Enhancing Recruitment Methodologies: Leveraging the Tailored Design Method to Survey Populations with Varied Engagement in Healthcare

Enhancing Recruitment Methodologies: Leveraging the Tailored Design Method to Survey Populations with Varied Engagement in Healthcare | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Enhancing Recruitment Methodologies: Leveraging the Tailored Design Method to Survey Populations with Varied Engagement in Healthcare Hari H Venkatachalam, Nina A. Sayer, Heather Belanger, Peter A. Toyinbo, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8234598/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background Successfully recruiting respondents to complete surveys is integral for ensuring representative samples and providing adequate power for analyses that researchers can extrapolate to the population of interest. However, obtaining survey responses for public health and health services research can be challenging, especially when working with populations with varied engagement with the providers of the institution conducting the study. Recruitment is even more challenging when selecting participants based on historical events, which they may not remember, such as a brief health screen. Many researchers implement the Tailored Design Method (TDM) to optimize response rates. Methods We surveyed a national sample of 9,775 US Iraq and Afghanistan war veterans who had been screened between 2007 and 2018 for post-deployment health concerns in the Veterans Health Administration (VHA). We implemented a modified TDM to recruit respondents through USPS mail and an online data collection platform (i.e., Qualtrics) between July 2020 and August 2021. Results Twenty-one percent (N = 2,025) responded. It took a median of 21 and 69 days to receive responses via electronic survey (i.e., Qualtrics) and USPS, respectively. Participants had varied levels of engagement with the VHA at the time of screening and survey. Respondents and non-respondents used equal amounts of neurology, physical medicine and rehabilitation, and mental health VHA services in the 6 months post-screens. At the time of the survey, 20.82% of respondents reported not having recently used VHA services. Respondents were more likely to be older, White, and female than non-respondents, although differences were small in magnitude. Compared to online respondents, paper respondents were more likely to be older, rural, and screened earlier, and less likely to live in the same state as the research team. Conclusions This manuscript describes TDM implementation techniques and modifications that comply with federal regulations, and factors associated with response rates and contact methods. Researchers in federal institutions seeking to optimize response rates may find our TDM adaptations useful when surveying populations drawn from historical cohorts or without regular interaction with the research institution. Figures Figure 1 Figure 2 Figure 3 Introduction Two main challenges hinder successfully recruiting individuals to respond to research surveys. First, researchers need to recruit enough respondents to sufficiently power analyses. Second, researchers must ensure that respondents are a representative sample to limit biases in the research findings (1–4). The Total Design Method, originally conceived by Don A. Dillman in the 1970s, created guidelines for survey data collectors to obtain high response rates and representative samples (5). Dillman’s techniques are rooted in social exchange theory and strive to improve survey response rates by removing costs and barriers while enhancing benefits and building trust (6). The growth in electronic surveying options prompted modifications to the Total Design Method. The most recent iteration of the technique, termed the Tailored Design Method (TDM), allows for surveying through both mail and electronic channels (6). Key characteristics of TDM include sending a token cash incentive with the survey request, personalizing communication with handwritten signatures, and providing contacted individuals with both web and mail-based options to complete the survey (6,7). Other recommendations include tailoring succinct, direct, and user-friendly questionnaires that reduce respondent burden (6). Strict implementation of Dillman’s recommendations has resulted in survey response rates greater than 70% in some civilian samples(8–12). However, other recruitment protocols have noted lower response rates between 12–40% in veteran samples and / or samples of individuals who did not have an ongoing relationship with the surveying institution (1,13–18). Lower response rates in some of the studies may occur due to a variety of factors, including regulatory constraints that prevent research institutions from implementing certain features of TDM. Within US government institutions, sending cash incentives to all contacted individuals has tax implications on income-based government benefits (19). Instead, Institutional Review Board-approved research studies traditionally only provide incentive payments post hoc to those who complete a survey (20). Sending post-survey incentive payments rather than initial token payments with the survey request has been shown to reduce response rates in surveyed populations, including veterans (7,13). There are also resource constraints on personalizing communication when performing large survey mailouts. Additionally, standardized self-report measures of health outcomes included in surveys may not be succinct or user friendly. These factors may have contributed to lower response rates for VHA survey studies conducted after Dillman developed TDM. Another consideration for TDM implementation is how to leverage relationships with contacted individuals. VHA research studies typically seek respondents who are engaged in VHA healthcare. This often includes restricting samples to individuals who had recent diagnoses or encounters with their healthcare provider (10,14,16). However, only surveying individuals who recently engaged in VHA healthcare limits the generalizability of the findings. VHA performs research in a variety of domains, such as satisfaction with care, perceived barriers and facilitators to seeking care, and quality of life studies that would benefit from the participation of individuals from populations with varied levels of engagement in healthcare (21–23). Successfully surveying individuals who are engaged and those who are less engaged in receiving VHA services can provide important information to inform quality improvement projects, especially initiatives to increase service utilization among non-users (24). This paper presents the survey methods of a large VHA study that sought to examine clinical outcomes of Afghanistan and Iraq war veterans who underwent the VHA mandated post-deployment screenings for Traumatic Brain Injury (TBI) and Mental Health (MH) conditions between 2007 and 2018 (25–27). We selected individuals based on a specific historical anchor timepoint: the date the veterans were assessed for TBI and MH conditions. This anchor timepoint preceded the survey data collection by up to twelve years. Because current engagement in VHA was not an inclusion criterion, those surveyed may not be invested in VHA clinical care or research. Within this report, we describe how we implemented TDM with adjustments for VHA context and regulations. To determine if we selected a representative sample with these methods, we examined differences between responders and non-responders. Our goal is to inform other survey studies that are conducted by large and / or federal healthcare organizations, and who seek to survey groups of individuals with varying levels of engagement with the research institution. Materials and Methods Human Ethics and Consent to Participate The study was approved by the Department of Veterans Affairs and Institutional Review Board. The study adheres to all state and federal ethical research guidelines. All participants completed altered informed consent either electronically or on paper prior to completing any study related activities. A clinical trial number was not applicable for this research study. Sample Selection We selected veterans from a cohort of VHA patients who completed a TBI screen between 10/01/2007 and 9/30/2018 and an MH screen within 7 days of the TBI screen ( N = 289,104). From this cohort, we selected a random sample of 9,775 veterans. To meet the objectives of the parent study (26), we stratified the sample by MH screen outcome (screened positive for: posttraumatic stress disorder, depression, and / or alcohol use disorder), TBI screen outcome (probable TBI vs. no TBI), gender (male or female), VHA region (i.e., North Atlantic, Southeast, Midwest, Continental, and Pacific), and year of TBI screen (i.e., grouped into three cohorts of 2008–09, 2010–12, and 2013–18). Data Sources To advance both research and medical care, VHA has developed large databases that house administrative data (e.g., diagnoses, types and frequency of medical visits, demographics) for all veterans in the national healthcare system. The current study paired administrative data with a survey that evaluated current functioning and satisfaction with VHA healthcare (26,28). We extracted TBI screen results; demographic variables of age, sex, race, and ethnicity; service utilization in the form of visit counts (i.e., Stop codes) in the domains of physical medicine and rehabilitation (PM&R), neurology, and mental health (MH); and email addresses, USPS mail addresses, and telephone numbers for each veteran from the administrative datasets (28,29). We also captured self-reported service utilization in the previous six months from survey respondents (26). We assessed rurality of home residence based on zip codes extracted from the USPS addresses and evaluated against a crosswalk generated by the Washington, Wyoming, Alaska, Montana, Idaho’s Rural Health Research Center Rural-Urban Commuting Area Codes (30). Modified TDM Contact Schedule The research team attempted to contact all individuals between July 2020 and August 2021. We ceased recruitment efforts after we reached the sample size necessary to power analyses for the parent study. Contacted individuals had both email and USPS mail addresses in the administrative datasets (n = 7,198), only USPS mail addresses (n = 2,499), or only email addresses (n = 78). The survey recruitment method involved a maximum of ten attempted contacts performed via USPS mail (i.e. USPS mail protocol), emails sent via the Qualtrics platform (i.e., email protocol), and / or telephone phone calls. Paper booklet versions of the survey were provided via the USPS mail protocol, and links to the electronic survey were provided via the email protocol. If the research team had both email addresses and USPS mail addresses, we completed the email protocol prior to initiating the USPS mail protocol. Contact attempts within each protocol included: (1) a pre-notice letter; (2) an email or USPS mailed letter with the survey; (3) an electronic or USPS mailed opt-out postcard; and (4) a follow-up email or USPS mailed letter with the survey. After the first attempted protocol, we performed three telephone contact attempts to confirm receipt of the survey and to provide the opportunity to address any questions or concerns. Subsequently, contact attempts 2, 3, and 4 were repeated via the USPS mail protocol for non-respondents from the email protocol that had both email and USPS mail addresses. Contacted individuals could access the electronic survey from the USPS mailed information via a URL or QR code. Conversely, contacted individuals could request a paper survey if they received the request via email. Contact attempts ceased after the contacted individual completed the survey, opted out, was identified as deceased, or when the email and / or USPS mail protocols were completed ( See Fig. 1 ). Modifications to TDM Where possible, we implemented TDM in accordance with Dillman’s guidelines. We made modifications to TDM in certain circumstances to address human resource limitations and to ensure alignment with VHA regulatory requirements. Consistent with TDM, we generated visually appealing contact letters and booklets, incorporating feedback from a test group of veterans. Respondents could complete the survey in either paper or electronic formats. Respondents could complete the electronic survey accessed from a QR code, an email-embedded link, or a shortened URL. Respondents who completed paper surveys returned them using a self-addressed pre-paid envelope. Key modifications of TDM for VHA regulatory requirements include retaining survey instruments in their validated forms and sending gift cards to survey respondents, rather than all contacted individuals. A complete list of TDM implemented features and adaptations is listed in Table 1 (6,31). Table 1 : Implemented Features of the Modified TDM for TBI Study Software Systems We sent emails and collected electronic surveys using a Qualtrics platform, a surveying platform that allowed customization (e.g., contact list generation), and automation (e.g., timed survey delivery) (32). Streamworks, a vendor specializing in data digitalization, converted our paper surveys into a digital format for data collation (33). We mailed paper surveys to Streamworks for scanning and storage in an electronic database. Analyses Assessed characteristics were race (white vs. non-white or mixed race), sex (female vs. male), ethnicity (Hispanic vs. non-Hispanic), rurality (rural vs. urban), proximity to research site (Florida vs. non-Florida), age (at the time of data collection), and the number of years between the TBI Screen and the survey. We performed two-sample t-tests for continuous variables and Chi-squared analyses for dichotomous variables. We employed multivariable logistic regression models comparing respondents against non-respondents, and those who responded by paper against those who responded by electronic survey. Adjusted Odds-Ratios for all predictor variables estimated the effect sizes. We assessed magnitude of the effect sizes using cut points of 1.68, 3.47, and 6.71 for adjusted Odds Ratios to refer to small, medium, and large effect sizes respectively (34). We performed descriptives statistical analyses on time between survey sent and receipt of returned survey, number of responses per day following each of the steps of the recruitment protocol, and service utilization around the anchoring timepoint and before survey completion. We additionally performed non-parametric statistical tests (i.e., Mann-Whitney U test) to determine if respondents and non-respondents had different service utilization around the anchoring timepoint. Results We attempted to contact 2,735 veterans by USPS mail only, 3,304 by email only, and 3,736 by both email and USPS mail. Ninety-three (0.95%) veterans died during the data collection period. Both USPS and email contact attempts failed for an additional 336 veterans (3.44%). By the end of data collection, 2,025 (20.72%) of the contacted veterans had responded to the survey, and 1,093 (11.0%) actively declined participation in the study. Most of the veterans we attempted to contact (n = 6,228, 63.71%) neither responded to the survey nor actively opted out of participating ( See Fig. 2 ). We noted spikes immediately after the delivery of each email during the Qualtrics email protocol. Responses