The Effects of Health Risk Assessments on Cafeteria Purchases:
Do New Information and Health Training Matter?
Mariana Carrera
Case Western Reserve University
Syeda A. Hasan
University of Illinois – Chicago
Silvia Prina
Case Western Reserve University
February 16, 2017
Abstract
Using a unique dataset combining health screenings data with cafeteria purchases and
nutritional content, we test whether the provision of tailored health information, via
health risk assessments, affects the eating behavior of hospital employees. We use an
event-study framework to estimate the effects of learning one’s cholesterol level on the
overall sample, on individuals with high cholesterol, and on individuals with previously
undiagnosed high cholesterol. We find a reduction in cafeteria spending in the latter two
groups which is consistently larger in the previously undiagnosed sample. While the
effects appear to persist over a period of five months, they are small and correspond at
most to one-third of the NIH recommended dietary change for persons with high
cholesterol. Also, these effects appear to be larger for medically-trained employees,
particularly physicians. This suggests that in a broader workforce population, health risk
assessments on their own might have smaller impacts on eating behavior.
JEL codes: I12, I18, D8
Keywords: health risk assessment, tailored information, high cholesterol, nutrition
_____________
We thank Eileen Seeholzer and Charles Thomas for providing us with the data and answering our many questions.
This paper benefited from comments from Carlos Chiapa, Silke Forbes, Anya Samek, Mark Votruba, and seminar
participants at the Health Services Research Seminar at the Center for Health Care Research and Policy at Case
Western Reserve University at the Health Services Research Seminar at the Center for Value-Based Care Research
at the Cleveland Clinic, and at the Center for Economic and Social Research (CESR) at the University of Southern
California. Alex Xu provided excellent research assistance. Carrera and Prina thank the Weatherhead School of
Management for generous research support.
1
1. Introduction
Many public health interventions involve the provision of health information with the aim of
inducing changes in behavior. But in several cases, such as the mandatory posting of calories on
restaurant menus and the distribution of “weight report cards” to parents of schoolchildren,
information provision appears to have only small effects on behavior. 1 One important challenge
in understanding these effects is the difficulty of identifying for whom the information is news.
A second challenge is dealing with heterogeneity in individuals’ understanding of the
information and the changes in behavior they should make. 2
Our study addresses these challenges in the context of the latest nationwide trend in health
information dissemination: workplace-sponsored health risk assessments (HRAs). Because
employers bear a large share of the health costs accrued by their employees, firms are
increasingly investing in frequent large-scale risk screenings in hopes of tracking and reducing
risk factors such as high cholesterol and high blood pressure in their employee populations.
Approximately half of workers in the United States were asked by their employers to complete a
HRA in 2012. 3 But despite their widespread adoption, little is known about HRAs’ effects on
health behavior (Oremus 2011, RAND 2013). Economic theory would suggest that the
individualized feedback on health risks provided by HRAs is most useful to employees who
currently face high risks, but are not yet aware of them. Fifteen percent of U.S. adults are
estimated to have undiagnosed high cholesterol, undiagnosed hypertension, or undiagnosed
diabetes, but we are not aware of any existing studies that measure HRAs’ effects on individuals
with these undiagnosed conditions. 4
1
Downs, Wisdom and Loewenstein (2015) provide a helpful review of the literature studying the effect of
nutritional information on eating behavior and test different ways of presenting nutritional information. The
provision of weight report cards to parents of schoolchildren has proven effective at improving parental knowledge
and shifting parental attitudes about child’s weight, but not at changing behaviors or children’s body mass index
(Chomitz et al. 2003; Grimmett et al. 2008; Kalich et al. 2008; Kubik et al. 2006; Prina and Royer 2014).
2
In a review of health information interventions in developing countries, Dupas (2011) discusses several studies
finding complementarities between information and education, which can make processing information easier.
3
A nationally representative employer survey conducted by RAND (2013) finds that approximately one-third of
firms with more than 50 employees conducted a health risk assessment in 2012. Because these programs are more
prevalent among larger firms, they estimate that more than half of U.S. workers were asked by their employer to
complete a health assessment in 2012 (RAND 2013). Of the firms conducting HRAs, 62% collected biometric
measures.
4
Based on NHANES (National Health and Nutrition Examination Survey) 1999-2006 data, as reported in NCHS
Data Brief #36 (April 2010) accessible at http://www.cdc.gov/nchs/data/databriefs/db36.pdf.
2
We study a sample of employees of a large regional hospital who took HRAs. Our setting and
data allow us to distinguish the HRA’s effect on behavior along two important dimensions: prior
awareness of the health risk, and whether or not they work in health-related occupations.
Physicians and nurses, for example, might take more actions in response to a new health risk
either because they are more familiar with its consequences or more knowledgeable about how to
reduce their risks.
To investigate whether HRAs lead to health behavior change, we assembled a unique dataset that
combines data from firm administrative records, workplace cafeteria transactions, and HRAs for
employees of a large regional hospital. Our ability to utilize individual-specific HRA data,
including biometric screening results, is particularly unusual. The vast majority of employers
contract with third-party vendors to conduct these assessments, with employers receiving only
aggregate statistics from those assessments. 5 In contrast, employees at this hospital had to
consent to their de-identified data being used for research purposes in order to receive the
participation incentive (a health insurance premium reduction). Also unusual is the availability of
high-frequency individual-level food consumption data, which we obtain from itemized receipts
of food purchases made at the hospital. Lastly, since most of the employees are covered by the
employer’s health insurance plan, we have access to (limited) information on their medical
expenditures. 6
The advantages of this unique dataset are manifold. First, we are able to measure the effects on
actual behavior (food purchases) as opposed to self-reported behavior (e.g. intention to increase
physical activity, to start a diet, etc.). Second, by using employees’ self-reported health
conditions in combination with their biometric screenings, we can measure whether receiving
news—a negative signal regarding one’s health—has a different impact than simply being
reminded of one’s health condition. Third, because employees had a period of eight months in
which they could complete their HRAs, temporal variation allows us to credibly identify the
5
This is partly because most employers lack the resources and expertise to collect and store HIPAA-protected health
information, and partly to assuage privacy concerns (employees are told that their personal data will not be seen by
their employer).
6
Specifically, we have been given the quintile of total annual medical expenditures for each employee in 2012.
3
effects of the information. Fourth, the high-frequency and detailed nature of our data allows us to
estimate short- and medium-run effects on purchases at the workplace cafeteria.
We focus on the cholesterol testing component of the HRA and estimate its effect on two
individual-level outcomes: weekly spending at the cafeteria and healthy share of spending,
defined as the share of weekly spending devoted to healthy items. To identify this effect, we use
the variation in dates when employees took their tests, comparing each employee’s eating
behavior after the test to their own eating behavior before the test while adjusting for trends over
time. We consider the effects on the overall sample of HRA completers (N=1,265), as well as on
individuals found to be at high risk based on their cholesterol levels (N=371) and individuals
within this high-risk group who were reportedly unaware of their high cholesterol, i.e.
undiagnosed (N=252). Only for the latter group is there a clear direction for the hypothesized
effect: to the extent that behavior responds to new information about a health risk, we would
expect previously undiagnosed individuals to begin eating less overall, and/or eating more
healthfully. In theory, the HRA could lead to the same changes on the part of individuals who
have already been diagnosed with high cholesterol and still have it, either because it reminds
them of their risks (i.e. a salience effect) or informs them that any lifestyle changes they have
made since diagnosis have not been sufficiently effective. For individuals who are found to have
normal levels of cholesterol, however, there is no clear prediction for how the HRA should affect
eating behavior.
Our results align with these predictions. While we find no overall effect of the cholesterol test for
the entire population, we estimate that high-risk individuals and undiagnosed high-risk
individuals reduced their weekly cafeteria spending by $0.85 and $1.12, respectively. These
changes are on the order of a 10-15% decrease in spending. The estimated effect on healthy
share of spending in these subsamples is positive but small (2-3 percentage points) and only
significant at the 10% level for the undiagnosed high-risk individuals. These effects appear to
persist for at least 15 weeks after the cholesterol test.
Interestingly, when we consider subgroups based on employee occupations, we find that the drop
in cafeteria spending among high-risk employees is larger among medically-trained employees
4
(in particular, physicians) than among the non-medical hospital occupations. To explore possible
reasons for this stronger response, we conducted a later survey asking a random sample of
employees about the effects of having high cholesterol, what foods should be avoided to reduce
risk, and their perceived responsibility to model good health behaviors for others (e.g. hospital
patients). Survey responses provide some suggestive evidence that knowledge about the
consequences of the health risk and sense of responsibility to model health behavior might be
important factors.
Our results remain consistent over a series of robustness checks including varying the
classification of healthy foods, the types of spending considered, and the inclusion of eateries
outside the main cafeteria. We also show that when we focus on the high-risk employees who eat
most frequently at the cafeteria, their drop in spending is larger since their spending is higher, but
still equivalent to 10-15% of their average spending.
Prior evidence on the effects of HRAs on behavior is not only limited, but also focuses primarily
on self-reported behavior (RAND 2013, Colkesen et al. 2011) and on the effects of HRAs
combined with other workplace wellness interventions. One exception is Huskamp and
Rosenthal (2009) who use health insurance claims data and a propensity score approach to
compare the health care services and spending of HRA completers against the most similar
employees of other firms. They find that HRA completion was associated with small increases in
physician visits, prescription drug spending, and cervical cancer screenings.
Our study adds to a small emerging literature on the effects of medical diagnoses on eating
behavior. In particular, Zhao et al. (2013) use longitudinal data from an annual Chinese survey
and a regression discontinuity design to find that, in the year following a hypertension diagnosis,
individuals reduce fat intake significantly, and high-income individuals make larger reductions.
Also, Oster (2015) uses household scanner data to infer diabetes diagnosis from purchases of
glucose testing products and estimate its effect on food purchases. She finds small calorie
reductions. Like Zhao et al. (2013), our data contains a rich set of individual health
characteristics, but like Oster (2015) our data are high frequency, which is important since
responses to the information may be short-lived. Unlike these two studies, we can pinpoint the
5
exact date of testing, identify individuals who are learning their risk for the first time, and study
differences by occupation categories. Lastly, we examine a widespread and expensive employer
health benefit (HRAs) whose effectiveness has not been proven.
2. Data
This study takes advantage of a unique combination of data sources for a sample of employees at
a large regional hospital. Our dataset contains, for each employee in the sample, (1) biometric
data and survey responses from a health risk assessment conducted in 2013; (2) demographic
characteristics (age, gender, ethnicity and job category); and (3) receipts from food purchases at
the hospital’s cafeteria and cafes from January 2013-April 2014.
All benefits-eligible employees of the hospital were invited to take the health risk assessment and
incentivized with a $200 health insurance subsidy for the following year. 7 In order to qualify for
the subsidy, employees were required to complete an online questionnaire as well as a biometric
screening. The health risk assessment was advertised in numerous ways including email, health
fairs, and posters in the hallway leading to the cafeteria. 8 The health assessment questionnaire
elicited self-reported health conditions and behaviors, and the biometric screening measured
individuals’ height, weight, blood pressure, glucose, and total cholesterol. 9 After completing the
biometric screening, individuals received a personal letter informing them of their cholesterol
and glucose measures and recommending follow-up with their healthcare provider if their results
were in the moderate- or high-risk range. 10
Based on the measures collected in the biometric screening, an employee was categorized as
being in low, moderate, or high risk for high cholesterol, high blood pressure, blood glucose, and
7
Employees could take the health risk assessment any time starting on April 1st 2013 until the end of the year.
However, only those employees who completed the HRA, including the biometric tests, by November 15th 2013
received a $200 annual health insurance subsidy for the year 2014 and were eligible for an additional $400 subsidy
for meeting additional program requirements.
8
Since employees might have seen the program advertised when they went to the cafeteria, there is a possibility of a
within-person correlation between frequenting the cafeteria and taking up the HRA. Nevertheless, this would bias
against finding a reduction in cafeteria spending at the time of the HRA.
9
The screening could be done during one of several health fairs throughout the year, or by appointment at the
employee health clinic. The complete set of biometric measures depends on the location where the screening was
done, but in both locations, total cholesterol and glucose were measured. These were the two measures required for
the incentive to be awarded.
10
A copy of this letter is provided after the Figures, Tables, and Appendix Tables.
6
BMI, based on broadly accepted thresholds for each measure. For example, individuals with a
total cholesterol reading of 200-239 were categorized as “moderate risk” while those with
readings of 240 or higher were categorized as “high risk.” Individuals could also be categorized
as “high risk” based on a high level of LDL (“bad cholesterol”), or a low level of HDL (“good
cholesterol”). In our empirical analysis, we define “high risk” as having a high-risk value on any
of the three types of cholesterol. 11
To assess whether employees were aware or unaware of their high cholesterol, we take
advantage of a question asked in the questionnaire portion of the health assessment: “Do you
have, or have you been told that you have any of the following health conditions?” where the
health conditions listed include diabetes, high cholesterol, and high blood pressure, among
others. Specifically, if an employee indicated that she had never been told she had high
cholesterol, but her biometric reading indicated a “high risk” cholesterol level, we consider her to
have “undiagnosed” high cholesterol.
Our study focuses on the measurement of cholesterol rather than blood pressure, glucose, or
BMI. 12 Although guidelines from the American College of Occupational and Environmental
Medicine emphasize that employer screenings are not a substitute for diagnosis by a physician,
the blood tests used to measure cholesterol are exactly what physicians use to diagnose high
cholesterol. 13 In contrast, diagnoses of high blood pressure or diabetes rely on a series of blood
pressure or glucose tests rather than just one reading. Also, blood pressure readings are easily
mismeasured and can be affected by recent consumption of caffeine, smoking, physical activity,
or the “white coat” effect (the mere experience of being examined can cause blood pressure to
spike). 14 As for glucose, the cut-off values for the “high risk” category depend on whether the
employee fasted for 8 hours or not; while the survey data includes self-reported fasting, we
11
Specifically, “high risk” means either the total cholesterol value exceeded 240, the LDL cholesterol value
exceeded 160, or the HDL cholesterol was lower than 40 for men and 45 for women. This is consistent with the
definition employed by the CDC, except the CDC now uses a uniform HDL cutoff of 40 for both men and women.
12
Results are consistent when considering blood pressure instead of total cholesterol. Results available upon request.
13
Joint Consensus Statement of the American College of Occupational and Environmental Medicine (May 2013)
http://www.acoem.org/uploadedFiles/Public_Affairs/Policies_And_Position_Statements/Guidelines/Position_State
ments/Biometric%20Hlth%20Screening%20Statement.pdf
14
We observe only the blood pressure measurements recorded, not the employee’s behavior before the reading nor
verbal discussion between the employee and the person conducting the screening. Some employees may have been
classified as “high risk” but verbally told not to worry if, for example, they drank coffee prior to the screening.
7
worry that these self-reports may not be fully reliable. In contrast, the measurement of total
cholesterol does not require fasting and is not sensitive to recent consumption of caffeine or
physical activity (Sidhu and Naugler 2012). Furthermore, cholesterol tests are far less frequently
conducted (the NCEP recommends every 5 years for healthy adults) than blood pressure
readings. Thus, the cholesterol test conveys information to employees that is more reliable,
novel, and salient than the blood pressure test.
