Preventive Medicine 74 (2015) 55–58
Contents lists available at ScienceDirect
Preventive Medicine journal homepage: www.elsevier.com/locate/ypmed
Does time pressure create barriers for people to receive preventive health services? Xiaoxi Yao a,⁎, Allard E. Dembe b, Thomas Wickizer c, Bo Lu d a
Mayo Clinic, Center for the Science of Health Care Delivery, USA The Ohio State University, College of Public Health, Center for Health Outcomes, Policy and Evaluation Studies, USA The Ohio State University, College of Public Health, Division of Health Services, Management and Policy, USA d The Ohio State University, College of Public Health, Division of Biostatistics, USA b c
a r t i c l e
i n f o
Available online 12 March 2015 Keywords: Time pressure Preventive health services Mammogram Pap smear Cancer screening Dental check-up Flu vaccination Work hours Overtime
a b s t r a c t Objective. Regular use of recommended preventive health services can promote good health and prevent disease. However, individuals may forgo obtaining preventive care when they are busy with competing activities and commitments. This study examined whether time pressure related to work obligations creates barriers to obtaining needed preventive health services. Methods. Data from the 2002–2010 Medical Expenditure Panel Survey (MEPS) were used to measure the work hours of 61,034 employees (including 27,910 females) and their use of five preventive health services (flu vaccinations, routine check-ups, dental check-ups, mammograms and Pap smear). Multivariable logistic regression analyses were performed to test the association between working hours and use of each of those five services. Results. Individuals working long hours (N 60 per week) were significantly less likely to obtain dental checkups (OR = 0.81, 95% CI: 0.72–0.91) and mammograms (OR = 0.47, 95% CI: 0.31–0.73). Working 51–60 h weekly was associated with less likelihood of receiving Pap smear (OR = 0.67, 95% CI: 0.46–0.96). No association was found for flu vaccination. Conclusions. Time pressure from work might create barriers for people to receive particular preventive health services, such as breast cancer screening, cervical cancer screening and dental check-ups. Health practitioners should be aware of this particular source of barriers to care. © 2015 Elsevier Inc. All rights reserved.
Introduction Regular use of preventive health services has been shown to be an effective way of promoting good health and avoiding disease. However, the inadequate utilization of preventive services has been consistently documented. For example, Americans only receive about half of the recommended preventive health services (McGlynn et al., 2003). In the past, many Americans had difficulty obtaining preventive care because of not having health insurance coverage or having limited health insurance benefits. Under the Patient Protection and Affordable Care Act (ACA), beginning in 2014, nearly everyone is required to be covered by health insurance and most health insurance plans cover a set of important preventive health services at no cost to patients. Although concerns over the cost of care may still present a barrier to care for many people, exploring and understanding the effect of nonfinancial barriers to obtaining preventive health services is especially important in the U.S., as well as in many other countries. ⁎ Corresponding author at: Research Fellow, Center for the Science of Health Care Delivery, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA. E-mail address: [email protected] (X. Yao).
http://dx.doi.org/10.1016/j.ypmed.2015.03.008 0091-7435/© 2015 Elsevier Inc. All rights reserved.
Individuals may face many kinds of non-financial barriers to care, including language and cultural issues (Shaw et al., 2009; DuBard and Gizlice, 2008), having no usual source of care (DeVoe et al., 2003), constraints on physician time, and difficulty in securing transportation to care facilities (Østbye et al., 2005). Individuals may also forgo obtaining health care because they believe it is not important, or because they are busy with other competing activities and commitments. This might be a particularly relevant issue for people who have a considerable amount of time pressure in their lives. Many working adults spend significantly more time at their jobs than at performing other daily activities. In the U.S., 33% of full-time employees work over 40 h per week, and 9% work at least 60 h per week (CPS, 2013). Because work is such an important component in people's lives, time pressure from work can have a significant impact on workers' ability to meet various personal, domestic and civic obligations. Finding time to attend to health care needs may be particularly challenging, because of the complexities in scheduling appointments, locating appropriate medical providers, and dealing with insurance issues. Additionally, it may be especially difficult for employed persons to take time away from their jobs, since working hours often overlap with the hours available at the offices of healthcare providers. Even if an individual makes time to seek treatment for an
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X. Yao et al. / Preventive Medicine 74 (2015) 55–58
acute illness episode, he/she may be less likely to find time to obtain discretionary preventive health services, such as routine check-ups, immunizations, and cancer screening. There has been only limited research conducted examining time pressure as a potential barrier to care. A recent Japanese study found that men working long hours (N 250 per month) made about half as many physician visits as those working fewer (100–200) hours per month (Sato et al., 2011). A 2007 Canadian study found similar results indicating a statistically significant inverse association between patients' long work hours (N 45 h per week) and utilization of general practitioner services (Fell et al., 2007). These findings are consistent with a qualitative study in Canada, which found that work responsibilities and work time were the most commonly mentioned individual barriers to accessing primary care (Wellstood et al., 2006). Another recent qualitative study of British men reported that long working hours often interfered with their ability to “take care of their health” (Coles et al., 2010). The aforementioned studies suggest that time pressure from work may be an important barrier to accessing health care. However, to date, there is only a sparse amount of research to demonstrate this connection, and none of the available studies has focused specifically on preventive health services. Additionally, the available data in this area is exclusively from countries outside of the U.S. This study therefore aims to determine the extent to which time pressure from work creates a barrier to accessing specific preventive health services among the U.S. working population. Materials and methods Data source This study utilizes data from the 2002–2010 Medical Expenditure Panel Survey (MEPS). MEPS is a nationally representative survey that collects information about health care use, expenditures, sources of payment, health insurance, and health status for the U.S. civilian non-institutionalized population (AHRQ, 2009b). Specific variables germane to this study were obtained from the MEPS database including demographic and socio-economic characteristics, health status, employment history, and health care utilization information.