tapered slowly after each touchpoint. ( See Fig. 3 ). Spikes in responses were not as apparent after steps of the USPS mail protocol. More respondents (67.11%) responded via the Qualtrics electronic survey than the paper survey. The research team received electronic responses more quickly than paper surveys. The median time to respond for a Qualtrics electronic survey was 21 days from first receiving the survey (IQR: 5–57 Days), while the median time to receive paper surveys was 69 Days (IQR: 42–123 Days) from delivery. This was expected due to the lag time in sending and receiving USPS mail versus emails. Bivariate analyses showed respondents were more likely to be White (χ 2 = 11.38, p < 0.01), female (χ 2 = 14.13, p < 0.01), older ( t -statistic = -12.12, p < 0.01), and screened earlier for post-deployment health concerns ( t -statistic = -3.31, p < 0.01) compared to non-respondents ( See Table 2 ). Bivariate analyses between survey response type showed those who responded by paper were more likely to be rural (χ 2 = 7.83, p = 0.01), older ( t -statistic = -7.88, p < 0.01), residents of a different state as the recruitment site (i.e., Florida) (χ 2 = 9.29, p < 0.01), and screened earlier for TBI and MH conditions ( t -statistic = -4.90, p < 0.01) compared to those who responded by electronic survey ( See Table 3 ). Multivariable logistic regression models revealed similar results as the bivariate analyses with respondents being more likely to be female (aOR 1.40, 95% C.I. 1.22, 1.61), older (aOR per 5-year increment, 1.16, 95% C.I. 1.13, 1.19), and White (aOR for White respondents 1.31, 95% C.I. 1.16, 1.47) compared to non-respondents ( See Table 4 ). Multivariable logistic regression models comparing paper survey respondents to electronic survey respondents revealed that respondents from Florida (i.e., research site) were less likely to respond by paper (aOR 0.610, 95% C.I. 0.417, 0.894). They were more likely to respond by paper if they were older (aOR per 5-year increment, 1.193, 95% C.I. 1.139, 1.249) and were residing in a rural zip code compared with an urban zip code (aOR 1.329, 95% C.I. 1.047, 1.688) ( See Table 5 ). Table 2 Demographic Comparisons between Response and Non-Response Respondent vs. Non-respondent Variable Respondent Non-respondent Statistical Test Race White 1440 (71.11%) 5179 (67.18%) Χ 2 (1, N = 9734) = 11.38, p = 0.0007 Non-White 585 (28.89%) 2530 (32.82%) Sex Female 349 (17.23%) 1073 (13.92%) Χ 2 (1, N = 9734) = 14.13, p = 0.0002 Male 1676 (82.77%) 6636 (86.08%) Ethnicity Hispanic 225 (11.49%) 935 (12.64%) Χ 2 (1, N = 9358) = 1.87, p = 0.1720 Non-Hispanic 1733 (88.51%) 6465 (87.36%) Rurality Rural 405 (20.59%) 1426 (19.24%) Χ 2 (1, 9379) = 1.81, p = 0.1791 Urban 1562 (79.41%) 5986 (80.76%) Age Age at Mailout 45.52 ± 11.44 42.11 ± 10.55 t (2989.9) 1 = -12.12, p < .0001 Proximity to Mailout Site Florida 184 (9.10%) 659 (26.39%) Χ 2 (1, 9633) = 0.39, p = 0.5324 Non-Florida 1838 (90.90%) 1838 (73.61%) Time Difference between Screen and Study Years before Recruitment 9.27 ± 2.94 9.03 ± 2.98 t (9728) = -3.31, p = 0.0009 Table 3 Demographic Comparisons between Response by Paper vs Electronic Survey Paper vs. Electronic Variable Paper Electronic Statistical Test Race White 464 (69.67%) 976 (71.82%) Χ 2 (1, N = 2025) = 1.00, p = 0.3164 Non-White 202 (30.33%) 383 (28.18%) Sex Female 101 (15.17%) 248 (18.25%) Χ 2 (1, N = 2025) = 2.98, p = 0.0843 Male 565 (84.83%) 1111 (81.75%) Ethnicity Hispanic 69 (10.82%) 156 (11.82%) Χ 2 (1, N = 1958) = 0.43, p = 0.5142 Non-Hispanic 569 (89.18%) 1164 (88.18%) Rurality Rural 157 (24.23%) 248 (18.80%) Χ 2 (1, N = 1967) = 7.83, p = 0.0052 Urban 491 (75.77%) 1071 (81.20%) Age Age at Mailout 48.50 ± 12.49 44.06 ± 10.60 t (1147.3) 1 = -7.88, p < .0001 Proximity to Mailout Site Florida 42 (6.32%) 142 (10.46%) Χ 2 (1, N = 2022) = 9.29, p = 0.0023 Non-Florida 623 (93.68%) 1215 (89.54%) Time Difference between Screen and Study Years before Recruitment 9.71 ± 2.66 9.06 ± 3.04 t (1489.3) 1 = -4.90, p < .0001 2 1 Satterthwaite approximation of degrees of freedom for unequal variances utilized Table 4 Multivariate Logistic Regression Models Predicting Response over Non-Response Parameter DF β Estimate O.R. and 95% Wald C.I. p -value Intercept 1 -2.587 < 0.001 Race (White vs. Non-White) 1 0.268 1.307 [1.163, 1.469] < 0.001 Sex (Female vs. Male) 1 0.339 1.404 [1.221, 1.613] < 0.001 Ethnicity (Hispanic vs. Non-Hispanic) 1 -0.129 0.879 [0.744, 1.039] 0.132 Rurality (Rural vs. Urban) 1 0.032 1.033 [0.907, 1.176] 0.624 Age at Mailout (in 5-year increments) 1 0.030 1.161 [1.133, 1.189] < 0.001 Proximity to Research Site (Florida vs. Non-Florida) 1 -0.016 0.984 [0.821, 1.179] 0.860 Years Before Mailout (in 1-year increments) 1 0.000 1.000 [0.982, 1.019] 0.970 Table 5 Multivariate Logistic Regression Models Predicting Response by Paper over Electronic Survey Parameter DF β Estimate O.R. and 95% Wald C.I. p -value Intercept 1 -2.738 < 0.001 Race (White vs. Non-White) 1 0.002 1.002 [0.802, 1.253 0.985 Sex (Female vs. Male) 1 -0.113 0.893 [0.682, 1.168] 0.409 Ethnicity (Hispanic vs. Non-Hispanic) 1 -0.033 0.968 [0.692, 1.355] 0.849 Rurality (Rural vs. Urban) 1 0.285 1.329 [1.047, 1.688] 0.020 Age at Mailout (in 5-year increments) 1 0.035 1.193 [1.139, 1.249] < 0.001 Proximity to Research Site (Florida vs. Non-Florida) 1 -0.494 0.610 [0.417, 0.894] 0.011 Years Before Mailout (in 1-year increments) 1 0.042 1.043 [1.006, 1.081] 0.021 Engagement in MH, neurology, and PM&R care after the TBI screen (an average of 9.08 years prior to the current survey) did not statistically differ between respondents and non-respondents in the six months prior or after the TBI screen (anchor timepoint for parent study) ( See Table 6 ). Seventy-five percent of respondents had ≤ 5 MH visits, while 75% percent of non-respondents ≤ 4 MH visits in the six months after the TBI screen. Seventy-five percent of both respondents and non-respondents had < 1 PM&R visit and 0 neurology visits in the 6 months after the TBI screen. Among those who had a positive MH screen, most respondents and non-respondents had less than 9 MH visits in the six months post-screen. Among those who had a positive TBI screen, most respondents had less than 7 PM&R visits and 0 neurology visits in the six months post-screen. Differences in service utilization between respondents and non-respondents were also not significant when stratified by TBI or MH screen outcome, with alpha adjusted to 0.008 using Bonferroni correction ( Results not shown ). Of the respondents, 51.10%, 76.57%, 53.25% reported not receiving services in the domains of MH, neurology, and PM&R, respectively, in the six months prior to responding to the survey. Additionally, 21% stated they had not received any VHA healthcare services in the 6 months prior to completing the survey. ( See Table 7 ). Table 6 Contacted Individuals’ Engagement with VHA Services Non-respondents ( n = 6528) Respondents ( n = 1741) Variable Mean ± SD IQR Mean ± SD IQR p Engagement with VHA Healthcare after TBI Screen MH visits 6 months prior to screen 0.2 ± 0.86 0–0 0.2 ± 1.09 0–0 0.4148 1 Neurology visits 6 months prior to screen 0.01 ± 0.15 0–0 0.01 ± 0.13 0–0 0.4734 1 PM&R visits 6 months prior to screen 0.11 ± 0.73 0–0 0.1 ± 0.57 0–0 0.3122 1 MH visits 6 months after the screen 4.64 ± 15.24 0–4 4.26 ± 12.81 0–5 0.4954 1 Neurology visits 6 months after the screen 0.09 ± 0.49 0–0 0.09 ± 0.47 0–0 0.5896 1 PM&R visits 6 months after the screen 1.73 ± 5.32 0–1 1.7 ± 4.12 0–1 0.3161 1 Engagement with VHA prior to Survey Completion ( n = 1,741) Variable N % (excluding missing) Did not receive any VHA services 6 months prior to survey 359 20.82% Did not receive PM&R services 6 months prior to survey 918 53.25% Did not receive MH services 6 months prior to survey 881 51.10% Did not receive Neurology services 6 months prior to survey 1320 76.57% Discussion Collecting representative data and ensuring adequate sample sizes are important factors for building robust datasets and successfully completing research studies. Researchers widely cite TDM as an effective method for obtaining these goals. The objective of this analysis was to examine response rates associated with implementation of TDM within a large, federal healthcare system, surveying respondents selected from a historical anchor timepoint (i.e., mandatory TBI and MH screens) with varying levels of healthcare engagement. TDM is based on social exchange theory that leverages trust and transactions. Likely because our sample included varied engagement and limitations that prevented strict implementation of TDM, our modified TDM resulted in a lower than expected response rate (21%) compared to what has been reported in some studies of civilian patients engaged in healthcare (8–12). Even with the lower response rate, we obtained a sample of the surveyed population that was representative in terms of demographics and VHA care utilization. Analyses showed the surveyed population had minimal engagement with MH, neurology, and PM&R within the VHA, with no statistical differences between respondents and non-respondents. In regards to the MH engagement, the number of sessions would be insufficient for a full course of evidence based psychotherapy for the disorders for which the veterans were screened (i.e., PTSD, depression, and alcohol use disorder) (35–37). Additionally, 21% of the respondents reported not having received any VHA services in the 6 months prior to completing the survey. These findings support that the surveyed sample had varied engagement with VHA care, and that respondents are representative of the surveyed population on service utilization characteristics. As the study sought to examine the long-term effects of the VHA TBI screening program (26), it needed feedback from both veterans engaged and not engaged in VHA care. Successfully obtaining responses from those with little or no engagement with VHA met the needs of the parent study. Possible reasons for the lack of engagement with VHA at the time of the survey may be due to the fact that most of the veterans completed the TBI and MH screens as part of their transition to civilian life and while relatively new to VHA healthcare. Combat veterans who served in the wars in Afghanistan and Iraq are eligible for free VHA healthcare benefits for 10 years after discharge from the Department of Defense (38). After 10 years, if the veteran does not have a VHA disability, he / she may be required to pay for some or all their healthcare depending on other factors, such as whether they have other insurance and/or service-connected disabilities. Veterans without VHA disabilities may have transitioned their healthcare to civilian providers after their 10 years of healthcare expired. Although there were no statistically significant differences between respondents and non-respondents in service utilization around the anchor timepoint, there were statistically significant differences in demographics between respondents and non-respondents. However, if we assessed the size of the differences using established effect sizes of adjusted Odds Ratio values of 1.68, 3.47, and 6.71 to refer to small, medium, and large effects, respectively (34), the demographic differences were small. This finding also suggests the sample’s representativeness in terms of these assessed demographic features to the overall VHA population that had post deployment screens. There were small effect size differences between those who responded via paper survey compared to those who responded via electronic survey. The odds of Florida residents responding via paper survey were 39% (95% CI: 11% − 58%) less than non-Florida residents. The odds of rural residents responding via paper were 33% higher (95% CI: 5% − 69%) than urban residents. Internet connectivity and familiarity with the emailing institution may be possible sources for these differences. Therefore, we recommend allowing respondents to complete surveys through both paper and electronic methods to ensure that the research team obtains a representative sample. Researchers may also use non-random sampling techniques, such as quota sampling, to ensure that underrepresented groups respond. Finally, subgroup analyses and multivariable modeling approaches can be used to check for sample biases and to account for differences in response rates in their models. Our modified TDM balanced federal regulatory constraints with the benefits of TDM to obtain a response rate that was sufficient for the needs of our larger study, although lower than anticipated based on research conducted in civilian samples by researchers who could employ all aspects of TDM. We offer six lessons learned for future researchers conducting survey studies within large healthcare organizations that survey similar populations. Lesson 1: Plan for a response rate lower than TDM advertises if you are studying historical cohorts, historical anchoring events, or involve individuals who are not engaged in healthcare at the research institution. This study recruited veterans who had undergone clinical screenings between two to twelve years prior to beginning study activities. After receiving our survey, some individuals reached out to the study team and stated they had never been screened for TBI or MH conditions, although the screening results appeared in the administrative datasets. The longer the time between the anchor point (i.e., TBI screen) and surveys, the less likely the individuals are to remember being screened, especially if the contacted individual screened negative for the condition (i.e., Recall Bias) (39). Additionally, many veterans screened for TBI or MH conditions did not seek VHA services after their screens (28). Therefore, the lower response rate from our modified TDM may be useful for researchers who are attempting to recruit those who completed health screens or medical procedures years prior to data collection and who are no longer affiliated with the research institution. Estimating a lower response rate will be useful when recruiting samples of individuals who may not currently be engaged in receiving services from the research institution. Lesson 2: Save research team members' time by automating or outsourcing tasks, including prioritizing email. Leveraging available resources helped with limited research staff. The use of Qualtrics and Streamworks provided the team with resources to efficiently implement the modified TDM. Qualtrics capabilities included generation of mailout lists and automated delivery of electronic surveys, removing the burden of having to individually email surveys to each contacted individual. Streamworks coded paper surveys by scanning them electronically, allowing for seamless merging of the electronic and paper datasets. Collating mail and stuffing envelopes are time-consuming tasks. We limited the number of instances that the team had to do so to improve efficiency. We first implemented the email protocol prior to the USPS mail protocol. Subsequently, we excluded respondents and those who opted out from the USPS mail protocol. We were able to utilize VHA mailroom staff to collate and seal the prenotice letter and send the opt-out cards without assistance from the research team. This resulted in the research team only having to collate and process mailings for the second and fourth mailouts from the USPS mail protocol. Lesson 3: Implement TDM’s multimodal and multiple touch-point approach