The dataset also contains information about food purchases at hospital eateries for each
employee. Employees can use their ID badges, which are required to be worn at all times, as
debit cards via payroll deduction. Employees are incentivized to use their ID cards at the time of
purchase through a 10% discount on all items. 15 This study focuses on purchases made at the
central cafeteria located in the main building, which includes purchases by 65% of employees
who are benefits eligible and accounts for 71% of employee spending at the hospital’s eateries. 16
The food offered at the cafeteria includes traditional home-style meals, pizza, sandwiches, salad
bar, and soups.
Aside from the main cafeteria, there are two cafés and some vending machines where employees
can buy a more limited selection of food and drinks. Our data (based on purchases made with
employees’ ID cards) includes the cafés but not the vending machines. Our analysis focuses on
the main cafeteria, but our results are robust to including all eateries. The USDA Food Access
Research Atlas shows that the hospital is located in an area with low income and low access to
food options, as there are no fast-food or restaurant options within half a mile. 17,
18
Hence, the
vast majority of employees eat at the cafeteria and/or bring food from home. In a survey we
conducted of 398 employees, 80.3% reported that they “never or rarely” purchase food outside of
the hospital during a workday, while only 12.5% reported that they “never or rarely” bring their
15
Prior to this study, no attempts were ever made to match and analyze individual-level cafeteria purchase data.
Therefore, employees had no reason to expect that their purchases would be tracked and studied.
16
The fact that 65% of employees eat at this cafeteria is partly explained by the fact that 31% of the employees in
the benefits-eligible sample are not located on the main campus but on satellite locations. Thus, the vast majority of
employees located on the main campus makes purchases at the main cafeteria.
17
http://www.ers.usda.gov/data-products/food-access-research-atlas/go-to-the-atlas.aspx.
18
Employees are unlikely to drive out for lunch. Since employees might be less likely to do so on cold days, rainy
days, and snowy days, we used weather data for the study period to check if daily cafeteria spending is affected by
the weather. Regression estimates (not reported) show that daily cafeteria spending does not increase when
temperatures are low, or when it rains or snows.
8
lunch from home. While 81% of employees reported that they bring their lunch from home on
two or more days per week, only 8.4% of employees reported that they eat outside of the hospital
on two or more days per week.
For the purposes of our study, a nutritionist collaborated with the cafeteria manager to categorize
all items sold during our sample period as either healthy, unhealthy, or ambiguous. 19 In our
sample, the most frequently purchased healthy items are the salad bar ($6.24 per pound, $3.13 on
average), small coffee ($1.67), side vegetable of the day ($0.92), and fruit bar. The most
frequently purchased unhealthy items are French fries ($1.22), breakfast potatoes ($1.19),
chicken tenders ($3.50), 3 sausage links ($1.35), and cheeseburger ($2.39), while the top
ambiguous items are bottled beverages ($1.38), 20 soup ($1.63), and chips ($1.07).
We also gathered nutritional information for 60% of the items weighted by frequency of
purchase. The nutritional information we were able to collect includes calories, grams of fat,
grams of saturated fats, and milligrams of dietary cholesterol. Appendix Table B1 shows the
complete list of items sold at the cafeteria during the study period. The items are in descending
order of frequency. For each item, we report its classification as healthy, unhealthy, or
ambiguous, as well as the number of calories, grams of fat and saturated fats, and milligrams of
dietary cholesterol, when this information is available. 21 Consistent with our classification of
healthy and unhealthy items, the average number of calories for healthy and unhealthy items is
178 and 233, respectively. The average healthy item has 5.8 grams of fat, 1.8 grams of saturated
fat, and 36.5 milligrams of dietary cholesterol, while these values are 11.2g, 4.0g, and 55.2mg,
respectively, for the average unhealthy item. 22
19
For example, the item “starch of the day” might be cooked in a healthy way on one day, but an unhealthy way on
another day. The data do not allow us to distinguish between these two preparations. For the main analysis we
categorize ambiguous items together with unhealthy items as “non-healthy.”
20
Because it includes a variety of carbonated soft drinks (both diet and regular) as well as bottled water, the item
“Bottled beverage” was coded it as “ambiguous.”
21
The details of how the nutritional information was collected are provided after the Figures, Tables, Appendix
Tables, and the sample letter employees received.
22
The only items classified as healthy despite containing saturated fats are items rich in protein such as hard boiled
eggs, cottage cheese, hummus, and Trailmix.
9
We collapse food purchases by week to create a panel of employee purchases. The two main
outcome variables that we study are (1) TotalSpending, an employee’s total weekly spending at
the main cafeteria, and (2) HealthyShare, the share of weekly spending devoted to healthy items,
although we also look at the specific nutritional measures in a secondary analysis.
2.1 Sample Characteristics
Of the 6,237 employees who were eligible to take the HRA in 2013, 46% completed the
cholesterol test as well as the health risk questionnaire. Because our identification strategy relies
on observing individuals both before and after their tests, we limit our study sample to those who
were employed for the entire period in which the test was made available and who made a
purchase at the cafeteria at least once prior to the beginning of the HRA period (January-March
2013) and at least once in January-April 2014. This results in a sample of 1,265 employees.
Table 1 shows socio-demographic characteristics for this sample (columns 1-2) and for the
remaining employees (columns 3-4)—those who were eligible to take the Health Risk
Assessment but did not complete it, combined with those who completed the HRA but did not
buy food at the cafeteria during the periods we require. In our sample, 79% of the individuals are
women and the average age is 46 years. The majority of employees in our sample are white
(77%) while 14% are black, and the remaining 9% are either Hispanic or Asian. Regarding
occupation, we categorize 36% of employees as professional (requiring a Bachelor’s degree or
higher), and the rest as non-professional. 23 We also divide employees into four occupational
categories: physicians, nurses, other professional occupations, and other non-professional
occupations. 24 Compared to the employees excluded from the sample, those in our sample are on
average 2.6 years older, more likely to be female, more likely to be white and less likely to be
black. The representation of physicians and nurses is similar in our sample and the remaining
23
We classified the following occupational categories as “professional”: directors, clinical directors, physicians,
clinical specialists, interns/residents, coordinators, non-physician practitioners, managers, professional support, and
administration. We classified the following occupational categories as “non-professional”: administrative support,
clinical nurses, licensed practical nurses, technicians, supervisors, support services, maintenance skilled trade, nonprofessional clinical staff, non-professional support, and other clinical/therapeutic.
24
The physician category includes: physicians, interns/residents, and non-physician practitioners, while the nurse
category includes clinical nurses and licensed practical nurses. The non-health professional category includes
professional directors, coordinators, managers, professional support, and administration. The non-health nonprofessional category includes administrative support, technicians, supervisors, support services, maintenance
skilled trade, and non-professional support.
10
population. For non-health related occupations, however, professionals are overrepresented in
our sample due to their greater likelihood of completing the health risk assessment.
We also report the distribution of employees across five healthcare cost quintiles. These quintiles
were created using 2012 medical claims, for every employee who appeared in the 2012 medical
claims database. 25 Unfortunately, for the employees who do not appear in any quintile (22% of
our sample, and 44% of all benefits-eligible employees), we cannot distinguish whether they had
$0 medical expenses in 2012 or opted out of the health insurance plans offered by the employer.
The employees in our sample are significantly more likely to appear in the quintile data, likely
due to the fact that employees opting out of the employer’s health insurance plan have less
reason to participate in the health risk assessment. 26 Conditional on appearing in the quintile
data, employees in our sample appear to have slightly higher healthcare spending than those who
do not appear in our sample, based on slightly lower representation in the lowest two quintiles
and higher representation in the upper two quintiles. 27
Table 2 reports the biometric statistics from the 2013 health assessment for our sample of 1,265
individuals. Since not all individuals were tested for all conditions, the number of employees in
each row differs. Twenty-nine percent of employees have a high-risk level of cholesterol (high
total cholesterol, high LDL, and/or low HDL cholesterol). Twenty four percent of the employees
have a high reading of their blood pressure. 28 Only 1.8 percent are found to have uncontrolled
high glucose, but 32% of the sample is overweight and 37% is obese. 29
On these health measures, our sample looks similar to a nationally representative sample
(NHANES conducted by the CDC) in 2011-2012. The NHANES data shows that 34.9% of
adults over the age of 20 were obese in 2012, 12.9% of U.S. adults over the age of 20 have high
total cholesterol and 17.4% have low HDL. In our sample, these fractions are 37%, 10.4%, and
25
We do not have access to the raw claims data, only to these summary measures.
Employees opting out of the medical plans offered by the employer could receive the $200 incentive via a Health
Savings Account, but not as a premium reduction.
27
A Kolmogorov-Smirnov test of equality of distributions yields a p-value of 0.088, so we can reject the hypothesis
that the distributions are the same at the 10% level.
28
High blood pressure is defined by the CDC as a systolic value at or above 140 or a diastolic value at or above 90.
29
According to the CDC, an individual is classified as overweight if her BMI is between 25 and 30, and obese if her
BMI is above 30.
26
11
14.4%, respectively, when we use the CDC-equivalent definition for low HDL. Also, in the
NHANES data, 12% are reported to have high blood pressure, although several readings were
done and 17.8% had a “high” value on the highest of these readings. 30 Therefore, the employees
in our sample seem to be of roughly similar health, overall, as the U.S. adult population.
Compared to the NHANES sample, employees in our sample who have high-risk levels of
cholesterol are somewhat less likely to have been told this before. As reported in columns 2 and
3, 29.2% of employees have high cholesterol, and 19.8% have undiagnosed high cholesterol.
Thus, about two-thirds of the individuals with high cholesterol were not aware of their condition.
The equivalent percentage in the NHANES sample is 50%. 31 In our empirical analysis, we will
study both the effect of providing information about having high cholesterol to the subsample
that suffers from this condition, as well as the effect of providing such information to the
subsample that is receiving this information for the first time.
The second panel of Table 2 shows cholesterol diagnosis rates by occupation. Physicians in our
sample are slightly more likely to be “high-risk” in cholesterol compared to other professional
employees, although the difference is not statistically significant. On the other hand, nurses in
our sample are significantly less likely to be “high-risk” in cholesterol, most likely due to the fact
that there is a higher representation of women and younger employees in this group. In the four
occupational groups, the fraction of “high-risk” employees who are undiagnosed ranges from
65% to 73%, with the medically trained having slightly higher fractions (70% for nurses and
73% for physicians).
2.2 Cafeteria spending
Our sample consists of 1,265 employees who completed the HRA questionnaire, were tested for
their total cholesterol, and ate at the main cafeteria at least once in the first quarter of 2013 and at
least once in January through April of 2014. In the average week, 64% of these employees
30
The CDC statistics were obtained from CDC Data Briefs No. 131 and 132 as well as the authors’ calculations
using NHANES microdata. Although a larger share of our sample (24%) had a high blood pressure reading, these
readings are sensitive to recent consumption of caffeine, smoking, and physical activity, and the hospital put no
restrictions on employees’ activities or consumption prior to their blood pressure readings.
31
In the NHANES data, people are also asked whether they have been told by a doctor that they have a given health
condition, so we can make this comparison using the same definition.
12
purchased at least one food or beverage at the hospital’s main cafeteria, and those who make a
purchase spend on average $11.86 per week. 32 This is approximately equivalent to three small
meals. For example, purchasing a cheeseburger, fries, and Diet Coke would cost $4.66, a bagel
with cream cheese, small soup, and fresh fruit would cost $3.48, and a scrambled eggs, breakfast
potatoes, and small juice would cost $3.43, summing to $11.57.
In Table 3, we summarize our key variables: weekly spending at the cafeteria and the share of
spending devoted to healthy items. We also summarize selected nutritional information (calories,
grams of saturated fat, and milligrams of dietary cholesterol) for the sample of items in which
nutritional information was available. Overall, the average employee in our sample spent $7.26
on food and beverages at the main cafeteria in the average week, spending 46% of this amount
on healthy items. 33 We also show how the means of these variables differ across employee
subgroups defined by cholesterol level, body mass index (BMI), gender, age, occupation, and
healthcare spending. There are clear differences which lend credence to the validity of our
healthy/unhealthy classification system: Employees found to have higher cholesterol levels and
higher BMI devote a smaller share of their weekly spending to healthy items, and also have
higher spending at the cafeteria. Women eat less, and more healthily, than men. Similarly, within
the subset of items for which we have nutritional information, we see that employees with higher
cholesterol levels and higher BMI consume more calories, grams of saturated fat, and milligrams
of dietary cholesterol, as do men compared to women. Also, older age groups spend more at the
cafeteria but devote a larger share of spending to healthy items. Physicians appear to have similar
total spending and healthy share of spending as other professional occupations, while nonprofessionals have a significantly lower healthy share. Finally, regarding healthcare cost, we do
not see any significant differences between employees with high spending (quintiles four and
five) versus low spending.
32
Note that within our sample, 31% of employees are not stationed at the main campus, which is why only 64% of
our sample makes a purchase in the average week.
33
This average value includes weeks in which an employee’s spending was zero. If we exclude these weeks, the
average is $11.63, but the healthy share average is the same since healthy share is only defined when spending is
nonzero.
13
Figures 1 and 2 show that both total weekly spending and healthy share of weekly spending are
noisy but have no clear trend over the study time period. The lowest points in total spending all
correspond to holiday weeks, but these weeks do not appear to have abnormal healthy shares.
2.3 Graphical analysis of cafeteria spending before and after HRA completion
To take an initial look at how total spending and healthy share changed after each individual’s
health risk assessment, we graphically present the results using an event study analysis. The goal
of our study is to measure the causal effect of the health assessment on two outcomes: Total
Spending, an employee’s total weekly spending at the cafeteria, and Healthy Share, the share of
weekly spending devoted to healthy items.
If learning their cholesterol level motivates individuals to shift towards healthier foods in the
cafeteria, we should see increases in the outcome HealthyShare in the weeks following the
cholesterol test. If it motivates them to improve their health by eating less overall, or substituting
food brought from home for food at the cafeteria, then we should see decreases in
Total Spending, employees’ weekly spending at the cafeteria. Taking advantage of the fact that
employees had a long period of time in which they could complete their health assessments and
chose different weeks to do so, we are able to include week fixed effects (wt) in addition to
individual fixed effects (di), where i represents individuals and t represents week. This allows us
to separate the effect of receiving tailored health information from seasonal trends in cafeteria
use and week-to-week menu variation. For this analysis, we use the following specification:
15
𝑌𝑌𝑖𝑖𝑖𝑖 = ∑−2
𝜏𝜏=−15 𝛽𝛽𝑖𝑖𝑖𝑖𝑖𝑖 𝑆𝑆𝑖𝑖𝑖𝑖𝑖𝑖 + ∑𝜏𝜏=0 𝛽𝛽𝑖𝑖𝑖𝑖𝑖𝑖 𝑆𝑆𝑖𝑖𝑖𝑖𝑖𝑖 + 𝑑𝑑𝑖𝑖𝑖𝑖 + 𝑤𝑤𝑡𝑡 + ε𝑖𝑖𝑖𝑖
(1)
where the event time indicator variables Siτt are a set of dummy variables indicating the amount
of time by which week t precedes or follows the week in which individual i completed her total
cholesterol test. 34 We estimate individual 𝛽𝛽𝑖𝑖𝑖𝑖 coefficients for each event week starting from 14
weeks prior to the health assessment and ending 14 weeks afterwards, with the exception of
In the week in which individual i completed her test (𝜏𝜏=0), Si0t =1 and all other event time dummies are equal to
zero. In the following week (t=1), Si1=1 and all other event time dummies are equal to zero. In the week immediately
preceding the week of the cholesterol test, St-1=1 and all other event time dummies are equal to zero, and so forth.
