Assisted ambulation to improve health outcomes for older medical inpatients (AMBULATE): study protocol for a randomized controlled trial.

BACKGROUND: Hospitalized older adults spend as much as 95% of their time in bed, which can result in adverse events and delay recovery while increasing costs. Observational studies have shown that general mobility interventions (e.g., ambulation) can mitigate adverse events and improve patients' functional status. Mobility technicians (MTs) may address the need for patients to engage in mobility interventions without overburdening nurses. There is no data, however, on the effect of MT-assisted ambulation on adverse events or functional status, or on the cost tradeoffs if a MT were employed. The AMBULATE study aims to determine whether MT-assisted ambulation improves mobility status and decreases adverse events for older medical inpatients. It will also include analyses to identify the patients that benefit most from MT-assisted mobility and assess the cost-effectiveness of employing a MT. METHODS: The AMBULATE study is a multicenter, single-blind, parallel control design, individual-level randomized trial. It will include patients admitted to a medical service in five hospitals in two regions of the USA. Patients over age 65 with mild functional deficits will be randomized using a block randomization scheme. Those in the intervention group will ambulate with the MT up to three times daily, guided by the Johns Hopkins Mobility Goal Calculator. The intervention will conclude at hospital discharge, or after 10 days if the hospitalization is prolonged. The primary outcome is the Short Physical Performance Battery score at discharge. Secondary outcomes are discharge disposition, length of stay, hospital-acquired complications (falls, venous thromboembolism, pressure ulcers, and hospital-acquired pneumonia), and post-hospital functional status. DISCUSSION: While functional decline in the hospital is multifactorial, ambulation is a modifiable factor for many patients. The AMBULATE study will be the largest randomized controlled trial to test the clinical effects of dedicating a single care team member to facilitating mobility for older hospitalized patients. It will also provide a useful estimation of cost implications to help hospital administrators assess the feasibility and utility of employing MTs. TRIAL REGISTRATION: Registered in the United States National Library of Medicine clinicaltrials.gov (# NCT05725928). February 13, 2023.

Personalized Disease Prevention (PDP): study protocol for a cluster-randomized clinical trial.

BACKGROUND: The US Preventive Services Task Force recommends 25 primary preventive services for middle-aged adults, but it can be difficult to do them all. METHODS: The Personalized Disease Prevention (PDP) cluster-randomized clinical trial will evaluate whether patients and their providers benefit from an evidence-based decision tool to prioritize preventive services based on their potential to improve quality-adjusted life expectancy. The decision tool will be individualized for patient risk factors and available in the electronic health record. This Phase III trial seeks to enroll 60 primary care providers (clusters) and 600 patients aged 40-75 years. Half of providers will be assigned to an intervention to utilize the decision tool with approximately 10 patients each, and half will be assigned to usual care. Mixed-methods follow-up will include collection of preventive care utilization from electronic health records, patient and physician surveys, and qualitative interviews. We hypothesize that quality-adjusted life expectancy will increase by more in patients who receive the intervention, as compared with controls. DISCUSSION: PDP will test a novel, holistic approach to help patients and providers prioritize the delivery of preventive services, based on patient risk factors in the electronic health record. TRIAL REGISTRATION: ClinicalTrials.gov NCT05463887. Registered on July 19, 2022.

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.

Project ACTIVE: a Randomized Controlled Trial of Personalized and Patient-Centered Preventive Care in an Urban Safety-Net Setting.

