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Deployment of a mixed-mode data collection strategy does not reduce nonresponse bias in a general population health survey.

Survey participation is declining (Hox and de Leeuw 1994; Hartge 1999; Steeh et al. 2001; de Leeuw and de Heer 2002; Tickle See Tcl/Tk and tickle packet.

(text, tool) Tickle - A text editor, file translator and TCL interpreter for the Macintosh.

Version 5.0v1. The text editor breaks the 32K limit (like MPW).
 et al. 2003; Curtin, Presser, and Singer 2005; Morton, Cahill, and Hartge 2006; Berk, Schur, and Feldman 2007); this trend is of great concern because response rate is the most widely used measure of survey quality (Atrostic et al. 2001) and nonresponse bias can be a serious threat to the validity of survey estimates (Sackett 1979; Barton et al. 1980). In an effort to increase response rates, and potentially reduce nonresponse bias, household surveys are increasingly turning to mixed-mode designs whereby instruments are designed to be administered in more than one mode, including mail, web, telephone, and/or in-person, and respondents In the context of marketing research, a representative sample drawn from a larger population of people from whom information is collected and used to develop or confirm marketing strategy.  are allowed to respond to the mode of their choice (De Leeuw 2005; Dillman, Smyth, and Christin 2009b). The attraction of mixed-mode designs is that the characteristics of nonrespondents may vary by the mode of data collection (Groves 2006) and a second mode will bring in potentially different types of respondents. For this reason (among others), the data collection protocols for three major surveys, the Consumer Assessment of Healthcare Providers and Systems, the Experience of Care and Health Outcomes studies, and the American Community Survey (ACS (Asynchronous Communications Server) See network access server. ), call for an initial contact by mail with telephone follow-up to encourage initial nonrespondents to mail in their completed questionnaires or to complete a telephone interview.

Available evidence supports the notion that some respondents exhibit mode preference (Siemiatycki 1979; Brambilla and McKinlay 1987; Link and Mokdad 2005) and that a sequential strategy of implementing multiple contacts allows prospective respondents to respond to a particular mode will improve response rates. For example, in work evaluating the effect of pairing a mixed mail and telephone methodology with a prepaid pre·pay  
tr.v. pre·paid, pre·pay·ing, pre·pays
To pay or pay for beforehand.



pre·payment n.
 cash incentive on response rates in a survey of Medicaid enrollees response rates increased considerably after telephone follow-ups, from 54 to 69 percent in the incentive condition, and from 45 to 64 percent in the nonincentive condition (Beebe et al. 2005). Similarly, Gallagher, Fowler, and Stringfellow (2000) found that approximately 34 percent of a sample of Medicaid enrollees responded to a mailed survey and another 10-13 percent responded by telephone. Finally, the ACS, a large national demographic survey conducted by the U.S. Census Bureau Noun 1. Census Bureau - the bureau of the Commerce Department responsible for taking the census; provides demographic information and analyses about the population of the United States
Bureau of the Census
, achieves a response rate of 56.2 percent to an initial mailed survey, an increase to 63.5 percent after telephone follow-up, and a final response rate of 95.4 percent after face-to-face interviews (Griffin and Obenski 2002).

Although these studies demonstrate the ability of mixed-mode surveys to increase response rates, they do not clarify their effect on response bias because little information on nonrespondents is available. Some research suggests that switching modes does bring in a different population from those that respond to the initial mode. For example, Fowler et al. (2002) found that telephone interviews with mail nonrespondents produced a less biased final sample in terms of gender and age in a sample of 800 health plan enrollees. In one of the few mixed-mode studies to have more detailed health-related information on the full sample of 1,900 adult patients enrolled in a randomized controlled trial A randomized controlled trial (RCT) is a scientific procedure most commonly used in testing medicines or medical procedures. RCTs are considered the most reliable form of scientific evidence because it eliminates all forms of spurious causality.  to promote smoking cessation smoking cessation Public health Temporary or permanent halting of habitual cigarette smoking; withdrawal therapies–eg, hypnosis, psychotherapy, group counseling, exposing smokers to Pts with terminal lung CA and nicotine chewing gum are often ineffective. , a telephone followed by mail design improved representativeness in a number of health-related areas, such as seeking treatment, cardio-pulmonary comorbidities, and substance abuse (Baines et al. 2007). However, these studies had limited information on respondents and nonrespondents (Fowler et al. 2002); used an atypical atypical /atyp·i·cal/ (-i-k'l) irregular; not conformable to the type; in microbiology, applied specifically to strains of unusual type.

a·typ·i·cal
adj.
 sequential strategy (e.g., telephone followed by mail versus mail followed by telephone (Baines et al. 2007); and focused on specialized spe·cial·ize  
v. spe·cial·ized, spe·cial·iz·ing, spe·cial·iz·es

v.intr.
1. To pursue a special activity, occupation, or field of study.

