Abstract
Introduction.
This study evaluates the real-world impact of a lifestyle change program (LCP) on healthcare utilization in a large health system.
Methods.
Using electronic health record (EHR) data from a large health system in northern California, United States, LCP participant and propensity-score-matched non-participant outcomes were compared in the second year post-participation: (1) overall healthcare utilization, and (2) utilization and medications related to cardiometabolic conditions and obesity. Adult LCP participants between 2010-2017 were identified and matched 1:1 with replacement to comparable non-participants. Participants without EHR activity in the 12-36 months before baseline, or with conditions or procedures associated with substantial weight change, were excluded. Statistical analysis and modeling were performed in 2021-22.
Results.
Compared to matched non-participants, LCP participants in the 12-24 months post-baseline were more likely to have specialty-care visits (+4.7%, 95% CI +1.8%,+7.6%), electronic communications (8.6%, 95% CI +5.6%,+11.7%), and urgent-care visits (+6.5%, 95% CI +3.0%,10.0%). Participants also had more office visits for cardiometabolic conditions and obesity (+1.72 visits/patient, 95% CI +1.05,+2.39).
Conclusions.
Compared with matched non-participants, LCP participation was associated with higher utilization of outpatient services post-participation. Additional research could assess whether this indicates an increase in preventive care that could lead to improved future outcomes.
INTRODUCTION
Multiple studies have found obesity to be associated with increased medical costs and healthcare resource utilization. This includes an increase in both healthcare visits and medication use.1-7 Obesity is also associated with a higher prevalence of cardiometabolic conditions.2,7,8 Weight stigma in healthcare can lead patients with obesity both to avoid preventive care and to receive lower-quality care due to implicit bias on the part of care providers, ultimately contributing to worse health outcomes.9-12 By 2030, the prevalence of obesity and its attributable medical expenses are predicted to extend to 50% of the United States (US) population, and to account for 16%-18% of total healthcare costs.13
Due to the negative health outcomes associated with obesity, clinical guidelines recommend use of intensive lifestyle interventions for prevention and management.14 The immediate goal of such interventions is typically weight loss, with the longer-term goal of reducing the risk of adverse health outcomes, including high healthcare utilization and its associated costs. Although studies have demonstrated that lifestyle change programs (LCPs) can be effective in facilitating weight loss, evidence regarding healthcare utilization of participants in LCPs remains limited and shows mixed results; some studies show no change after participation, while others show slight reductions in certain types of utilization, such as inpatient and ED visits.14-21 No studies were found that looked specifically at healthcare utilization related to cardiometabolic conditions and obesity, although such utilization could be more likely to be affected by LCP participation.
The goal of this study was to evaluate the impact of a real-world implementation of a US Centers for Disease Control (CDC)-approved LCP on both general healthcare utilization and orders specific to cardiometabolic disorders and obesity. Outcomes were examined between one and two years after participation in the program. The authors hypothesized that participation in the LCP could result in sustained reductions in healthcare utilization, specifically in ED/inpatient hospital encounters and in outcomes related to obesity and associated cardiometabolic conditions.
METHODS
Study Sample
This electronic health records (EHR)-based observational study was conducted at Sutter Health, a large, not-for-profit, community-based healthcare system in northern California, United States (US). Sutter Health serves over three million patients annually with a network of 23 acute hospitals, over 12,000 physicians, and other healthcare services (e.g., home health). This study was reviewed and approved by Sutter Health’s Institutional Review Board.
Sutter Health utilizes a group-based, 12-month, structured LCP, known as the Group Lifestyle Balance™ (GLB) program. The program is based on the original Diabetes Prevention Program (DPP) and comprises three phases.22–24 The core phase includes weekly sessions for the first 12 weeks. The transition phase includes weekly or biweekly sessions over an additional 12 weeks. Lastly, the support phase includes monthly or bimonthly sessions for the remainder of the year (see Appendix). Sutter Health first implemented the LCP in 2010. Between 2010-2017, the program was offered at 20 different clinics, and facilitated by certified diabetes educators. The target population was individuals at high risk for type 2 diabetes (T2D) or with clinical evidence of pre-diabetes, based on diagnoses or elevated HbA1c. Given the program’s focus on weight loss, it is open to all patients with elevated cardiometabolic risk, including those with diagnosed type 2 diabetes.
