. 2018;67: 1–11. pmid:29596406
- 22. Elgazzar R, Nolan TS, Joseph JJ, Aboagye-Mensah EB, Azap RA, Gray DM. Community-engaged and community-based participatory research to promote American Heart Association Life’s Simple 7 among African American adults: A systematic review. PLoS ONE. 2020;15: e0238374. pmid:32870944
- 23. Joseph JJ, Glover A, Olayiwola JN, Rastetter M, Allen J (Chip) , Knight K, et al. Mask Up: Academic-Community-Government Partnerships to Advance Public Health During COVID-19. Population Health Management. 2021; pop.2020.0305. pmid:33467989
- 24. Gray DM, Nolan TS, Gregory J, Joseph JJ. Diversity in clinical trials: an opportunity and imperative for community engagement. The Lancet Gastroenterology & Hepatology. 2021;6: 605–607. pmid:34246352
- 25. Joseph JJ, Nolan TS, Williams A, McKoy A, Zhao S, Aboagye-Mensah E, et al. Improving Cardiovascular Health in Black Men Through a 24-Week Community-Based Team Lifestyle Change Intervention: The Black Impact Pilot Study. American Journal of Preventive Cardiology. 2022; 100315. pmid:35146467
- 26.
Melnyk BM, Morrison-Beedy D, editors. Intervention research: designing, conducting, analyzing, and funding. New York: Springer Pub; 2012.
- 27. The Diabetes Prevention Program. Reduction in the Incidence of Type 2 Diabetes with Lifestyle Intervention or Metformin. New England Journal of Medicine. 2002;346: 393–403. pmid:11832527
- 28. Bail J, Nolan TS, Vo JB, Gisiger-Camata S, Meneses K. Engaging an Urban African American Community to Deliver Cognitive Health Education to Breast Cancer Survivors. Journal of Cancer Education. 2018;33: 870–874. pmid:28032260
- 29. Gray DM, Nolan TS, Bignall ONR, Gregory J, Joseph JJ. Reckoning with Our Trustworthiness, Leveraging Community Engagement. Population Health Management. 2021; pop.2021.0158. pmid:34271849
- 30.
Centers for Medicare and Medicaid Services, Billioux A, Verlander K, Centers for Medicare and Medicaid Services, Anthony S, Centers for Medicare and Medicaid Services, et al. Standardized Screening for Health-Related Social Needs in Clinical Settings: The Accountable Health Communities Screening Tool. NAM Perspectives. 2017;7. https://doi.org/10.31478/201705b
- 31.
Centers for Medicare and Medicaid Services. The Accountable Health Communities Health-Related Social Needs Screening Tool. [cited 17 May 2021]. Available: https://innovation.cms.gov/files/worksheets/ahcm-screeningtool.pdf.
- 32. Community Care Coordination Learning Network and The Pathways Community HUB Certification Program. Pathways Community HUB Manual: A Guide to Identify and Address Risk Factors, Reduce Costs, and Improve Outcomes. Agency for Healthcare Research and Quality (AHRQ). 2016; 74.
- 33. Redding S, Conrey E, Porter K, Paulson J, Hughes K, Redding M. Pathways community care coordination in low birth weight prevention. Matern Child Health J. 2015;19: 643–650. pmid:25138628
- 34. Zeigler BP, Redding S, Leath BA, Carter EL, Russell C. Guiding Principles for Data Architecture to Support the Pathways Community HUB Model. eGEMs. 2016;4: 1. pmid:26870743
- 35.
Zeigler BP, Carter EL, Redding SA, Leath BA, Russell C. Care Coordination: Formalization of Pathways for Standardization and Certification Report for Project: Health System Modeling and Simulation: Coordinated Care Example. Rockville, MD: Rockville Institute; 2014. Report No.: National Science Foundation Grant Award No. CMMI-1235364. Available: http://www.rockvilleinstitute.org/files/Care_Coordination_Formalization_of_Pathways_for_Standardization_and_Certification.pdf.
- 36. Golnick C, Asay E, Provost E, Van Liere D, Bosshart C, Rounds-Riley J, et al. Innovative primary care delivery in rural Alaska: a review of patient encounters seen by community health aides. Int J Circumpolar Health. 2012;71: 18543. pmid:22765934
- 37. Redding M, Hoornbeek J, Zeigler BP, Kelly M, Redding S, Falletta L, et al. Risk Reduction Research Initiative: A National Community-Academic Framework to Improve Health and Social Outcomes. Popul Health Manag. 2019;22: 289–291. pmid:30102575
- 38. Alley DE, Asomugha CN, Conway PH, Sanghavi DM. Accountable Health Communities—Addressing Social Needs through Medicare and Medicaid. N Engl J Med. 2016;374: 8–11. pmid:26731305
- 39. Zeigler BP, Redding SA, Leath BA, Carter EL. Pathways Community HUB: A Model for Coordination of Community Health Care. Population Health Management. 2014;17: 199–201. pmid:25127204
- 40.
Diet History Questionnaire III (DHQ III). [cited 1 Jun 2021]. Available: https://epi.grants.cancer.gov/dhq3/index.html.
