Utilizing the Probation Office as an Opportunity to Screen for Cardiometabolic Outcomes: A Feasibility Study

Abstract

This cross-sectional study examined whether the probation office setting was feasible to screen adults on probation for cardiometabolic risk factors, measure risk profiles, and estimate the prevalence of obesity, hypertension, hypercholesterolemia, and diabetes. During June and August 2019, screening included blood pressure, anthropometrics, total and high-density lipoprotein (HDL) cholesterol, and glucose. A survey included demographics, medical history, and current medication. The participation rate was 36% (N = 202). The screening identified 5% had hypercholesterolemia, 38% of men and 50% of women had low HDL cholesterol, 70% had overweight/obesity, 31% of men and 55% of women had elevated waist circumferences, and 26.7% had Stage 1 hypertension. Of individuals with a history of hypertension (n = 74), 77% had elevated blood pressure. Of those with a history of diabetes (n = 27), 22% had hyperglycemia, independent of whether they reported being prescribed medication. The screening identified 11% with Stage 2 hypertension, 27% with Stage 1 hypertension, 22% with elevated blood pressure, and 5% with hyperglycemia. Our findings suggest it is feasible to identify individuals at high risk for cardiometabolic disorders during routine probation office visits. These data can then be used to provide referrals for treatment to improve long-term health outcomes.

Introduction

Individuals in U.S. jails and prisons have higher rates of chronic disease than the general U.S. population (Binswanger et al., 2009, 2012; Gates & Bradford, 2015; Houle, 2011; Leddy et al., 2009; Maruschak et al., 2015). It is reasonable to expect a similar trend among adults on community correctional supervision, such as probation, because many cycle in and out of the correctional system; however, the empirical evidence is limited. At the end of 2018, about 1.1% of U.S. adults were on probation supervision (Kaeble & Alper, 2020). Rhode Island had double the rate of adults on probation (2.1%) than the national average (Kaeble & Alper, 2020).

In 2016, we conducted a cross-sectional study through a survey in a probation office in Rhode Island and found that people on probation supervision faced high estimated rates of unemployment (59%), food insecurity (70%), and homelessness (23%; Dong et al., 2018). In addition, we conducted a qualitative study with 22 adults on probation supervision and found that routine health care was of relatively low priority compared with substance use treatment, employment, housing, and food access (Dong et al., 2018). The large proportion of individuals involved with the probation system makes this a critical public health issue.

Given the challenges with health equity related to social determinants of health, such as economic stability (Dong et al., 2018; Pager et al., 2009; Pager, 2003; Plugge et al., 2014; Roddy & Morash, 2020) and access to housing (McNiel et al., 2005; Plugge et al., 2014; Polcin et al., 2018), food (Dong et al., 2018; Wang et al., 2013), and health care (Plugge et al., 2014), we hypothesized that people on probation were at an increased risk for cardiometabolic disease compared with the general population and, therefore, may benefit from screening for cardiovascular risk to identify opportunities for risk reduction.

Adults on probation have frequent mandatory visits to the probation office and this setting represents a novel opportunity to conduct cardiometabolic risk screenings and improve access to health care for this vulnerable population. Screening should be feasible in this setting, which is similar to community wellness programs that have effectively provided screenings for cardiometabolic risk (Grant et al., 2004; Misra et al., 2016; Shellman, 2000; VanWorme et al., 2012; Willis et al., 2014). In the probation office setting, a similar model has been successful for hepatitis C screening (Zaller et al., 2016).

Finding successful approaches to identify and link people to health care will contribute to identifying high-risk individuals, thereby lowering the development or progression of chronic diseases, resulting in lowered rates of premature disability and mortality, improved quality of life, and reduced long-term health care costs. Before designing an intervention, it is critical to establish the efficacy. The aim of this study was to assess the feasibility of using the probation setting as a platform to screen for cardiovascular disease (CVD) and diabetes risk factors and measure the risk profile and estimated prevalence of obesity, hypertension, hypercholesterolemia, and hyperglycemia in the probation population.

Method

Study Participants

This was a cross-sectional single visit feasibility study conducted between June and August 2019 in one probation office in Rhode Island. Study visits were conducted 2 weekdays per week. Eligible participants were under active probation supervision, at least 18 years of age, English-speaking, and able to provide informed consent. Less than 5% of the individuals who report to this probation office were non-English speaking.

Participants were recruited in the waiting room of the probation office. Methods of recruitment included fliers in the waiting room and probation officers informing their clients of the study opportunity. All participants provided informed consent in a private room. This study was approved by the institutional review board at Tufts University (No. 13007). Individuals received $10 for participating in the study. Each study visit was approximately 30 to 45 minutes in length.

