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Absenteeism and Indirect Economic Burden Associated With Primary and Secondary Hypogonadism

A Retrospective Matched Cohort Analysis of Employed, Commercially Insured Patients in the U.S.

Hepp, Zsolt PharmD, MS; Kim, Gilwan PharmD, MS; Lenhart, Gregory MS; Johnson, Barbara H. MBA

Journal of Occupational and Environmental Medicine: August 2018 - Volume 60 - Issue 8 - p 724–731
doi: 10.1097/JOM.0000000000001323
ORIGINAL ARTICLES
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Objective: The aim of this study was to evaluate the indirect economic burden incurred by patients with primary and secondary hypogonadism (HG) compared with non-HG controls using real-world data.

Methods: In this retrospective cohort study using a large US administrative claims database, adult males with primary or secondary HG were selected from 2010 to 2014. Non-HG controls had no evidence of HG from 2009 to 2014 and were matched on age, insurance type, and geographic region to HG patients. Outcomes included absenteeism and associated costs.

Results: HG (vs non-HG) patients had a significant 15% increase in nonrecreational absenteeism hours (adjusted odds ratio 1.15, P = 0.002) and associated costs ($2152 vs $1172, P < 0.001) post-index after adjusting for pre-period differences.

Conclusion: The indirect economic burden of HG is significant. Further research is needed to test whether treatment with testosterone can help alleviate the indirect burden associated with HG.

AbbVie, Inc., Chicago, Illinois (Dr Hepp); Truven Health Analytics, an IBM Company, Cambridge, Massachusetts (Dr Kim, Dr Lenhart, Johnson).

Address correspondence to: Zsolt Hepp, PharmD, MS, AbbVie Inc, North Chicago, IL 60064 (zsolt.hepp@abbvie.com).

Virginia Noxon provided writing and other editorial support for this manuscript.

This study was funded by AbbVie, Inc. The sponsor was involved in the study design, data analysis, and interpretation of results, and provided critical review of the manuscript. Zsolt Hepp is an AbbVie employee and may own AbbVie stock or stock options. Barbara H. Johnson, Gregory Lenhart, and Gilwan Kim are employees of Truven Health Analytics LLC, an IBM Company, which received funding from AbbVie for the conduct of this study. Dr. Noxon is an employee of Truven Health Analytics LLC, an IBM Company, which received funding from AbbVie for these services.

The authors report no conflicts of interest.

This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc-nd/4.0

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BACKGROUND

Male hypogonadism (HG) is a disorder of persistent testosterone deficiency that is associated with various signs and symptoms such as loss of libido, erectile dysfunction, depression, lethargy, etc.1 HG is classified as primary HG or secondary HG, which are caused by either testicular failure, or hypothalamic or pituitary dysfunction, respectively.2 Currently, there is a lack of knowledge on the incidence and prevalence of primary and secondary HG.3 HG is associated with a number of signs and symptoms and has been shown to significantly impact a patient's quality of life. For example, one study in HG patients demonstrated that abnormal erectile function was significantly associated with lower quality of life.4 The primary treatment for HG is testosterone replacement therapy (TRT), which can be delivered via several different routes of administration (topical, oral, etc).2

In a private insurance administrative database study that examined the direct and indirect costs of HG using a broad definition, outside of primary and secondary, employees with HG had higher risk-adjusted direct costs ($9291 vs. $5248, P < 0.0001) and indirect costs ($2729 vs $1840, P < 0.0001) than the control group.5 Another study in primary and secondary HG patients found that HG was associated with significantly higher total direct costs than a matched control cohort ($8813 vs $5992; P < 0.001).6

To date, there are no studies that have explored absenteeism specifically among primary and secondary HG patients. The objective of this study is to add to the existing literature by describing absenteeism and the associated costs incurred by primary or secondary HG patients compared with non-HG control patients.

