HHS Public Access Author manuscript Author Manuscript
JAMA Otolaryngol Head Neck Surg. Author manuscript; available in PMC 2017 June 01. Published in final edited form as:
JAMA Otolaryngol Head Neck Surg. 2016 June 1; 142(6): 607–609. doi:10.1001/jamaoto.2016.0188.
Higher Health Care Costs in Middle-aged US Adults With Hearing Loss Annie N. Simpson, PhD, Department of Healthcare Leadership and Management, Medical University of South Carolina, Charleston; Department of Otolaryngology–Head & Neck Surgery, Medical University of South Carolina, Charleston
Author Manuscript
Kit N. Simpson, DrPH, and Department of Healthcare Leadership and Management, Medical University of South Carolina, Charleston Judy R. Dubno, PhD Department of Otolaryngology–Head & Neck Surgery, Medical University of South Carolina, Charleston
Author Manuscript
Age-related hearing loss affects more than 60% of US adults older than 70 years and has been associated with increased risk of hospitalization,1 decreased quality of life,2,3 and increased risk of functional and cognitive decline.4 The onset of hearing loss is gradual, with prevalence tripling from the age of 50 years to 60 years.3 However, the association between hearing loss in older middle-aged adults (aged 55-64 years) and the use of health care has not been studied. We compared the costs of health care for a matched cohort of privately insured individuals with and without a diagnosis of hearing loss.
Methods Using the Truven Health MarketScan database from January 1, 2009, to December 31, 2013, of privately insured individuals with at least 18 months of coverage, we extracted data on a cohort of patients aged 55 to 64 years with an International Classification of Diseases, Ninth Revision, code for hearing loss (codes V41.2, V72.1x, 388.00, 388.01, 388.40, 388.43, 388.44, 388.5, 389.1x, and 389.2x). We then propensity score matched them with a comparison group based on age, sex, US region, insurance type, beneficiary status, Charlson
Author Manuscript
Corresponding Author: Annie N. Simpson, PhD, Department of Healthcare Leadership and Management, Medical University of South Carolina, 151-B Rutledge Ave, Mail Stop Code 962, Charleston, SC 29425 ([email protected]). Author Contributions: Dr A. N. Simpson had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: All authors. Acquisition, analysis, or interpretation of data: All authors. Drafting of the manuscript: A. N. Simpson, K. N. Simpson. Critical revision of the manuscript for important intellectual content: All authors. Statistical analysis: A. N. Simpson, K. N. Simpson. Obtained funding: A. N. Simpson, Dubno. Administrative, technical, or material support: A. N. Simpson, Dubno. Study supervision: A. N. Simpson, Dubno. Conflict of Interest Disclosures: None reported.
Simpson et al.
Page 2
Author Manuscript
comorbidity score,5 use of outpatient services, and baseline presence of 22 chronic conditions (asthma, carditis, chronic obstructive pulmonary disease, chronic renal failure, conductive heart disease, congestive heart failure, cystic fibrosis, diabetes mellitus type 2, diverticulitis, epilepsy, heart valve disease, hepatitis, human immunodeficiency virus, hypertension, multiple sclerosis, Parkinson disease, pulmonary heart disease, rheumatoid arthritis, schizophrenia, systemic lupus, senile dementia, and sickle cell disease). Owing to very small cell sizes, cystic fibrosis, diverticulitis, heart valve disease, human immunodeficiency virus, multiple sclerosis, and schizophrenia were not used in the final matching model. Six months of baseline data for each individual were used for matching. Patients with diagnoses of late effects of stroke, coma, or paralysis were excluded. Health care bills for up to 18 months of follow-up after baseline were summed by patient to calculate total payments for inpatient services, outpatient services, prescription medication, and cost of hearing services. Payments were compared by group using gamma distributed, generalized, linear log-transformed models, adjusting for baseline covariates (demographic characteristics, chronic conditions, Charlson comorbidity score, number of days in the follow-up period, and use of hearing services). Data analysis was conducted using SAS, version 9.4 (SAS Institute Inc), with α < .05. The study protocol was approved by the Medical University of South Carolina Institutional Review Board. Patient consent was not required because data were deidentified and the Medical University of South Carolina Institutional Review Board classified this study as nonhuman research.