BACKGROUND: Evidence-based preventive care in the USA is underutilized, diminishing population health and worsening health disparities. We developed Project ACTIVE, a program to improve adherence with preventive care goals through personalized and patient-centered care. OBJECTIVE: To determine whether Project ACTIVE improved utilization of preventive care and/or estimated life expectancy compared to usual care. DESIGN: Single-site randomized controlled trial. PARTICIPANTS: Cluster-randomized 140 English or Spanish speaking adult patients in primary care with at least one of twelve unfulfilled preventive care goals based on USPSTF grade A and B recommendations. INTERVENTION: Project ACTIVE employs a validated mathematical model to predict and rank individualized estimates of health benefit that would arise from improved adherence to different preventive care guidelines. Clinical staff engaged the participant in a shared medical decision-making (SMD) process to identify highest priority unfulfilled clinical goals, and health coaching staff engaged the participant to develop and monitor action steps to reach those goals. MAIN MEASURES: Change in number of unfulfilled preventive care goals from USPSTF grade A and B recommendations and change in overall gain in estimated life expectancy. KEY RESULTS: In an intent-to-treat analysis, Project ACTIVE increased the average number of fulfilled preventive care goals out of 12 by 0.68 in the intervention arm compared with 0.15 in the control arm (mean difference [95% CI] 0.53 [0.19-0.86]), yielding a gain in estimated life expectancy of 8.8 months (3.8, 14.2). In a per-protocol analysis, Project ACTIVE increased fulfilled preventive care goals by 0.80 in the intervention arm compared with 0.16 in the control arm (mean difference [95% CI], 0.65 [0.25-1.04]), yielding a gain in estimated life expectancy of 13.7 months (6.2, 21.2). Among the 12 preventive care goals, more improvement occurred for alcohol use, hypertension, hyperlipidemia, depression, and smoking. CONCLUSIONS: Project ACTIVE improved unfulfilled preventive care goals and improved estimated life expectancy. CLINICAL TRIAL REGISTRATION NUMBER: NCT04211883.

Opportunities, Pitfalls, and Alternatives in Adapting Electronic Health Records for Health Services Research.

Electronic health records (EHRs) offer the potential to study large numbers of patients but are designed for clinical practice, not research. Despite the increasing availability of EHR data, their use in research comes with its own set of challenges. In this article, we describe some important considerations and potential solutions for commonly encountered problems when working with large-scale, EHR-derived data for health services and community-relevant health research. Specifically, using EHR data requires the researcher to define the relevant patient subpopulation, reliably identify the primary care provider, recognize the EHR as containing episodic (i.e., unstructured longitudinal) data, account for changes in health system composition and treatment options over time, understand that the EHR is not always well-organized and accurate, design methods to identify the same patient across multiple health systems, account for the enormous size of the EHR, and consider barriers to data access. Associations found in the EHR may be nonrepresentative of associations in the general population, but a clear understanding of the EHR-based associations can be enormously valuable to the process of improving outcomes for patients in learning health care systems. In the context of building 2 large-scale EHR-derived data sets for health services research, we describe the potential pitfalls of EHR data and propose some solutions for those planning to use EHR data in their research. As ever greater amounts of clinical data are amassed in the EHR, use of these data for research will become increasingly common and important. Attention to the intricacies of EHR data will allow for more informed analysis and interpretation of results from EHR-based data sets.

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.

An Alternative Mathematical Modeling Approach to Estimating a Reference Life Expectancy.

Background. Reference life expectancies inform frequently used health metrics, which play an integral role in determining resource allocation and health policy decision making. Existing reference life expectancies are not able to account for variation in geographies, populations, and disease states. Using a computer simulation, we developed a reference life expectancy estimation that considers competing causes of mortality, and is tailored to population characteristics. Methods. We developed a Monte Carlo microsimulation model that explicitly represented the top causes of US mortality in 2014 and the risk factors associated with their onset. The microsimulation follows a birth cohort of hypothetical individuals resembling the population of the United States. To estimate a reference life expectancy, we compared current circumstances with an idealized scenario in which all modifiable risk factors were eliminated and adherence to evidence-based therapies was perfect. We compared estimations of years of potential years life lost with alternative approaches. Results. In the idealized scenario, we estimated that overall life expectancy in the United States would increase by 5.9 years to 84.7 years. Life expectancy for men would increase from 76.4 years to 82.5 years, and life expectancy for women would increase from 81.3 years to 86.8 years. Using age-75 truncation to estimate potential years life lost compared to using the idealized life expectancy underestimated potential health gains overall (38%), disproportionately underestimated potential health gains for women (by 70%) compared to men (by 40%), and disproportionately underestimated the importance of heart disease for white women and black men. Conclusion. Mathematical simulations can be used to estimate an idealized reference life expectancy among a population to better inform and assess progress toward targets to improve population health.

