Antibiotics potentially used in response to bioterrorism and the risk of major congenital malformations.

This study was designed to assess the association between pregnancy-related exposures to antibiotics recommended for use in the event of a bioterrorism attack and major congenital malformations. A retrospective cohort study included 30 049 infants from Tennessee Medicaid born between 1985 and 2000 identified from computerised state databases. Infants with fetal exposures to ciprofloxacin, azithromycin, doxycycline and amoxicillin (antibiotics recommended for potential bioterrorism attacks) (n = 24 521) and erythromycin (included as a positive control) (n = 2128) were compared with infants with no fetal exposure to any antibiotics (n = 3400). Major congenital malformations identified from computerised records were confirmed through medical record review. Overall, 869 (2.9%) of infants in the cohort had a confirmed major congenital malformation, with major malformations ranging from 2.5% to 3.0% among the antibiotic-specific exposure groups. No increased risk was present in multivariable analyses for any malformations and for malformations of specific organ systems. In conclusion, these data suggest that ciprofloxacin, azithromycin, doxycycline or amoxicillin use by pregnant women should not result in a greater incidence of overall major congenital malformations in infants whose mothers take these medications, though a large increase in risk cannot be ruled out.

Computerized definitions showed high positive predictive values for identifying hospitalizations for congestive heart failure and selected infections in Medicaid enrollees with rheumatoid arthritis.

PURPOSE: Computerized definitions are used to identify serious infections and congestive heart failure leading to hospitalizations in studies of medication safety. However, information on their accuracy is limited. We evaluated the ability of computerized definitions to identify these conditions as the reason for admission among patients diagnosed with rheumatoid arthritis (RA). METHODS: Medical charts were randomly selected from a systematic sample of hospitalizations for selected conditions in a cohort of Medicaid patients with RA. We calculated positive predictive values (PPVs) for computerized definitions for community-acquired pneumonia, invasive pneumococcal disease, sepsis, opportunistic mycoses, and congestive heart failure using charts reviews as gold standard and computed inter-reviewer agreement statistics. RESULTS: From 2667 hospitalizations, 336 (13%) records were selected for review. A total of 277 charts (82%) were available. Based on any discharge diagnosis, PPVs for hospitalizations due to community-acquired pneumonia, invasive pneumococcal disease, sepsis, and opportunistic mycoses were 84, 100, 80, and 62%, respectively. Restricting definitions to principal diagnoses yielded higher PPVs, 95% for pneumonia and 100% for other diagnoses. The PPV of a principal diagnosis for congestive heart failure was 100%. Inter-reviewer agreement was at least 77% for all outcomes. CONCLUSION: These findings suggest that computerized definitions can identify congestive heart failure and selected infections leading to hospitalization in Medicaid patients with RA.

Positive predictive value of computerized records for major congenital malformations.

PURPOSE: To assess the positive predictive value of computerized records in a linked database of vital records and infant claims, with medical record confirmation to detect congenital malformations in a Medicaid population. METHODS: Study subjects were selected from cases identified for three studies of congenital malformations in the Tennessee Medicaid (TennCare) population including 173 827 (studies 1 and 2) and 519 465 (study 3) mother/infant pairs. Possible malformations were identified from computerized databases of birth certificates linked with maternal and infant claims. Medical records were reviewed for all possible congenital malformations and positive predictive values were calculated for each data source and for each malformation. RESULTS: Among 1430 potential congenital malformations identified from either birth certificates or inpatient claims, 67.7% were confirmed by medical record review. The positive predictive value varied considerably depending on the data source and the organ system. For example, cardiac defects had a very low positive predictive value when identified from birth certificates, and somewhat higher positive predictive value when identified from inpatient claims. Orofacial defects had 90.9% positive predictive value from birth certificates and inpatient claims. Requiring evidence of a diagnostic or therapeutic procedure increased the positive predictive value to >90% for specific defects, but substantially reduced the number of included cases. CONCLUSIONS: Depending on the defect, computerized claims data linked to vital records offer opportunities for identifying birth defects in populations of vulnerable persons. However, for many defects, medical record confirmation is likely to be required to provide valid identification of malformation occurrence.