Frequency of nonaspirin NSAID-relevant coexisting medical conditions in the primary-care setting: a retrospective database review

Abstract

Background

Coexisting medical conditions and concomitant medications contribute to treatment challenges primary-care professionals (PCPs) face daily. The current study assessed the extent and distribution of nonaspirin NSAID-relevant coexisting medical conditions of interest (CMCOI) in patients visiting PCPs.

Methods

This retrospective database review analyzed data from three large health-care claim databases to identify the frequency of nonaspirin NSAID-relevant CMCOI among adults aged ≥18 years with a PCP visit in 2013. Claim databases employed were the Truven Health MarketScan^® Commercial Claims and Encounters database, representative of the privately insured (PI) population; Truven Health MarketScan Multi-State Medicaid, representative of the Medicaid population (Medicaid); and Truven MarketScan Medicare Supplemental, representative of the Medicare population with employer-based supplemental Medicare insurance (Medicare-Supplement). Nonaspirin NSAID-relevant CMCOI, asthma, cardiovascular risk factors, gastrointestinal bleeding risk factors, and renal insufficiency were chosen based on US NSAID over-the-counter Drug Facts label warnings. Frequency of CMCOI was determined for those without and with a musculoskeletal diagnosis.

Results

In each database, ≥19% (19.0% PI, 29.9% Medicaid, 33.6% Medicare-Supplement) had a musculoskeletal diagnosis. A greater proportion of individuals with a musculoskeletal diagnosis had one or more CMCOI compared with those without a musculoskeletal diagnosis (61.3% vs 50.4% PI, 78.1% vs 66.8% Medicaid, 87.1% vs 82.3% Medicare-Supplement). The frequency of one or more CMCOI increased with age in each database. Across databases among CMCOI, cardiovascular risk factors were most common, followed by gastrointestinal bleeding risk factors, and proportions were higher among those with a musculoskeletal diagnosis.

Conclusion

These data confirm the high frequency of nonaspirin NSAID-relevant CMCOI among patients presenting to PCPs for musculoskeletal diagnosis, as well as among older patients. These analyses reinforce the critical role health-care professionals can play in identifying patients with nonaspirin NSAID-relevant CMCOI, providing those patients with ongoing guidance on appropriate choice and use of over-the-counter analgesics, and educating patients about the impact aging, health status, concomitant conditions, and medicines have on selection of all medicines, including analgesics.

Keywords:

• coexisting medical conditions
• NSAIDs
• musculoskeletal disease
• health claim databases
• OTC analgesics

Supplementary material

The Truven Health MarketScan^® Commercial Claims and Encounters database (privately insured; [PI]) database is representative of a PI population of >121 million Americans from ~350 companies/employers in a wide range of industries and occupations in the US. The Truven Health MarketScan Multi-State Medicaid (Medicaid) database contains data on >17 million Medicaid enrollees from 12 contributing states. This database contains a blinded subset of Medicaid data derived from the pooled health-care experience of ~6 million Medicaid enrollees that is made available to private companies. This database represents ~12 million Medicaid beneficiaries each year. The Truven MarketScan Medicare Supplement (Medicare-Supplement) database is representative of Medicare-eligible active and retired employees and their Medicare-eligible dependents who are enrolled in employer-sponsored supplemental plans (predominantly fee-for-service plans). Medicare-Supplement represents >9 million PI Americans and a wide range of industries and occupations in the US. Only plans where both the Medicare-paid amounts and the employer-paid amounts were available and evident on the claims were selected for this database.

Each database includes inpatient and outpatient services, pharmacy claims, and health-plan enrollment data for all beneficiaries for services provided. Medical claim-capture information on inpatient and outpatient care includes date, type, and place of service, provider type, and payment information. ICD9-CM diagnosis codes, ICD9-CM procedure codes, and Current Procedural Terminology codes are provided for each medical claim. Pharmacy claim files capture the National Drug Code (NDC), dispensing date, quantity of drug, number of days supplied, and payment information.

Enrollment/eligibility records contain enrollment information and demographics (ie, year of birth, sex). For the Medicare-Supplement database, only individuals with both an employer-sponsored plan and Medicare benefits are included, indicating a high-cost health-care benefits plan and higher socioeconomic status. To allow for ease of use, data from these sources were converted to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), version 5 (OMOP CDM) (Observational Health Sciences and Informatics Example ETLs) before any analyses were performed. The conversion of the databases to the OMOP CDM included the mapping of ICD-9-CM and NDC codes to Systematized Nomenclature of Medicine (SNOMED) and RxNorm standard concepts, respectively. To construct diagnosis definitions in the databases, one or more standard SNOMED concepts (OMOP Vocabulary version 5.0, April 3, 2015) was used for each of the four conditions/risk groups of interest, and all terms from the lists were reviewed to exclude terms that were not consistent with the concept of interest. For example, for gastrointestinal bleeding risk, all descendant terms (using the SNOMED hierarchy) of the SNOMED concepts for gastrointestinal hemorrhage and gastrointestinal ulcer were used. In addition, gastrointestinal bleeding risk also includes all drugs linked to the RxNorm concepts for anticoagulants and corticosteroids. For cardiovascular risk, all descendant terms of the SNOMED concepts for cerebrovascular disease, heart failure, disorder of coronary artery, acute myocardial infarction, and hypertensive disorder were used.

Acknowledgments

The authors thank Jesse A Berlin of Johnson and Johnson and Erica A Voss Stanochof Janssen Research and Development for their input and support in the development of this project. Johnson & Johnson Consumer, McNeil Consumer Healthcare Division, provided funding for the study and for support provided to KE Boyle Consultants, LLC for this study (KEB).

Author contributions

LB, AEM, CB, and RW were responsible for the protocol/study design, data review and analysis, critical review and revision of manuscript, final approval of the version to be published, agreement to be accountable for all aspects of the work, and questions being appropriately investigated and resolved; KEB was responsibile for drafting the manuscript, data review and analysis, critical review and revision of the manuscript, final approval of the version to be published, agreement to be accountable for all aspects of the work and questions being appropriately investigated and resolved.

Disclosure

LB and AEM are employees of Johnson & Johnson Consumer Inc, McNeil Consumer Healthcare Division. RW and CB are employees of Janssen Research and Development. KEB is a consultant to Johnson & Johnson Consumer Inc, McNeil Consumer Healthcare Division. The authors report no other conflicts of interest in this work.