Author Manuscript Results
Author Manuscript
A total of 561 764 individuals were included in the study. Groups were balanced (standardized mean differences on all covariates,
JAMA Otolaryngol Head Neck Surg. Author manuscript; available in PMC 2017 June 01. Published in final edited form as:
JAMA Otolaryngol Head Neck Surg. 2016 June 1; 142(6): 607–609. doi:10.1001/jamaoto.2016.0188.
Higher Health Care Costs in Middle-aged US Adults With Hearing Loss Annie N. Simpson, PhD, Department of Healthcare Leadership and Management, Medical University of South Carolina, Charleston; Department of Otolaryngology–Head & Neck Surgery, Medical University of South Carolina, Charleston
Author Manuscript
Kit N. Simpson, DrPH, and Department of Healthcare Leadership and Management, Medical University of South Carolina, Charleston Judy R. Dubno, PhD Department of Otolaryngology–Head & Neck Surgery, Medical University of South Carolina, Charleston
Author Manuscript
Age-related hearing loss affects more than 60% of US adults older than 70 years and has been associated with increased risk of hospitalization,1 decreased quality of life,2,3 and increased risk of functional and cognitive decline.4 The onset of hearing loss is gradual, with prevalence tripling from the age of 50 years to 60 years.3 However, the association between hearing loss in older middle-aged adults (aged 55-64 years) and the use of health care has not been studied. We compared the costs of health care for a matched cohort of privately insured individuals with and without a diagnosis of hearing loss.
Methods Using the Truven Health MarketScan database from January 1, 2009, to December 31, 2013, of privately insured individuals with at least 18 months of coverage, we extracted data on a cohort of patients aged 55 to 64 years with an International Classification of Diseases, Ninth Revision, code for hearing loss (codes V41.2, V72.1x, 388.00, 388.01, 388.40, 388.43, 388.44, 388.5, 389.1x, and 389.2x). We then propensity score matched them with a comparison group based on age, sex, US region, insurance type, beneficiary status, Charlson
Author Manuscript
Corresponding Author: Annie N. Simpson, PhD, Department of Healthcare Leadership and Management, Medical University of South Carolina, 151-B Rutledge Ave, Mail Stop Code 962, Charleston, SC 29425 ([email protected]). Author Contributions: Dr A. N. Simpson had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: All authors. Acquisition, analysis, or interpretation of data: All authors. Drafting of the manuscript: A. N. Simpson, K. N. Simpson. Critical revision of the manuscript for important intellectual content: All authors. Statistical analysis: A. N. Simpson, K. N. Simpson. Obtained funding: A. N. Simpson, Dubno. Administrative, technical, or material support: A. N. Simpson, Dubno. Study supervision: A. N. Simpson, Dubno. Conflict of Interest Disclosures: None reported.
Simpson et al.
Page 2
Author Manuscript
comorbidity score,5 use of outpatient services, and baseline presence of 22 chronic conditions (asthma, carditis, chronic obstructive pulmonary disease, chronic renal failure, conductive heart disease, congestive heart failure, cystic fibrosis, diabetes mellitus type 2, diverticulitis, epilepsy, heart valve disease, hepatitis, human immunodeficiency virus, hypertension, multiple sclerosis, Parkinson disease, pulmonary heart disease, rheumatoid arthritis, schizophrenia, systemic lupus, senile dementia, and sickle cell disease). Owing to very small cell sizes, cystic fibrosis, diverticulitis, heart valve disease, human immunodeficiency virus, multiple sclerosis, and schizophrenia were not used in the final matching model. Six months of baseline data for each individual were used for matching. Patients with diagnoses of late effects of stroke, coma, or paralysis were excluded. Health care bills for up to 18 months of follow-up after baseline were summed by patient to calculate total payments for inpatient services, outpatient services, prescription medication, and cost of hearing services. Payments were compared by group using gamma distributed, generalized, linear log-transformed models, adjusting for baseline covariates (demographic characteristics, chronic conditions, Charlson comorbidity score, number of days in the follow-up period, and use of hearing services). Data analysis was conducted using SAS, version 9.4 (SAS Institute Inc), with α < .05. The study protocol was approved by the Medical University of South Carolina Institutional Review Board. Patient consent was not required because data were deidentified and the Medical University of South Carolina Institutional Review Board classified this study as nonhuman research.
Author Manuscript Results
Author Manuscript
A total of 561 764 individuals were included in the study. Groups were balanced (standardized mean differences on all covariates,
