Prevention Priorities: Guidance for Value-Driven Health Improvement

Effect of Individualized Preventive Care Recommendations vs Usual Care on Patient Interest and Use of Recommendations: A Pilot Randomized Clinical Trial

Importance

This randomized clinical trial examines the feasibility and acceptability of a decision-making tool for increasing patient interest in individualized recommendations for preventive care services.

Objective

To pilot a tool to help patients compare life expectancy gains from evidence-based preventive services.

Design, Setting, and Participants

This randomized clinical trial examined patient and physician responses to a pilot decision tool incorporating personalized risk factors at 3 US primary care clinics between 2017 and 2020. Eligible patients were between ages 45 to 70 years with 2 or more high-risk factors. Patients were followed-up after 1 year.

Interventions

The gain in life expectancy associated with guideline adherence to each recommended preventive service was estimated. Personalized estimates incorporating risk factors in electronic health records were displayed in a physician-distributed visual aid. During development, physicians discussed individualized results with patients using shared decision-making (SDM). During the trial, patients were randomized to receive individualized recommendations or usual care (nonmasked, parallel, 1:1 ratio).

Main Outcomes and Measures

Primary outcome was patient interest in individualized recommendations, assessed by survey. Secondary outcomes were use of SDM, decisional comfort, readiness to change, and preventive services received within 1 year.

Results

The study enrolled 104 patients (31 development, 39 intervention, 34 control), of whom 101 were included in analysis (mean [SD] age, 56.5 [5.3] years; 73 [72.3%] women; 80 [79.2%] Black patients) and 20 physicians. Intervention patients found the tool helpful and wanted to use it again, rating it a median 9 of 10 (IQR, 8-10) and 10 of 10 (8-10), respectively. Compared with the control group, intervention patients more often correctly identified the service least likely (18 [46%] vs 0; P = .03) to improve their life expectancy. A greater number of patients also identified the service most likely to improve their life expectancy (26 [69%] vs 10 [30%]; P = .07), although this result was not statistically significant. Intervention patients reported greater mean [SD] improvement in SDM (4.7 [6.9] points) and near-term readiness to change (13.8 points for top-3-ranked recommendations). Point estimates indicated that patients in the intervention group experienced greater, although non-statistically significant, reductions in percentage of body weight (-2.96%; 95% CI, -8.18% to 2.28%), systolic blood pressure (-6.42 mm Hg; 95% CI, -16.12 to 3.27 mm Hg), hemoglobin A1c (-0.68%; 95% CI, -1.82% to 0.45%), 10-year atherosclerotic cardiovascular disease risk score (-1.20%; 95% CI, -3.65% to 1.26%), and low-density lipoprotein cholesterol (-8.46 mg/dL; 95% CI, -26.63 to 9.70 mg/dL) than the control group. Nineteen of 20 physicians wanted to continue using the decision tool in the future.

Conclusions and Relevance

In this clinical trial, an individualized preventive care decision support tool improved patient understanding of primary prevention and demonstrated promise for improved shared decision-making and preventive care utilization.

Trial Registration

ClinicalTrials.gov Identifier: NCT03023813.

Modeling Strategies to Optimize Cancer Screening in USPSTF Guideline-Noncompliant Women

Importance

In 2018, only half of US women obtained all evidence-based cancer screenings. This proportion may have declined during the COVID-19 pandemic because of social distancing, high-risk factors, and fear.

Objective

To evaluate optimal screening strategies in women who obtain some, but not all, US Preventive Services Task Force (USPSTF)-recommended cancer screenings.

Design, Setting, and Participants

This modeling study was conducted from January 31, 2017, to July 20, 2020, and used 4 validated mathematical models from the National Cancer Institute's Cancer Intervention and Surveillance Modeling Network using data from 20 million simulated women born in 1965 in the US.

Interventions

Forty-five screening strategies were modeled that combined breast, cervical, colorectal, and/or lung cancer (LC) screenings; restricted to 1, 2, 3 or 4 screenings per year; or all eligible screenings once every 5 years.

