Simulations showed that validation of database-derived diagnostic criteria based on a small subsample reduced bias

Validity of claims data for identifying cancer incidence in the Japan public health center-based prospective study for the next generation

PURPOSE

This study determined the validity of claims-based definitions for identifying the incidence of total and site-specific cancers in a population-based cohort study.

METHODS

Claims data were obtained for 21 946 participants aged 40-74 years enrolled in the Japan Public Health Center-based Prospective Study for the Next Generation. We defined total and site-specific cancer incidence using combinations of codes from claims data, including diagnosis and procedure codes for cancer therapy. Data from the cancer registry were used as the gold standard to evaluate validity.

RESULTS

Among 21 946 participants, 454 total, 89 stomach, 67 colorectal, 51 lung, 39 breast and 99 prostate invasive cancer cases were newly diagnosed in the cancer registry. For invasive cancer, the sensitivity and specificity of the definition that combined codes for diagnosis and procedures for cancer therapy were 87.0% and 99.4% for total, 88.8% and 99.9% for stomach, 80.6% and 99.9% for colorectal, 86.3% and 99.9% for lung, 100% and 99.9% for breast and 91.9% and 99.9% for prostate cancer, respectively. Furthermore, for invasive and/or in situ cancer, the sensitivity and specificity of the definition were 84.5% and 99.5% for total, 66.7% and 99.9% for colorectal and 100% and 99.9% for breast cancer.

CONCLUSIONS

Our findings suggest that claims-based definitions using diagnosis and procedure codes generally have high validity for total, stomach, lung, breast and prostate cancer incidence, but may underestimate colorectal cancer incidence.

Risk of Incident Diabetes Mellitus Associated With the Dosage and Duration of Oral Glucocorticoid Therapy in Patients With Rheumatoid Arthritis

Objective

To quantify the risk of incident diabetes mellitus (DM) associated with the dosage, duration, and timing of glucocorticoid (GC) use in patients with rheumatoid arthritis (RA).

Methods

We undertook a cohort study using 2 databases: a UK primary care database (the Clinical Practice Research Datalink [CPRD]) including 21,962 RA patients (1992–2009) and the US National Data Bank for Rheumatic Diseases (NDB) including 12,657 RA patients (1998–2013). Information on the dosage and timing of GC use was extracted. DM in the CPRD was defined using Read codes, at least 2 prescriptions for oral antidiabetic medication, or abnormal blood test results. DM in the NDB was defined through patient self‐reports. Data were analyzed using time‐dependent Cox models and a novel weighted cumulative dose (WCD) model that accounts for dosage, duration, and timing of treatment.

Results

The hazard ratio (HR) was 1.30 (95% confidence interval [95% CI] 1.17–1.45) and 1.61 (95% CI 1.37–1.89) in current GC users compared to nonusers in the CPRD and the NDB, respectively. A range of conventional statistical models consistently confirmed increases in risk with the GC dosage and duration. The WCD model showed that recent GC use contributed the most to the current risk of DM, while doses taken >6 months previously did not influence current risk. In the CPRD, 5 mg of prednisolone equivalent dose for the last 1, 3, and 6 months was significantly associated with HRs of 1.20, 1.43, and 1.48, respectively, compared to nonusers.

Conclusion

GC use is a clinically important and quantifiable risk factor for DM. Risk is influenced by the dosage and treatment duration, although only for GC use within the last 6 months.

Pharmacosurveillance without borders: electronic health records in different countries can be used to address important methodological issues in estimating the risk of adverse events

OBJECTIVES

Evaluate methodological advantages and limitations of an international pharmacosurveillance system based on electronic health records (EHRs).

STUDY DESIGN AND SETTINGS

Type 2 diabetes was used as an exemplar. Cohorts of newly treated diabetics were followed in each country (Quebec, Canada; Massachusetts, United States; Manchester, UK) from 2009 to 2012 using local EHR systems. Cox proportional hazards models were used to assess the risk of cardiovascular events.

RESULTS

A total of 44,913 newly treated diabetics were identified; 82.6% (United States) to 93.1% (Canada) were started on biguanides; 13% of patients failed to fill initial prescriptions. An increased risk of cardiovascular events with sulfonylureas was observed when dispensing [hazard ratio (HR): 2.83] vs. EHR prescribing (HR: 2.47) data were used. The addition of clinical data produced a threefold to 10-fold increase in comorbidity for obesity and renal disease, but had no impact on the risk of different hypoglycemic therapies. The risk of cardiovascular events with sulfonylureas was higher in the United States [HR: 3.4; 95% confidence interval (CI): 2.1, 5.5] compared to England (HR: 1.3; 95% CI: 1.1, 1.6).

CONCLUSION

An international surveillance system based on EHRs may provide more timely information about drug safety and new opportunities to estimate potential sources of bias and health system effects on drug-related outcomes.

Diagnostic accuracy of algorithms to identify hepatitis C status, AIDS status, alcohol consumption and illicit drug use among patients living with HIV in an administrative healthcare database

PURPOSE

This study aims to develop and evaluate diagnostic algorithms for AIDS, hepatitis C status, alcohol abuse and illicit drug use in the administrative healthcare database of the Province of Quebec, Canada (Régie de l'assurance-maladie du Québec (RAMQ)).

METHODS

We selected HIV-positive patients contributing to both the RAMQ database and a local clinical database, which was used as gold standard. We developed algorithms to identify the diagnoses of interest in RAMQ using data from hospital discharge summaries and medical and pharmaceutical claims databases. We estimated and compared sensitivity, specificity, positive predictive and negative predictive values and area under receiver operating curve for each algorithm.

