Paid sick leave and preventive health care service use among U.S. working adults

Preventive Medicine 99 (2017) 58–62

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Preventive Medicine journal homepage: www.elsevier.com/locate/ypmed

Paid sick leave and preventive health care service use among U.S. working adults☆ LeaAnne DeRigne a,⁎, Patricia Stoddard-Dare b, Cyleste Collins b, Linda Quinn c a b c

School of Social Work, Florida Atlantic University, 777 Glades Road, Boca Raton, FL 33431, United States School of Social Work, Cleveland State University, 2121 Euclid Ave, Cleveland, OH 44115, United States Department of Mathematics, Cleveland State University, 2121 Euclid Ave, Cleveland, OH 44115, United States

a r t i c l e

i n f o

Article history: Received 17 October 2016 Received in revised form 12 December 2016 Accepted 30 January 2017 Available online 09 February 2017 Keywords: Sick leave policy Preventive health services Blood pressure Pap smears Flu shots Cholesterol Fasting blood sugar

a b s t r a c t Managing work and health care can be a struggle for many American workers. This paper explored the relationship between having paid sick leave and receiving preventive health care services, and hypothesized that those without paid sick leave would be less likely to obtain a range of preventive care services. In 2016, cross-sectional data from a sample of 13,545 adults aged 18–64 with current paid employment from the 2015 National Health Interview Survey (NHIS) were examined to determine the relationship between having paid sick leave and obtaining eight preventive care services including: (1) blood pressure check; (2) cholesterol check; (3) fasting blood sugar check; (4) having a flu shot; (5) having seen a doctor for a medical visit; (6) getting a Pap test; (7) getting a mammogram; (8) getting tested for colon cancer. Findings from multivariable logistic regressions, holding 10 demographic, work, income, and medical related variables stable, found respondents without paid sick leave were significantly less likely to report having used six of eight preventive health services in the last 12 months. The significant findings remained robust even for workers who had reported having been previously told they had risk factors related to the preventive services. These findings support the idea that without access to paid sick leave, American workers risk foregoing preventive health care which could lead to the need for medical care at later stages of disease progression and at a higher cost for workers and the American health care system as a whole. © 2017 Elsevier Inc. All rights reserved.

1. Introduction The benefits of preventive health care services include preventing illness and disease, slowing disease progression, and saving lives (Maciosek et al., 2010). It can also reduce health care expenses which topped $3 trillion in the United States in 2014 by keeping disease from progressing and becoming costlier to treat (Health expenditures, n.d.). Federal health care initiatives have embraced the increased use of preventive health care services for Americans (Health expenditures, n.d.; Yong et al., 2011; Healthy people 2020, n.d.). Nevertheless, The Centers on Disease Control and Prevention report that Americans use clinical preventive care at about half the recommended rate (CDC. Preventive health care, n.d.). Twenty million people have gained health insurance coverage through the Affordable Care Act (ACA) (Health insurance coverage ☆ Conflict of interest: There are no conflicts of interest for any of the authors of this article. There was no funding for this research from any source. No financial disclosures were reported by the authors of this paper. ⁎ Corresponding author. E-mail addresses: [email protected] (L. DeRigne), [email protected] (P. Stoddard-Dare), [email protected] (C. Collins), [email protected] (L. Quinn).

http://dx.doi.org/10.1016/j.ypmed.2017.01.020 0091-7435/© 2017 Elsevier Inc. All rights reserved.

and the affordable care act, 2010–2016, n.d.). Since 2010, the ACA eliminated the cost-sharing requirements for 15 preventive screenings recommended by the U.S. Preventive Services Task Force including those for blood pressure, cholesterol, diabetes, and certain cancer screenings as well as influenza vaccinations for all insured adults (Preventive services covered under the ACA, n.d.). Despite this progress, barriers to accessing preventive health care services still exist. Managing both employment and health care can be a struggle for many working Americans. Of the 68% of the civilian workforce that has paid sick leave benefits, only 31% of part-time workers have access to them (Selected paid leave benefits, n.d.). Compared to 22 similarly developed countries, the United States is the only one that does not mandate employers to provide paid sick leave benefits or include paid sick leave in a universal social insurance plan (Heymann et al., 2010). Sick leave not only allows workers to self-quarantine when they are ill, but also gives them time away from work to see a doctor for health care. When sick leave is unpaid or unavailable, workers may not be able to afford time away from work to get timely and preventive medical care. In the U.S., legislative momentum is building toward providing sick leave benefits for more workers. Legislation mandating paid sick leave has passed in 7 states (Connecticut, California, Massachusetts, Oregon, Vermont, Arizona, Washington), 29 cities, two counties and