to communication. A key feature of TDM is using both paper mail and electronic modes for responses and using multiple methods of contact. Our team used USPS mail, email, and telephone methods to obtain responses from contacted individuals. The burden of performing multiple mailouts using both electronic and USPS mail methods is apparent. This methodology, however, ameliorates some of the logistical challenges the team faced. For example, because we were working with a historical cohort, the research team may not have updated contact information for everyone. Using multiple modes of contact decreased the number of individuals with neither a valid mailing address nor email address (3%; n = 336). Limiting the survey response type to electronic surveys and limiting the communication strategy to emails may be appealing to research teams with limited human resources. However, our large number of paper responses reveals that giving the option to respond via paper survey is key to ensuring our response rate. Although we first attempted to contact all individuals by email and all USPS mail communication offered the option to access the electronic survey via a tiny URL and a QR code, a third of respondents (32.88%) still chose to respond via paper mail. Statistically, paper respondents tended to be older and from rural zip codes. Ensuring a paper option for survey response allows representation from these groups that may have connectivity or access issues with electronic surveys. Furthermore, the multiple touchpoints for the mail and email protocols were required to achieve our response rates. We noted a spike in response after each of the email touchpoints. Such bursts were harder to identify with the USPS protocol, due to mail delivery lag. The findings from the email protocol highlights that the repeated touchpoints were resulting in increases in responses. Lesson 4: Follow Dillman’s Recommendation for Visual Appeal of the Survey Dillman highlights visual appeal to encourage responses. Certain elements, such as an ink signature, were difficult to implement with the large number of mailouts. We utilized an image of a blue color signature block from the Principal Investigator on mailouts. We used an iterative process with input from multiple stakeholders to ensure a clear layout and visual appeal of the survey. First, Streamworks generated a booklet survey to appeal to the reader and to easily scan the paper surveys into digital data. Second, a professional graphics team member designed several cover page options. Third, both Streamworks and the graphics team used color images, borders, and Department of Veterans Affairs logos and branding to improve the visual appeal of the booklet. Finally, veteran stakeholders provided feedback on the readability of the survey (e.g., font size, contrast, ease of following directions), and preference for the cover page. Lesson 5: Establish an efficient process for incentive payments for respondents Although our team was unable to provide an initial token incentive payment for all contacted individuals, we were able to establish an efficient process for paying respondents post-hoc. The survey requested respondents enter their preferred delivery address for their gift card, which increased insurance that they would receive payment. The tracking database assisted the team to ensure all respondents received the incentive. The established procedures for returned incentives facilitated the delivery and handling of incentives. Lesson 6: Follow Dillman’s recommendations for building trust and increasing rewards Our research team used Dillman’s overarching social exchange theoretical model to inform decisions we made throughout the study. Ensuring transparency of the research process and following regulatory requirements were central to building trust. We provided payments in a timely manner and reiterated at each touchpoint that we ensured anonymity of individual data in any results we published. Limitations and Strengths Following a modified TDM, we achieved a 21% response rate, which provided sufficient power for our analyses but is lower than previously published studies in other populations. While results demonstrated that certain characteristics had statistically significant associations with whether a veteran responded to the survey or not, or responded via paper or electronic survey, the effect sizes were generally negligible. Our current method may be helpful for researchers conducting surveys in large and / or federal healthcare organizations but may be less relevant for researchers in other settings that have more flexibility to strictly follow TDM. Future Applications Surveying is an important part of research activities. Gathering a sufficiently sized and representative sample is a central concern for any researcher that uses surveying for their study. Our goal was to assist researchers who are planning future studies using our protocol as a template for implementing TDM, especially when asking questions about historical anchoring events and surveying large populations of individuals who may not be engaged in healthcare services. Table 1 Implemented Features of the Modified TDM for TBI Study TDM implemented features and adaptations for VHA Survey SURVEY LAYOUT STRATEGIES Created a user-friendly booklet for the survey, with a double-stapled binding. Used validated instruments in surveys and retained matrix-style questions. Asked each instrument sequentially. Instructions for the survey were short and located on the inside cover of the booklet (i.e., page 2) and on the first screen of the electronic survey. The Informed Consent document was separate from the survey. Depending on whether the Informed Consent document was sent via the email protocol or the USPS mail protocol, contacted individuals could access it through a link or on pages separate from the survey. Instructions for individual measures were included on the same page as the question items. Used 14 + font sizes and bolded the questions. Used instrument names to separate groups of questions. Increased font contrast on the electronic surveys to improve the readability on phones. Institutional graphics artists developed several customized covers for the survey, which were tested with a group of veteran stakeholders. All electronic surveys had “back buttons” so respondents could return to previously answered questions. Respondents were able to leave questions blank on both the paper and the electronic surveys, but we did not include a “Refuse to answer” option. Added conditional logic to electronic surveys to skip irrelevant questions. On the paper survey, the survey text prompted respondents to skip questions that were not relevant to them. The final page/screen only contained a thank-you message and two organizational logos. QUALITY CONTROL AND ADMINISTRATIVE STRATEGIES Performed literature reviews for best practices for TDM implementation. Undeliverable USPS mail or emails triggered stopping the respective protocols. Phone calls were used to attempt to obtain current contact information. Team obtained preferred USPS mailing addresses for incentives through a question on the electronic survey. Team completed three follow up calls for any returned incentives. Generated unique ID numbers to limit communication of any Protected Health Information and Personally Identifiable Information. Created a Microsoft Access database for tracking progress through the recruitment protocol, opt-outs, respondents’ survey completion, and gift card delivery. MAIL AND EMAIL COMMUNICATION STRATEGIES Research team contacted individuals using USPS mail, email, and telephone methods. Email protocol preceded USPS mail protocol. Mailouts and emails occurred in spaced gaps of 1–2 weeks. Contacted individuals could respond to the paper surveys via self-addressed envelopes. Contacted individuals could respond to electronic surveys via QR codes, shortened URLs, and links embedded in emails. TELEPHONIC COMMUNICATION STRATEGIES Research team made three phone attempts, on varying days and times. Discontinued attempts if contacted individual declined participation, hung up, or number was inaccurate. Calls staggered by 2–3 days and batched by participant’s time zone. Utilized a detailed voicemail script. Generated protocols to handle suicide ideation. Recorded a personalized voicemail message for the study phone number. TRUST BUILDING STRATEGIES / REDUCING RESPONSE BURDEN AND COSTS Survey respondents received gift cards upon completing the survey, rather than using a token cash incentive. Highlighted the importance of the research in contact letters, e.g. “This study will help the VA better understand how to improve healthcare for all veterans across the country.” Letters contained personalized headers, e.g. “Dear John Doe,” accomplished through mail merge options in Microsoft Word. All letters thanked respondents for their time and assistance. Used an image of the Principal Investigator’s signature in blue ink on all communication. Electronic surveying option provided to remove mailing burden. Paper survey respondents were able to return surveys using pre-paid, self-addressed envelopes. Survey could not be completely anonymized due to study needs to connect respondents to their health record. Ensured confidentiality of responses in all communication. A test group (i.e., local Veteran Engagement Counsel) reviewed and provided feedback on the survey. Limited the first wave to 50 contacted individuals to limit the impact of any undetected errors. Published results from the study and made them publicly available. Declarations Ethics approval and consent to participate The study was reviewed and approved by University of South Florida IRB and the James A. Haley Veterans’ Hospital Research and Development Committee. It adheres to all federal research ethical guidelines including the Declaration of Helsinki. All participants completed an altered informed consent by reading the informed consent form and either clicking on a link that stated, “I consent to the study” or completing it as part of the submitted paper survey. Consent for publication The informed consent asked all participants to publish their data in aggregate form. All participants provided altered informed consent prior to data collection. Availability of data and materials Specific data from this study are not available. Competing interests The authors report no financial conflicts of interest. Funding This work was supported by Merit Review Award Number (IIR 17-051) from the United States (U.S.) Department of Veterans Affairs Health Service R&D (HSR&D). The views, opinions, and/or findings contained in this article are those of the authors and should not be construed as an official Department of Veterans Affairs position or any other federal agency, policy, or decision unless so designated by other official documentation. Authors' contributions Hari Venkatachalam: Conceptualization, software; data curation; formal analysis; writing – original draft (lead). Shannon Miles: Conceptualization, methodology, writing – original draft, supervision, project administration. Nina Sayer: Conceptualization; writing – original draft; funding acquisition. Heather Belanger: Conceptualization; methodology; funding acquisition; writing – original draft. Stephen Luther: Conceptualization; methodology; writing – original draft; formal analysis; supervision; funding acquisition. Peter A. Toyinbo: Methodology, writing – original draft; formal analysis. Acknowledgements We would like to thank the Veterans who gave their time and expertise to improve healthcare for future Veterans. References Bayley PJ, Kong JY, Helmer DA, Schneiderman A, Roselli LA, Rosse SM, et al. Challenges to be overcome using population-based sampling methods to recruit veterans for a study of post-traumatic stress disorder and traumatic brain injury. BMC Med Res Methodol. 2014 Apr 8;14(48):1–9. Harrington KM, Nguyen XMT, Song RJ, Hannagan K, Quaden R, Gagnon DR, et al. Gender differences in demographic and health characteristics of the Million Veteran Program cohort. Womens Health Issues. 2019 June 25;29(S1):S56–66. Luoto TM, Tenovuo O, Kataja A, Brander A, Öhman J, Iverson GL. Who gets recruited in mild Traumatic Brain Injury research? J Neurotrauma. 2012 Dec 21;30(1):11–6. Jang M, Vorderstrasse A. Socioeconomic status and racial or ethnic differences in participation: Web-based survey. JMIR Res Protoc. 2019 Apr 10;8(4):e11865. Hoddinott SN, Bass MJ. The dillman total design survey method. Can Fam Physician Med Fam Can. 1986 Nov;32:2366–8. Dillman DA, Smyth JD, Christian LM. Internet, phone, mail, and mixed-mode surveys: The tailored design method. 4th ed. Hoboken, New Jersey: Wiley; 2015. Millar MM, Dillman DA. Improving response to web and mixed-mode surveys. Public Opin Q. 2011 May 18;75(2):249–69. Al Khalaf K, O’Dowling Keane S, da Mata C, McGillycuddy CT, Chadwick BL, Lynch CD. Response rates to questionnaire-based studies in the contemporary dental literature: A systematic review. J Dent. 2022 Sept 8;126:104284. Dillman DA, Dolsen DE, Machlis GE. Increasing response to personally-delivered mail-back questionnaires. J Off Stat. 1995 June;11(2):129. Kazzazi F, Haggie R, Forouhi P, Kazzazi N, Malata CM. Utilizing the Total Design Method in medicine: Maximizing response rates in long, non-incentivized, personal questionnaire postal surveys. Patient Relat Outcome Meas. 2018 June;9:169–72. Monroe MC, Adams DC. Increasing response rates to web-based surveys. J Ext [Internet]. 2012 Dec 1 [cited 2025 Nov 27];50(6). Available from: https://tigerprints.clemson.edu/joe/vol50/iss6/34/ Smyth JD, Dillman DA, Christian LM, O’Neill AC. Using the internet to survey small towns and communities: Limitations and possibilities in the early 21st century. Am Behav Sci. 2010 May;53(9):1423–48. Coughlin SS, Aliaga P, Barth S, Eber S, Maillard J, Mahan CM, et al. The effectiveness of a monetary incentive on response rates in a survey of recent U.S. veterans. Surv Pract [Internet]. 2011 Jan 31 [cited 2025 Nov 27];4(1). Available from: https://surveypractice.scholasticahq.com/article/3059-the-effectiveness-of-a-monetary-incentive-on-response-rates-in-a-survey-of-recent-u-s-veterans Culpepper WJ, Wallin MT, Magder LS, Perencevich E, Royal W, Bradham DD, et al. VHA Multiple Sclerosis Surveillance Registry and its similarities to other contemporary multiple sclerosis cohorts. J Rehabil Res Dev. 2015;52(3):263–72. Derrick JL, Eliseo-Arras RK, Hanny C, Britton M, Haddad S. Comparison of internet and mailing methods to recruit couples into research on unaided smoking cessation. Addict Behav. 2017 Dec;75:12–6. deRussy AJ, Jones AL, Austin EL, Gordon AJ, Gelberg L, Gabrielian SE, et al. Insights for conducting large-scale surveys with veterans who have experienced homelessness. J Soc Distress Homelessness. 2021 Dec 28;32(1):123–34. Pflugeisen BM, Mou J. Patient satisfaction with virtual obstetric care. Matern Child Health J. 2017 July;21(7):1544–51. Vogt D, Perkins DF, Copeland LA, Finley EP, Jamieson CS, Booth B, et al. The Veterans Metrics Initiative study of US veterans’ experiences during their transition from military service. BMJ Open. 2018 June 11;8(6):e020734. Largent EA, Eriksen W, Barg FK, Greysen SR, Halpern SD. Participants’ perspectives on payment for research participation: A qualitative study. Ethics Hum Res. 2022 Nov;44(6):14–22. U.S. Department of Health and Human Services. Attachment A - Addressing Ethical Concerns Offers of Payment to Research Participants [Internet]. 