2.
 patient populations (Fowler et al. 2002; Baines et al. 2007) that render the generalizability of their results unclear.

In a general population survey utilizing a mixed-mode, mail followed by telephone data collection approach, this article reports a systematic analysis of survey nonresponse bias using extensive sociodemographic and health-related information on both respondents and nonrespondents to a general population survey. Our primary focus is to assess whether nonresponse bias was reduced by the utilization of a mixed-mode, mail and telephone data collection design.

METHODS

Survey and Procedures

The data on response status come from a sequential mixed-mode, mail and telephone survey on recent gastrointestinal gastrointestinal /gas·tro·in·tes·ti·nal/ (-in-tes´ti-n'l) pertaining to or communicating with the stomach and intestine.

gas·tro·in·tes·ti·nal
adj.
Abbr.
 symptoms conducted between September 2005 and April 2006 by the Mayo Clinic Survey Research Center. Further details of the study and its methods are available elsewhere (Beebe et al. 2007, 2011). The population for the study survey included noninstitutionalized residents of Olmsted County, Minnesota Olmsted County is a county located in the U.S. state of Minnesota, founded in 1855. As of 2000, the population was 124,277. Its county seat is Rochester6. Geography
According to the U.S.
, aged 18 and older as identified in a purchased list-based sample.

The study population is the 6,939 eligible cases that were sent a mailed survey packet. Initial nonresponders were sent a second survey 3 weeks later. A telephone interview was attempted approximately 2 weeks later for remaining nonrespondents. The overall response rate for the survey was 51.2 percent (American Association for Public Opinion Research 2006). The response rates for the first and second mailings were 24.1 and 38.3 percent, respectively.

The sampling frame for the study was linked to administrative data from the Rochester Epidemiology epidemiology, field of medicine concerned with the study of epidemics, outbreaks of disease that affect large numbers of people. Epidemiologists, using sophisticated statistical analyses, field investigations, and complex laboratory techniques, investigate the cause  Project (REP). Each health care provider in Olmsted County (home of Mayo Clinic, Olmsted Medical Center, and the Rochester Family Medicine Clinic) uses a unit medical record system whereby all data collected on an individual are assembled in one place. Each participating site also solicits and documents permission from patients for their records to be used. Currently, 95 percent of patients have granted this permission. The REP includes medical diagnoses, hospital admissions and surgical procedures Surgical procedures have long and possibly daunting names. The meaning of many surgical procedure names can often be understood if the name is broken into parts. For example in splenectomy, "ectomy" is a suffix meaning the removal of a part of the body. "Splene-" means spleen. , and demographic information. Overall, at least 98 percent of the Olmsted county population has been seen by a REP provider at some point (Melton mel·ton  
n.
A heavy woolen cloth used chiefly for making overcoats and hunting jackets.



[After Melton Mowbray, an urban district of central England.]
 1996; St Sauver et al. 2011). Approximately 97 percent of the cases in the sample file were matched to members in the REP database. Primary analyses focused on the 6,716 individuals for whom health care information was available. This study was approved by the Mayo Clinic and Olmsted Medical Center IRBs.

Measures

Respondents include those who completed a mailed survey or telephone interview (at least two-thirds of the items completed). Nonrespondents include those who refused or could not be contacted. Respondents are further categorized cat·e·go·rize  
tr.v. cat·e·go·rized, cat·e·go·riz·ing, cat·e·go·riz·es
To put into a category or categories; classify.



cat
 by whether they completed the survey at the first or second mailings, or completed the telephone interview.

Selected demographic variables were obtained from the REP frame, including age, gender, and race/ethnicity. Race/ethnicity was classified as white versus other because sample sizes did not permit analysis of specific minority cultural groups. All medical and surgical diagnoses received

by patients at a health care site participating in the REP are coded using either Hospital Adaptation of the International Classification of Diseases (Commission on professional and hospital activities 1973) or the International Classification of Diseases, 9th Edition (ICD-9) codes. Also included was the formal diagnosis in the past decade of a number of disease statuses (see Table 1) dichotomized as presence or absence of each condition. The severity-weighted Charlson Index (Charlson et al. 1987; Deyo, Cherkin, and Ciol 1992) based on these diagnoses was used to provide a summary score of comorbidity. The Charlson measure is an effective method of estimating future morbidity and mortality Morbidity and Mortality can refer to:
  • Morbidity & Mortality, a term used in medicine
  • Morbidity and Mortality Weekly Report, a medical publication
See also
  • Morbidity, a medical term
  • Mortality, a medical term
 in longitudinal studies longitudinal studies,
n.pl the epidemiologic studies that record data from a respresentative sample at repeated intervals over an extended span of time rather than at a single or limited number over a short period.
 (Charlson et al. 1987) and therefore has utility as a measure of current health.