Measures

To determine the feasibility of using the probation office to screen for CVD and diabetes risk factors, we recorded the voluntary participation rate. For individuals who declined to participate, if provided, we recorded their reasons.

Research team members conducted the screening measurements for CVD and diabetes risk factors for people on probation supervision. We arranged two rooms in the probation office, similar to a worksite wellness screening health fair. One room was allocated to point-of-care measurement of total cholesterol, high-density lipoprotein (HDL) cholesterol, and glucose concentrations. Blood was obtained by finger stick and put on a test strip for analysis by an automated portable machine (CardioCheck Plus testing analyzer). Results were available within 5 minutes and provided to the participants. Participants provided information about the last time they had consumed food or beverages while they awaited their finger stick results. After this, participants were taken to the second room where anthropometric measurements, blood pressure, and a brief survey were offered.

Anthropometric measurements included height, weight, and waist circumference. Height was measured to the nearest 0.1 cm by a stadiometer (Seca, Model 213) with shoes removed. Weight was measured to the nearest 0.1 kg by a digital scale (Tanita, Model TBF-410) wearing light clothing and shoes removed. Waist circumference was measured to the nearest 0.1 cm through a tape measure (Gulick II) in the horizontal plane midway between the lowest ribs and the iliac crest with the participant standing with arms at their sides, feet positioned close together, weight evenly distributed across their feet, and wearing light clothing (World Health Organization, 2008).

A brief questionnaire was administered through an audio computer-assisted self-interview (ACASI) to allow for privacy and elicit responses that were less likely to be subject to social desirability responses compared with interviewer-administered surveys (Hewett et al., 2004; Simões et al., 2006; Turner et al., 1998). The ACASI minimizes potential literacy issues associated with self-administered paper surveys because the questionnaire is delivered through a laptop. Participants wear headphones to listen to each survey question and response options and can directly enter responses into the laptop.

The 20-minute survey included questions about sociodemographic characteristics, medical history, and current prescribed medication regimens and adherence. Participants were asked if they were ever told by a health care provider that they had high blood cholesterol, hypertension, or diabetes. For any affirmative response, participants were further asked if they were prescribed medication and, if so, whether they were currently taking the medication.

Blood pressure was measured after taking the survey and being seated for at least 20 minutes. Three consecutive blood pressure measurements were taken on the right arm using an automated blood pressure cuff (Omron HEM 907XL) with the participant seated with feet flat on the floor and arm resting comfortably; average blood pressure was reported (Action to Control et al., 2008; SPRINT Research Group et al., 2015).

Analysis

To determine the feasibility of using the probation office to screen for CVD and diabetes risk factors, we calculated the percentage of participants who were queried and agreed to participate. Descriptive statistics were calculated for participant characteristics and the risk factor data. An inadequate blood sample collected from two participants precluded biochemical analysis. Stata v15 (StataCorp, College Station, TX) was used for all statistical analyses.

Results

The study had 202 participants, predominantly male, with a mean age of 39.6 years. Table 1 depicts participant characteristics by gender. Approximately half of the participants were non-Hispanic White, one-fifth non-Hispanic Black, and one-fifth Hispanic/Latinx. Almost half of the participants had spent 18 to 24 months on probation. Approximately one-third of participants self-reported currently being homeless. The majority of participants had at least a high school/general educational development level education, were never married, were unemployed, had an annual income ≤$5,000, sometimes had access to a car, smoked daily, had government health insurance, and had a primary care physician.

Table 1.

Characteristics of a Sample of Adults on Probation in Rhode Island (N = 202)

