Development and Evaluation of the Algorithm CErtaInty Tool (ACE-IT) to Assess Electronic Medical Record and Claims-based Algorithms' Fit for Purpose for Safety Outcomes.

INTRODUCTION: Electronic health record (EHR) or medical claims-based algorithms (i.e., operational definitions) can be used to define safety outcomes using real-world data. However, existing tools do not allow researchers and decision-makers to adequately appraise whether a particular algorithm is fit for purpose (FFP) to support regulatory decisions on drug safety surveillance. Our objective was to develop a tool to enable regulatory decision-makers and other stakeholders to appraise whether a given algorithm is FFP for a specific decision context. METHODS: We drafted a set of 77 generic items informed by regulatory guidance documents, existing instruments, and publications. The outcome of ischemic stroke served as an exemplar to inform the development of draft items. The items were designed to be outcome independent. We conducted a three-round online Delphi panel to develop and refine the tool and achieve consensus on items (> 70% agreement) among panel participants composed of regulators, researchers from pharmaceutical organizations, academic clinicians, methodologists, pharmacoepidemiologists, and cardiologists. We conducted a qualitative analysis of panel responses. Five pairs of reviewers independently evaluated two ischemic stroke algorithm validation studies to test its application. We developed a user guide, with explanation and elaboration for each item, guidance on essential and additional elements for user responses, and an illustrative example of a complete assessment. Furthermore, we conducted a 2-h online stakeholder panel of 16 participants from regulatory agencies, academic institutions, and industry. We solicited input on key factors for an FFP assessment, their general reaction to the Algorithm CErtaInty Tool (ACE-IT), limitations of the tool, and its potential use. RESULTS: The expert panel reviewed and made changes to the initial list of 77 items. The panel achieved consensus on 38 items, and the final version of the ACE-IT includes 34 items after removal of duplicate items. Applying the tool to two ischemic stroke algorithms demonstrated challenges in its application and identified shared concepts addressed by more than one item. The ACE-IT was viewed positively by the majority of stakeholders. They identified that the tool could serve as an educational resource as well as an information-sharing platform. The time required to complete the assessment was identified as an important limitation. We consolidated items with shared concepts and added a preliminary screen section and a summary assessment box based on their input. The final version of the ACE-IT is a 34-item tool for assessing whether algorithm validation studies on safety outcomes are FFP. It comprises the domains of internal validity (24 items), external validity (seven items), and ethical conduct and reporting of the validation study (three items). The internal validity domain includes sections on objectives, data sources, population, outcomes, design and setting, statistical methods, reference standard, accuracy, and strengths and limitations. The external validity domain includes items that assess the generalizability to a proposed target study. The domain on ethics and transparency includes items on ethical conduct and reporting of the validation study. CONCLUSION: The ACE-IT supports a structured, transparent, and flexible approach for decision-makers to appraise whether electronic health record or medical claims-based algorithms for safety outcomes are FFP for a specific decision context. Reliability and validity testing using a larger sample of participants in other therapeutic areas and further modifications to reduce the time needed to complete the assessment are needed to fully evaluate its utility for regulatory decision-making.

Durability of the Single-Dose Ad26.COV2.S Vaccine in the Prevention of COVID-19 Infections and Hospitalizations in the US Before and During the Delta Variant Surge.