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METHODS

Data Sources

Retrospective data from the Truven Health MarketScan® Commercial Claims and Encounters (CCAE) and Health and Productivity Management (HPM) Databases were used to conduct this study.7 The CCAE database profiles the health care experience (inpatient, outpatient, and pharmacy) of individuals with employer-sponsored primary health insurance. The HPM database contains workplace absence, short-term disability, long-term disability, and workers’ compensation data from a subset of Truven Health Analytics’ employer clients. The database is fully linkable to the corresponding medical and pharmacy claims data in the CCAE for these employees. Combined, these databases provide a source of data that includes diagnoses, medical utilization and cost, pharmacy utilization and cost, and nonrecreational absenteeism among employed patients with commercial insurance. The data were fully de-identified before analysis in compliance with Health Insurance Portability and Accountability Act regulations (HIPAA), and thus, Institutional Review Board approval was not sought or required.

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Study Population: HG Patients

Prevalent HG Patients

Male patients with at least one nondiagnostic medical claim for primary or secondary HG [International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes related to testosterone deficiency] between January 1, 2010, and January 1, 2014, were initially included in the HG sample population (please see the code list in Appendix A). Index date was the date of first primary or secondary HG diagnosis. This sample was further restricted to adults (age ≥18 years at the index date) with evidence of TRT during the 12 months before, on, or in the 12 months following the index date. The final population was also required to have continuous enrollment with absenteeism eligibility and medical and pharmacy benefits in the 12 months before and post index date.

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Study Population: Non-HG Control Patients (Matched Population)

Male patients with no claims for primary or secondary HG and no evidence of TRT between January 1, 2009, and December 31, 2014, were initially included in the control sample population. Patients were then matched on age, health plan type, and geographic region to the prevalent HG population at a 3:1 ratio (three non-HG patients to one HG patient). Non-HG patients were assigned the index date of their matched HG counterparts. The final control population was restricted to adults (≥18 years at index date) with continuous enrollment with absenteeism eligibility and medical and pharmacy benefits in the 12 months before and post index date.

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Study Outcomes: Absenteeism

The main outcome observed was any absenteeism indicating missed work due to sickness, disability, leave, vacation, or other paid time off in the post-index period. Pre-index absenteeism was also observed. Nonrecreational absenteeism was defined as absenteeism due to sickness, disability, leave, or other. Otherwise stated, nonrecreational absenteeism is any absenteeism minus vacation (recreational time off). Average total absentee hours and attributable productivity cost were reported for both any and nonrecreational absence. Productivity cost was calculated on the basis of the estimated hourly wage of $25.14, which is the 2016 Bureau of Labor Statistics average hourly earnings of all employees on private nonfarm payrolls, seasonally adjusted.8

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Covariates

Demographic characteristics, including age, gender, health plan type, urban area, and geographic region, were measured on the index date. Urban areas were defined by metropolitan statistical areas (MSAs), which are areas with a population of at least 50,000.9

Clinical characteristics measured in the pre-index period were the Deyo Charlson Comorbidity Index (DCI), select comorbid conditions, and concomitant medications of interest. Comorbid conditions were determined by the presence of an ICD-9 code (Appendix B) on a nondiagnostic medical claim during the pre-index period. Comorbid conditions of interest included chronic pulmonary disease, mild to moderate diabetes, complicated diabetes, headache/migraine, pain, hypothyroidism, coronary heart disease, hyperlipidemia, fatigue, insomnia/sleep disturbances, obesity, hypertension, and osteoporosis. Concomitant medications were determined by the presence of NDC codes on outpatient pharmacy claims that included androgenic steroids, antidiabetics, nonsteroidal anti-inflammatories, and glucocorticoids.

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Statistical Analysis

Bivariate analysis on all study measures was performed with categorical variables presented as the number and proportion of patients in each category and continuous variables presented as means and standard deviations (SD). Results were stratified as prevalent HG versus matched non-HG Chi-square tests and t tests or their nonparametric equivalents were utilized to test for significant differences in the values of measures of the prevalent HG versus matched non-HG.