data, researchers should keep all responses in the analysis, and adjust personlevel survey weights to generate nationally representative estimates (AHRQ, 2009a). The aforementioned sample population pertains to analyses of the relationship between long work hours and flu vaccinations and routine check-ups. For the analysis of the relationship between long work hours and dental checkups, the study cohort was further limited to workers having dental insurance. For that analysis there was a total of 26,997 eligible distinct respondents who provided a total of 42,369 responses. For the analysis of long work hours in relation to obtaining a mammogram, the study population was limited to women aged 50–64 who did not report having breast cancer. There were 3337 eligible women and the number of responses was also the same (3337), because the recommended mammography frequency for those women is once every two years. Thus, there was only one assessment of that outcome made, that is, at the end of the two-year panel period. Similarly, there was also only one assessment for cervical cancer screening, which determined whether the eligible women had a Pap smear during the past three years. The study population for that analysis consisted of 8340 eligible women aged 21–64 who did not report having a hysterectomy or cervical cancer. Independent variable Eligible cohort members were asked about how many hours they usually worked per week for their current main job and any current secondary jobs. The usual work hours per week were assessed five times during the two-year panel period; at the beginning of the first year, part way through the first year, at the end of the first year (which also was the beginning of the second year), part way through the second year, and at the end of the second year. Using these assessments, a mean value of work hours per week was calculated for the first year and a separate value for the second year. Each of those values was used as the independent variable in analyses involving annual flu-vaccinations, annual routine check-ups, and biennial dental checkups. For the analysis involving mammograms and Pap smears, the mean number of work hours across all five assessments was used as the independent variable in the regression model. Statistical analysis
Selected preventive health services The MEPS database contained information for five types of preventive health services that constituted the outcome variables used in this study: 1) an annual flu vaccination, 2) an annual routine check-up, 3) dental checkups at least twice a year, 4) a biennial mammogram for women aged 50–64, and 5) a Pap smear every three years for women aged 21–64. The age range and the frequency of breast cancer and cervical cancer screening were determined based on guidelines from the U.S. Preventive Services Task Force (USPSTF) (Moyer, 2012; USPSTF, 2009). Study population The study population was defined as all full-time workers aged 18 through 64 having private health insurance. We confined the study population to those covered by private health insurance, so as to control for the significant effect on access to services that arises between insured and uninsured individuals, and among the various types of health insurance in the U.S. Workers were considered working “full time” if they reported working at least 35 h per week in all the survey rounds during the study period. MEPS uses an overlapping panel design. Every year a new panel is selected and assessments are made across a two-year panel duration. Depending on the nature of the specific question being asked, it is possible that multiple interim assessments may be made within a panel period or even within a particular year. In cases where multiple responses were made within a particular year, the responses were averaged so that there was one mean value for the first year of the panel and another distinct average value for the second year assessment. Using these selection criteria, there were a total of 61,034 responses available for the analysis among the 38,108 distinct individuals who responded in either the first year or second year of the panel. Some individuals responded in both years and contributed two responses. Although two responses from one person are not independent from each other, the U.S. Agency for Healthcare Research and Quality (AHRQ) suggested that when pooling multiple years of MEPS
Multivariable logistic regression analyses were performed separately for each of the four outcome variables, measured in binary form (1 = received a preventive health services; 0 = did not receive the service). The average hours per week for each employee was the main predictor variable. All the regression models were adjusted for age, gender, race, education, family income, self-perceived physical health status, self-perceived mental health status, size of employer, occupation, metropolitan statistical area status, and the year of data collection. Stratum, cluster and adjusted person-level survey weight variables were used in the regression to account for the survey design and generate nationally representative estimates. In the analyses, the exposure variable was treated categorically, with 35–40 h per week as the referent category, and “long-hour” exposure categories of 41–50, 51–60, and over 60 h per week. SAS Statistical Software, version 9.2 was used to perform the multivariable logistic regression analyses.
