Association of patient characteristics and insurance type with anti-obesity medications prescribing and fills.

AIM: To characterize factors associated with the receipt of anti-obesity medication (AOM) prescription and fill. MATERIALS AND METHODS: This retrospective cohort study used electronic health records from 1 January 2015 to 30 June 2023, in a large health system in Ohio and Florida. Adults with a body mass index ≥30 kg/m2 who attended ≥1 weight-management programme or had an initial AOM prescription between 1 July 2015 and 31 December 2022, were included. The main measures were a prescription for an AOM (naltrexone-bupropion, orlistat, phentermine-topiramate, liraglutide 3.0 mg and semaglutide 2.4 mg) and an AOM fill during the study follow-up. RESULTS: We identified 50 678 adults, with a mean body mass index of 38 ± 8 kg/m2 and follow-up of 4.7 ± 2.4 years. Only 8.0% of the cohort had AOM prescriptions and 4.4% had filled prescriptions. In the multivariable analyses, being a man, Black, Hispanic and other race/ethnicity (vs. White), Medicaid, traditional Medicare, Medicare Advantage, self-pay and other insurance types (vs. private insurance) and fourth quartile of the area deprivation index (vs. first quartile) were associated with lower odds of a new prescription. Hispanic ethnicity, being a man, Medicaid, traditional Medicare and Medicare Advantage insurance types, liraglutide and orlistat (vs. naltrexone-buproprion) were associated with lower odds of AOM fill, while phentermine-topiramate was associated with higher odds. Among privately insured individuals, the insurance carrier was associated with both the odds of AOM prescription and fill. CONCLUSIONS: Significant disparities exist in access to AOM both at the prescribing stage and getting the prescription filled based on patient characteristics and insurance type.

Healthcare and Social Needs Assessment and Response Quality Among Black Men.

To understand Black men's healthcare and social needs and determine if the resources that healthcare systems offer meet expectations. We surveyed men who had previously participated in at least one Minority Men's Health Fair in Cleveland, Ohio. In this descriptive study, we spoke with men up to three times (i.e., phases) between May and October 2020 by email and/or telephone. Phase 1 was a needs assessment survey. Phase 2 involved outreach to those who identified a need to provide a resource. Phase 3 determined whether the resource met individuals' needs. We described the demographic characteristics of the survey respondents, the percentage of men reporting a need and wanting a resource, and whether the resource resolved their need. Of the 768 men contacted, 275 completed the survey (36% response rate). The majority of respondents were 50-69 years old, identified as Black, and had at least a bachelor's degree. Eighty-five percent reported a need, of which wellness, financial, and healthcare access were among the top-reported needs. Among the men identifying a need, 35% were interested in a resource. Resources that were provided for employment, behavioral health, oral health, vision, or wellness needs were deemed insufficient. A few individuals reported that resources for food/personal hygiene, financial support, health care access, annual health screening, and medication met their needs. Among men with healthcare and social needs, only a fraction were interested in a resource, and fewer reported that the resource met their needs. These results warrant a greater understanding of what constitutes a resolution of healthcare and social needs from patients' perspectives.

Early- and later-stage persistence with antiobesity medications: A retrospective cohort study.

OBJECTIVE: The study's objective was to examine the percentage of patients with an initial antiobesity medication (AOM) fill who were persistent with AOM at 3, 6, and 12 months and to characterize factors associated with persistence at 12 months. METHODS: This retrospective cohort study used electronic health records from January 2015 to July 2023 in a large health system in Ohio and Florida and included adults with BMI ≥30 kg/m2 who had an initial AOM prescription filled between 2015 and 2022. RESULTS: The authors identified 1911 patients with a median baseline BMI of 38 (IQR, 34-44). Over time, 44% were persistent with AOM at 3 months, 33% at 6 months, and 19% at 12 months. Across categories of AOM, the highest 1-year persistence was in patients receiving semaglutide (40%). Semaglutide (adjusted odds ratio [AOR] = 4.26, 95% CI: 3.04-6.05) was associated with higher odds of 1-year persistence, and naltrexone-bupropion (AOR = 0.68, 95% CI: 0.46-1.00) was associated with lower odds, compared with phentermine-topiramate. Among patients who were persistent at 6 months, a 1% increase in weight loss at 6 months was associated with 6% increased odds of persistence at year 1 (AOR = 1.06, 95% CI: 1.03-1.09). CONCLUSIONS: Later-stage persistence with AOM varies considerably based on the drug and the weight loss at 6 months.