Importance: Vaccination against the SARS-CoV-2 virus is critical to control the pandemic. Randomized clinical trials demonstrated efficacy of the single-dose Ad26.COV2.S COVID-19 vaccine, but data on longer-term protection in clinical practice and effectiveness against variants are needed. Objective: To assess the association between receiving the Ad26.COV2.S vaccine and COVID-19-related infections and hospitalizations before and during the Delta variant surge. Design, Setting, and Participants: This cohort study included adults aged 18 years and older who were newly Ad26.COV2.S-vaccinated matched to as many as 10 unvaccinated individuals by date, location, age, sex, and comorbidity index. This was followed by 1:4 propensity score matching on COVID-19 risk factors. Data were collected from US insurance claims data from March 1, 2020, through August 31, 2021. Exposures: Vaccination with Ad26.COV2.S vs no vaccination. Main Outcomes and Measures: Vaccine effectiveness (VE) was estimated for recorded COVID-19 infection and COVID-19-related hospitalization, nationwide and in subgroups by age, high-risk factors, calendar time, and states with high incidences of the Delta variant. VE estimates were corrected for underrecording of vaccinations in insurance data. Results: Among 422 034 vaccinated individuals (mean [SD] age, 54.7 [17.4] years; 236 437 [56.0%] women) and 1 645 397 matched unvaccinated individuals (mean [SD] age, 54.5 [17.5] years; 922 937 [56.1%] women), VE was 76% (95% CI, 75%-77%) for COVID-19 infections and 81% (95% CI, 78%-82%) for COVID-19-related hospitalizations. VE was stable for at least 180 days after vaccination and over calendar time. Among states with high Delta variant incidence, VE during June to August 2021 was 74% (95% CI, 71%-77%) for infections and 81% (95% CI, 75%-86%) for hospitalizations. VE for COVID-19 was higher in individuals younger than 65 years (78%; 95% CI, 77%-79%) and lower in immunocompromised patients (64%; 95% CI, 59%-68%). All estimates were corrected for vaccination underrecording; uncorrected VE, which served as a lower bound, was 66% (95% CI, 64%-67%) for any recorded COVID-19 infection and 72% (95% CI, 69%-74%) for COVID-19-related hospitalization. Conclusions and Relevance: This cohort study in US clinical practice showed stable VE of Ad26.COV2.S for at least 6 months before as well as during the time the Delta variant emerged and became dominant.

Cardiovascular risk prediction model and stratification in patients with type 2 diabetes enrolled in a Medicare Advantage plan.

BACKGROUND: Cardiovascular (CV) risk tools have been developed both nationally and internationally to identify patients at risk for developing CV disease or experiencing a CV event. However, these tools vary widely in the definitions of endpoints, the time at which the endpoints are measured, patient populations, and their validity. The primary limitation of some of the most commonly utilized tools is the lack of specificity for a type 2 diabetes (T2D) population and/or among older patients. OBJECTIVE: To develop a predictive model within an older population of patients with T2D to identify patients at risk for CV events. METHODS: This retrospective cohort study used claims, laboratory, and enrollment data during the 2011-2018 study period. Patients with T2D were identified based on diagnoses and/or medications from 2012-2013. The patient cohort was split into 3 different datasets. The holdout dataset included only those patients residing in the northeastern United States. The rest of the sample was then randomly split: 70% for the training dataset, which were used to fit the predictive model, and 30% for the test dataset to assess internal validity. The primary outcome was the first composite CV event defined as at least 1 of the following: inpatient hospitalization for myocardial infarction, ischemic stroke, unstable angina, or heart failure; or any evidence of revascularization. A survival model for the composite outcome was fitted with baseline demographic and clinical characteristics prognostic for the dependent variable utilizing augmented backwards elimination. For assessing model performance, accuracy, sensitivity, specificity, and the c-statistic were used. Patients were ranked as having a low, moderate, or high probability of a future CV event. RESULTS: A total of 362,791 patients were identified. The holdout dataset included only those patients residing in the northeastern United States (n = 8,303). There were 248,142 patients included in the training dataset and 106,346 patients in the test dataset. The proportion with at least 1 observed composite CV event was 20.9%. The final model included 42 variables. The c-statistic was 0.68, and the accuracy, sensitivity, and specificity were approximately 63%. Results were consistent across the training, test, and holdout samples. The optimal cut points minimizing the difference in sensitivity and specificity for low-, moderate-, and high-risk future CV events were determined to be less than 0.18, 0.18-0.63, and greater than 0.63, respectively, in the training dataset at 5 years. The 5-year observed event risk was 11%, 27%, and 51% for patients classified as low, moderate, and high risk of a future CV event, respectively. CONCLUSIONS: A model predicting CV events among older patients with T2D using administrative claims to identify those at risk may be used for focusing interventions to prevent future events. DISCLOSURES: This study was funded by Boehringer Ingelheim (BI) and conducted as part of the BI-Humana Research Collaboration. Caplan is employed by Humana Healthcare Research, Inc., a wholly owned subsidiary of Humana Inc., which received fees to conduct the study from the sponsor BI. At the time of the study, Hayden and Harvey were employees of Humana Healthcare Research, Inc. Additionally, Prewitt, who owns stock in Humana Inc, and Chiguluri are employees of Humana Inc. Kattan, associated with the Cleveland Clinic in Ohio, served as a consultant to BI, and Pimple and Goss are employees of BI. Luthra was employed by BI for the duration of the study. Portions of this work were accepted as an abstract and presented as a poster at the American Diabetes Association 2020 virtual meeting, June 12-16, 2020.

