This document summarizes a study that further analyzes the relationship between age and self-reported sleep disturbances using data from the 2006 and 2011 Behavioral Risk Factor Surveillance System surveys. Logistic regression analysis was conducted on categorical sleep disturbance variables. The results found that for both males and females, the odds of reporting a sleep complaint generally decreased with increasing age, though some variations existed. These findings did not contradict prior research and in many cases corroborated it.

1. A Further Analysis of Sleep Disturbances by Age
Phillip Richardson
California Polytechnic University, San Luis Obispo
parichar@calpoly.edu
Abstract
This project is a further analysis on Grandner et al.’s work on sleep disturbances by age range. The
goal was to determine if the results found in Grandner et al.’s work were consistent with more recent
BRFSS data. The method used was logistic regression, creating categorical response variables from the
original quantitative response variables. I found that there does not appear to be an increasing relationship
between self reported sleep disturbances and increasing age. These findings did not contradict Grandner et
al.’s findings, and in many cases corroborated it.
I. Introduction
Is there a relationship between age, sleep disturbances and day time tiredness? This is the
question that I will explore in my project. To start, a recent article written by Grandner et al. (2012),
Age and Sleep Disturbances among American Men and Women, investigated the relationship
between age and sleep disturbances. The article found that increasing age was not associated
with an increase in the odds of having a sleep complaint or a day time tiredness complaint,
after adjusting for demographic covariates. This result conflicts with the general idea that sleep
complaints and day time tiredness worsen as age increases. The National Sleep Foundation states
that, “Many older adults, though certainly not all, also report being less satisfied with sleep and
more tired during the day.” Because of the unexpected nature of the results I decided to explore
the results from Grandner et al.’s (2012) article in more detail.
The goal of this analysis is to replicate and expand on the analysis done by Grandner et al.
(2012). The original data used was from the 2006 Behavioral Risk Factor Surveillance System
(BRFSS) survey. The questions from the 2006 survey were replicated in the survey distributed in
2011. This gave me another set of data to compare to the 2006 results. The main hypothesis follows
from the original article. I hypothesize that in 2011, sleep complaints and day time tiredness will
increase with increasing age ranges after adjusting for demographic covariates. The results of this
analysis will hopefully answer whether or not 2006 was an anomaly, or whether there is some
evidence against the idea that sleep and energy worsen as we get older.
II. Methods
The data used in the analyses were collected from the BRFSS surveys in 2006 and 2011. The
BRFSS survey is an annual, random-digit-dialing telephone survey. It is collected state to state with
3 main modules. The core component is the part of the survey that every state must implement;
the optional modules consist of questions devised from CDC branches. States can opt in to
optional modules. The last component is the state added questions. These are questions that states
ask to be included in their copy of the survey. Each participant of the survey was assigned an
appropriate weight by the BRFSS survey team to ensure the data is representative. The BRFSS is
one of the largest health based telephone surveys in the United States.
There were statistical issues in analyzing the 2011 survey data. First, in 2011 the CDC updated
their survey methodology and included cell phone sampling for households that didn’t have
1

