A Further Analysis of Sleep Disturbances by Age

Phillip Richardson

California Polytechnic University, San Luis Obispoparichar@calpoly.edu

Abstract

This project is a further analysis on Grandner et al.’s work on sleep disturbances by age range. Thegoal was to determine if the results found in Grandner et al.’s work were consistent with more recentBRFSS data. The method used was logistic regression, creating categorical response variables from theoriginal quantitative response variables. I found that there does not appear to be an increasing relationshipbetween self reported sleep disturbances and increasing age. These findings did not contradict Grandner etal.’s findings, and in many cases corroborated it.

I. Introduction

Is there a relationship between age, sleep disturbances and day time tiredness? This is thequestion 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 relationshipbetween age and sleep disturbances. The article found that increasing age was not associatedwith 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 sleepcomplaints and day time tiredness worsen as age increases. The National Sleep Foundation statesthat, “Many older adults, though certainly not all, also report being less satisfied with sleep andmore tired during the day.” Because of the unexpected nature of the results I decided to explorethe 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 in2011. This gave me another set of data to compare to the 2006 results. The main hypothesis followsfrom the original article. I hypothesize that in 2011, sleep complaints and day time tiredness willincrease with increasing age ranges after adjusting for demographic covariates. The results of thisanalysis will hopefully answer whether or not 2006 was an anomaly, or whether there is someevidence 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. TheBRFSS survey is an annual, random-digit-dialing telephone survey. It is collected state to state with3 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 tooptional modules. The last component is the state added questions. These are questions that statesask to be included in their copy of the survey. Each participant of the survey was assigned anappropriate weight by the BRFSS survey team to ensure the data is representative. The BRFSS isone 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 updatedtheir survey methodology and included cell phone sampling for households that didn’t have

1

landlines. This update means that the results from 2011 can’t be rigorously compared to theresults from 2006, according to BRFSS itself. Another issue stems from the sleep questions beingan 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 subsetfor 2006 was created to analyze the new response variable QLREST2. This subset only included 3states: Delaware, Rhode Island, and Hawaii. The data from 2006 and 2011 were used to createprimary subsets for each analysis. The way the data was subset was by omitting people who hadambiguous or no responses to the response variables and covariates. An ambiguous response iscategorized by responding “Don’t know” or “Refused” as these responses don’t add any usefulinformation 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 haveyou had trouble falling asleep or staying asleep or sleeping too much?” ADENERGY was, “Overthe 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 orsleep?” Due to the distribution of the responses for both ADSLEEP and ADENERGY with modesat 0 and 14 the logical next step was to create dichotomous variables. The new response variablescreated were Sleep Complaint and Energy Complaint. These two variables categorize people whohad less than 6 days of sleep disturbances as someone with no sleep complaint, and people whohad 6 or more days of sleep disturbances as someone with a sleep complaint. Because QLREST2had a larger range of 30 possible days of sleep disturbances for its response values I created 3different types of response variables. The first variable that I created, issue, categorized people intono 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 calledcutoff that used a similar cutoff, but at 14 days, to determine if people had many complaints orfew complaints. Lastly, I created an ordinal response variable ordered, with 4 categories. Thesecategories are 0 days of sleep complaints, 1-10 days, 11-20 days, and 21-30 days. These newresponse 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 andsleep/energy complaints. The covariates include the following: Race (White, Black/AfricanAmerican, 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 ofthe analyses. Depression was separated into its own analyses due to its known impact on sleepand energy; it had the ability to skew the impact of other covariates.

The main statistical analysis used was logistic regression on binary and ordinal responsevariables. 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 asignificance level of 0.05 and were two tailed. There were 2 models implemented for each responsevariable. 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

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 Grandneret al. (2012) all models were stratified by gender due to a significant interaction between sex andage.