Results Table 1 summarizes the frequencies and percentages of workers' demographic, socioeconomic and health characteristics. The mean age of respondents was 43.0 years old. The majority of the workers were male (55.7%) and white (82.7%). On average, respondents worked 45.7 h per week during the study period. Nearly half (43.1%) of them averaged working over 40 h per week, and 6.2%, on average, worked more than 60 h per week. Table 2 summarizes the percentage of workers in each of the demographic, socioeconomic, health and work-hour categories that received each kind of recommended preventive health service. Overall, most women (86.8%) received a biennial mammogram and a pap smear every three years (94.7%). Additionally, the majority (61.5%) of respondents received an annual routine check-up, and 53.2% had dental check-
X. Yao et al. / Preventive Medicine 74 (2015) 55–58 Table 1 Demographic characteristics of sample, MEPS data, 2002–2010 (N = 61,034). Characteristic Age 18–34 35–49 50–64 Gender Male Female Race White Non-white Education High school and below College degree and above Family income Poor/near poor/low income Middle income High income Self-reported physical health status Poor/fair/good Very good Excellent Self-reported mental health status Poor/fair/good Very good Excellent Employer workforce size 1–49 50–100 101–500 501+ Metropolitan statistical area Yes No Average weekly work hours 35–40 41–50 51–60 N60
Frequency
Weighted %
14,969 26,651 19,414
26.0 42.1 31.9
33,124 27,910
55.7 44.3
46,564 14,470
82.7 17.3
39,945 21,089
61.3 38.7
7177 21,291 32,566
8.0 31.9 60.1
20,082 25,089 15,862
30.2 42.0 27.8
13,400 22,260 25,373
20.5 36.7 42.8
23,567 9335 13,367 11,424
41.1 16.1 23.1 19.7
51,395 9639
15.5 84.5
36,804 14,234 6436 3560
56.9 25.5 11.4 6.2
ups at least twice a year. However, only 29.5% of respondents reported obtained an annual flu vaccination. As indicated in Table 3, respondents working more than 60 h per week had significantly lower odds of getting dental check-ups and mammograms than those working 35–40 h per week. Working 51–60 h per week was also related to lower likelihood of receiving a Pap smear. In contrast, people working more than 60 h per week were generally more likely to get a routine check-up than those working 35–40 weekly. No association was observed between work hours and the likelihood of getting a flu vaccination. Conclusions and discussion Previous studies have looked at many kinds of barriers to preventive care. Our study adds to this body of literature by finding that time pressure from work might also be an important barrier for people, impeding their ability to obtain recommended preventive health services, particularly breast cancer screening, cervical cancer screening and routine dental check-ups. The effect was especially great with respect to obtaining a mammogram: women working over 60 h per week were only half as likely to receive a mammogram every two years, compared to those working less than 60 h per week. While these results are intriguing, the findings should be interpreted cautiously. Working long (N 60) hours was actually found to increase the odds of receiving a routine check-up, contrary to the initial hypothesis. A possible explanation for this unexpected finding is that in some occupations and industries where long-hour schedules are prevalent (e.g. transportation and health care), employers have mandatory requirements for obtaining initial and periodic medical
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Table 2 Weighted percent (%) of workers receiving each of five types of preventive health services, 2002–2010.
Overall Average weekly work hours 35–40 41–50 51–60 N60 Age 18–34 35–49 50–64 Gender Male Female Race White Non-white Education High school and below College degree and above Family income Poor/near poor/low income Middle income High income Self-reported physical health status Poor/fair/good Very good Excellent Self-reported mental health status Poor/fair/good Very good Excellent Employer workforce size 1–49 50–100 101–500 501+ Metropolitan statistical area Yes No
Flu Routine vaccination check-up
Dental Mammogram Pap check-up smear
29.5
61.5
53.2
86.8
94.7
30.1 29.2 28.9 27.2
62.5 59.4 60.7 62.8
52.2 55.8 54.4 48.8
86.6 89.0 86.7 78.0
94.3 96.0 93.1 95.4
21.2 26.4 40.4
49.8 59.6 73.5
47.9 53.0 58.1
NA NA 86.8
95.9 95.0 93.0
25.3 34.8
54.8 69.9
49.4 57.9
NA 86.8
NA 94.7
30.3 26.0
60.4 66.7
55.3 44.0
86.5 88.9
94.5 95.5
26.5
60.1
46.8
84.6
93.4
34.3
63.7
63.1
90.9
96.3
23.3
54.9
32.8
80.6
91.1
26.1 32.2
58.1 64.2
44.2 60.1
83.5 88.8
93.2 95.9
32.5 29.3 26.6
67.6 60.9 55.9
46.2 55.1 58.0
85.2 87.5 88.2
93.9 94.5 95.7
30.4 30.2 28.5
64.0 62.0 59.9
44.8 54.3 56.2
85.4 87.2 87.4
94.1 93.7 95.8
24.2 29.5 33.6 36.4
59.0 61.1 62.6 65.2
51.7 52.4 53.4 56.7
86.6 88.3 87.9 84.9
93.9 95.9 94.8 95.2
29.3 31.0
61.8 59.8
54.0 48.5
87.6 83.1
95.0 92.9
examinations, which might counteract the hypothesized restrictive effect of working long hours on the use of routine check-ups. For cervical cancer screening, the effect was only seen when comparing 51–60 h per week to 35–40 h per week. No such association was found for women working more than 60 h per week. One reason might be that working women with private health insurance have very high rates of complying with the cervical cancer screening guideline (over 90%). Therefore, the number of women who did not receive a Pap smear within the past three years was relatively small in the over 60 h per week group. The small sample size in that group limited the ability to detect statistically significant results. There was no observed association between working long hours and getting a flu vaccination. Receiving a flu vaccination is relatively convenient and only requires a small amount of time, while dental check-ups and cancer screening are available in fewer locations and generally require making an appointment and taking the time to complete the office visit. Also, workers can receive a flu shot after regular work hours and on weekends, while considerably fewer providers offer dental care or cancer screening after regular work hours.