The authors identified in LCP (GLB) participants ≥18 years of age, with ≥1 LCP program encounter between January 1, 2010 (first implementation of the program within the health system) and December 31, 2017 (end of study period). The date of the first LCP encounter was defined as the baseline. Patients with usual care, ≥18 years of age, with no record of an LCP program encounter were also identified in the EHR. Their baseline was assigned as a random office encounter during the study period. All patients were required to have ≥12 to 36 months of EHR activity prior to baseline to confirm prior health system engagement and to extract medical history data. Participants with coded diagnoses for conditions or procedures associated with substantial weight change in the 12 months prior or 24 months after baseline, including metastatic cancer, pregnancy, gastric bypass surgery, or end-stage kidney disease were excluded. International Classification of Diseases (ICD) codes for each condition are included in the Appendix.
Self-reported patient demographic characteristics (i.e., date of birth, sex, race/ethnicity, and preferred spoken language) and primary insurance payer were also extracted. At the time of this analysis, gender identity was not recorded separately from biological sex in the Sutter Health EHR, so the sex designation in this study may reflect current gender identity at the time of data extraction for transgender individuals. Median household income based on ZIP code was used as a proxy for socioeconomic status.
The following clinical characteristics recorded in the 12 months prior to baseline were extracted: weight (kg), body mass index (BMI), smoking status, and clinic-measured systolic and diastolic BP. The following comorbidities were identified: prediabetes, T2D, hypertension, dyslipidemia, metabolic syndrome, atherosclerotic cardiovascular disease, and depression, using a combination of diagnosis codes, medication orders, biometrics, and/or laboratory values as described elsewhere.15,25 The Charlson comorbidity index (CCI) for each participant was calculated as a measure of overall disease burden.26,27 A validated screening tool from the American Diabetes Association was used to classify patients as having a high risk for T2D in the absence of clinical prediabetes.28
Medication orders active at baseline were used to identify prescription-based weight loss products, appetite suppressants, glucose lowering therapies (GLTs), and antihypertensive medications. GLTs were further categorized into those that promote weight loss (i.e., metformin and glucagon-like peptide-1 receptor agonists) and those that do not.15 Patients were classified as having an established Sutter Health primary-care provider and the number of outpatient encounters and telephonic/electronic encounters were quantified. The presence of a preventive visit or influenza immunization in the 12 months prior to baseline was also determined as a potential measure of health engagement and motivation.
Measures
The outcomes of interest fell into two domains: (1) overall healthcare utilization, and (2) office visits, ED visits, hospitalizations, and medication orders related to cardiometabolic disease and obesity. Healthcare utilization outcomes included outpatient visits (primary care and specialist visits), email/patient portal consultation, telephone consultation, hospitalization, ED visits, and urgent care visits. Medication orders for anti-hypertension, anti-diabetes, and anti-hyperlipidemia medications were examined. Association of visits with cardiometabolic conditions or obesity was determined according to visit-associated diagnoses (see Appendix for more detail).
Healthcare utilization and medication order outcomes overall and for cardiometabolic conditions were computed in the 12-24 months after program initiation. Because prior analyses by the authors showed that the majority of participants had low session attendance and did not have clinically-significant weight loss at 24 months, two secondary analyses were conducted to explore whether the participants who went on to have clinically-significant weight loss also had different utilization outcomes (compared with matched non-participants) than participants who did not.25
Statistical Analysis
Analyses were conducted in SAS version 9.4 (SAS Institute, Cary NC) and R version 4.0.4 (www.r-project.org). Descriptive statistics were computed for all baseline demographics, clinical characteristics, healthcare utilization, and medication orders. Mean values were calculated for continuous variables and percentages were calculated for categorical variables.
The authors estimated propensity scores as the probability of attending the LCP conditional on baseline demographics, clinical characteristics, baseline healthcare utilization and medication order variables. Main terms logistic regression was used to construct 3 propensity score models: for the full study population, and then within the subgroups with <5% and 5% or more weight loss at 24 months. LCP participants were matched to patients with usual care one-to-one on the logit of the propensity score, with exact matching on sex, ±2 unit matching on weight, and ±3 unit matching on BMI, with replacement. This method is well-established and is described in detail elsewhere.29-31 To ensure sufficient covariate balance, matching and balance assessment was performed separately for the overall study population and for the subgroup analyses.