Table 1

3

Table 2

4

III. Results

After dropping cases with missing or non-informational values for their covariates or responsevariables, we were left with 11,953 participants in the 2006 subset and 12,134 participants in the2011 subset. A breakdown of the participants’ unweighted demographics for each subset can befound in Tables 1 and 2 (2006 & 2011) respectively. The 2011 subset was used for the Sleep/Energycomplaint response variables, while the 2006 subset was used for the issue/cutoff /ordered responsevariables. 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 theanalysis 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 femalesthan males. The highest prevalence of complaints for males occurred at 25-30 years old, whilefor females it occurred at 35-40 years old. There was an overall downward trend in prevalence ofcomplaints from ages 18-40, with a few peaks in between. However, between ages 40 and 55 therewas a peak in prevalence followed by another sharp decrease in prevalence of both sleep andenergy complaints. For Sleep Complaints, males had their lowest prevalence at ages 80+, whilefemales had their lowest prevalence at ages 70-75. For Energy Complaints, males and females bothhad 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 theodds ratio estimates of all the covariates from Model 1. These estimated odds ratios are stratifiedby 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 ofinterest, age, are also represented in Figures 2 and 3 (2011). Figure 2 contains the estimated oddratios for Sleep Complaint by age, while Figure 3 contains the estimated odds ratios for EnergyComplaint by age. Both figures include Models 1 and 2.

5

Table 3

6

Table 4

7

Figure 2

Figure 3

8

The estimated odds ratios of Sleep Complaint by age for males have an overall decreasingtrend in both Model 1 and Model 2. They can be seen in Tables 5 and 6 (2011). Table 5 representsModel 1 and Table 6 represents Model 2. This culminates in the 80+ age range having the lowestestimated odds of a sleep complaint. For females there is a similar pattern, but with more variationin the estimated odds ratios. A difference was found in female’s estimated odds ratios of SleepComplaint by age. Females had their lowest odds of a sleep complaint at ages 75-79, compared tomales lowest estimated odds of a sleep complaint being at ages 80+. For Energy Complaint inModel 1 there was a similar decreasing pattern in the estimated odds ratios for Energy Complaintby age, as age increased, in both males and females. In Model 2 the estimated odds ratios ofEnergy Complaint by age peaked at ages 25-29 and then decreased until ages 35-39. From ages35-79 the estimated odds ratios stayed relatively similar with some variance. For males in Model 2the estimated odds ratios lay below 1 for a majority of the ages, but from ages 18-35 we see thehighest estimated odds of an energy complaint. For females, the estimated odds ratios dippedbelow 1 from ages 65-79. Similarly, the highest estimated odds ratios occurred between ages 18-35for females.

For nearly all covariates in the 2011 analysis females have higher estimated odds of a sleep orenergy complaint than males. There are a few covariates with large estimated odds ratios for bothgenders. General health shows a drop in the estimated odds of a sleep/energy complaint as itimproves. Time since last checkup shows the highest estimated odds of a sleep disturbance forpeople who have never been in for a checkup. Depression shows an extreme increase in estimatedodds ratios from no depression to moderate/severe. The rest of the covariates have estimatedodds ratios at each level that are close to 1.

The next analysis follows from the 2006 subset. For the response variable QLREST2 there werethree 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) displaygraphs of the estimated odds ratios for Sleep Complaints by age. Complaint types are determinedby the issue and cutoff levels. All analyses are stratified by gender as in previous analyses. Forthe response variable issue the highest estimated odds ratios are at ages 18-35 for both males andfemales in both Model 1 and Model 2. Both models show a decrease in the estimated odds ratiosas the age range increases. Females experienced a slight increase in estimated odds ratios fromages 40-44, but the estimated odds ratios return to decreasing from ages 45-80+. For the responsevariable 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 estimatedodds ratios for males spiked at ages 30-34 and at ages 40-44. Males’ estimated odds ratios dropbelow 1 from ages 65-79 in both Model 1 and 2. Females have an increase in their estimatedodds ratios of many sleep complaints from ages 18-34. At ages 35-44 the estimated odds ratiosdecreased, but there is a second peak at ages 45-49. From ages 50-79 the estimated odds ratiosbegin decreasing again. Females maintain estimated odds ratios above 1 for all ages in both Model1 and Model 2.