Also ascertained as·cer·tain  
tr.v. as·cer·tained, as·cer·tain·ing, as·cer·tains
1. To discover with certainty, as through examination or experimentation. See Synonyms at discover.

2.
 was whether each subject had a surgical or nonsurgical procedure at one of the hospitals in Olmsted County in the past decade. Finally, the number of emergency room (ER) visits, outpatient outpatient /out·pa·tient/ (-pa-shent) a patient who comes to the hospital, clinic, or dispensary for diagnosis and/or treatment but does not occupy a bed.

out·pa·tient
n.
 clinic visits, and hospital admissions during the 2 years that covered when the survey was in the field (2005 and 2006) were calculated. Utilization was dichotomized and cut-offs were chosen to facilitate analysis and interpretation, informed by the items' marginal distributions to identify natural breaks, and designed to accord with prior authorization prior authorization,
n See predetermination.

prior authorization Health insurance A cost containment measure that provides full payment of health benefits only if the hospitalization or medical treatment has been
 studies in Olmsted county using the REP (Jacobsen et al. 1999).

Statistical Analysis

The key research question was, "What effect did deploying a mixed-mode, mail and telephone data collection strategy have on nonresponse bias?" Using the distribution from the total eligible sample as population estimates, we used chi-square goodness-of-fit tests to compare respondents by mode (first and second mailing and phone) to the population. Note that throughout we refer to the survey protocol as reflecting a mixed-mode design, we acknowledge that part of our analysis, looking at response patterns between the first and second mailings, is not an evaluation of mixing modes but rather an evaluation of a second contact in the same mode. Multivariable logistic regression In statistics, logistic regression is a regression model for binomially distributed response/dependent variables. It is useful for modeling the probability of an event occurring as a function of other factors.  analysis was used to assess whether our mixed-mode design affected sample representation across data collection phases, including all sociodemographic and health-related variables. Three regression regression, in psychology: see defense mechanism.
regression

In statistics, a process for determining a line or curve that best represents the general trend of a data set.
 models were analyzed an·a·lyze  
tr.v. an·a·lyzed, an·a·lyz·ing, an·a·lyz·es
1. To examine methodically by separating into parts and studying their interrelations.

2. Chemistry To make a chemical analysis of.

3.
, considering three outcomes: (1) probability of responding to the first mailing (versus second mailing or phone or nonresponse), (2) probability of responding to either mailing (versus phone or nonresponse), and (3) probability of any response (versus nonresponse). Odds ratios (adjusted for all predictors included in the model) and 95 percent confidence intervals were estimated. All analyses were performed using SAS (1) (SAS Institute Inc., Cary, NC, www.sas.com) A software company that specializes in data warehousing and decision support software based on the SAS System. Founded in 1976, SAS is one of the world's largest privately held software companies. See SAS System.  v. 9.1 software, p-values less than 0.05 were considered statistically significant.

RESULTS

Table 1 assesses the differences between responders and the population by response mode where we compare respondents reached after the first and second mailings and via telephone (first three columns of Table 1) to the population. Demographically, men are under-represented in the first mail contact with 48.9 percent being male, compared to 52.6 percent of the population. Older people, particularly those over 65 and white individuals are over-represented in the first mail contact.

With respect to health status, individuals with a severity-weighted Charlson score of two or more are over-represented by about 12 percent in the first mail contact. For most of the measured health conditions, the sample reached by mail (either contact) closely matched the population, with the exception of other cancer types where the sample responding to the first mailing was significantly more likely to have cancer (15 percent) compared to the population (11.8 percent), an over-representation of approximately 27 percent. The telephone mode brought in respondents with some of the other health conditions that were less representative of the population. That is, telephone respondents were less likely to have congestive heart failure congestive heart failure, inability of the heart to expel sufficient blood to keep pace with the metabolic demands of the body. In the healthy individual the heart can tolerate large increases of workload for a considerable length of time. , cerebrovascular disease cerebrovascular disease Neurology Any vascular disease affecting cerebral arteries–eg ASHD, diabetic vasculopathy, HTN, which may cause a CVA or TIA with neurologic sequelae–speech, vision, movement of variable duration. , moderate/severe renal disease Renal disease
Kidney disease.

Mentioned in: Glycogen Storage Diseases

hypertension High blood pressure Cardiovascular disease An abnormal ↑ systemic arterial pressure, corresponding to a systolic BP of > 160 mm Hg
 and other cancer than were the total eligible sample. With respect to office visits and procedures, early respondents were heavier utilizers than the population. The same was true (but to a lesser degree) for those responding to the second mailing with respect to office visits. The sample obtained with the second mail survey was less likely to have a hospitalization hospitalization /hos·pi·tal·iza·tion/ (hos?pi-t'l-i-za´shun)
1. the placing of a patient in a hospital for treatment.