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Washington, DC to date (A Better Balance, n.d.). At the federal level, in 2015, the Obama Administration signed an executive order requiring any company that contracts with the federal government to provide a minimum of seven paid sick leave days (Weisman, 2015). Anderson's Health Behavior Model provides a theoretical framework for explaining health service use, and posits that behaviors can be predicted by analyzing predisposing, enabling and need factors (Kehrer et al., 1972). Predisposing factors are often measured using demographic characteristics including sex, age, race/ethnicity, and marital status. Enabling factors include socio-economic variables such as education, income, employment status and health insurance. Need factors are measured by general health status. The objective of this research was to examine the extent to which paid sick leave benefits are an enabling factor in explaining use of preventive health care services. While previous research has evaluated paid sick leave in relation to cancer screenings, outpatient care, and having seen a medical professional, this is the first study to analyze these variables using data collected after the implementation of the ACA (Peipins et al., 2012; Cook, 2011). We also include preventive care variables never before analyzed in relation to paid sick leave including blood pressure, cholesterol, diabetes, and flu vaccinations. Since more workers are insured, and the ACA mandates that private insurers cover routine preventive screenings at no cost to the insured, what other factors contribute to low rates of preventive care use? We test the hypothesis that workers without access to paid sick leave benefits will be less likely to get needed preventive health care services than workers with access to paid sick leave benefits after controlling for sociodemographic variables.

2. Methods 2.1. Data sources and analytic sample The NHIS has been conducted annually since 1957 and is managed by the National Center for Health Statistics (NCHS) which uses a multistage area probability design to produce a representative sample of noninstitutionalized U.S. households. The interviews, in which respondents are asked about an array of health topics, are conducted with households in person or by phone with a randomly selected adult household member using computer-assisted personal interviewing (CAPI). More information on the NHIS is available at http://www.cdc.gov/nchs/nhis/ about_nhis.htm. In 2016, the authors downloaded NHIS Public Release 2015 data from the National Center for Health Statistics (NCHS) website. The NHIS Basic Module contains the Family Core, Persons Core, and Sample Adult Core. The Family Core collects information on all family members in sampled households. The Sample Persons Core data are recorded for each person within each family. The Sample Adult Core data are recorded for one randomly selected adult within each family. By combining these data modules, and keeping the sample adult as the observational unit, data can be used from across the three datasets. The analytic sample of 13,545 adults is derived from the merged Sample Adult (n = 33,672) dataset. It includes adults aged 18–64 (excluded 1253 with age 65 or over) with current paid employment at the same job for at least one year. Respondents who reported that they work without pay, work in a family business, are self-employed, looking for work, or not working (excluded = 14.522) were excluded from the sample. Respondents that had missing data for an independent variable or any of the control variables were excluded (excluded = 1323). The paid sick leave variable used measures whether the worker has paid sick leave in their current job; however, the dependent variables used focuses on a previous 12-month time frame. To safeguard the validity of our measures, and assure we correctly classified workers paid sick leave status, we excluded those workers who had been employed for b 12 months (excluded = 3029).

