Abstracts / Annals of Epidemiology 23 (2013) 581e598592

P48. Convergent Validity of Parent-Reported ADHD Diagnosis:A Cross-Study Comparison

S.N. Visser, M.L. Danielson, R.H. Bitsko, J.R. Holbrook. Centers for DiseaseControl and Prevention

Purpose: Parent-reported survey data have been used over the past twodecades as an efficient way to monitor the prevalence of childhood condi-tions, including Attention-Deficit/Hyperactivity Disorder (ADHD). However,these data have not been clinically validated. This study contrasts the prev-alence of diagnosed ADHD in California using estimates from administrativemedical records and parent-reported survey data.Methods: The prevalence of diagnosed ADHD from 2001-2010 was recentlypublished using Kaiser Permanente Southern California (KPSC) medicalrecords (Getahun et al., 2013). On the 2007 National Survey of Children'sHealth (NSCH), parents reported whether their child had ever been diag-nosed with ADHD. NSCH data were restricted to allow for more directcomparison to the Getahun sample. SUDAAN was used for analyses toaccount for the NSCH complex sampling design.Results: Getahun et al. reported that 39,200 of 842,830 (4.9%) children aged5-11 had been diagnosedwith ADHD. Restricting the NSCH sample to insuredchildren of the same age in California, the estimated prevalence was 4.7%(95% CI: 2.5%-8.4%).Conclusion: After restricting for demographics, estimates of parent reportedADHD in California closely approximate the prevalence of documentedADHD diagnosis in KPSC medical records. This study provides evidence ofconvergent validity that demonstrates the usefulness of surveys with parentreport for monitoring the prevalence of ADHD.

P49-S. Bayesian Estimation of the Accuracy of ICD-9-CMANDCPT-4-Based Algorithms to Identify CholecystectomyProcedures inAdministrativeDataWithoutaReferenceStandard

S.R. Jafarzadeh, D.K. Warren, K.B. Nickel, A.E. Wallace, D. Mines, V.J. Fraser,M.A. Olsen. Division of Infectious Diseases, Department of InternalMedicine, Washington University School of Medicine, St Louis, MO

Purpose: A major concern when using administrative data for research iscoding accuracy and difficulty in ascertaining if a condition of interest is trulypresent, especially when a reference standard such as chart review isunavailable. We used a Bayesian latent class model to evaluate two algo-rithms to identify cholecystectomy procedures in administrative data, basedon International Classification of Diseases, 9th Edition, Clinical Modification(ICD-9-CM) and Current Procedural Terminology (CPT-4) codes, withoutrelying on a reference standard.Methods: Medical claims data from a large US commercial insurer for1,571,467members aged 18-64 with an inpatient hospital stay between 2006through 2010 were cross-classified according to the presence of ICD-9-CMand CPT-4 procedure codes for cholecystectomy. Assuming a multinomialsampling distribution for the observed cross-classified data, the accuracy ofICD-9-CM- and CPT-4-based algorithms were estimated when used sepa-rately and also jointly in a parallel scheme, where either algorithm beingpositive was considered a positive outcome.Results: The sensitivity and specificity were 0.82 [probability interval (PI):0.73, 0.91] and 0.99 (PI: 0.99, 0.99) for ICD-9-CM-, and 0.92 (PI: 0.86, 0.97)and 0.99 (PI: 0.99, 0.99) for CPT-4-based algorithms, respectively. Theparallel-joint scheme yielded a sensitivity and specificity of 0.99 (PI: 0.97,0.99) and 0.99 (PI: 0.99, 0.99), respectively.Conclusion: When used individually, the ICD-9-CM-based algorithm hadlower accuracy for identifying cholecystectomy procedures in administrativedata, compared to either the CPT-4-based algorithm or parallel-jointapproach, potentially due to the limited number of ICD-9-CM procedurecodes (up to 5) available on each hospital claim.