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X. Yao et al. / Preventive Medicine 74 (2015) 55–58
Table 3 The association between average weekly work hours and obtaining care for each of five preventive health services; adjusted regression results, odds ratios and 95% confidence intervals. Average weekly work hours
Flu vaccination
Routine check-up
Dental check-up
Mammogram
Pap smear
35–40 41–50 51–60 N60
– 0.98 (0.92–1.04) 0.98 (0.90–1.07) 0.92 (0.83–1.02)
– 0.96 (0.91–1.02) 1.01 (0.94–1.09) 1.15⁎⁎ (1.05–1.27)
– 1.03 (0.95–1.11) 0.96 (0.88–1.05) 0.81⁎⁎⁎ (0.72–0.91)
– 1.19 (0.88–1.62) 0.92 (0.61–1.39) 0.47⁎⁎⁎ (0.31–0.73)
– 1.24 (0.92, 1.67) 0.67⁎ (0.46, 0.96) 1.06 (0.60, 1.88)
⁎ p b 0.05. ⁎⁎ p b 0.01. ⁎⁎⁎ p b 0.001.
Because this study utilized secondary data, we were not able to follow up with these workers to examine whether the abovementioned reasons correctly explained the observed inconsistency in the preventive health behaviors. Future research is needed to explore how people make decisions for receiving different preventive health services when they work long hours. A deeper understanding of this decision making process would shed light on how the behaviors could be changed.
Limitations As with any survey based on self-reported information, there may be inaccuracies related to recall errors or misclassification in subjects' responses. Also, the measure of work hours used in the analyses was the average of the number of work hours reported in each round of MEPS (based on a maximum of 5 estimations, over two years). This method creates an inherent limitation on accurately capturing the precise exposure to long work hours. For example, a person who reported working an average of 50 h per week during each of the three rounds in a particular year, and another person who reported 40, 40, and 70 h per week during that year would both have the same number of average work hours used in the regression equation. The first person could be said to have had a chronic exposure to extensive work hours, while the second person experienced a temporary sharp increase in work hours during the third round in that year. It is not known whether or not the two patterns of exposures result in the same net effect. However, in general, most full-time workers' average weekly hours remained relatively stable during the study period, so it is unlikely that this limitation would have had a major impact on the results. Another limitation of the study is that we limited to workers with private health insurance. Therefore, the findings might not be generalized to low-income workers covered by Medicaid. The findings of this study suggest that time pressure from work creates barriers for individuals to receive particular preventive health services, such as cervical cancer screening, breast cancer screening and dental check-ups. Health care practitioners, especially those in primary care settings, should be aware of this distinctive source of barriers to accessing healthcare. Patients in occupations where long-hour schedules are prevalent could be targeted for health promotion programs. Some potential components of such programs may include health education regarding the importance and recommended frequency of preventive care, how to manage time and stress, and the location and contact information of nearby health care facilities. Providing care after regular work hours or on weekends may also help address the difficulties facing by people who are under a lot of time pressure from work (Coles et al., 2010). Workplace health promotion programs may be a particularly appropriate strategy for improving the use of preventive services. During the past several decades, there has been a growing interest in workplace wellness programs among employers (Goetzel and Ozminkowski, 2008). Typically, health promotion efforts include health education, screening of risk factors, and incentives for modifying health behaviors. When appropriately implemented, these workplace programs show great potential for promoting employees' health and generating cost savings for the employer (Baicker et al., 2010; Merrill et al., 2011). Our
study suggests that health promotion programs need to give special attention to employees working long-hour schedules. For example, employers could provide customized educational materials for addressing the unique challenges that workers face, and identifying ways of potentially alleviating sources of time pressure, such as offering flexible schedules or better transportation options (e.g., ride sharing). Work-life counseling offered through employee assistance programs or through private counselors might enable many employees to develop effective strategies for improving work-life balance and facilitating adherence to the recommended preventive health services (Goetzel and Ozminkowski, 2008). Conflict of interest None of the authors have any conflicts of interest, or financial disclosures.
References Agency for Healthcare Research and Quality, 2009a. MEPS HC-036: 1996–2007 pooled estimation file. Available from: http://meps.ahrq.gov/mepsweb/data_stats/download_ data/pufs/h36/h36u07doc.shtml [Accessed 1/26/2015]. Agency for Healthcare Research and Quality, 2009b. Survey background. Available from: http://meps.ahrq.gov/mepsweb/about_meps/survey_back.jsp ([Accessed 1/26/2015]). Baicker, K., Cutler, D., Song, Z., 2010. Workplace wellness programs can generate savings. Health Aff. 29 (2), 304–311. Coles, R., Watkins, F., Swami, V., Jones, S., Woolf, S., Stanistreet, D., 2010. What men really want: a qualitative investigation of men's health needs from the Halton and St Helens Primary Care Trust men's health promotion project. Br. J. Health Psychol. 