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2.2. Measures The primary independent variable, paid sick leave, was measured using a self-reported response (y/n) to the question “Do you have paid sick leave on this MAIN job or business?” The dependent variables included in this analysis represented eight preventive health screenings and services recommended for adults by the U.S. Preventive Service Taskforce and covered by the ACA (Starfield et al., 2008; U.S. Preventive Services Task Force, n.d.; Preventive services covered under the ACA, n.d.). Questions asked respondents about receipt of the following services from a doctor, nurse, or other health professional, “during the past 12 months did you receive…” 1) a blood pressure check; 2) cholesterol check; 3) fasting test for high blood sugar or diabetes; 4) influenza vaccine (shot or flu mist); 5) mammogram (only females); 6) Pap smear or Pap test (only females); 7) any test for colon cancer (including blood stool tests, colonoscopy and sigmoidoscopy); and 8) have they seen or talked to a “general” doctor or health care provider about their health. Consistent with the Andersen model, three categories of ten theoretically relevant control variables related to the adult respondent's preventive health care service utilization were included. The three categories included 1) predisposing factors, 2) enabling factors, and 3) need factors. Variables that composed the predisposing factors included sex, age (in years), race/ethnicity (Hispanic, Non-Hispanic white, NonHispanic black, non-Hispanic other), and marital status (married/unmarried). Enabling factors included highest level of education (less than high school, high school degree or GED, some college), full-time work status (working ≥35 h in a previous week), total family income (ranging from less than $35,000 to greater than $100,000) and health insurance coverage (insured or uninsured). Need factors included having a limiting health condition (y/n), and health status (excellent/very good/good or fair/poor). Because some of the guidelines for covered preventive care services under the ACA are specific to sex, age, pre-existing conditions and/or have health conditions (e.g., being overweight or obese), our analysis took these factors into account. To explore the extent to which receipt of paid sick leave was related to preventive care services for respondents who were at higher risk, the analyses used additional controls for ever having “been told by a doctor or other health professional that…” the respondent had: hypertension, high cholesterol, diabetes or pre-diabetes, coronary heart disease, had a heart attack, any kind of heart condition or heart disease, angina, asthma, or a stroke. Analyses using the interactions of these specific variables with paid sick leave were performed but are not reported since they added little explanatory information. 2.3. Statistical analysis approach Eight separate multivariable logistic regressions were performed using paid sick leave as the primary independent variable to explain each of the outcome variables related to obtaining preventive medical services in the last year, and including each of the control variables listed above. Analysis were run separately for the entire analytic sample and for a subset of adults based on published guidelines regarding age, sex, weight and risk factors (see footnotes Table 2). All analyses were performed using SAS software (version 9.4) in the fall of 2016. 3. Results In the sample, a total of 8505 (62.8%) had paid sick leave benefits, and 5040 (37.2%) did not. The sample's full demographic profile is displayed in Table 1. Based on multivariable logistic regressions performed to examine the relationship between paid sick leave and obtaining preventive care services, for workers without paid sick leave, the odds were that

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Table 1 Sample characteristics (n = 13,545). Control variables

Outcome

n

%

Gender

Male Female Unmarried Married Less than high school High school graduate/GED Any college Hispanic Non-Hispanic white Non-Hispanic black Non-Hispanic other race ≥35 h/wk b35 h/wk Uninsured Insured Poor/fair Good/very good/excellent Yes No b$35 K $35 K–b$75 K $75 K–b$100 K $100 K and over

6763 6782 5878 7667 1178

49.9 50.1 43.4 56.6 8.7

2843

21.0

9524 2342 8553

70.3 17.5 63.8

1663

12.4

848

6.3

11,056 2489 1501 12,044 819 12,726

81.6 18.4 11.1 88.9 6.0 94.0

651 12,894 3232 4487 1967 3859

4.8 95.2 23.9 33.1 14.5 28.5

Marital status Education

Race and ethnicity

Full time work Insurance coverage Health status

Limiting health condition Family income

Odds ratioa,b Preventive not done (past 12 months)

n

OR

LCL

UCL

Nagelkerke R-square

Seen/talked to a general doctor Blood pressure checked by doctor/nurse/health professional Influenza shot or spray Cholesterol checked by doctor/nurse/health professionalc Fasting test for high blood sugar/diabetesd Test for colon cancere Pap smear/testf Mammogramg

13,321 1.19 1.09 1.30 15.6 13,211 1.30 1.17 1.45 24.9 13,256 1.61 1.47 1.75 14.3 11,291 1.40 1.27 1.54 25.6 6277

1.24 1.09 1.40 18.5

4385 6596 3848

1.04 0.88 1.23 3.5 1.23 1.10 1.39 7.4 1.09 0.93 1.28 9.2

LCL = lower confidence limit; OR = odds ratio; PSL = paid sick leave; UCL = upper confidence limit. Authors' analysis of data from CDC/NCHS, National Health Interview Survey, 2015. a The odds ratio (with 95% confidence interval) of not having the preventive done comparing adults without PSL to adults with PSL. b Model controls for age, sex, marital status, race/ethnicity, education, full/part work status, insurance coverage, family income, health status, whether adult has limiting health condition. c Includes only (males, aged 35+) or (males, aged 20–34, with a risk of heart disease) or (females, aged 45+) or (females, aged 20–44, with a risk of heart disease). d Includes only (adults told they have hypertension) or (BMI ≥ 25 and aged 40+). e Includes only adults aged 50+. f Includes only females, aged 18+. g Includes only females, aged 40+ yrs.