Patient Portal Message Volume and Time Spent on the EHR: an Observational Study of Primary Care Clinicians.

BACKGROUND: As patient-initiated messaging rises, identifying variation in message volume and its relationship to clinician workload is essential. OBJECTIVE: To describe the association between variation in message volume over time and time spent on the electronic health record (EHR) outside of scheduled hours. DESIGN: Retrospective cohort study. PARTICIPANTS: Primary care clinicians at Cleveland Clinic Health System. MAIN MEASURES: We categorized clinicians according to their number of quarterly incoming medical advice messages (i.e., message volume) between January 2019 and December 2021 using group-based trajectory modeling. We assessed change in quarterly messages and outpatient visits between October-December 2019 (Q4) and October-December 2021 (Q12). The primary outcome was time outside of scheduled hours spent on the EHR. We used mixed effects logistic regression to describe the association between incoming portal messages and time spent on the EHR by clinician messaging group and at the clinician level. KEY RESULTS: Among the 150 clinicians, 31% were in the low-volume group (206 messages per quarter per clinician), 47% were in the moderate-volume group (505 messages), and 22% were in the high-volume group (840 messages). Mean quarterly messages increased from 340 to 695 (p < 0.001) between Q4 and Q12; mean quarterly outpatient visits fell from 711 to 575 (p = 0.005). While time spent on the EHR outside of scheduled hours increased modestly for all clinicians, this did not significantly differ by message group. Across all clinicians, each additional 10 messages was associated with an average of 12 min per quarter of additional time spent on the EHR (p < 0.001). CONCLUSIONS: Message volume increased substantially over the study period and varied by group. While messages were associated with additional time spent on the EHR outside of scheduled hours, there was no significant difference in time spent on the EHR between the high and low message volume groups.

Trends, Distribution, and Impact of Pediatric Messages in a Large Health System From 2019 to 2021.

This cohort study assesses changes in the volume of medical advice messages between 2019 and 2021, variation among pediatricians, and the association of volume with time spent working on the electronic health record outside clinical hours.

Health-Related Social Needs: Which Patients Respond to Screening and Who Receives Resources?

BACKGROUND: Health systems are screening patients for health-related social needs (HRSN) but the optimal approach is unknown. OBJECTIVE: To describe the variation in responding to an HRSN questionnaire delivered via patient portal, and whether referral to and resources provided by social workers differed by response status. DESIGN: Retrospective observational study. PARTICIPANTS: Primary care patients with a visit between June 2020 and January 2022. INTERVENTION: HRSN questionnaire MAIN MEASURES: We identified each patient's index visit (e.g., date of their first questionnaire response for responders or their first visit within the study period for non-responders). Through the EHR, we identified patients' demographic characteristics. We linked the area deprivation index (ADI) to each patient and grouped patients into quintiles. We used multilevel logistic regressions to identify characteristics associated with responding to the questionnaire and, for responders, reporting a need. We also determined if responder status was associated with receiving a social worker referral or receiving a resource. We included patient demographics and ADI quintile as fixed variables and practice site as a random variable. KEY RESULTS: Our study included 386,997 patients, of which 51% completed at least one HRSN questionnaire question. Patients with Medicaid insurance (AOR: 0.62, 95%CI: 0.61, 0.64) and those who lived in higher ADI neighborhoods had lower adjusted odds of responding (AOR: 0.76, 95% CI: 0.75, 0.78 comparing quintile 5 to quintile 1). Of responders, having Medicaid insurance (versus private) increased the adjusted odds of reporting each of the HRSN needs by two- to eightfold (p < 0.01). Patients who completed a questionnaire (versus non-responders) had similar adjusted odds of receiving a referral (AOR: 0.91, 95% CI: 0.80, 1.02) and receiving a resource from a SW (AOR: AOR: 1.18, 95%CI: 0.79, 1.77). CONCLUSION: HRSN questionnaire responses may not accurately represent the needs of patients, especially when delivered solely via patient portal.