Main Outcomes and Measures

Modeled life-years gained from restricted cancer screenings as a fraction of those attainable from full compliance with USPSTF recommendations (maximum benefits). Results were stratified by LC screening eligibility (LC-eligible/ineligible). We repeated the analysis with 2018 adherence rates, evaluating the increase in adherence required for restricted screenings to have the same population benefit as USPSTF recommendations.

Results

This modeling study of 20 million simulated US women found that it was possible to reduce screening intensity to 1 carefully chosen test per year in women who were ineligible for LC screening and 2 tests per year in eligible women while maintaining 94% or more of the maximum benefits. Highly ranked strategies screened for various cancers, but less often than recommended by the USPSTF. For example, among LC-ineligible women who obtained just 1 screening per year, the optimal strategy frequently delayed breast and cervical cancer screenings by 1 year and skipped 3 mammograms entirely. Among LC-eligible women, LC screening was essential; strategies omitting it provided 25% or less of the maximum benefits. The top-ranked strategy restricted to 2 screenings per year was annual LC screening and alternating fecal immunochemical test with mammography (skipping mammograms when due for cervical cancer screening, 97% of maximum benefits). If adherence in a population of LC-eligible women obtaining 2 screenings per year were to increase by 1% to 2% (depending on the screening test), this model suggests that it would achieve the same benefit as USPSTF recommendations at 2018 adherence rates.

Conclusions and Relevance

This modeling study of 45 cancer screening strategies suggests that women who are noncompliant with cancer screening guidelines may be able to reduce USPSTF-recommended screening intensity with minimal reduction in overall benefits.

Assessment of Physician Priorities in Delivery of Preventive Care

IMPORTANCE

Primary care physicians have limited time to discuss preventive care, but it is unknown how they prioritize recommended services.

OBJECTIVE

To understand primary care physicians' prioritization of preventive services.

DESIGN, SETTING, AND PARTICIPANTS

This online survey was administered to primary care physicians in a large health care system from March 17 to May 12, 2017. Physicians were asked whether they prioritize preventive services and which factors contribute to their choice (5-point Likert scale). Results were analyzed from July 8, 2017, to September 19, 2019.

EXPOSURES

A 2 × 2 factorial design of 2 hypothetical patients: (1) a 50-year-old white woman with hypertension, type 2 diabetes, hyperlipidemia, obesity, a 30-pack-year history of smoking, and a family history of breast cancer; and (2) a 45-year-old black man with hypertension, hyperlipidemia, obesity, a 30-pack-year history of smoking, and a family history of colorectal cancer. Two visit lengths (40 minutes vs 20 minutes) were given. Each patient was eligible for at least 11 preventive services.

MAIN OUTCOMES AND MEASURES

Physicians rated their likelihood of discussing each service during the visit and reported their top 3 priorities for patients 1 and 2. Physician choices were compared with the preventive services most likely to improve life expectancy, using a previously published mathematical model.

RESULTS

Of 241 physicians, 137 responded (57%), of whom 74 (54%) were female and 85 (62%) were younger than 50 years. Physicians agreed they prioritized preventive services (mean score, 4.27 [95% CI, 4.12-4.42] of 5.00), mostly by ability to improve quality (4.56 [95% CI, 4.44-4.68] of 5.00) or length (4.53 [95% CI, 4.40-4.66] of 5.00) of life. Physicians reported more prioritization in the 20- vs 40-minute visit, indicating that they were likely to discuss fewer services during the shorter visit (median, 5 [interquartile range {IQR}, 3-8] vs 11 [IQR, 9-13] preventive services for patient 1, and 4 [IQR, 3-6] vs 9 [IQR, 8-11] for patient 2). Physicians reported similar top 3 priorities for both patients: smoking cessation, hypertension control, and glycemic control for patient 1 and smoking cessation, hypertension control, and colorectal cancer screening for patient 2. Physicians' top 3 priorities did not usually include diet and exercise or weight loss (ranked in their top 3 recommendations for either patient by only 48 physicians [35%]), although these were among the 3 preventive services most likely to improve life expectancy based on the mathematical model.