Mean Age (yrs) Family size Independent variable Paid sick leave Dependent variables Seen/talked to a general doctor, past 12 m (missing = 181) Blood pressure checked by doctor/nurse/health professional, past 12 m (missing = 294) Influenza shot or spray, past 12 m (missing = 246) Cholesterol checked by doctor/nurse/health professional, past 12 m (missing = 363) Fasting test for high blood sugar/diabetes, past 12 m (missing = 378) Test for colon cancer, past 12 m (missing = 5930) Pap smear/test, past 12 m (missing = 6938) Mammogram, past 12 m (missing = 8064)

Outcome Yes No Outcome Yes No Yes No Yes No Yes No Yes No Yes No Yes No Yes No

Std Dev 42.6 12.0 2.5 1.4 n % 8505 62.8 5040 37.2 n % 8576 64.2 4788 35.8 10,698 80.7 2553 19.3 5174 38.9 8125 61.1 7983 60.6 5199 39.4 5691 43.2 7476 56.8 1135 14.9 6480 85.1 3724 56.4 2883 43.6 2406 43.9 3075 56.1

Table 2 Logistic regressions comparing adults without paid sick leave to adults with paid sick leave (odds ratios).

Data source: CDC/NCHS, National Health Interview Survey, 2015.

they were significantly less likely to have accessed six of the eight preventive services in the last year (all p-values were b0.001) compared to workers with paid sick leave (see Table 2). These findings are after controlling for ten predisposing, enabling, and need control variables, and knowledge of previous medical conditions. Not having paid sick leave increased by 30% the odds that workers did not have a blood pressure check in the last 12 months, a 40% increase in the odds of not having had a cholesterol check, 24% of not having a fasting blood sugar check, 61% of not having received a flu vaccine, 19% of not having seen or talked to a doctor, and 23% of not having had a Pap smear. Paid sick leave was not a significant factor in having undergone two cancer-related screenings: for having had a colon cancer test or having had a mammogram in the last 12 months (p-values N 0.05). Adjusted risk percentages for each of the significant dependent variables are shown in Fig. 1. It is clear that after adjusting for all the control

variables, adults without paid sick leave have at least a 3.8% to as much as 9.7% higher risk of not obtaining preventive health services than adults with paid sick leave. The impact of paid sick leave was robust for analyses examining preventive care services for respondents who reported having previously been told by a doctor or other health professional that they had hypertension, high cholesterol, coronary heart disease, a heart attack, any kind of heart condition or heart disease, asthma, or stroke. The findings also did not vary when the analyses used the entire analytic sample rather than being limited according to the published recommended age, sex, and weight guidelines. 4. Discussion Three main findings emerge from this research. First, workers who lack paid sick leave are significantly less likely to have received preventive health care screenings in the last 12 months, even among those who had been previously told they have a condition which places them at higher medical risk. These findings demonstrate that even when insured adults are provided with free preventive screenings, paid sick leave is a significant factor associated with actually utilizing the screenings. Revisiting the Andersen model, these findings support the notion that paid sick leave is an important enabling factor with regard to utilizing preventive health care screenings. Without preventive screenings and/or early detection and intervention, conditions such as heart disease and stroke can develop from untreated high cholesterol and high blood pressure. Similarly, screening for blood sugar is important to detect, prevent and/or control diabetes, and has the potential to reduce premature mortality (CDC, n.d.; Piper et al., 2014). Additionally, treating these diseases after they develop, as opposed to intervening in earlier stages is an expensive endeavor with estimates suggesting an annual cost burden of $48.6 billion for hypertension and $245 billion for diabetes (CDC. 2014 national diabetes statistics report, n.d.; CDC. High blood pressure fact sheet, n.d.). Such costs should be considered carefully when weighing the costs and benefits of offering paid sick leave. This study illuminates the importance of the role paid sick leave plays in the lives of employees - without workplace support of health prevention,

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Fig. 1. Adjusted risk of NOT having these preventive services performed (when they are recommended).