P50. Recruitment Strategies and Participation in a Case ControlStudy on Childhood Hunger

A.D. Liese, L.H. Martini, C.L. Draper, M.P. Burke, J. Probst, C.E. Blake,D.A. Freedman, B.A. Bell, S.J. Jones. University of South Carolina, Columbia, SC

Purpose: In the United States, childhood hunger is associated strongly withpoverty and minority race and affected 350,000 households in 2011. Given

that participation in research tends to be lower in minority and economicallydisadvantaged populations, recruitment to a case control study of childhoodhunger is challenging. We aimed to present successful recruitment strategiesand participation data from our study.Methods: Participants were recruited in-person and through flyers from fourtypes of food system sites in urban and rural South Carolina; we also allowedfor respondent-driven sampling (RDS). The study aimed for 200 families withchildren in three categories, including very low food secure with children(VLFSC, i.e. hunger), food insecure (FI), food secure (FS).Results: Of 1,011 screened, 790 met eligibility criteria and 542 (69%)participated. Recruitment rates differed by food system site. Eligible personswere most likely to participate if they were VLFSC or FI than FS (70% vs. 72%vs. 64%), lived in rural vs. urban areas (58% vs. 49%), and were recruitedthrough RDS (83% vs. 62-69% depending on site). In multivariate logisticmodels including demographics, recruitment site was the only significantpredictor (p-value <0.05) of participation. About half the participants (48%)were recruited from emergency food assistance sites; 25% through RDS; 18%from traditional food system outlets targeting adults or children; and 9%from child-oriented emergency services.Conclusion: In an era of declining participation rates, we successfullyrecruited a large sample of households into a study of childhood hunger.

P51. Developing a Case-Based Curriculum to TeachEpidemiology and Biostatistics to Clinicians

A.M. Rodday, J.K. Paulus. Tufts University Sackler School of GraduateBiomedical Sciences, Boston, MA

Purpose: Despite covering common methodological topics, courses inEpidemiology and Biostatistics are often developed independently with littlecoordination between faculty and curricula. As a result, students may havedifficulty integrating and applying these concepts to the design, analysis andinterpretation of their own studies. We are developing an integrated, case-based course using modern, real-world clinical examples.Methods: Working with faculty in our Clinical and Translational Sciencegraduate program, we first identified core methodological competencies ourstudents are expected to master. Competencies included randomized trials,case-control studies, cohort studies, confounding, effect modification,propensity scores, linear regression, logistic regression, and survival analysis.To identify clinical examples and datasets corresponding to these compe-tencies, we surveyed faculty members for interesting clinical questions andasked whether they would be willing to share their data for educationalpurposes; we also reviewed publicly-available datasets.Results: To date, we have obtained data that correspondwith 4 different casescenarios and are in the process of developing clinical vignettes and exer-cises. Clinical topics include the effect of soda consumption, cardiac cathe-terization and mortality (using propensity score adjustment), acupunctureand pain, and time-to-prostate cancer recurrence.Conclusion: The case-based course integrating Epidemiology and Biosta-tistics will be offered to our graduate students for the first time in Spring2014. We will evaluate the course using qualitative questionnaires.

P52-S. Propensity Score Matching and Missing Data Imputationin a Large Claims-Based Dataset

J. Poeran, M. Mazumdar, S. Memtsoudis, Y. Ma. Department of Public Health,Division of Biostatistics and Epidemiology, Weill Cornell Medical Center,New York, NY

Purpose: Logistic regression (LR) and propensity score (PS) analysis havedemonstrated different database conditions for each strategy to have theleast unbiased results. With this ongoing study we aim to compare LR withPS analysis with additional consideration of missing data.Methods: Using the claims-based Premier Perspective database (2006-2010)we studied anesthetic technique effects with LR in 528495 orthopedicpatients. Next, we applied PS analysis and compared mean covariate differ-ence between exposure groups (general vs. neuraxial anesthesia) before andafter PS matching to quantify bias reduction. Odds ratios (OR; general vs.neuraxial anesthesia) from LR and PS analysis were compared for 30-daymortality. Multiple imputation will be applied in the next phase to tackle theup to 30% missing data across all covariates in the study. LR and PS analysiswill be compared using the imputed data.