Absolute standardized mean differences (SMDs) were calculated to assess covariate balance between LCP participants and usual-care matched comparators. An absolute SMD<10 (equivalent to 10% for binary variables) was considered a negligible difference.32 Additional regression adjustment was performed during effect estimation if any variables were not sufficiently balanced after matching.33
The estimated average LCP impact/difference (average treatment effect amongst the treated) and 95% confidence intervals for healthcare utilization and medication orders in the 12-24 months after baseline were reported. If regression adjustment was required, an adjusted average difference was computed using linear regression.33 Utilization for any reason and utilization related to cardiometabolic conditions or obesity were considered separately. Propensity score matching and computation of treatment effect estimates (including any required regression adjustment) were performed using the R package Matching, and Abadie-Imbens standard errors (appropriate for matching estimators when matching with replacement is used) were used for confidence interval estimates.33,34
RESULTS
A total of 1,737 LCP participants who met study criteria were identified, of whom 490 (28.2%) had 5% or more and 1,247 (71.8%) had <5% weight loss at 24 months. The demographic characteristics of the LCP and comparison populations pre- and post-match are shown in Table 1. Similarly, the baseline clinical and utilization characteristics pre- and post-match are shown in Table 2. Analogous tables for the two subgroup analyses are provided in the Appendix.
Table 1.
Demographic characteristics of LCP participants and comparators, pre- and post-match.
| Characteristic | LCP Participants N=1,737 | Comparison Pool (pre-match) N=222,334 | Matched Comparators N=1,737 |
|---|---|---|---|
| Mean Age (years) | 54.7 | 52.0a | 55.1 |
| Female (%) | 77.4 | 60.4a | 77.4 |
| Race/ethnicity (%) | |||
| Black/African American (NH) | 3.8 | 2.9 | 4.1 |
| Asian (NH) | 5.4 | 17.2a | 5.7 |
| Hispanic | 12.2 | 11.6 | 10.6 |
| White (NH) | 69.5 | 58.0a | 69.7 |
| Other (NH) | 2.6 | 2.5 | 2.9 |
| Unknown | 6.4 | 7.9 | 6.9 |
| Established PCP (%) | 93.1 | 52.1a | 91.9 |
| Insurance Payer (%) | |||
| Commercial (FFS/PPO/HMO | 59.2 | 58.2 | 56.2 |
| Medicare (FFS/ HMO) | 20.7 | 20.8 | 23.7 |
| Medicaid | 1.7 | 2.0 | 1.6 |
| Other/ Self | 2.0 | 1.4 | 1.8 |
| Unknown | 16.4 | 17.6 | 16.7 |
| Median Household Income (%) | |||
| <$50,000 | 17.3 | 14.4 | 17.3 |
| ≥$50,000 to <$75,000 | 29.4 | 24.0a | 29.6 |
| ≥$75,000 to <$100,000 | 24.9 | 21.8 | 23.9 |
| ≥$100,000 | 20.7 | 29.9a | 21.4 |
| Unknown Income | 7.8 | 9.9 | 7.8 |
Note: FFS=Fee-for-service; HMO=Health maintenance organization; LCP= lifestyle change program; PPO=Preferred provider organization; NH=Non-Hispanic
Percentages are reported for categorical/binary variables, means are reported for continuous variables.
Absolute standardized difference > 10.
Table 2.
Baseline clinical and utilization characteristics of LCP participants and comparators, pre- and post-match.