2. the term of confinement in a hospital.
 admission than the population. Finally, the sample obtained after the first mailing significantly under-represented individuals who had used the ER.

Table 2 provides the results of the multivariable logistic regression analyses that included all sociodemographic and health-related variables. The first of the three regression analyses (Model 1) shows the likelihood of response to the first mail contact compared to not responding to that initial contact, revealing biases in the sample gathered from the first mail contact. Adjusting for selected demographics The attributes of people in a particular geographic area. Used for marketing purposes, population, ethnic origins, religion, spoken language, income and age range are examples of demographic data. , health status and utilization, older adults (50-65 and 65+) are more likely to respond to the first mail contact than are 18- to <35-year-olds. White individuals are more likely to respond as are those with three or more office visits. Individuals with one or more ER visits are less likely to respond. Most important, the results indicate younger people, those from minority cultural groups, ER users, and those who have fewer doctor visits would have been under-represented if estimates had been based only on respondents to the initial mailing.

With a few exceptions, the above biases persist after considering the sample characteristics following the second mailing of the survey (Model 2), and after additional respondents completed the survey by phone (Model 3). Adding a second mailing and a phone mode did not measurably reduce the biases that were observed in the mail sample; however, it does appear to reduce, but not eliminate, the over-representation of older persons that was observed in the first mailing. The over-representation of high-utilizers of clinics and low users of the ER that was observed after the first mail contact remains substantially unchanged after the second mail attempt and after the phone mode.

Interestingly, individuals with congestive heart failure were less likely to be a respondent In Equity practice, the party who answers a bill or other proceeding in equity. The party against whom an appeal or motion, an application for a court order, is instituted and who is required to answer in order to protect his or her interests.  once the mode switched to telephone (odds ratio [OR] = 0.61, p = 0.001), indicating that the third contact resulted in a respondent population that may be less representative of the underlying population. Of note, in a similar set of models that used the severity-weighted Charlson score as a predictor or response status instead of individual diseases, all the demographic and utilization relationships remained the same (data not shown). Across all three models, however, individuals with a weighted Charlson score of two or more were less likely to be respondents.

DISCUSSION

There is ample evidence that attaining high levels of survey participation is increasingly difficult (Hox and de Leeuw 1994; Hartge 1999; Steeh et al. 2001; Tickle et al. 2003; Curtin, Presser, and Singer 2005; Morton, Cahill, and Hartge 2006; Berk, Schur, and Feldman 2007) and that deployment of a mixed-mode data collection protocol can be an effective way of increasing survey response rates (Gallagher, Fowler, and String-fellow 2000; Griffin and Obenski 2002; Beebe et al. 2005). However, emerging evidence suggests that a low response rate does not necessarily portend major study bias (Groves 2006; Groves and Peytcheva 2008) and little evidence that mixing modes minimizes the latter. In our general population survey with an overall response rate of 51.2 percent, contrary to expectations, we found that switching modes from a mail survey to a telephone interview did notuniformly increase the representativeness of the responding sample. Indeed, we found evidence that switching modes may make the sample less representative of the population in terms of at least one clinical variable. Incidentally, we also found that a second contact in the same mode did not increase sample representativeness either.

Our finding that switching mode did not increase the representation of the final sample runs counter to the few studies investigating this issue. In the two studies most similar to our study with respect to order of contact, this approach yielded a more representative sample, although only one study had health and health care utilization for nonrespondents (Gallagher, Fowler, and Stringfellow 1999; Fowler et al. 2002). However, the populations from neither study were representative of the general population and, as such, may be more attuned at·tune  
tr.v. at·tuned, at·tun·ing, at·tunes
1. To bring into a harmonious or responsive relationship: an industry that is not attuned to market demands.

2.
 to the nuances of data collection strategies and more susceptible to the deployment of specific modes. Tacit support for this notion is supplied by the juxtaposition juxtaposition /jux·ta·po·si·tion/ (-pah-zish´un) apposition.

jux·ta·po·si·tion
n.
The state of being placed or situated side by side.
 of two studies deploying a mixed-mode design representing the converse (logic) converse - The truth of a proposition of the form A => B and its converse B => A are shown in the following truth table:

A B | A => B B => A ------+---------------- f f | t t f t | t f t f | f t t t | t t
 of ours: initial telephone contact followed by another mode (e.g., mail, web). Whereas switching to a mailed survey after a telephone interview reached a segment of the population quite different from the segment that would have been reached through telephone alone among adult patients enrolled in a trial to promote treatment for relapsed smokers at five Veteran's Administration Centers (Baines et al. 2007), a similar effect was not seen in a similarly designed general population survey of close to 9,000 households, albeit in an area unrelated to health (Dillman et al. 2009a).