2. landlines. This update means that the results from 2011 can’t be rigorously compared to the
results from 2006, according to BRFSS itself. Another issue stems from the sleep questions being
an optional module. It was asked by 2 states in 2011 compared to 38 states who asked it in 2006.
The two states who asked the sleep questions in 2011 were New Hampshire and New Mexico.
This limits the generalizability of the analysis done for the sleep questions in 2011. A new subset
for 2006 was created to analyze the new response variable QLREST2. This subset only included 3
states: Delaware, Rhode Island, and Hawaii. The data from 2006 and 2011 were used to create
primary subsets for each analysis. The way the data was subset was by omitting people who had
ambiguous or no responses to the response variables and covariates. An ambiguous response is
categorized by responding “Don’t know” or “Refused” as these responses don’t add any useful
information to the analysis.
The three original BRFSS response variables used in the 2006 study were ADSLEEP, ADEN-
ERGY, and QLREST2. ADSLEEP was the question, “Over the last 2 weeks, how many days have
you had trouble falling asleep or staying asleep or sleeping too much?” ADENERGY was, “Over
the last 2 weeks, how many days have you felt tired or had little energy?” Lastly, QLREST2 asked,
“During the past 30 days, for about how many days have you felt you did not get enough rest or
sleep?” Due to the distribution of the responses for both ADSLEEP and ADENERGY with modes
at 0 and 14 the logical next step was to create dichotomous variables. The new response variables
created were Sleep Complaint and Energy Complaint. These two variables categorize people who
had less than 6 days of sleep disturbances as someone with no sleep complaint, and people who
had 6 or more days of sleep disturbances as someone with a sleep complaint. Because QLREST2
had a larger range of 30 possible days of sleep disturbances for its response values I created 3
different types of response variables. The first variable that I created, issue, categorized people into
no complaints or any complaint defining anyone with 0 disturbances as no complaint and anyone
> 0 disturbances as having a sleep complaint. Secondly, I created another response variable called
cutoff that used a similar cutoff, but at 14 days, to determine if people had many complaints or
few complaints. Lastly, I created an ordinal response variable ordered, with 4 categories. These
categories are 0 days of sleep complaints, 1-10 days, 11-20 days, and 21-30 days. These new
response variables represent 3 different ways to look at the QLREST response in analysis.
The covariates selected for this analysis were chosen by the authors of the original arti-
cle to reduce confounding factors enough to observe the true relationship between age and
sleep/energy complaints. The covariates include the following: Race (White, Black/African
American, Hispanic/Latino, Asian/Other and Multiracial), education (less than high school,
high school graduate, some college, college graduate), income level (< $10,000, $10,000-$15,000,
$15,000-$20,000, $20,000-$25,000, $25,000-$35,000, $35,000-$50,000, $50,000-$75,000, > $75,000),
general health (excellent, very good, good, fair, poor), time since last checkup (within the past yr,
within the past 2 yr, within the past 5 yr, 5 or more yr ago, never), and depressed mood (none,
mild, moderate/severe). Depressed mood was an optional variable and only included in half of
the analyses. Depression was separated into its own analyses due to its known impact on sleep
and energy; it had the ability to skew the impact of other covariates.
The main statistical analysis used was logistic regression on binary and ordinal response
variables. All the logistic regression was implemented in SAS software using PROC SURVEY-
LOGISTIC and using the weight designated by the BRFSS survey. All tests conducted had a
significance level of 0.05 and were two tailed. There were 2 models implemented for each response
variable. Model 1 contains age (reference = 80 yr or older), education (ref = college grad), race
(ref = White), income (ref = $75,000 + per yr), general health (ref = excellent), and time since last
2

3. medical checkup (ref = never).
Model 1 : Odds =
1
e(β0 + β1∗age + β2∗education + β3∗race + β4∗income + β5∗genhlth + β6∗checkup)
Model 2 : Odds =
1
e(β0 + β1∗age + β2∗education + β3∗race + β4∗income + β5∗genhlth + β6∗checkup + β7∗Depression)
I used the reference points for each covariate as designated in the original article. As in Grandner
et al. (2012) all models were stratified by gender due to a significant interaction between sex and
age.
Table 1
3

4. Table 2
4

5. III. Results
After dropping cases with missing or non-informational values for their covariates or response
variables, we were left with 11,953 participants in the 2006 subset and 12,134 participants in the
2011 subset. A breakdown of the participants’ unweighted demographics for each subset can be
found in Tables 1 and 2 (2006 & 2011) respectively. The 2011 subset was used for the Sleep/Energy
complaint response variables, while the 2006 subset was used for the issue/cutoff/ordered response
variables. The prevalence of complaints in 2011 was computed for ADSLEEP and ADENERGY
(Figure 1 2011) over age and separated by sex. The analysis of the 2011 subset replicates the
analysis done by Grandner et al. (2012) in their original article.
Figure 1
The prevalence of complaints in 2011, for both Sleep and Energy, were higher for females
than males. The highest prevalence of complaints for males occurred at 25-30 years old, while
for females it occurred at 35-40 years old. There was an overall downward trend in prevalence of
complaints from ages 18-40, with a few peaks in between. However, between ages 40 and 55 there
was a peak in prevalence followed by another sharp decrease in prevalence of both sleep and
energy complaints. For Sleep Complaints, males had their lowest prevalence at ages 80+, while
females had their lowest prevalence at ages 70-75. For Energy Complaints, males and females both
had their lowest complaints at ages 65-70 with a sharp increase in prevalence from ages 70-80+.
The results of the 2011 analyses can be found in Tables 3, 4, 5, and 6 (2011). Table 3 includes the
odds ratio estimates of all the covariates from Model 1. These estimated odds ratios are stratified
by gender. Table 4 includes the estimated odds ratios for all the covariates in Model 2. Once again,
these estimated odds ratios are stratified by gender. The estimated odds ratios for the covariate of
interest, age, are also represented in Figures 2 and 3 (2011). Figure 2 contains the estimated odd
ratios for Sleep Complaint by age, while Figure 3 contains the estimated odds ratios for Energy
Complaint by age. Both figures include Models 1 and 2.
5