Racial, ethnic and neighborhood socioeconomic differences in incidence of dementia: A regional retrospective cohort study.

BACKGROUND: Evidence on the effects of neighborhood socioeconomic disadvantage on dementia risk in racially and ethically diverse populations is limited. Our objective was to evaluate the relative extent to which neighborhood disadvantage accounts for racial/ethnic variation in dementia incidence rates. Secondarily, we evaluated the spatial relationship between neighborhood disadvantage and dementia risk. METHODS: In this retrospective study using electronic health records (EHR) at two regional health systems in Northeast Ohio, participants included 253,421 patients aged >60 years who had an outpatient primary care visit between January 1, 2005 and December 31, 2015. The date of the first qualifying visit served as the study baseline. Cumulative incidence of composite dementia outcome, defined as EHR-documented dementia diagnosis or dementia-related death, stratified by neighborhood socioeconomic deprivation (as measured by Area Deprivation Index) was determined by competing-risk regression analysis, with non-dementia-related death as the competing risk. Fine-Gray sub-distribution hazard ratios were determined for neighborhood socioeconomic deprivation, race/ethnicity, and clinical risk factors. The degree to which neighborhood socioeconomic position accounted for racial/ethnic disparities in the incidence of composite dementia outcome was evaluated via mediation analysis with Poisson rate models. RESULTS: Increasing neighborhood disadvantage was associated with increased risk of EHR-documented dementia diagnosis or dementia-related death (most vs. least disadvantaged ADI quintile HR = 1.76, 95% confidence interval = 1.69-1.84) after adjusting for age and sex. The effect of neighborhood disadvantage on this composite dementia outcome remained after accounting for known medical risk factors of dementia. Mediation analysis indicated that neighborhood disadvantage accounted for 34% and 29% of the elevated risk for composite dementia outcome in Hispanic and Black patients compared to White patients, respectively. CONCLUSION: Neighborhood disadvantage is related to the risk of EHR-documented dementia diagnosis or dementia-related death and accounts for a portion of racial/ethnic differences in dementia burden, even after adjustment for clinically important confounders.

Relationship Between Oral Temperature and Bacteremia in Hospitalized Patients.

BACKGROUND: Early recognition and treatment of bacteremia can be lifesaving. Fever is a well-known marker of bacteremia, but the predictive value of temperature has not been fully explored. OBJECTIVE: To describe temperature as a predictor of bacteremia and other infections. DESIGN: Retrospective review of electronic health record data. SETTING: A single healthcare system comprising 13 hospitals in the United States. PATIENTS: Adult medical patients admitted in 2017 or 2018 without malignancy or immunosuppression. MAIN MEASURES: Maximum temperature, bacteremia, influenza and skin and soft tissue (SSTI) infections based on blood cultures and ICD-10 coding. KEY RESULTS: Of 97,174 patients, 1,518 (1.6%) had bacteremia, 1,392 (1.4%) had influenza, and 3,280 (3.3%) had an SSTI. There was no identifiable temperature threshold that provided adequate sensitivity and specificity for bacteremia. Only 45% of patients with bacteremia had a maximum temperature ≥ 100.4˚F (38˚C). Temperature showed a U-shaped relationship with bacteremia with highest risk above 103˚F (39.4˚C). Positive likelihood ratios for influenza and SSTI also increased with temperature but showed a threshold effect at ≥ 101.0 ˚F (38.3˚C). The effect of temperature was similar but blunted for patients aged ≥ 65 years, who frequently lacked fever despite bacteremia. CONCLUSIONS: The majority of bacteremic patients had maximum temperatures below 100.4 ˚F (38.0˚C) and positive likelihood ratios for bacteremia increased with high temperatures above the traditional definition of fever. Efforts to predict bacteremia should incorporate temperature as a continuous variable.