For general populations, switching modes may be more akin to a multiple attempt strategy, perceived only as an increased effort on our part to enlist en·list  
v. en·list·ed, en·list·ing, en·lists

v.tr.
1. To engage (persons or a person) for service in the armed forces.

2. To engage the support or cooperation of.

v.
 cooperation, rather than the introduction of a new method, per se. As such, our results are more aligned with the literature investigating the effects of multiple attempts on response rates (Keeter et al. 2000; Davern et al. 2010). The impact of additional measures to enlist participation, such as multiple contacts and/or switching modes, may actually bring in respondents for whom the topic is less salient, leading to an under-representation of those who are less healthy and higher utilizers. This interpretation is consistent with Leverage-Salience Theory proposed by Groves and colleagues (Groves, Singer, and Coming 2000; Groves, Presser, and Dipko 2004), which posits that survey features, such as mode, could have variable leverage for different types of sample members and that switching modes may make a given survey more or less salient for certain types of people, thus increasing or decreasing participation. Regardless of the cause, it appears that use of a mixed-mode approach does not represent a wholesale good when considering use among general population samples, particularly if the topic of survey pertains to health.

In considering our findings, we note potentially important limitations. Our data may not be generalizable gen·er·al·ize  
v. gen·er·al·ized, gen·er·al·iz·ing, gen·er·al·iz·es

v.tr.
1.
a. To reduce to a general form, class, or law.

b. To render indefinite or unspecific.

2.
 to the U.S. population because the racial composition of the population is predominantly pre·dom·i·nant  
adj.
1. Having greatest ascendancy, importance, influence, authority, or force. See Synonyms at dominant.

2.
 white; the prevalence of clinical disease status may vary by ethnicity ethnicity Vox populi Racial status–ie, African American, Asian, Caucasian, Hispanic , but at a minimum our data are probably generalizable to the U.S. white population. Additionally, our study relied on the medical chart to determine disease status and utilization, which may be subject to underreporting of mild symptoms or disease status. However, we assume that more severe symptoms or disease conditions would have been charted and that utilization history was accurately characterized char·ac·ter·ize  
tr.v. character·ized, character·iz·ing, character·iz·es
1. To describe the qualities or peculiarities of: characterized the warden as ruthless.

2.
 as payment is based on such documentation. Finally, this relatively health-literate population has been heavily surveyed and lives in close proximity to a well-known medical center with close community ties that may have reduced nonresponse bias; the results may not apply in all other U.S. population-based studies.

Survey researchers usually work with fixed resources and are faced with difficult choices of how to allocate efforts to maximize study goals. The choice to use multiple modes of data collection is increasingly popular because it is assumed to serve multiple goals. First, starting with a relatively inexpensive mode such as mail allows one to reach a substantial proportion of the sample at relatively low costs. Second, multiple modes typically are effective at reaching the goal of achieving higher response rates. The research presented here, however, suggests that it is overly simplistic sim·plism  
n.
The tendency to oversimplify an issue or a problem by ignoring complexities or complications.



[French simplisme, from simple, simple, from Old French; see simple
 to assume that reaching higher response rates in itself is consistent with a goal of reduced bias. Finally, sample size is also an important goal of survey research, especially when it comes to providing precise estimates for small subpopulations. Balancing the competing goals of survey research will always prove difficult, but further study of which types of designs actually reduce nonresponse bias is essential for informed decisions about how to allocate efforts.

ACKNOWLEDGMENTS

Joint Acknowledgment/Disclosure Statement: Supported by funds from the National Cancer Institute (R03 CA132974; PI: Beebe) and the Mayo Clinic Foundation for Education and Research. The study was made possible by the Rochester Epidemiology Project (R01 AG034676 from the National Institute on Aging; PI: Rocca).

Disclosures: None.

Disclaimers: None.

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Any of the ornamental rock-garden or border plants that make up the genus Silene, of the pink family, consisting of about 500 species of herbaceous plants found throughout the world.
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adj.
Running in the direction of the long axis of the body or any of its parts.
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[Medieval Latin epid
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SUPPORTING INFORMATION

Additional supporting information may be found in the online version of this article:

Appendix SA1: Author Matrix.

Please note: Wiley-Blackwell is not responsible for the content or functionality of any supporting materials supplied by the authors. Any queries (other than missing material) should be directed to the corresponding author for the article.

