Overview of Recruitment Strategies to Reach the Million Milestone and Characterization of the Million Veteran Program Cohort

Overview of Recruitment Strategies to Reach the Million Milestone and Characterization of the Million Veteran Program Cohort | 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 Overview of Recruitment Strategies to Reach the Million Milestone and Characterization of the Million Veteran Program Cohort Stacey B. Whitbourne, April R. Williams, Jessica V. Brewer, Jennifer E. Deen, and 22 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8272722/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The Department of Veterans Affairs (VA) Million Veteran Program (MVP) began in 2011 with the goal of recruiting at least one million Veterans to participate in a large population genetic and health research program. The Million Milestone Campaign (MMC) was implemented between 2022 and 2023 to reach pre-pandemic recruitment rates and the millionth enrollee by Veterans Day of 2023. The objectives are to describe and evaluate the effectiveness of MMC recruitment strategies and to characterize the cumulative MVP cohort. Methods During the MMC, multiple recruitment strategies, including paper invitation mailings, recruitment calls, mass emails, enroll by mail invitations, and digital marketing, were implemented using a “surround sound” model. Strategy effectiveness was assessed via enrollment rates for each enrollment modality from September 2022 through July 2024. Email and digital media reach were measured by click-through rates and ads viewed. Standard mean differences were used to compare the demographics of enrollees during the MMC with those of enrollees prior, and the cumulative MVP cohort with those of non-MVP VA users. Results More than 6.5 million Veterans were contacted and 109,912 enrolled during the MMC. Enrollment rates from recruitment calls (13.7%) were highest, followed by invitations to enroll by mail (7.8%), email (0.8%), and paper invitations (0.6%). Emails yielded the most enrollments (n = 46,999, 43% of total enrollments), followed by recruitment calls (n = 22,069, 20%), paper invitations (n = 16,062, 14.6%), and invitations to enroll by mail (n = 7,762,7.1%). There were small differences in demographics between those enrolled during the MMC and prior enrollees (n = 906,672); likewise, the cumulative cohort (N = 1,016,584) of largely male participants resembles the non-MVP VA user population. Conclusions In the year leading to the culmination of one million MVP participants, the multi-strategy MMC was effective in recruiting nearly 110,000 Veterans and informing ongoing recruitment. Combined strategies used in a surround sound model of recruitment yielded enrollments that aligned with pre-pandemic rates and accumulated one-tenth the largest cohort of Veterans in VA research history. MVP’s integration within the VHA as a learning health care system, along with linkages to health records and other data sources, is a resource for investigators to improve Veteran health care with precision medicine. Veterans Genetics Health Research Population study Background Health care delivery has improved incrementally in recent decades, as initiatives to understand the relationship between genetics and health have amassed various resources for research. Of particular utility are large-scale population cohorts, such as the UK Biobank,( 1 ) the Kaiser Permanente Research Program on Genes, Environment, and Health,( 2 ) the Department of Veterans Affairs (VA) Million Veteran Program (MVP)( 3 ) and the National Institutes of Health All of Us Research Program.( 4 ) The success of these research programs depends in large part on their complex recruitment and enrollment methodologies, continued engagement with participants, and robust infrastructures for collecting, storing, and analyzing specimens and other data as resources for scientific endeavors. In 2006, the VA established a plan to initiate genetic health research for Veterans by developing a data resource that scientists could use to better understand the role of genes, health, lifestyle, and military experiences to provide personalized health care for Veterans. Following focus groups with Veterans to determine support for a genetic research program( 5 ) and pilot work to establish the feasibility of enrolling Veterans into a large-scale genetic and health research program, the VA Office of Research and Development (ORD) began recruitment for MVP in 2011. A sample of at least one million Veterans was estimated to both sufficiently power genetic research studies and be generally representative of the larger VA user population.( 3 ) MVP provides a unique and substantial scientific contribution to the fields of genetics, omics, and public health. As of this writing, the cohort represents approximately 11% of Veterans Health Administration (VHA) users, who consented to provide blood specimens for genetic testing, complete surveys, permit access to health records, and agree to be re-contacted about additional research opportunities. Research using MVP data has led to over 450 scientific publications and has initiated translational projects to improve clinical care in the VA healthcare system.( 6 ) Standard MVP recruitment and enrollment efforts have been described previously.( 3 ) Between 2012 and 2019, the average annual enrollment was nearly 100,000 participants. With the onset of the COVID-19 pandemic in March 2020, MVP temporarily closed sites for in-person recruitment and enrollment activities and transitioned primarily to data collection activities (e.g., surveys) and piloting remote specimen collection. In addition to engaging Veterans through outreach efforts to increase return of the MVP Baseline and Lifestyle Surveys from participants,( 7 ) the MVP COVID-19 Survey was developed and distributed to all eligible MVP participants to gather their COVID-19-specific health experiences.( 8 ) In-person recruitment and enrollment activities restarted at limited capacity at the end of 2020. A campaign was initiated in October 2022 to recruit the millionth Veteran participant by November 11th, 2023 (Veteran’s Day), referred to hereafter as the Million Milestone Campaign (MMC). Approximately 96,000 enrollments were needed within the timeframe to reach the objective, and on November 8th, 2023, MVP successfully reached the historic milestone. The objectives of this work are to: 1) describe the effectiveness of the strategies implemented during the MMC period and 2) characterize the cumulative MVP cohort through July 2024. Methods Ethical and Eligibility Considerations The VA Central Institutional Review Board (IRB) approved the MVP protocol (#10–02) in 2010 with continued oversight and ongoing annual review. At the onset of MVP in 2011, eligibility requirements to participate in MVP included VHA user status (i.e., the Veteran needed to have at least one visit at a VHA medical facility in the 12-month period leading up to enrolling). In January 2019, the VHA user status requirement was removed, thereby allowing any Veteran, regardless of their VHA use, to participate. A Veteran is defined as someone who has served in the active military, naval, or air service and was discharged under conditions other than dishonorable. The exclusion criteria included being incarcerated, and beingcognitively unable to provide consent. Compensation is not provided for participation. MMC Recruitment and Enrollment Infrastructure With the goal of reaching the millionth participant by Veterans Day 2023, MVP developed the MMC using a variety of existing and novel strategies that ran between September 08, 2022, and November 8, 2023. Recruitment and enrollment activities were centrally managed by the MVP