Long-term Outcomes of a 1-year Hypertension Quality Improvement Initiative in a Large Health System.

BACKGROUND: Understanding whether practices retain outcomes attained during a quality improvement (QI) initiative can inform resource allocation. OBJECTIVE: We report blood pressure (BP) control and medication intensification in the 3 years after a 2016 QI initiative ended. RESEARCH DESIGN: Retrospective cohort. SUBJECTS: Adults with a diagnosis of hypertension who had a primary care visit in a large-integrated health system between 2015 and 2019. MEASURES: We report BP control (<140/90 mm Hg) at the last reading of each year. We used a multilevel regression to identify the adjusted propensity to receive medication intensification among patients with an elevated BP in the first half of the year. To examine variation, we identified the average predicted probability of control for each practice. Finally, we grouped practices by the proportion of their patients whose BP was controlled in 2016: lowest performing (<75%), middle (≥75%-<85%), and highest performing (≥85%). RESULTS: The dataset contained 184,981 patients. From 2015 to 2019, the percentage of patients in control increased from 74% to 82%. In 2015, 38% of patients with elevated BP received medication intensification. This increased to 44% in 2016 and 50% in 2019. Practices varied in average BP control (from 62% to 91% in 2016 and 68% to 90% in 2019). All but one practice had a substantial increase from 2015 to 2016. Most maintained the gains through 2019. Higher-performing practices were more likely to intensify medications than lower-performing practices. CONCLUSIONS: Most practices maintained gains 3 years after the QI program ended. Low-performing practices should be the focus of QI programs.

Relationship Between Primary Care Providers' Perceptions of Alcohol Use Disorder And Pharmacotherapy Prescribing Rates.

AIMS: Acamprosate, naltrexone and disulfiram are underprescribed for alcohol use disorder (AUD) with marked variability among primary care providers (PCPs). We aimed to identify differences between high and low prescribers of medications for AUD (MAUD) with regard to knowledge, experiences, prioritization and attitudes. METHODS: We surveyed PCPs from a large healthcare system with at least 20 patients with AUD. Prescribing rates were obtained from the electronic health record (EHR). Survey responses were scored from strongly disagree (1) to strongly agree (5). Multiple imputation was used to generate attitude scores for 7 missing subjects. PCPs were divided into groups by the median prescribing rate and attitude. Comparisons were made using Wilcoxon rank-sum and regression. RESULTS: Of the 182 eligible PCPs, 68 (37.4%) completed the survey. Most indicated willingness to attend an educational course (57.4%). Compared with low prescribers, high prescribers viewed the effectiveness of medications more favorably (short term 4.0 vs 3.7, P = 0.02; long term 3.5 vs 3.2, P = 0.04) and were more likely to view prescribing as part of their job (3.9 vs 3.4, P = 0.04). PCPs with positive attitudes (72.4%, CI 60.9-83.8%) had a prescribing rate of 5.0% (CI 3.5-6.5%) compared to 1.9% (CI 0.5-3.4%) among those with negative attitudes (P = 0.028). When stratified by attitude, belief in effectiveness was associated with higher prescribing among PCPs with positive attitudes but not those with negative attitudes. CONCLUSIONS: PCPs indicated an interest in learning to prescribe MAUD. However, education alone may not be effective unless physicians have positive attitudes towards patients with AUD.