34
14
week 𝑡𝑡 = −1 which serves as the comparison period. We group weeks 15 (-15) together with all
periods more than 15 weeks after (before) an employee’s cholesterol test.
The purpose of estimating the time effects prior to the health assessment (-1 to -15) is to verify
that the timing of the cholesterol test is exogenous to other factors influencing eating habits. If it
were the case that individuals decided to do the total cholesterol test at a time when their
behavior is trending in a more healthy direction, then the event time effects for weeks preceding
the total cholesterol test should be increasingly negative going further into the past. The eventstudy framework also allows us to examine whether the effects of the health assessment on
eating behavior decline over time, as we consider weeks further in distance from the health
assessment.
For both dependent variables we consider three samples: the entire set of employees who
completed the cholesterol test, the subset who were classified as “high-risk” due to their
cholesterol level, and the smaller subset who were classified as high-risk but had never before
been told that they had high cholesterol, whom we refer to as “undiagnosed high-risk.” Our
hypothesis is that any increase in healthy share or reduction in total spending will be larger for
the high-risk than the whole sample, and larger still for those who were undiagnosed.
The regression results from estimating (1) are shown graphically in Figure 3, which contains 6
plots. 35 The three plots on the left side of the figure show results for TotalSpending while the
plots on the right side show results for HealthyShare. Within each column, the three plots
correspond to the full sample, high-risk, and undiagnosed high-risk, respectively.
On the top left plot of Figure 3, corresponding to all employees in our sample, we see that none
of the post-period time dummies (Event Weeks 1-15) is significantly different from zero. 36
However, the two plots below this one show that in both the high-risk and undiagnosed high-risk
subsamples, many of the weekly dummies are significantly negative in the post-test period,
35
Coefficient estimates are also reported in Appendix Table A1.
However, the average TotalSpending is significantly higher during the week of the cholesterol test (Week 0),
relative to the week prior (Week -1). This could be because employees scheduled their tests on a week when they
knew they would be around campus for more days or time than usual. It could also be that employees who fasted in
the morning before their glucose test purchased more food than usual at the cafeteria that day.
36
15
suggesting that the high-risk undiagnosed employees, in particular, are responding to the news of
their health condition by consuming less food at the workplace cafeteria. In the group of highrisk employees, it appears that the effects over weeks 9-14 are somewhat smaller than the effects
over the first 8 weeks, but this is less evident in the undiagnosed subsample. Furthermore, the
last coefficient plotted (labeled “15”) represents the effect estimated over weeks 15+, which
occur on average 27 weeks after the test. 37 This coefficient is statistically significant in the
undiagnosed high-risk sample, borderline significant in the high-risk sample, and of similar
magnitude as the averaged effects of weeks 1-14. Thus, it appears that taking a cholesterol test
with a result indicating high cholesterol leads to a sustained drop in cafeteria spending of $1-$2
per week.
In all three samples, none of the pre-test event week coefficients are significantly different from
zero, indicating that the levels of spending of the average employee in our sample were not
significantly different from the week prior to the cholesterol test, and no consistent trends are
evident.
In the three graphs for HealthyShare, appearing on the right column of Figure 3, the only eventweek coefficient that is significantly different from zero is Week 0, the week of the test, for the
undiagnosed high-risk sample. Most of the weekly coefficients after the test are above zero,
however, in both the high-risk and undiagnosed high-risk samples. This suggests that there might
be a small but noisy shift towards healthier purchases at the cafeteria in these subsamples, which
we will attempt to estimate with a less restrictive specification in the next section.
3. Empirical Strategy
Since we see no trend in the pre-test coefficients, the graphical results of Figure 3 support our
identification assumption that the timing of the cholesterol test is quasi-exogenous. We also find
that any effects on cafeteria spending and healthy share are relatively stable over the range of
post-test weeks we observe, implying that the following pre-post regression framework is
appropriate.
37
Because the employees who took the test on the last possible week are only observed for 15 weeks after the test,
this is an unbalanced comparison of effects. Some employees are observed up until week 55.
16
Consistent with the above event study, the unit of observation is an individual’s cafeteria
purchases over a calendar week. Similarly, we test for causal effects of the health assessment on
TotalSpending and HealthyShare. When the dependent variable is HealthyShare, we weight the
observations by total spending. For each outcome, the estimating equation is:
𝑌𝑌𝑖𝑖𝑖𝑖 = 𝛽𝛽1 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑡𝑡𝑖𝑖𝑖𝑖 + 𝛽𝛽2 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑘𝑘𝑖𝑖𝑖𝑖 + 𝑑𝑑𝑖𝑖 + 𝑤𝑤𝑡𝑡 + 𝜖𝜖𝑖𝑖𝑖𝑖
(2)
We use the dummy variable PostTest to test whether employees’ eating habits change from the
period before to the period after they complete the total cholesterol test required for the HRA. As
in the analysis above, we include week fixed effects (wt) and individual fixed effects (di).
Standard errors are clustered at the individual level, since spending behavior might be
autocorrelated over time.
The key assumption to our identification strategy is that the week in which an employee decides
to take her total cholesterol test is not driven by changes in other factors that might shift her food
preferences. Since employees could schedule their cholesterol test at a time of their own
convenience, it is important to discuss challenges to the exogeneity assumption. Factors that
might make a given week more convenient for a given employee could include: being at the
workplace more often than usual, being less busy at the workplace than usual, or being for some
reason more mindful of one’s health than usual. All of these factors could be viewed as
temporary shocks to an employee’s use of the workplace cafeteria in addition to shocking the
probability of completing the cholesterol test. Furthermore, although we can identify the date in
which each employee took the cholesterol test, we do not know exactly when each employee was
informed about her test result. 38 According to the director of the wellness program of the
hospital, employees should have received their test result by two weeks after their test, but in
many cases they received it earlier.
38
Employees were mailed their test result after lab processing and data entry were completed. There are no records
of test mailing dates.
17
For these reasons, our main specification includes a separate coefficient for TestWeek (a dummy
variable equal to one in the week of the health assessment) in order to exclude this possibly
problematic period from the pre-post comparison. We conduct robustness checks in which we
vary the length of this excluded time period. None of these changes affect our results.
4. Results: The impact of health information on cafeteria purchases
Our main results are shown in Tables 4 and 5. 39 We show the estimated effects for
TotalSpending (columns 1-3) and for HealthyShare (columns 4-6), for the same three samples
used in the event study: the full sample, the high-risk sample, and the undiagnosed high-risk
sample.
4.1 The effects for all employees and those receiving new information
Table 4 reports in the first panel the estimates for the overall sample. We find a statistically
significant drop in TotalSpending of 85 cents per week in the high-risk sample, and of $1.12 per
week in the undiagnosed high-risk samples. This reduction corresponds to a 9.8% drop for the
high risk sample ($0.85/$8.64, where $8.64 is average weekly cafeteria spending of our high risk
sample prior to their cholesterol test) and a 12.9% drop for the undiagnosed high risk sample
($1.11/$8.60, where $8.60 is average weekly cafeteria spending of our undiagnosed high risk
sample prior to their cholesterol test). On average, across all employees who took the cholesterol
test, however, there is no significant change in spending. For the outcome of HealthyShare,
Column (4) shows a zero effect (the coefficient is 0.001 and its confidence interval is (-0.012;
0.015)) of the cholesterol test, on average. When we restrict the sample to high-risk and
undiagnosed high-risk subsamples, however, the estimated effect increases to .02 and .03,
respectively. However, the coefficient is significant at the 10% level only for the latter group.
Although we find larger point estimates for both TotalSpending and HealthyShare among the
previously undiagnosed, we cannot reject the hypothesis that they are equal to the effects on the
high-risk diagnosed population. Without observing employees’ current priors about their
cholesterol levels, we cannot rule out the possibility that those who have been told they had high
39
These tables only show the estimates for the coefficient of interest, i.e. PostTest. The TestWeek coefficients,
which appear as the Week 0 effects in Figure 3 for the main sample, are available upon request for other
specifications.
18
cholesterol in the past are still receiving a negative signal (e.g. they might have tried to improve
their cholesterol and might not expect to learn that they are still high risk). Furthermore, there
may also be salience effects: an individual who already knows he has high cholesterol might still
improve his behavior in the short term after being reminded of it. However, when we run the
regressions separately for the group of High Risk employees who were not undiagnosed, both the
effects on spending and on healthy share are much smaller and statistically insignificant (not
currently shown in tables).
4.2 Do medically trained employees respond differently?
Since our study takes place at a large regional hospital, it is important to consider the potential
role of health-related training on the employees’ responses to health-related information. To this
end, we estimate results for each the four categories described earlier: physicians, nurses, other
professionals, and other nonprofessionals.
Results in the second to fifth panel of Table 4 show significant reductions in total spending at the
cafeteria for physicians at high-risk and both physicians and nurses with undiagnosed high-risk.
There appears to be no significant impact for either professional or nonprofessional employees in
non-health related occupations. In addition, the impact for health professionals (e.g. physicians)
is larger than the one for health non-professionals (e.g. nurses). In additional specifications
available upon request, we estimate effects for the four occupational groups within one
regression with interactions between a post period dummy and each occupation category. 40
Estimates show that among the undiagnosed high-risk employees, the effect for physicians is
statistically different at the 5% level from the nonprofessional non-medically trained employees.
The interaction term for nurses is also negative but of a smaller magnitude and not statistically
significant. Hence, it seems that the results on total weekly spending at the cafeteria are primarily
driven by employees with medical training, in particular, physicians. The difference in results
40
We also include interactions for the weekly fixed effects with each occupational group to account for their
different work schedules, for example physicians and nurses do not exhibit the same drop in consumption during
holiday weeks as non-medical employees.
19
between physicians and nurses could be a combination of the influence of additional medical
training and/or a reflection of their demographic differences. 41
To explore possible reasons for this stronger response, we conducted a later survey about the
effects of having high cholesterol, what foods should be avoided to reduce risk, and their
perceived responsibility, as hospital employees, to model good health behaviors for patients of
the hospital. We invited a random sample of 1,000 employees to participate and obtained
responses from 460 employees, of whom 56 were health professionals (physicians, interns,
residents, or non-physician practitioners), 94 were health non-professionals (nurses), 110 were
non-health professionals and 181 were non-health non-professionals. 42
Based on the survey responses, medically-trained employees believe more health risks to be
associated with high cholesterol. Although medically-trained and non-medically trained
employees were equally likely to be aware that high cholesterol increases one’s risk of a heart
attack (96.6% vs. 96.7%), the medically-trained were more likely than the non-medically trained
to agree with the other correct statements that high cholesterol increases the risk of stroke (95%
vs. 87%, p<.01), clogged arteries (97% vs. 91%, p<.05), kidney disease (43% vs. 27%, p<.01),
high blood pressure (73% vs. 68%, p=0.35) and cancer (25% vs. 13%, p<.01). We also included
some decoy choices, and the medically-trained were more likely to erroneously indicate that high
cholesterol increases the risk of nerve damage in the hands and feet (25% vs. 18%, p<.10) and
insignificantly more likely to erroneously indicate that it increases the risk of bone fracture (10%
vs. 8.4%, p=0.57).
By contrast, in response to a question about what types of foods should be avoided by a person
seeking to reduce her LDL cholesterol, there were few significant differences in the answers
selected by the medically-trained and non-medically trained. This suggests knowledge regarding
how to improve one’s diet is not a driving force behind our stronger results for the medicallytrained versus others.
41
In the next section we find that older employees exhibit larger reductions in spending than younger employees, as
do men relative to women. Unfortunately, the small size of our sample does not give us power to jointly estimate the
effects of occupation, age, and gender with precision.
42
The survey sample is not drawn from our study sample since the online survey was conducted in the fall of 2016,
three years after the HRA we study. The participation incentive was entry in a drawing to win one of two iPads.
20
Our survey results also suggest that medically-trained employees feel a stronger sense of
responsibility to model healthy behavior at the workplace. When asked whether they agree with
the statement “In my role at HospitalName, I have a responsibility to our patients to lead by
example and practice healthy lifestyle behaviors,” 82% of medically-trained vs. 68% of nonmedically trained employees selected either “Agree” or “Strongly agree” (p<.01).
These results are important for two reasons. First, they suggest that provision of tailored health
information via HRAs may have larger effects when delivered to people who are more likely to
know the risks associated with the health information received and who feel a strong
responsibility to model health behavior for others. However, more work is needed, given that
employees self-select into the HRAs as well as their professions. Second, these results also
suggest caution about generalizing our findings to the U.S. population or other employer.
4.3 Heterogeneity by demographics and risk levels
In subsequent panels of Table 4 and Table 5, we explore whether employees whose cardiac risk
varies along dimensions other than high cholesterol respond differently to the information that
they have high cholesterol. In Table 4, we report estimates of equation (1) within three age
groups: age 40 and below, age 40-55, and above age 55. 43 It appears that older employees might
be more likely to make changes in their eating habits, with the group of undiagnosed high-risk
over age 55 having largest point estimates for both total spending and healthy share of spending.
In Table 5 we explore heterogeneity by gender and race in the first four panels and by different
health categories in the remaining panels. Among female employees (who represent 79% of our
total sample, 72% of our high risk sample, and 69% of our undiagnosed high-risk sample), we
see a statistically significant shift towards healthier spending in the undiagnosed high-risk
subsample. For the high-risk and high-risk undiagnosed subsample, the coefficients indicate that
after their cholesterol test, the percentage of cafeteria spending allocated to healthy foods
increases by 2.5 percentage points and 3.8 percentage points, respectively—a 6% and a 9%
increase relative to the average healthy share of high-risk and high-risk undiagnosed women in
43
The pattern is consistent when considering different age groups (e.g. over age 40, 50, and 60).
21
our sample, respectively. Among males, the corresponding coefficients are close to zero but have
large standard errors. The effects of the cholesterol test on total spending, however, are larger in
magnitude for men than for women. Based on the large difference in sample size of men and
women, we are reluctant to draw any firm conclusions from these comparisons. Across ethnicity
we find few differences in the effects of the cholesterol test on healthy share.
We see similarly large and significant effects on healthy share among the high risk and
undiagnosed high risk employees who are simultaneously found to be high risk based on blood
pressure, and among the undiagnosed high risk employees who have an immediate family
member suffering from heart disease or high cholesterol. 44 These increased effects remain
present even if we exclude employees over the age of 55 (results not shown), suggesting that
they are not driven by the possible correlation between age and other cardiac risk factors.
However, among the subpopulation of obese individuals, we do not see larger effects than in the
general high-risk population, despite the fact that their weight places them at greater risk.