Characteristics	All (N = 202), mean (±SD) or n (%)	Men (N = 169), mean (±SD) or n (%)	Women (N = 33), mean (±SD) or n (%)
Age, years (range 18–70)	39.6 (12.1)	38.6 (11.7)	44.7 (13.2)
Race/ethnicity
White, non-Hispanic	92 (45.5)	74 (43.8)	18 (54.6)
Black, non-Hispanic	42 (20.8)	38 (22.5)	4 (12.1)
Hispanic/Latinx, any race	43 (21.3)	37 (21.9)	6 (18.2)
Other	25 (12.4)	20 (11.8)	5 (15.2)
Current length of time on probation (months)
<6	76 (37.6)	58 (34.9)	18 (54.5)
6 to <12	20 (9.9)	15 (8.9)	5 (15.2)
12 to <18	8 (4.0)	7 (4.1)	1 (3.0)
18 to <24	97 (48.0)	88 (52.1)	9 (27.3)
Homeless	63 (31.2)	49 (29.0)	14 (42.4)
Education
Less than high school	57 (28.2)	48 (28.4)	9 (27.3)
High school/GED	83 (41.1)	72 (42.6)	11 (33.3)
Trade/technical	15 (7.4)	11 (6.5)	4 (12.1)
Some college/college graduate	43 (21.3)	35 (20.7)	8 (24.2)
Some graduate school/graduate school degree	4 (2.0)	3 (1.8)	1 (3.0)
Marital status
Married	11 (5.5)	9 (5.3)	2 (6.1)
Widowed/divorced	42 (20.8)	29 (17.2)	16 (48.5)
Separated	22 (10.9)	19 (11.2)	3 (9.1)
Never married	99 (49.0)	87 (51.5)	12 (36.4)
Living with partner	28 (13.9)	25 (14.8)	3 (9.1)
Employment
Full time	45 (22.3)	43 (25.4)	2 (6.0)
Part time	17 (8.4)	14 (8.3)	3 (9.1)
Occasional	15 (7.4)	12 (7.1)	3 (9.1)
Unemployed	125 (61.9)	100 (59.2)	25 (75.8)
Annual income
>$30,000	19 (9.4)	19 (11.2)	0 (0.0)
$20,001–$30,000	25 (12.4)	24 (14.2)	1 (3.0)
$10,001–$20,000	36 (17.8)	30 (17.8)	6 (18.2)
$5,001–$10,000	31 (15.4)	25 (14.8)	6 (18.2)
≤$5,000	72 (35.6)	54 (32.0)	18 (54.6)
Don't know	16 (7.9)	14 (8.3)	2 (6.1)
Not provided	3 (1.5)	3 (1.8)	0 (0.0)
Access to a car
Always	44 (21.8)	38 (22.5)	6 (18.2)
Sometimes	86 (42.6)	71 (42.0)	15 (45.5)
Never	72 (35.6)	60 (35.5)	12 (36.4)
History of cigarette use
Never smoked	57 (28.2)	49 (29.0)	8 (24.2)
Not currently	16 (7.9)	13 (7.7)	3 (9.1)
Daily	111 (55.0)	89 (52.7)	22 (66.7)
Less than daily	18 (8.9)	18 (10.7)	0 (0.0)
Health insurance	170 (84.2)	138 (81.7)	32 (97.0)
Type of health insurance
Government	143 (70.8)	115 (68.0)	28 (84.8)
Private	13 (6.4)	11 (6.5)	2 (6.1)
Other	14 (6.9)	12 (7.1)	2 (6.1)
None	31 (15.4)	30 (17.8)	1 (3.0)
Don't know	1 (0.5)	1 (0.5)	0 (0)
Have a primary care physician	126 (62.4)	102 (60.4)	24 (72.7)

GED, general educational development; SD, standard deviation.

Of those approached, there was a 36.4% participation rate. The three most common reasons people declined to participate were lack of interest (66%), not enough time to stay for the study visit (15%), and having an appointment to see a doctor soon (3%). The demographics of nonparticipants were similar to participants with respect to gender and race/ethnicity.

Table 2 lists the estimated prevalence of chronic disease among people on probation based on self-report and summarized wellness screening data. Approximately one-third of our participants self-reported ever being told by a health care provider that they had high blood pressure or hypertension. During the wellness screening, approximately one-third of the participants had normal blood pressure and 22% had Stage 2 hypertension (≥140 mm Hg systolic or ≥90 mm Hg diastolic).

Table 2.

Cardiovascular Disease and Diabetes Risk Factors Among a Sample of Adults on Probation in Rhode Island (N = 202), Self-Reported and From the Wellness Screening

	Self-reported, n (%)	Wellness screening, n (%) or mean (±SD)
Blood pressure
Systolic blood pressure		125 (16)
Diastolic blood pressure		78 (12)
Normal (<120 mm Hg systolic and <80 mm Hg diastolic)		68 (34)
Elevated (120–129 mm Hg systolic and <80 mm Hg diastolic)		35 (17.3)
Stage 1 hypertension (130–139 mm Hg systolic or 80–89 mm Hg diastolic)		54 (26.7)
Stage 2 hypertension (≥140 mm Hg systolic or ≥90 mm Hg diastolic)	74 (36.6)	45 (22.3)
Total cholesterol		160 (43)
High total cholesterol (>240 mg/dL)	54 (26.7)	10 (5.0)
HDL cholesterol
Men		47 (13)
Women		55 (21)
Low HDL cholesterol (mg/dL)	Not asked
Men <40		63 (37.5)
Women <50		16 (50.0)
Blood glucose, ≥2 hours postprandial		116 (42)
High blood glucose, ≥2 hours postprandial	27 (13.4)	14 (6.9)
Body mass index	Not asked
Underweight		4 (2.0)
Normal		56 (27.7)
Overweight		66 (32.7)
Obese		76 (37.6)
Waist circumference
Men		97.6 (17.1)
Women		91.8 (15.0)
High waist circumference (cm)	Not asked
Men >102		52 (31.0)
Women >88		18 (54.6)