Analysis of the drivers of cost of management when patients with brain metastases are treated with upfront radiosurgery.

OBJECTIVES: We aimed to assess the driving factors for increased cost of brain metastasis management when using upfront stereotactic radiosurgery (SRS). PATIENT AND METHODS: 737 patients treated with upfront SRS without whole brain radiotherapy (WBRT). Patients were evaluated for use of craniotomy, length of hospital stay, need for rehabilitation or facility placement, and use of salvage SRS or salvage WBRT. Costs of care of these interventions were estimated based on 2013 Medicare reimbursements. Multiple linear regression was performed to determine factors that predicted for higher cost of treatment per month of life, as well as highest cumulative cost of care for brain metastasis. RESULTS: Mean cost of brain metastasis management per patient was $42,658, and $4673 per month of life. Upfront SRS represented the greatest contributor of total cost of brain metastasis management over a lifetime (49%), followed by use of any salvage SRS (21%), use of initial surgery (14%), use of salvage surgery (10%), hospitalization (3%) and cost of salvage WBRT (3%). Multiple linear regression identified brain metastasis velocity (BMV) (p < 0.001), use of cavity-directed SRS (<0.001), and CNS symptoms at time of presentation (p = 0.005) as factors that increased costs of care per month of survival. Use of salvage WBRT decreased per month cost of care in patients requiring salvage (p < 0.001). CONCLUSION: The cost of upfront SRS is the greatest contributor to cost of brain metastasis management when using upfront SRS. Higher BMV, progressive systemic disease and presence of symptoms are associated with increased cost of care.

Measuring Population Health in a Large Integrated Health System to Guide Goal Setting and Resource Allocation: A Proof of Concept.

In integrated health care systems, techniques that identify successes and opportunities for targeted improvement are needed. The authors propose a new method for estimating population health that provides a more accurate and dynamic assessment of performance and priority setting. Member data from a large integrated health system (n = 96,246, 73.8% female, mean age = 44 ± 0.01 years) were used to develop a mechanistic mathematical simulation, representing the top causes of US mortality in 2014 and their associated risk factors. An age- and sex-matched US cohort served as comparator group. The simulation was recalibrated and retested for validity employing the outcome measure of 5-year mortality. The authors sought to estimate potential population health that could be gained by improving health risk factors in the study population. Potential gains were assessed using both average life years (LY) gained and average quality-adjusted life years (QALYs) gained. The simulation validated well compared to integrated health system data, producing an AUC (area under the curve) of 0.88 for 5-year mortality. Current population health was estimated as a life expectancy of 84.7 years or 69.2 QALYs. Comparing potential health gain in the US cohort to the Kaiser Permanente cohort, eliminating physical inactivity, unhealthy diet, smoking, and uncontrolled diabetes resulted in an increase of 1.5 vs. 1.3 LY, 1.1 vs. 0.8 LY, 0.5 vs. 0.2 LY, and 0.5 vs. 0.5 LY on average per person, respectively. Using mathematical simulations may inform efforts by integrated health systems to target resources most effectively, and may facilitate goal setting.

Long-term Natural History of Lower Urinary Tract Symptoms Following Radical Retropubic Prostatectomy: A Prospective 15-Year Longitudinal Study.