15 (4), 921–939. Current Population Survey (CPS), 2013. Table 19: Persons at Work in Agriculture and Nonagricultural Industries by Hours of Work. Bureau of Labor Statistics (Available from: http://www.bls.gov/cps/cpsaat19.htm [Accessed 1/26/2015]). DeVoe, J.E., Fryer, G.E., Phillips, R., Green, L., 2003. Receipt of preventive care among adults: insurance status and usual source of care. Am. J. Public Health 93 (5), 786–791. DuBard, A.C., Gizlice, Z., 2008. Language spoken and differences in health status, access to care, and receipt of preventive services among US Hispanics. Am. J. Public Health 98 (11), 2021–2028. Fell, D.B., Kephart, G., Curtis, L.J., et al., 2007. The relationship between work hours and utilization of general practitioners in four Canadian provinces. Health Serv. Res. 42 (4), 1483–1498. Goetzel, R.Z., Ozminkowski, R.J., 2008. The health and cost benefits of work site healthpromotion programs. Annu. Rev. Public Health 29, 303–323. McGlynn, E.A., Asch, S.M., Adams, J., et al., 2003. The quality of health care delivered to adults in the United States. N. Engl. J. Med. 348 (26), 2635–2645. Merrill, R.M., Aldana, S.G., Garrett, J., Ross, C., 2011. Effectiveness of a workplace wellness program for maintaining health and promoting healthy behaviors. J. Occup. Environ. Med. 53 (7), 782–787. Moyer, V.A., 2012. Screening for cervical cancer: U.S. Preventive Services Task Force recommendation statement. Ann. Intern. Med. 156, 880–891. Østbye, T., Yarnall, K., Krause, K.M., Pollak, K.I., Gradison, M., Michener, J., 2005. Is there time for management of patients with chronic diseases in primary care? Ann. Fam. Med. 3 (3), 209–214. Sato, K., Yamazaki, S., Hayashino, Y., et al., 2011. Associations between hours worked, symptoms and health resource utilization among full-time male Japanese workers. J. Occup. Health 53, 197–204. Shaw, S.J., Huebner, C., Armin, J., Orzech, K., Vivian, J., 2009. The role of culture in health literacy and chronic disease screening and management. J. Immigr. Minor. Health 11 (6), 460–467. U.S. Preventive Services Task Force (USPSTF), 2009. Screening for breast cancer: recommendation statement. Ann. Intern. Med. 151, 716–726. Wellstood, K., Wilson, K., Eyles, J., 2006. ‘Reasonable access’ to primary care: assessing the role of individual and system characteristics. Health Place 12 (2), 121–130.
Contents lists available at ScienceDirect
Preventive Medicine journal homepage: www.elsevier.com/locate/ypmed
Does time pressure create barriers for people to receive preventive health services? Xiaoxi Yao a,⁎, Allard E. Dembe b, Thomas Wickizer c, Bo Lu d a
Mayo Clinic, Center for the Science of Health Care Delivery, USA The Ohio State University, College of Public Health, Center for Health Outcomes, Policy and Evaluation Studies, USA The Ohio State University, College of Public Health, Division of Health Services, Management and Policy, USA d The Ohio State University, College of Public Health, Division of Biostatistics, USA b c
a r t i c l e
i n f o
Available online 12 March 2015 Keywords: Time pressure Preventive health services Mammogram Pap smear Cancer screening Dental check-up Flu vaccination Work hours Overtime
a b s t r a c t Objective. Regular use of recommended preventive health services can promote good health and prevent disease. However, individuals may forgo obtaining preventive care when they are busy with competing activities and commitments. This study examined whether time pressure related to work obligations creates barriers to obtaining needed preventive health services. Methods. Data from the 2002–2010 Medical Expenditure Panel Survey (MEPS) were used to measure the work hours of 61,034 employees (including 27,910 females) and their use of five preventive health services (flu vaccinations, routine check-ups, dental check-ups, mammograms and Pap smear). Multivariable logistic regression analyses were performed to test the association between working hours and use of each of those five services. Results. Individuals working long hours (N 60 per week) were significantly less likely to obtain dental checkups (OR = 0.81, 95% CI: 0.72–0.91) and mammograms (OR = 0.47, 95% CI: 0.31–0.73). Working 51–60 h weekly was associated with less likelihood of receiving Pap smear (OR = 0.67, 95% CI: 0.46–0.96). No association was found for flu vaccination. Conclusions. Time pressure from work might create barriers for people to receive particular preventive health services, such as breast cancer screening, cervical cancer screening and dental check-ups. Health practitioners should be aware of this particular source of barriers to care. © 2015 Elsevier Inc. All rights reserved.
Introduction Regular use of preventive health services has been shown to be an effective way of promoting good health and avoiding disease. However, the inadequate utilization of preventive services has been consistently documented. For example, Americans only receive about half of the recommended preventive health services (McGlynn et al., 2003). In the past, many Americans had difficulty obtaining preventive care because of not having health insurance coverage or having limited health insurance benefits. Under the Patient Protection and Affordable Care Act (ACA), beginning in 2014, nearly everyone is required to be covered by health insurance and most health insurance plans cover a set of important preventive health services at no cost to patients. Although concerns over the cost of care may still present a barrier to care for many people, exploring and understanding the effect of nonfinancial barriers to obtaining preventive health services is especially important in the U.S., as well as in many other countries. ⁎ Corresponding author at: Research Fellow, Center for the Science of Health Care Delivery, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA. E-mail address: [email protected] (X. Yao).
http://dx.doi.org/10.1016/j.ypmed.2015.03.008 0091-7435/© 2015 Elsevier Inc. All rights reserved.