2019 [cited 2025 Nov 28]. Available from: https://www.hhs.gov/ohrp/sachrp-committee/recommendations/attachment-a-september-30-2019/index.html Purcell N, Zamora K, Bertenthal D, Abadjian L, Tighe J, Seal KH. How VA Whole Health coaching can impact veterans’ health and quality of life: A mixed-methods pilot program evaluation. Glob Adv Health Med. 2021 Mar 5;10:2164956121998283. U.S. Department of Veterans Affairs, Office of Research & Development. About the Office of Research & Development [Internet]. 2025 [cited 2025 Nov 28]. Available from: https://www.research.va.gov/about/default.cfm Washington DL, Farmer MM, Mor SS, Canning M, Yano EM. Assessment of the healthcare needs and barriers to VA use experienced by women veterans: Findings from the National Survey of Women Veterans. Med Care. 2015 Apr;53:S23–31. Pietrzak RH, Southwick SM, Meffert BN, Morabito DM, Sawicki DA, Hausman C, et al. US veterans who do and do not utilize Veterans Affairs health care services: Demographic, military, medical, and psychosocial characteristics. Prim Care Companion CNS Disord [Internet]. 2019 Jan 17 [cited 2025 Nov 27];21(1). Available from: https://www.psychiatrist.com/pcc/veterans-who-do-and-do-not-utilize-va-services Returning home from Iraq and Afghanistan: Preliminary assessment of readjustment needs of veterans, service members, and their families [Internet]. Washington, D.C.: National Academies Press; 2010 [cited 2025 Nov 27]. Available from: http://www.nap.edu/catalog/12812 Miles SR, Toyinbo PA, Belanger HG, Venkatachalam HH, Luther SL, Sayer NA. Long-term clinical outcomes associated with the Veterans Health Administration’s Traumatic Brain Injury and mental health screens. J Head Trauma Rehabil. 2025 Sept;40(5):E349–59. U.S. Department of Veterans Affairs, Veterans Health Administration. Screening and evaluation of possible Traumatic Brain Injury in Operation Enduring Freedom (OEF) and Operation Iraqi Freedom (OIF) veterans [Internet]. 2010 [cited 2025 Nov 28]. Available from: https://www.va.gov/optometry/docs/vha_directive_2010-012_screening_and_evaluation_of_possible_tbi_in_oef-oif_veterans.pdf Miles SR, Sayer NA, Belanger HG, Venkatachalam HH, Kozel FA, Toyinbo PA, et al. Comparing outcomes of the Veterans Health Administration’s Traumatic Brain Injury and Mental Health screening programs: Types and frequency of specialty services used. J Neurotrauma. 2023 Jan 1;40(1–2):102–11. U.S. Department of Veterans Affairs. Traumatic brain injury screening and evaluation data. [Internet]. 2024 [cited 2025 July 1]. Available from: https://vaww.vhadataportal.med.va.gov/Data-Sources/Traumatic-Brain-Injury WWAMI Rural Health Research Center [Internet]. 2005 [cited 2025 Nov 28]. Available from: https://depts.washington.edu/uwruca/ruca-data.php Dillman DA, Smyth JD, Christian LM. List of guidelines in internet, phone, mail, and mixed-mode surveys: The Tailored Design Method, 4th edition. [Internet]. 2015 [cited 2025 Nov 27]. Available from: https://higheredbcs.wiley.com/legacy/college/dillman/1118456149/pdf/List_of_Guidelines_Internet_Phone_Surveys4e.pdf Qualtrics [Internet]. 2025 [cited 2025 Nov 27]. Available from: https://www.qualtrics.com/ Streamworks, LLC [Internet]. [cited 2025 Nov 28]. Available from: https://streamworksmn.com/services/compliance-communications Chen H, Cohen P, Chen S. How big is a big odds ratio? Interpreting the magnitudes of odds ratios in epidemiological studies. Commun Stat - Simul Comput. 2010 Apr 6;39(4):860–4. U.S. Department of Veterans Affairs and U.S. Department of Defense. VA/DoD clinical practice guideline for the management of substance use disorders [Internet]. 2021 [cited 2025 Nov 28]. Available from: https://www.healthquality.va.gov/guidelines/MH/sud/VADODSUDCPGProviderSummary.pdf U.S. Department of Veterans Affairs and U.S. Department of Defense. VA/DoD clinical practice guideline for the management of major depressive disorder [Internet]. 2022 [cited 2025 Nov 28]. Available from: https://www.healthquality.va.gov/guidelines/MH/mdd/VADODMDDCPG_ProviderSummary_Final_508_updated.pdf U.S. Department of Veterans Affairs and U.S. Department of Defense. VA/DoD clinical practice guideline: Management of posttraumatic stress disorder and acute stress disorder [Internet]. 2023 [cited 2025 Nov 28]. Available from: https://www.healthquality.va.gov/HEALTHQUALITY/guidelines/MH/ptsd/VA-DOD-CPG-PTSD-Quick-Reference-Guide.pdf U.S. Department of Veterans Affairs. VA priority groups [Internet]. 2024 [cited 2025 Nov 28]. Available from: https://www.va.gov/health-care/eligibility/priority-groups/ Althubaiti A. Information bias in health research: Definition, pitfalls, and adjustment methods. J Multidiscip Healthc. 2016 May 4;9:211–7. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 04 Feb, 2026 Reviews received at journal 02 Feb, 2026 Reviews received at journal 14 Jan, 2026 Reviewers agreed at journal 08 Jan, 2026 Reviewers agreed at journal 18 Dec, 2025 Reviewers invited by journal 18 Dec, 2025 Editor assigned by journal 18 Dec, 2025 Editor invited by journal 16 Dec, 2025 Submission checks completed at journal 16 Dec, 2025 First submitted to journal 15 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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method\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8234598/v1/d2f9128b1d7c7a23a9c75307.png"},{"id":99307884,"identity":"47add57d-a263-4096-aa46-ca01d252adfd","added_by":"auto","created_at":"2025-12-31 16:06:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":218052,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eElectronic responses per day from the start of the e-mail protocol\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8234598/v1/38762c8099bfa425f673f9cb.png"},{"id":100356110,"identity":"edfe9d2b-0c9d-4852-a7a1-48887e29beb7","added_by":"auto","created_at":"2026-01-16 06:52:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2252887,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8234598/v1/7434d328-4423-4dbe-92c1-75e569733896.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Enhancing Recruitment Methodologies: Leveraging the Tailored Design Method to Survey Populations with Varied Engagement in Healthcare","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTwo main challenges hinder successfully recruiting individuals to respond to research surveys. First, researchers need to recruit enough respondents to sufficiently power analyses. Second, researchers must ensure that respondents are a representative sample to limit biases in the research findings (1\u0026ndash;4). The Total Design Method, originally conceived by Don A. Dillman in the 1970s, created guidelines for survey data collectors to obtain high response rates and representative samples (5).\u003c/p\u003e \u003cp\u003eDillman\u0026rsquo;s techniques are rooted in social exchange theory and strive to improve survey response rates by removing costs and barriers while enhancing benefits and building trust (6). The growth in electronic surveying options prompted modifications to the Total Design Method. The most recent iteration of the technique, termed the Tailored Design Method (TDM), allows for surveying through both mail and electronic channels (6). Key characteristics of TDM include sending a token cash incentive with the survey request, personalizing communication with handwritten signatures, and providing contacted individuals with both web and mail-based options to complete the survey (6,7). Other recommendations include tailoring succinct, direct, and user-friendly questionnaires that reduce respondent burden (6). Strict implementation of Dillman\u0026rsquo;s recommendations has resulted in survey response rates greater than 70% in some civilian samples(8\u0026ndash;12).\u003c/p\u003e \u003cp\u003eHowever, other recruitment protocols have noted lower response rates between 12\u0026ndash;40% in veteran samples and / or samples of individuals who did not have an ongoing relationship with the surveying institution (1,13\u0026ndash;18). Lower response rates in some of the studies may occur due to a variety of factors, including regulatory constraints that prevent research institutions from implementing certain features of TDM. Within US government institutions, sending cash incentives to all contacted individuals has tax implications on income-based government benefits (19). Instead, Institutional Review Board-approved research studies traditionally only provide incentive payments post hoc to those who complete a survey (20). Sending post-survey incentive payments rather than initial token payments with the survey request has been shown to reduce response rates in surveyed populations, including veterans (7,13). There are also resource constraints on personalizing communication when performing large survey mailouts. Additionally, standardized self-report measures of health outcomes included in surveys may not be succinct or user friendly. These factors may have contributed to lower response rates for VHA survey studies conducted after Dillman developed TDM.\u003c/p\u003e \u003cp\u003eAnother consideration for TDM implementation is how to leverage relationships with contacted individuals. VHA research studies typically seek respondents who are engaged in VHA healthcare. This often includes restricting samples to individuals who had recent diagnoses or encounters with their healthcare provider (10,14,16). However, only surveying individuals who recently engaged in VHA healthcare limits the generalizability of the findings. VHA performs research in a variety of domains, such as satisfaction with care, perceived barriers and facilitators to seeking care, and quality of life studies that would benefit from the participation of individuals from populations with varied levels of engagement in healthcare (21\u0026ndash;23). Successfully surveying individuals who are engaged and those who are less engaged in receiving VHA services can provide important information to inform quality improvement projects, especially initiatives to increase service utilization among non-users (24).\u003c/p\u003e \u003cp\u003eThis paper presents the survey methods of a large VHA study that sought to examine clinical outcomes of Afghanistan and Iraq war veterans who underwent the VHA mandated post-deployment screenings for Traumatic Brain Injury (TBI) and Mental Health (MH) conditions between 2007 and 2018 (25\u0026ndash;27). We selected individuals based on a specific historical anchor timepoint: the date the veterans were assessed for TBI and MH conditions. This anchor timepoint preceded the survey data collection by up to twelve years. Because current engagement in VHA was not an inclusion criterion, those surveyed may not be invested in VHA clinical care or research. Within this report, we describe how we implemented TDM with adjustments for VHA context and regulations. To determine if we selected a representative sample with these methods, we examined differences between responders and non-responders. Our goal is to inform other survey studies that are conducted by large and / or federal healthcare organizations, and who seek to survey groups of individuals with varying levels of engagement with the research institution.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eHuman Ethics and Consent to Participate\u003c/h2\u003e \u003cp\u003eThe study was approved by the Department of Veterans Affairs and Institutional Review Board. The study adheres to all state and federal ethical research guidelines. All participants completed altered informed consent either electronically or on paper prior to completing any study related activities. A clinical trial number was not applicable for this research study.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSample Selection\u003c/h3\u003e\n\u003cp\u003eWe selected veterans from a cohort of VHA patients who completed a TBI screen between 10/01/2007 and 9/30/2018 and an MH screen within 7 days of the TBI screen (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;289,104). From this cohort, we selected a random sample of 9,775 veterans. To meet the objectives of the parent study (26), we stratified the sample by MH screen outcome (screened positive for: posttraumatic stress disorder, depression, and / or alcohol use disorder), TBI screen outcome (probable TBI vs. no TBI), gender (male or female), VHA region (i.e., North Atlantic, Southeast, Midwest, Continental, and Pacific), and year of TBI screen (i.e., grouped into three cohorts of 2008\u0026ndash;09, 2010\u0026ndash;12, and 2013\u0026ndash;18).\u003c/p\u003e\n\u003ch3\u003eData Sources\u003c/h3\u003e\n\u003cp\u003eTo advance both research and medical care, VHA has developed large databases that house administrative data (e.g., diagnoses, types and frequency of medical visits, demographics) for all veterans in the national healthcare system. The current study paired administrative data with a survey that evaluated current functioning and satisfaction with VHA healthcare (26,28). We extracted TBI screen results; demographic variables of age, sex, race, and ethnicity; service utilization in the form of visit counts (i.e., Stop codes) in the domains of physical medicine and rehabilitation (PM\u0026amp;R), neurology, and mental health (MH); and email addresses, USPS mail addresses, and telephone numbers for each veteran from the administrative datasets (28,29). We also captured self-reported service utilization in the previous six months from survey respondents (26). We assessed rurality of home residence based on zip codes extracted from the USPS addresses and evaluated against a crosswalk generated by the Washington, Wyoming, Alaska, Montana, Idaho\u0026rsquo;s Rural Health Research Center Rural-Urban Commuting Area Codes (30).\u003c/p\u003e\n\u003ch3\u003eModified TDM Contact Schedule\u003c/h3\u003e\n\u003cp\u003eThe research team attempted to contact all individuals between July 2020 and August 2021. We ceased recruitment efforts after we reached the sample size necessary to power analyses for the parent study. Contacted individuals had both email and USPS mail addresses in the administrative datasets (n\u0026thinsp;=\u0026thinsp;7,198), only USPS mail addresses (n\u0026thinsp;=\u0026thinsp;2,499), or only email addresses (n\u0026thinsp;=\u0026thinsp;78).\u003c/p\u003e \u003cp\u003eThe survey recruitment method involved a maximum of ten attempted contacts performed via USPS mail (i.e. USPS mail protocol), emails sent via the Qualtrics platform (i.e., email protocol), and / or telephone phone calls. Paper booklet versions of the survey were provided via the USPS mail protocol, and links to the electronic survey were provided via the email protocol. If the research team had both email addresses and USPS mail addresses, we completed the email protocol prior to initiating the USPS mail protocol. Contact attempts within each protocol included: (1) a pre-notice letter; (2) an email or USPS mailed letter with the survey; (3) an electronic or USPS mailed opt-out postcard; and (4) a follow-up email or USPS mailed letter with the survey. After the first attempted protocol, we performed three telephone contact attempts to confirm receipt of the survey and to provide the opportunity to address any questions or concerns. Subsequently, contact attempts 2, 3, and 4 were repeated via the USPS mail protocol for non-respondents from the email protocol that had both email and USPS mail addresses.