Multivariate analysis using a regression model was conducted to determine the impact of HG on nonrecreational absence hours adjusting for pre-index absenteeism, DCI, and seven comorbid conditions (pain, heart disease, pulmonary disease, hypothyroidism, mild or moderate diabetes, complicated diabetes, and headache/migraine). Direct 1:3 matching (one HG patient to three non-HG patients) was done on demographics [age (5-year interval), health plan, geographic region, and index year] to account for underlying differences in the prevalent HG population and non-HG population.

Results were considered statistically significant at the P less than 0.05 level. All analyses were done using SAS 9.4 (SAS Inc, Cary, North Carolina).

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RESULTS

Prevalent HG and Matched Non-HG Patients

There were 2300 prevalent HG patients and 250,044 unmatched (6899 matched) non-HG patients who met the final inclusion criteria (Fig. 1). Before matching, both cohorts had similar demographics excluding age and geographic region. After matching (all 1:3 except one 1:2), the mean [SD] age was 49.38 years in the prevalent HG population and 49.28 years in the matched non-HG population. The majority (>50%) of patients in both cohorts resided in the South U.S. geographic region and in an urban area determined by MSA status (yes, no, unknown). Among prevalent HG patients, 71.3% had primary HG (Table 1).

FIGURE 1

FIGURE 1

TABLE 1

TABLE 1

Compared with the matched non-HG cohort, the prevalent HG cohort had a significantly higher mean DCI (0.63 vs 0.19, P < 0.001) and significantly higher proportion of patients with chronic pulmonary disease (7.3% vs 3.7%, P < 0.001), coronary heart disease (7.1% vs 3.8%, P < 0.001), mild to moderate diabetes (15.1% vs 6.7%, P < 0.001), complicated diabetes (3.7% vs 0.9%, P < 0.001), hypothyroidism (5.8% vs 1.6%, P < 0.001), and pain (56.7% vs 31.3%, P < 0.001) in the pre-index period. Also, compared with the matched non-HG cohort, the prevalent HG cohort had a significantly higher proportion of patients who used androgenic steroids (62.9% vs 0.0%, P < 0.001), antihypertensives (50.4% vs 28.5%, P < 0.001), mental health medications (43.5% vs 15.5%, P < 0.001), narcotic opioids (42.4% vs 21.6%, P < 0.001), and antihyperlipidemics (41.7% vs 23.8%, P < 0.001) in the pre-index period (Table 2).

TABLE 2

TABLE 2

Compared with the matched non-HG cohort, the prevalent HG cohort had a significantly higher proportion of patients with any absenteeism in the pre- and post-index periods [(84.3% vs 80.5%, P < 0.001) and (84.6% vs 81.2%, P < 0.001)], respectively. The prevalent HG cohort also had a significantly higher proportion of patients with nonrecreational absenteeism in the pre- and post-index periods when compared with the matched non-HG cohort (73.6% vs 67.7%, P < 0.001) and (74.5% vs 67.9%, P < 0.001), respectively. Significantly more absenteeism hours per patient in the pre- and post-index period were observed in the prevalent HG cohort than the matched non-HG cohort for any absenteeism (234.5 vs 196.2 hours and 251.7 vs 202.4 hours, both P < 0.001) and nonrecreational absenteeism (73.4 vs 45.7 hours and 85.6 vs 46.6 hours, both P < 0.001) (Fig. 2A, and B). Significantly higher associated cost in the pre- and post-index periods was observed in the prevalent HG cohort than the matched non-HG cohort for any absenteeism ($5895 vs $4932 and $6329 vs $5088, both P < 0.001) and nonrecreational absenteeism ($1844 vs $1149 and $2152 vs $1172, both P < 0.001) (Fig. 3A and 3B).