workers are foregoing health screenings for conditions that could eventually threaten their ability to work. We also found that workers without paid sick leave are 1.6 times less likely to have received an influenza vaccination in the past 12 months: the public health implications of not receiving a flu vaccination are immense. For example, during the H1N1 outbreak of 2009 the CDC estimated that an additional seven million people were infected by those who did not stay home while they were sick, which resulted in 1500 additional deaths during the outbreak (CDC, 2010). This research adds to a growing body of work that indicates the importance of paid sick leave in the lives of America's workers. Past research has shown that workers without paid sick leave are more likely to delay and forego needed health care for both themselves and their family members and are less likely to have seen a medical professional in the last 12 months (DeRigne et al., 2016). Research has found a relationship between having paid sick leave and cancer screenings including clinical breast examinations, pap tests, colonoscopy and mammograms (Peipins et al., 2012; Wilson et al., 2014). Workers with paid sick leave have also been found to be more likely to use outpatient care (Cook, 2011). A recent study has found that children with mothers who lack paid sick leave are less likely to receive routine well child check-ups, dental checkups, flu shots, and obesity and vision screening (Shepherd-Banigan et al., 2016). In contrast to previous findings from Peipins et al. (2012) and Wilson, Wang & Stimpson (2014), this study found that colon cancer screenings and mammography were not significantly related to paid sick leave (Peipins et al., 2012; Wilson et al., 2014). There are several possible explanations for this finding. With regard to mammography, it might be that mammography services are becoming more widely available to workers at all levels and might not require paid sick time to due to increasing mobile and other types of innovative screening units for mammography (Peek and Han, 2007). Also, while the NHIS screening questions ask respondents to reflect on services they received within a 12-month time frame, mammograms are not necessarily indicated on 12-month cycle for all women, so it might be that the survey time frame fails to capture incidents of such screening. Similarly, with regard to colon cancer screening, the variables Peipin's et al. (2012)

examined included ever having a colonoscopy or fecal blood home test in a 10 year time period. In our study, the time frame for any colon cancer test was in the last 12 months (Peipins et al., 2012). Finally, one of the objectives of this study was to replicated the Peipins et al. (2012) study and Wilson et al. (2014) with additional exclusion criteria to our sample (being employed at the current job for at least 12 months) to ensure the time period since the respondent had the job matched the measured prevention service time frame, which the Peipins et al. (2012) study and Wilson et al. (2014) did not do. This strengthens our findings in comparison to previous research that did not know whether the respondent actually had sick leave during the time period they are being asked to recall or if they only had it at the time of the survey. Paid sick leave is more than simply a workplace benefit—it should properly be thought of as a public health policy. The two most common ways to offer paid sick leave are through mandating employer funded benefits or through a universal social insurance program funded through taxes. The Healthy Families Act, introduced in Congress in 2015, uses the employer funded model and would allow workers to earn up to seven days of paid sick leave if their employer has N15 employees and seven unpaid days for employers who have fewer than 15 (Delauro, n.d.). The bill introduced in both the House and the Senate has been referred to subcommittees but has seen no further action (Delauro, n.d.). Our findings add to the body of evidence that policy planners can use when assessing mandatory paid sick leave as a means of increasing access to preventive care. Our findings can also be used by medical professionals, health system planners, and human resource managers who may consider expansion of access to evening and weekend hours for appointments as well as mobile, community-based, and workplace health and wellness services. Such alternative approaches are important for people who don't have access to time off of work, and might be especially important in the administration of influenza vaccinations. This is particularly relevant for service related employees, food preparation workers and personal care assistance workers who have low access to paid sick leave coverage (14%, 19%, 25% respectively) (Selected paid leave benefits, n.d.; Institute for Women's Policy Research, n.d.). When these workers forego essential preventive health care, particularly influenza vaccination, public health is at risk.

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5. Limitations An important limitation to this research is that we cannot establish causal relationships between the variables using only one year of the NHIS data. Longitudinal data collected on the same individuals would help us to more fully understand people's preventive health care service use over time. A second limitation is that the NHIS relies on self-report data rather than actual medical records. However, research has found that recall within 12 months is not as susceptible to telescoping or memory decay as time periods beyond 12 months (Bhandari and Wagner, 2006). Self-report data is more accurate for rare, infrequent health care events (annual cancer screenings or flu shots) than for more routine and frequent outpatient visits (Bhandari and Wagner, 2006). A final limitation is that the findings cannot be generalized to workers who have not been employed at the same workplace for the previous 12 months. Future research should explore if this group is disproportionately represented among those who lack paid sick leave. Knowing about this population is particularly valuable because the Healthy Families Act doesn't allow workers to carry accrued sick leave to new employment. 6. Conclusion This research adds to a modest but important body of knowledge that stakeholders can consider when weighing the value of paid sick leave. Here we find workers without paid sick leave are less likely to receive needed preventive care. Future research should explore the costs and benefits of mandating universal access to paid sick leave in light of these findings. Transparency document The Transparency document associated with this article can be found, in the online version. Acknowledgments • LeaAnne DeRigne, Patricia Stoddard-Dare, Cyleste Collins, Linda Quinn all contributed to the authorship of this paper. • Conflicts of interest: none. • This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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