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National Health Statistics Reports
Number 14, March 2009
Wireless Substitution: Statelevel Estimates From the National Health Interview Survey, JanuaryDecember 2007
PDF Version (585 KB)
by Stephen J. Blumberg, Ph.D., and Julian V. Luke, Division of Health Interview Statistics, National Center for Health Statistics, and Gestur Davidson, Ph.D.; Michael E. Davern, Ph.D.; TzyChyi Yu, M.H.A., Ph.D.; and Karen Soderberg, M.S., the State Health Access Data Assistance Center, University of Minnesota
Abstract
Objectives  This report presents statelevel estimates of the percentage of households that do not have a landline telephone, but do have at least one wireless telephone. These wirelessonly households made up 14.7% of U.S. households in 2007. The report also presents statelevel estimates of the percentage of adults living in wirelessonly households. These wirelessonly adults made up 13.6% of U.S. adults in 2007.
Methods  A twosample modeling strategy was used to estimate the prevalence of wirelessonly households and adults by state. This modeling was based on data from the 2007 National Health Interview Survey and the 2008 Current Population Survey’s Annual and Social Economic Supplement.
Results  The results show that the prevalence of wirelessonly households and adults in 2007 varied substantially across states. Statelevel estimates ranged from 5.1% (Vermont) to 26.2% (Oklahoma) of households and from 4.0% (Delaware) to 25.1% (Oklahoma) of adults. In addition, approximately one out of four adults (25.4%) living in the District of Columbia were wirelessonly.
Introduction
The National Health Interview Survey (NHIS) is the most widely cited source for data on the number of American homes that only have wireless telephones. Every 6 months, the National Center for Health Statistics (NCHS) releases a report with the most uptodate estimates available from the federal government concerning the size and characteristics of the wirelessonly population (1). That report, published as part of the NHIS Early Release Program, presents national and regional estimates. For example, the latest results show that more than one out of every six American homes (17.5%) had only wireless telephones during the first half of 2008 (1).
Most major survey research organizations in the United States, including NCHS, have not traditionally included wireless telephone numbers when conducting randomdigitdial telephone surveys. The exclusion of households with only wireless telephones has potential implications for results from health surveys, political polls, and other research conducted using randomdigitdial methods. Indeed, the potential for bias due to incomplete coverage of the U.S. household population (that is, due to noncoverage of wirelessonly and phoneless households) remains a real and growing threat to health surveys conducted only on landline telephones (24).
For this reason, survey systems that have relied on randomdigitdial surveys for years have been testing methods for including samples of wirelessonly households. These systems include several conducted by the Centers for Disease Control and Prevention (CDC), including the Behavioral Risk Factor Surveillance System, the National Immunization Survey, and the State and Local Area Integrated Telephone Survey. These three systems collect data and produce results at the state level. For them to effectively combine samples of wirelessonly households with samples of landline households from random digitdial surveys, statelevel estimates of the prevalence of wirelessonly households are needed. Yet, direct statelevel estimates of this prevalence have not been available from NHIS data because the sample size of NHIS is insufficient for direct reliable annual estimates for most states.
This report presents results of modeled estimates of the prevalence of wirelessonly households and wirelessonly adults at the state level, using data from the 2007 NHIS and the 2008 Current Population Survey’s (CPS) Annual and Social Economic Supplement (ASEC). In contrast to the NHIS, the CPS has sufficient sample size for direct reliable annual demographic estimates for all states, but does not include questions necessary to identify wirelessonly households. By incorporating data from both surveys, the modeled estimates presented here take advantage of the unique strengths of both surveys. To our knowledge, these estimates are the first statelevel estimates of the size of this population available from the federal government.
Methods
A twosample modeling strategy was used to estimate the prevalence of wirelessonly households and of adults living in such households, by state. This strategy used an optimal blend of direct estimates and synthetic estimates (5,6). First, NHIS data were used to fit multivariate regression models predicting wirelessonly status using covariates from NHIS that were also available from the CPS. The model was then used with CPS data to obtain average statelevel synthetic estimates (or predictions). Standard errors (SEs) for these mean statelevel synthetic estimates were also obtained using Stata version 10 with the Delta method (7). Next, NHIS data were used to directly obtain statelevel prevalence estimates and their corresponding (and often large) SEs. Finally, a blended overall estimate was calculated as the weighted sum of the synthetic and direct estimates for each state, where the weights reflected the relative precision of each estimate.
More detail regarding this estimation methodology is available in the "Technical Notes".
Results
Results from the twosample modeling strategy show great variation in the prevalence of wirelessonly households across states (see Figures 1 and 2). Householdlevel estimates ranged from a low of 5.1% in Vermont to a high of 26.2% in Oklahoma (see Table).
Other states with a high prevalence of wirelessonly households include Utah (25.5%), Nebraska (23.2%), Arkansas (22.6%), Idaho (22.1%), and Iowa (22.2%). Other states with a low prevalence of wirelessonly households include Connecticut (5.6%), Delaware (5.7%), South Dakota (6.4%), Rhode Island (7.9%), New Jersey (8.0%), and Hawaii (8.0%).
Similarly, results show great variation in the prevalence of wirelessonly adults across states, ranging from a low of 4.0% in Delaware to a high of 25.1% in Oklahoma (see Table) . An ostensibly but not significantly higher prevalence rate was observed for adults living in the District of Columbia (25.4%). Other states with a high prevalence of wirelessonly adults include Utah (23.9%), Nebraska (22.4%), Kentucky (21.6%), Idaho (21.3%), and Arkansas (21.2%). Other states with a low prevalence of wirelessonly adults include Vermont (4.6%), Connecticut (4.8%), Rhode Island (5.3%), Montana (5.4%), and New Jersey (6.1%).
Conclusion
Because of the absence of statelevel prevalence estimates for the wirelessonly population, survey researchers interested in combining statelevel samples of wirelessonly households with samples of landline households have relied on national or regional estimates of the relative sizes of these two populations (8). Similarly, telecommunications companies seeking greater understanding of conditions in state and local markets have relied on regional estimates of the prevalence of wirelessonly households (9). The results in this report clearly show that, for many states, national and regional estimates are not sufficiently accurate for these purposes.
Results from the twosample modeling strategy show great statelevel variation in the prevalence of wirelessonly households, even within regions. The range of prevalence exceeded 8 percentage points in the Northeast region and 20 percentage points in the South region. In fact, in the Midwest region, the state with the lowest prevalence (South Dakota, 6.4%) borders the state with the highest prevalence (Nebraska, 23.2%). Similar ranges within regions were observed for estimates of the prevalence of wirelessonly adults.