Association of SARS-CoV-2 Seropositive Antibody Test With Risk of Future Infection.

Importance: Understanding the effect of serum antibodies to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on susceptibility to infection is important for identifying at-risk populations and could have implications for vaccine deployment. Objective: The study purpose was to evaluate evidence of SARS-CoV-2 infection based on diagnostic nucleic acid amplification test (NAAT) among patients with positive vs negative test results for antibodies in an observational descriptive cohort study of clinical laboratory and linked claims data. Design, Setting, and Participants: The study created cohorts from a deidentified data set composed of commercial laboratory tests, medical and pharmacy claims, electronic health records, and hospital chargemaster data. Patients were categorized as antibody-positive or antibody-negative according to their first SARS-CoV-2 antibody test in the database. Main Outcomes and Measures: Primary end points were post-index diagnostic NAAT results, with infection defined as a positive diagnostic test post-index, measured in 30-day intervals (0-30, 31-60, 61-90, >90 days). Additional measures included demographic, geographic, and clinical characteristics at the time of the index antibody test, including recorded signs and symptoms or prior evidence of coronavirus 2019 (COVID) diagnoses or positive NAAT results and recorded comorbidities. Results: The cohort included 3 257 478 unique patients with an index antibody test; 56% were female with a median (SD) age of 48 (20) years. Of these, 2 876 773 (88.3%) had a negative index antibody result, and 378 606 (11.6%) had a positive index antibody result. Patients with a negative antibody test result were older than those with a positive result (mean age 48 vs 44 years). Of index-positive patients, 18.4% converted to seronegative over the follow-up period. During the follow-up periods, the ratio (95% CI) of positive NAAT results among individuals who had a positive antibody test at index vs those with a negative antibody test at index was 2.85 (95% CI, 2.73-2.97) at 0 to 30 days, 0.67 (95% CI, 0.6-0.74) at 31 to 60 days, 0.29 (95% CI, 0.24-0.35) at 61 to 90 days, and 0.10 (95% CI, 0.05-0.19) at more than 90 days. Conclusions and Relevance: In this cohort study, patients with positive antibody test results were initially more likely to have positive NAAT results, consistent with prolonged RNA shedding, but became markedly less likely to have positive NAAT results over time, suggesting that seropositivity is associated with protection from infection. The duration of protection is unknown, and protection may wane over time.

Real-world data suggest antibody positivity to SARS-CoV-2 is associated with a decreased risk of future infection.