Evaluating and Modeling Neighborhood Diversity and Health Using Electronic Health Records.

BACKGROUND: Electronic health records (EHRs) provide researchers with abundant sample sizes, detailed clinical data, and other advantages for performing high-quality observational health research on diverse populations. We review and demonstrate strategies for the design and analysis of cohort studies on neighborhood diversity and health, including evaluation of the effects of race, ethnicity, and neighborhood socioeconomic position on disease prevalence and health outcomes, using localized EHR data. METHODS: Design strategies include integrating and harmonizing EHR data across multiple local health systems and defining the population(s) of interest and cohort extraction procedures for a given analysis based on the goal(s) of the study. Analysis strategies address inferential goals, including the mechanistic study of social risks, statistical adjustment for differences in distributions of social and neighborhood-level characteristics between available EHR data and the underlying local population, and inference on individual neighborhoods. We provide analyses of local variation in mortality rates within Cuyahoga County, Ohio. RESULTS: When the goal of the analysis is to adjust EHR samples to be more representative of local populations, sampling and weighting are effective. Causal mediation analysis can inform effects of racism (through racial residential segregation) on health outcomes. Spatial analysis is appealing for large-scale EHR data as a means for studying heterogeneity among neighborhoods even at a given level of overall neighborhood disadvantage. CONCLUSIONS: The methods described are a starting point for robust EHR-derived cohort analysis of diverse populations. The methods offer opportunities for researchers to pursue detailed analyses of current and historical underlying circumstances of social policy and inequality. Investigators can employ combinations of these methods to achieve greater robustness of results. HIGHLIGHTS: EHR data are an abundant resource for studying neighborhood diversity and health.When using EHR data for these studies, careful consideration of the goals of the study should be considered in determining cohort specifications and analytic approaches.Causal mediation analysis, stratification, and spatial analysis are effective methods for characterizing social mechanisms and heterogeneity across localized populations.

Primary care physician referral practices regarding BRCA1/2 genetic counseling in a major health system.

PURPOSE: The United States Preventive Services Task Force recommends primary care physicians refer patients at high risk for BRCA1/2 mutations to genetic testing when appropriate. The objective of our study was to describe referrals for BRCA1/2 testing in a large integrated health system and to assess factors associated with referral. METHODS: This retrospective cohort study includes female patients between 18 and 50 years who had a primary care visit in the Cleveland Clinic Health System between 2010 and 2019. We used multivariable logistic regression to estimate differences in the odds of a woman being referred for BRCA1/2 testing by patient factors and referring physician specialty. We also assessed variation in referrals by physicians. RESULTS: Among 279,568 women, 5% were high risk. Of those, 22% were referred for testing. Black patients were significantly less likely to be referred than white patients (aOR 0.87; 95% CI 0.77, 0.98) and Jewish patients were more likely to be referred than non-Jewish patients (aOR 2.13; 95% CI 1.68, 2.70). Patients primarily managed by OB/GYN were significantly more likely to be referred than those cared for via Internal/Family Medicine (aOR 1.45; 95% CI 1.30, 1.61). Less than a quarter of primary care physicians ever referred a patient for testing. CONCLUSION: The majority of primary care patients at high risk for a BRCA1/2 mutation were not referred for testing, and over a decade, most physicians never referred a single patient. Internal/Family Medicine physicians, in particular, need support in identifying and referring women who could benefit from testing.

Reduced Cognitive Burden and Increased Focus: A Mixed-methods Study Exploring How Implementing Scribes Impacted Physicians.

