Self-report is often used to estimate health care utilization. However, the accuracy of such data is of paramount concern. The authors conducted a systematic review of 42 studies that evaluated the accuracy of self-report utilization data, where utilization was defined as a visit to a clinical provider or entity. They also present a broad conceptual model that identifies major issues to consider when collecting, analyzing, and reporting such data. The results show that self-report data are of variable accuracy. Factors that affect accuracy include (1) sample population and cognitive abilities, (2) recall time frame, (3) type of utilization, (4) utilization frequency, (5) questionnaire design, (6) mode of data collection, and (7) memory aids and probes.

Chronic conditions are among the most common causes of death and disability in the United States. Patients with such conditions receive disproportionate amounts of health care services and therefore cost more per capita than the average patient. This study assesses the prevalence among the Department of Veterans Affairs (VA) health care users and VA expenditures (costs) of 29 common chronic conditions. The authors used regression to identify the marginal impact of these conditions on total, inpatient, outpatient, and pharmacy costs. Excluding costs of contracted medical services at non-VA facilities, total VA health care expenditures in fiscal year 1999 (FY1999) were $14.3 billion. Among the 3.4 million VA patients in FY1999, 72 percent had 1 or more of the 29 chronic conditions, and these patients accounted for 96 percent of the total costs ($13.7 billion). In addition, 35 percent (1.2 million) of VA health care users had 3 or more of the 29 chronic conditions. These individuals accounted for 73 percent of the total cost. Overall, VA health care users have more chronic diseases than the general population.

Phenome-wide association studies (PheWAS) have been proposed as a possible aid in drug development through elucidating mechanisms of action, identifying alternative indications, or predicting adverse drug events (ADEs). Here, we select 25 single nucleotide polymorphisms (SNPs) linked through genome-wide association studies (GWAS) to 19 candidate drug targets for common disease indications. We interrogate these SNPs by PheWAS in four large cohorts with extensive health information (23andMe, UK Biobank, FINRISK, CHOP) for association with 1683 binary endpoints in up to 697,815 individuals and conduct meta-analyses for 145 mapped disease endpoints. Our analyses replicate 75% of known GWAS associations (P < 0.05) and identify nine study-wide significant novel associations (of 71 with FDR < 0.1). We describe associations that may predict ADEs, e.g., acne, high cholesterol, gout, and gallstones with rs738409 (p.I148M) in PNPLA3 and asthma with rs1990760 (p.T946A) in IFIH1. Our results demonstrate PheWAS as a powerful addition to the toolkit for drug discovery.

This article reports how we matched Common Procedure Terminology (CPT) codes with Medicare payment rates and aggregate Veterans Affairs (VA) budget data to estimate the costs of every VA ambulatory encounter. Converting CPT codes to encounter-level costs was more complex than a simple match of Medicare reimbursements to CPT codes. About 40 percent of the CPT codes used in VA, representing about 20 percent of procedures, did not have a Medicare payment rate and required other cost estimates. Reconciling aggregated estimated costs to the VA budget allocations for outpatient care produced final VA cost estimates that were lower than projected Medicare reimbursements. The methods used to estimate costs for encounters could be replicated for other settings. They are potentially useful for any system that does not generate billing data, when CPT codes are simpler to collect than billing data, or when there is a need to standardize cost estimates across data sources.