Search

Objectives To develop recommendations regarding the use of weights to reduce selection bias for commonly performed analyses using electronic health record (EHR)-linked biobank data. Materials and methods We mapped diagnosis (ICD code) data to standardized phecodes from 3 EHR-linked biobanks with varying recruitment strategies: All of Us (AOU; n  = 244 071), Michigan Genomics Initiative (MGI; n  = 81 243), and UK Biobank (UKB; n  = 401 167). Using 2019 National Health Interview Survey data, we constructed selection weights for AOU and MGI to represent the US adult population more. We used weights previously developed for UKB to represent the UKB-eligible population. We conducted 4 common analyses comparing unweighted and weighted results. Results For AOU and MGI, estimated phecode prevalences decreased after weighting (weighted-unweighted median phecode prevalence ratio [MPR]: 0.82 and 0.61), while UKB estimates increased (MPR: 1.06). Weighting minimally impacted latent phenome dimensionality estimation. Comparing weighted versus unweighted phenome-wide association study for colorectal cancer, the strongest associations remained unaltered, with considerable overlap in significant hits. Weighting affected the estimated log-odds ratio for sex and colorectal cancer to align more closely with national registry-based estimates. Discussion Weighting had a limited impact on dimensionality estimation and large-scale hypothesis testing but impacted prevalence and association estimation. When interested in estimating effect size, specific signals from untargeted association analyses should be followed up by weighted analysis. Conclusion EHR-linked biobanks should report recruitment and selection mechanisms and provide selection weights with defined target populations. Researchers should consider their intended estimands, specify source and target populations, and weight EHR-linked biobank analyses accordingly. [ABSTRACT FROM AUTHOR]

Background Health care administrative database research frequently uses standard medical codes to identify diagnoses or procedures. The aim of this review was to establish the diagnostic accuracy of codes used in administrative data research to identify nontuberculous mycobacterial (NTM) disease, including lung disease (NTMLD). Methods We searched Ovid Medline, Embase, Scopus, Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, and ClinicalTrials.gov from inception to April 2019. We included studies assessing the diagnostic accuracy of International Classification of Diseases, 9th edition, Clinical Modification (ICD-9-CM) diagnosis codes to identify NTM disease and NTMLD. Studies were independently assessed by 2 researchers, and the Quality Assessment of Diagnostic Accuracy Studies 2 tool was used to assess bias and quality. Results We identified 5549 unique citations. Of the 96 full-text articles reviewed, 7 eligible studies of moderate quality (3730 participants) were included in our review. The diagnostic accuracy of ICD-9-CM diagnosis codes to identify NTM disease varied widely across studies, with positive predictive values ranging from 38.2% to 100% and sensitivity ranging from 21% to 93%. For NTMLD, 4 studies reported diagnostic accuracy, with positive predictive values ranging from 57% to 64.6% and sensitivity ranging from 21% to 26.9%. Conclusions Diagnostic accuracy measures of codes used in health care administrative data to identify patients with NTM varied across studies. Overall the positive predictive value of ICD-9-CM diagnosis codes alone is good, but the sensitivity is low; this method is likely to underestimate case numbers, reflecting the current limitations of coding systems to capture NTM diagnoses. [ABSTRACT FROM AUTHOR]