Of course, for survey researchers and telecommunications companies interested in local areas, these statelevel prevalence estimates still may not be sufficiently specific. For example, national estimates suggest that adults living in metropolitan areas are more likely to live in wirelessonly households than are adults living in nonmetropolitan areas. Variation across local areas within a state should be expected, just as there was variation across states within a region. NCHS intends to continue working with the University of Minnesota to use twosample modeling strategies like this one to produce estimates of telephone status for large metropolitan areas. Meanwhile, researchers may find the statelevel model specifications (in the "Technical Notes") useful for creating completely synthetic predictions for local areas or other subpopulations of interest.
Survey researchers and telecommunication companies using the estimates presented in this report should be aware that these estimates are based on 2007 data. The number of American homes with only wireless telephones continues to grow (1). The estimate from the first half of 2008, that 17.5% of households were wirelessonly, is nearly 3 percentage points higher than the estimate for the 2007 calendar year (14.7%). Similarly, the estimated prevalence of wirelessonly adults has grown from 13.6% in 2007 to 16.1% in the first half of 2008. We do not know if the rates of growth in each state are comparable to the national rates, or whether they vary substantially (as did the overall prevalence rates by state). Regardless, it is very likely that the current statelevel prevalence rates of wirelessonly households and adults are greater than the estimates presented here.
For More Information
For more information about the implications of wirelessonly households for health surveys based on landline telephone interviews, see other reports (14). For more information about the design, content, and use of the NHIS, please see the NHIS website.
References
 Blumberg SJ, Luke JV. Wireless substitution: Early release of estimates based on data from the National Health Interview Survey, January  June 2008. National Center for Health Statistics. 2008. Available from: Early Releases of Selected Estimates from the National Health Interview Survey.
 Blumberg SJ, Luke JV. Coverage bias in traditional telephone surveys of lowincome and young adults. Public Opinion Quarterly 71:73449. 2007.
 Blumberg SJ, Luke JV, Cynamon ML. Telephone coverage and health survey estimates: Evaluating the need for concern about wireless substitution. Am J Public Health 96:92631. 2006.
 Blumberg SJ, Luke JV, Cynamon ML, Frankel MR. Recent trends in household telephone coverage in the United States. In JM Lepkowski et al. (eds.), Advances in telephone survey methodology 5686. New York: John Wiley and Sons, Inc. 2008.
 Schirm AL, DiCarlo JR. Using Bayesian shrinkage methods to derive state estimates of poverty, food stamp program eligibility, and food stamp program participation [PDF  232 KB]. Washington, DC: Mathematica Policy Research, Inc. 1998.
 National Research Council. Small area income and poverty estimates: Priorities for 2000 and beyond. Panel on Estimates of Poverty for Small Geographical Areas, CF Citro and G Kalton, editors. Committee on National Statistics. Washington, DC: National Academy Press. 2000.
 Greene WH. Econometric Analysis, 5th Ed. Upper Saddle River, NJ: Prentice Hall. 2003.
 AAPOR Cell Phone Task Force. Guidelines and considerations for survey researchers when planning and conducting RDD and other telephone surveys in the U.S. with respondents reached via cell phone numbers [PDF  140 KB]. American Association for Public Opinion Research. 2008.
 Petitions of Qwest Corporation for Forbearance Pursuant to 47 U.S.C. § 160(c) in the Denver, MinneapolisSt. Paul, Phoenix, and Seattle Metropolitan Statistical Areas, WC Docket No. 0797, Memorandum Opinion and Order, FCC 08174. Federal Communications Commission [PDF  374 KB]. July 25, 2008.
 Davern M, Jones A, Lepkowski J, Davidson G, Blewitt LA. Estimating regression standard errors with data from the Current Population Survey’s public use file. Inquiry 44:21124. 2007.
Acknowledgements
This research was supported by a grant from the Robert Wood Johnson Foundation to the State Health Access Data Assistance Center at the University of Minnesota. The authors are solely responsible for the content of this report. Gestur Division of the University of Minnesota was the primary author of the "Technical Notes." We thank Jane Gentleman, Graham Kalton, Chris Moriarty, and Allen Schirm for their comments on earlier versions of the "Technical Notes."
Suggested citation
Blumberg SJ, Luke JV, Davidson G, Davern ME, Yu T, Soderberg K. Wireless substitution: Statelevel estimates from the National Health Interview Survey, JanuaryDecember 2007. National health statistics report; no 14. Hyattsville, MD: National Center for Health Statistics. 2009.
Copyright information
All material appearing in this report is in the public domain and may be reproduced or copied without permission; citation as to source, however, is appreciated.
Technical Notes
Data sources
The statelevel estimates presented in this report are based on data from the 2007 National Health Interview Survey (NHIS) and the 2008 CPS Annual and Social Economic Supplement. NHIS is a multipurpose health survey conducted by CDC’s NCHS. The CPS is an annual demographic survey conducted by the U.S. Census Bureau for the Bureau of Labor Statistics.
NHIS is an annual multistage probability household survey of a large sample of households drawn from the civilian noninstitutionalized household population of the United States. This facetoface survey interview is administered by trained field representatives from the U.S. Census Bureau. NHIS interviews are conducted continuously throughout the year to collect information on health status, healthrelated behaviors, and health care utilization. The survey also includes information about household telephones and whether anyone in the household has a wireless telephone (also known as a cellular telephone, cell phone, or mobile phone).
The sample for NHIS is stratified by state, which allows use of NHIS for producing statelevel estimates. However, the current NHIS sample size is not sufficient to provide reliable annual statelevel estimates for most states. In 2007, household telephone status information was obtained for 29,079 households; of these, 28,492 had sufficient nonmissing data for the covariates to be included in the multivariate regression analyses presented here.
The CPS is a multistage probability household survey that provides data on labor force participation and unemployment. Data are collected through a combination of facetoface and telephone interviews. The ASEC is added one time per year to the monthly CPS (from February through April) and is used to produce household income, family poverty, and health insurance coverage estimates. The reference period for these ASEC items is the prior calendar year; the 2008 CPS ASEC uses 2007 as its reference period
NHIS and CPS sampling weights adjust for the probability of selection of each household, and they are adjusted for nonresponse. The results reported in this report are based on weighted estimates. StataSE v.10 software was used to account for the complex survey designs.
Definition of a wirelessonly household
For each family contacted by NHIS, one adult family member was asked whether "you or anyone in your family has a working cellular telephone." A family can be an individual or a group of two or more related persons living together in the same housing unit. Thus, a family can consist of only one person, and more than one family can live in a household (including, for example, a household where there are multiple singleperson families, as when unrelated roommates are living together).