Importance There is limited evidence regarding whether the presence of serum antibodies to SARS-CoV-2 is associated with a decreased risk of future infection. Understanding susceptibility to infection and the role of immune memory is important for identifying at-risk populations and could have implications for vaccine deployment. Objective The purpose of this study was to evaluate subsequent evidence of SARS-CoV-2 infection based on diagnostic nucleic acid amplification test (NAAT) among individuals who are antibody-positive compared with those who are antibody-negative, using real-world data. Design This was an observational descriptive cohort study. Participants The study utilized a national sample to create cohorts from a de-identified dataset composed of commercial laboratory test results, open and closed medical and pharmacy claims, electronic health records, hospital billing (chargemaster) data, and payer enrollment files from the United States. Patients were indexed as antibody-positive or antibody-negative according to their first SARS-CoV-2 antibody test recorded in the database. Patients with more than 1 antibody test on the index date where results were discordant were excluded. Main Outcomes/Measures Primary endpoints were index antibody test results and post-index diagnostic NAAT results, with infection defined as a positive diagnostic test post-index, as measured in 30-day intervals (0-30, 31-60, 61-90, >90 days). Additional measures included demographic, geographic, and clinical characteristics at the time of the index antibody test, such as recorded signs and symptoms or prior evidence of COVID-19 (diagnoses or NAAT+) and recorded comorbidities. Results We included 3,257,478 unique patients with an index antibody test. Of these, 2,876,773 (88.3%) had a negative index antibody result, 378,606 (11.6%) had a positive index antibody result, and 2,099 (0.1%) had an inconclusive index antibody result. Patients with a negative antibody test were somewhat older at index than those with a positive result (mean of 48 versus 44 years). A fraction (18.4%) of individuals who were initially seropositive converted to seronegative over the follow up period. During the follow-up periods, the ratio (CI) of positive NAAT results among individuals who had a positive antibody test at index versus those with a negative antibody test at index was 2.85 (2.73 - 2.97) at 0-30 days, 0.67 (0.6 - 0.74) at 31-60 days, 0.29 (0.24 - 0.35) at 61-90 days), and 0.10 (0.05 - 0.19) at >90 days. Conclusions Patients who display positive antibody tests are initially more likely to have a positive NAAT, consistent with prolonged RNA shedding, but over time become markedly less likely to have a positive NAAT. This result suggests seropositivity using commercially available assays is associated with protection from infection. The duration of protection is unknown and may wane over time; this parameter will need to be addressed in a study with extended duration of follow up.

Amyotrophic lateral sclerosis among patients with a Medicare Advantage prescription drug plan; prevalence, survival and patient characteristics.

Objective: To estimate amyotrophic lateral sclerosis (ALS) prevalence, 5-year survival, and explore factors associated with survival in a Medicare population. Methods: A validated administrative claims algorithm was used to classify individual's ages 18-89 years at index date (first claim with a diagnosis of motor neuron disease or ALS between 1 January 2007 and 31 December 2011) with Medicare Advantage prescription drug coverage into mutually exclusive categories: ALS, no ALS, and possible ALS. Crude prevalence and cumulative survival from index date to the date of death, disenrollment or end of the study were calculated. Cox-proportional hazards were used to estimate and explore factors associated with survival. Results: Of 2631 eligible individuals, the algorithm identified 1271 (48 %), 1157 (44 %), 203 (8 %) as ALS, no ALS and possible ALS, respectively. The 5-year period prevalence and the 2011 point prevalence of ALS were 20.5 and 11.8 per 100,000, respectively. Evidence of death was documented in 81%, 35%, and 1.6% of the ALS, no ALS or possible ALS groups, respectively. Unadjusted median survival time was 388, 542 and 1473 days for the ALS, no ALS and possible ALS groups, respectively. Seeing a psychiatrist or neurologist at the index visit, having respiratory or genitourinary comorbidities, and the number of pre-index acute inpatient admissions were associated with shorter survival. Conclusions: Surveillance data from a Medicare population demonstrated a higher prevalence of ALS. Results highlight the need for effective ALS treatment options and resources for patients with ALS who will likely face limited therapeutic choices and care options at the end of life.

Understanding Prediabetes in a Medicare Advantage Population Using Data Adaptive Techniques.