Background: Many patients with common cancers are late diagnosed.Objectives: Identify consultation profiles and clinical features in patients with the seven most common cancers, who had consulted a general practitioner (GP) frequently before their cancer diagnosis.Methods: A case-control study was conducted in Region Västra Götaland, Sweden. A total of 2570 patients, diagnosed in 2011 with prostate, breast, colorectal, lung, gynaecological and skin cancers including malignant melanoma, and 9424 controls were selected from the Swedish Cancer Register and a regional health care database. Diagnostic codes [International Statistical Classification of Diseases and Related Health Problems, 10th revision (ICD-10)] from primary care for patients with ≥4 GP consultations registered in the year before cancer diagnosis were collected. Likelihood ratios (LRs) were calculated for variables associated with the different cancers.Results: Fifty-six percent of the patients had consulted a GP four or more times in the year before cancer diagnosis. Alarm symptoms or signs represented 60% of the codes with the highest LR, but only 40% of the 10 most prevalent codes. Breast lump had the highest LR, 11.9 [95% confidence interval (CI) 8.0-17.8]; abnormalities of plasma proteins had an LR of 5.0 (95% CI 3.0-8.2) and abnormal serum enzyme levels had an LR of 4.6 (95% CI 3.6-5.9). Early clinical features associated with cancer had been registered already at the first two GP consultations.Conclusion: One out of six clinical features associated with cancer were presented by cancer patients with four or more pre-referral consultations already at the two first consultations. These early clinical features that were focal and had benign characteristics might have been missed diagnostic opportunities. [ABSTRACT FROM AUTHOR]

Current data collected by medical examiners and coroners are incomplete and inadequate to evaluate the factors that lead to fatalities involving prescription opioids. Determining cause of death is critically important. Two methods are proposed to improve consistency and accuracy in the collection and analysis of decedent data in opioid-related poisoning deaths. First, an improved death certificate is needed to collect evaluative data, including: extent to which opioids were judged to 1) cause, 2) contribute to, or 3) be present in investigated deaths; extent to which opioids as a cause of death were found 1) alone, 2) combined with other prescription drugs, 3) combined with alcohol, or 4) combined with illicit drugs; the time of death; the presence or absence of a valid prescription; and the estimated quantity of opioids taken proximal to death. Patient characteristics for analysis include age, gender, race/ethnicity, geographic area (particularly whether urban or rural), body mass index, duration of opioid usage and daily average dose during the last 2 weeks of life, and histories of chronic pain/medical conditions, substance abuse, and mental illness/psychiatric diagnoses. Second, expanding the scope of opioid toxicology categories used to classify and code cause-of-death data reported by death investigators would improve identification of individual drugs and classes most often associated with overdose deaths. Formulation-specific codes should be added to facilitate consistent recording of findings by death investigators and entry into national vital statistics databases. [ABSTRACT FROM AUTHOR]

Pladevall M (The University of Texas-Houston School of Public Health, E-607 RAS, Houston, Texas 77225, USA), Goff D C, Nichaman M Z, Chan F, Ramsey D, Ortiz C and Labarthe D R. An assessment of the validity of ICD Code 410 to identify hospital admissions for myocardial infarction: The Corpus Christi Heart Project. International Journal of Epidemiology 1996; 25: 948–952.Background The identification of myocardial infarction (MI) is typically based on finding events designated by a nosologist with the appropriate international Classification of Diseases (ICD) code, currently code 410. These codes are applied based on review of medical records or death certificates. However, other factors, including reimbursement considerations, may influence the coding process, especially for hospitalizations. Thus, the validity of using ICD code 410 to identify MI must be assessed Methods The Corpus Christi Heart Project (CCHP) is a population-based surveillance programme for hospitalized MI. Patients were identified using concurrent ascertainment in coronary care units and retrospective review of medical records. Events were validated as definite or possible MI using data regarding chest pain, electrocardiographic changes and cardiac enzymes. The validity of using ICD code 410 to identify cases of MI was assessed by calculating the sensitivity, specificity, predictive values and efficiency of ICD code 410 versus the CCHP ‘gold standard’. Results Use of ICD code 410 identified 80.9% (401/496) of definite MI, but only 19.0% (243/1280) of possible MI. Only 12.3% (90/734) of discharges with an ICD 410 code received a ‘no MI’ designation based on the ‘gold standard’. The efficiency of ICD code 410 for identifying MI was 92.0% for definite MI and 77.1% for definite and possible MI. Conclusions The use of ICD code 410 to identify hospitalized cases of MI results in a modestly biased overestimate of the number of definite MI hospitalizations; however, this approach warrants consideration due to the expense of validation procedures. [ABSTRACT FROM PUBLISHER]