BACKGROUND: Understanding how medical scribes impact care delivery can inform decision-makers who must balance the cost of hiring scribes with their contribution to alleviating clinician burden. OBJECTIVE: The objective of this study was to understand how scribes impacted provider efficiency and satisfaction. DESIGN: This was mixed-methods study. PARTICIPANTS: Internal and family medicine clinicians were included. MEASURES: We administered structured surveys and conducted unstructured interviews with clinicians who adopted scribes. We collected average days to close charts and quantity of after-hours clinical work in the 6 months before and after implementation using electronic health record data. We conducted a difference in difference (DID) analysis using a multilevel Poisson regression. RESULTS: Three themes emerged from the interviews: (1) charting time is less after training; (2) clinicians wanted to continue working with scribes; and (3) scribes did not reduce the overall inbox burden. In the 6-month survey, 76% of clinicians endorsed that working with a scribe improved work satisfaction versus 50% at 1 month. After implementation, days to chart closure decreased [DID=0.38 fewer days; 95% confidence interval (CI): -0.61, -0.15] the average minutes worked after hours on clinic days decreased (DID=-11.5 min/d; 95% CI: -13.1, -9.9) as did minutes worked on nonclinical days (DID=-24.9 min/d; 95% CI: -28.1, -21.7). CONCLUSIONS: Working with scribes was associated with reduced time to close charts and reduced time using the electronic health record, markers of efficiency. Increased satisfaction accrued once scribes had experience.

Linking Primary Care Patients to Mental Health Care via Behavioral Health Social Workers: A Stepped-Wedge Study.

OBJECTIVE: Demand for systematic linkage of patients to behavioral health care has increased because of the widespread implementation of depression screening. This study assessed the impact of deploying behavioral health social workers (BHSWs) in primary care on behavioral health visits for depression or anxiety. METHODS: This quasi-experimental, stepped-wedge study included adults with a primary care visit between 2016 and 2019 at Cleveland Clinic, a large integrated health system. BHSWs were deployed in 40 practices between 2017 and 2019. Patients were allocated to a control group (diagnosed before BHSW deployment) and an intervention group (diagnosed after deployment). Data were collected on behavioral health visits (i.e., to therapists and psychiatrists) within 30 days of the diagnosis. Multilevel logistic regression models identified associations between BHSW deployment period and behavioral health visit, adjusted for demographic variables and clustering within each group. RESULTS: Of 68,659 persons with a diagnosis, 21% had a depression diagnosis, 49% an anxiety diagnosis, and 31% both diagnoses. In the period after BHSW deployment, the proportion of patients with depression who had a behavioral health visit increased by 10 percentage points, of patients with anxiety by 9 percentage points, and of patients with both disorders by 11 percentage points. The adjusted odds of having a behavioral health visit was higher in the postdeployment period for patients with depression (adjusted odds ratio [AOR]=4.35, 95% confidence interval [CI]=3.50-5.41), anxiety (AOR=4.27, 95% CI=3.57-5.11), and both (AOR= 3.26, 95% CI=2.77-3.84). CONCLUSIONS: Integration of BHSWs in primary care was associated with increased behavioral health visits.

Physicians' Views on Utilization of an Electronic Health Record-Embedded Calculator to Assess Risk for Venous Thromboembolism among Medical Inpatients: A Qualitative Study.

Background Venous thromboembolism (VTE) causes preventable in-hospital morbidity. Pharmacologic prophylaxis reduces VTE in at-risk patients but also increases bleeding. To increase appropriate prescribing, a risk calculator to guide prophylaxis decisions was developed. Despite efforts to promote its use, providers accessed it infrequently. Objective This study aimed to understand provider perspectives on VTE prophylaxis and facilitators and barriers to using the risk calculator. Design This is a qualitative study exploring provider perspectives on VTE prophylaxis and the VTE risk calculator. Participants We interviewed attending physicians and advanced practice providers who used the calculator, and site champions who promoted calculator use. Providers were categorized by real-world usage over a 3-month period: low (<20% of the time), moderate (20-50%), or high (>50%). Approach During semistructured interviews, we asked about experiences with VTE, calculator use, perspectives on its implementation, and experiences with other risk assessment tools. Once thematic saturation was reached, transcripts were analyzed using content analysis to identify themes. Results Fourteen providers participated. Five were high utilizers, three were moderate utilizers, and six were low utilizers. Three site champions participated. Eight major themes were identified as follows: (1) ease of use, (2) perception of VTE risk, (3) harms of thromboprophylaxis, (4) overestimation of calculator use, (5) confidence in own ability, (6) underestimation of risk by calculator, (7) variability of trust in calculator, and (8) validation to withhold prophylaxis from low-risk patients. Conclusions While providers found the calculator is easy to use, routine use may be hindered by distrust of its recommendations. Inaccurate perception of VTE and bleeding risk may prevent calculator use.