To produce the statistics for this report, families were identified as wireless families if anyone in the family had a working cellular telephone. Households were identified as wirelessonly if they included at least one wireless family and if there were no working landline telephones inside the household. To determine if there was a working landline telephone inside the household, survey respondents were asked if there was "at least one phone inside your home that is currently working and is not a cell phone."
Household telephone status (rather than family telephone status) is used in this report because most telephone surveys draw samples of households rather than families. Adults are identified as wirelessonly if they live in a wirelessonly household. Individual ownership or use of cellular telephones is not determined.
Twosample statelevel estimation
The goal was to develop a robust set of blended direct and synthetic estimates of the wirelessonly population. We used both the NHIS and the CPS Annual and Social Economic Supplement in a twosample modeling approach (5,6). Specifically, we first fitted a multivariate regression model using NHIS data. NHIS is the only survey that provides information on wirelessonly households. We then used data from the CPS to derive statelevel predictions through the NHISfitted model. The CPS has a larger sample size and a survey design that produces reliable, staterepresentative estimates. We then blended the direct estimates from the NHIS and the modeled estimates from the CPS data and NHIS model to yield a set of improved statelevel estimates. We undertook this modeling exercise separately for two units of analysis: for the household and for adults aged 18 years and older. For ease of exposition, we describe the process undertaken using the household as the unit of analysis. The process was the same when adults were the unit of analysis.
Formally, this twosample strategy has five steps. First, using the multinomial logistic (MNL) regression command for survey data in Stata 10.1 ("svy: mlogit") and confidential internal NHIS data files, we fitted a fixedeffects model (at the level of state) on this national sample of 28,492 households. The three categories for our multinomial dependent variable were wirelessonly, landline (with or without the presence of a wireless telephone), and phoneless. With access to the detailed confidential NHIS sample design and using Stata’s survey suite commands, we were able to take full account of the complex survey design to obtain robust standard errors for this multivariate model.
Second, using publicly available CPS data files, we recycled the CPS data on all our model covariates (i.e., recycled predictions) through our NHISfitted model to obtain the average state modelbased estimates. These average state modelbased estimates were obtained as the sum of the modelestimated probabilities of each household in a state being wirelessonly divided by the number of sample observations at the level of the state. Symbolically denoting the j^{th} state’s estimated rate of wirelessonly (wo) using the MNL model as we refer to these , as our synthetic estimates.
Third, we used the Stata command "adjust" to obtain the standard errors of these mean statelevel model predictions . With access to the publicly available CPS data files only, we could not take full account of the complex survey design when calculating these standard errors. Instead, we used the Delta method (7) with the Stata command "adjust," and we identified the lowest level of identifiable geography in the publicly available CPS data files as the strata variable. This alternative method has been shown to yield standard errors for multivariate regression models of dichotomous dependent variables that are very close to those obtained when the full confidential survey design is available (10). As such, these standard errors are likely to fully reflect both sampling and modelbased imputation errors.
Fourth, we used the NHIS survey data and Stata ("svy: mean") to obtain the direct prevalence estimate for the wirelessonly variable for each state, denoted by DE_{wo,j}, along with the standard error of these means, . We call these values of DE_{wo,j} our direct estimates (Table I). As noted earlier, we were able to take full account of the complex NHIS design to obtain these standard errors.
Finally, we formed the blended wirelessonly estimate as the weighted sum of the synthetic estimate and direct estimate for each state, where the weights reflect the relative precision of each state’s pair of synthetic and direct estimates, as:
We used a MNL regression model with the three categories of wirelessonly, landline, and phoneless rather than a binomial logistic regression model with the two categories of wirelessonly and "all others." We used three categories because, though the prevalence of phoneless households is quite small, our MNL regression analyses revealed that the phoneless equation’s coefficients were almost as large and significant as those for the wirelessonly equation. Thus, combining the landline and phoneless categories into a single "all others" category and estimating a binomial logistic regression for wirelessonly would have resulted in heterogeneity within this combined "all others" category. As a consequence, the wirelessonly equation’s coefficients would likely have been biased.
We tested the appropriateness of our use of a MNL regression model rather than the use of much more computationally demanding multinomial probit regression models (MNP). As part of their estimation approach, MNL models make the assumption — referred to as Independence from Irrelevant Alternatives (IIA) — that the coefficients in a MNL model will not depend on whether an outcomelevel within the multinomial response variable is included in the estimation or excluded (and its data removed). MNP does not make this assumption and consequently it is often recommended as the appropriate estimator. MNP is very computationally demanding, however. The IIA assumption underlying MNL can be tested empirically, and there are two variants of it formulated as Hausman tests, plus a third IIA test known as the SmallHsiao test. In our particular modeling example, the IIA tests can be simply described as assessing whether the coefficients of the wirelessonly equation in a MNL model are significantly different from the coefficients estimated from a wirelessonly binomial logistic regression model that excludes the phoneless data. Two of the three tests favored the IIA assumption. Therefore, we believe our use of a MNL model is appropriate.
Selection of a fixedeffects model
We note that the NHIS has a relatively large sample size—although not designed for representative annual statelevel estimates—and that the CPS has an even greater sample size and is designed to produce representative annual statelevel estimates. Additionally, we note that, with our model specification, we were able to account for significant variation in wirelessonly households. Given all these considerations, we believe it was appropriate that differences in each state’s direct and synthetic estimates should reflect only the differences across NHIS and CPS surveys in their covariate means, , where these covariates have been shown empirically to be significant predictors of whether households are wirelessonly. For this reason, we believe that a fixed effect modeling strategy is a better specification than a nonfixed effect strategy would have been.
Assume that after estimating our fixed effects model with NHIS data we had generated a mean predicted NHIS value for each state by recycling NHIS data through the model. Let these values be denoted as . We in fact generated a mean predicted value for each state by recycling the CPS data through the model. Let these values be denoted as . Finally, we also used NHIS data to produce a direct prevalence estimate for each state. Let these values be denoted as DE_{NHIS}.
Given these three estimates, for any state, we can form the ratio:
which is identically equal to 1.0. By the first order, conditions of the logistic model (for binomial or MNL regressions), when a model is a fixed effects model at the state level, then for each state,
,
in which case, the above equation becomes:
In other words, in our statelevel fixed effects models, the difference of any state’s direct and synthetic estimates in the estimated proportion of households that are wirelessonly reflects—solely— the weighted differences across the NHIS and CPS surveys in their covariate means, . That is, differences in direct and synthetic estimates are a weighted average across NHIS and CPS surveys of their covariate means, , with these weights being the size of the coefficients in our MNL regression model.
Selection of variables used in the models
The wirelessonly classification is a collective attribute or characteristic of a household. It is either present in a household or not present, and its significance is that no member of that household can be contacted with a landline associated with that household. (By contrast, all persons in a landline household can be contacted with a landline, in the telephonesurveybased sense that any one household member contacted on a landline can tell the survey interviewer about himself or herself and all others in the household.) As a collective household characteristic, the wirelessonly status of households is best modeled using variables that are also measured at the household level. We also used householdlevel variables when we estimated our adultlevel models. That is, in the adultlevel models, we looked for characteristics of the household that predicted whether that adult resides within a wirelessonly household.
Given the nature of our twosample modeling approach, the variables that could be used in the model had to be available in both the NHIS and CPS surveys. In addition, we required identical coding of all responses in these two sets of variables. We used previous work on predictors of telephone status (4) as a starting point for choosing our variables. In addition, as suggested by their work, we tested the importance and significance of several interactions between variables for home ownership, age ranges within the household, and number of persons in the household.
In addition, we expected that there would be a direct relationship between the prevalence of wirelessonly households in a state and the number of wireless telephones per capita. From the Federal Communications Commission’s Automated Reporting Management Information System database, we obtained the number of wireless telephone subscriptions as of December 2006 and June 2007, and we divided these respectively by the U.S. Census Bureau’s July 1, 2006, and July 1, 2007, population estimates. We then entered these two statewide values of wireless telephones per capita into the NHIS data set in conjunction with the NHIS variable that designates whether a household participated in the survey during the first half of the year or the second half, to form one overall statespecific cell subscriber variable. This statelevel variable could be used in a fixedeffects model because there was variation in it within a state, and due to rapidly rising numbers of wireless telephone subscriptions, there was adequate variability. Indeed, the effect of this "withinstate" estimator—it relates variation in a statelevel variable within the individual states to variation in wirelessonly prevalence within the individual states—was quantitatively large, positive, and significant. For the householdlevel model, β = 5.05 and p = .015.
MNL regression results
Table II presents the MNL regression results for the final householdlevel model for the wirelessonly equation. Table III presents the MNL regression results for the final adultlevel model for the wirelessonly equation.
Variability of the estimates
For policy purposes, it’s natural to seek some quantitative measure of the level of uncertainty surrounding the estimates, analogous to a 95% confidence interval (CI). Of course, these blended estimates are a weighted average of two random variables, where the weights are complex functions of each of the two estimates’ SEs and thus these weights must be considered themselves to be random variables. This high level of complexity holds even if we assume, appropriately, that the NHIS and CPS survey samples are independent.
A full bootstrap procedure involving the simultaneous resampling of both the estimation sample (NHIS) and the prediction sample (CPS) could be conceptualized as a complex way of obtaining 95% CI for our blended estimates. Given this high level of complexity, we instead derived a simple quantitative measure of the level of uncertainty about these blended estimates, which due to its construction we refer to as the widest plausible interval for our blended estimates. Our direct estimates, DE_{wo,j}, have lower and upper 95% CI values, denoted as and , and our synthetic estimates, , similarly have lower and upper 95% CI values, denoted and . A maximal difference in our blended estimates could be conceived of as arising from taking the upper bounds of both our direct estimates and synthetic estimates and weighting these two upper bound values by their precision, as is done in our overall blended estimate, and then taking the lower bounds of both our direct estimates and synthetic estimates and again weighting these two lower bound values by their precision. The weights  although complex functions of SEs  are considered fixed for this purpose.
In equation form, we have for our widest plausible interval:
and
Clearly, this widest plausible interval is a very conservative measure of uncertainty because it uses the upper bounds of both direct and synthetic estimates simultaneously and the lower bounds of both direct and synthetic estimates simultaneously. Although they are not CIs and almost certainly exceed in width the CIs that might be obtained from a full bootstrapping of the blended estimates, we believe these estimates of uncertainty nevertheless serve a useful purpose. These estimates are included in the Table.
Calibration of the estimates
The values of our blended estimates have been adjusted to give an overall weighted average that equals the overall NHIS wirelessonly prevalence rate. This process is know in the small area of estimation literature as "raking" the blended estimates, and it is a process usually undertaken when the stateweighted average prevalence rate from the blended methodology does not perfectly match the overall directly estimated national rate (6).
First, we calculated each state's total weight from the CPS data, formed as the product of the state's average surveyweight times the state's sample size in the CPS. Next, we multiplied each state's total weight by it's blended estimate. Then, we summed these state products and divided this sum by the total of all the states' CPS weights. This yielded a rate of .1416 for households. Finally, we formed the ratio of the overall national NHIS rate to the stateweighted blended estimate rate, which is the "raking factor." This "raking factor" was applied to each state's blended estimate to arrive at a final raked blended estimate. The direct estimate from the NHIS was .147 for households, and this yielded a "raking factor" of 1.0381. The raking factor for the adult estimates was 1.0599.
The small size of these raking factors (i.e., the discrepancy between the directly estimated national rate and the initial stateweighted average prevalence rate from the blended methodology) provides empirical support for the appropriateness of our modeling procedures. Bias introduced by the modeling procedures, if any, was minor.
Figures
Figure 1. Statelevel comparisons of the percentage of wirelessonly households, modeled estimates: United States, 2007
Figure 2. Modeled statelevel estimates of the percentage of wirelessonly households: United States, 2007
Tables
Table. Modeled statelevel estimates of the percentage of wirelessonly households and the percentage of adults living in wirelessonly households: United States, 2007