In the last panel of Table 5, we consider effects among employees who had above-average
medical expenditures in the previous year. This population could include people who face health
problems entirely unrelated to cardiovascular disease. Within this group, we find no statistically
significant changes in eating patterns.
4.4 Analysis of changes in calories, fat, and cholesterol in cafeteria purchases
In addition to our binary classification of food items at the cafeteria into healthy and unhealthy,
we also have nutritional information for a subset of these food items. We would expect high-risk
and high-risk undiagnosed employees to reduce their consumption of calories, especially from
saturated fat and dietary cholesterol, following NIH guidelines. 45
44
The HRA questionnaire asks whether each health problem is found in their family (parent, brother, or sister).
Other health problems asked about include cancer, asthma, high blood pressure, and diabetes.
45
The National Heart, Lung, and Blood Institute (National Institute of Health, 2005) recommends people with high
cholesterol to reduce caloric intake, as well as the intake of fats and saturated fats. Specifically, a person with high
cholesterol should reduce her daily caloric intake from saturated fats to less than 7% of her daily total caloric intake.
Furthermore, the National Heart, Lung, and Blood Institute also advises to limit the amount of dietary cholesterol to
less than 200 milligrams per day.
22
Table 6 estimates the effect of the HRA on calories, grams of fat, grams of saturated fat,
milligrams of dietary cholesterol, and the share of weekly recommended consumption of
saturated fat based on NIH guidelines, among the subset of purchases for which this information
was available. 46 In addition, the first row of Table 6 shows the estimated effect on total spending
at the cafeteria for the items included in this nutritional analysis. The effects in dollar terms are
smaller than the ones shown in Table 4 for the entire sample, consistent with the fact that we
could only gather information for 60% of the items. In percentage terms, however, the spending
reductions are comparable to our main results (9-10% decrease in spending).
Using the nutritional measures as separate dependent variables, regression results are consistent
with the ones we find for total spending at the cafeteria. The provision of tailored health
information significantly reduces the consumption of calories, grams of fat and saturated fats,
milligrams of cholesterol, and the share of weekly-recommended consumption of saturated fats
based on NIH guidelines. The impact is stronger for high-risk employees and strongest for highrisk undiagnosed. Columns (4)-(6) show the size of the changes in percentage terms. The drops
in nutritional measures range from 7-9% for high-risk employees and from 11-13% for high-risk
undiagnosed employees. The fact that these drops are of similar magnitudes as the drops in total
spending suggests changes might be primarily driven by reductions in total spending at the
cafeteria rather than any major change in the composition of food intake.
The primary dietary recommendation for individuals with high cholesterol is to reduce
consumption of saturated fat to 7% or less of one’s total caloric intake. We attempt to compare
the reduction in saturated fat we observe in cafeteria purchases to the (unobserved) amount
required for each person to reach the recommended level. Since we do not know how previously
diagnosed individuals with high cholesterol may have already modified their eating habits, we do
the comparison only for the previously undiagnosed.
46
The share of weekly recommended saturated fats is built as follows. First, we estimate for each employee the
basal metabolic rate (BMR), i.e. the daily caloric intake to keep one’s weight constant using the Mifflin St Jeor
Equation, using employee data on weight, height, age, and sex. Then, we then use the BMR and the NIH guidelines
to calculate the maximum recommended caloric intake from saturated fats for each employee. Next, we convert to
grams multiplying by 9. Finally, we build the share of weekly recommended saturated fats based on NIH guidelines
as the share of saturated fats consumed on a weekly basis by each employee at all on-site eateries over the maximum
recommended level of saturated fat intake.
23
We make the assumption that before they knew of this health condition, their consumption of
saturated fats matched that of the overall U.S. adult population. According to the nationally
representative NHANES estimates, the average saturated fat intake of U.S. adults aged 30-60 is
11-13% of their caloric intake. Therefore, an individual newly diagnosed with high cholesterol
who eats like the average American should decrease their consumption of saturated fat by 3646%. Within the subset of food purchases for which we observe nutritional information, we
observe a reduction of saturated fat of approximately 12% in the undiagnosed high-risk group.
Hence, if this were indicative of a diet-wide change, it would mean individuals were going one
third of the way towards the recommended level on average.
However, there are a few reasons why we believe that this number overstates the likely reduction
in overall saturated fat intake as a percentage of total calories. First, the reduction in saturated fat
is mostly driven by a reduction in spending rather than a shift towards other foods, and it is likely
that food from home was consumed in substitution. While food brought from home is likely to
be healthier than cafeteria food, it may very well contain some saturated fat, making the net
change in saturated fat consumed at the workplace smaller than our estimate suggests. Second, to
the extent that some groups do shift towards lower-fat foods in the cafeteria, this may be a more
elastic type of food consumption since it is unconstrained by meal preparation time and
knowledge. Therefore, we believe a 12% reduction in saturated fat can be viewed as an upper
bound of the overall impact of learning for the first time that one has high cholesterol on one’s
saturated fat consumption. However, while the effect is small, it is not negligible.
5. Robustness checks
The robustness checks described below are presented in Table 7. Following the format of Tables
4 and 5, each row of each table shows the estimates of Equation (1) for the entire sample, highrisk, and undiagnosed high-risk samples, under a different robustness check.
A limitation of our dataset is that not all employees took the HRA; thus our results may not
generalize to the full population. In the first panel of Table 7, we use inverse probability
weighting to predict the effects if all employees were forced to take the HRA. We first estimate a
logit model predicting HRA participation, using employees’ gender, age, healthcare cost quintile,
24
and occupation as determinants. Then, we use the inverse of the predicted probabilities resulting
from this model as weights when estimating Equation 1 on the sample of HRA participants. This
approach relies on the assumption that conditional on the aforementioned characteristics, the
decision to participate is not correlated with unobservable characteristics affecting the behavioral
response. Our results suggest that both the spending reduction and the shift towards healthier
foods would be more pronounced, on average, if all employees participated.
Next, in the second panel of Table 7, we limit the sample to those employees who make
purchases most frequently at the cafeteria over the 16-month span of our sample. Specifically,
we focus on employees who have a median weekly spending value of at least $10 over the 69
weeks in our sample period. The sample sizes drop by approximately 70%, and while the results
for HealthyShare lose statistical significance, the results for total weekly spending are larger in
magnitude than in the main specification. In fact, the increase in magnitude is consistent with the
spending effect being of the same proportion in this group as in the sample as a whole. Since
larger changes in response to the health risk information are seen among those who eat at the
cafeteria more often, it is possible that this effect might generalize beyond the subset of
purchases we observe. We also run some additional robustness checks in Appendix Table A2.
Third, our binary classification of food items is admittedly a coarse measure of their nutritional
value. Beverages in particular are difficult to classify. For example, both coffee and the
discounted “diet soda of the month” are in the “healthy” category, due to their lack of sugar,
while the catch-all billing code for “20 oz. bottled beverages” is considered “uncertain” and thus
grouped with unhealthy items in our main analysis.
In the third and fourth panels of Table 7, we exclude all beverages from the analysis to focus on
food spending, and then broaden the spending data to include all food and all beverage purchases
at the workplace, including smaller cafés outside the main cafeteria. Results are quite similar to
the results in the first row of Table 4.
Finally, in the last three panels of Table 7, we estimate effects of the cholesterol test on spending
within each of the three categories: strictly unhealthy, ambiguous, and healthy foods. The results
25
show a large and highly significant drop in spending on unhealthy items, representing a decrease
of 15-19% among the high-risk and undiagnosed high-risk employees, who spend on average
$3.09 per week on unhealthy items. Based on estimates of the price elasticity of food consumed
away from home (Andreyeva et al. 2010), the 15% reduction we see in the purchases of
unhealthy food would require a 18.5% price increase (95% confidence interval (0.14; 0.27)) to
achieve via taxes, in the absence of a health risk assessment. Spending on other items also drops,
but less dramatically. Since the average high-risk employee spends $1.76 per week on
ambiguous items, we find that high-risk and undiagnosed high-risk employees reduce their
spending on ambiguous items by 10% and 16%, respectively. By contrast, the spending effects
for healthy items are smaller in percentage terms (5-7% relative to the weekly average of $3.94)
and statistically insignificant.
6. Conclusion
Using a unique dataset containing hospital employees’ health risk assessment data and their
workplace cafeteria purchases, we estimate how the receipt of tailored health information affects
eating behavior. Since employees reported whether they had ever been told they had high
cholesterol, we can estimate the effect of new, negative information about one’s health. Our
results show that, in response, they reduce their total spending at the workplace cafeteria, and
reduce their spending on unhealthy items more drastically. However, considering the NIH
dietary guidelines for people diagnosed with high cholesterol, the changes we identify are
relatively small. Even under the assumption that the proportional reductions we see at the
workplace are representative of a diet-wide shift, the reduction in saturated fat we find
corresponds to one third of the recommended dietary change.
Also, the effects we find are strongest in medically trained individuals. Survey evidence seems to
indicate that nutritional knowledge is not a driving force behind our finding, but knowledge
about the consequences of the health risk and a sense of responsibility to model health behavior
might be important. This would suggest that in a broader workforce population, health risk
assessments on their own might have smaller impacts on eating behavior, and there might be
value to combining them with educational interventions or physician visits.
26
Overall we interpret the magnitudes of our results as an upper bound estimate of how firms
should expect their employees to respond to tailored health information provided via HRAs.
Another important reason to view our results as an upper bound is the fact that we only observe a
portion of employees’ weekly food consumption, raising two concerns. First, there might be
substitution with food consumption elsewhere, or food brought from home, that offsets the
reduction in calories we estimate at the workplace. Second, the type of consumption we observe
might be a more elastic type of consumption than others, since it is not constrained by family
preferences or cooking time. If shifting towards healthier foods is easier in meals purchased
away from home, our results might be an overestimate of overall dietary changes. 47
In addition, since not all employees participated in the health risk assessment, our analysis could
suffer from sample selection bias. This is a ubiquitous problem in this literature because
participation in HRAs is not mandatory. 48 However, when we use inverse probability weighting
as a selection correction technique, we do not find any meaningful change in the estimated
effects.
Given the newfound popularity of health risk assessments at the workplace, more work is needed
to better understand their value in improving health and reducing healthcare costs. This paper
contributes evidence on one goal of HRAs: spurring employees to improve their health
behaviors, but we find that the magnitude of improvements is rather small. We also present
somewhat surprising evidence that health professionals, who one might expect to be accurately
informed about their own health, are equally likely to be undiagnosed with high cholesterol, but
respond more strongly to this new information. For future studies of health information
provision, our findings underscore the importance of distinguishing new from previously known
information and the potential relevance of health-related human capital.
47
However, on average, food away from home is associated with higher total calories and saturated fat (Nguyen and
Powell 2014; Mancino et al. 2009; Paeratakul et al. 2003; Bowman and Vinyard 2004; Binkley 2008; Larson et al.
2011).
48
According to the RAND study (2013), the national average participation rate for biometric tests is 46%, which is
exactly equal to the share of eligible employees at this firm who completed the cholesterol test.
27
References
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RAND. 2013. “Workplace Wellness Programs Study.” Final Report by Soeren Mattke,
Hangsheng Liu, John P. Caloyeras, Christina Y. Huang, Kristin R. Van Busum, Dmitry
Khodyakov, and Victoria Shier.
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28
Figures
Figure 1: Average total weekly spending at the cafeteria for the study sample.
Figure 2: Share of weekly spending devoted to healthy items for the study sample.
29
Figure 3: Event study estimates for total weekly spending (left side) and healthy share of spending
(right side) at the cafeteria for the full sample, the high-risk and undiagnosed high-risk subsamples.
Note: The excluded time dummy is the week preceding the test (-1). The coefficients labeled “-15”
(and “15”) represent all weeks that precede (follow) the cholesterol test by 15 weeks or more. The
panel is balanced within the (-15,+15) interval but not outside it. Bars represent 95% confidence
intervals.
30
Table 1: Descriptive Statistics
All employees who took the
cholesterol test and ate at the
cafeteria at least once in Jan-March
2013 and at least once in Jan-April
2014.
Obs.
Mean
(1)
(2)
All remaining employees eligible to
take the Health Risk Assessment
Obs.
Mean
(3)
(4)
P-value3
(5)
Female
1,265
0.790
4,972
0.721
0.000
Age
1,265
46.0
4,972
43.4
0.000
0.43
0.37
0.20
0.000
0.67
0.22
0.06
0.05
0.000
0.000
0.118
0.351
0.15
0.24
0.16
0.22
0.537
0.023
0.23
0.38
0.17
0.46
0.000
0.000
0.201
0.208
0.205
0.192
0.194
0.501
0.088
Age below 40
Age 40-54
Age above 55
0.32
0.46
0.23
Ethnicity
White
Black
Asian
Hispanic
1,265
Occupational categories1
Health-trained
Physicians (including residents)
Nurses
1,265
Non-health trained
Other professional occupations
Other nonprofessional occupations
Healthcare cost quintiles
In 2012 claims data
1
2
3
4
5
Not in 2012 claims data2
4,209
0.77
0.14
0.05
0.04
4,972
991
274 / 1265
2,490
0.178
0.189
0.201
0.224
0.209
0.219
2482 / 4972
0.002
1
The non-health professional category includes professional directors, coordinators, managers, professional support, and administration. The non-health non-
professional category includes administrative support, technicians, supervisors, support services, maintenance skilled trade, and non-professional support.
2
Health care cost quintiles are created using 2012 medical claims data for employees with at least one claim. For those who do not appear in the claims data,
we cannot determine whether they had $0 medical expenses or were not covered by the insurance plans offered by the employer. 4 The p-values shown are
based on a t-test of mean equality (female, age, ethnicity and occupational categories and "Not in 2012 claims data") or a Kolmogorov-Smirnov equality-ofdistributions test (Age categories, Healthcare cost quintiles).
Table 2: Biometric Statistics from 2013 Health Assessment
All employees who took the cholesterol test and ate at the cafeteria
at least once in Jan-March 2013 and at least once in Jan-April 2014.
Obs. (Employees tested)
(1)
Fraction of people at
high risk1
(2)
Fraction of people at
high risk and undiagnosed2
(3)
Cholesterol
1,265
29.2%
19.8%
Blood Pressure
1,060
24.1%
14.5%
Glucose
1,254
1.8%
0.4%
Overweight
971
32.4%
Obese
971
36.7%
Cholesterol by occupation
Health-trained
Physicians (including residents)
Nurses
163
303
31.9%
22.1%
23.3%
15.5%
351
447
26.8%
33.9%
18.0%
22.2%
Non-health trained
Other professional occupations
Other nonprofessional occupations
1
High risk for cholesterol was defined as total cholesterol at or above 240, LDL at or above 160, or HDL below 40 (Men) or 45 (Women). High risk for blood pressure was
defined as a systolic value above or at 140 or diastolic blood pressure above or at 90. High risk for glucose was defined as glucose above 126 (if fasting) or 200 (if not
fasting). Being overweight was defined as a value from 25 to 30 for BMI. Being obese was defined as a value above or at 30 for BMI. 2Undiagnosed was defined as
answering "No" to the question: "Do you have, or have you been told that you have any of the following health conditions?" for the health conditions high cholesterol, high
blood pressure, and diabetes, respectively.