FIGURE 2

FIGURE 2

FIGURE 3

FIGURE 3

After adjusting for the DCI and prevalent conditions where significant differences remained after matching (pain, heart disease, pulmonary disease, hypothyroidism, diabetes, and migraine/headache), nonrecreational absenteeism hours among the HG cohort continued to be significantly higher than the matched non-HG cohort (75.5 vs 49.5 hours) (Fig. 4). After adjustment for clinical characteristics, the regression model showed that the presence of prevalent HG increased nonrecreational absence hours by 1.5 times (P < 0.0001). Using the method of recycled predictions that included adjusting for pre-index nonrecreational absenteeism, the presence of prevalent HG increased nonrecreational absence hours by 1.15 times (P = 0.002). An increase in nonrecreational absence hours was also observed for a one-point increase in DCI (1.1 times, P < 0.0001) and the presence of pain (1.4 times, P < 0.0001) adjusting for all other variables in the model. In contrast, a decrease in nonrecreational absence hours was observed with the presence of hypothyroidism (0.7 times, P = 0.0002) adjusting for all other variables in the model. It is important to note that during the pre-index, patients are likely already experiencing absenteeism due to HG, as this study used diagnosis and treatment to define cases, therefore, adjusting for pre-index absenteeism is conservative.

FIGURE 4

FIGURE 4

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DISCUSSION

Patients with primary and secondary HG had significantly higher absenteeism (overall and nonrecreational) hours missed from work than their matched non-HG controls. Possible factors contributing to this increased absence are patients feeling so lethargic that they are not able to function in their jobs, or succumbing to overwhelming depression so they call in sick. Total productivity costs associated with nonrecreational hours missed from work were significantly higher for HG patients than matched non-HG controls indicating that HG is associated with significant indirect economic burden. Consistent with previous studies, this study showed that patients with primary and secondary HG have an increased comorbid burden compared with demographically similar patients without HG.5,6 This study builds on previous literature that shows patients with primary HG have an increased economic burden from direct medical cost and indirect (absenteeism) cost5 by including secondary HG. The annual economic burden associated with HG nonrecreational absenteeism ($2152) was markedly higher than literature reporting on patients with other chronic illnesses such as diabetes ($272 to $277)10,11 and irritable bowel syndrome with diarrhea (IBS-D) ($1642).12 Two of these studies had a control group of patients who did not have diabetes or IBS-D. In these studies, the annual cost of absenteeism was $160 for nondiabetics10 and $977 for those without IBS-D,12 which both have around a 70% increase in indirect cost among patients with the chronic condition. In contrast, HG patients in this study had an 84% increase in cost compared with matched controls, indicating a potentially higher economic burden of HG than other chronic conditions.

Direct comparisons between these studies and the current results are difficult, as the literature results rely on self-reported measures in different datasets.10–12 A more direct comparison between absenteeism results is available in the study by Ershler et al13 using MarketScan HPM data for annualized cost of anemia in selected chronic conditions including cancer and inflammatory bowel disease (IBD). Ershler et al13 reported the annual indirect cost among anemia patients with cancer and IBD was $3389 and $2808, respectively. HG patients do incur markedly higher annual any absenteeism cost ($6329) than cancer and IBD patients, but there are some differences to note. The wage rate used in the study by Ershler et al13 is slightly lower ($23/hour vs $25/hour); short-term disability (STD) was included and Ershler et al13 did not specify if absenteeism was limited to nonrecreational. If the cost of cancer and IBD only reflected STD and nonrecreational absenteeism than patients with HG may have a similar economic burden of nonrecreational absenteeism as anemic patients with cancer or IBD. Productivity cost burden estimates of HG in the current study while notable are very likely conservative. In their study measuring the effects of work loss productivity, Nicholson et al14 find empirical support that the use of a multiplier may be necessary for analyzing the costs associated with time lost because there are costs associated with absenteeism that are not related to just the hours that a worker is absent from work.