Address correspondence to Timothy J. Beebe, Ph.D., Associate Professor of Health Services Research, Mayo Clinic College of Medicine, 200 First Street SW, Rochester, MN 55905; e-mail: beebe.timothy@mayo.edu. Timothy J. Beebe, Ph.D., Lindsey Haas, B.A., and Jeanette Y. Ziegenfuss, Ph.D, are with the Division of Health Care Policy & Research, Mayo Clinic, and Survey Research Center, Mayo Clinic, Rochester, MN. Donna D. McAlpine, Ph.D., is with the Division of Health Policy & Management, University of Minnesota School of Public Health The University of Minnesota School of Public Health, located in Minneapolis, Minnesota, is a professional school of the University of Minnesota. The school offers a 15 masters programs and four doctoral programs, which culminate in one of the following degrees: Master of Public , Minneapolis, MN. Sarah Jenkins, M.S., is with the Division of Biomedical bi·o·med·i·cal
adj.
1. Of or relating to biomedicine.

2. Of, relating to, or involving biological, medical, and physical sciences.
 Statistics and Informatics Same as information technology and information systems. The term is more widely used in Europe. , Mayo Clinic, Rochester, MN. Michael E. Davern, Ph.D., is with NORC NORC National Opinion Research Center
NORC Naturally Occurring Retirement Community
NORC National Organization for Research at the University of Chicago
NORC Naval Ordnance Research Calculator
NORC North Oakland Republican Club (Waterford, MI) 
 at University of Chicago, IL.

DOI: 10.1111/j.1475-6773.2011.01369.x
Table 1: Characteristics of the Population by Survey Response Phase

                                   First Mail      Second Mail
Variable                           (N = 1617)      (N = 957)

Background
Male                                 790 (48.9%)   515 (53.8%)
Age
  18 to <35                          209 (12.9%)   147 (15.4%)
  35 to <50                          403 (24.9%)   315 (32.9%)
  50 to <65                          510 (31.5%)   304 (31.8%)
  65+                                495 (30.6%)   191 (20%)
White                              1,467 (90.7%)   858 (89.7%)
Clinical
  Charlson (weighted), 2+            451 (27.9%)   222 (23.2%)
  Myocardial infarct                  72 (4.5%)     37 (3.9%)
  Congestive heart failure            80 (4.9%)     35 (3.7%)
  Peripheral vascular disease         90 (5.6%)     44 (4.6%)
  Cerebrovascular disease            124 (7.7%)     58 (6.1%)
  Chronic pulmonary disease          253 (15.6%)   137 (14.3%)
  Ulcer                              103 (6.4%)     48 (5%)
  Mild liver disease                  37 (2.3%)     19 (2%)
  Diabetes                           170 (10.5%)    84 (8.8%)
  Diabetes with organ damage          46 (2.8%)     23 (2.4%)
  Moderate/severe renal disease       93 (5.8%)     45 (4.7%)
  Moderate/severe liver disease        9 (0.6%)      1 (0.1%)
  Metastatic solid tumor              34 (2.1%)     17 (1.8%)
  Rheumatologic disease               43 (2.7%)     20 (2.1%)
  Other cancer                       242 (15%)     113 (11.8%)
Utilization
  3+ Clinic office                  1169 (72.3%)   636 (66.5%)
  Any ER admission                   451 (27.9%)   276 (28.8%)
  Any hospital admission             365 (22.6%)   183 (19.1%)
  1+ Surgical or nonsurgical       1,039 (64.3%)   552 (57.7%)
    procedures in last 10 years

                                   Phone         Total Responders
Variable                           (N = 863)     (N = 3437)

Background
Male                               451 (52.3%)   1,756 (51.1%)
Age
  18 to <35                        147 (17%)       503 (14.6%)
  35 to <50                        356 (41.3%)   1,074 (31.2%)
  50 to <65                        245 (28.4%)   1,059 (30.8%)
  65+                              115 (13.3%)     801 (23.3%)
White                              761 (88.2%)   3,086 (89.8%)
Clinical
  Charlson (weighted), 2+          190 (22%)       863 (25.1%)
  Myocardial infarct                36 (4.2%)      145 (4.2%)
  Congestive heart failure          20 (2.3%)      135 (3.9%)
  Peripheral vascular disease       38 (4.4%)      172 (5%)
  Cerebrovascular disease           40 (4.6%)      222 (6.5%)
  Chronic pulmonary disease        145 (16.8%)     535 (15.6%)
  Ulcer                             47 (5.4%)      198 (5.8%)
  Mild liver disease                18 (2.1%)       74 (2.2%)
  Diabetes                          86 (10%)       340 (9.9%)
  Diabetes with organ damage        27 (3.1%)       96 (2.8%)
  Moderate/severe renal disease     31 (3.6%)      169 (4.9%)
  Moderate/severe liver disease      3 (0.31%)      13 (0.4%)
  Metastatic solid tumor            15 (l.7%)       66 (1.9%)
  Rheumatologic disease             16 (l.9%)       79 (2.3%)
  Other cancer                      80 (9.3%)      435 (12.7%)
Utilization
  3+ Clinic office                 530 (61.4%)   2,335 (67.9%)
  Any ER admission                 278 (32.2%)   1,005 (29.2%)
  Any hospital admission           186 (21.6%)     734 (21.4%)
  1+ Surgical or nonsurgical       494 (57.2%)   2,085 (60.7%)
    procedures in last 10 years