9

Table 5

10

Table 6

11

Figure 4

Figure 5

12

The analysis of the ordered response variable required using a cumulative logit function due toit not being a binary response variable. The cumulative logit function produces cumulative logodds, which can be then transformed into individual probabilities for specific covariate levels. Inmy 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 6and 7 (2006) show the individual estimated probability values for each ordered level of a sleepcomplaint by age for both Model 1 and Model 2 respectively. Once again, the analysis is stratifiedby gender. Male’s and female’s estimated probability of having no sleep disturbances increasedas age increased, while the probability of 1-10 days of sleep disturbances decreased slowly asage increased for both males and females. At ages 60-64 the estimated probability of no sleepdisturbances became higher than the estimated probability of 1-10 days of sleep disturbances. Theestimated probability of having 11-20 days and 21-30 days of sleep disturbances decreased steadilyover time.

Figure 6

Figure 7

13

IV. Discussion

The results for the 2011 analysis show that both males and females experience higher oddsof sleep disturbances at nearly every age, and that there is an overall downward trend in sleepcomplaints as participants got older. Day time tiredness saw a trend in which there were higherodds of a day time tiredness complaint (compared to ref = 80+) for male subjects age 65-79 whendepression is not included, and ages 35-50 and 54-79 when depression was included. Females didnot have the same extremeness in their trend. Females had ages 75-79 having higher odds of aday time tiredness complaint when depression was not included, and ages 65-79 having higherodds when depression was included. For the 2006 analysis there was evidence that, for the typicalsubject, the probability of a sleep complaint decreased as age increased. The only age range thatsaw a slight increase in probability of a sleep complaint was age 30-34 for males and females inboth models.

Findings from both 2011 and 2006 analyses either corroborate or don’t contradict the findingsmade by Grandner et al. (2012). For sleep complaints both analyses confirm that self-reportedsleep complaints don’t tend to increase as people get older, in fact they appear to decrease. Energycomplaints were a bit harder to infer from. In 2011 day time tiredness complaints didn’t have adistinct increasing or decreasing direction in their pattern. The odds ratio of day time tirednesscomplaints had a slightly downward trend, but it dropped below 1, meaning that the odds ofa day time tiredness complaint became higher for younger ages compared to 80 year olds. Theodds ratio trend wasn’t monotone increasing as age increased, so it didn’t directly contradict thefindings in the original article.

One issue that comes along with these analyses is where the data is coming from. Thesequestions are all answered by a person who is self-reporting. There is no external data on eachperson to supplement their answers. That can mean that while the probability of a sleep complaintis decreasing, actual sleep problems are going unnoticed. For example, an older subject could feelthat only getting 6 hours of sleep a night is normal to them. They would then report themselvesas 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 womenfrom age 40-55. This could be possibly due to side effects of menopause as the same increase isnot 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 age18 that has never gone away. That isn’t something this data set can adjust for, and a cross sectionalstudy is not feasible. There is also the possibility that someone of poor health in younger ageswon’t live to see 80+, which narrows down that age group to healthier people who would be lesslikely to have sleep complaints.

The analyses done in this project, and the analysis done in the original article are importantbecause they challenge current beliefs about sleep. As the original study states, this could helpchallenge the assumption that sleeping problems are common with increasing age. These findingscan help lead to an improvement in current sleep research and hopefully a long term solution tothe problem of sleep disturbances. The findings can improve our understanding of who suffersfrom sleep disturbances most, and who needs more attention.

14

References

“Aging and Sleep.” Aging & Sleep Information. National Sleep Foundation, n.d. Web. 14 May2015.

Centers for Disease Control. Behavioral Risk Factor Surveillance System. Atlanta,Georgia: Department of Health and Human Services, Centers for Disease Control andPrevention, 2007

Centers for Disease Control. Behavioral Risk Factor Surveillance System. Atlanta,Georgia: Department of Health and Human Services, Centers for Disease Control andPrevention, 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 Americanmen and women: data from the U.S. behavioral risk factor surveillance system. SLEEP2012;35(3):395-406.

SAS Institute. Base SAS 9.3 Procedures Guide: Statistical Procedures. Cary, NC: SASInstitute; 2008.

15