Households  Adults  

State  Percent  Widest plausible interval  Percent  Widest plausible interval 
Alabama  13.9  9.718.1  12.2  8.116.4 
Alaska  11.7  8.914.8  13.3  7.319.5 
Arizona  18.9  14.523.1  17.1  13.620.4 
Arkansas  22.6  18.726.4  21.2  16.825.6 
California  9.0  8.19.8  8.4  7.79.1 
Colorado  16.7  13.220.3  15.2  12.518.2 
Connecticut  5.6  3.47.8  4.8  2.76.9 
Delaware  5.7  4.86.8  4.0  2.85.4 
District of Columbia  20.0  15.524.5  25.4  15.234.1 
Florida  16.8  13.919.4  15.5  12.817.8 
Georgia  16.5  12.919.9  15.0  11.618.1 
Hawaii  8.0  6.59.6  8.2  7.48.8 
Idaho  22.1  18.925.3  21.3  19.023.9 
Illinois  16.5  14.118.7  15.2  12.817.1 
Indiana  13.8  10.316.9  13.0  8.916.8 
Iowa  22.2  9.834.1  18.9  7.829.3 
Kansas  16.8  12.820.6  15.2  11.918.1 
Kentucky  21.4  11.730.4  21.6  11.530.8 
Louisiana  15.0  10.219.6  13.8  9.617.9 
Maine  13.4  10.516.5  12.0  10.613.9 
Maryland  10.8  9.112.6  9.8  8.311.5 
Massachusetts  9.3  7.910.7  8.4  7.19.8 
Michigan  16.3  12.719.7  15.3  11.618.7 
Minnesota  17.4  14.420.3  16.5  14.718.2 
Mississippi  19.1  11.426.3  20.3  12.627.0 
Missouri  9.9  6.812.9  8.4  6.210.6 
Montana  9.2  8.010.6  5.4  4.56.4 
Nebraska  23.2  13.232.7  22.4  12.731.2 
Nevada  10.8  8.813.0  10.1  9.011.3 
New Hampshire  11.6  9.214.3  8.9  7.211.0 
New Jersey  8.0  6.010.0  6.1  4.87.5 
New Mexico  21.1  11.329.6  20.5  10.428.8 
NewYork  11.4  10.013.0  10.6  9.412.2 
North Carolina  16.3  13.619.0  14.8  12.317.3 
North Dakota  16.9  6.727.2  18.1  4.432.2 
Ohio  14.0  11.316.6  13.1  11.015.3 
Oklahoma  26.2  12.938.8  25.1  14.634.6 
Oregon  17.7  14.520.8  18.1  15.020.8 
Pennsylvania  10.8  8.613.0  9.2  7.311.2 
Rhode Island  7.9  0.115.6  5.3  0.311.0 
South Carolina  20.6  14.526.0  19.2  13.824.0 
South Dakota  6.4  5.77.1  6.8  6.17.6 
Tennessee  20.3  16.123.4  20.8  14.925.2 
Texas  20.9  18.323.0  19.5  17.021.2 
Utah  25.5  16.932.8  23.9  15.230.9 
Vermont  5.1  4.95.4  4.6  4.54.9 
Virginia  10.8  8.812.9  10.0  7.912.2 
Washington  16.3  12.420.2  15.6  12.219.0 
West Virginia  11.6  8.314.5  10.6  4.616.1 
Wisconsin  15.2  11.918.4  13.6  10.816.3 
Wyoming  11.4  10.812.2  13.0  12.314.2 
DATA SOURCES: CDC/NCHS, National Health Interview Survey, 2007, and U.S. Census Bureau, Current Population Survey, Annual and Social Economic Supplement, 2008. Estimates were calculated by the State Health Access Data Assistance Center, University of Minnesota.
Table I. Direct statelevel estimates of the percentage of wirelessonly households and the percentage of adults living in wirelessonly households: United States, 2007