Boston, MA and Palo Alto, CA Coordinating Centers. The ORD MVP Program Office in Washington, DC, managed social media and advertising campaigns as well as general program oversight. The MVP Info Center located in Canandaigua, NY, answered incoming calls, conducted outbound calls, and assisted with administrative activities. The MVP local sites, located at VA medical facilities across the US, conducted standard recruitment and enrollment activities as part of the MMC. MVP Online, the online platform launched in 2019, provided Veterans with the options to enroll online, schedule in-person visits to complete enrollment in-person and/or provide a blood specimen, complete available surveys, request an at-home blood specimen kit, and follow their enrollment progress. Recruitment Strategies to Reach the MMC The MMC employed a surround sound model, commonly used in marketing,(9) where a combination of messages across various formats interact synergistically to achieve the desired outcome. The MMC strategies included the following: 1) invitation mailings; 2) email campaigns; 3) recruitment calls; 4) invitations to enroll by mail; and 5) digital marketing. Veterans were able to enroll in-person, online, or by mail. Paper Invitation Mailings. Since MVP’s inception, paper invitation mailings have been the primary strategy for recruitment and enrollment. Ongoing refinements to the mailing methodology have included modifications to the criteria for participant selection on the basis of VHA utilization frequency or distance to an MVP local site, the timing of mailings, the number of mailings, along with design enhancements and modifications. Standard MVP recruitment consists of up to three paper invitation mailings distributed to eligible Veterans. As part of the MMC, the volumes for those mailings were increased along with a fourth invitation sent to Veterans not yet enrolled. All four invitation mailings were distributed to Veterans (N=2,848,310) on the basis of their previous invitation status and time since the last invitation. Recruitment Calls . Recruitment calls were made to 161,558 Veterans who had previous contact with MVP and upcoming VA visits. As part of the MMC, the rate of standard outbound recruitment calls made to Veterans increased by approximately 12% compared with the previous year. Enroll by Mail Invitation . A random sample of Veterans (N=100,000) who had shown interest in MVP but had not yet enrolled were sent an invitation to enroll by mail. The packet included a cover letter with instructions to review the enclosed MVP enrollment and consent documents, along with a postage-paid return envelope. The completed and returned documents were then reviewed to verify enrollment validity. Email. During the MMC period, MVP sent out seven promotional emails. A total of 20,773,244 emails were sent to 5,532,594 Veterans who had valid email addresses available in their VA health records. The emails included general information about MVP, instructions on how to learn more about enrolling in MVP, and links to the MVP online platform. Key improvements made to the email distribution system before the MMC allowed us to contact more than 3 million Veterans within a 24-hour period. Digital Marketing . As part of the MMC, MVP launched a digital marketing strategy. Materials consisted of photos, videos, and calls to action directed at Veterans to learn more about MVP and how to enroll. A variety of digital platforms were used, including Google and Bing search advertisements, Facebook, Instagram, and Google Video and graphic advertisements that appear on websites, web applications, and social media, known as display ads. Analyses Effectiveness was measured for each strategy except the digital marketing using the enrollment rate, which is the number of enrollees divided by the number of unique Veterans contacted through each strategy. Effectiveness was assessed for each enrollment modality and overall enrollment for the MMC recruitment strategies. Enrollment was attributed to the final recruitment strategy contact event before enrollment and presented as a proportion of the total enrollments up to July 2024. MVP participants who enrolled but did not receive mailings, emails, recruitment calls, or enroll by mail invitations during the MMC may have learned about MVP at VA facilities, offsite MVP events, or engaged with MVP digital marketing campaigns. These enrollees were attributed to an “Other” strategy, and an enrollment rate is not reported because the number of unique Veterans contacted is unknown. Measures of reach for the digital marketing strategy were reported as impressions (number of ads seen by the target audience) and click-through rate (CTR; the number of clicks divided by the total number of emails opened). Descriptive statistics (n, %, mean, SD) were used to describe the demographic characteristics of the MVP enrollees prior to the MMC and those who enrolled during the MMC period. Comparisons were made to describe any significant differences between the enrollees before and after the MMC using standard mean differences (SMD; small: 0.2-0.5, medium or moderate: 0.5-0.8, large: >0.8). (10) SMDs were selected because, owing to the large sample sizes, p -values from traditional t -tests for group differences are less meaningful.(11) Descriptive statistics (n, %, mean, SD) were used to describe the demographics and health status outcomes of the cumulative MVP cohort by sex (biological sex: female, male). Comparisons of demographics were made to identify any differences between the MVP cohort and non-MVP VHA users via SMDs. Analyses were conducted using the MVP Roster version 24.1 which includes Veterans enrolled through July 31, 2024. The MVP Roster is a research-ready dataset curated to reflect active MVP participants and includes self-report survey response data. Demographic data are matched and validated using the VA Corporate Data Warehouse (CDW)(12) and the Observational Medical Outcomes Partnership (OMOP).(13) All descriptive statistics were calculated using SAS Enterprise Guide 8.3 (SAS Institute Inc., Cary, NC, USA). Results Table 1 provides an overview of the effectiveness (enrollment rates) of the recruitment strategies used during the 2022–2023 MMC period. In total, 6,584,999 unique Veterans were contacted, resulting in an enrollment of 109,912 participants (1.7% enrollment rate). Among the enrollees, 60.7% enrolled in-person (n = 66,748), 32.6% enrolled online (n = 35,832), and 6.7% enrolled by mail (n = 7,332). Paper invitation mailings contributed 14.6% to the total enrollment, recruitment calls contributed 20.1%, email campaigns accounted for 42.8%, invitations to enroll by mail contributed 7.1%, and other sources accounted for the remaining 15.4% of the total MVP enrollment during the MMC period. Among MVP participants who received a paper invitation mailing as their last recruitment contact, 16,062 enrolled, resulting in a 0.6% enrollment rate. Approximately two-thirds of these enrollments (n = 10,673) occurred in-person, about one-third (n = 5,381) were online, and fewer than 10 occurred by mail. Recruitment calls (n = 161,558) to Veterans resulted in 22,069 enrollments (3.7% enrollment rate), with the majority enrolling in-person (n = 21,503). Additionally, the invitation to enroll by mail was sent to 100,000 unique Veterans, resulting in 7,762 enrollments, yielding a 7.8% enrollment rate. Table 1 Effectiveness of the Million Milestone Campaign (MMC) Recruitment Strategies as Enrollment Rates by Enrollment Modality Enrollment Count (n) and Rates (%) by Enrollment Modality 1 MMC Recruitment Strategy Unique Veterans Contacted 2 In-Person Online By Mail Overall Enrollment Rates (%) 1 MVP Enrollment n (%) 3 Paper Invitation Mailings 4 2,848,310 10,673 (0.4) 5,381 (0.2) < 10 (< 0.0) 0.6 16,062 (14.6) Recruitment Calls 5 161,558 21,503 (13.3) 444 (0.3) 122 (0.1) 13.7 22,069 (20.1) Invitation to Enroll by Mail 6 100,000 225 (0.2) 349 (0.3) 7,188 (7.2) 7.8 7,762 (7.1) Email Campaigns 7 5,532,594 18,870 (0.3) 28,115 (0.5) 14 (0.0) 0.8 46,999 (42.8) Other 8 - 15,477 (-) 1,543 (-) 0 (-) - 17,020 (15.5) Total Unique Veterans Contacted 6,584,999 Total (% of MVP Enrollments) 66,748 (60.7) 35,832 (32.6) 7,332 (6.7) 109,912 1. Enrollment Rate (%): The percentage is calculated as the number of participants who enrolled having received their latest contact via the strategy divided by the number of unique Veterans contacted using the strategy. 