\u003c/p\u003e \u003cp\u003eContacted individuals could access the electronic survey from the USPS mailed information via a URL or QR code. Conversely, contacted individuals could request a paper survey if they received the request via email. Contact attempts ceased after the contacted individual completed the survey, opted out, was identified as deceased, or when the email and / or USPS mail protocols were completed (\u003cem\u003eSee\u003c/em\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eModifications to TDM\u003c/h3\u003e\n\u003cp\u003e Where possible, we implemented TDM in accordance with Dillman\u0026rsquo;s guidelines. We made modifications to TDM in certain circumstances to address human resource limitations and to ensure alignment with VHA regulatory requirements. Consistent with TDM, we generated visually appealing contact letters and booklets, incorporating feedback from a test group of veterans. Respondents could complete the survey in either paper or electronic formats. Respondents could complete the electronic survey accessed from a QR code, an email-embedded link, or a shortened URL. Respondents who completed paper surveys returned them using a self-addressed pre-paid envelope. Key modifications of TDM for VHA regulatory requirements include retaining survey instruments in their validated forms and sending gift cards to survey respondents, rather than all contacted individuals. A complete list of TDM implemented features and adaptations is listed in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e1\u003c/span\u003e (6,31).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e1\u003c/span\u003e: \u003cb\u003eImplemented Features of the Modified TDM for TBI Study\u003c/b\u003e\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSoftware Systems\u003c/h2\u003e \u003cp\u003eWe sent emails and collected electronic surveys using a Qualtrics platform, a surveying platform that allowed customization (e.g., contact list generation), and automation (e.g., timed survey delivery) (32). Streamworks, a vendor specializing in data digitalization, converted our paper surveys into a digital format for data collation (33). We mailed paper surveys to Streamworks for scanning and storage in an electronic database.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAnalyses\u003c/h3\u003e\n\u003cp\u003eAssessed characteristics were race (white vs. non-white or mixed race), sex (female vs. male), ethnicity (Hispanic vs. non-Hispanic), rurality (rural vs. urban), proximity to research site (Florida vs. non-Florida), age (at the time of data collection), and the number of years between the TBI Screen and the survey. We performed two-sample t-tests for continuous variables and Chi-squared analyses for dichotomous variables. We employed multivariable logistic regression models comparing respondents against non-respondents, and those who responded by paper against those who responded by electronic survey. Adjusted Odds-Ratios for all predictor variables estimated the effect sizes. We assessed magnitude of the effect sizes using cut points of 1.68, 3.47, and 6.71 for adjusted Odds Ratios to refer to small, medium, and large effect sizes respectively (34). We performed descriptives statistical analyses on time between survey sent and receipt of returned survey, number of responses per day following each of the steps of the recruitment protocol, and service utilization around the anchoring timepoint and before survey completion. We additionally performed non-parametric statistical tests (i.e., Mann-Whitney U test) to determine if respondents and non-respondents had different service utilization around the anchoring timepoint.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eWe attempted to contact 2,735 veterans by USPS mail only, 3,304 by email only, and 3,736 by both email and USPS mail. Ninety-three (0.95%) veterans died during the data collection period. Both USPS and email contact attempts failed for an additional 336 veterans (3.44%). By the end of data collection, 2,025 (20.72%) of the contacted veterans had responded to the survey, and 1,093 (11.0%) actively declined participation in the study. Most of the veterans we attempted to contact (n\u0026thinsp;=\u0026thinsp;6,228, 63.71%) neither responded to the survey nor actively opted out of participating (\u003cem\u003eSee\u003c/em\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe noted spikes immediately after the delivery of each email during the Qualtrics email protocol. Responses tapered slowly after each touchpoint. (\u003cem\u003eSee\u003c/em\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Spikes in responses were not as apparent after steps of the USPS mail protocol.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMore respondents (67.11%) responded via the Qualtrics electronic survey than the paper survey. The research team received electronic responses more quickly than paper surveys. The median time to respond for a Qualtrics electronic survey was 21 days from first receiving the survey (IQR: 5\u0026ndash;57 Days), while the median time to receive paper surveys was 69 Days (IQR: 42\u0026ndash;123 Days) from delivery. This was expected due to the lag time in sending and receiving USPS mail versus emails.\u003c/p\u003e \u003cp\u003eBivariate analyses showed respondents were more likely to be White (χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;11.38, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), female (χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;14.13, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), older (\u003cem\u003et\u003c/em\u003e-statistic = -12.12, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and screened earlier for post-deployment health concerns (\u003cem\u003et\u003c/em\u003e-statistic = -3.31, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) compared to non-respondents (\u003cem\u003eSee\u003c/em\u003e Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Bivariate analyses between survey response type showed those who responded by paper were more likely to be rural (χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;7.83, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01), older (\u003cem\u003et\u003c/em\u003e-statistic = -7.88, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), residents of a different state as the recruitment site (i.e., Florida) (χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;9.29, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and screened earlier for TBI and MH conditions (\u003cem\u003et\u003c/em\u003e-statistic = -4.90, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) compared to those who responded by electronic survey (\u003cem\u003eSee\u003c/em\u003e Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMultivariable logistic regression models revealed similar results as the bivariate analyses with respondents being more likely to be female (aOR 1.40, 95% C.I. 1.22, 1.61), older (aOR per 5-year increment, 1.16, 95% C.I. 1.13, 1.19), and White (aOR for White respondents 1.31, 95% C.I. 1.16, 1.47) compared to non-respondents (\u003cem\u003eSee\u003c/em\u003e Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Multivariable logistic regression models comparing paper survey respondents to electronic survey respondents revealed that respondents from Florida (i.e., research site) were less likely to respond by paper (aOR 0.610, 95% C.I. 0.417, 0.894). They were more likely to respond by paper if they were older (aOR per 5-year increment, 1.193, 95% C.I. 1.139, 1.249) and were residing in a rural zip code compared with an urban zip code (aOR 1.329, 95% C.I. 1.047, 1.688) (\u003cem\u003eSee\u003c/em\u003e Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic Comparisons between Response and Non-Response\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRespondent vs. Non-respondent\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVariable\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eRespondent\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eNon-respondent\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eStatistical Test\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1440 (71.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5179 (67.18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eΧ\u003csup\u003e2\u003c/sup\u003e (1, \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9734)\u0026thinsp;=\u0026thinsp;11.38,\u003c/p\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0007\u003c/p\u003e\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e585 (28.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2530 (32.82%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e349 (17.23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1073 (13.92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eΧ\u003csup\u003e2\u003c/sup\u003e (1, \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9734)\u0026thinsp;=\u0026thinsp;14.13,\u003c/p\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1676 (82.77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6636 (86.08%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEthnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e225 (11.49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e935 (12.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eΧ\u003csup\u003e2\u003c/sup\u003e (1, \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9358)\u0026thinsp;=\u0026thinsp;1.87,\u003c/p\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.1720\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1733 (88.51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6465 (87.36%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRurality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e405 (20.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1426 (19.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eΧ\u003csup\u003e2\u003c/sup\u003e (1, 9379)\u0026thinsp;=\u0026thinsp;1.81,\u003c/p\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.1791\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1562 (79.41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5986 (80.76%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge at Mailout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.52\u0026thinsp;\u0026plusmn;\u0026thinsp;11.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.11\u0026thinsp;\u0026plusmn;\u0026thinsp;10.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e(2989.9)\u003csup\u003e1\u003c/sup\u003e = -12.12,\u003c/p\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eProximity to Mailout Site\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlorida\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e184 (9.10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e659 (26.39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eΧ\u003csup\u003e2\u003c/sup\u003e (1, 9633)\u0026thinsp;=\u0026thinsp;0.39,\u003c/p\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.5324\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-Florida\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1838 (90.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1838 (73.61%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime Difference between Screen and Study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYears before Recruitment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.27\u0026thinsp;\u0026plusmn;\u0026thinsp;2.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.03\u0026thinsp;\u0026plusmn;\u0026thinsp;2.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e(9728) = -3.31,\u003c/p\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic Comparisons between Response by Paper vs Electronic Survey\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePaper vs. Electronic\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVariable\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ePaper\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eElectronic\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eStatistical Test\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e464 (69.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e976 (71.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eΧ\u003csup\u003e2\u003c/sup\u003e (1, \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2025)\u0026thinsp;=\u0026thinsp;1.00,\u003c/p\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.3164\u003c/p\u003e\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e202 (30.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e383 (28.18%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101 (15.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e248 (18.25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eΧ\u003csup\u003e2\u003c/sup\u003e (1, \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2025)\u0026thinsp;=\u0026thinsp;2.98,\u003c/p\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0843\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e565 (84.83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1111 (81.75%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEthnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69 (10.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e156 (11.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eΧ\u003csup\u003e2\u003c/sup\u003e (1, \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1958)\u0026thinsp;=\u0026thinsp;0.43,\u003c/p\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.5142\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e569 (89.18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1164 (88.18%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRurality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e157 (24.23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e248 (18.80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eΧ\u003csup\u003e2\u003c/sup\u003e (1, \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1967)\u0026thinsp;=\u0026thinsp;7.83,\u003c/p\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e491 (75.77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1071 (81.20%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge at Mailout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.50\u0026thinsp;\u0026plusmn;\u0026thinsp;12.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.06\u0026thinsp;\u0026plusmn;\u0026thinsp;10.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e(1147.3)\u003csup\u003e1\u003c/sup\u003e = -7.88,\u003c/p\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eProximity to Mailout Site\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlorida\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (6.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e142 (10.46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eΧ\u003csup\u003e2\u003c/sup\u003e (1, \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2022)\u0026thinsp;=\u0026thinsp;9.29,\u003c/p\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-Florida\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e623 (93.68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1215 (89.54%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime Difference between Screen and Study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYears before Recruitment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.71\u0026thinsp;\u0026plusmn;\u0026thinsp;2.