OBJECTIVE: To provide insights into the long-term impact of radical retro-pubic prostatectomy (ORRP) on lower urinary tract symptoms (LUTS) which are age and prostate dependent and adversely impact quality of life. METHODS: 1995 men undergoing ORRP enrolled in a prospective longitudinal outcomes study. The American Urological Association Symptom Index was self-administered before ORRP and at predetermined time-points after surgery. A multivariate generalized linear model was used to evaluate the association of time since ORRP with American Urological Association symptom score (AUASS). McNemar's test and paired sample t-tests were used to assess whether the proportion of men with clinically significant LUTS (CSLUTS) defined by an AUASS >7 or mean AUASS differed significantly between the time-dependent assessments, respectively. RESULTS: The 15-year mean adjusted AUASS was similar to baseline (7.00 vs 6.85, P = .66). Throughout the 15 years of follow-up, the proportion of men with CSLUTS was lower than baseline with the exception of the 3 month and 15 year assessments. Among men with baseline clinically insignificant LUTS (CILUTS), the mean adjusted AUASS at 15 years was significantly greater than baseline (6.09 vs 3.19, P < .001). Among men with baseline CSLUTS, ORRP led to a significant decrease in mean adjusted AUASS between baseline and 15 years (13.26 vs 8.67, P < .001). CONCLUSION: ORRP favorably affects the long-term natural history of LUTS. The long-term economic and quality of life benefits of ORRP on LUTS should inform the risks and benefits of RP for treatment of localized prostate cancer.

Long-Term Continence Outcomes in Men Undergoing Radical Prostatectomy: A Prospective 15-Year Longitudinal Study.

PURPOSE: We examined the time dependent rates of urinary continence following open retropubic radical prostatectomy. MATERIALS AND METHODS: A total of 1,995 men treated with radical prostatectomy were enrolled in a prospective longitudinal outcomes study. The UCLA-PCI-UFS (UCLA-Prostate Cancer Index-Urinary Function Index) was administered at baseline, and 3, 6, 12, 24, 96, 120 and 180 months after open retropubic radical prostatectomy. Urinary continence was defined by 1 pad or less in 24 hours. Two multiple regression models were constructed to evaluate the association of time since open retropubic radical prostatectomy with the UCLA-PCI-UFI score and urinary continence. RESULTS: The decrease in urinary continence rates between baseline and 15 years (99.6% vs 87.2%, p <0.001), and 2 and 15 years (95.3% vs 87.2%, p = 0.021) were statistically significant. Urinary continence rates were consistently higher in the younger group at all time points. CONCLUSIONS: A significant decrease in urinary continence rates was observed between baseline and 2 years, and between 2 and 15 years in the entire cohort. Urinary continence rates in age matched men in the general population who were followed longitudinally for 15 years were comparable to those in our study population. This suggests that while open retropubic radical prostatectomy causes primarily sphincteric urinary incontinence, it may be protective for subsequent benign prostatic hyperplasia mediated urinary incontinence.

Provider Variation in Antibiotic Prescribing and Outcomes of Respiratory Tract Infections.

OBJECTIVES: Inappropriate antibiotic use for respiratory tract infection (RTI) is an ongoing problem linked to the emergence of drug resistance and other adverse effects. Less is known about the prescribing practices of individual physicians or the impact of physician prescribing habits on patient outcomes. We studied the prescribing practices of providers for acute RTIs in an integrated health system, identified patient factors associated with receipt of an antibiotic and assessed the relation between providers' adjusted prescribing rates and a number of patient outcomes. METHODS: This was a retrospective analysis of adults with an RTI visit to any primary care providers across the Cleveland Clinic Health System in 2011-2012. Patients with a history of chronic obstructive pulmonary disease or immunocompromised status were excluded. Logistic regression was used to examine patient factors associated with receipt of an antibiotic. RESULTS: Of 31,416 patients with an RTI, 54.8% received an antibiotic. Patient factors associated with antibiotic prescribing included white race (odds ratio [OR] 1.35, P < 0.001), presence of fever (OR 1.66, P < 0.001), and a diagnosis of bronchitis (OR 10.98, P < 0.001) or sinusitis (OR 33.85, P < 0.001). Among 290 providers with ≥10 RTI visits, adjusted antibiotic prescribing rates ranged from 0% to 100% (mean 49%). Antibiotics were prescribed more often for sinusitis (OR 33.85, P < 0.001), bronchitis (OR 10.98, P < 0.001), or pharyngitis (OR 1.76, P < 0.001) compared with upper respiratory tract infection. Patients who were prescribed antibiotics at the index visit were more likely to return for RTI within 1 year (adjusted OR 1.26, P < 0.001). Emergency department visits for respiratory complications were rare and not associated with antibiotic receipt. CONCLUSIONS: Antibiotic prescribing for RTI varies widely among physicians and cannot be explained by patient factors. Patients prescribed antibiotics for RTI were more likely to return for RTI.