| Characteristic | LCP Participants N=1,737 | Comparison Pool (pre-match) N=222,334 | Matched Comparators N=1,737 |
|---|---|---|---|
| Weight, kg (mean) | 100.7 | 78.7 a | 100.8 |
| BMI, kg/m2 (mean) | 36.1 | 27.8 a | 36.1 |
| Smoking Status (%) | |||
| Current | 5.6 | 9.6 a | 6.2 |
| Ever | 28.4 | 21.3 a | 30.3 |
| Never | 64.7 | 66.5 | 62.1 |
| Unknown | 1.3 | 2.6 a | 1.3 |
| Cardiometabolic Risk Group (%) | |||
| Type 2 Diabetes (%) | 26.9 | 11.4 a | 31.4 |
| High Risk for Diabetes (%) | 47.4 | 38.8 a | 44.7 |
| Overweight/Obesity Only (%) | 23.7 | 30.2 a | 22.9 |
| Other (%) | 2.0 | 19.6 a | 1.0 |
| Metabolic Syndrome (%) | 28.0 | 7.8 a` | 30.1 |
| Hypertension (%) | 48.9 | 31.1 a | 51.5 |
| Dyslipidemia (%) | 49.4 | 31.2 a | 51.3 |
| ASCVD (%) | 8.7 | 8.2 | 8.3 |
| Depression (%) | 22.7 | 11.3a | 22.3 |
| Charlson comorbidity index (%) | |||
| 0 | 50.2 | 68.0 | 49.1 |
| 1-2 | 42.9 | 26.9 | 42.3 |
| 3-4 | 5.9 | 3.8 | 7.1 |
| 5-6 | 0.9 | 0.8 | 1.1 |
| >6 | 0.1 | 0.5 | 0.4 |
| Outpatient visit, count (mean) | 9.57 | 5.8a | 9.71 |
| Type of healthcare utilization | |||
| Tele/electronic, count (mean) | 14.3 | 8.1a | 14.0 |
| Preventive care (%) | 33.1 | 23.2a | 29.9 |
| Immunization (%) | 24.0 | 20.8 | 25.8 |
| Primary care (%) | 67.9 | 50.1a | 65.6 |
| Specialty care (%) | 63.1 | 43.8a | 62.7 |
| Email/patient portal (%) | 76.3 | 43.2a | 75.9 |
| Telephone (%) | 95.7 | 80.6a | 95.6 |
| Inpatient hospital (%) | 0.3 | 0.5 | 0.2 |
| Emergency department (%) | 2.6 | 3.4 | 2.6 |
| Urgent Care (%) | 29.6 | 20.3a | 28.7 |
| Medication orders | |||
| Count (mean) | 5.07 | 3.3a | 5.13 |
| High blood pressure medication (%) | 51.0 | 32.7a | 53.9 |
| Anti-hyperlipidemia medication (%) | 34.5 | 22.3a | 37.8 |
| Weight loss medication (%) | 7.5 | 2.3a | 7.2 |
| Weight loss/glucose lowering therapy (%) | 18.1 | 6.5a | 20.4 |
| Other glucose lowering therapy (%) | 11.5 | 5.2a | 14.0 |
Note: ASCVD= Atherosclerotic Cardiovascular Disease; LCP= lifestyle change program
Percentages are reported for categorical/binary variables, means are reported for continuous variables.
Absolute standardized difference > 10.
The estimated differences between LCP participants and their matched comparators in terms of general healthcare utilization in the 12-24 months after baseline are shown in Table 3. Amongst LCP participants, the number of patients with specialist visits was 4.7% higher than their matched comparators (95% CI +1.8%,+7.6%), and the count of specialist visits per patient was 0.93 more (95% CI +0.34,+1.52). The overall percentage of LCP participants who had urgent care visits in the 12-24 months after baseline was also higher than their matched comparators (+6.5%, 95% CI +3.0%,10.0%), with a 0.09-visit increase/patient (95% +0.002, +0.19). LCP participants were also 8.6% more likely to have an electronic communication than their matched comparators (95% CI +5.6%,11.7%), with an average difference of 4.40 contacts/patient (95% CI +3.50,+5.30). Office visit counts specific to cardiometabolic conditions and obesity (Table 3) were also higher in the 12-24 months after baseline (+1.72 visits/patient, 95% CI +1.05,+2.39). Differences in the other utilization and medication measures (including ED encounters and hospitalizations) were not found (Table 3). Within the two subgroups based on weight loss, similar patterns to the full cohort were observed (see Appendix). In contrast to the full analysis, the subgroup with 5% or more weight loss did not have differences in urgent-care utilization, while the subgroup with <5% weight loss had 0.14 more urgent care visits/patient (95% CI +0.03,+0.25) than their matched comparators. Both subgroups had higher office visit counts related to cardiometabolic disease and obesity (5% or more: +1.50 visits/patient, 95% CI +0.30, 2.60; <5%: +1.84 visits/patient, 95% CI +1.12,+2.56).