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.06\u0026thinsp;\u0026plusmn;\u0026thinsp;3.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e(1489.3)\u003csup\u003e1\u003c/sup\u003e = -4.90,\u003c/p\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.0001\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eSatterthwaite approximation of degrees of freedom for unequal variances utilized\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate Logistic Regression Models Predicting Response over Non-Response\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eDF\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eβ Estimate\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eO.R. and\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e95% Wald C.I.\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003ep\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e-value\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace (White vs. Non-White)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.307 [1.163, 1.469]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (Female vs. Male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.404 [1.221, 1.613]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity (Hispanic vs. Non-Hispanic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.879 [0.744, 1.039]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRurality (Rural vs. Urban)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.033 [0.907, 1.176]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.624\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at Mailout (in 5-year increments)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.161 [1.133, 1.189]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProximity to Research Site (Florida vs. Non-Florida)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.984 [0.821, 1.179]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.860\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYears Before Mailout (in 1-year increments)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000 [0.982, 1.019]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.970\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate Logistic Regression Models Predicting Response by Paper over Electronic Survey\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eDF\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eβ Estimate\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eO.R. and\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e95% Wald C.I.\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003ep\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e-value\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace (White vs. Non-White)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.002 [0.802, 1.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.985\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (Female vs. Male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.893 [0.682, 1.168]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.409\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity (Hispanic vs. Non-Hispanic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.968 [0.692, 1.355]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.849\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRurality (Rural vs. Urban)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.329 [1.047, 1.688]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at Mailout (in 5-year increments)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.193 [1.139, 1.249]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProximity to Research Site (Florida vs. Non-Florida)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.610 [0.417, 0.894]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYears Before Mailout (in 1-year increments)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.043 [1.006, 1.081]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eEngagement in MH, neurology, and PM\u0026amp;R care after the TBI screen (an average of 9.08 years prior to the current survey) did not statistically differ between respondents and non-respondents in the six months prior or after the TBI screen (anchor timepoint for parent study) (\u003cem\u003eSee\u003c/em\u003e Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Seventy-five percent of respondents had\u0026thinsp;\u0026le;\u0026thinsp;5 MH visits, while 75% percent of non-respondents\u0026thinsp;\u0026le;\u0026thinsp;4 MH visits in the six months after the TBI screen. Seventy-five percent of both respondents and non-respondents had\u0026thinsp;\u0026lt;\u0026thinsp;1 PM\u0026amp;R visit and 0 neurology visits in the 6 months after the TBI screen. Among those who had a positive MH screen, most respondents and non-respondents had less than 9 MH visits in the six months post-screen. Among those who had a positive TBI screen, most respondents had less than 7 PM\u0026amp;R visits and 0 neurology visits in the six months post-screen. Differences in service utilization between respondents and non-respondents were also not significant when stratified by TBI or MH screen outcome, with alpha adjusted to 0.008 using Bonferroni correction (\u003cem\u003eResults not shown\u003c/em\u003e).\u003c/p\u003e \u003cp\u003eOf the respondents, 51.10%, 76.57%, 53.25% reported not receiving services in the domains of MH, neurology, and PM\u0026amp;R, respectively, in the six months prior to responding to the survey. Additionally, 21% stated they had not received any VHA healthcare services in the 6 months prior to completing the survey. (\u003cem\u003eSee Table\u0026nbsp;7\u003c/em\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eContacted Individuals\u0026rsquo; Engagement with VHA Services\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eNon-respondents\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6528)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eRespondents\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1741)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIQR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIQR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eEngagement with VHA Healthcare after TBI Screen\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMH visits 6 months prior to screen\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u0026ndash;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.4148\u003csup\u003e\u003cb\u003e1\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNeurology visits 6 months prior to screen\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u0026ndash;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.4734\u003csup\u003e\u003cb\u003e1\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePM\u0026amp;R visits 6 months prior to screen\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u0026ndash;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.3122\u003csup\u003e\u003cb\u003e1\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMH visits 6 months after the screen\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e4.64\u0026thinsp;\u0026plusmn;\u0026thinsp;15.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e4.26\u0026thinsp;\u0026plusmn;\u0026thinsp;12.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.4954\u003csup\u003e\u003cb\u003e1\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNeurology visits 6 months after the screen\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u0026ndash;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.5896\u003csup\u003e\u003cb\u003e1\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePM\u0026amp;R visits 6 months after the screen\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.73\u0026thinsp;\u0026plusmn;\u0026thinsp;5.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u0026ndash;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.3161\u003csup\u003e\u003cb\u003e1\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEngagement with VHA prior to Survey Completion\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(\u003c/b\u003e\u003cb\u003en\u003c/b\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;1,741)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVariable\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003e% (excluding missing)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDid not receive any VHA services 6 months prior to survey\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e20.82%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDid not receive PM\u0026amp;R services 6 months prior to survey\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e53.25%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDid not receive MH services 6 months prior to survey\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e51.10%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDid not receive Neurology services 6 months prior to survey\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e76.57%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eCollecting representative data and ensuring adequate sample sizes are important factors for building robust datasets and successfully completing research studies. Researchers widely cite TDM as an effective method for obtaining these goals. The objective of this analysis was to examine response rates associated with implementation of TDM within a large, federal healthcare system, surveying respondents selected from a historical anchor timepoint (i.e., mandatory TBI and MH screens) with varying levels of healthcare engagement.\u003c/p\u003e \u003cp\u003eTDM is based on social exchange theory that leverages trust and transactions. Likely because our sample included varied engagement and limitations that prevented strict implementation of TDM, our modified TDM resulted in a lower than expected response rate (21%) compared to what has been reported in some studies of civilian patients engaged in healthcare (8\u0026ndash;12).\u003c/p\u003e \u003cp\u003eEven with the lower response rate, we obtained a sample of the surveyed population that was representative in terms of demographics and VHA care utilization. Analyses showed the surveyed population had minimal engagement with MH, neurology, and PM\u0026amp;R within the VHA, with no statistical differences between respondents and non-respondents. In regards to the MH engagement, the number of sessions would be insufficient for a full course of evidence based psychotherapy for the disorders for which the veterans were screened (i.e., PTSD, depression, and alcohol use disorder) (35\u0026ndash;37). Additionally, 21% of the respondents reported not having received any VHA services in the 6 months prior to completing the survey. These findings support that the surveyed sample had varied engagement with VHA care, and that respondents are representative of the surveyed population on service utilization characteristics. As the study sought to examine the long-term effects of the VHA TBI screening program (26), it needed feedback from both veterans engaged and not engaged in VHA care. Successfully obtaining responses from those with little or no engagement with VHA met the needs of the parent study.\u003c/p\u003e \u003cp\u003ePossible reasons for the lack of engagement with VHA at the time of the survey may be due to the fact that most of the veterans completed the TBI and MH screens as part of their transition to civilian life and while relatively new to VHA healthcare. Combat veterans who served in the wars in Afghanistan and Iraq are eligible for free VHA healthcare benefits for 10 years after discharge from the Department of Defense (38). After 10 years, if the veteran does not have a VHA disability, he / she may be required to pay for some or all their healthcare depending on other factors, such as whether they have other insurance and/or service-connected disabilities. Veterans without VHA disabilities may have transitioned their healthcare to civilian providers after their 10 years of healthcare expired.\u003c/p\u003e \u003cp\u003eAlthough there were no statistically significant differences between respondents and non-respondents in service utilization around the anchor timepoint, there were statistically significant differences in demographics between respondents and non-respondents. However, if we assessed the size of the differences using established effect sizes of adjusted Odds Ratio values of 1.68, 3.47, and 6.71 to refer to small, medium, and large effects, respectively (34), the demographic differences were small. This finding also suggests the sample\u0026rsquo;s representativeness in terms of these assessed demographic features to the overall VHA population that had post deployment screens.\u003c/p\u003e \u003cp\u003eThere were small effect size differences between those who responded via paper survey compared to those who responded via electronic survey. The odds of Florida residents responding via paper survey were 39% (95% CI: 11% \u0026minus;\u0026thinsp;58%) less than non-Florida residents. The odds of rural residents responding via paper were 33% higher (95% CI: 5% \u0026minus;\u0026thinsp;69%) than urban residents. Internet connectivity and familiarity with the emailing institution may be possible sources for these differences. Therefore, we recommend allowing respondents to complete surveys through both paper and electronic methods to ensure that the research team obtains a representative sample. Researchers may also use non-random sampling techniques, such as quota sampling, to ensure that underrepresented groups respond. Finally, subgroup analyses and multivariable modeling approaches can be used to check for sample biases and to account for differences in response rates in their models.\u003c/p\u003e \u003cp\u003eOur modified TDM balanced federal regulatory constraints with the benefits of TDM to obtain a response rate that was sufficient for the needs of our larger study, although lower than anticipated based on research conducted in civilian samples by researchers who could employ all aspects of TDM. We offer six lessons learned for future researchers conducting survey studies within large healthcare organizations that survey similar populations.\u003c/p\u003e \u003cp\u003e \u003cem\u003eLesson 1: Plan for a response rate lower than TDM advertises if you are studying historical cohorts, historical anchoring events, or involve individuals who are not engaged in healthcare at the research institution.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThis study recruited veterans who had undergone clinical screenings between two to twelve years prior to beginning study activities. After receiving our survey, some individuals reached out to the study team and stated they had never been screened for TBI or MH conditions, although the screening results appeared in the administrative datasets. The longer the time between the anchor point (i.e., TBI screen) and surveys, the less likely the individuals are to remember being screened, especially if the contacted individual screened negative for the condition (i.e., Recall Bias) (39).