2. N = Total number of Veterans who received contact with a given recruitment strategy between September 8, 2022, and November 8, 2023. Strategies are not mutually exclusive 3. %: Proportion of total MVP enrollment between September 8, 2022, and July 31, 2024, attributed to the recruitment strategy. 4. Paper Invitation Mailings: Living Veterans were eligible for a paper invitation if they had an address in the VA system, received care at or lived within 75 miles of an MVP/VA facility, and had not opted out of contact. Veterans are sent paper invitations up to 4 times before they are excluded from additional mailings. 5. Recruitment Calls: Any living Veteran who had at least one paper invitation mailed to them and who had not already enrolled nor opted out of contact. 6. Invitation to Enroll by Mail: Veterans were mailed a packet with enrollment information and consent documents. 7. Email Campaigns: Any living Veteran who was not enrolled, not opted out of contact, and had a working email address 8. Other: Veterans who enrolled via any modality with no other contact from MVP via mailings, email, recruitment calls, or an enroll by mail invitation. These Veterans may have learned about MVP from MVP staff at VA facilities, at off-site MVP events, or engaged with MVP’s digital media campaigns (including emails not directly sent from MVP, newsletters and blogs) across federal and non-governmental affinity organizations. Across the seven promotional emails (n = 20,773,244) to 5,532,594 Veterans, 44.0% (n = 9,132,353) were opened, and the CTR was 21.6% (n = 1,971,390). The email campaigns resulted in a total of 46,999 enrollments (0.8% enrollment rate); among these, 40.1% (n = 18,870) enrolled in-person, 59.8% (n = 28,115) enrolled online, and a few (n = 14) enrolled by mail. Among the remaining enrollments (n = 17,020, 15.6%) that were attributed to 'Other' strategies as their last contact, the majority (n = 15,477; 91%) occurred in-person at local sites. Across all the digital marketing strategies employed, there were a total of 33,984,656 impressions. Facebook/Instagram (n = 8,384,569), Google Video (n = 14,128,699), and display ads (n = 11,076,619) accounted for 98.8% of the ads viewed. These impressions resulted in 160,226 clicks to MVP Online, yielding an overall CTR of 0.47%. The CTRs varied across the three most used platforms: Facebook/Instagram ads had a 1.1% CTR (n = 94,588), Google Video ads had a 0.11% CTR (n = 15,678), and display ads had a 0.27% CTR (n = 30,005). Table 2 provides a comparison of MVP participants enrolled before and after the MMC. Among the 109,912 enrollees during the MMC, most were male (n = 94,885, 86.5%) and 60.1% were born between 1945 and 1970. Nearly three-quarters were white (n = 78,621, 71.5%), 13.5% (n = 14,501) were Black, 1.3% (n = 1,386) were Asian, 1.3% (n = 1,443) were American Indian/Alaska Native/Native Hawaiian/Other Pacific Islander, 3.3% (n = 3,577) were multiple races, and 8.3% (n = 8,408) were of Latino/Hispanic ethnicity. Most (n = 63,695, 68.0%) served in the 1990 or later service eras. Compared with the cohort of MVP enrollees prior to the MMC (n = 906,672), those enrolled during the MMC period had similar distributions by sex (SMD: 0.10) and ethnicity (SMD: 0.0), and small differences in the age group born before 1930 (SMD: -0.22), race (SMD range: -0.12, 0.27), and service era (SMD range: -0.24, 0.37). Table 3 provides an overview of MVP demographic characteristics and Veterans’ military service era for the total cohort (N = 1,016,584) and is stratified by male (n = 909,486; 89.5%) and female Veterans (n = 106,707; 10.5%). The demographic characteristics of the total VHA population (N = 10,984,668) are also reported for reference. Comparisons using SMDs between the MVP cohort with available VHA data (n = 985,500) and the non-MVP VHA population (n = 9,999,168) are presented. The VHA population included 93.1% male and 6.9% female Veterans, and there was a small difference (SMD = 0.12) between the MVP and non-MVP VHA distributions by sex. Table 2 Demographic and Military Service for MMC MVP Enrollees (N = 109,912) Compared with Prior MVP Enrollees (N = 906,672) Total MMC Enrollees 1 (n = 109,912) MVP Enrollees Prior to MMC 2 (n = 906,672) MMC Enrollees Compared with Pre-MMC Enrollees n (%) n ( %) SMD 3 Age at Enrollment (years), mean ± SD 61.0 (15.1) 61.3 (14.4) -0.01 Age at 2018 (Median across 2011–2024), mean ± SD 56.5 (15.1) 63.9 (14.9) -0.49 Sex, % Male 94,885 (86.5%) 814,601 (89.9%) Female 14,832 (13.5%) 91,875 (10.1%) 0.10 Birth Year > 1990 4,711 (4.3%) 12,942 (1.4%) 0.17 1985- 4,993 (4.5%) 27,070 (3.0%) 0.08 1980- 7,098 (6.5%) 35,655 (3.9%) 0.11 1975- 6,705 (6.1%) 30,713 (3.4%) 0.13 1970- 8,562 (7.8%) 39,512 (4.4%) 0.14 1965- 11,007 (10.0%) 54,346 (6.0%) 0.15 1960- 12,554 (11.4%) 74,692(8.2%) 0.11 1955- 12,740 (11.6%) 98,971 (10.9%) 0.02 1950- 11,972 (10.9%) 122,040 (13.5%) -0.08 1945- 17,805 (16.2%) 200,473 (22.1%) -0.15 1940- 7,515 (6.8%) 92,192 (10.2%) -0.12 1930- 3,922 (3.6%) 89,002 (9.8%) -0.25 < 1930 328 (0.3%) 29,064 (3.2%) -0.22 Hispanic/Latino Yes 8,408 (8.3%) 74,939 (8.4%) No 93,417 (91.7%) 818,837 (91.6%) 0.00 Races White 78,621 (71.5%) 657,454 (72.5%) -0.02 Black 14,501 (13.3) 157,766 (17.4%) -0.12 Asian 1,386 (1.3%) 10,049 (1.1%) 0.01 American Indian/Alaska Native 702 (0.6%) 4,933 (0.5%) 0.01 Native Hawaiian/Other Pacific Islander 741 (0.7%) 4,568(0.5%) 0.02 Multiple 3,577 (3.3%) 44,539 (4.9%) 0.02 Service Era September 2001 or later 21,981 (23.5%) 122402 (14.1%) 0.24 August 1990 to August 2001 41,714 (44.5%) 240591 (27.7%) 0.36 May 1975 to July 1990 24,837 (26.5%) 207912 (23.9%) 0.06 August 1964 to April 1975 34,181 (36.5%) 414555 (47.7%) -0.23 February 1955 to July 1964 3,722 (4.0%) 79853 (9.2%) -0.21 July 1950 to January 1955 1,198 (1.3%) 49743 (5.7%) -0.24 January 1947 to June 1950 103 (0.1%) 6340 (0.7%) -0.10 December 1941 to December 1946 181 (0.2%) 20030 (2.3%) -0.19 November 1941 or earlier 24 (0.03%) 726 (0.08%) -0.02 Missing 16,210 (14.8%) 38,138 (4.2%) 0.37 Multi-Service 26,685 (24.3%) 202,488 (22.3%) 0.05 1. Total MVP participants enrolled N = 109,912 during the Million Milestone Campaign (MMC): September 8, 2022, through July 31, 2024. 