Patient Perspectives on Self-Monitoring of Blood Glucose When not Using Insulin: a Cross-sectional Survey.

BACKGROUND: Professional societies have recommended against use of self-monitoring blood glucose (SMBG) in non-insulin-treated type 2 diabetes (NITT2D) to control blood sugar levels, but patients are still monitoring. OBJECTIVE: To understand patients' motivation to monitor their blood sugar, and whether they would stop if their physician suggested it. DESIGN: Cross-sectional in-person and electronic survey conducted between 2018 and 2020. PARTICIPANTS: Adults with type 2 diabetes not using insulin who self-monitor their blood sugar. MAIN MEASURES: The survey included questions about frequency and reason for using SMBG, and the impact of SMBG on quality of life and worry. It also asked, "If your doctor said you could stop checking your blood sugar, would you?" We categorized patients based on whether they would stop. To identify the characteristics independently associated with desire to stop SMBG, we performed a logistic regression using backward stepwise selection. KEY RESULTS: We received 458 responses. The common reasons for using SMBG included the doctor wanted the patient to check (67%), desire to see the number (65%), and desire to see if their medications were working (61%). Forty-eight percent of respondents stated that using SMBG reduced their worry about their diabetes and 61% said it increased their quality of life. Fifty percent would stop using SMBG if given permission. In the regression model, respondents who said that they check their blood sugar levels because "I was told to" were more likely to want to stop (AOR: 1.69, 95%CI: 1.11, 2.58). Those that used SMBG due to habit and to understand their diabetes better had lower odds of wanting to stop (AOR: 0.33, 95%CI: 0.18-0.62; AOR: 0.60, 95%CI: 0.39-0.93, respectively). CONCLUSIONS: Primary care physicians should discuss patients' reasons for using SMBG and offer them the option of discontinuing.

Do patients who have newly identified prediabetes lose weight in the following year?

BACKGROUND: Identifying a window of opportunity when patients are motivated to lose weight might improve the effectiveness of weight loss counseling. The onset of chronic disease could create such a window. OBJECTIVE: To determine whether identifying prediabetes was associated with subsequent weight loss. METHODS: Our retrospective cohort study included adults with obesity and a primary care visit between 2015 and 2017. Data were collected and analysed in 2019/2020. We compared patients who developed prediabetes [haemoglobin A1c (HbA1c) ≥5.7 and <6.5] to patients with a normal HbA1c (<5.7). We ran linear regression models to identify the association between identifying prediabetes and percent body mass index (BMI) change at 6 and 12 months. The adjusted model controlled for demographic characteristics at baseline, Charlson comorbidity score, and metformin, antipsychotic, antidepressant and antiobesity medication prescribed in either the first 3 months (for the 6-month outcome) or first 9 months (for 12-month outcome) and clustering within physician. RESULTS: Of 11 290 participants, 43% developed prediabetes. At 6 months, 15% of the prediabetes group lost ≥5% of their BMI compared with 13% of the comparison group. The results were similar at 12 months with 18% of the prediabetes group losing ≥5% of their BMI compared with 17%. The prediabetes group lost a higher percentage of their BMI (ß = -0.7% versus -0.3% at 6 months and ß = -0.5% versus 0.01% at 12 months). CONCLUSIONS: While the percent of BMI change was small, patients with newly identified prediabetes lost more weight than a comparison group.