RESULTS

Four hundred twenty patients contributed to both databases. Prevalence of conditions of interest in the clinical database was as follows: AIDS 233 (55%), hepatitis C infection 105 (25%), alcohol abuse 106 (25%), illicit drug use 144 (34%) and intravenous drug use 107 (25%). Sensitivity to detect AIDS, hepatitis C, alcohol abuse, illicit drug use and intravenous drug use was 46% [95%CI: 39-53], 26% [18-35], 50% [37-57], 64% [55-72] and 70% [61-79], respectively. Specificity to detect these conditions was 91% [86-95], 97% [94-98], 92% [88-95], 95% [92-97] and 90% [87-93], respectively. Positive predictive values were 87% [80-92], 71% [54-85], 68% [56-78], 87% [79-93] and 72% [62-80], respectively. Area under receiver operating curve varied from 0.62 [0.57-0.65] for hepatitis C to 0.80 [0.76-0.85] for intravenous drug use.

CONCLUSIONS

Sensitivity was low to detect AIDS, alcohol abuse, illicit drug use and especially hepatitis C in RAMQ. Researchers must be aware of the potential for residual confounding and must consider additional methods to control for confounding.

Development and validation of a predictive algorithm to identify adult asthmatics from medical services and pharmacy claims databases

OBJECTIVE

To develop and validate the accuracy of a predictive model to identify adult asthmatics from administrative health care databases.

STUDY SETTING

An existing electronic medical record project in Montreal, Quebec.

STUDY DESIGN

One thousand four hundred and thirty-one patients with confirmed asthma status were identified from primary care physician's electronic medical record.

DATA COLLECTION/EXTRACTION METHODS

Therapeutic indication of asthma in an electronic prescription and/or confirmed asthma from an automated problem list were used as the gold standard. Five groups of asthma-specific markers were identified from administrative health care databases to estimate the probability of the presence of asthma. Cross-validation evaluated the diagnostic ability of each predictive model using 50 percent of sample.

PRINCIPAL FINDINGS

The best performance in discriminating between the patients with asthma and those without it included indicators from medical service and prescription claims databases. The best-fitting algorithm had a sensitivity of 70 percent, a specificity of 94 percent, and positive predictive value of 65 percent. The prescriptions claims-specific algorithm demonstrated a nearly equal performance to the model with medical services and prescription claims combined.

CONCLUSIONS

Our algorithm using asthma-specific markers from administrative claims databases provided moderate sensitivity and high specificity.

Bias-variance trade-off in pharmacoepidemiological studies using physician-preference-based instrumental variables: a simulation study

PURPOSE

Instrumental variables (IV) methodology removes bias due to unobserved confounding by replacing in the analysis the treatment with another variable--the instrument--that is well correlated with the treatment and independent of confounders. Recently, physician drug preference, operationalized as the treatment prescribed to the previous patient of the same physician, was proposed as an instrument in database studies comparing two competing drugs. We assessed, in simulations, how the performance of the IV estimates depends on the strength of this instrument.

METHODS

The 'physician preference' instrument correlates well with the treatment only if physician preferences affect the treatment received by a large fraction of patients. Yet, often there is a subgroup of patients whose treatment cannot be affected by physician's preferences. The larger this subgroup is, the weaker the instrument. We investigated the impact of weakening this instrument on the performance of IV estimates, by comparing risk difference estimates from the conventional and IV analyses in the presence of an unobserved confounder, for both continuous and binary outcomes.

RESULTS

The IV estimates were uniformly less biased than the conventional estimates, but had higher variance. Accordingly, the bias-variance trade-off favors the IV estimates only when physician' preference is a strong instrument. Still, the coverage rate of the 95%CI for the IV estimates was very close to the nominal 95%, while it was consistently lower for the conventional estimates.

CONCLUSION

Researchers should consider using the 'physician preference' instrument when comparing two competing drugs, but should be aware of the underlying assumptions.

Use of nonbiologic disease-modifying antirheumatic drugs and risk of infection in patients with rheumatoid arthritis

OBJECTIVE

Rheumatoid arthritis (RA) is associated with increased frequency of and mortality from infections, which may be related to host factors, RA itself, inflammation, or medication side effects. This study was undertaken to determine the effect of nonbiologic disease-modifying antirheumatic drugs (DMARDs) on infection risk in RA.

METHODS

We performed a retrospective, longitudinal study of a population-based RA cohort in British Columbia, Canada, followed from January 1996 to March 2003 using administrative data. We evaluated mild infections (requiring a physician visit or antibiotics) and serious infections (requiring or complicating hospitalization). Adjusted risk of mild and serious infections associated with DMARD exposure was estimated using generalized estimating equation extension of multivariate Poisson regression models, after adjusting for baseline covariates (age, sex, RA duration, socioeconomic status) and time-dependent covariates (corticosteroids, comorbidity, prior infections).

RESULTS

A total of 27,710 individuals with RA provided 162,710 person-years of followup. Of these, 25,608 (92%) had at least 1 mild infection and 4,941 (18%) had at least 1 serious infection. Use of DMARDs without corticosteroids was associated with a small decrease in mild infection risk of statistical significance but unclear clinical significance (adjusted rate ratio [RR] 0.90, 95% confidence interval [95% CI] 0.88-0.93 relative to no corticosteroid or DMARD use). Use of DMARDs without corticosteroids was not associated with increased serious infection risk (adjusted RR 0.92, 95% CI 0.85-1.0). Use of corticosteroids increased the risk of mild and serious infections.

CONCLUSION

Our results indicate that use of nonbiologic DMARDs, including methotrexate, does not increase the risk of infection in RA, whereas use of corticosteroids does. This has important implications for counseling individuals with RA concerning risks and benefits of DMARDs.