2. Total MVP participants enrolled N = 906,672 prior to the MMC: January 1, 2011, through September 7, 2022 3. SMD: Standard Mean Difference Table 3 Demographic and Military Service Era for MVP Participants (N = 1,016,584) and Total VHA Users (N = 10,984,668) Male N = 909,486 (89.5%) Female N = 106,707 (10.5%) Total MVP 1 N = 1,016,584 Total VHA 2 N = 10,984,668 MVP Compared with Non-MVP VHA Users SMD 3 n (%) n (%) n (%) % 4 Age at Enrollment (years), mean ± SD 62.5 (14.1) 50.7 (13.5) 61.3 (14.5) Age at 2018 (Median across 2011–2024), mean ± SD 64.4 (14.6) 51.7 (13.9) 63.1 (15.1) 64.4 (18.8) -0.07 Sex, % Male 909,486 (100) 0 90,9486 (89.5) 93.1 Female 0 106,707 (100) 106,707 (10.5) 6.9 0.12 Birth Year > 1990 13,025 (1.4) 4,580 (4.3) 17,653 (1.7) 3.3 -0.13 1985- 24,709 (2.7) 7,333 (6.9) 32,063 (3.2) 4.7 -0.09 1980- 32,110 (3.5) 10,609 (9.9) 42,753 (4.2) 5.1 -0.05 1975- 27,869 (3.1) 9,524 (8.9) 37,418 (3.7) 4.0 -0.02 1970- 37,365 (4.1) 10,675 (10.0) 48,074 (4.7) 4.7 0.00 1965- 53,039 (5.8) 12,278 (11.5) 65,353 (6.4) 5.6 0.04 1960- 72,003 (7.9) 15,192 (14.2) 87,246 (8.6) 6.7 0.08 1955- 96,334 (10.6) 15,328 (14.4) 111,711 (11.0) 8.0 0.12 1950- 123,044 (13.5) 10,933 (10.3) 134,012 (13.2) 9.7 0.13 1945- 212,706 (23.4) 5,549 (5.2) 218,278 (21.5) 16.4 0.15 1940- 97,339 (10.7) 2,350 (2.2) 99,707 (9.8) 8.7 0.04 1930- 91,226 (10.0) 1,685 (1.6) 92,924 (9.1) 13.5 -0.15 < 1930 28,717 (3.2) 671 (0.6) 29,392 (2.9) 9.8 -0.30 Hispanic/Latino Yes 73,431 (8.2) 9,915 (9.6) 83,347 (8.4) 6.1 No 818,349 (91.8) 93,904 (90.4) 912,254 (91.6) 93.9 0.03 Races White 670,513 (76.4) 65,561 (64.5) 736,075 (75.2) 79.7 -0.07 Black 146,283 (16.7) 25,984 (25.6) 172,267 (17.6) 16.7 0.07 Asian 4,776 (0.5) 859 (0.8) 5,635 (0.6) 0.8 -0.01 American Indian/Alaska Native 9,984 (1.1) 1,451 (1.4) 11,435 (1.2) 1.1 0.00 Native Hawaiian/Other Pacific Islander 4,608 (0.5) 701 (0.7) 5,309 (0.5) 0.9 0.00 Multiple 41,103 (4.7) 7,012 (6.9) 48,116 (4.9) 0.8 0.01 Service Era September 2001 or later 115,307 (13.3) 28,788 (29.7) 144,096 (15.0) 11.3 -0.02 August 1990 to August 2001 225,264 (26.0) 55,801 (57.6) 281,066 (29.2) 30.4 0.03 May 1975 to July 1990 198,520 (22.9) 33,145 (34.2) 231,666 (24.1) 9.7 0.09 August 1964 to April 1975 430,989 (49.8) 13,515 (14.0) 444,507 (46.2) 40.9 0.17 February 1955 to July 1964 81,542 (9.4) 1,749 (1.8) 83,291 (8.7) 2.4 -0.10 July 1950 to January 1955 49,342 (5.7) 823 (0.9) 50,165 (5.2) 8.3 -0.20 January 1947 to June 1950 6,360 (0.7) 65 (0.1) 6,425 (0.7) 0.2 -0.05 December 1941 to December 1946 6,360 (0.7) 462 (0.5) 19,802 (2.1) 7.8 -0.29 November 1941 or earlier 731 (0.1) 19 (0.0) 750 (0.1) 0.0 -0.02 Missing 43,993 (4.8) 9,968 (9.3) 54,348 (5.4) 0.3 -0.05 Multi-Service 198,415 (21.8) 30,757 (28.8) 229,173 (22.5) 11.3 -0.02 1. Total MVP participants N = 1,016,584 as of July 2024 2. Total VHA Users N = 10,984,668 as of July 2024 with VHA health records 3. MVP participants with VHA data (n = 985,500) compared with Non-MVP VHA Users (n = 9,999,168) using SMD (Standard Mean Difference) 4. VHA population presented as percent only The mean age at the time of enrollment was 61.3 years (SD = 14.5), with females being younger at an average age of 50.7 years (SD = 13.5) compared to males at 62.5 years (SD = 14.1). In the MVP cohort, most males (58.2%) were in the 5-year birth cohorts between 1940 and 1960, whereas more than half of the females (50.4%) were in the 1950 to 1970 birth cohorts. In contrast, the total VHA user population had a concentration of births within the 1930 to 1955 range (48.3%). The largest birth year group difference between MVP participants and non-MVP VHA users was among those born before 1930 (SMD = -0.30). Hispanic representation among MVP participants was 8.4% of the total cohort (8.2% of males and 9.6% of females), compared to 6.1% in the total VHA population (SMD = 0.03). Among male MVP participants, 76.4% were White, 16.7% were Black, and 4.7% reported multiple races. For female MVP participants, 64.5% were White, 25.6% were Black, and 6.9% indicated multiple races. The total VHA users comprised 79.7% White, 16.7% Black, and 0.8% multiple races. Overall, racial group representation in the MVP cohort compared with non-MVP VHA users was similar (SMD range: -0.07 to 0.01). Nearly half of the male MVP participants (49.8%) served in the U.S. military between August 1964 and April 1975, whereas more than half of the female MVP participants (57.6%) served between August 1990 and August 2001. There were minor differences between the MVP and non-MVP VHA user groups regarding service era (SMD range: -0.29 to 0.17). Table 4 provides a health profile of the MVP cohort by sex, including various health characteristics and VHA health care utilization patterns. General health rated as good or better was reported by 61.9% of males, 66.2% of females, and 61.9% overall. The Patient Health Questionnaire-2 (PHQ-2) identified 11.7% of MVP participants as screening positive for depression, with lower endorsement by males (11.1%, n = 46,295) than females (17.6%,n = 7,203). Less than a third of males (32%, n = 290,669) and a small proportion of females (15.4%, n = 16,476) had three or more comorbidities, as calculated using the weighted Charlson Comorbidity Index. Overall and for both males and females, the mean BMI (kg/m 2 ) was near or above the obese range (29.8 [SD = 5.8], 29.8 [SD = 5.7], and 30.2 [SD = 6.6], respectively). Table 4 MVP Health Profile and VHA Care Utilization Male n = 909,486 (89.5%) n (%) Female n = 106,707 (10.5%) n (%) Total MVP N = 1,016,584 N (%) General Health 1 Excellent 26,547 (5.0) 2,880 (5.3) 29,427 (5.0) Very Good 108,707 (20.4) 12,283 (22.4) 120,991 (20.5) Good 194,715 (36.5) 21,062 (38.5) 215,779 (36.6) Fair 155,549 (29.1) 14,928 (27.3) 170,477 (28.9) Poor 48,748 (9.1) 3,601 (6.6) 52,350 (8.9) Depression 2 46,295 (11.1) 7,203 (17.6) 53,501 (11.7) Charlson Comorbidity Index 3 0 58,094 (54.4) 347,720 (38.2) 406,122 (40) 1 or 2 32,148 (30.1) 271,097 (29.8) 303,305 (29.8) >=3 16,465 (15.4) 290,669 (32.0) 307,157 (30.2) BMI 4 (kg/m 2 ), mean ± SD 29.8 (5.7) 30.2 (6.6) 29.8 (5.8) Dietary DASH score 5 mean ± SD 23.6 (5.2) 25.2 (5.2) 23.8 (5.2) Physical Activity 6 (MET hour/week), mean ± SD 4.5 (5.4) 5.0 (5.7) 4.5 (5.4) Smoking Status 7 Never 217,933 (26.3) 21,568 (22.7) 239,521 (25.9) Former 390,079 (47.0) 31,686 (33.3) 421,835 (45.6) Current 221,922 (26.7) 41,897 (44.0) 263,841 (28.5) Drinking Status 8 Never 189,995 (54.2) 16,911 (55.9) 206,909 (54.4) Former 134,244 (38.3) 10,058 (33.3) 144,303 (37.9) Current 26,158 (7.5) 3,259 (10.8) 29,417 (7.7) Percent of Care at VA None 61,603 (11.6) 5,890 (10.8) 67,494 (11.5) 1–25% 103,636 (19.5) 8,234 ( 15 ) 111,871 (19.1) 26–50% 38,047 (7.2) 3,353 (6.1) 41,400 (7.1) 51–75% 35,773 (6.7) 3,628 (6.6) 39,402 (6.7) 76–99% 88,741 (16.7) 11,043 (20.2) 99,785 (17.1) 100% 202,687 (38.2) 22,626 (41.3) 225,313 (38.5) 1. General Health was assessed using a single item “VR12 Q1- In general, would you say your health is:” with response options from the Veterans RAND 12 item Health Survey (VR-12). N missing = 427,560. 2. PHQ_2: Patient Health Questionnaire (PHQ-2): 2-item measure to assess a patient’s mental health. A score greater than 3 indicated depression. 3. Charlson Comorbidity Index considers the number of and seriousness of comorbid conditions or diseases. 4. BMI: n missing = 14,693. 5. Dietary DASH Score: Range 7–35; higher score indicates a diet agrees more with the DASH diet guidelines. 6. MET: metabolic equivalent. The American Heart Association guidelines suggest 150 minutes or 8.3 MET hours/week as an optimal physical activity level. 24 7. Smoking Status: n missing = 91,387. 