The current results indicate that HG patients incur a significant economic burden due to nonrecreational absenteeism; however, there are significant limitations that include those inherent in any retrospective analysis. The potential for misclassification of HG and comorbid conditions is present, as HG patients were identified and comorbid conditions were captured through administrative claims data as opposed to medical records. In addition, the severity of HG and comorbid conditions could not be distinguished in the study. As with any claims databases, the MarketScan Research Databases rely on administrative claims data for clinical detail. These data are subject to data coding limitations and data entry error. In addition, there was no specific data available to specify the reasons for hours missed from work; thus, nonrecreational absenteeism hours do not necessarily imply the absenteeism hours from HG. Also, the average wage used to calculate the absenteeism associated costs is not specific to wages based on the type of industry. As indirect cost incurred by employers depends on the average wage within a particular industry and organization, these results do not necessarily reflect all employers.

In conclusion, HG patients incur significant indirect cost and further research is needed to determine if treatment with testosterone (TRT) can help alleviate the indirect burden associated with primary and secondary HG.

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Appendix A: ICD-9 Codes for Primary and Secondary Hypogonadism

Table

Table

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Appendix B: Codes for Comorbid Conditions

Table

Table

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REFERENCES

1. Petak SM, Nankin HR, Spark RF, Swerdloff RS, Rodriguez-Rigau LJ. American Association of Clinical E. American Association of Clinical Endocrinologists Medical Guidelines for clinical practice for the evaluation and treatment of hypogonadism in adult male patients: 2002 update. Endocr Pract 2002; 8:440–456.
2. Kumar P, Kumar N, Thakur DS, Patidar A. Male hypogonadism: symptoms and treatment. J Adv Pharm Technol Res 2010; 1:297–301.
3. Araujo AB, O’Donnell AB, Brambilla DJ, et al. Prevalence and incidence of androgen deficiency in middle-aged and older men: estimates from the Massachusetts Male Aging Study. J Clin Endocrinol Metab 2004; 89:5920–5926.
4. Hwang TI, Lo HC, Tsai TF, Chiou HY. Association among hypogonadism, quality of life and erectile dysfunction in middle-aged and aged male in Taiwan. Int J Impot Res 2007; 19:69–75.
5. Kaltenboeck A, Foster S, Ivanova J, et al. The direct and indirect costs among U.S. privately insured employees with hypogonadism. J Sex Med 2012; 9:2438–2447.
6. Grabner M, Bodhani A, Khandelwal N, Palli S, Bonine N, Khera M. Clinical characteristics, health care utilization and costs among men with primary or secondary hypogonadism in a US commercially insured population. J Sex Med 2017; 14:88–97.
7. Huse D. The Value of Measuring Health and Productivity Costs. Cambridge, MA: Truven Health Analytics; 2015.
8. Bureau of Labor Statistics UDoL. Table B-3. Average Hourly and Weekly Earnings of All Employees on Private Nonfarm Payrolls by Industry Sector, Seasonally Adjusted. 2016. Available at: https://www.bls.gov/opub/ee/2016/ces/table3a_201606.pdf. Accessed October 2016.
9. Bureau USC. Geographic Terms and Concepts-Core Based Statistical Areas and Related Statistical Areas. Geography. 2012. Available at: https://www.census.gov/geo/reference/gtc/gtc_cbsa.html. Accessed October 2016.
10. Bishu KG, Gebregziabher M, Dismuke CE, Egede LE. Quantifying the incremental and aggregate cost of missed workdays in adults with diabetes. J Gen Intern Med 2015; 30:1773–1779.
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12. Buono JL, Carson RT, Flores NM. Health-related quality of life, work productivity, and indirect costs among patients with irritable bowel syndrome with diarrhea. Health Qual Life Outcomes 2017; 15:35.
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14. Nicholson S, Pauly MV, Polsky D, Sharda C, Szrek H, Berger ML. Measuring the effects of work loss on productivity with team production. Health Econ 2006; 15:111–123.
Keywords:

absenteeism; hypogonadism; indirect cost

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