Table 3.
Healthcare utilization differences between LCP participants and matched comparators 12-24 months post-baseline (N=1,737)
| General healthcare utilization | ||
|---|---|---|
| Measure Name | LCP Participants | Estimated Differencea (95% CI) |
| Office visits | ||
| Primary care, any visit (%) | 89.1 | −0.3 (−2.6, 2.0) |
| Primary care, counts | 3.71 | 0.09 (−0.16, 0.35) |
| Specialist, any visit (%) | 83.5 | 4.7 (1.8, 7.6) |
| Specialist, counts | 6.26 | 0.93 (0.34, 1.52) |
| Email Consultation/Patient Portal, any (%) | 82.1 | 8.6 (5.6, 11.7) |
| Email Consultation/Patient Portal, count | 9.65 | 4.40 (3.50, 5.30) |
| Telephone consultation, any (%) | 91.0 | 1.4 (−0.9, 3.6) |
| Telephone consultation, count | 6.86 | 0.26 (−0.37, 0.88) |
| Hospitalization, any (%) | 3.6 | 0.06 (−1.35, 1.47) |
| Hospitalization, count per 1,000 | 40.3 | −9.2 (−28.9, 10.5) |
| Emergency Room Visit, any (%) | 6.7 | 0.1 (−1.8, 2.1) |
| Emergency Room Visit, count per 1,000 | 88.7 | −5.3 (−36.3, 25.7) |
| Urgent Care, any (%) | 34.8 | 6.5 (3.0, 10.0)b |
| Urgent Care, counts | 0.63 | 0.09 (0.002, 0.19) |
| Cardiometabolic disease or obesity-related healthcare utilization and medication orders | ||
| Office visits, any (%) | 97.9 | 0.5 (−0.7, 1.6) |
| Office visits, count | 9.78 | 1.72 (1.05, 2.39) |
| Hospitalization, any (%) | 0.5 | −0.4 (−1.1, 0.2) |
| Hospitalization, count per 1,000 | 5.2 | −4.4 (−11.0, 2.2) |
| Emergency Room Visit, any (%) | 4.5 | −0.2 (−1.8, 1.4) |
| Emergency Room Visit, count per 1,000 | 55.8 | −5.6 (−28.6, 17.3) |
| Medications | ||
| Anti-hypertension (%) | 51.4 | −1.8 (−5.5, 1.8) |
| Anti-diabetes (%) | 26.3 | −2.3 (−5.6, 1.0) |
| Anti-hyperlipidemia (%) | 34.7 | −1.3 (−4.9, 2.4) |
Note: LCP=lifestyle change program; Boldface indicates statistical significance (p<0.05).
Between LCP participants and matched comparators, adjusted for clinic location.
DISCUSSION
In this study, the authors evaluated the impact of a real-world implementation of a CDC-approved LCP on healthcare utilization in a large, mixed-payer, integrated healthcare system. Compared with matched usual-care comparators, LCP participants were found to have higher counts of specialist visits, urgent care visits, electronic communications, and office visits related to obesity and cardiometabolic conditions during the 12-24 months after baseline; no difference in primary care visits was observed. Inpatient and ED utilization for LCP participants during the 12-24 months after baseline were found to be similar to matched non-participants, even within hospital utilization related to cardiometabolic disease and obesity. These patterns were consistent within participant subgroups defined by weight loss (5% or more vs. <5% weight loss); although the 5% or more weight loss subgroup did not show a difference in urgent care visits. Given the small size of the overall difference, this could simply reflect the difference in sample size between the two subgroups and an associated difference in statistical power.
This study’s general ED and inpatient hospital utilization findings are similar to that of two small matched analyses of inpatient and ED utilization outcomes among virtual DPP participants, and contrast with the results of a larger study (N=3,319) of an in-person DPP intervention.18–20 The latter study, however, considered only Medicare FFS beneficiaries, and the 3-year reductions in ED encounters and hospitalizations were small (−9 encounters per 1000 per quarter).18 The effect estimates over the three-year period were also quite variable, with the most consistent difference in hospital utilization observed within the first 1.5 years.18 These studies did not analyze obesity- or cardiometabolic-disease-related hospital utilization. If the true differences at short-term follow-up are small, then much larger studies may be required to have sufficient statistical power to detect changes in hospital utilization. It is also possible that a longer follow-up study would be required to detect effects given the long timelines for disease progression for both T2D and cardiovascular disease.