CONCLUSIONS AND RELEVANCE

In this survey study, physicians prioritized preventive services under time constraints, but priorities did not vary across patients. Physicians did not prioritize lifestyle interventions despite large potential benefits. Future research should consider whether physicians and patients would benefit from guidance on preventive care priorities.

Assessing Patient Interest in Individualized Preventive Care Recommendations

Background. Few Americans obtain all 41 guideline-recommended preventive services for nonpregnant adults. We assessed patient interest in prioritizing their preventive care needs. Methods. We conducted a mixed-methods study, with 4 focus groups (N = 28) at a single institution and a nationwide survey (N = 2,103). Participants were middle-aged and older adults with preventive care needs. We obtained reactions to written materials describing the magnitude of benefit from major preventive services, including both absolute and relative benefits. Recommendations were individualized for patient risk factors (“individualized preventive care recommendations”). Focus groups assessed patient interest, how patients would want to discuss individualized recommendations with their providers, and potential for individualized recommendations to influence patient decision making. Survey content was based on focus groups and analyzed with logistic regression. Results. Patients expressed strong interest in individualized recommendations. Among survey respondents, an adjusted 88.2% (95% confidence interval [CI] = 86.7% to 89.7%) found individualized recommendations very easy to understand, 77.2% (95% CI = 75.3% to 79.1%) considered them very useful, and 64.9% (95% CI = 62.8% to 67.0%) highly trustworthy (each ≥6/7 on Likert scale). Three quarters of participants wanted to receive their own individualized recommendations in upcoming primary care visits (adjusted proportion = 77.5%, 95% CI = 75.6% to 79.4%). Both focus group and survey participants supported shared decision making and reported that individualized recommendations would improve motivation to obtain preventive care. Half of survey respondents reported that they would be much more likely to visit their doctor if they knew individualized recommendations would be discussed, compared with 4.2% who would not be more likely to visit their doctor. Survey respondents already prioritized preventive services, stating they were most likely to choose quick/easy preventive services and least likely to choose expensive preventive services (adjusted proportions, 63.8% and 8.5%, respectively). Results were consistent in sensitivity analyses. Conclusions. Individualized preventive care recommendations are likely to be well received in primary care and might motivate patients to improve adherence to evidence-based care.

Association Between Number of Preventive Care Guidelines and Preventive Care Utilization by Patients

INTRODUCTION

The number of preventive care guidelines is rapidly increasing. It is unknown whether the number of guideline-recommended preventive services is associated with utilization.

METHODS

The authors used Poisson regression of 390,778 person-years of electronic medical records data from 2008 to 2015, in 80,773 individuals aged 50-75 years. Analyses considered eligibility for 11 preventive services most closely associated with guidelines: tobacco cessation; control of obesity, hypertension, lipids, or blood glucose; influenza vaccination; and screening for breast, cervical, or colorectal cancers, abdominal aortic aneurysm, or osteoporosis. The outcome was the rate of preventive care utilization over the following year. Results were adjusted for demographics and stratified by the number of disease risk factors (smoking, obesity, hypertension, hyperlipidemia, diabetes). Data were collected in 2016 and analyzed in 2017.

RESULTS

Preventive care utilization was lower when the number of guideline-recommended preventive services was higher. The adjusted rate of preventive care utilization decreased from 38.67 per 100 (95% CI=38.16, 39.18) in patients eligible for one guideline-recommended service to 31.59 per 100 (95% CI=31.29, 31.89) in patients eligible for two services and 25.43 per 100 (95% CI=24.68, 26.18) in patients eligible for six or more services (p-trend<0.001). Results were robust to disease risk factors and observed for all but two services (tobacco cessation, obesity reduction). However, for any given number of guideline-recommended services, patients with more disease risk factors had higher utilization rates.

CONCLUSIONS

The rate of preventive care utilization was lower when the number of guideline-recommended services was higher. Prioritizing recommendations might improve utilization of high-value services.