HDL, high-density lipoprotein.

More than one-quarter of participants reported that they had ever been told by a health care professional that they had high cholesterol. Based on the wellness screening, the mean total cholesterol concentration was in the normal range; however, more than one-third of men and half of women had low HDL cholesterol levels.

Thirteen percent of participants reported that they had ever been told by a health care professional that they had diabetes. Approximately 7% of the participants had a high 2-hour postprandial blood glucose concentration. Seventy percent of the participants were overweight or obese and approximately one-third of the men and more than half of the women had high waist circumference measurements.

Figure 1 shows the cascade of care for participants with self-reported high blood pressure. Of the individuals who reported having high blood pressure, 53% were prescribed pharmacotherapy, but only 80% reported currently taking the antihypertensive medication. During the wellness screening, of those with self-reported hypertension, 42% were classified as having Stage 2 hypertension, 26% Stage 1 hypertension, and 10% elevated blood pressure (120–129 mm Hg systolic blood pressure and >80 mm Hg diastolic blood pressure), indicating most (77%) did not have their blood pressure under adequate control. At our wellness screening, an additional 11% were identified with Stage 2 hypertension, 27% with Stage 1 hypertension, and 22% with elevated blood pressure.

Fig. 1.

Cascade of care for people on probation who self-reported high blood pressure. This Sankey diagram shows the cascade of care for people with high blood pressure by who was (A) and was not (B) prescribed antihypertensive medication.

• (A) Patients who were prescribed antihypertensive medication:
  • •Of those who took the medication, those with normal blood pressure are represented by A1 flowing to D and those with abnormal blood pressure by A2 flowing to C.
  • •Of those who did not take the medication, those with abnormal blood pressure are represented by A3 flowing to C and those with normal blood pressure by A4 flowing to D.
• (B) Patients who were not prescribed antihypertensive medication:
  • •Those with abnormal blood pressure are represented by B flowing to C and those with normal blood pressure by B flowing to D.

Overall, 74 participants reported being told by a health care professional that they had high blood pressure or hypertension, of whom 39 were prescribed medication and only 31 were currently taking this medication. During the wellness screening, abnormal blood pressure was found in 57 of the individuals with a history of high blood pressure (77%) and 17 had controlled blood pressure.

Of the individuals who reported having diabetes, approximately two-thirds were prescribed medications and of those, 83% reported currently taking the medications (Fig. 2). On the basis of our wellness screening, six individuals who self-reported having diabetes had elevated 2-hour postprandial blood glucose concentrations. Our wellness screening identified another 5% of the participants with elevated blood glucose concentrations.

Fig. 2.

Cascade of care for people on probation who self-reported diabetes. This Sankey diagram shows the cascade of care for people with diabetes by who was (A) and was not (B) prescribed diabetes medication.

• (A) Patients who were prescribed diabetes medication:
  • •Of those who took the medication, those with normal blood glucose concentrations are represented by A1 flowing to C and those with abnormal blood glucose concentrations by A2 flowing to D.
  • •Of those who did not take the medication, those with normal blood glucose concentrations are represented by A3 flowing to C and those with abnormal blood glucose concentrations by A4 flowing to D.
• (B) Patients who were not prescribed diabetes medication:
  • •Those with normal blood glucose concentrations are represented by B flowing to C and those with abnormal concentrations by B flowing to D.

Overall, 27 participants reported being told by a health care professional that they had high blood glucose or diabetes, of whom 18 were prescribed medication and only 15 were currently taking this medication. During the wellness screening, abnormal blood glucose concentrations were found in 6 of the individuals with diabetes (22%) and 21 had controlled blood glucose.

Discussion

Our study demonstrated that it is feasible to use the probation office to screen for cardiometabolic risk among adults on probation, with a participation rate of 36%. A systematic review that compared nonparticipation in population-based disease prevention programs (Koopmans et al., 2012) found three studies that focused on cardiometabolic risk programs and the range of nonparticipation was 25% to 99%. Of note, the potential participants were naive to the study before their visit to the probation office. If we had a longer recruitment period, individuals would have become more aware of the wellness screenings and may have factored more time into their probation visit to participate.