The objective was to identify individuals with undiagnosed prediabetes from administrative data using adaptive techniques. The data source was a national Medicare Advantage Prescription Drug (MAPD) plan administrative data set. A retrospective, cross-sectional study developed and evaluated data adaptive logistic regression, decision tree, neural network, and ensemble predictive models for metabolic syndrome and prediabetes using 3 mutually exclusive cohorts (N = 279,903). The misclassification rate (MCR), average squared error (ASE), c-statistics, sensitivity (SN), and false positive (FP) rates were compared to select the final predictive models. MAPD individuals with continuous enrollment from 2013 to 2014 were included. Metabolic syndrome and prediabetes were defined using clinical guidelines, diagnosis, and laboratory data. A total of 512 variables identified through subject matter expertise in addition to utilizing all data available were evaluated for the modeling. The ensemble model demonstrated better discrimination (c-statistics, MCR, and ASE of 0.83, 0.24, and 0.16, respectively), high SN, and low FP rate in predicting metabolic syndrome than the individual data adaptive modeling techniques. Logistic regression demonstrated better discrimination (c-statistics, MCR, and ASE of 0.67, 0.13, and 0.11 respectively), high SN, and low FP rate in predicting prediabetes than the other adaptive modeling techniques or ensemble methods. The scored data predicted prediabetes in 44% of the MAPD population, which is comparable to 2005-2006 National Health and Nutrition Examination Survey prediabetes rates of 41%. The logistic regression model demonstrated good performance in predicting undiagnosed prediabetes in MAPD individuals.

Association of obesity with healthcare resource utilization and costs in a commercial population.

OBJECTIVE: To examine the association of obesity with healthcare resource utilization (HRU) and costs among commercially insured individuals. METHODS: This retrospective observational cohort study used administrative claims from 1 January 2007 to 1 December 2013. The ICD-9-CM status codes (V85 hierarchy) from 2008 to 2012 classified body mass index (BMI) into the World Health Organizations' BMI categories. The date of first observed BMI code was defined as the index date and continuous eligibility for one year pre- and post- index date was ensured. Post-index claims determined individuals' HRU and costs. Sampling weights developed using the entropy balance method and National Health and Nutrition Examination Survey data ensured representation of the US adult commercially insured population. Baseline characteristics were described across BMI classes and associations between BMI categories, and outcomes were examined using multivariable regression. RESULTS: The cohort included 9651 individuals with BMI V85 codes. After weighting, the BMI distribution was: normal (31.1%), overweight (33.4%), obese class I (22.0%), obese class II (8.1%) and obese class III (5.4%). Increasing BMI was associated with greater prevalence of cardiometabolic conditions, including hypertension, type 2 diabetes and metabolic syndrome. The use of antihypertensives, antihyperlipidemics, antidiabetics, analgesics and antidepressants rose with increasing BMI. Greater BMI level was associated with increased inpatient, emergency department and outpatient utilization, and higher total healthcare, medical and pharmacy costs. CONCLUSIONS: Increasing BMI was associated with higher prevalence of cardiometabolic conditions and higher HRU and costs. There is an urgent need to address the epidemic of obesity and its clinical and economic impacts.

Positive predictive value between medical-chart body-mass-index category and obesity versus codes in a claims-data warehouse.

OBJECTIVE: To evaluate the positive predictive value of claims-based V85 codes for identifying individuals with varying degrees of BMI relative to their measured BMI obtained from medical record abstraction. METHODS: This was a retrospective validation study utilizing administrative claims and medical chart data from 1 January 2009 to 31 August 2015. Randomly selected samples of patients enrolled in a Medicare Advantage Prescription Drug (MAPD) or commercial health plan and with a V85 claim were identified. The claims-based BMI category (underweight, normal weight, overweight, obese class I-III) was determined via corresponding V85 codes and compared to the BMI category derived from chart abstracted height, weight and/or BMI. The positive predictive values (PPVs) of the claims-based BMI categories were calculated with the corresponding 95% confidence intervals (CIs). RESULTS: The overall PPVs (95% CIs) in the MAPD and commercial samples were 90.3% (86.3%-94.4%) and 91.1% (87.3%-94.9%), respectively. In each BMI category, the PPVs (95% CIs) for the MAPD and commercial samples, respectively, were: underweight, 71.0% (55.0%-87.0%) and 75.9% (60.3%-91.4%); normal, 93.8% (85.4%-100%) and 87.8% (77.8%-97.8%); overweight, 97.4% (92.5%-100%) and 93.5% (84.9%-100%); obese class I, 96.9 (90.9%-100%) and 97.2% (91.9%-100%); obese class II, 97.0% (91.1%-100%) and 93.0% (85.4%-100%); and obese class III, 85.0% (73.3%-96.1%) and 97.1% (91.4%-100%). CONCLUSIONS: BMI categories derived from administrative claims, when available, can be used successfully particularly in the context of obesity research.