6. Table 3
6

7. Table 4
7

8. Figure 2
Figure 3
8

9. The estimated odds ratios of Sleep Complaint by age for males have an overall decreasing
trend in both Model 1 and Model 2. They can be seen in Tables 5 and 6 (2011). Table 5 represents
Model 1 and Table 6 represents Model 2. This culminates in the 80+ age range having the lowest
estimated odds of a sleep complaint. For females there is a similar pattern, but with more variation
in the estimated odds ratios. A difference was found in female’s estimated odds ratios of Sleep
Complaint by age. Females had their lowest odds of a sleep complaint at ages 75-79, compared to
males lowest estimated odds of a sleep complaint being at ages 80+. For Energy Complaint in
Model 1 there was a similar decreasing pattern in the estimated odds ratios for Energy Complaint
by age, as age increased, in both males and females. In Model 2 the estimated odds ratios of
Energy Complaint by age peaked at ages 25-29 and then decreased until ages 35-39. From ages
35-79 the estimated odds ratios stayed relatively similar with some variance. For males in Model 2
the estimated odds ratios lay below 1 for a majority of the ages, but from ages 18-35 we see the
highest estimated odds of an energy complaint. For females, the estimated odds ratios dipped
below 1 from ages 65-79. Similarly, the highest estimated odds ratios occurred between ages 18-35
for females.
For nearly all covariates in the 2011 analysis females have higher estimated odds of a sleep or
energy complaint than males. There are a few covariates with large estimated odds ratios for both
genders. General health shows a drop in the estimated odds of a sleep/energy complaint as it
improves. Time since last checkup shows the highest estimated odds of a sleep disturbance for
people who have never been in for a checkup. Depression shows an extreme increase in estimated
odds ratios from no depression to moderate/severe. The rest of the covariates have estimated
odds ratios at each level that are close to 1.
The next analysis follows from the 2006 subset. For the response variable QLREST2 there were
three separate response variables created. These response variables were issue, cutoff, and ordered.
Models 1 and 2 were used in the analysis of these three responses. Figures 4 and 5 (2006) display
graphs of the estimated odds ratios for Sleep Complaints by age. Complaint types are determined
by the issue and cutoff levels. All analyses are stratified by gender as in previous analyses. For
the response variable issue the highest estimated odds ratios are at ages 18-35 for both males and
females in both Model 1 and Model 2. Both models show a decrease in the estimated odds ratios
as the age range increases. Females experienced a slight increase in estimated odds ratios from
ages 40-44, but the estimated odds ratios return to decreasing from ages 45-80+. For the response
variable cutoff, males and females experienced different patterns in their estimated odds ratios.
Males have their highest estimated odds of many sleep complaints at ages 18-24 in both models,
and the estimated odds ratios decreased as age increased with a few exceptions. The estimated
odds ratios for males spiked at ages 30-34 and at ages 40-44. Males’ estimated odds ratios drop
below 1 from ages 65-79 in both Model 1 and 2. Females have an increase in their estimated
odds ratios of many sleep complaints from ages 18-34. At ages 35-44 the estimated odds ratios
decreased, but there is a second peak at ages 45-49. From ages 50-79 the estimated odds ratios
begin decreasing again. Females maintain estimated odds ratios above 1 for all ages in both Model
1 and Model 2.
9