About two-thirds of nonparticipants (66%) reported they were not interested in participation, and we did not inquire further about why. Perhaps some potential participants were mistrustful about whether the information would be provided to their probation officer, or their responses would impact their probation sentence. Greater efforts are needed to clarify these issues. This should be considered for improving delivery of the program.

We also contribute to the limited data available about the health status of people on probation. The majority of our participants had profiles that indicate high cardiometabolic risk: high smoking rates, low HDL cholesterol levels, high blood glucose levels, high blood pressure, body mass index ranges in the overweight and obese categories, and high waist circumferences. Elevated total cholesterol and blood glucose levels were less favorable compared with the general population.

Our wellness screening identified an additional 11% with high blood pressure and 5% with elevated blood glucose who reported not being told by a health care professional that they had these risk factors. Our study identified individuals with a history of high blood pressure and diabetes who had elevated blood pressure and glucose as well as individuals not yet diagnosed. These factors also support the implementation of a wellness screening at the probation office.

Approximately half of adults on probation participating in our study had Stage 1 or Stage 2 hypertension. In addition, many participants on treatment for elevated blood pressure and diabetes had values that were not within normal ranges during our wellness screening. Some who were prescribed medications to treat elevated blood pressure and/or high blood sugar reported not taking their medications and some who were taking prescribed medications still did not have the measures under control.

Most reported having health insurance and a primary care physician. Rhode Island has a state mandate for health insurance and offers health insurance for free or low cost for people with limited income, which enabled most participants to have coverage for medical visits. This situation might not be generalizable to other states. We did not assess access to health care, including prescribed medications and frequency of blood pressure and glucose monitoring. Based on findings from our qualitative study among a convenience sample of adults on probation, health care was ranked a low priority relative to substance use treatment, employment, housing, and food access (Dong et al., 2018).

At the study probation office, probation officers encouraged their clients to participate in the study but were not aware of who actually participated. Study visits were conducted in private rooms without probation officers present. During the consent process, it was made clear to potential study participants that there were no benefits or penalties based on their participation decision. Only a summary report of the findings was shared with the probation officers at the end of the study. No individual identifiers were used so as to maintain confidentiality. We recognize that the culture in this probation office was supportive of people undergoing the wellness screening and participation was kept as confidential as possible, which might not occur in all probation settings.

Although our study has many strengths, there were some limitations too. We were at only one probation office, which limits the generalizability of our findings. Also, this probation office has the physical space and privacy to offer wellness screenings, which might not be common among other probation offices. In addition, the research team conducted the cardiometabolic risk factors screening. Future steps include identifying ways to make the screenings sustainable.

We enrolled a convenience sample, although the sample from our study is reflective of the ethnic, gender, and socioeconomic status of this particular probation office. At this probation office, the percentage of non-English speaking individuals was less than 5%. If wellness screenings were implemented nationwide, using the ACASI would make possible the opportunity to conduct the screening in multiple languages. In addition, this sample may better reflect the real world since, programmatically, people are not forced to participate in the screening.

Conclusion

It is feasible to provide screenings for cardiometabolic risk factors at a probation office to a population that might not have access to health care regularly. We provided screening to a high-risk population in a setting that they frequent often to identify people who have modifiable risk factors that can be treated. Finding novel ways to bring health care services to people on community supervision may improve long-term health outcomes. In addition, providing these screenings as a mechanism to increase patient awareness of cardiovascular risk can help lead to better health outcomes.

These findings will also help inform the development of an intervention to help screen and link individuals to health care. Systems approaches that include efforts to target screening, link to care, and provide education regarding medication use and risk reduction are also needed for this population.

Authors' Note

The funding sources had no involvement in the study design; collection, analysis, and interpretation of data; the writing of the report; and the decision to submit the article for publication.

Author Disclosure Statement

D.E.W. has consulted for Janssen Biopharmaceuticals. The other authors disclosed no conflicts of interest with respect to the research, authorship, or publication of this article.

Funding Information

Support for this study was provided by the Tufts Collaborates pilot grant program from the Office of the Vice Provost at Tufts University; National Institute on Drug Abuse (NIDA) Grant No. R25DA037190, The Lifespan/Brown Criminal Justice Research Program on Substance Use, HIV, and Comorbidities; NIH CTSA Grant No. UL1TR002544.