Households  Adults  

State  Percent  95% confidence interval  Percent  95% confidence interval 
Alabama  12.5  5.819.2  11.7  5.018.4 
Alaska  9.8  1.118.6  11.2  1.820.6 
Arizona  18.8  13.424.2  17.1  13.021.1 
Arkansas  22.3  17.527.0  20.7  14.826.5 
California  8.4  7.39.4  7.7  6.88.6 
Colorado  18.1  12.224.1  17.0  10.923.2 
Connecticut  6.1  2.99.2  5.0  1.98.1 
Delaware  7.7  0.315.0  5.0  0.010.4 
District of Columbia  23.0  15.330.8  25.7  17.134.3 
Florida  15.8  13.118.5  14.5  12.017.0 
Georgia  15.5  11.619.4  13.7  10.017.3 
Hawaii  7.6  5.210.0  8.2  7.49.0 
Idaho  23.2  18.528.0  21.5  17.625.4 
Illinois  16.3  13.718.9  15.0  12.917.2 
Indiana  13.8  10.517.2  13.5  9.217.8 
Iowa  23.6  10.536.7  21.4  9.933.0 
Kansas  15.8  11.720.0  13.9  10.617.2 
Kentucky  22.0  13.130.9  22.8  13.232.3 
Louisiana  13.8  8.319.2  13.2  8.417.9 
Maine  17.3  4.729.8  15.7  4.027.3 
Maryland  10.1  7.312.9  8.9  6.411.5 
Massachusetts  8.9  6.910.9  8.0  6.010.0 
Michigan  17.4  13.021.8  16.5  11.921.2 
Minnesota  16.2  12.320.0  15.7  13.517.8 
Mississippi  18.2  10.925.5  19.1  12.325.8 
Missouri  9.9  6.213.7  8.3  5.411.3 
Montana  5.0  0.59.4  3.1  1.84.3 
Nebraska  22.3  12.032.6  20.8  12.229.5 
Nevada  10.3  5.814.7  9.0  7.410.5 
New Hampshire  12.1  0.025.3  9.1  0.019.8 
New Jersey  7.9  5.310.5  5.9  4.17.6 
New Mexico  20.3  12.627.9  19.4  11.727.1 
New York  12.8  9.216.5  12.1  8.515.8 
North Carolina  15.9  12.219.6  14.6  11.218.1 
North Dakota  14.3  0.030.4  15.3  0.032.0 
Ohio  14.5  10.818.2  13.7  10.317.1 
Oklahoma  27.4  14.240.6  26.5  16.336.7 
Oregon  17.3  13.521.1  17.4  14.320.5 
Pennsylvania  11.0  8.014.0  9.6  6.712.5 
Rhode Island  6.8  0.014.4  4.4  0.010.5 
South Carolina  19.6  14.224.9  18.3  13.223.4 
South Dakota  4.8  3.06.6  5.5  4.36.6 
Tennessee  19.9  16.923.0  20.2  15.924.5 
Texas  20.6  18.322.8  18.7  16.820.7 
Utah  24.0  17.330.7  22.2  15.628.8 
Vermont  3.5  0.08.8  2.8  0.06.3 
Virginia  11.9  8.315.6  10.9  7.014.7 
Washington  16.6  11.222.1  16.1  11.420.7 
West Virginia  10.0  7.212.9  9.6  4.614.7 
Wisconsin  16.3  12.120.4  15.2  11.319.2 
Wyoming  14.2  4.623.7  16.8  10.023.5 
0.0 Quantity more than zero but less than 0.05.
DATA SOURCE: CDC/NCHS, National Health Interview Survey, 2007.
Table IIa. Multinomial logistic regression results for the fixedeffects householdlevel model, where predictor is age.
Age  Coefficient  Standard error  tvalue  pvalue 

All adults in the household are less than or equal to 30 years of age  1.866711  0.089851  20.78  <.001 
Household includes an adult 65 years or older  1.539320  0.124413  12.37  <.001 
All adults in the household are between 31 and 44 years of age, inclusive  0.415542  0.073732  5.64  <.001 
Table IIb. Multinomial logistic regression results for the fixedeffects householdlevel model, where predictor is home ownership status.
Home ownership status  Coefficient  Standard error  tvalue  pvalue 

Renting  1.116378  0.068147  16.38  <.001 
Interaction of "renting" and "all adults in the household are less than or equal to 30 years of age"  0.565510  0.114927  4.92  <.001 
Table IIc. Multinomial logistic regression results for the fixedeffects householdlevel model, where predictor is sex.
Sex  Coefficient  Standard error  tvalue  pvalue 

All adults in the household are male  0.892477  0.068968  12.94  <.001 
All adults in the household are female  0.319934  0.072536  4.41  <.001 
Table IId. Multinomial logistic regression results for the fixedeffects householdlevel model, where predictor is household structure.
Household structure  Coefficient  Standard error  tvalue  pvalue 

Household includes unrelated adults  0.609820  0.078856  7.73  <.001 
Household includes children under 18 years of age  0.246420  0.087131  2.83  0.005 
Household includes only one or two adults  0.158120  0.097213  1.63  0.105 
Table IIe. Multinomial logistic regression results for the fixedeffects householdlevel model, where predictor is race or ethnicity.
Race/ethnicity  Coefficient  Standard error  tvalue  pvalue 

