Dataset Information

Measuring Preventive Care Delivery: Comparing Rates Across Three Data Sources.

ABSTRACT: Preventive care delivery is an important quality outcome, and electronic data reports are being used increasingly to track these services. It is highly informative when electronic data sources are compared to information manually extracted from medical charts to assess validity and completeness.This cross-sectional study used a random sample of Medicaid-insured patients seen at 43 community health centers in 2011 to calculate standard measures of correspondence between manual chart review and two automated sources (electronic health records [EHRs] and Medicaid claims), comparing documentation of orders for and receipt of ten preventive services (n=150 patients/service). Data were analyzed in 2015.Using manual chart review as the gold standard, automated EHR extraction showed near-perfect to perfect agreement (?=0.96-1.0) for services received within the primary care setting (e.g., BMI, blood pressure). Receipt of breast and colorectal cancer screenings, services commonly referred out, showed moderate (?=0.42) to substantial (?=0.62) agreement, respectively. Automated EHR extraction showed near-perfect agreement (?=0.83-0.97) for documentation of ordered services. Medicaid claims showed near-perfect agreement (?=0.87) for hyperlipidemia and diabetes screening, and substantial agreement (?=0.67-0.80) for receipt of breast, cervical, and colorectal cancer screenings, and influenza vaccination. Claims showed moderate agreement (?=0.59) for chlamydia screening receipt. Medicaid claims did not capture ordered or unbilled services.Findings suggest that automated EHR and claims data provide valid sources for measuring receipt of most preventive services; however, ordered and unbilled services were primarily captured via EHR data and completed referrals were more often documented in claims data.

SUBMITTER: Bailey SR

PROVIDER: S-EPMC5067199 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature

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Measuring Preventive Care Delivery: Comparing Rates Across Three Data Sources.

Bailey Steffani R SR Heintzman John D JD Marino Miguel M Hoopes Megan J MJ Hatch Brigit A BA Gold Rachel R Cowburn Stuart C SC Nelson Christine A CA Angier Heather E HE DeVoe Jennifer E JE

American journal of preventive medicine 20160810 5

<h4>Introduction</h4>Preventive care delivery is an important quality outcome, and electronic data reports are being used increasingly to track these services. It is highly informative when electronic data sources are compared to information manually extracted from medical charts to assess validity and completeness.<h4>Methods</h4>This cross-sectional study used a random sample of Medicaid-insured patients seen at 43 community health centers in 2011 to calculate standard measures of corresponden ...[more]

PMID: 27522472

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Project description:BACKGROUND:Multimorbidity is a global health issue, particularly for older adults in the primary care setting. An adequate portrayal of its epidemiology is essential to properly identify and understand the health care needs of this population. This study aimed to compare the differences in the prevalence of selected chronic conditions and multimorbidity, including its associated characteristics, using health survey/self-reported (SR) information only, administrative (Adm) data only and the combined (either) sources. METHODS:This was a secondary analysis of survey data from the first cycle of the Longitudinal Survey on Senior's Health and Health Services Use linked to health-Adm data. The analytical sample consisted of 1625 community-dwelling older adults (≥65 years) recruited in the waiting rooms of primary health clinics in a selected administrative region of the province of Quebec. Seventeen chronic conditions were assessed according to two different data sources. We examined the differences in the observed prevalence of chronic conditions and multimorbidity and the agreement between data sources. RESULTS:The prevalence of each of the 17 chronic conditions ranged from 1.2 to 68.7% depending on the data source. The agreement between different data sources was highly variable, with kappa coefficients (κ) ranging from 0.05 to 0.73. Multimorbidity was very high in this population, with an estimated prevalence of up to 95.9%. In addition, we found that the association between sociodemographic and behavioural factors and the presence of multimorbidity varied according to the different data sources and thresholds. CONCLUSIONS:This is the first study to simultaneously investigate chronic conditions and multimorbidity prevalence among primary care older adults using combined SR and health-Adm data. Our results call attention to (1) the possibility of underestimating cases when using a single data source and (2) the potential benefits of integrating information from different data sources to increase case identification. This is an important aspect of characterizing the health care needs of this fast-growing population.