8. Drinking Status: n missing = 635,955. Differences in health behaviors between male and female Veterans were observed. Adherence to a preventive Dietary Approaches to Stop Hypertension (DASH) diet, with a maximum score of 35, was 23.8 (SD = 5.2) overall; 23.6 (SD = 5.2) for males and 25.2 (SD = 5.2) for females. Females in the cohort reported more physical activity per week (5.0 MET hours/week, SD = 5.7) compared with males (4.5 MET hours/week, SD = 5.4). A higher proportion of females were current smokers (n = 41,897, 44%) compared with males (n = 221,922, 26.7%) and current drinkers (n = 3,259, 10.8% vs. n = 26,158, 7.5%). Overall, more than a third (n = 225,313, 38.5%) of the MVP cohort relied on the VA for 100% of their care, with a higher proportion of female Veterans utilizing VA care (n = 22,626, 41.3%) than males (n = 202,687, 38.2%). Discussion The present work summarizes the recruitment strategies and enrollment outcomes of the MMC to achieve the million milestone for MVP, as well as the characteristics of the overall cohort as of July 2024. Reaching the million milestone is a significant and historic marker for scientific research, particularly in genetics and health. As of July 2024, the cohort includes a large and diverse sample of Veterans (n = 1,016,584) enrolled in MVP, representing almost one-tenth of the entire VHA population, with minor demographic differences compared with non-MVP VHA users. During the 2022–2023 MMC period, approximately 6.5 million VHA Veterans were contacted, resulting in nearly 110,000 enrollments and a 1.7% overall MVP enrollment rate. The variety of strategies implemented during the MMC demonstrates the effectiveness of a surround sound model that used a combination of approaches to achieve an outcome and helped overcome the recruitment barriers experienced following the COVID-19 pandemic. Paper invitation mailings accounted for approximately 15% of enrollments during the MMC. Importantly many Veterans who received paper invitations also received recruitment calls. Although recruitment calls are time- and resource-intensive, making contacting every Veteran who received a paper invitation impractical, they had the highest effectiveness rate (14%) among all strategies used in the MMC. Taken together, the combination of paper invitations and recruitment calls accounted for 35% of enrollments during the MMC. During the 2022–2023 MMC period, MVP used mass email invitations as a standard recruitment strategy for the first time, which proved to be a powerful recruitment tool, accounting for 42.8% of enrollments. Prior to the MMC, online enrollments made up approximately 20% of annual enrollees. This proportion increased to about one-third of enrollments during the MMC period, likely due to the multiple mass email invitations distributed because of technological enhancements realized during that timeframe. Although the enrollment rates from email campaigns alone were lower than those from the combined strategy of paper invitations and recruitment calls, the low effort required to send emails to a large audience with email addresses and the rapid response time made emails an effective tool. MVP operational reports show that approximately 95% of online enrollments occurred within one or two days of a mass email invitation, highlighting the importance of this strategy for efficient enrollments. Invitations to enroll by mail accounted for just over 7% of overall enrollments, with a high enrollment rate within this strategy (about 8%). Enrolling by mail is an important recruitment method, particularly for reaching populations who do not have internet access, prefer not to use an online process, or are unable to enroll in person owing to geographic constraints or limited transportation options. This strategy has been utilized in other population health studies( 14 ) with completion rates and costs similar to those of in-person enrollment and collection. Other strategies accounted for more than 15% of enrollments, with most (approximately 91%) of these enrollments occurring in person. Although it is not possible to discern the exact source that motivated these Veterans to participate, we assume that they likely learned about the program from MVP at a local site or through engagement with digital media because they did not receive contact via the other recruitment strategies during the MMC timeframe. The digital marketing strategy effectively promoted MVP by leveraging advertising across digital domains. An integrative literature review on the use of social media for participant recruitment in research revealed that just over half (51%) of the studies examined employed social media alongside other recruitment methods.( 15 ) Research indicates that social media has the advantage of reaching specific populations, such as individuals with certain medical conditions, those with low incomes, and racial and ethnic groups that are historically underrepresented in research.( 16 , 17 ) Digital recruitment strategies for the MMC including mass emails, social media, and online advertising campaigns, enabled engagement with Veterans who may not frequently use VHA services, or who live in rural areas, and do not typically respond to printed mailings, or generally may not participate in research. Using novel and existing recruitment methods for the MMC extended reach beyond standard MVP mailing and in-person recruitment efforts. A systematic review and meta-analysis of recruitment strategies in large population biobank cohort studies( 18 ) found that participation rates were not significantly tied to a particular recruitment strategy. The MMC strategies described here demonstrated the success of the surround sound model in enrolling close to 110,000 Veterans to reach the million milestone. These participants included Veterans who were not previously enrolled through MVP’s traditional recruitment efforts at local sites, as well as those who may have been hesitant to volunteer or had limited resources to engage in the research program. In the future, MVP aims to expand as broadly as possible to provide enrollment opportunities for all Veterans, such as through continued mass emails or remote enrollment locations, regardless of their geographic location or access to technology. This work represents the first characterization of the MVP cohort with more than a million participants, including comparisons between those who enrolled before the MMC and those who enrolled during the MMC, and between the cumulative MVP cohort and the non-MVP VHA user population. Overall, only minor differences were identified in the pre-MMC and MMC enrollee comparisons and between the cumulative MVP cohort and non-MVP VHA users in terms of sex, birth year, race, ethnicity, and service era. Most participants in the MVP cohort were male. However, the female participants make up the largest sample of any VA research study to date (approximately 107,000), which is significant given that female Veterans constitute the fastest-growing population of VHA users.( 19 ) The female MVP participants were slightly younger and represented more diverse racial and ethnic groups. Additionally, more than 425,000 MVP participants reported military service 1990 and later, which comprises the fastest growing service eras.( 20 ) The data resources developed from MVP offer opportunities for future population-level research to focus on a variety of understudied Veteran experiences, such as toxic exposures, sex-specific health needs, and rurality. Among the MVP cohort, most survey respondents reported good or better health, which aligns with findings from a 2024 report by the Survey of Enrollees indicating that 62% of Veterans using the VA healthcare system reported good, very good, or excellent health.( 21 ) However, health records revealed that most MVP participants in the cohort had at least one comorbidity and a mean BMI in the overweight range. Self-report survey responses revealed that female participants are more physically active and follow a diet more closely aligned with the heart-healthy DASH dietary pattern. Paradoxically, a greater proportion of female MVP participants reported being current smokers and drinkers. This discrepancy could be due to the older average age of male Veterans compared to female Veterans. Importantly, nearly 55% of the MVP cohort utilize VHA for most or all of their healthcare needs. This closely matches the Survey of Enrollees, which reported that 46% of more than 8 million Veterans either had no healthcare needs outside the VA or did not use non-VA healthcare services.