Individuals may face many kinds of non-financial barriers to care, including language and cultural issues (Shaw et al., 2009; DuBard and Gizlice, 2008), having no usual source of care (DeVoe et al., 2003), constraints on physician time, and difficulty in securing transportation to care facilities (Østbye et al., 2005). Individuals may also forgo obtaining health care because they believe it is not important, or because they are busy with other competing activities and commitments. This might be a particularly relevant issue for people who have a considerable amount of time pressure in their lives. Many working adults spend significantly more time at their jobs than at performing other daily activities. In the U.S., 33% of full-time employees work over 40 h per week, and 9% work at least 60 h per week (CPS, 2013). Because work is such an important component in people's lives, time pressure from work can have a significant impact on workers' ability to meet various personal, domestic and civic obligations. Finding time to attend to health care needs may be particularly challenging, because of the complexities in scheduling appointments, locating appropriate medical providers, and dealing with insurance issues. Additionally, it may be especially difficult for employed persons to take time away from their jobs, since working hours often overlap with the hours available at the offices of healthcare providers. Even if an individual makes time to seek treatment for an
56
X. Yao et al. / Preventive Medicine 74 (2015) 55–58
acute illness episode, he/she may be less likely to find time to obtain discretionary preventive health services, such as routine check-ups, immunizations, and cancer screening. There has been only limited research conducted examining time pressure as a potential barrier to care. A recent Japanese study found that men working long hours (N 250 per month) made about half as many physician visits as those working fewer (100–200) hours per month (Sato et al., 2011). A 2007 Canadian study found similar results indicating a statistically significant inverse association between patients' long work hours (N 45 h per week) and utilization of general practitioner services (Fell et al., 2007). These findings are consistent with a qualitative study in Canada, which found that work responsibilities and work time were the most commonly mentioned individual barriers to accessing primary care (Wellstood et al., 2006). Another recent qualitative study of British men reported that long working hours often interfered with their ability to “take care of their health” (Coles et al., 2010). The aforementioned studies suggest that time pressure from work may be an important barrier to accessing health care. However, to date, there is only a sparse amount of research to demonstrate this connection, and none of the available studies has focused specifically on preventive health services. Additionally, the available data in this area is exclusively from countries outside of the U.S. This study therefore aims to determine the extent to which time pressure from work creates a barrier to accessing specific preventive health services among the U.S. working population. Materials and methods Data source This study utilizes data from the 2002–2010 Medical Expenditure Panel Survey (MEPS). MEPS is a nationally representative survey that collects information about health care use, expenditures, sources of payment, health insurance, and health status for the U.S. civilian non-institutionalized population (AHRQ, 2009b). Specific variables germane to this study were obtained from the MEPS database including demographic and socio-economic characteristics, health status, employment history, and health care utilization information.
data, researchers should keep all responses in the analysis, and adjust personlevel survey weights to generate nationally representative estimates (AHRQ, 2009a). The aforementioned sample population pertains to analyses of the relationship between long work hours and flu vaccinations and routine check-ups. For the analysis of the relationship between long work hours and dental checkups, the study cohort was further limited to workers having dental insurance. For that analysis there was a total of 26,997 eligible distinct respondents who provided a total of 42,369 responses. For the analysis of long work hours in relation to obtaining a mammogram, the study population was limited to women aged 50–64 who did not report having breast cancer. There were 3337 eligible women and the number of responses was also the same (3337), because the recommended mammography frequency for those women is once every two years. Thus, there was only one assessment of that outcome made, that is, at the end of the two-year panel period. Similarly, there was also only one assessment for cervical cancer screening, which determined whether the eligible women had a Pap smear during the past three years. The study population for that analysis consisted of 8340 eligible women aged 21–64 who did not report having a hysterectomy or cervical cancer. Independent variable Eligible cohort members were asked about how many hours they usually worked per week for their current main job and any current secondary jobs. The usual work hours per week were assessed five times during the two-year panel period; at the beginning of the first year, part way through the first year, at the end of the first year (which also was the beginning of the second year), part way through the second year, and at the end of the second year. Using these assessments, a mean value of work hours per week was calculated for the first year and a separate value for the second year. Each of those values was used as the independent variable in analyses involving annual flu-vaccinations, annual routine check-ups, and biennial dental checkups. For the analysis involving mammograms and Pap smears, the mean number of work hours across all five assessments was used as the independent variable in the regression model. Statistical analysis
Selected preventive health services The MEPS database contained information for five types of preventive health services that constituted the outcome variables used in this study: 1) an annual flu vaccination, 2) an annual routine check-up, 3) dental checkups at least twice a year, 4) a biennial mammogram for women aged 50–64, and 5) a Pap smear every three years for women aged 21–64. The age range and the frequency of breast cancer and cervical cancer screening were determined based on guidelines from the U.S. Preventive Services Task Force (USPSTF) (Moyer, 2012; USPSTF, 2009). Study population The study population was defined as all full-time workers aged 18 through 64 having private health insurance. We confined the study population to those covered by private health insurance, so as to control for the significant effect on access to services that arises between insured and uninsured individuals, and among the various types of health insurance in the U.S. Workers were considered working “full time” if they reported working at least 35 h per week in all the survey rounds during the study period. MEPS uses an overlapping panel design. Every year a new panel is selected and assessments are made across a two-year panel duration. Depending on the nature of the specific question being asked, it is possible that multiple interim assessments may be made within a panel period or even within a particular year. In cases where multiple responses were made within a particular year, the responses were averaged so that there was one mean value for the first year of the panel and another distinct average value for the second year assessment. Using these selection criteria, there were a total of 61,034 responses available for the analysis among the 38,108 distinct individuals who responded in either the first year or second year of the panel. Some individuals responded in both years and contributed two responses. Although two responses from one person are not independent from each other, the U.S. Agency for Healthcare Research and Quality (AHRQ) suggested that when pooling multiple years of MEPS
Multivariable logistic regression analyses were performed separately for each of the four outcome variables, measured in binary form (1 = received a preventive health services; 0 = did not receive the service). The average hours per week for each employee was the main predictor variable. All the regression models were adjusted for age, gender, race, education, family income, self-perceived physical health status, self-perceived mental health status, size of employer, occupation, metropolitan statistical area status, and the year of data collection. Stratum, cluster and adjusted person-level survey weight variables were used in the regression to account for the survey design and generate nationally representative estimates. In the analyses, the exposure variable was treated categorically, with 35–40 h per week as the referent category, and “long-hour” exposure categories of 41–50, 51–60, and over 60 h per week. SAS Statistical Software, version 9.2 was used to perform the multivariable logistic regression analyses.