Project description:IntroductionThe Veterans Choice Program expanded Veteran access to community care. The Veterans Choice Program may negatively impact the receipt of preventive care services owing to care fragmentation. This study assesses 10 measures of preventive care in Veterans with the Department of Veterans Affairs coverage before and after the Veterans Choice Program.MethodsThe study population included Veterans who responded to the National Health Interview Survey during the 2 time periods before and after Veterans Choice Program implementation: January 2011-October 2014 and November 2015-December 2018. Outcomes were preventive care services categorized as cardiovascular risk reduction (cholesterol monitoring, blood pressure monitoring, aspirin use), infectious disease prevention (influenza vaccination and HIV testing), and diabetes care (fasting blood glucose monitoring, podiatry visits, ophthalmology visits, influenza vaccination, and pneumonia vaccination). Two different analyses were conducted: (1) unadjusted and multivariable-adjusted pre-post analysis and (2) difference-in-differences analyses. Analyses were conducted in 2019.ResultsMeasures of cardiovascular risk reduction and influenza vaccination were not statistically different before and after Veterans Choice Program implementation using the 2 different analytic approaches. In unadjusted pre-post analysis, after Veterans Choice Program implementation, Veterans with Veterans Affairs coverage had increased HIV testing (66.1%‒75.4%, p=0.008), podiatry visits (22.4%‒38.3%, p=0.01), and ophthalmology visits (62.2%‒77.2%, p=0.02). Using multivariable adjustment for participant sociodemographic factors, Veterans Choice Program implementation was associated with higher odds of podiatry visits (AOR=2.28, 95% CI=1.24, 4.20, p=0.009) and ophthalmology visits (AOR=2.11, 95% CI=1.13, 3.94, p=0.02) among Veterans with diabetes. In difference-in-differences analyses, Veterans Choice Program implementation was associated with increased podiatry visits (AOR=2.95, 95% CI=1.49, 5.83, p=0.002) among Veterans with diabetes and Veterans Affairs coverage compared with that among those with other coverage types, but no statistically significant effect was observed for ophthalmology visits.ConclusionsVeterans with Veterans Affairs coverage and diabetes had an increase in podiatry visits after Veterans Choice Program implementation. There was no evidence that Veterans Choice Program implementation had a negative impact on the receipt of preventive care services among Veterans with Veterans Affairs coverage.

Project description:ImportancePrimary care physicians have limited time to discuss preventive care, but it is unknown how they prioritize recommended services.ObjectiveTo understand primary care physicians' prioritization of preventive services.Design, setting, and participantsThis 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.ExposuresA 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 measuresPhysicians 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.ResultsOf 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 relevanceIn 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.

Project description:BackgroundEstimating multimorbidity (presence of two or more chronic conditions) using administrative data is becoming increasingly common. We investigated (1) the concordance of identification of chronic conditions and multimorbidity using self-report survey and administrative datasets; (2) characteristics of people with multimorbidity ascertained using different data sources; and (3) whether the same individuals are classified as multimorbid using different data sources.MethodsBaseline survey data for 90,352 participants of the 45 and Up Study-a cohort study of residents of New South Wales, Australia, aged 45 years and over-were linked to prior two-year pharmaceutical claims and hospital admission records. Concordance of eight self-report chronic conditions (reference) with claims and hospital data were examined using sensitivity (Sn), positive predictive value (PPV), and kappa (?).The characteristics of people classified as multimorbid were compared using logistic regression modelling.ResultsAgreement was found to be highest for diabetes in both hospital and claims data (? = 0.79, 0.78; Sn = 79%, 72%; PPV = 86%, 90%). The prevalence of multimorbidity was highest using self-report data (37.4%), followed by claims data (36.1%) and hospital data (19.3%). Combining all three datasets identified a total of 46 683 (52%) people with multimorbidity, with half of these identified using a single dataset only, and up to 20% identified on all three datasets. Characteristics of persons with and without multimorbidity were generally similar. However, the age gradient was more pronounced and people speaking a language other than English at home were more likely to be identified as multimorbid by administrative data.ConclusionsDifferent individuals, with different combinations of conditions, are identified as multimorbid when different data sources are used. As such, caution should be applied when ascertaining morbidity from a single data source as the agreement between self-report and administrative data is generally poor. Future multimorbidity research exploring specific disease combinations and clusters of diseases that commonly co-occur, rather than a simple disease count, is likely to provide more useful insights into the complex care needs of individuals with multiple chronic conditions.

Dataset Information

Measuring Preventive Care Delivery: Comparing Rates Across Three Data Sources.

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Measuring Preventive Care Delivery: Comparing Rates Across Three Data Sources.

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