( 21 ) Understanding the health profile of the MVP sample has significant implications for the future development of VHA resources and policies. There are important limitations to consider when interpreting the effectiveness of the recruitment strategies and the overall cohort description. For example, the recruitment efforts during the MMC were not mutually exclusive, since Veterans could have been contacted through more than one strategy before enrolling. Attributing enrollment to the recruitment strategy associated with the last recorded contact with a Veteran may be imprecise due to the concurrent use of multiple recruitment strategies. As noted, enrollment rates from other strategies cannot be calculated since the denominator of unique Veterans contacted was unknown. Despite these limitations, the surround sound method of broadcasting MVP through multiple channels may have influenced the decision to participate. While this work does not account for the number and combinations of contact strategies used, future efforts will assess their effects on overall MVP enrollment. Although MVP participants are generally comparable to the broader VHA population, MVP uses a convenience sampling approach. Ongoing work aims to better understand how representative the MVP sample is of the overall VHA population across various health domains. MVP's integration within the VHA as a learning healthcare system, along with its linkages to health records and other data sources, facilitates investigations that can lead to advancements specific to Veteran health, directly translating into improvements in clinical care. Additionally, ongoing pilot work is exploring the feasibility and capability of returning clinically actionable genetic findings to MVP participants.( 22 , 23 ) Conclusions The MMC successfully reached its target of one million MVP participants by employing a variety of recruitment strategies. Ongoing MVP recruitment efforts aim to ensure representation of Veteran subpopulations to support research that addresses the evolving healthcare needs of all Veterans. The substantial number of MVP participants and the resulting curated health and genetic data provide a novel and historic resource, significantly benefiting scientific research and supporting clinical care improvements within the VHA healthcare system.( 6 ) Continued investigations using MVP data can lead to discoveries about Veteran-specific health experiences, including mental health conditions, toxic exposures, and general population health concerns related to aging, cardiometabolic diseases, and cancer. Owing to the generous participation and ongoing engagement of Veterans in MVP, researchers throughout the VA and approved academic collaborators continue to produce a large body of research using the MVP data repository. Abbreviations DASH: Dietary Approaches to Stop Hypertension MMC : Million Milestone Campaign MVP: Million Veteran Program ORD: Office of Research and Development VA : Department of Veterans Affairs VHA : Veterans Health Administration Declarations Ethics approval and consent to participate This research was conducted according to the guidelines of the Declaration of Helsinki and approved by the Department of Veteran Affairs Central IRB (protocol code MVP001, approved in 2010). The MVP protocol is approved by the Department of Veterans Affairs VA Central Institutional Review Board (#10–02). All participants provide consent to participate in MVP. Consent for publication MVP participants consent to their information being used for scientific publications and that they will not be individually identified. Availability of data and materials As described in detail in Gaziano’s MVP introduction paper(3) and MVP website [https://mvp. va.gov], access to MVP data and/or samples is governed by the scope of MVP informed consent and VA policies and requires scientific review by appropriate VA review committees. Data are currently available to VA investigators and other approved partners with plans for expanding to non-VA investigators in the future. Enquiries can be directed to the MVP Program Office [ [email protected] ]. For accessing the resource, a consortium approach is strongly encouraged; collaborators from university affiliates and other organizations working with VA investigators are encouraged [https://www.research.va.gov/MVP/research.cfm]. Competing interests The authors declare that they have no competing interests. Funding This research is based on data from the VA Million Veteran Program supported by award MVP#000 from the Department of Veterans Affairs. This publication does not represent the views of the Department of Veterans Affairs or the U.S. government. The authors have no financial disclosures. Authors' contributions SBW: Conceptualization, Methodology, Investigation, Resources, Writing - Original Draft, Writing - Review & Editing, Supervision ARW: Writing - Original Draft, Writing - Review & Editing, Project Administration JVB: Conceptualization, Methodology, Writing - Original Draft, Writing - Review & Editing JED: Conceptualization, Methodology, Resources, Writing - Review & Editing EML: Software, Formal Analysis, Resources, Data Curation, Writing - Review & Editing SAM: Software, Formal Analysis, Resources, Data Curation, Writing - Review & Editing YL: Software, Formal Analysis, Resources, Data Curation, Writing - Review & Editing YH: Resources, Data Curation, Writing - Review & Editing, Supervision MP: Software, Formal Analysis, Resources, Data Curation, Writing - Review & Editing SW: Software, Formal Analysis, Resources, Data Curation, Writing - Review & Editing XTN: Conceptualization, Writing - Review & Editing, Supervision LC: Resources, Writing - Review & Editing AV: Resources, Writing - Review & Editing EN: Resources, Writing - Review & Editing AF: Resources, Writing - Review & Editing KMH: Conceptualization, Methodology, Writing - Review & Editing RP: Resources, Writing - Review & Editing SS: Resources, Writing - Review & Editing LES: Resources, Writing - Review & Editing SP: Resources, Writing - Review & Editing KC: Conceptualization, Writing - Review & Editing, Supervision JM: Resources, Writing - Review & Editing GH: Resources, Writing - Review & Editing PST: Conceptualization, Resources, Writing - Review & Editing, Supervision SM: Conceptualization, Resources, Writing - Review & Editing, Supervision JMG: Conceptualization, Resources, Writing - Review & Editing, Supervision Acknowledgements The authors thank the Veteran participants for their generous contributions. The authors would also like to acknowledge the following: MVP Program Office - Sumitra Muralidhar, Ph.D., Program Director US Department of Veterans Affairs, 810 Vermont Avenue NW, Washington, DC 20420 - Jennifer Moser, Ph.D., Associate Director, Scientific Programs US Department of Veterans Affairs, 810 Vermont Avenue NW, Washington, DC 20420 - Jennifer E. Deen, B.S., Associate Director, Cohort & Public Relations US Department of Veterans Affairs, 810 Vermont Avenue NW, Washington, DC 20420 MVP Executive Committee - Co-Chair: Philip S. Tsao, Ph.D. VA Palo Alto Health Care System, 3801 Miranda Avenue, Palo Alto, CA 94304 - Co-Chair: Sumitra Muralidhar, Ph.D. US Department of Veterans Affairs, 810 Vermont Avenue NW, Washington, DC 20420 - J. Michael Gaziano, M.D., M.P.H. VA Boston Healthcare System, 150 S. Huntington Avenue, Boston, MA 02130 - Elizabeth Hauser, Ph.D. Durham VA Medical Center, 508 Fulton Street, Durham, NC 27705 - Amy Kilbourne, Ph.D., M.P.H. VA HSR&D, 2215 Fuller Road, Ann Arbor, MI 48105 - Michael Matheny, M.D., M.S., M.P.H. VA Tennessee Valley Healthcare System, 1310 24th Ave. South, Nashville, TN 37212 - Dave Oslin, M.D. Philadelphia VA Medical Center, 3900 Woodland Avenue, Philadelphia, PA 19104 - Deepak Voora, MD Durham VA Medical Center, 508 Fulton Street, Durham, NC 27705 MVP Co-Principal Investigators - J. Michael Gaziano, M.D., M.P.H. VA Boston Healthcare System, 150 S. Huntington Avenue, Boston, MA 02130 - Philip S. Tsao, Ph.D. VA Palo Alto Health Care System, 3801 Miranda Avenue, Palo Alto, CA 94304 MVP Core Operations - Jessica V. Brewer, M.P.H., Director, MVP Cohort Operations VA Boston Healthcare System, 150 S. Huntington Avenue, Boston, MA 02130 - Mary T. Brophy M.D., M.P.H., Director, VA Central Biorepository VA Boston Healthcare System, 150 S. Huntington Avenue, Boston, MA 02130 - Kelly Cho, M.P.H, Ph.D., Director, MVP Phenomics VA Boston Healthcare System, 150 S. Huntington Avenue, Boston, MA 02130 - Lori Churby, B.S., Director, MVP Regulatory Affairs VA Palo Alto Health Care System, 3801 Miranda Avenue, Palo Alto, CA 94304 - Scott L. DuVall, Ph.D., Director, VA Informatics and Computing Infrastructure (VINCI) VA Salt Lake City Health Care System, 500 Foothill Drive, Salt Lake City, UT 84148 - Saiju Pyarajan Ph.D., Director, Data and Computational Sciences VA Boston Healthcare System, 150 S. Huntington Avenue, Boston, MA 02130 - Robert Ringer, Pharm.D., Director, VA Albuquerque Central Biorepository New Mexico VA Health Care System, 1501 San Pedro Drive SE, Albuquerque, NM 87108 - Luis E. Selva, Ph.D., Director, MVP Biorepository Coordination VA Boston Healthcare System, 150 S. Huntington Avenue, Boston, MA 02130 - Shahpoor (Alex) Shayan, M.S., Director, MVP PRE Informatics VA Boston Healthcare System, 150 S. Huntington Avenue, Boston, MA 02130 - Brady Stephens, M.S., Principal Investigator, MVP Information Center Canandaigua VA Medical Center, 400 Fort Hill Avenue, Canandaigua, NY 14424 - Stacey B. Whitbourne, Ph.D., Director, MVP Cohort Development and Management VA Boston Healthcare System, 150 S. Huntington Avenue, Boston, MA 02130 MVP Local Site Investigators - Samuel Aguayo, M.D., Phoenix VA Health Care System, 650 E. Indian School Road, Phoenix, AZ 85012 - Sunil Ahuja, M.D., South Texas Veterans Health Care System, 7400 Merton Minter Boulevard, San Antonio, TX 78229 - Kathrina Alexander, M.D., Veterans Health Care System of the Ozarks, 1100 North College Avenue, Fayetteville, AR 72703 - Harshitha Kota, M.D., Columbia VA Health Care System, Garners Ferry Road, Columbia, SC 29209 - Prakash Balasubramanian, M.D., William S. Middleton Memorial Veterans Hospital, 2500 Overlook Terrace, Madison, WI 53705 - Zuhair Ballas, M.D., Iowa City VA Health Care System, 601 Highway 6 West, Iowa City, IA 52246-2208 - Jean Beckham, Ph.D., Durham VA Medical Center, 508 Fulton Street, Durham, NC 27705 - Sujata Bhushan, M.D., VA North Texas Health Care System, 4500 S. Lancaster Road, Dallas, TX 75216 - Edward Boyko, M.D., VA Puget Sound Health Care System, 1660 S. Columbian Way, Seattle, WA 98108-1597 - David Cohen, M.D., Portland VA Medical Center, 3710 SW U.S. Veterans Hospital Road, Portland, OR 97239 - Louis Dellitalia, M.D., Birmingham VA Medical Center, 700 S. 19th Street, Birmingham AL 35233 - Mona Brake, M.D., Robert J. Dole VA Medical Center, 5500 East Kellogg Drive, Wichita, KS 67218-1607 - Joseph Fayad, M.D., VA Southern Nevada Healthcare System, 6900 North Pecos Road, North Las Vegas, NV 89086 - Daryl Fujii, Ph.D., VA Pacific Islands Health Care System, 459 Patterson Rd, Honolulu, HI 96819 - Saib Gappy, M.D., John D. Dingell VA Medical Center, 4646 John R Street, Detroit, MI 48201 - Frank Gesek, Ph.D., White River Junction VA Medical Center, 163 Veterans Drive, White River Junction, VT 05009 - Jennifer Greco, M.D., Sioux Falls VA Health Care System, 2501 W 22nd Street, Sioux Falls, SD 57105 - Michael Godschalk, M.D., Richmond VA Medical Center, 1201 Broad Rock Blvd., Richmond, VA 23249 - Todd W. Gress, M.D., Ph.D., Hershel “Woody” Williams VA Medical Center, 1540 Spring Valley Drive, Huntington, WV 25704 - Samir Gupta, M.D., M.S.C.S., VA San Diego Healthcare System, 3350 La Jolla Village Drive, San Diego, CA 92161 - Salvador Gutierrez, M.D., Edward Hines, Jr. VA Medical Center, 5000 South 5th Avenue, Hines, IL 60141 - John Harley, M.D., Ph.D., Cincinnati VA Medical Center, 3200 Vine Street, Cincinnati, OH 45220 - Tze Shien Lo, M.D., Fargo VA Health Care System, 2101 N. Elm, Fargo, ND 58102 - Mark Hamner, M.D., Ralph H. Johnson VA Medical Center, 109 Bee Street, Mental Health Research, Charleston, SC 29401 - Adriana Hung, M.D., M.P.H., VA Tennessee Valley Healthcare System, 1310 24th Avenue, South Nashville, TN 37212 - Robin Hurley, M.D., W.G. (Bill) Hefner VA Medical Center, 1601 Brenner Ave, Salisbury, NC 28144 - Radhika Manhapra, M.D., Hampton VA Medical Center, 100 Emancipation Drive, Hampton, VA 23667 - Frank Jacono, M.D., VA Northeast Ohio Healthcare System, 10701 East Boulevard, Cleveland, OH 44106 - Darshana Jhala, M.D., Philadelphia VA Medical Center, 3900 Woodland Avenue, Philadelphia, PA 19104 - Scott Kinlay, M.B.B.S., Ph.D., VA Boston Healthcare System, 150 S. Huntington Avenue, Boston, MA 02130 - Gerald Wayne Dryden, Jr., M.D., Ph.D., Louisville VA Medical Center, 800 Zorn Avenue, Louisville, KY 40206 - Brooks Robey, M.D., Southeast Louisiana Veterans Health Care System, 2400 Canal Street, New Orleans, LA 70119 - Peter Liang, M.D., M.P.H., VA New York Harbor Healthcare System, 423 East 23rd Street, New York, NY 10010 - Suthat Liangpunsakul, M.D., M.P.H., Richard Roudebush VA Medical Center, 1481 West 10th Street, Indianapolis, IN 46202 - Jack Lichy, M.D., Ph.D., Washington DC VA Medical Center, 50 Irving St, Washington, D. C. 20422 - C. Scott Mahan, M.D., Charles George VA Medical Center, 1100 Tunnel Road, Asheville, NC 28805 - Ronnie Marrache, M.D., VA Maine Healthcare System, 1 VA Center, Augusta, ME 04330 - Stephen Mastorides, M.D., James A. Haley Veterans’ Hospital, 13000 Bruce B. Downs Blvd, Tampa, FL 33612 - Amneet S. Rai, Pharm.D., VA Sierra Nevada Health Care System, 975 Kirman Avenue, Reno, NV 89502 - Kristin Mattocks, Ph.D., M.P.H., Central Western Massachusetts Healthcare System, 421 North Main Street, Leeds, MA 01053 - Adriana Martin, M.D., Ph.D., Southern Arizona VA Health Care System, 3601 S 6th Avenue, Tucson, AZ 85723 - Jonathan Moorman, M.D., Ph.D., James H. Quillen VA Medical Center, Corner of Lamont & Veterans Way, Mountain Home, TN 37684 - Timothy Morgan, M.D., VA Long Beach Healthcare System, 5901 East 7th Street Long Beach, CA 90822 - Maureen Murdoch, M.D., M.P.H., Minneapolis VA Health Care System, One Veterans Drive, Minneapolis, MN 55417 - James Norton, Ph.D., VA Health Care Upstate New York, 113 Holland Avenue, Albany, NY 12208 - Olaoluwa Okusaga, M.D., Michael E. DeBakey VA Medical Center, 2002 Holcombe Blvd, Houston, TX 77030 - Kris Ann Oursler, M.D., Salem VA Medical Center, 1970 Roanoke Blvd, Salem, VA 24153 - Elizabeth S. Bast, M.D., M.P.H., Miami VA Health Care System, 1201 NW 16th Street, 11 GRC, Miami FL 33125 - Robert Zwolak, M.D., Ph.D., Manchester VA Medical Center, 718 Smyth Road, Manchester, NH 03104 - Seema Joshi, MD, FACP, ABOIM, VA Eastern Kansas Health Care System, 4101 S 4th Street Trafficway, Leavenworth, KS 66048 - Michael Rauchman, M.D., St. Louis VA Health Care System, 915 North Grand Blvd, St. Louis, MO 63106 - Paul Dougherty, D.C., Syracuse VA Medical Center, 800 Irving Avenue, Syracuse, NY 13210 - Satish Sharma, M.D., Providence VA Medical Center, 830 Chalkstone Avenue, Providence, RI 02908 - River Smith, Ph.D., Eastern Oklahoma VA Health Care System, 1011 Honor Heights Drive, Muskogee, OK 74401 - Peruvemba Sriram, M.D., N. FL/S. GA Veterans Health System, 1601 SW Archer Road, Gainesville, FL 32608 - Patrick Strollo, Jr., M.D., VA Pittsburgh Health Care System, University Drive, Pittsburgh, PA 15240 - Neeraj Tandon, M.D., Overton Brooks VA Medical Center, 510 East Stoner Ave, Shreveport, LA 71101 - Philip Tsao, Ph.D., VA Palo Alto Health Care System, 3801 Miranda Avenue, Palo