- 41. Thompson FE, Subar AF, Brown CC, Smith AF, Sharbaugh CO, Jobe JB, et al. Cognitive research enhances accuracy of food frequency questionnaire reports: results of an experimental validation study. Journal of the American Dietetic Association. 2002;102: 212–225. pmid:11846115
- 42. Subar AF, Thompson FE, Kipnis V, Midthune D, Hurwitz P, McNutt S, et al. Comparative Validation of the Block, Willett, and National Cancer Institute Food Frequency Questionnaires. American Journal of Epidemiology. 2001;154: 1089–1099. pmid:11744511
- 43. Kipnis V. Structure of Dietary Measurement Error: Results of the OPEN Biomarker Study. American Journal of Epidemiology. 2003;158: 14–21. pmid:12835281
- 44. Coleman KJ, Ngor E, Reynolds K, Quinn VP, Koebnick C, Young DR, et al. Initial Validation of an Exercise “Vital Sign” in Electronic Medical Records. Medicine & Science in Sports & Exercise. 2012;44: 2071–2076. pmid:22688832
- 45. Sherin KM, Sinacore JM, Li XQ, Zitter RE, Shakil A. HITS: a short domestic violence screening tool for use in a family practice setting. Fam Med. 1998;30: 508–512. pmid:9669164
- 46. Shakil A, Donald S, Sinacore JM, Krepcho M. Validation of the HITS domestic violence screening tool with males. Fam Med. 2005;37: 193–198. pmid:15739135
- 47. Lloyd-Jones DM, Hong Y, Labarthe D, Mozaffarian D, Appel LJ, Van Horn L, et al. Defining and Setting National Goals for Cardiovascular Health Promotion and Disease Reduction: The American Heart Association’s Strategic Impact Goal Through 2020 and Beyond. Circulation. 2010;121: 586–613. pmid:20089546
- 48. Berkowitz SA, Hulberg AC, Standish S, Reznor G, Atlas SJ. Addressing Unmet Basic Resource Needs as Part of Chronic Cardiometabolic Disease Management. JAMA Intern Med. 2017;177: 244. pmid:27942709
- 49. Gottlieb LM, DeSalvo K, Adler NE. Healthcare sector activities to identify and intervene on social risk: an introduction to the American Journal of Preventive Medicine supplement. American journal of preventive medicine. 2019;57: S1–S5. pmid:31753275
- 50. Brewster AL, Kunkel S, Straker J, Curry LA. Cross-Sectoral Partnerships By Area Agencies On Aging: Associations With Health Care Use And Spending. Health Affairs. 2018;37: 15–21. pmid:29309226
- 51. Mays GP, Mamaril CB, Timsina LR. Preventable Death Rates Fell Where Communities Expanded Population Health Activities Through Multisector Networks. Health Affairs. 2016;35: 2005–2013. pmid:27834240
- 52. Caleyachetty R, Echouffo-Tcheugui JB, Muennig P, Zhu W, Muntner P, Shimbo D. Association between cumulative social risk and ideal cardiovascular health in US adults: NHANES 1999–2006. International Journal of Cardiology. 2015;191: 296–300. pmid:25984898
- 53. Laraia BA. Food Insecurity and Chronic Disease. Advances in Nutrition. 2013;4: 203–212. pmid:23493536
- 54. Palakshappa D, Speiser JL, Rosenthal GE, Vitolins MZ. Food Insecurity Is Associated with an Increased Prevalence of Comorbid Medical Conditions in Obese Adults: NHANES 2007–2014. Journal of general internal medicine. 2019;34: 1486–1493. pmid:31161567
- 55. Seligman HK, Laraia BA, Kushel MB. Food Insecurity Is Associated with Chronic Disease among Low-Income NHANES Participants. The Journal of Nutrition. 2010;140: 304–310. pmid:20032485
- 56. Myers CA, Martin CK, Newton RL, Apolzan JW, Arnold CL, Davis TC, et al. Cardiovascular Health, Adiposity, and Food Insecurity in an Underserved Population. Nutrients. 2019;11: 1376. pmid:31248113
- 57. Saiz AM, Aul AM, Malecki KM, Bersch AJ, Bergmans RS, LeCaire TJ, et al. Food insecurity and cardiovascular health: Findings from a statewide population health survey in Wisconsin. Preventive Medicine. 2016;93: 1–6. pmid:27612573
- 58. Cabeza de Baca T, Burroughs Peña MS, Slopen N, Williams D, Buring J, Albert MA. Financial strain and ideal cardiovascular health in middle-aged and older women: Data from the Women’s health study. American Heart Journal. 2019;215: 129–138. pmid:31323455
- 59. Sims M, Kershaw KN, Breathett K, Jackson EA, Lewis LM, Mujahid MS, et al. Importance of Housing and Cardiovascular Health and Well-Being: A Scientific Statement From the American Heart Association. Circ: Cardiovascular Quality and Outcomes. 2020;13. pmid:32673512
- 60. Kangovi S, Mitra N, Grande D, Huo H, Smith RA, Long JA. Community Health Worker Support for Disadvantaged Patients With Multiple Chronic Diseases: A Randomized Clinical Trial. Am J Public Health. 2017;107: 1660–1667. pmid:28817334
- 61. Kangovi S, Mitra N, Norton L, Harte R, Zhao X, Carter T, et al. Effect of Community Health Worker Support on Clinical Outcomes of Low-Income Patients Across Primary Care Facilities: A Randomized Clinical Trial. JAMA Intern Med. 2018;178: 1635. pmid:30422224
- 62. Watt TT, Appel L, Lopez V, Flores B, Lawhon B. A Primary Care-Based Early Childhood Nutrition Intervention: Evaluation of a Pilot Program Serving Low-Income Hispanic Women. J Racial and Ethnic Health Disparities. 2015;2: 537–547. pmid:26863560
- 63. Loskutova NY, Tsai AG, Fisher EB, LaCruz DM, Cherrington AL, Harrington TM, et al. Patient Navigators Connecting Patients to Community Resources to Improve Diabetes Outcomes. The Journal of the American Board of Family Medicine. 2016;29: 78–89. pmid:26769880
- 64. Dorr DA, Wilcox A, Burns L, Brunker CP, Narus SP, Clayton PD. Implementing a Multidisease Chronic Care Model in Primary Care Using People and Technology. Disease Management. 2006;9: 1–15. pmid:16466338