\u003c/p\u003e \u003cp\u003eAdditionally, many veterans screened for TBI or MH conditions did not seek VHA services after their screens (28). Therefore, the lower response rate from our modified TDM may be useful for researchers who are attempting to recruit those who completed health screens or medical procedures years prior to data collection and who are no longer affiliated with the research institution. Estimating a lower response rate will be useful when recruiting samples of individuals who may not currently be engaged in receiving services from the research institution.\u003c/p\u003e \u003cp\u003e \u003cem\u003eLesson 2: Save research team members' time by automating or outsourcing tasks, including prioritizing email.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eLeveraging available resources helped with limited research staff. The use of Qualtrics and Streamworks provided the team with resources to efficiently implement the modified TDM. Qualtrics capabilities included generation of mailout lists and automated delivery of electronic surveys, removing the burden of having to individually email surveys to each contacted individual. Streamworks coded paper surveys by scanning them electronically, allowing for seamless merging of the electronic and paper datasets.\u003c/p\u003e \u003cp\u003eCollating mail and stuffing envelopes are time-consuming tasks. We limited the number of instances that the team had to do so to improve efficiency. We first implemented the email protocol prior to the USPS mail protocol. Subsequently, we excluded respondents and those who opted out from the USPS mail protocol. We were able to utilize VHA mailroom staff to collate and seal the prenotice letter and send the opt-out cards without assistance from the research team. This resulted in the research team only having to collate and process mailings for the second and fourth mailouts from the USPS mail protocol.\u003c/p\u003e \u003cp\u003e \u003cem\u003eLesson 3: Implement TDM\u0026rsquo;s multimodal and multiple touch-point approach to communication.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eA key feature of TDM is using both paper mail and electronic modes for responses and using multiple methods of contact. Our team used USPS mail, email, and telephone methods to obtain responses from contacted individuals. The burden of performing multiple mailouts using both electronic and USPS mail methods is apparent. This methodology, however, ameliorates some of the logistical challenges the team faced. For example, because we were working with a historical cohort, the research team may not have updated contact information for everyone. Using multiple modes of contact decreased the number of individuals with \u003cem\u003eneither\u003c/em\u003e a valid mailing address nor email address (3%; n\u0026thinsp;=\u0026thinsp;336).\u003c/p\u003e \u003cp\u003eLimiting the survey response type to electronic surveys and limiting the communication strategy to emails may be appealing to research teams with limited human resources. However, our large number of paper responses reveals that giving the option to respond via paper survey is key to ensuring our response rate. Although we first attempted to contact all individuals by email and all USPS mail communication offered the option to access the electronic survey via a tiny URL and a QR code, a third of respondents (32.88%) still chose to respond via paper mail. Statistically, paper respondents tended to be older and from rural zip codes. Ensuring a paper option for survey response allows representation from these groups that may have connectivity or access issues with electronic surveys. Furthermore, the multiple touchpoints for the mail and email protocols were required to achieve our response rates. We noted a spike in response after each of the email touchpoints. Such bursts were harder to identify with the USPS protocol, due to mail delivery lag. The findings from the email protocol highlights that the repeated touchpoints were resulting in increases in responses.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eLesson 4: Follow Dillman\u0026rsquo;s Recommendation for Visual Appeal of the Survey\u003c/h2\u003e \u003cp\u003eDillman highlights visual appeal to encourage responses. Certain elements, such as an ink signature, were difficult to implement with the large number of mailouts. We utilized an image of a blue color signature block from the Principal Investigator on mailouts. We used an iterative process with input from multiple stakeholders to ensure a clear layout and visual appeal of the survey. First, Streamworks generated a booklet survey to appeal to the reader and to easily scan the paper surveys into digital data. Second, a professional graphics team member designed several cover page options. Third, both Streamworks and the graphics team used color images, borders, and Department of Veterans Affairs logos and branding to improve the visual appeal of the booklet. Finally, veteran stakeholders provided feedback on the readability of the survey (e.g., font size, contrast, ease of following directions), and preference for the cover page.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eLesson 5: Establish an efficient process for incentive payments for respondents\u003c/h2\u003e \u003cp\u003eAlthough our team was unable to provide an initial token incentive payment for all contacted individuals, we were able to establish an efficient process for paying respondents post-hoc. The survey requested respondents enter their preferred delivery address for their gift card, which increased insurance that they would receive payment. The tracking database assisted the team to ensure all respondents received the incentive. The established procedures for returned incentives facilitated the delivery and handling of incentives.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLesson 6: Follow Dillman\u0026rsquo;s recommendations for building trust and increasing rewards\u003c/h2\u003e \u003cp\u003eOur research team used Dillman\u0026rsquo;s overarching social exchange theoretical model to inform decisions we made throughout the study. Ensuring transparency of the research process and following regulatory requirements were central to building trust. We provided payments in a timely manner and reiterated at each touchpoint that we ensured anonymity of individual data in any results we published.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and Strengths\u003c/h2\u003e \u003cp\u003e Following a modified TDM, we achieved a 21% response rate, which provided sufficient power for our analyses but is lower than previously published studies in other populations. While results demonstrated that certain characteristics had statistically significant associations with whether a veteran responded to the survey or not, or responded via paper or electronic survey, the effect sizes were generally negligible. Our current method may be helpful for researchers conducting surveys in large and / or federal healthcare organizations but may be less relevant for researchers in other settings that have more flexibility to strictly follow TDM.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eFuture Applications\u003c/h2\u003e \u003cp\u003eSurveying is an important part of research activities. Gathering a sufficiently sized and representative sample is a central concern for any researcher that uses surveying for their study. Our goal was to assist researchers who are planning future studies using our protocol as a template for implementing TDM, especially when asking questions about historical anchoring events and surveying large populations of individuals who may not be engaged in healthcare services.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eImplemented Features of the Modified TDM for TBI Study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"1\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTDM implemented features and adaptations for VHA Survey\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e SURVEY LAYOUT STRATEGIES\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreated a user-friendly booklet for the survey, with a double-stapled binding.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUsed validated instruments in surveys and retained matrix-style questions. Asked each instrument sequentially.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInstructions for the survey were short and located on the inside cover of the booklet (i.e., page 2) and on the first screen of the electronic survey.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe Informed Consent document was separate from the survey. Depending on whether the Informed Consent document was sent via the email protocol or the USPS mail protocol, contacted individuals could access it through a link or on pages separate from the survey. Instructions for individual measures were included on the same page as the question items.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUsed 14\u0026thinsp;+\u0026thinsp;font sizes and bolded the questions. Used instrument names to separate groups of questions. Increased font contrast on the electronic surveys to improve the readability on phones. Institutional graphics artists developed several customized covers for the survey, which were tested with a group of veteran stakeholders.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll electronic surveys had \u0026ldquo;back buttons\u0026rdquo; so respondents could return to previously answered questions.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespondents were able to leave questions blank on both the paper and the electronic surveys, but we did not include a \u0026ldquo;Refuse to answer\u0026rdquo; option.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdded conditional logic to electronic surveys to skip irrelevant questions. On the paper survey, the survey text prompted respondents to skip questions that were not relevant to them.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe final page/screen only contained a thank-you message and two organizational logos.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQUALITY CONTROL AND ADMINISTRATIVE STRATEGIES\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerformed literature reviews for best practices for TDM implementation.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUndeliverable USPS mail or emails triggered stopping the respective protocols. Phone calls were used to attempt to obtain current contact information. Team obtained preferred USPS mailing addresses for incentives through a question on the electronic survey. Team completed three follow up calls for any returned incentives.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenerated unique ID numbers to limit communication of any Protected Health Information and Personally Identifiable Information.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreated a Microsoft Access database for tracking progress through the recruitment protocol, opt-outs, respondents\u0026rsquo; survey completion, and gift card delivery.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMAIL AND EMAIL COMMUNICATION STRATEGIES\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResearch team contacted individuals using USPS mail, email, and telephone methods. Email protocol preceded USPS mail protocol.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMailouts and emails occurred in spaced gaps of 1\u0026ndash;2 weeks.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContacted individuals could respond to the paper surveys via self-addressed envelopes. Contacted individuals could respond to electronic surveys via QR codes, shortened URLs, and links embedded in emails.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTELEPHONIC COMMUNICATION STRATEGIES\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResearch team made three phone attempts, on varying days and times. Discontinued attempts if contacted individual declined participation, hung up, or number was inaccurate. Calls staggered by 2\u0026ndash;3 days and batched by participant\u0026rsquo;s time zone.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUtilized a detailed voicemail script. Generated protocols to handle suicide ideation. Recorded a personalized voicemail message for the study phone number.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTRUST BUILDING STRATEGIES / REDUCING RESPONSE BURDEN AND COSTS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurvey respondents received gift cards upon completing the survey, rather than using a token cash incentive.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHighlighted the importance of the research in contact letters, e.g. \u0026ldquo;This study will help the VA better understand how to improve healthcare for all veterans across the country.\u0026rdquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLetters contained personalized headers, e.g. \u0026ldquo;Dear John Doe,\u0026rdquo; accomplished through mail merge options in Microsoft Word. All letters thanked respondents for their time and assistance. Used an image of the Principal Investigator\u0026rsquo;s signature in blue ink on all communication.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElectronic surveying option provided to remove mailing burden. Paper survey respondents were able to return surveys using pre-paid, self-addressed envelopes.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurvey could not be completely anonymized due to study needs to connect respondents to their health record. Ensured confidentiality of responses in all communication.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA test group (i.e., local Veteran Engagement Counsel) reviewed and provided feedback on the survey. Limited the first wave to 50 contacted individuals to limit the impact of any undetected errors.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublished results from the study and made them publicly available.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was reviewed and approved by University of South Florida IRB and the James A. Haley Veterans’ Hospital Research and Development Committee. It adheres to all federal research ethical guidelines including the Declaration of Helsinki. All participants completed an altered informed consent by reading the informed consent form and either clicking on a link that stated, “I consent to the study” or completing it as part of the submitted paper survey.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe informed consent asked all participants to publish their data in aggregate form. All participants provided altered informed consent prior to data collection.\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSpecific data from this study are not available.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors report no financial conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Merit Review Award Number (IIR 17-051) from the United States (U.S.) Department of Veterans Affairs Health Service R\u0026amp;D (HSR\u0026amp;D). The views, opinions, and/or findings contained in this article are those of the authors and should not be construed as an official Department of Veterans Affairs position or any other federal agency, policy, or decision unless so designated by other official documentation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHari Venkatachalam: Conceptualization, software; data curation; formal analysis; writing – original draft (lead). Shannon Miles: Conceptualization, methodology, writing – original draft, supervision, project administration. Nina Sayer: Conceptualization; writing – original draft; funding acquisition. Heather Belanger: Conceptualization; methodology; funding acquisition; writing – original draft. Stephen Luther: Conceptualization; methodology; writing – original draft; formal analysis; supervision; funding acquisition. \u0026nbsp;Peter A. Toyinbo: Methodology, writing – original draft; formal analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank the Veterans who gave their time and expertise to improve healthcare for future Veterans.