All persons in the household are Hispanic  0.406733  0.065491  6.21  <.001 
All persons in the household are nonHispanic black  0.198420  0.074202  2.67  0.008 
Table IIf. Multinomial logistic regression results for the fixedeffects householdlevel model, where predictor is household poverty status.
Household poverty status  Coefficient  Standard error  tvalue  pvalue 

Nearpoor household  0.326522  0.061096  5.34  <.001 
Poor household  0.241867  0.083452  2.90  0.004 
Table IIg. Multinomial logistic regression results for the fixedeffects householdlevel model, where predictor is other demographics.
Other demographics  Coefficient  Standard error  tvalue  pvalue 

Household includes at least one adult with a job or business  0.402410  0.076572  5.26  <.001 
At least one person in the household has a 4year college degree or higher education  0.174650  0.050613  3.45  0.001 
Number of persons in the household  0.104420  0.037508  2.78  0.006 
Table IIh. Multinomial logistic regression results for the fixedeffects householdlevel model, where predictor is statelevel estimate of the number of wireless subscribers per capita.
Statelevel estimate of the number of wireless subscribers per capita  Coefficient  Standard error  tvalue  pvalue 

Estimate from December 2006 or June 2007, depending on date of interview  5.047416  2.055141  2.46  0.015 
Table IIi. Multinomial logistic regression results for the fixedeffects householdlevel model, where predictor is state.
State  Coefficient  Standard error  tvalue  pvalue 

Alabama  1.272225  0.238382  5.34  <.001 
Alaska  1.417395  0.469514  3.02  0.003 
Arizona  1.607334  0.256893  6.26  <.001 
Arkansas  2.014621  0.200072  10.07  <.001 
Colorado  1.179756  0.197636  5.97  <.001 
Connecticut  0.154310  0.356241  0.43  0.665 
Delaware  0.155140  0.173824  0.89  0.373 
District of Columbia  2.797410  1.508089  1.85  0.065 
Florida  1.165666  0.133942  8.70  <.001 
Georgia  1.232165  0.171628  7.18  <.001 
Hawaii  0.252015  0.169125  1.49  0.137 
Idaho  2.273645  0.333961  6.81  <.001 
Illinois  1.296925  0.173017  7.50  <.001 
Indiana  1.497265  0.331355  4.52  <.001 
Iowa  2.035218  0.469920  4.33  <.001 
Kansas  1.393183  0.218114  6.39  <.001 
Kentucky  1.879856  0.413259  4.55  <.001 
Louisiana  1.079136  0.231553  4.66  <.001 
Maine  1.782551  0.380313  4.69  <.001 
Maryland  0.486007  0.152081  3.20  0.002 
Massachusetts  0.344432  0.136055  2.53  0.012 
Michigan  1.674510  0.273125  6.13  <.001 
Minnesota  1.563082  0.239372  6.53  <.001 
Mississippi  2.179626  0.340533  6.40  <.001 
Missouri  0.672953  0.304624  2.21  0.028 
Montana  0.829946  0.351831  2.36  0.019 
Nebraska  1.866004  0.333427  5.60  <.001 
Nevada  0.379044  0.156361  2.42  0.016 
New Hampshire  1.136946  0.255660  4.45  <.001 
New Jersey  0.170389  0.202940  0.84  0.402 
New Mexico  2.004247  0.667762  3.00  0.003 
New York  0.530698  0.122055  4.35  <.001 
North Carolina  1.333125  0.185659  7.18  <.001 
North Dakota  1.243878  0.417006  2.98  0.003 
Ohio  1.229178  0.213456  5.76  <.001 
Oklahoma  2.236250  0.406260  5.50  <.001 
Oregon  1.615444  0.228845  7.06  <.001 
Pennsylvania  1.048467  0.266023  3.94  <.001 
Rhode Island  0.564256  0.622844  0.91  0.366 
South Carolina  1.929321  0.264777  7.29  <.001 
South Dakota  0.228156  0.257332  0.89  0.376 
Tennessee  1.119440  0.262129  4.27  <.001 
Texas  1.454220  0.149066  9.76  <.001 
Utah  2.551921  0.344626  7.40  <.001 
Vermont  0.750223  0.479845  1.56  0.119 
Virginia  0.987165  0.198595  4.97  <.001 
Washington  1.195146  0.204184  5.85  <.001 
West Virginia  2.273231  0.507029  4.48  <.001 
Wisconsin  1.757524  0.416058  4.22  <.001 
Wyoming  0.720274  0.127978  5.63  <.001 
Table IIj. Multinomial logistic regression results for the fixedeffects householdlevel model, where predictor is constant.
Constant  Coefficient  Standard error  tvalue  pvalue 

Value  7.645990  1.725050  4.43  <.001 
DATA SOURCES: CDC/NCHS, National Health Interview Survey (NHIS), 2007, and Federal Communications Commission, Automated Reporting Management Information System, 20062007. NHIS sample size = 28,492 households.
Table IIIa. Multinomial logistic regression results for the fixedeffects adultlevel model, where predictor is age.
Age  Coefficient  Standard error  tvalue  pvalue 

All adults in the household are less than or equal to 30 years of age  1.799508  0.090836  19.81  <.001 
Household includes an adult 65 years or older  1.382090  0.134494  10.28  <.001 
All adults in the household are between 31 and 44 years of age, inclusive  0.301825  0.072248  4.18  <.001 
Table IIIb. Multinomial logistic regression results for the fixedeffects adultlevel model, where predictor is home ownership status.
Home ownership status  Coefficient  Standard error  tvalue  pvalue 

Renting  1.143872  0.067472  16.95  <.001 
Interaction of "renting" and all "adults in the household are less than or equal to 30 years of age"  0.565430  0.112602  5.02  <.001 
Table IIIc. Multinomial logistic regression results for the fixedeffects adultlevel model, where predictor is sex.
Sex  Coefficient  Standard error  tvalue  pvalue 