Table 3: Cafeteria Spending Statistics Across Sub-Groups Pre-HRA, by week
Healthy share2
Calories
Grams of
Saturated Fat
(2)
(3)
(4)
(5)
Milligrams of
Dietary
Cholesterol
(6)
44,049 / 1265
7.26
0.46
448.73
6.34
110.02
Low risk
30,749 / 904
6.66
0.48
407.38
5.66
93.84
High risk
13,300 / 371
8.64
0.41
544.92
7.92
147.67
BMI in healthy range
10,314 / 302
5.81
0.50
363.96
5.03
77.89
Overweight
10,868 / 320
7.27
0.48
426.30
5.87
104.31
Obese
12,744 / 357
8.58
0.39
558.70
8.08
142.14
9,576 / 268
9.67
0.42
599.28
8.56
143.84
34,473/ 1007
6.59
0.47
405.65
5.70
100.35
Under 40
14,563 / 401
6.94
0.43
439.54
6.03
99.10
40-55
19,859 / 580
7.31
0.46
441.23
6.19
112.06
Over 55
9,627 / 294
7.66
0.50
479.06
7.13
122.95
Physicians
6,063 / 163
6.99
0.54
348.27
4.51
72.50
Nurses
11,164 / 303
4.87
0.46
290.88
3.99
66.65
Other professionals
9,371 / 351
7.98
0.53
439.98
6.06
101.40
Other nonprofessionals
17,451 / 448
8.50
0.40
589.36
8.61
155.40
Low spending
19,716 / 562
7.11
0.47
451.67
6.38
112.36
High spending
14,224 / 429
7.55
0.44
452.80
6.49
115.17
Full sample
Total
Observations/
Individuals
spending
(1)
1
Cholesterol screening3
BMI4
Gender
Male
Female
Age
Occupation category5
Healthcare spending6
Notes:1Total spending is the employee’s total weekly spending at the cafeteria. 2Healthy share is the share of weekly spending devoted to healthy
items.3High cholesterol was defined as total cholesterol at or above 240, LDL at or above 160, or HDL below 40 (Men) or 45 (Women). 4Being
overweight was defined as a value from 25 to 30 for BMI. Being obese was defined as a value above or at 30 for BMI. 5The non-health professional
category includes professional directors, coordinators, managers, professional support, and administration. The non-health non-professional category
includes administrative support, technicians, supervisors, support services, maintenance skilled trade, and non-professional support. 6Employees in the
fourth and fifth quintiles of 2012 medical claims spending are classified as "high spending" while the first three quintiles are considered "low spending".
The sample sizes for "Low spending" and "High spending" do not sum to the size of our full sample because not every employee appears in the medical
claims data.
Table 4: Impact of the HRA information on cafeteria purchases
Total spending1
All employees
who took the
cholesterol test
(1)
Full sample
Healthy share2
Only employees at
high risk3
(2)
Only employees at
high risk and
undiagnosed4
(3)
All employees
who took the
cholesterol test
(4)
Only employees at
high risk3
(5)
Only employees at
high risk and
undiagnosed4
(6)
-0.0952
(0.134)
-0.850***
(0.285)
-1.118***
(0.346)
0.00137
(0.007)
0.018
(0.012)
0.0274*
(0.016)
Observations (employee-weeks)
Observations (employees)
87,285
1,265
25,461
369
17,250
250
54,786
1,265
16,796
369
11,503
250
Physicians5
-0.239
(0.422)
-1.962**
(0.736)
-2.545***
(0.913)
0.00164
(0.0199)
0.0165
(0.0361)
0.0407
(0.0584)
Observations (employee-weeks)
Observations (employees)
11247
163
3588
52
2622
38
7000
163
2272
52
1701
38
Nurses7
-0.0287
(0.216)
-0.296
(0.472)
-1.117**
(0.548)
0.0195
(0.0147)
0.0222
(0.0256)
0.0589*
(0.0297)
Observations (employee-weeks)
Observations (employees)
20907
303
4623
67
3243
47
10999
303
2554
67
1792
47
Other Professionals6
-0.102
(0.253)
-0.690
(0.615)
-0.998
(0.715)
-0.00244
(0.0118)
0.00159
(0.0192)
0.0130
(0.0270)
Observations (employee-weeks)
Observations (employees)
24219
351
6900
100
4623
67
15632
351
4752
100
3102
67
Other Non-Professionals8
0.0205
(0.239)
-0.652
(0.427)
-0.349
(0.487)
-0.00225
(0.0120)
0.0293
(0.0191)
0.0261
(0.0262)
Observations (employee-weeks)
Observations (employees)
30912
448
10350
150
6762
98
21155
448
7218
150
4908
98
Age 40 and below
0.3
(0.278)
-0.971
(0.700)
-1.011
(0.806)
-0.00565
(0.013)
0.0239
(0.030)
0.0345
(0.033)
Observations (employee-weeks)
Observations (employees)
27,600
400
6,210
90
5,520
80
16,710
400
4,201
90
3,797
80
Ages 40-55
-0.315
(0.195)
-0.984**
(0.421)
-1.132**
(0.476)
-0.00964
(0.011)
0.00213
(0.016)
0.0162
(0.025)
Observations (employee-weeks)
Observations (employees)
39,468
572
12,696
184
7,728
112
24,976
572
8,345
184
5,072
112
Age 55 and over
-0.243
(0.252)
-0.879
(0.577)
-1.384**
(0.673)
0.0263**
(0.012)
0.0494**
(0.021)
0.0500*
(0.026)
Observations (employee-weeks)
Observations (employees)
20,217
293
6,555
95
4,002
58
13,100
293
4,250
95
2,634
58
Notes: 1Total spending is the employee’s total weekly spending at the cafeteria. 2Healthy share is the share of weekly spending devoted to healthy items. 3High risk was defined as total
cholesterol at or above 240, LDL at or above 160, or HDL below 40 (Men) or 45 (Women). 4Undiagnosed was defined as answering "No" to the question: "Do you have, or have you been
told that you have any of the following health conditions?" for high cholesterol. Statistically significant coefficients are indicated as follows: *10%; **5%; ***1%. 5The health professional
category includes: physicians, interns/residents, and non-physician practicioners. 6The health non-professional category includes clinical nurses and licensed practical nurses. 7The nonhealth professional category includes professional directors, coordinators, managers, professional support, and administration. 8The non-health non-professional category includes
administrative support, technicians, supervisors, support services, maintenance skilled trade, and non-professional support.
Table 5: Impact of the HRA information on cafeteria purchases by demographic characteristics
Total spending1
Healthy share2
All employees
who took the
cholesterol test
(1)
Only employees at
high risk3
(2)
Only employees at
high risk and
undiagnosed4
(3)
All employees
who took the
cholesterol test
(4)
Only employees at
high risk3
(5)
Only employees at
high risk and
undiagnosed4
(6)
Females
0.0592
(0.142)
-0.499*
(0.301)
-0.852**
(0.403)
0.00158
(0.008)
0.0251*
(0.013)
0.0380**
(0.018)
Observations (employee-weeks)
Observations (employees)
68,931
999
18,285
265
11,937
173
42,131
999
11,853
265
7,807
173
-0.803**
(0.352)
-2.696***
(0.729)
-2.567***
(0.655)
-0.00033
(0.015)
-0.0041
(0.028)
-0.00164
(0.042)
Observations (employee-weeks)
Observations (employees)
18,354
266
7,176
104
5,313
77
12,655
266
4,943
104
3,696
77
White employees
-0.0108
(0.146)
-0.588*
(0.327)
-0.942**
(0.394)
0.00213
(0.00756)
0.0183
(0.0142)
0.0237
(0.0202)
Observations (employee-weeks)
Observations (employees)
67,551
979
18,699
271
12,213
177
42,435
979
12,290
271
8,076
177
Non-white employees
-0.428
(0.322)
-1.651***
(0.561)
-1.482**
(0.680)
-0.000401
(0.0178)
0.0176
(0.0227)
0.0368
(0.0242)
Observations (employee-weeks)
Observations (employees)
19,734
286
6,762
98
5,037
73
12,351
286
4,506
98
3,427
73
High risk based on results of
blood pressure test
-0.462
(0.417)
-1.378*
(0.725)
-1.395*
(0.769)
-0.00209
(0.018)
0.0520**
(0.022)
0.0626**
(0.030)
Observations (employee-weeks)
Observations (employees)
17,595
255
6,624
96
4,623
67
12,004
255
4,554
96
3,197
67
Family history of heart disease
or high cholesterol
-0.00187
(0.170)
-0.432
(0.339)
-0.979**
(0.446)
-0.000917
(0.009)
0.0246
(0.015)
0.0595***
(0.021)
Observations (employee-weeks)
Observations (employees)
53,061
769
16,077
233
9,591
139
33,095
769
10,358
233
6,287
139
High risk based on weight
(obesity)
-0.261
(0.285)
-0.788*
(0.442)
-1.295**
(0.528)
0.0193*
(0.011)
0.0187
(0.018)
0.0116
(0.022)
Observations (employee-weeks)
Observations (employees)
24,564
356
10,764
156
7,590
110
16,047
356
7,062
156
5,013
110
High healthcare costs in 2012
(quintiles 4-5)
0.164
(0.215)
-0.0828
(0.417)
0.0519
(0.499)
-0.00876
(0.011)
0.0288*
(0.017)
0.0377
(0.025)
Observations (employee-weeks)
Observations (employees)
29,601
429
10,074
146
5,658
82
19,153
429
7,143
146
4,214
82
Males
Notes: 1Total spending is the employee’s total weekly spending at the cafeteria. 2Healthy share is the share of weekly spending devoted to healthy items. 3High risk was defined as total
cholesterol at or above 240, LDL at or above 160, or HDL below 40 (Men) or 45 (Women). 4Undiagnosed was defined as answering "No" to the question: "Do you have, or have you been told
that you have any of the following health conditions?" for high cholesterol. Statistically significant coefficients are indicated as follows: *10%; **5%; ***1%.
Table 6: Impact of the HRA information on cafeteria purchases
Total change after the HRA
All employees
Only employees at
who took the
Only employees at
high risk and
high risk1
undiagnosed2
cholesterol test
(1)
(2)
(3)
Total Spending on items with
nutritional information
-0.00163
(0.081)
-0.445***
(0.171)
-0.432**
(0.212)
Observations (employee-weeks)
Observations (employees)
83,628
1212
24,357
353
16,422
238
-10.95
(10.760)
-49.06**
(22.140)
-64.12***
(24.330)
Observations (employee-weeks)
Observations (employees)
83,628
1212
24,357
353
16,422
238
Grams of fat
-0.456
(0.501)
-2.193**
(0.990)
-3.059***
(1.100)
Observations (employee-weeks)
Observations (employees)
83,628
1212
24,357
353
16,422
238
Grams of saturated fat
-0.112
(0.172)
-0.595*
(0.335)
-0.950***
(0.365)
Observations (employee-weeks)
Observations (employees)
83,628
1212
24,357
353
16,422
238
Milligrams of dietary cholesterol
2.732
(4.660)
-11.14
(9.351)
-15.97
(11.500)
Observations (employee-weeks)
Observations (employees)
83,628
1212
24,357
353
16,422
238
-0.00168
(0.003)
-0.00732
(0.005)
-0.0132**
(0.006)
64,032
928
18,699
271
13,041
189
Calories
Share of weekly recommended saturated fats
based on NIH guidelines3
Observations (employee-weeks)
Observations (employees)
Change as a share of pre-HRA average
All employees
Only employees at
who took the
Only employees at
high risk and
high risk1
undiagnosed2
cholesterol test
(4)
(5)
(6)
0.000
-0.099
-0.094
-0.025
-0.090
-0.115
-0.025
0.093
-0.128
-0.018
-0.075
-0.118
0.025
-0.075
-0.112
-0.018
-0.071
-0.127
Notes: 1High risk was defined as total cholesterol at or above 240, LDL at or above 160, or HDL below 40 (Men) or 45 (Women). 2Undiagnosed was defined as answering "No" to the question: "Do you
have, or have you been told that you have any of the following health conditions?" for high cholesterol. 3The NIH guidelines that a person with high cholesterol should reduce her daily caloric intake
from saturated fats to less than 7% of her daily total caloric intake. While we do not observe an individual’s daily caloric intake, we estimate for each employee the basal metabolic rate (BMR), i.e. the
daily caloric intake to keep one’s weight constant using the Mifflin St Jeor Equation. We then use this estimate to calculate the maximum caloric intake from saturated fats for each individual which we
convert in grams multiplying by 9. Statistically significant coefficients are indicated as follows: *10%; **5%; ***1%.
Table 7: Robustness checks
Total spending1
Healthy Share2
All employees
who tested for
cholesterol
(1)
Only employees at
high risk3
(2)
Only employees at
high risk and
undiagnosed4
(3)
All employees
who tested for
cholesterol
(4)
Only employees at
high risk3
(5)
Only employees at
high risk and
undiagnosed4
(6)
Inverse probability weighting
to account for selection into HRA
-0.136
(0.203)
-1.247***
(0.331)
-1.389***
(0.339)
0.00456
(0.00940)
0.0382***
(0.0138)
0.0455***
(0.0173)
Observations (employee-weeks)
Observations (employees)
87285
1265
25461
369
17250
250
54786
1265
16796
369
11503
250
Frequent cafeteria visitors5
-0.547
(0.390)
-1.465*
(0.759)
-2.719***
(0.946)
0.00412
(0.0104)
0.0228
(0.0163)
0.0257
(0.0249)
Observations (employee-weeks)
Observations (employees)
20,493
297
7,176
104
4,761
69
19,293
297
6,690
104
4,424
69
Food only: Main cafeteria
-0.115
(0.114)
-0.791***
(0.234)
-1.054***
(0.281)
-0.00366
(0.008)
0.0196
(0.0126)
0.0237
(0.0154)
Observations (employee-weeks)
Observations (employees)
87,285
1,265
25,461
369
17,250
250
52,063
1,265
16,058
369
10,933
250
Including all onsite eateries
-0.0937
(0.178)
-1.168***
(0.371)
-1.218**
(0.483)
-0.000181
(0.00571)
0.0180*
(0.0101)
0.0224
(0.0138)
Observations (employee-weeks)
Observations (employees)
87,285
1,265
25,461
369
17,250
250
66,053
1,265
20,014
369
13,740
250
Spending on unhealthy items
-0.0906
(0.0628)
-0.485***
(0.135)
-0.578***
(0.163)
Observations (employee-weeks)
Observations (employees)
87975
1,265
25599
371
17388
252
Spending on ambiguous items
-0.0294
(0.0455)
-0.172*
(0.0968)
-0.282**
(0.116)
Observations (employee-weeks)
Observations (employees)
87975
1,265
25599
371
17388
252
0.0246
(0.0897)
-0.210
(0.170)
-0.274
(0.210)
87,975
1,265
25,599
371
17,388
252
Spending on healthy items
Observations (employee-weeks)
Observations (employees)
Notes:
1
Total
spending
is
the
employee’s
total
weekly
spending
at
the
cafeteria.
2
Healthy
share
is
the
share
of
weekly
spending
devoted
to
healthy
items.