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBayley PJ, Kong JY, Helmer DA, Schneiderman A, Roselli LA, Rosse SM, et al. Challenges to be overcome using population-based sampling methods to recruit veterans for a study of post-traumatic stress disorder and traumatic brain injury. BMC Med Res Methodol. 2014 Apr 8;14(48):1\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eHarrington KM, Nguyen XMT, Song RJ, Hannagan K, Quaden R, Gagnon DR, et al. Gender differences in demographic and health characteristics of the Million Veteran Program cohort. Womens Health Issues. 2019 June 25;29(S1):S56\u0026ndash;66. \u003c/li\u003e\n\u003cli\u003eLuoto TM, Tenovuo O, Kataja A, Brander A, \u0026Ouml;hman J, Iverson GL. Who gets recruited in mild Traumatic Brain Injury research? J Neurotrauma. 2012 Dec 21;30(1):11\u0026ndash;6. \u003c/li\u003e\n\u003cli\u003eJang M, Vorderstrasse A. Socioeconomic status and racial or ethnic differences in participation: Web-based survey. JMIR Res Protoc. 2019 Apr 10;8(4):e11865. \u003c/li\u003e\n\u003cli\u003eHoddinott SN, Bass MJ. The dillman total design survey method. Can Fam Physician Med Fam Can. 1986 Nov;32:2366\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eDillman DA, Smyth JD, Christian LM. Internet, phone, mail, and mixed-mode surveys: The tailored design method. 4th ed. Hoboken, New Jersey: Wiley; 2015. \u003c/li\u003e\n\u003cli\u003eMillar MM, Dillman DA. Improving response to web and mixed-mode surveys. Public Opin Q. 2011 May 18;75(2):249\u0026ndash;69. \u003c/li\u003e\n\u003cli\u003eAl Khalaf K, O\u0026rsquo;Dowling Keane S, da Mata C, McGillycuddy CT, Chadwick BL, Lynch CD. Response rates to questionnaire-based studies in the contemporary dental literature: A systematic review. J Dent. 2022 Sept 8;126:104284. \u003c/li\u003e\n\u003cli\u003eDillman DA, Dolsen DE, Machlis GE. Increasing response to personally-delivered mail-back questionnaires. J Off Stat. 1995 June;11(2):129. \u003c/li\u003e\n\u003cli\u003eKazzazi F, Haggie R, Forouhi P, Kazzazi N, Malata CM. Utilizing the Total Design Method in medicine: Maximizing response rates in long, non-incentivized, personal questionnaire postal surveys. Patient Relat Outcome Meas. 2018 June;9:169\u0026ndash;72. \u003c/li\u003e\n\u003cli\u003eMonroe MC, Adams DC. Increasing response rates to web-based surveys. J Ext [Internet]. 2012 Dec 1 [cited 2025 Nov 27];50(6). Available from: https://tigerprints.clemson.edu/joe/vol50/iss6/34/\u003c/li\u003e\n\u003cli\u003eSmyth JD, Dillman DA, Christian LM, O\u0026rsquo;Neill AC. Using the internet to survey small towns and communities: Limitations and possibilities in the early 21st century. Am Behav Sci. 2010 May;53(9):1423\u0026ndash;48. \u003c/li\u003e\n\u003cli\u003eCoughlin SS, Aliaga P, Barth S, Eber S, Maillard J, Mahan CM, et al. The effectiveness of a monetary incentive on response rates in a survey of recent U.S. veterans. Surv Pract [Internet]. 2011 Jan 31 [cited 2025 Nov 27];4(1). Available from: https://surveypractice.scholasticahq.com/article/3059-the-effectiveness-of-a-monetary-incentive-on-response-rates-in-a-survey-of-recent-u-s-veterans\u003c/li\u003e\n\u003cli\u003eCulpepper WJ, Wallin MT, Magder LS, Perencevich E, Royal W, Bradham DD, et al. VHA Multiple Sclerosis Surveillance Registry and its similarities to other contemporary multiple sclerosis cohorts. J Rehabil Res Dev. 2015;52(3):263\u0026ndash;72. \u003c/li\u003e\n\u003cli\u003eDerrick JL, Eliseo-Arras RK, Hanny C, Britton M, Haddad S. Comparison of internet and mailing methods to recruit couples into research on unaided smoking cessation. Addict Behav. 2017 Dec;75:12\u0026ndash;6. \u003c/li\u003e\n\u003cli\u003edeRussy AJ, Jones AL, Austin EL, Gordon AJ, Gelberg L, Gabrielian SE, et al. Insights for conducting large-scale surveys with veterans who have experienced homelessness. J Soc Distress Homelessness. 2021 Dec 28;32(1):123\u0026ndash;34. \u003c/li\u003e\n\u003cli\u003ePflugeisen BM, Mou J. Patient satisfaction with virtual obstetric care. Matern Child Health J. 2017 July;21(7):1544\u0026ndash;51. \u003c/li\u003e\n\u003cli\u003eVogt D, Perkins DF, Copeland LA, Finley EP, Jamieson CS, Booth B, et al. The Veterans Metrics Initiative study of US veterans\u0026rsquo; experiences during their transition from military service. BMJ Open. 2018 June 11;8(6):e020734. \u003c/li\u003e\n\u003cli\u003eLargent EA, Eriksen W, Barg FK, Greysen SR, Halpern SD. Participants\u0026rsquo; perspectives on payment for research participation: A qualitative study. Ethics Hum Res. 2022 Nov;44(6):14\u0026ndash;22. \u003c/li\u003e\n\u003cli\u003eU.S. Department of Health and Human Services. Attachment A - Addressing Ethical Concerns Offers of Payment to Research Participants [Internet]. 2019 [cited 2025 Nov 28]. Available from: https://www.hhs.gov/ohrp/sachrp-committee/recommendations/attachment-a-september-30-2019/index.html\u003c/li\u003e\n\u003cli\u003ePurcell N, Zamora K, Bertenthal D, Abadjian L, Tighe J, Seal KH. How VA Whole Health coaching can impact veterans\u0026rsquo; health and quality of life: A mixed-methods pilot program evaluation. Glob Adv Health Med. 2021 Mar 5;10:2164956121998283. \u003c/li\u003e\n\u003cli\u003eU.S. Department of Veterans Affairs, Office of Research \u0026amp; Development. About the Office of Research \u0026amp; Development [Internet]. 2025 [cited 2025 Nov 28]. Available from: https://www.research.va.gov/about/default.cfm\u003c/li\u003e\n\u003cli\u003eWashington DL, Farmer MM, Mor SS, Canning M, Yano EM. Assessment of the healthcare needs and barriers to VA use experienced by women veterans: Findings from the National Survey of Women Veterans. Med Care. 2015 Apr;53:S23\u0026ndash;31. \u003c/li\u003e\n\u003cli\u003ePietrzak RH, Southwick SM, Meffert BN, Morabito DM, Sawicki DA, Hausman C, et al. US veterans who do and do not utilize Veterans Affairs health care services: Demographic, military, medical, and psychosocial characteristics. Prim Care Companion CNS Disord [Internet]. 2019 Jan 17 [cited 2025 Nov 27];21(1). Available from: https://www.psychiatrist.com/pcc/veterans-who-do-and-do-not-utilize-va-services\u003c/li\u003e\n\u003cli\u003eReturning home from Iraq and Afghanistan: Preliminary assessment of readjustment needs of veterans, service members, and their families [Internet]. Washington, D.C.: National Academies Press; 2010 [cited 2025 Nov 27]. Available from: http://www.nap.edu/catalog/12812\u003c/li\u003e\n\u003cli\u003eMiles SR, Toyinbo PA, Belanger HG, Venkatachalam HH, Luther SL, Sayer NA. Long-term clinical outcomes associated with the Veterans Health Administration\u0026rsquo;s Traumatic Brain Injury and mental health screens. J Head Trauma Rehabil. 2025 Sept;40(5):E349\u0026ndash;59. \u003c/li\u003e\n\u003cli\u003eU.S. Department of Veterans Affairs, Veterans Health Administration. Screening and evaluation of possible Traumatic Brain Injury in Operation Enduring Freedom (OEF) and Operation Iraqi Freedom (OIF) veterans [Internet]. 2010 [cited 2025 Nov 28]. Available from: https://www.va.gov/optometry/docs/vha_directive_2010-012_screening_and_evaluation_of_possible_tbi_in_oef-oif_veterans.pdf\u003c/li\u003e\n\u003cli\u003eMiles SR, Sayer NA, Belanger HG, Venkatachalam HH, Kozel FA, Toyinbo PA, et al. Comparing outcomes of the Veterans Health Administration\u0026rsquo;s Traumatic Brain Injury and Mental Health screening programs: Types and frequency of specialty services used. J Neurotrauma. 2023 Jan 1;40(1\u0026ndash;2):102\u0026ndash;11. \u003c/li\u003e\n\u003cli\u003eU.S. Department of Veterans Affairs. Traumatic brain injury screening and evaluation data. [Internet]. 2024 [cited 2025 July 1]. Available from: https://vaww.vhadataportal.med.va.gov/Data-Sources/Traumatic-Brain-Injury\u003c/li\u003e\n\u003cli\u003eWWAMI Rural Health Research Center [Internet]. 2005 [cited 2025 Nov 28]. Available from: https://depts.washington.edu/uwruca/ruca-data.php\u003c/li\u003e\n\u003cli\u003eDillman DA, Smyth JD, Christian LM. List of guidelines in internet, phone, mail, and mixed-mode surveys: The Tailored Design Method, 4th edition. [Internet]. 2015 [cited 2025 Nov 27]. Available from: https://higheredbcs.wiley.com/legacy/college/dillman/1118456149/pdf/List_of_Guidelines_Internet_Phone_Surveys4e.pdf\u003c/li\u003e\n\u003cli\u003eQualtrics [Internet]. 2025 [cited 2025 Nov 27]. Available from: https://www.qualtrics.com/\u003c/li\u003e\n\u003cli\u003eStreamworks, LLC [Internet]. [cited 2025 Nov 28]. Available from: https://streamworksmn.com/services/compliance-communications\u003c/li\u003e\n\u003cli\u003eChen H, Cohen P, Chen S. How big is a big odds ratio? Interpreting the magnitudes of odds ratios in epidemiological studies. Commun Stat - Simul Comput. 2010 Apr 6;39(4):860\u0026ndash;4. \u003c/li\u003e\n\u003cli\u003eU.S. Department of Veterans Affairs and U.S. Department of Defense. VA/DoD clinical practice guideline for the management of substance use disorders [Internet]. 2021 [cited 2025 Nov 28]. Available from: https://www.healthquality.va.gov/guidelines/MH/sud/VADODSUDCPGProviderSummary.pdf\u003c/li\u003e\n\u003cli\u003eU.S. Department of Veterans Affairs and U.S. Department of Defense. VA/DoD clinical practice guideline for the management of major depressive disorder [Internet]. 2022 [cited 2025 Nov 28]. Available from: https://www.healthquality.va.gov/guidelines/MH/mdd/VADODMDDCPG_ProviderSummary_Final_508_updated.pdf\u003c/li\u003e\n\u003cli\u003eU.S. Department of Veterans Affairs and U.S. Department of Defense. VA/DoD clinical practice guideline: Management of posttraumatic stress disorder and acute stress disorder [Internet]. 2023 [cited 2025 Nov 28]. Available from: https://www.healthquality.va.gov/HEALTHQUALITY/guidelines/MH/ptsd/VA-DOD-CPG-PTSD-Quick-Reference-Guide.pdf\u003c/li\u003e\n\u003cli\u003eU.S. Department of Veterans Affairs. VA priority groups [Internet]. 2024 [cited 2025 Nov 28]. Available from: https://www.va.gov/health-care/eligibility/priority-groups/\u003c/li\u003e\n\u003cli\u003eAlthubaiti A. Information bias in health research: Definition, pitfalls, and adjustment methods. J Multidiscip Healthc. 2016 May 4;9:211\u0026ndash;7. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-research-methodology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmrm","sideBox":"Learn more about [BMC Medical Research Methodology](http://bmcmedresmethodol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmrm/default.aspx","title":"BMC Medical Research Methodology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8234598/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8234598/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSuccessfully recruiting respondents to complete surveys is integral for ensuring representative samples and providing adequate power for analyses that researchers can extrapolate to the population of interest. However, obtaining survey responses for public health and health services research can be challenging, especially when working with populations with varied engagement with the providers of the institution conducting the study. Recruitment is even more challenging when selecting participants based on historical events, which they may not remember, such as a brief health screen. Many researchers implement the Tailored Design Method (TDM) to optimize response rates.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe surveyed a national sample of 9,775 US Iraq and Afghanistan war veterans who had been screened between 2007 and 2018 for post-deployment health concerns in the Veterans Health Administration (VHA). We implemented a modified TDM to recruit respondents through USPS mail and an online data collection platform (i.e., Qualtrics) between July 2020 and August 2021.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eTwenty-one percent (N\u0026thinsp;=\u0026thinsp;2,025) responded. It took a median of 21 and 69 days to receive responses via electronic survey (i.e., Qualtrics) and USPS, respectively. Participants had varied levels of engagement with the VHA at the time of screening and survey. Respondents and non-respondents used equal amounts of neurology, physical medicine and rehabilitation, and mental health VHA services in the 6 months post-screens. At the time of the survey, 20.82% of respondents reported not having recently used VHA services. Respondents were more likely to be older, White, and female than non-respondents, although differences were small in magnitude. Compared to online respondents, paper respondents were more likely to be older, rural, and screened earlier, and less likely to live in the same state as the research team.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003e This manuscript describes TDM implementation techniques and modifications that comply with federal regulations, and factors associated with response rates and contact methods. Researchers in federal institutions seeking to optimize response rates may find our TDM adaptations useful when surveying populations drawn from historical cohorts or without regular interaction with the research institution.\u003c/p\u003e","manuscriptTitle":"Enhancing Recruitment Methodologies: Leveraging the Tailored Design Method to Survey Populations with Varied Engagement in Healthcare","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-22 17:19:49","doi":"10.21203/rs.3.rs-8234598/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-04T06:16:10+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-02T19:22:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-14T22:46:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"46320025247707032480459288996011309040","date":"2026-01-08T15:24:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"88637949447418376289278512207940790539","date":"2025-12-18T16:42:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-18T16:23:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-18T12:58:44+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-16T17:22:35+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-16T16:50:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Research Methodology","date":"2025-12-16T00:17:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-research-methodology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmrm","sideBox":"Learn more about [BMC Medical Research Methodology](http://bmcmedresmethodol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmrm/default.aspx","title":"BMC Medical Research Methodology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"57c129fa-4481-4a08-a4f5-9c2c1e58cd54","owner":[],"postedDate":"December 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-23T20:08:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-22 17:19:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8234598","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8234598","identity":"rs-8234598","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}