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.

Implications of false-positive results for future cancer screenings.

BACKGROUND: False-positive cancer screening results may affect a patient's willingness to obtain future screening. METHODS: The authors conducted logistic regression analysis of 450,484 person-years of electronic medical records (2006-2015) in 92,405 individuals aged 50 to 75 years. Exposures were false-positive breast, prostate, or colorectal cancer screening test results (repeat breast imaging or negative breast biopsy ≤3 months after screening mammography, repeat prostate-specific antigen [PSA] test ≤3 months after PSA test result ≥4.0 ng/mL or negative prostate biopsy ≤3 months after any PSA result, or negative colonoscopy [without biopsy/polypectomy] ≤6 months after a positive fecal occult blood test). Outcomes were up-to-date status with breast or colorectal cancer screening. Covariates included prior screening history, clinical information (eg, family history, obesity, and smoking status), comorbidity, and demographics. RESULTS: Women were more likely to be up to date with breast cancer screening if they previously had false-positive mammography findings (adjusted odds ratio [AOR], 1.43 [95% confidence interval, 1.34-1.51] without breast biopsy and AOR, 2.02 [95% confidence interval, 1.56-2.62] with breast biopsy; both P<.001). The same women were more likely to be up to date with colorectal cancer screening (AOR range, 1.25-1.47 depending on breast biopsy; both P<.001). Men who previously had false-positive PSA testing were more likely to be up to date with colorectal cancer screening (AOR, 1.22 [P = .039] without prostate imaging/biopsy and AOR, 1.60 [P = .028] with imaging/biopsy). Results were stronger for individuals with more false-positive results (all P≤.005). However, women with previous false-positive colorectal cancer fecal occult blood test screening results were found to be less likely to be up to date with breast cancer screening (AOR, 0.73; P<.001). CONCLUSIONS: Patients who previously had a false-positive breast or prostate cancer screening test were more likely to engage in future screening. Cancer 2018;124:2390-8. © 2018 American Cancer Society.

A risk prediction model to allow personalized screening for cervical cancer.

IMPORTANCE: Cervical cancer screening guidelines are in evolution. Current guidelines do not differentiate recommendations based on individual patient risk. OBJECTIVE: To derive and validate a tool for predicting individualized probability of cervical intraepithelial neoplasia grade 2 or higher (CIN2+) at a single time point, based on demographic factors and medical history. DESIGN: The study design consisted of an observational cohort with hierarchical generalized linear regression modeling. SETTING: The study was conducted in a setting of 33 primary care practices from 2004 to 2010. PARTICIPANTS: The participants of the study were women aged ≥ 30 years. MAIN OUTCOME AND MEASURES: CIN2+ was the main outcome on biopsy, and the following predictors were included: age, race, marital status, insurance type, smoking history, median income based on zip code, prior human papilloma virus (HPV) results. RESULTS: The final dataset included 99,319 women. Of these, 745 (0.75%) had CIN2+. The multivariable model had a C-statistic of 0.81. All factors but race were independently associated with CIN2+. The model categorized women as having below-average CIN2+ risk (0.15% predicted vs. 0.12% observed risk), average CIN2+ risk (0.42% predicted vs. 0.36% observed), and above-average CIN2+ risk (1.76% predicted vs. 1.85% observed). Before screening, women at below-average risk had a risk of CIN2+ well below that of women with ASCUS and HPV negative (0.12 vs. 0.20%). CONCLUSIONS AND RELEVANCE: A multivariable model using data from the electronic health record was able to stratify women across a 50-fold gradient of risk for CIN2+. After further validation, use of a similar model could enable more targeted cervical cancer screening.