The observed increases in outpatient services represent a new contribution to the literature surrounding the impact of LCPs on future healthcare utilization. This finding contrasts with one study in a large integrated healthcare delivery system in California, US, in which adults with obesity who participated in a weight management program had reductions from baseline in office visits and overall healthcare touches over a 5-year follow-up period. That study, however, had no comparison group, so it is not known what would have occurred under usual care.17 The higher number of outpatient visits and electronic communications observed in the current study among LCP participants as compared to matched non-participants could indicate overall higher engagement with the healthcare system post-participation. This could be a positive outcome, especially given the barriers that patients with obesity can face when seeking high-quality care.9,11,35 It is unclear whether the higher urgent care utilization could serve as an early indicator of disease progression.
Limitations
This study has several limitations. First, as with all studies based on EHR data, only healthcare utilization information that was documented could be recorded. Because Sutter Health is an open healthcare system, it is possible that patients could have received care from non-Sutter physicians and facilities. To reduce as much as possible the impact of differences in data availability on this study’s outcomes, the authors were careful to match patients within the same geography and with a similar utilization history. Second, as with all matched analyses, it was assumed that the matched comparators accurately represent LCP participants’ outcomes under usual care. Matching was therefore conducted using a variety of variables, including not only demographic and general health-related variables, but also prior healthcare utilization and medications. Finally, the majority of participants (73%) did not achieve the program’s goal of 5% weight loss, and the mean number of sessions completed per participant was 5.2 (SD=6.4), which could have reduced the ability of the program to meaningfully impact healthcare utilization.25 Nonetheless, this does reflect the real impact of the program’s implementation within this healthcare system, and it is also unknown whether additional weight gain was averted by program participation.
CONCLUSIONS
In this real-world study, LCP participation was associated with increased utilization of outpatient services in year two post-participation. This suggests that LCPs could benefit participants by encouraging engagement with the healthcare system, possibly increasing uptake of preventive care services. Although changes in hospital utilization could not be detected, increases in preventive care can result in reduced future hospital utilization, so it is possible that the observed increase in outpatient services could be an early indicator of future reduced hospital utilization that was not yet realized in the relatively short two-year follow-up period.36-38 This increase was observed within both subgroups based on weight loss, so this could be a benefit of LCP participation regardless of weight loss goal achievement; if so, this implies positive outcomes of LCPs that extend beyond weight loss alone. Given the complexity of the relationship between lifestyle and long-term health, and the association of weight stigmatization with poor metabolic health, this could be important for patients and providers when considering the possible benefits of LCP participation, and of health systems when considering program implementation.39,40
Supplementary Material
ACKNOWLEDGEMENTS
KA, SS, and RR designed the study. SS designed the statistical analysis, and QH conducted the analysis. NS and SS performed the literature search. All authors were involved in writing the paper and approved the final manuscript. The authors thank Hsiao-Ching (Claire) Huang for her initial work on study data and analysis. They also gratefully acknowledge Kevin Hays and Pragati Kenkare for their work in preparing the study data.
This work was funded by a grant under the Award Number R18DK110739 from the National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). The research presented in this paper is that of the authors and does not reflect the official policy of the NIH. The authors declare that they have no competing interests. No financial disclosures were reported by the authors of this paper.
Footnotes
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CREDIT AUTHOR STATEMENT
Sylvia E.K. Sudat: Conceptualization, Methodology, Supervision, Writing – Original Draft, Writing – Review & Editing
Qiwen Huang: Formal Analysis, Data Curation, Software, Writing – Review & Editing
Nina Szwerinski: Writing – Original Draft, Writing – Review & Editing, Project Administration
Robert J. Romanelli: Conceptualization, Writing – Original Draft, Writing – Review & Editing, Supervision, Funding Acquisition
Kristen M. J. Azar: Conceptualization, Writing – Original Draft, Writing – Review & Editing, Supervision, Funding Acquisition
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