Results Table 1 summarizes the frequencies and percentages of workers' demographic, socioeconomic and health characteristics. The mean age of respondents was 43.0 years old. The majority of the workers were male (55.7%) and white (82.7%). On average, respondents worked 45.7 h per week during the study period. Nearly half (43.1%) of them averaged working over 40 h per week, and 6.2%, on average, worked more than 60 h per week. Table 2 summarizes the percentage of workers in each of the demographic, socioeconomic, health and work-hour categories that received each kind of recommended preventive health service. Overall, most women (86.8%) received a biennial mammogram and a pap smear every three years (94.7%). Additionally, the majority (61.5%) of respondents received an annual routine check-up, and 53.2% had dental check-
X. Yao et al. / Preventive Medicine 74 (2015) 55–58 Table 1 Demographic characteristics of sample, MEPS data, 2002–2010 (N = 61,034). Characteristic Age 18–34 35–49 50–64 Gender Male Female Race White Non-white Education High school and below College degree and above Family income Poor/near poor/low income Middle income High income Self-reported physical health status Poor/fair/good Very good Excellent Self-reported mental health status Poor/fair/good Very good Excellent Employer workforce size 1–49 50–100 101–500 501+ Metropolitan statistical area Yes No Average weekly work hours 35–40 41–50 51–60 N60
Frequency
Weighted %
14,969 26,651 19,414
26.0 42.1 31.9
33,124 27,910
55.7 44.3
46,564 14,470
82.7 17.3
39,945 21,089
61.3 38.7
7177 21,291 32,566
8.0 31.9 60.1
20,082 25,089 15,862
30.2 42.0 27.8
13,400 22,260 25,373
20.5 36.7 42.8
23,567 9335 13,367 11,424
41.1 16.1 23.1 19.7
51,395 9639
15.5 84.5
36,804 14,234 6436 3560
56.9 25.5 11.4 6.2
ups at least twice a year. However, only 29.5% of respondents reported obtained an annual flu vaccination. As indicated in Table 3, respondents working more than 60 h per week had significantly lower odds of getting dental check-ups and mammograms than those working 35–40 h per week. Working 51–60 h per week was also related to lower likelihood of receiving a Pap smear. In contrast, people working more than 60 h per week were generally more likely to get a routine check-up than those working 35–40 weekly. No association was observed between work hours and the likelihood of getting a flu vaccination. Conclusions and discussion Previous studies have looked at many kinds of barriers to preventive care. Our study adds to this body of literature by finding that time pressure from work might also be an important barrier for people, impeding their ability to obtain recommended preventive health services, particularly breast cancer screening, cervical cancer screening and routine dental check-ups. The effect was especially great with respect to obtaining a mammogram: women working over 60 h per week were only half as likely to receive a mammogram every two years, compared to those working less than 60 h per week. While these results are intriguing, the findings should be interpreted cautiously. Working long (N 60) hours was actually found to increase the odds of receiving a routine check-up, contrary to the initial hypothesis. A possible explanation for this unexpected finding is that in some occupations and industries where long-hour schedules are prevalent (e.g. transportation and health care), employers have mandatory requirements for obtaining initial and periodic medical
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Table 2 Weighted percent (%) of workers receiving each of five types of preventive health services, 2002–2010.
Overall Average weekly work hours 35–40 41–50 51–60 N60 Age 18–34 35–49 50–64 Gender Male Female Race White Non-white Education High school and below College degree and above Family income Poor/near poor/low income Middle income High income Self-reported physical health status Poor/fair/good Very good Excellent Self-reported mental health status Poor/fair/good Very good Excellent Employer workforce size 1–49 50–100 101–500 501+ Metropolitan statistical area Yes No
Flu Routine vaccination check-up
Dental Mammogram Pap check-up smear
29.5
61.5
53.2
86.8
94.7
30.1 29.2 28.9 27.2
62.5 59.4 60.7 62.8
52.2 55.8 54.4 48.8
86.6 89.0 86.7 78.0
94.3 96.0 93.1 95.4
21.2 26.4 40.4
49.8 59.6 73.5
47.9 53.0 58.1
NA NA 86.8
95.9 95.0 93.0
25.3 34.8
54.8 69.9
49.4 57.9
NA 86.8
NA 94.7
30.3 26.0
60.4 66.7
55.3 44.0
86.5 88.9
94.5 95.5
26.5
60.1
46.8
84.6
93.4
34.3
63.7
63.1
90.9
96.3
23.3
54.9
32.8
80.6
91.1
26.1 32.2
58.1 64.2
44.2 60.1
83.5 88.8
93.2 95.9
32.5 29.3 26.6
67.6 60.9 55.9
46.2 55.1 58.0
85.2 87.5 88.2
93.9 94.5 95.7
30.4 30.2 28.5
64.0 62.0 59.9
44.8 54.3 56.2
85.4 87.2 87.4
94.1 93.7 95.8
24.2 29.5 33.6 36.4
59.0 61.1 62.6 65.2
51.7 52.4 53.4 56.7
86.6 88.3 87.9 84.9
93.9 95.9 94.8 95.2
29.3 31.0
61.8 59.8
54.0 48.5
87.6 83.1
95.0 92.9
examinations, which might counteract the hypothesized restrictive effect of working long hours on the use of routine check-ups. For cervical cancer screening, the effect was only seen when comparing 51–60 h per week to 35–40 h per week. No such association was found for women working more than 60 h per week. One reason might be that working women with private health insurance have very high rates of complying with the cervical cancer screening guideline (over 90%). Therefore, the number of women who did not receive a Pap smear within the past three years was relatively small in the over 60 h per week group. The small sample size in that group limited the ability to detect statistically significant results. There was no observed association between working long hours and getting a flu vaccination. Receiving a flu vaccination is relatively convenient and only requires a small amount of time, while dental check-ups and cancer screening are available in fewer locations and generally require making an appointment and taking the time to complete the office visit. Also, workers can receive a flu shot after regular work hours and on weekends, while considerably fewer providers offer dental care or cancer screening after regular work hours.