3
High risk was defined as total cholesterol at or above 240, LDL at or above 160, or HDL below 40 (Men) or 45 (Women). 4Undiagnosed was defined as answering "No" to the question: "Do you have, or
have you been told that you have any of the following health conditions?" for the health condition high cholesterol. 5Frequent cafeteria visitors are those who make at least one purchase at the cafeteria in
almost all weeks, with fewer than 15 weeks of $0 spending. Statistically significant coefficients are indicated as follows: *10%; **5%; ***1%.
Appendix for Online Publication
Appendix Table A1: Effect of Receiving a Cholesterol Report on Food Purchases at the Cafeteria
Total spending1
15+ weeks before cholesterol test
14 weeks before cholesterol test
13 weeks before cholesterol test
12 weeks before cholesterol test
11 weeks before cholesterol test
10 weeks before cholesterol test
9 weeks before cholesterol test
8 weeks before cholesterol test
7 weeks before cholesterol test
6 weeks before cholesterol test
5 weeks before cholesterol test
4 weeks before cholesterol test
3 weeks before cholesterol test
2 weeks before cholesterol test
Week of cholesterol test
1 week after cholesterol test
Healthy share2
All employees
who tested for
cholesterol
(1)
Only employees at
high risk3
Only employees at
high risk and
undiagnosed4
Only employees at
high risk3
Only employees at
high risk and
undiagnosed4
(3)
All employees
who tested for
cholesterol
(4)
(2)
(5)
(6)
0.00745
(0.260)
-0.0868
(0.296)
-0.315
(0.274)
-0.0615
(0.296)
-0.460
(0.282)
0.148
(0.272)
0.252
(0.269)
0.0790
(0.280)
-0.108
(0.250)
-0.0947
(0.263)
0.326
(0.248)
0.233
(0.257)
0.197
(0.246)
0.317
(0.226)
0.634***
(0.243)
-0.218
(0.228)
-0.729
(0.573)
-0.953
(0.669)
-0.563
(0.620)
-0.847
(0.669)
-1.182*
(0.635)
-0.585
(0.593)
0.0686
(0.551)
-0.283
(0.647)
-0.804
(0.557)
-0.598
(0.606)
-0.302
(0.495)
-0.0132
(0.540)
-0.567
(0.498)
-0.452
(0.465)
0.0574
(0.540)
-1.057**
(0.487)
-0.594
(0.680)
-1.047
(0.739)
-0.853
(0.734)
-1.030
(0.781)
-1.425**
(0.692)
-0.577
(0.680)
0.373
(0.696)
0.124
(0.805)
-0.498
(0.674)
-0.962
(0.686)
-0.00609
(0.595)
-0.183
(0.602)
-0.437
(0.558)
-0.303
(0.490)
-0.204
(0.540)
-1.154**
(0.531)
-0.00755
(0.0128)
-0.00328
(0.0145)
0.000622
(0.0137)
-0.0113
(0.0139)
-0.00975
(0.0137)
-0.0108
(0.0142)
-0.00139
(0.0130)
0.0138
(0.0133)
0.00571
(0.0134)
0.00815
(0.0135)
-0.00659
(0.0127)
0.00339
(0.0133)
0.0200
(0.0135)
-0.00492
(0.0132)
0.0111
(0.0122)
0.00371
(0.0129)
-0.0188
(0.0240)
-0.0428*
(0.0245)
-0.0130
(0.0233)
-0.0411
(0.0250)
-0.0208
(0.0251)
-0.0448*
(0.0259)
-0.000662
(0.0225)
-0.0152
(0.0229)
-0.00893
(0.0232)
-0.00185
(0.0241)
-0.00995
(0.0228)
-0.0102
(0.0237)
0.0138
(0.0245)
-0.0119
(0.0214)
0.0178
(0.0197)
0.0102
(0.0216)
-0.00623
(0.0343)
-0.0441
(0.0349)
-3.56e-05
(0.0297)
-0.0328
(0.0331)
-0.0262
(0.0337)
-0.0528
(0.0358)
0.00357
(0.0310)
-0.00626
(0.0297)
-0.00594
(0.0294)
-0.00714
(0.0312)
-0.00816
(0.0287)
-0.00567
(0.0325)
0.0198
(0.0311)
-0.00404
(0.0254)
0.0542**
(0.0223)
0.0198
(0.0272)
2 weeks after cholesterol test
3 weeks after cholesterol test
4 weeks after cholesterol test
5 weeks after cholesterol test
6 weeks after cholesterol test
7 weeks after cholesterol test
8 weeks after cholesterol test
9 weeks after cholesterol test
10 weeks after cholesterol test
11 weeks after cholesterol test
12 weeks after cholesterol test
13 weeks after cholesterol test
14 weeks after cholesterol test
15+ weeks after cholesterol test
Week and Individual Fixed Effects
Total number of observations at the
person/week level
Total number of individuals
R2 (overall)
-0.0329
(0.244)
-0.0288
(0.250)
-0.125
(0.264)
-0.239
(0.262)
-0.321
(0.267)
-0.155
(0.258)
0.270
(0.264)
-0.0977
(0.271)
0.133
(0.281)
0.221
(0.267)
0.0977
(0.285)
0.0798
(0.275)
0.187
(0.284)
0.146
(0.257)
Yes
-1.126**
(0.480)
-1.438***
(0.501)
-1.827***
(0.584)
-1.736***
(0.594)
-1.884***
(0.555)
-1.598***
(0.546)
-0.980*
(0.553)
-1.193**
(0.577)
-1.473**
(0.609)
-0.837
(0.610)
-1.196*
(0.635)
-0.700
(0.575)
-0.710
(0.639)
-1.103*
(0.573)
Yes
-1.317**
(0.548)
-1.737***
(0.505)
-2.185***
(0.661)
-1.800**
(0.708)
-1.615**
(0.636)
-1.781***
(0.628)
-1.231**
(0.616)
-1.281**
(0.601)
-1.746***
(0.630)
-1.225*
(0.676)
-2.083***
(0.706)
-1.052
(0.683)
-1.189
(0.764)
-1.522**
(0.696)
Yes
0.0167
(0.0127)
0.0105
(0.0136)
-0.00451
(0.0136)
0.00620
(0.0140)
0.00463
(0.0152)
0.0150
(0.0140)
-0.0121
(0.0139)
-0.0117
(0.0146)
-0.00383
(0.0142)
-0.00121
(0.0140)
0.00809
(0.0147)
-0.0110
(0.0142)
-0.00401
(0.0146)
0.000141
(0.0134)
Yes
0.0160
(0.0210)
0.00244
(0.0209)
-0.00777
(0.0241)
0.0124
(0.0244)
0.00348
(0.0242)
0.0276
(0.0241)
0.000483
(0.0230)
0.00941
(0.0252)
0.0211
(0.0243)
-0.0119
(0.0219)
0.0180
(0.0235)
-0.0192
(0.0226)
-0.00839
(0.0241)
0.0167
(0.0222)
Yes
0.0341
(0.0267)
0.0350
(0.0273)
0.0118
(0.0293)
0.0106
(0.0315)
0.0236
(0.0321)
0.00682
(0.0297)
0.0153
(0.0301)
0.0219
(0.0342)
0.0284
(0.0325)
0.00453
(0.0289)
0.0340
(0.0320)
-0.00964
(0.0321)
0.0114
(0.0334)
0.00929
(0.0308)
Yes
87,285
1,265
0.538
25,461
369
0.555
17,250
250
0.538
54,786
1,265
0.421
16,796
369
0.418
11,503
250
0.406
Notes: There is no event week dummy for the week preceding the cholesterol test, so all results are relative to that week. 1Total spending is the employee’s total weekly spending at the cafeteria.
2
Healthy share is the share of weekly spending devoted to healthy items. 3High risk was defined as total cholesterol at or above 240, LDL at or above 160, or HDL below 40 (Men) or 45
(Women). 4Undiagnosed was defined as answering "No" to the question: "Do you have, or have you been told that you have any of the following health conditions?" for high cholesterol.
Statistically significant coefficients are indicated as follows: *10%; **5%; ***1%.
Table A2: Other robustness checks
Total spending1
Healthy Share2
All employees
who tested for
cholesterol
(1)
Only employees at
high risk3
(2)
Only employees at
high risk and
undiagnosed4
(3)
All employees
who tested for
cholesterol
(4)
Only employees at
high risk3
(5)
Only employees at
high risk and
undiagnosed4
(6)
Balanced in event-time5
-0.0408
(0.154)
-0.817**
(0.331)
-1.085***
(0.415)
-0.00198
(0.00783)
0.0167
(0.0140)
0.0245
(0.0201)
Observations (employee-weeks)
Observations (employees)
87,285
1,265
25,461
369
17,250
250
54,786
1,265
16,796
369
11,503
250
Varying excluded weeks:6
Excluding weeks -1 to +1
-0.0789
(0.144)
-0.809***
(0.308)
-1.095***
(0.386)
0.00105
(0.00742)
0.0188
(0.0131)
0.0280
(0.0187)
Observations (employee-weeks)
Observations (employees)
87,285
1,265
25,461
369
17,250
250
54,786
1,265
16,796
369
11,503
250
Varying excluded weeks:6
Week of HRA considered pre-test
-0.155
(0.129)
-0.907***
(0.277)
-1.142***
(0.330)
0.0000282
(0.00666)
0.0146
(0.0113)
0.0185
(0.0158)
Observations (employee-weeks)
Observations (employees)
87,285
1,265
25,461
369
17,250
250
54,786
1,265
16,796
369
11,503
250
Varying excluded weeks:6
Week of HRA considered post-test
-0.0255
(0.128)
-0.696***
(0.267)
-0.950***
(0.326)
0.00257
(0.00657)
0.0192*
(0.0111)
0.0326**
(0.0149)
Observations (employee-weeks)
Observations (employees)
87,285
1,265
25,461
369
17,250
250
54,786
1,265
16,796
369
11,503
250
Excluding holiday weeks7
-0.0914
(0.136)
-0.853***
(0.291)
-1.137***
(0.354)
0.000423
(0.00702)
0.0174
(0.0119)
0.0255
(0.0166)
Observations (employee-weeks)
Observations (employees)
83,490
1,265
24,354
369
16,500
250
52,663
1,265
16,143
369
11,054
250
-0.0239
(0.0673)
-0.337**
(0.148)
-0.367**
(0.174)
0.00376
(0.00584)
0.0174
(0.0109)
0.0302*
(0.0166)
87,285
1,265
25,461
369
17,250
250
54891
1,265
16827
369
11527
250
Items rather than spending8
Observations (employee-weeks)
Observations (employees)
Notes:
3
1
Total
spending
is
the
employee’s
total
weekly
spending
at
the
cafeteria.
2
Healthy
share
is
the
share
of
weekly
spending
devoted
to
healthy
items.
4
High risk was defined as total cholesterol at or above 240, LDL at or above 160, or HDL below 40 (Men) or 45 (Women). Undiagnosed was defined as answering "No" to the question: "Do you have, or
have you been told that you have any of the following health conditions?" for the health condition high cholesterol. 5This specification includes all individuals only from the period 13 weeks prior to their
test to 23 weeks following their test. 6In our main specification, week 0, i.e. the week of the health risk assessment, is excluded. 7This specification drops the weeks of New Year's Day, Thanksgiving and
Christmas because they are outliers in weekly spending. 8In the regressions with HealthyShare as the dependent variable, HealthyShare is the share of items that are classified as healthy, and observations
are weighted by the number of items purchased. Statistically significant coefficients are indicated as follows: *10%; **5%; ***1%.
Appendix Table A2: Other robustness checks
Also in Appendix Table A2 we run some additional robustness checks. First, we limit our
sample to a balanced panel in event-time, with each individual appearing only from 13
weeks prior to their test to 23 weeks after their test, the longest span of time before and
after the test that we can observe for all of the individuals in our sample. The results
indicate that our event-time results are not affected by compositional change.
Next, we vary the length of the test period excluded from the pre-post analysis (as
described in Section 3, we effectively exclude the test week from our main analysis).
First, we exclude the period from the week prior to the week following each individual’s
cholesterol test, then we do not exclude any weeks, defining the week of the cholesterol
test as part of the pre-period and in another check as part of the post-period. Finally, since
Thanksgiving is the week with the lowest overall spending at the cafeteria, and occurs
soon after the end of the period in which employees were incentivized to complete their
health risk assessments, we drop this week along with the two other weeks of lowest
spending (the first week in 2013 and the last week in 2013) from the analysis. Overall,
none of these additional checks produces results that differ substantially from our main
specification.
Another possible concern is that following the cholesterol test, employees might feel
guilty about an unhealthy purchase and choose to pay it in cash, instead of with their ID
card. However, if this were occurring, we would expect to see a decrease in the share of
cafeteria sales linked to employee ID cards as the year progressed, and in particular after
the months of May, October, and November, in which completion of the health
assessment was most common. In fact, as shown in Appendix Figure 1, the share of
revenue linked to ID cards hovers between 70 and 75% over our sample period, showing
no support for this hypothesis.
A concern with using total spending and healthy share of spending as our outcome
variables is that healthy items might cost more, on average, than unhealthy items. Thus,
the individuals who shift towards healthier items might not decrease their spending as
much as individuals who simply choose to eat less at the cafeteria. To address this
concern, in the sixth panel of Appendix Table 2, we run a robustness check using total
items purchased, and healthy share of items purchased, as the dependent variables. The
results for healthy share of items indicate that the high-risk and undiagnosed high-risk
employees increase their healthy shares by 1.7 and 3 percentage points, respectively.1 In
magnitude, these estimated effects using healthy share of items are only slightly larger
than the effects on healthy share of spending shown on Table 4, suggesting that there is
not a large difference in average item price between healthy and unhealthy items.
Appendix Figure 1. Distribution of cholesterol test dates, and the share of weekly
cafeteria revenue not linked to an employee ID card. The Health Risk Assessment
could be taken any time between April 1 and November 15, 2013. As shown in the bar
graph, the most popular weeks were the week of the first health fair in early May, and
the last possible week in November.
1
In the regressions with healthy share of items as the dependent variable, the observations are weighted by
total items purchased, because healthy share has smaller variance when more items are purchased.