                                   Total Sample
Variable                           (N = 6716)

Background
Male                               3,530 (52.6%)
Age
  18 to <35                        1,127 (16.8%)
  35 to <50                        2,201 (32.8%)
  50 to <65                        1,887 (28.1%)
  65+                              1,501 (22.3%)
White                              5,833 (86.9%)
Clinical
  Charlson (weighted), 2+          1,652 (24.6%)
  Myocardial infarct                 308 (4.6%)
  Congestive heart failure           334 (5%)
  Peripheral vascular disease        334 (5%)
  Cerebrovascular disease            467 (7%)
  Chronic pulmonary disease        1,011 (15.1%)
  Ulcer                              374 (5.6%)
  Mild liver disease                 154 (2.3%)
  Diabetes                           661 (9.8%)
  Diabetes with organ damage         199 (3%)
  Moderate/severe renal disease      373 (5.6%)
  Moderate/severe liver disease       28 (0.4%)
  Metastatic solid tumor             134 (21%)
  Rheumatologic disease              153 (2.3%)
  Other cancer                       790 (11.8%)
Utilization
  3+ Clinic office                 4,192 (62.4%)
  Any ER admission                 2,027 (30.2%)
  Any hospital admission           1,468 (21.9%)
  1+ Surgical or nonsurgical       3,873 (57.7%)
    procedures in last 10 years

* Significant difference from total sample, p < 0.05.

Table 2: Multivariable Logistic Regression Models Examining the Effect
of Response Phase on Each Variable

                                                 Model 1: Response
                                                after First Mailing
                                                versus All Others

Variable                      Comparison        OR (95% CI)       p
Background
Gender                      Males versus     0.91 (0.81, 1.02)   0.10
                              females
Age                         18 to <35 (ref)
                            35 to <50        0.95 (0.78, 1.15)   0.58
                            50 to <65        1.51 (1.25, 1.83)  <0.001
                            65+              2.12 (1.72, 2.62)  <0.001
Race                        White versus     1.40 (1.15, 1.70)  <0.001
                              other
  Clinical
  Myocardial infarct        Present versus   0.77 (0.57, 1.04)   0.09
                              absent
  Congestive heart          Present versus   0.82 (0.61, 1.11)   0.20
    failure                   absent
  Peripheral vascular       Present versus   0.96 (0.73, 1.25)   0.76
    disease                   absent
  Cerebrovascular           Present versus   0.88 (0.70, 1.12)   0.30
    disease                   absent
  Chronic pulmonary         Present versus   0.92 (0.78, 1.09)   0.34
    disease                   absent
  Ulcer                     Present versus   1.02 (0.80, 1.30)   0.89
                              absent
  Mild liver disease        Present versus   0.93 (0.63, 1.36)   0.69
                              absent
  Diabetes                  Present versus   0.92 (0.73, 1.14)   0.43
                              absent
  Diabetes with organ       Present versus   0.81 (0.55, 1.20)   0.30
    damage                    absent
  Moderate/severe renal     Present versus   0.83 (0.63, 1.09)   0.17
    disease                   absent
  Metastatic solid tumor    Present versus   0.69 (0.45, 1.06)   0.09
                              absent
  Rheumatologic disease     Present versus   0.89 (0.61, 1.28)   0.53
                              absent
  Other cancer              Present versus   1.17 (0.97, 1.40)   0.10
                              absent
Utilization
  Clinic office visits      3+ versus <3     1.73 (1.51, 1.97)  <0.001
    2005-2006
  ER admission 2005-2006    Any versus none  0.70 (0.61, 0.82)  <0.001
  Hospital admission        Any versus none  1.02 (0.86, 1.20)   0.86
    2005-2006
  Number of surgical or     1+ versus none   1.11 (0.98, 1.26)   0.10
  nonsurgical procedures
    in last 10 years

                                                Model 2: Response
                                              after Mailing 1 or 2
                                                versus All Others