All adults in the household are male  0.873861  0.073431  11.90  <.001 
All adults in the household are female  0.277123  0.073664  3.76  <.001 
Table IIId. Multinomial logistic regression results for the fixedeffects adultlevel model, where predictor is household structure.
Household structure  Coefficient  Standard error  tvalue  pvalue 

Household includes unrelated adults  0.616546  0.079639  7.74  <.001 
Household includes children under 18 years of age  0.208900  0.091222  2.29  0.023 
Household includes only one or two adults  0.155960  0.094759  1.65  0.101 
Table IIIe. Multinomial logistic regression results for the fixedeffects adultlevel model, where predictor is race or ethnicity.
Race/ethnicity  Coefficient  Standard error  tvalue  pvalue 

All persons in the household are Hispanic  0.356096  0.072708  4.90  <.001 
All persons in the household are nonHispanic black  0.128510  0.075034  1.71  0.088 
Table IIIf. Multinomial logistic regression results for the fixedeffects adultlevel model, where predictor is household poverty status.
Household poverty status  Coefficient  Standard error  tvalue  pvalue 

Nearpoor household  0.362912  0.063380  5.73  <.001 
Poor household  0.363780  0.083803  4.34  <.001 
Table IIIg. Multinomial logistic regression results for the fixedeffects adultlevel model, where predictor is other demographics.
Other demographics  Coefficient  Standard error  tvalue  pvalue 

Household includes at least one adult with a job or business  0.473183  0.081272  5.82  <.001 
At least one person in the household has a 4year college degree or higher education  0.194480  0.054364  3.58  <.001 
Number of persons in the household  0.111140  0.037656  2.95  0.003 
Table IIIh. Multinomial logistic regression results for the fixedeffects adultlevel model, where predictor is statelevel estimate of the number of wireless subscribers per capita.
Statelevel estimate of the number of wireless subscribers per capita  Coefficient  Standard error  tvalue  pvalue 

Estimate from December 2006 or June 2007, depending on date of interview  4.280246  2.203306  1.94  0.053 
Table IIIi. Multinomial logistic regression results for the fixedeffects adultlevel model, where predictor is state.
State  Coefficient  Standard error  tvalue  pvalue 

Alabama  1.091400  0.268269  4.07  <.001 
Alaska  1.617397  0.555650  2.91  0.004 
Arizona  1.480033  0.258784  5.72  <.001 
Arkansas  1.918280  0.219244  8.75  <.001 
Colorado  1.105028  0.189786  5.82  <.001 
Connecticut  0.250100  0.381567  0.66  0.513 
Delaware  0.493910  0.309229  1.60  0.111 
District of Columbia  1.915190  1.650917  1.16  0.247 
Florida  1.092798  0.130196  8.39  <.001 
Georgia  1.161430  0.177353  6.55  <.001 
Hawaii  0.385860  0.088463  4.36  <.001 
Idaho  2.182128  0.354031  6.16  <.001 
Illinois  1.250206  0.185081  6.75  <.001 
Indiana  1.443714  0.373140  3.87  <.001 
Iowa  1.624521  0.558052  2.91  0.004 
Kansas  1.273341  0.206036  6.18  <.001 
Kentucky  1.865205  0.423037  4.41  <.001 
Louisiana  1.073218  0.210920  5.09  <.001 
Maine  1.586943  0.389421  4.08  <.001 
Maryland  0.489238  0.145614  3.36  0.001 
Massachusetts  0.314262  0.141998  2.21  0.028 
Michigan  1.624691  0.291254  5.58  <.001 
Minnesota  1.489495  0.233542  6.38  <.001 
Mississippi  2.214357  0.343132  6.45  <.001 
Missouri  0.512488  0.277719  1.85  0.066 
Montana  0.252518  0.388014  0.65  0.516 
Nebraska  1.856366  0.359632  5.16  <.001 
Nevada  0.357242  0.124442  2.87  0.004 
New Hampshire  0.782579  0.273309  2.86  0.004 
New Jersey  0.049300  0.182665  0.27  0.787 
New Mexico  1.958091  0.645469  3.03  0.003 
New York  0.528292  0.111597  4.73  <.001 
North Carolina  1.192146  0.193867  6.15  <.001 
North Dakota  1.502527  0.487668  3.08  0.002 
Ohio  1.190100  0.208526  5.71  <.001 
Oklahoma 
2.133292  0.383744  5.56  <.001 
Oregon 
1.598406  0.239406  6.68  <.001 
Pennsylvania 
0.874146  0.278287  3.14  0.002 
Rhode Island 
0.136707  0.670396  0.20  0.839 
South Carolina 
1.794088  0.254067  7.06  <.001 
South Dakota 
0.300735  0.273356  1.10  0.272 
Tennessee 
1.273948  0.266752  4.78  <.001 
Texas 
1.404436  0.149124  9.42  <.001 
Utah 
2.377257  0.371590  6.40  <.001 
Vermont 
0.576505  0.515766  1.12  0.265 
Virginia 
0.901419  0.225554  4.00  <.001 
Washington 
1.167482  0.200486  5.82  <.001 
West Virginia 
2.015832  0.648016  3.11  0.002 
Wisconsin 
1.532290  0.430925  3.56  <.001 
Wyoming 
0.991193  0.132367  7.49  <.001 
Table IIIj. Multinomial logistic regression results for the fixedeffects adultlevel model, where predictor is constant.
Constant  Coefficient  Standard error  tvalue  pvalue 

Value  7.029340  1.848191  3.80  <.001 
DATA SOURCES: CDC/NCHS, National Health Interview Survey (NHIS), 2007, and Federal Communications Commission, Automated Reporting Management Information System, 20062007. NHIS sample size = 53,770 adults.