Predictors of all-cause 30 day readmission among Medicare patients with type 2 diabetes.

OBJECTIVE: Readmission is costly among patients with type 2 diabetes (T2DM) in Medicare Advantage Prescription Drug Plans; identifying high-risk patients is necessary for targeting reduction programs. The objective of this study was to develop a claims-based algorithm to predict all-cause 30 day readmission among patients with T2DM. METHODS: This study used administrative data from 1 January 2012 through 31 January 2014. The cohort included hospitalized T2DM patients, aged 18-90 with ≥12 months' continuous enrollment before an unplanned hospital admission and ≥1 month of enrollment post-discharge, excluding patients in long-term care >30 days pre-index. Multivariate logistic regression predicted the likelihood of readmission following hospitalization in 2013. The analytic file was randomly split into training and test datasets to build and validate the model. Candidate variables included physician and patient demographics, baseline clinical conditions, and healthcare utilization metrics. Clinical conditions were classified using the Healthcare Cost and Utilization Project clinical classification system for ICD-9-CM. RESULTS: Of 63,237 individuals, 17.1% experienced a readmission. Of nearly 200 candidate variables, 14 were predictors of readmission, including total cumulative number of days for inpatient stays and the number of emergency department visits in the baseline period. Male gender, older age, and certain comorbidities were associated with higher likelihood of readmission. The final model demonstrated good discriminant ability (c-statistic = 0.82). CONCLUSIONS: This study provided evidence that certain patient characteristics and healthcare utilization are predictive of readmission. An algorithm with good discriminant ability was developed which could be used to target readmission reduction programs. Physician gender, specialty, and ownership status did not appear to influence the likelihood of readmission.

A comparison of entropy balance and probability weighting methods to generalize observational cohorts to a population: a simulation and empirical example.

PURPOSE: We compared methods to control bias and confounding in observational studies including inverse probability weighting (IPW) and stabilized IPW (sIPW). These methods often require iteration and post-calibration to achieve covariate balance. In comparison, entropy balance (EB) optimizes covariate balance a priori by calibrating weights using the target's moments as constraints. METHODS: We measured covariate balance empirically and by simulation by using absolute standardized mean difference (ASMD), absolute bias (AB), and root mean square error (RMSE), investigating two scenarios: the size of the observed (exposed) cohort exceeds the target (unexposed) cohort and vice versa. The empirical application weighted a commercial health plan cohort to a nationally representative National Health and Nutrition Examination Survey target on the same covariates and compared average total health care cost estimates across methods. RESULTS: Entropy balance alone achieved balance (ASMD ≤ 0.10) on all covariates in simulation and empirically. In simulation scenario I, EB achieved the lowest AB and RMSE (13.64, 31.19) compared with IPW (263.05, 263.99) and sIPW (319.91, 320.71). In scenario II, EB outperformed IPW and sIPW with smaller AB and RMSE. In scenarios I and II, EB achieved the lowest mean estimate difference from the simulated population outcome ($490.05, $487.62) compared with IPW and sIPW, respectively. Empirically, only EB differed from the unweighted mean cost indicating IPW, and sIPW weighting was ineffective. CONCLUSION: Entropy balance demonstrated the bias-variance tradeoff achieving higher estimate accuracy, yet lower estimate precision, compared with IPW methods. EB weighting required no post-processing and effectively mitigated observed bias and confounding. Copyright © 2016 John Wiley & Sons, Ltd.

The Influence of Respondent Characteristics on the Validity of Self-Reported Survey Responses.