Appendix Table B1: Food Items Information and Classification
Food Item
20 oz bot bev
Salad Bar
French Fries
Coffee Small
Brlfast Potatos
Soup 8 oz
Lays Chips
Coffee Medium
Veg of the Day
Gold Tea
Fruit Bar
Milk
Chix Tndr Brd
3 links sausage
Bottled Water
Soup 16 oz
Starch of Day
Bagel
Cheeseburger
Diet Coke Spec
Wings
3 pieces bacon
Scrambled Eggs
Juice Lg
Whole Fruit
Omlt Westrn
Donut
Spec Chix Sand
muffin
Cream Cheese
Coffee Large
Juice Small
Breadstick
Deli Bar
Sushi Asst
Scr Egg Deal
Vitamin Water
Calypso
Sliced Bread
Sour Cream
Seafood Bar
Bkf EggChsWrap
Onion Rings
Br Sandwich/Meat
Pasta Bar
Chili 8 oz
Honest Tea
2Hard Boiled Egg
Greek Yogurt
Auto Refill
Cornbread Muffin
Asian Bar
Hamburger
Powerade
Purchase Average
Grams
Grams of
Dietary
frequency price Unhealthy Healthy Ambiguous Beverage Calories of fat saturated fats cholesterol
95,172
60,308
42,562
36,800
34,360
27,681
24,622
22,363
21,143
19,842
19,329
18,889
18,860
18,488
18,141
17,580
17,358
17,352
17,241
16,389
16,047
15,746
13,590
13,301
11,972
11,744
11,062
10,994
10,765
10,322
10,307
9,809
9,580
9,579
9,483
9,042
8,803
8,481
8,347
7,838
7,660
7,557
7,472
7,395
7,321
7,235
6,787
6,724
6,687
6,539
6,294
6,177
5,947
5,921
1.35
3.35
1.21
1.6
1.17
1.63
1.07
1.8
0.92
1.93
1.9
0.62
3.5
1.35
1.32
2.94
0.91
0.83
2.39
1.06
3.18
1.44
1.31
1.75
0.74
3.2
0.97
3.42
1.34
0.28
1.95
0.95
0.63
2.85
6.86
3.57
1.95
1.91
0.3
0.28
4.36
2.7
1.18
2.36
4.32
2.04
1.85
0.85
1.85
1.27
0.53
5.27
1.93
1.62
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
219.42
0
150
199.56
160
5
50
80
10.13
0
3.6
5.81
5.5
0.1
0
0
3.38
0
0.94
2.45
0.75
0
0
0
0
0
0
20.78
0
0
0
0
146.67
373.33
315
0
399.13
134.78
120
683.02
0
713.52
120.81
150
120
108
170
302.5
512.83
390
90
6
143.33
110
4.33
12
31.5
0
11.63
1.35
1
36.96
0
46.38
9.01
10
0
0.2
12
16.25
16.97
13.5
9
0.1
0
4
2.67
1.33
10.5
0
4.91
0
0
15.13
0
14.27
3.16
3
0
0.05
4
4.25
5.79
3.5
6
0
0
1
20
93.33
52.5
0
41.56
0
0
121.91
0
317.51
28.86
420
0
0
255
25
99.27
40
30
0
0
0
420.81
48
300
130
60
22.61
0
0
1
5
7.1
0
0
0
3
448.86
0
0
0
20
160
8
1
0
175.5
94
160
80
5.5
0
10
0
2.87
0
3
0
19.5
0
420
10
623.02
65
32.96
0
12.63
0
106.91
0
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Appendix Table B1 (continued): Food Items Information and Classification
Food Item
Blk Bean Burger
Burger Bar
Grill Chix Sand
Dan Yogurt
Trail Mix
Snackwells
Pierogi Bar
Chicken Bar
1Hard Boiled Egg
Grp/Chs/Crax LG
Chili 16 oz
Lorna Doone
1 Ea Hot Dog
Fried egg 1
Smart Wat 1 Litr
Pepperoni Pizza
Bowl Hot Cereal
Br Sand Grill
Butter
Cereal Box Deal
Andou Sge
Turkey&Stuffing
pretzel hummus
Portugues Flatbr
Hot Cereal 8 oz
Peanut Butter
Protein Meal Bar
Core Power
Pita Bread
Cheese Pizza
Parfait Small
Chix Picat/rice
Fajita Bar
Falafil Wrap
Rice Pudding
salty nut bar
V8 Fusion
Pizza of Week
Extra Cheese
BAC 2 item
Chili Dog
Chef Chicken Sal
Naked Juice
Pancake - 1
Hummus
Fiber One Bar
Jelly
French Tst-1 pc
Saus Gravy/Bisc
egg gyro pita
CP Chix Sal Toss
Popcorn Campbell
Crackers
Purchase Average
Grams
Grams of
Dietary
frequency price Unhealthy Healthy Ambiguous Beverage Calories of fat saturated fats cholesterol
5,913
5,824
5,562
5,141
5,035
5,011
4,983
4,953
4,818
4,693
4,644
4,618
4,378
4,365
4,298
4,226
4,170
4,096
3,851
3,727
3,707
3,517
3,448
3,326
3,252
3,239
3,112
3,075
2,970
2,960
2,795
2,775
2,704
2,659
2,642
2,641
2,543
2,523
2,362
2,274
2,217
2,182
2,149
2,107
2,102
2,077
2,058
2,028
2,016
1,894
1,886
1,877
1,833
1.63
3.19
2.98
1.06
0.97
0.57
4.18
3.63
0.41
2.81
3.38
0.56
1.67
0.84
1.88
3.72
1.22
3.08
0.1
1.76
1.92
2.7
2.14
3.81
1.22
0.28
1.33
1.6
0.93
3.17
2.19
3.1
5.09
4.84
2.62
1.12
1.61
4.63
0.47
6.74
2.18
6.46
3.45
0.91
3.34
1.04
0.1
1.21
1.77
3.71
5.04
3.91
0.09
X
X
X
X
X
X
392.02
109.25
280
210
7.96
1.4
17
5
2.63
0.78
4.5
1.5
70.41
5.5
0
0
78
5.3
1.6
212
351
100
270
89
0
420
11
3
15
6.8
0
20
5.73
1.5
5.5
1.9
0
10
39
0
25
210
0
35
50
312.5
5.5
5.5
3.5
1.88
15
12.5
315
21
3
0
120
138
240
10
10.5
3.5
1.5
1.6
2
0
0
15
380
16
8
25
170
105
400
8
0
18
2.5
0
9
0
0
30
200
4
1
10
140
35
130
4
0
2.25
2
0
0.5
0
0
37.5
126
7.11
1.61
2.5
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Appendix Table B1 (continued): Food Items Information and Classification
Food Item
Apple Cider PT
Croissant
Tea Sml
chicken only
Soup & Sand Chip
Pita Chips
Cheese Packet
Cold Cereal Box
Omlt Ham&Chs
Grl Chs Only
Side Mac Cheese
Oatmeal Bar
Soy Milk
Hispanic Bar
Chix Marsala/Ndl
rst pork/2 sides
Chix/2 sides
Dessert of Day
Cottage Cheese
Apple Tart
BestBiteEggOnly
BAC 3 item
Quiche egg wht
Pizza by slice
2 veg sausage
Bkfst Ques Delux
Stk Sand/Fries
Egg Salad Wrap
burrito
Roll
Manicotti
Chicken Wrap
Fuze
Salmon/2 sides
English Muffin
French Tst Stick
Italian Bar
Tabooleh
CP Chix Pasta
PorkChop/2 sides
Omlt Chs
sm frsh frt cup
1 pork chop
2 Ea Hot Dogs
Pop Tarts
Tea Med
Pancake Bar
Greek Salad
BAC a la carte
Smart Water
Gelatin
Veal Parm/Pasta
Rib Burn Off Tkt
Purchase Average
Grams
Grams of
Dietary
frequency price Unhealthy Healthy Ambiguous Beverage Calories of fat saturated fats cholesterol
1,800
1,798
1,792
1,767
1,720
1,709
1,695
1,637
1,611
1,601
1,552
1,529
1,515
1,458
1,458
1,452
1,425
1,371
1,355
1,342
1,342
1,320
1,283
1,270
1,262
1,246
1,228
1,225
1,219
1,164
1,150
1,098
1,098
1,097
1,092
1,082
1,073
1,067
1,058
1,049
1,045
1,044
1,036
1,023
1,022
1,003
982
980
978
966
944
937
929
0.04
0.64
2.02
2.06
3.46
1.29
0.43
1.24
2.57
2.01
1.2
0.82
1.13
4.71
3.04
3.78
3.59
1.06
0.71
2.57
2.01
9.65
1.91
2.48
1.31
2.15
2.95
4.07
1.31
0.5
3.34
5.76
1.81
4.17
0.59
0.93
4.32
2.88
3.95
4.32
2.1
2.57
0.83
3.03
0.87
2
3.13
6.29
3.72
1.71
0.81
3.41
1.02
X
X
X
X
X
X
X
X
X
X
202.5
3
0.38
0
X
X
135
3.25
0.5
0
90
3
2
15
16.67
0
0
0
540
400
30
10
11
3
50
0
0
0
0
0
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Appendix Table B1 (continued): Food Items Information and Classification
Food Item
Pasta Salad Toss
Parfait Lg
Br Sand/no meat
2 pork chops
roast pork
Dawg Bar
Applesauce
ch paprik/spaetz
Bkfst Ques Veg
Hot Choc
Chick Gyro Wrap
Autumn Rasp Sala
Meat Lasagna
Turkey only
Chix Tndr Grl
Whole Wing
rice/veg almonds
chic pot pie
salmon only
StirFry Chix/Ric
salmon/ 1 side
Fet Alfredo
Veg Stfd Pep
Beef Stew/noodle
Grl Ham Chs only
veg lasagna
Sole Stfd/2 side
Fish Sand only
Chef Turk Burg
Potsticker/Rice
CP Special
Apple Cider QT
Chix Skewer/Rice
tort/veg
Bourbon Bar
Mujadara
Capcino Small
BestBite Bkfst
Jambalaya
Fish Taco
Crn Bf Hash
Htdog/ch or frut
Syrup cup
Baja Wrap
Lg Sugar Cookie
Pork Deal CP
Grl Chs Tom
Eggplant Rollett
Dole fruit cris
Chix Cacciatore
Biscuit Bar
Salata only
lg sugar cookie
Purchase Average
Grams
Grams of
Dietary
frequency price Unhealthy Healthy Ambiguous Beverage Calories of fat saturated fats cholesterol
929
887
882
882
856
827
788
779
766
763
759
754
753
751
734
699
660
659
657
657
624
617
614
607
590
590
590
584
583
583
582
581
579
575
572
560
539
539
533
532
521
519
519
518
509
505
504
502
499
496
484
481
481
3.3
3.59
1.84
2.81
2.02
1.92
0.68
2.95
1.7
1.06
5.62
6.47
2.45
2.24
3.04
0.64
2.77
2.53
2.84
3.74
3.88
2.81
1.95
2.88
2.9
2.43
4.1
1.22
3.75
3.94
3.63
0.2
3.27
2.63
5.1
6.85
1.28
2.66
3.3
2.68
1.25
2.42
0.25
4.97
0.68
4.4
1.96
4.11
1.31
3.32
2.55
2.51
0.17
X
X
X
X
X
X
X
50
0
0
0
101.93
6.63
2.04
45.36
67.5
0
0
0
65
0
0
0
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Appendix Table B1 (continued): Food Items Information and Classification
Food Item
Spag/Mballs
Shrimp PoBoy
tuna melt only
Fuze Big
Baba Ghanoug
Soup 32 oz
Sole Stfd
Big Burger only
Sushi Salad
Muffin 6-7pm
Pasta/Sauce
CP PrmRib/Pot
Tukey Deal CP
Tofu Stir Fry
Cracker Jacks
cod/2 sides
Flt/shrmp/2 side
Fish Sand/Slaw
Spec Salad
crust pizza slic
Mt/Veg Rice Bwl
Falafal SALAD
catfish po boy
Rustic Cod/2side
Tea Lg
Cod Rustic only
Chili Bar
mealoaf/pot
Angel Hair Pasta
steak sand only
Cntry Stk & Eggs
Capcino Medium
CP BBQ Bf Hgie
Beef Ravioli
CP Chix/Pot
Chix Phil/Fries
Honey
Capcino Large
Soft Pretzel
Eye Rnd/side
Meatloaf
Meatloaf Stacker
rst beef/2 sides
Seafood Newbrg
Chix Sand Combo
Cntry Bkfst
Turkey/2 sides
Pulled Pork Sand
Hot Cereal 16 oz
Tummy Yummy
ChixFetcini Alfr
Bf/Chix Philly
BAC 4 item
Purchase Average
Grams
Grams of
Dietary
frequency price Unhealthy Healthy Ambiguous Beverage Calories of fat saturated fats cholesterol
467
466
463
456
445
432
427
426
425
424
407
402
396
392
391
388
381
379
371
367
360
360
353
352
348
346
343
337
329
326
324
318
309
308
307
306
304
303
301
300
297
295
293
287
285
283
281
281
279
275
274
274
271
2.96
2.67
3.51
0.51
3.36
2.96
2.94
0.54
3.15
0.71
1.91
5.13
4.16
3.75
0.5
4.14
8.65
2.82
3.93
3.47
4.71
3.58
2.96
4.45
1.99
2.84
2.55
2.94
4.06
1.8
0.22
1.01
4.34
2.5
3.89
3.42
0.08
0.62
1.91
3.88
2.16
4.89
3.74
3.44
0.02
1.76
3.88
3.76
1.75
0.78
3.49
5.88
8.18
X
X
X
X
X
16.67
0
0
0
798.25
23.25
9.81
83.13
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Appendix Table B1 (continued): Food Items Information and Classification
Food Item
Coffee Card
Grill Brat Hoagi
Waffle Qtr
CP RotChix/2 sid
Popcorn 2 pkg
Cupcake
EggplntRol/pasta
French Dip/Pot
Greek Bar
meatloaf/2 sides
Lettuce Wrap
Bagel 6-7pm
CP Hand Tsd Sald
Chkpea Wat
Egg Roll Minh
Entree Mac Chees
Trky Mtloaf
Chicago Hot Dog
Grape Leaves
roast beef/pot
Roast Beef
Veal parm only
Beef Shaw Wrap
Omlt EgWhtTurk
Grl Chs Tom Bac
Chix/1 side
Caesar Salad Mah
Eggplant Sand
Dinner Roll
EggWht Brk Sand
Powerade Kids
Stfd Squash
Tuna Cass
Paczkis
pretzel/chez
peanuts
sweet bread
st cabb/potat
Candy Bar
cranb/nut salad
Bkd Potato
Fuji Apples
Californ Chix/Rc
Lester Special
Granola crunc bl
Big Burger
Chili 32 oz
small waterm cup
Fried Rice
Chix pad Thai
chdog/ch or frut
catfish only
Frd Grn Tom Sand
Purchase Average
Grams
Grams of
Dietary
frequency price Unhealthy Healthy Ambiguous Beverage Calories of fat saturated fats cholesterol
269
269
267
267
266
264
260
260
258
257
254
254
254
251
248
244
242
242
238
232
228
223
223
221
220
220
220
219
216
211
203
203
202
194
193
193
192
191
191
184
183
182
180
179
178
172
172
171
170
169
167
167
167
1.31
2.96
0.5
4.75
6.4
1.01
3.9
3.09
3.26
3.64
3.86
0.38
3.47
3.29
1.3
2.33
2.75
2
4.71
2.99
2.12
2.08
1.62
1.16
2.74
3.14
4.99
4.72
0.24
0.4
0.55
2.29
2.91
1.92
1.53
0.17
0.06
2.86
0.73
6.03
-0.14
1.26
4.25
5.49
0.08
4.11
2.62
1.71
2
2.78
2.6
2.43
4.75
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
702
22
78
Appendix Table B1 (continued): Food Items Information and Classification
Food Item
Port Brsd Cod
CP Brat Special
Soba Ndl Bowl
Gen Tao Chick
Bisque Twrs
Eggplant Parmesn
Chic Caes Sal
And Sge RiceBean
Black Hstry Spec
Fried Chix/2side
OpnFc Turk Pastr
Fish Sand Deal
salis stk/pot
Broc Strfry/rice
Saus Rustica
Catfish Sandwich
Wing Leg Thigh
apple slic bag
Chix Asp Penne
Cinnamon Roll
Beef Tips
1shrmpskew/rice
Grl RstBf&ChsSnd
Cobbler
Fatoush
ch ques only
Ital Sge Marinar
CP Crv Chix Deal
BfTeriyaki/Rice
Filet/2 side
Grill Quesadilla
Portabello Burge
Gyro/Fries
Monte Cristo
CP Sge Hoagie
Indians Hot Dog
Filet
Crepe
Bf Tip/Port Mush
Rattoulle/Pasta
chili bread bowl
Beef Burg/Rice
CP Rotis Chix
OpnFcd Brisket
trail mix
Ham Carved/1 sid
Nacho Supreme
Tortellini Toss
Trky Mtlf/2 side
Salmon
spinach sal/oil
Pork Carved CP
reuben sandwich
Purchase Average
Grams
Grams of
Dietary
frequency price Unhealthy Healthy Ambiguous Beverage Calories of fat saturated fats cholesterol
167
162
162
161
160
158
157
157
154
153
147
145
144
142
139
139
139
138
138
138
136
135
135
134
134
132
131
131
131
131
129
126
125
124
123
122
119
117
116
114
114