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X. Yao et al. / Preventive Medicine 74 (2015) 55–58
Table 3 The association between average weekly work hours and obtaining care for each of five preventive health services; adjusted regression results, odds ratios and 95% confidence intervals. Average weekly work hours
Flu vaccination
Routine check-up
Dental check-up
Mammogram
Pap smear
35–40 41–50 51–60 N60
– 0.98 (0.92–1.04) 0.98 (0.90–1.07) 0.92 (0.83–1.02)
– 0.96 (0.91–1.02) 1.01 (0.94–1.09) 1.15⁎⁎ (1.05–1.27)
– 1.03 (0.95–1.11) 0.96 (0.88–1.05) 0.81⁎⁎⁎ (0.72–0.91)
– 1.19 (0.88–1.62) 0.92 (0.61–1.39) 0.47⁎⁎⁎ (0.31–0.73)
– 1.24 (0.92, 1.67) 0.67⁎ (0.46, 0.96) 1.06 (0.60, 1.88)
⁎ p b 0.05. ⁎⁎ p b 0.01. ⁎⁎⁎ p b 0.001.
Because this study utilized secondary data, we were not able to follow up with these workers to examine whether the abovementioned reasons correctly explained the observed inconsistency in the preventive health behaviors. Future research is needed to explore how people make decisions for receiving different preventive health services when they work long hours. A deeper understanding of this decision making process would shed light on how the behaviors could be changed.
Limitations As with any survey based on self-reported information, there may be inaccuracies related to recall errors or misclassification in subjects' responses. Also, the measure of work hours used in the analyses was the average of the number of work hours reported in each round of MEPS (based on a maximum of 5 estimations, over two years). This method creates an inherent limitation on accurately capturing the precise exposure to long work hours. For example, a person who reported working an average of 50 h per week during each of the three rounds in a particular year, and another person who reported 40, 40, and 70 h per week during that year would both have the same number of average work hours used in the regression equation. The first person could be said to have had a chronic exposure to extensive work hours, while the second person experienced a temporary sharp increase in work hours during the third round in that year. It is not known whether or not the two patterns of exposures result in the same net effect. However, in general, most full-time workers' average weekly hours remained relatively stable during the study period, so it is unlikely that this limitation would have had a major impact on the results. Another limitation of the study is that we limited to workers with private health insurance. Therefore, the findings might not be generalized to low-income workers covered by Medicaid. The findings of this study suggest that time pressure from work creates barriers for individuals to receive particular preventive health services, such as cervical cancer screening, breast cancer screening and dental check-ups. Health care practitioners, especially those in primary care settings, should be aware of this distinctive source of barriers to accessing healthcare. Patients in occupations where long-hour schedules are prevalent could be targeted for health promotion programs. Some potential components of such programs may include health education regarding the importance and recommended frequency of preventive care, how to manage time and stress, and the location and contact information of nearby health care facilities. Providing care after regular work hours or on weekends may also help address the difficulties facing by people who are under a lot of time pressure from work (Coles et al., 2010). Workplace health promotion programs may be a particularly appropriate strategy for improving the use of preventive services. During the past several decades, there has been a growing interest in workplace wellness programs among employers (Goetzel and Ozminkowski, 2008). Typically, health promotion efforts include health education, screening of risk factors, and incentives for modifying health behaviors. When appropriately implemented, these workplace programs show great potential for promoting employees' health and generating cost savings for the employer (Baicker et al., 2010; Merrill et al., 2011). Our
study suggests that health promotion programs need to give special attention to employees working long-hour schedules. For example, employers could provide customized educational materials for addressing the unique challenges that workers face, and identifying ways of potentially alleviating sources of time pressure, such as offering flexible schedules or better transportation options (e.g., ride sharing). Work-life counseling offered through employee assistance programs or through private counselors might enable many employees to develop effective strategies for improving work-life balance and facilitating adherence to the recommended preventive health services (Goetzel and Ozminkowski, 2008). Conflict of interest None of the authors have any conflicts of interest, or financial disclosures.
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