OBJECTIVE: To examine concordance between member self-reports and the organization's administrative claims data for two key health factors: number of chronic conditions, and number of prescription drugs. DATA: Medicare Advantage plan claims data and member survey data from 2011 to 2012. DESIGN: Mailed surveys to 15,000 members, enrolled minimum 6 months, drawn from a random sample of primary care physician practices with at least 200 members. METHODS: Descriptive statistics were generated for extent of concordance. Multivariable logistic regressions were used to analyze the association of selected respondent characteristics with likelihood of concordance. FINDINGS: Concordance for number of chronic conditions was 58.4 percent, with 27.3 percent under-reporting, 14.2 percent over-reporting. Concordance for number of prescription drugs was 56.6 percent with 38.9 percent under-reporting, 4.5 percent over-reporting. Number of prescriptions and assistance in survey completion were associated with higher likelihood of concordance for chronic conditions. Assistance in survey completion and number of chronic conditions were associated with higher concordance, and age and number of prescriptions were associated with lower concordance, for prescription drugs. CONCLUSIONS: Self-reported number of chronic conditions and prescription medications are not in high concordance with claims data. Health care researchers and policy makers using patient self-reported data should be aware of these potential biases.

Self-reported physical activity and bone mineral density in urban adolescent girls.

BACKGROUND: This observational study aimed to examine the prevalence of activities of daily living, as well as the impact of leisure time activities, on bone mineral density in urban adolescent girls. METHODS: Patients completed a 23-item physical activity questionnaire at baseline, recording time spent in various activities in the previous 7 days. In addition to leisure time activities, activities of daily life were also considered. Activities were characterized and scored by metabolic intensity (METPA) and mechanical strain on bone (MECHPA). The METPA score for each activity is the product of the metabolic intensity of the activity and the time spent in the activity. The MECHPA score estimates the mechanical strain on bone from ground reaction forces. The logged scores were divided into quartiles with the lowest quartile as the reference group. RESULTS: Four hundred fifty-five females (ages 12-18 years) completed the survey (62% black and 38% non-black). The log of the overall METPA score was a significant predictor of bone mineral density (i.e. higher METPA score predicted a higher bone mineral density, P = 0.03). A MECHPA score in the highest quartile was associated with a higher bone mineral density (P < 0.05) when compared to the other MECHPA quartiles. CONCLUSIONS: In this population of urban adolescent girls, activities of daily living were reported with a higher frequency than sports activities. Results indicated a positive association between the time spent in metabolically intense activities and bone mineral density. There also appears to be a threshold effect for the relationship between activities with the highest mechanical strain and bone mineral density.

Over-the-counter decorative contact lenses: Cosmetic or Medical Devices? A Case Series.

PURPOSE: To illustrate the implications of the unauthorized sale and unmonitored wearing of decorative cosmetic contact lenses resulting in ocular complications and to determine the prevalence of the use of cosmetic contact lenses obtained from unlicensed providers by adolescents. METHODS: Observational case report, structured interview, and survey in a retrospective, observational, clinical practice setting. Reported sources of contact lenses were categorized as provider and nonprovider, and associations within the data were reviewed by using a Pearson correlation and chi-square test. RESULTS: Twelve patients (eight female and four male) were seen urgently for acute eye pain and redness after wearing plano decorative contact lenses. None of the patients had previously worn a contact lens. None of the lenses were dispensed by eye care professionals. Four patients developed blinding infections requiring hospital admission. Causative organisms included staphylococci, Pseudomonas, and acanthamoeba. One patient required a penetrating keratoplasty. One hundred fifty-nine patients were surveyed. Thirty-seven (23%) used decorative contact lenses. Lenses were obtained from an unlicensed provider 51% of the time. Education about lens care and handling was significantly associated with acquiring lenses from licensed providers (R = 0.74, P < 0.0001). CONCLUSIONS: Colored noncorrective contact lenses are being dispensed without a prescription or fitting from unlicensed vendors. Patients who acquire lenses from unauthorized providers are significantly less likely to be instructed on appropriate lens use and care. Consequently, uninformed lens wearers are experiencing acute vision-threatening infections and inflammation.

Sudden painless unilateral vision loss caused by branch retinal artery occlusion: implications for the primary care physician.

We report a case of sudden onset visual loss caused by branch retinal artery occlusion. Systematic search for the cause of branch retinal artery occlusion revealed Factor V Leiden mutation and antiphospholipid antibody syndrome as the cause. Implications for diagnosis and management are discussed.