114
114
112
111
110
109
106
106
104
104
103
101
2.49
3.16
3.64
4.11
0
2.24
4.72
4.09
4.05
4.41
4.37
3.82
2.4
3.87
4.3
2.13
0.99
0.01
4.3
0.17
3.06
3.87
3.9
2.21
2.37
3.99
2.99
4.36
4.1
5.91
3.87
3.58
4.83
1.97
3.13
1.43
4.06
3.89
3.19
3.53
0.59
2.95
2.45
3.19
0.9
4.03
3.02
4.02
4.45
1.91
5.52
2.42
3.7
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
270
16
4.5
Appendix Table B1 (continued): Food Items Information and Classification
Food Item
Cookie 6-7pm
ch parm/pasta
1 stuf pep/pot
Chili Fries
Protein Shake
Ham 2 sides
Chef Salad
CP Sz Rib/Rice
BfTeriyaki Combo
Chix Tnder Combo
Szechw Stir Fry
St cab only
EggplntPar/pasta
BAC rice
Pumpkin Pie
Pepr Stk/Rice
Shrimp Skew/Side
Shrimp Jumbo
CP Grill Burger
Bkfst Skillet
Tea Card
Tilapia/1 side
pap chic only
Stuf Shells
Gen Tsao Combo
2chops 1 side
Turkey Burger
Cod Lem Cpr
CP Cal Cobb Sld
Trky Mtbl Sub
Nacho Chips
brat & kraut
Caramel Apple
CP 1/4 Hot Dog
Turkey Carved CP
Stk Sand/Pret bn
sm ches/crac cup
Chic Philly Wrap
Rst Beef Bar
CP Rib to go
Sge Hoagie/Chips
Grld Slider 2ea
Metro Deli Sub
CP PstaPrim Alf
CP Chix Sand onl
CP Tortellini
GenTsao/Rice
Cin Churro
Grill Gyros
Grill Chi Philly
Shrimp Frd Rice
Pork Bals Apple
CntryFrdStk only
Purchase Average
Grams
Grams of
Dietary
frequency price Unhealthy Healthy Ambiguous Beverage Calories of fat saturated fats cholesterol
101
100
99
98
98
96
96
93
93
93
92
92
91
91
91
90
89
89
88
88
88
87
87
86
85
84
84
84
83
83
82
81
81
80
79
79
78
77
77
76
76
75
75
75
75
74
73
73
72
72
72
72
70
0.73
2.57
2.49
2.12
0.47
3.71
2.99
2.83
4.78
0.6
3.25
2.09
3.55
1.08
4.36
2.93
3.42
4.33
2.73
-0.09
1.37
3.17
2.12
2.78
4.43
3.7
2.4
3.38
4.43
3.33
1.89
2.22
1.16
2.61
2.65
4.09
0.75
2.83
4.31
9.71
4.28
2.5
-0.07
3.49
3.78
3.89
4.1
0.75
3.33
2.67
4.1
3.38
2.03
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Appendix Table B1 (continued): Food Items Information and Classification
Food Item
Sfd Sld Crois
Crois Sandwich
Fried Chicken
LE Perch Deal
kielbasi only
CP Rst Veg Prmvr
Italian Sandwich
Grl Chix Hoagie
Mush Risotto Chx
1 stuf pep only
CP Chix only
Cott cheese/frt
Chix Phil only
Tuna/Salmon Cup
Str Fry/Rice
Pasta Only
CP Srln Rst Deal
Grl Sp Chix Snd
CP Chix Pot Pie
Pecan Pie
Chil Dog Comb Ch
tuna melt/side
shrimp grits
Steel Oats
Swt Pot Sal
Quiche/only
BAC Bowl
Whole Lg Pizza
CP Prime Rib
LE Perch Sand
Ham only
blue Pancakes
french dip au ju
CP Pasta Salad
Chic Strog/Ndl
Ceasar Salad
Meatloaf/pot
Crab Slider 2
Mix Dried Frt/Nu
chex mix
Pasta w/Chix
Cod Lem Cpr deal
Grill Ruebn Trky
wing bar
CrmlBan FrTst
Whole Pickle
meatball sub
soy nut but cup
Pork BlAp Deal
Kifta Wrap
roast bf/veg pot
Eye Rnd only
Grl Meatloaf
Purchase Average
Grams
Grams of
Dietary
frequency price Unhealthy Healthy Ambiguous Beverage Calories of fat saturated fats cholesterol
70
70
69
69
68
68
66
65
65
65
63
63
62
62
61
61
59
58
58
57
57
57
57
56
56
56
55
54
53
53
53
52
52
52
51
51
51
50
49
49
49
48
48
47
46
44
43
42
40
40
40
37
36
2.88
2.68
2.37
7.07
1.54
3.28
4.16
4.07
4.4
1.81
2.95
0.22
2.48
0
2.87
0.92
5.21
3.75
4.43
7.73
0.63
4.27
4.11
0.12
0.21
2.89
5.73
1.33
3.59
3.61
2.22
2.79
2.33
3.19
3
4.15
2.99
4.14
0.66
0.31
5.8
4.78
0.74
4.07
2.12
0.1
2.62
0.18
4.43
0.66
2.9
2.24
4.88
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Appendix Table B1 (continued): Food Items Information and Classification
Food Item
Beef Tip/2 veg
Corn Bf Snd Bar
fish only
CntryFrdStk/2 sd
CP Asian Chi Sal
Hot Dog Combo FF
Metro Sub Deal
Ham Carved Only
Catfish/2 sides
Egg Gryo Pita
Hoppin John Deal
cod
Thai Veg Curry
Chil Dog Comb FF
CntryFrdStk/Pot
Apple Pie Whl
Soul Food Bar
ch parm only
Fish Sandwich
kiel/krt/pot
salisbury steak
Saus Pepp Only
Taco Sal Bar
small truffles
BBQ Bf Snd Combo
Tilapia
CrnBf & Cab only
Med Veg Wrap
Slc Chs Pizza
oat honey bar
Pork/Sauerkraut
CrnBeef/Cab Meal
Grld Slider 1ea
lg truffles
CP Crv Chix
PldPrk Sand Comb
cod/rice
Soy Nuts Bag
Cavatappi
Crab Slider 1
Hoppin John
Mix Frt Small
Spaetzel only
FrnchTst/Berries
Prtzl Stk/Chs
BBQ Beef Sand
Grill Panini
Corn Dog
Crisp ch Pita
Slc Pep Pizza
Coffee Bag
chic only
brat only
Purchase Average
Grams
Grams of
Dietary
frequency price Unhealthy Healthy Ambiguous Beverage Calories of fat saturated fats cholesterol
36
36
36
35
35
35
35
35
35
35
33
32
31
31
30
29
28
28
27
27
26
25
24
24
23
23
23
22
22
22
20
20
20
19
19
19
18
18
17
17
17
16
16
16
16
16
16
15
14
14
14
13
13
4.99
5.95
2.63
3.89
4.43
0.56
0.17
2.56
1
4.41
3.99
2.08
5.19
0.95
2.68
7.68
4.72
2.06
-0.35
4.32
1.38
2.73
4.38
2.92
3.19
2.46
3.39
0.56
0.64
0.18
2.89
3.99
1.89
5.5
1.57
3.5
3.39
0.22
1.93
2.75
3.29
1.22
0.52
0.28
0.94
2.77
0.41
0.5
2.62
0.96
2.97
2.34
1.79
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
52.5
X
X
X
X
3
0.63
5
Appendix Table B1 (continued): Food Items Information and Classification
Food Item
lg veg cup/dip
Med Tilap/2sides
brkfst pizza sli
Kashi Bar
baked pot bar
Med Tilapia
CP Sirloin Rst
Potato Skin Bar
Corned Beef Only
Pita Pkt Bar
Hot Choc Card
Rotis Chix Only
Rotis Chix Deal
Dr Day Coupon
Soup FS
Mash Swt Pot
Lester Rib only
bbq chic
Tort Chip Mltigr
Chic Stog only
pineapple
Olive Bar
Soup EVS
Apricot
Syrup 2 btl
Syrup 1 btl
SgeGrvFish Bar
Marnt Sal Bar
Micro Meal Astd
bbq ribs
Cranberries
Nestle Cookie
Beef pot pie
Figs
sweet pot fries
Rotis Ribs Only
Pumpkin Ravioli
Chili FS
Cashews
Baklava
Pancakes
4 Corn Muffins
Chili EVS
Purchase Average
Grams
Grams of
Dietary
frequency price Unhealthy Healthy Ambiguous Beverage Calories of fat saturated fats cholesterol
11
10
10
10
10
9
8
8
8
7
7
7
6
6
6
6
5
4
4
4
4
4
4
4
3
3
3
3
2
2
2
2
2
2
2
2
2
2
2
2
1
1
1
0.3
3.99
0.9
0
2.38
2.09
4.43
4.45
2.69
2.99
0.72
6.99
0
0.92
0.69
0.46
4
1.47
0.71
2.22
0
6.31
2.06
0
8.99
4.99
2.73
4.18
0
0
0
1.22
0
0
0
0
4.1
2.61
0
0
2.29
1.99
2.61
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Source: Nutritionist from the large regional hospital. Nutrition measured come from an online database (www.myftnesspal.com) or from the
manufacturer.
REDACTED hospital address
REDACTED hospital phone
REDACTED hospital email
.td
.name
.address
.dob
Dear (Mr./Ms.) (last name)
Congratulations on taking time to assess your health as part the REDACTED Program. I have
reviewed your recent test results.
Your glucose test:
The glucose test measures the amount of sugar or glucose in your blood. Your body keeps the
sugar level in your blood in a normal range so that your body has energy to work as it should.
Why was this test done?
To check or screen for diabetes mellitus or for pre-diabetes mellitus
Your glucose test results:
.last glucose .date
Your blood sugar result was (normal, a little high, very high).
What does your test result mean?
If you did not eat for 8 hours before the test, then:
• 70 to 99 milligrams per deciliter (mg/dL) is normal for adults.
• 100 to 125 mg/dL is called pre-diabetes.
• 126 mg/dL or higher can mean you have diabetes.
If you DID eat in the 8 hours before the test, then:
• 70 to 120 milligrams per deciliter (mg/dL) is normal for adults.
• 121 to 199 mg/dL may be pre-diabetes.
• 200 mg/dL or higher can mean you have diabetes.
Your cholesterol test:
This blood test measured 2 kinds of cholesterol in the blood: the total cholesterol, which is the
sum of all the types of cholesterol in your blood and the HDL cholesterol, which is the “good” or
protective cholesterol.
Why are these tests done?
These tests help to assess your risk for getting clogged arteries which can cause heart disease or
stroke. Your risk for clogged arteries is higher if you have a high level of total cholesterol.
A high level of HDL in your blood reduces your risk. High cholesterol or low HDL cholesterol
does not cause symptoms so you may not know that your levels are not healthy.
Your cholesterol test results:
.last cholesterol .date
Your total cholesterol result was (normal, a little high, very high).
.lasthdl .date
Your HDL cholesterol result was (too low, a little low, normal)
What do the test results mean?
If your total cholesterol is
• less than 200: healthy.
• 200 to 239: a little too high.
• 240 or above: too high.
If your HDL is
• 50 or higher for men and 55 or higher for women: normal.
• 41-49 for men and 45-54 for women: a little low.
• 40 or less for men and 44 or less for women: too low.
Your cholesterol levels may be high or your HDL cholesterol level may be low because:
• You have an inherited tendency to have abnormal levels of lipids.
• You smoke.
• You don't get enough exercise.
• You eat too much saturated (particularly animal) fat.
• You have medical conditions.
• You are overweight or obese .
• You take certain medicines, such as steroids, beta blockers, or birth control pills.
What if my test result is not normal?
Test results are only a part of your health. Sometimes a test needs to be repeated to check the
first result. Talk to your healthcare provider about your result and ask questions.
If your test results are not normal, ask your healthcare provider:
• if you need additional tests
• what you can do to work toward a normal value
• when you need to be tested again.
If you do not have a healthcare provider and would like help getting one, call the REDACTED
appointment line at ###-#### for help getting an appointment with a primary care provider.
The REDACTED clinical team received your Glucose and Cholesterol results after you
completed the blood draw at a REDACTED Lab.
To view your other lab results, you may log into your account at REDACTED.
To take your online Health Assessment, log on to REDACTED. Create your user name and
password, then click on Health Assessment. The Health Assessment is a series of questions that
will take you about 30 minutes to complete.
All REDACTED levels require the 2013 online Health Assessment and Biometrics be completed
by Nov. 15, 2013. Call REDACTED (Employee Health Clinic) to make an appointment to have
your height, weight and blood pressure checked with the REDACTED nurse. This will complete
the Biometric requirements. The level you choose to complete is your choice, but your rewards
increase with each completed level.
For a list of the requirements and deadlines, visit REDACTED.
Send questions to REDACTED or call REDACTED.
We look forward to seeing your health and wellness transformation!
Yours in Wellness,
The REDACTED Team
Collection of Nutritional Information
Nutritional information was collected in two ways. First, for pre-packaged (e.g. yogurt)
or basic items (e.g. fruit) with bar codes we collected nutritional information through an
online database (www.myftnesspal.com). Second, for cooked but non-recipe based items
(e.g. French fries, burgers, mashed potatoes) the cafeteria manager shared with us the
online ordering system, which provides nutritional information. Furthermore, we adjusted
the nutritional information provided by the online ordering system, by the portion size
served in the cafeteria.
For all cooked recipe based items (e.g. salmon two sides), with the exception of soups
and chili, we were unable to calculate nutritional information. Other categories excluded
are 20oz bottle beverages since it is not possible to distinguish between diet and high
calorie, as well as salad bar and fruit bar for obvious reasons.
Lastly, many billing codes correspond to items, which have different flavors (e.g. for
yogurt cherry, vanilla, etc.) or varieties (e.g. for milk skim, 2%, and chocolate). For these
items we average over the varieties to calculate nutritional values.