10. Table 5
10

11. Table 6
11

12. Figure 4
Figure 5
12

13. The analysis of the ordered response variable required using a cumulative logit function due to
it not being a binary response variable. The cumulative logit function produces cumulative log
odds, which can be then transformed into individual probabilities for specific covariate levels. In
my analysis I focused on the covariate levels that occurred most frequently in the 2006 subset.
The most frequently occurring covariate levels included: white race, college graduate education,
very good general health, income of >$75,000 a year, and had a checkup in the last year. Figures 6
and 7 (2006) show the individual estimated probability values for each ordered level of a sleep
complaint by age for both Model 1 and Model 2 respectively. Once again, the analysis is stratified
by gender. Male’s and female’s estimated probability of having no sleep disturbances increased
as age increased, while the probability of 1-10 days of sleep disturbances decreased slowly as
age increased for both males and females. At ages 60-64 the estimated probability of no sleep
disturbances became higher than the estimated probability of 1-10 days of sleep disturbances. The
estimated probability of having 11-20 days and 21-30 days of sleep disturbances decreased steadily
over time.
Figure 6
Figure 7
13

14. IV. Discussion
The results for the 2011 analysis show that both males and females experience higher odds
of sleep disturbances at nearly every age, and that there is an overall downward trend in sleep
complaints as participants got older. Day time tiredness saw a trend in which there were higher
odds of a day time tiredness complaint (compared to ref = 80+) for male subjects age 65-79 when
depression is not included, and ages 35-50 and 54-79 when depression was included. Females did
not have the same extremeness in their trend. Females had ages 75-79 having higher odds of a
day time tiredness complaint when depression was not included, and ages 65-79 having higher
odds when depression was included. For the 2006 analysis there was evidence that, for the typical
subject, the probability of a sleep complaint decreased as age increased. The only age range that
saw a slight increase in probability of a sleep complaint was age 30-34 for males and females in
both models.
Findings from both 2011 and 2006 analyses either corroborate or don’t contradict the findings
made by Grandner et al. (2012). For sleep complaints both analyses confirm that self-reported
sleep complaints don’t tend to increase as people get older, in fact they appear to decrease. Energy
complaints were a bit harder to infer from. In 2011 day time tiredness complaints didn’t have a
distinct increasing or decreasing direction in their pattern. The odds ratio of day time tiredness
complaints had a slightly downward trend, but it dropped below 1, meaning that the odds of
a day time tiredness complaint became higher for younger ages compared to 80 year olds. The
odds ratio trend wasn’t monotone increasing as age increased, so it didn’t directly contradict the
findings in the original article.
One issue that comes along with these analyses is where the data is coming from. These
questions are all answered by a person who is self-reporting. There is no external data on each
person to supplement their answers. That can mean that while the probability of a sleep complaint
is decreasing, actual sleep problems are going unnoticed. For example, an older subject could feel
that only getting 6 hours of sleep a night is normal to them. They would then report themselves
as having no sleep complaint when, by definition, they are suffering a sleep deficiency.
One of the anomalies in the patterns of sleep complaints is the increased probability in women
from age 40-55. This could be possibly due to side effects of menopause as the same increase is
not there for males of the same age. Another issue is that this type of data is not longitudinal;
meaning someone who has a sleep complaint at age 75 could have had a sleep complaint from age
18 that has never gone away. That isn’t something this data set can adjust for, and a cross sectional
study is not feasible. There is also the possibility that someone of poor health in younger ages
won’t live to see 80+, which narrows down that age group to healthier people who would be less
likely to have sleep complaints.
The analyses done in this project, and the analysis done in the original article are important
because they challenge current beliefs about sleep. As the original study states, this could help
challenge the assumption that sleeping problems are common with increasing age. These findings
can help lead to an improvement in current sleep research and hopefully a long term solution to
the problem of sleep disturbances. The findings can improve our understanding of who suffers
from sleep disturbances most, and who needs more attention.
14

15. References
“Aging and Sleep.” Aging & Sleep Information. National Sleep Foundation, n.d. Web. 14 May
2015.
Centers for Disease Control. Behavioral Risk Factor Surveillance System. Atlanta,
Georgia: Department of Health and Human Services, Centers for Disease Control and
Prevention, 2007
Centers for Disease Control. Behavioral Risk Factor Surveillance System. Atlanta,
Georgia: Department of Health and Human Services, Centers for Disease Control and
Prevention, 2012
Grandner MA; Martin JL; Patel NP; Jackson NJ; Gehrman PR; Pien G; Perlis ML;
Xie D; Sha D; Weaver T; Gooneratne NS. Age and sleep disturbances among American
men and women: data from the U.S. behavioral risk factor surveillance system. SLEEP
2012;35(3):395-406.
SAS Institute. Base SAS 9.3 Procedures Guide: Statistical Procedures. Cary, NC: SAS
Institute; 2008.
15