Variable                      Comparison        OR (95% CI)       p
Background
Gender                      Males versus     0.97 (0.87, 1.08)   0.57
                              females
Age                         18 to <35 (ref)
                            35 to <50        1.00 (0.85, 1.17)   0.98
                            50 to <65        1.53 (1.30, 1.81)  <0.001
                            65+              1.84 (1.53, 2.21)  <0.001
Race                        White versus     1.51 (1.29, 1.78)  <0.001
                              other
  Clinical
  Myocardial infarct        Present versus   0.78 (0.60, 1.02)   0.07
                              absent
  Congestive heart          Present versus   0.77 (0.59, 1.01)   0.06
    failure                   absent
  Peripheral vascular       Present versus   0.98 (0.77, 1.25)   0.86
    disease                   absent
  Cerebrovascular           Present versus   0.88 (0.71, 1.09)   0.24
    disease                   absent
  Chronic pulmonary         Present versus   0.91 (0.79, 1.06)   0.22
    disease                   absent
  Ulcer                     Present versus   0.98 (0.79, 1.23)   0.88
                              absent
  Mild liver disease        Present versus   0.85 (0.61, 1.20)   0.36
                              absent
  Diabetes                  Present versus   0.88 (0.72, 1.08)   0.23
                              absent
  Diabetes with organ       Present versus   0.80 (0.56, 1.13)   0.20
    damage                    absent
  Moderate/severe renal     Present versus   0.84 (0.66, 1.07)   0.15
    disease                   absent
  Metastatic solid tumor    Present versus   0.71 (0.48, 1.04)   0.08
                              absent
  Rheumatologic disease     Present versus   0.88 (0.63, 1.23)   0.45
                              absent
  Other cancer              Present versus   1.16 (0.98, 1.37)   0.09
                              absent
Utilization
  Clinic office visits      3+ versus <3     1.77 (1.57, 1.99)  <0.001
    2005-2006
  ER admission 2005-2006    Any versus none  0.75 (0.66, 0.85)  <0.001
  Hospital admission        Any versus none  0.92 (0.80, 1.07)   0.28
    2005-2006
  Number of surgical or     1+ versus none   1.06 (0.95, 1.19)   0.27
  nonsurgical procedures
    in last 10 years

                                                   Model 3: Any
                                                  Response versus
                                                    No Response

Variable                      Comparison        OR (95% CI)       p
Background
Gender                      Males versus     0.96 (0.87, 1.06)   0.45
                              females
Age                         18 to <35 (ref)
                            35 to <50        1.13 (0.98, 1.31)   0.10
                            50 to <65        1.47 (1.26, 1.72)  <0.001
                            65+              1.41 (1.18, 1.68)  <0.001
Race                        White versus     1.57 (1.35, 1.83)  <0.001
                              other
  Clinical
  Myocardial infarct        Present versus   0.90 (0.70, 1.17)   0.45
                              absent
  Congestive heart          Present versus   0.61 (0.47, 0.80)  <0.001
    failure                   absent
  Peripheral vascular       Present versus   1.03 (0.81, 1.31)   0.80
    disease                   absent
  Cerebrovascular           Present versus   0.81 (0.66, 1.01)   0.06
    disease                   absent
  Chronic pulmonary         Present versus   1.03 (0.89, 1.18)   0.71
    disease                   absent
  Ulcer                     Present versus   1.05 (0.84, 1.30)   0.69
                              absent
  Mild liver disease        Present versus   0.78 (0.56, 1.09)   0.14
                              absent
  Diabetes                  Present versus   0.97 (0.80, 1.18)   0.79
                              absent
  Diabetes with organ       Present versus   0.86 (0.61, 1.20)   0.37
    damage                    absent
  Moderate/severe renal     Present versus   0.80 (0.63, 1.01)   0.06
    disease                   absent
  Metastatic solid tumor    Present versus   0.80 (0.55, 1.16)   0.23
                              absent
  Rheumatologic disease     Present versus   0.85 (0.61, 1.19)   0.35
                              absent
  Other cancer              Present versus   1.08 (0.91, 1.28)   0.37
                              absent
Utilization
  Clinic office visits      3+ versus <3     1.66 (1.49, 1.86)  <0.001
    2005-2006
  ER admission 2005-2006    Any versus none  0.83 (0.73, 0.94)   0.003
  Hospital admission        Any versus none  0.93 (0.81, 1.08)   0.35
    2005-2006
  Number of surgical or     1+ versus none   1.10 (0.99, 1.23)   0.07
  nonsurgical procedures
    in last 10 years
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Title Annotation:RESEARCH ARTICLE
Author:Beebe, Timothy J.; McAlpine, Donna D.; Ziegenfuss, Jeanette Y.; Jenkins, Sarah; Haas, Lindsey; Daver
Publication:Health Services Research
Article Type:Survey
Geographic Code:1U4MN
Date:Aug 1, 2012
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