Alto, CA 94304-1290 - Gerardo Villarreal, M.D., New Mexico VA Health Care System, 1501 San Pedro Drive, S.E. Albuquerque, NM 87108 - Michael K. Ong, M.D., Ph.D., VA Greater Los Angeles Health Care System, 11301 Wilshire Blvd, Los Angeles, CA 90073 - Jessica Walsh, M.D., VA Salt Lake City Health Care System, 500 Foothill Drive, Salt Lake City, UT 84148 - John Wells, Ph.D., Edith Nourse Rogers Memorial Veterans Hospital, 200 Springs Road, Bedford, MA 01730 - Jeffrey Whittle, M.D., M.P.H., Clement J. Zablocki VA Medical Center, 5000 West National Avenue, Milwaukee, WI 53295 - Mary Whooley, M.D., San Francisco VA Health Care System, 4150 Clement Street, San Francisco, CA 94121 - Daniel J. Hogan, M.D., Bay Pines VA Healthcare System, 10,000 Bay Pines Blvd Bay Pines, FL 33744 - Peter Wilson, M.D., Atlanta VA Medical Center, 1670 Clairmont Road, Decatur, GA 30033 - Junzhe Xu, M.D., VA Western New York Healthcare System, 3495 Bailey Avenue, Buffalo, NY 14215-1199 - Shing Shing Yeh, Ph.D., M.D., Northport VA Medical Center, 79 Middleville Road, Northport, NY 11768 - Robyn Chase, D.O., Northern Arizona VA Health Care System, 500 Highway 89 North Prescott, AZ 86313 - Eknath Naik, M.D., Ph.D., West Palm Beach VA Medical Center, 7305 North Military Trail West Palm Beach, FL 33410 - Ismene Petrakis, M.D., VA Connecticut Healthcare System 950 Campbell Avenue West Haven, CT 06516 - Andrew Yen, M.D., VA Northern California Health Care System 10535 Hospital Way, Mather, CA 95655 References Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, et al. UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age. PLOS Med. 2015 Mar 31;12(3):e1001779. Hedderson MM, Ferrara A, Avalos LA, Van den Eeden SK, Gunderson EP, Li DK, et al. The Kaiser Permanente Northern California research program on genes, environment, and health (RPGEH) pregnancy cohort: study design, methodology and baseline characteristics. BMC Pregnancy Childbirth. 2016 Nov 29;16(1):381. Gaziano JM, Concato J, Brophy M, Fiore L, Pyarajan S, Breeling J, et al. Million Veteran Program: A mega-biobank to study genetic influences on health and disease. J Clin Epidemiol. 2016 Feb 1;70:214–23. All of Us Research Program Investigators, Denny JC, Rutter JL, Goldstein DB, Philippakis A, Smoller JW, et al. The “All of Us” Research Program. N Engl J Med. 2019 Aug 15;381(7):668–76. Kaufman D, Murphy J, Erby L, Hudson K, Scott J. Veterans’ attitudes regarding a database for genomic research. Genet Med. 2009 May 1;11(5):329–37. Science Corner | Veterans Affairs [Internet]. [cited 2025 Sep 10]. Available from: https://www.mvp.va.gov/pwa/science Nguyen XMT, Whitbourne SB, Li Y, Quaden RM, Song RJ, Nguyen HNA, et al. Data Resource Profile: Self-reported data in the Million Veteran Program: survey development and insights from the first 850 736 participants. Int J Epidemiol. 2023 Feb 1;52(1):e1–17. Whitbourne SB, Nguyen XMT, Song RJ, Lord E, Lyden M, Harrington KM, et al. Million Veteran Program’s response to COVID-19: Survey development and preliminary findings. PloS One. 2022;17(4):e0266381. Briggs R, Stipp H. How Internet Advertising Works. In: Webvertising: The Ultimate Internet Advertising Guide [Internet]. Wiesbaden: Vieweg+Teubner Verlag; 2000 [cited 2025 Jul 1]. p. 99–128. Available from: https://doi.org/10.1007/978-3-322-86793-3_11 Andrade C. Mean Difference, Standardized Mean Difference (SMD), and Their Use in Meta-Analysis: As Simple as It Gets. J Clin Psychiatry. 2020 Sep 22;81(5):20f13681. Sullivan GM, Feinn R. Using Effect Size—or Why the P Value Is Not Enough. J Grad Med Educ. 2012 Sep;4(3):279–82. Corporate Data Warehouse (CDW) [Internet]. 2023 [cited 2025 Jul 1]. Available from: https://www.hsrd.research.va.gov/for_researchers/cdw.cfm Data Standardization – OHDSI [Internet]. [cited 2025 Jul 1]. Available from: https://www.ohdsi.org/data-standardization/ Koenig MR, Wesselink AK, Kuriyama AS, Chaiyasarikul A, Hatch EE, Wise LA. Feasibility of mail-based biospecimen collection in an online preconception cohort study. Front Reprod Health [Internet]. 2023 Jan 9 [cited 2025 Jul 1];4. Available from: https://www.frontiersin.org/journals/reproductive-health/articles/10.3389/frph.2022.1052231/full Darko EM, Kleib M, Olson J. Social Media Use for Research Participant Recruitment: Integrative Literature Review. J Med Internet Res. 2022 Aug 4;24(8):e38015. Topolovec-Vranic J, Natarajan K. The Use of Social Media in Recruitment for Medical Research Studies: A Scoping Review. J Med Internet Res. 2016 Nov 7;18(11):e5698. Baldyga K, Iloputaife I, Taffet G, LaGanke N, Manor B, Lipsitz LA, et al. Comparison of targeted web-based advertising versus traditional methods for recruiting older adults into clinical trials. J Am Geriatr Soc. 2025;73(1):182–92. Van Zon SKR, Scholtens S, Reijneveld SA, Smidt N, Bültmann U. Active recruitment and limited participant-load related to high participation in large population-based biobank studies. J Clin Epidemiol. 2016 Oct;78:52–62. Goldstein KM, Pace R, Dancu C, Raman SR, Bridges-Curry Z, Klimek-Johnson P, et al. An Evidence Map of the Women Veterans’ Health Literature, 2016 to 2023: A Systematic Review. JAMA Netw Open. 2025 Apr 22;8(4):e256372. Veterans Health Administration, Office of Policy and Planning [Internet]. [cited 2025 Aug 26]. National Center for Veterans Analysis and Statistics. Available from: https://www.va.gov/vetdata/Veteran_Population.asp Department of Veterans Affairs. 2024 Survey of Veteran Enrollees’ Health and use of Health Care. Washington, D.C.: Office of Strategic Planning and Analysis; 2024 Jan. Vassy JL, Brunette CA, Yi T, Harrison A, Cardellino MP, Assimes TL, et al. Design and pilot results from the Million Veteran Program Return Of Actionable Results (MVP-ROAR) Study. Am Heart J. 2024 Oct;276:99–109. Montgomery RB, Lynch JA, Brown J, Maxwell KN, Kabilovic N, Stoll K, et al. Remote delivery of cancer genetic testing in veterans with metastatic prostate cancer: A Million Veteran Program study. J Clin Oncol. 2024 Jun 1;42(16_suppl):1541–1541. Kaminsky LA, Montoye AHK. Physical Activity and Health: What Is the Best Dose? J Am Heart Assoc . 2014;3(5). doi:10.1161/jaha.114.001430 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8272722","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":560845085,"identity":"d6fb4dfb-1dbc-4d55-b44a-1d31be78afaf","order_by":0,"name":"Stacey B. Whitbourne","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYDADA2YGNgiLvYGAUja4FmYok+cAsVoYYFokEvDrkJ/f/PDDxzaGxO3s/McefGy7l8cv+cbwA8Mvm0Rc7jM4xmYsOROoZWczM7vhzLbiYsnZOcYSjH1puLWwMZgx87Yx5G44zMwmzduWkLjhdlqCBGPPYWOcDmtj/8b8F1nL/pvHkn/g08JwjMeMmRHFFgnmYxIMPw7L4dJhcCynWLLnnEQ9UIu54YxzCYkzziQfs0hsSMOpRb75+MYPP8psjA3OH3z24ENZQmJ/+8HmGx/+2PDgdBgIMLJJoIkktuHVAAR/iBAZBaNgFIyCkQsAMXFV62YkH+oAAAAASUVORK5CYII=","orcid":"","institution":"VA Boston Healthcare System","correspondingAuthor":true,"prefix":"","firstName":"Stacey","middleName":"B.","lastName":"Whitbourne","suffix":""},{"id":560845088,"identity":"e9333c2a-72c9-4787-9678-4579082f071f","order_by":1,"name":"April R. Williams","email":"","orcid":"","institution":"VA Boston Healthcare System","correspondingAuthor":false,"prefix":"","firstName":"April","middleName":"R.","lastName":"Williams","suffix":""},{"id":560845098,"identity":"3bca7269-c928-48c2-88ce-a54859966664","order_by":2,"name":"Jessica V. Brewer","email":"","orcid":"","institution":"VA Boston Healthcare System","correspondingAuthor":false,"prefix":"","firstName":"Jessica","middleName":"V.","lastName":"Brewer","suffix":""},{"id":560845101,"identity":"eb8b1718-d60d-4189-9681-9c3e0875e6dd","order_by":3,"name":"Jennifer E. Deen","email":"","orcid":"","institution":"Veterans Health Administration","correspondingAuthor":false,"prefix":"","firstName":"Jennifer","middleName":"E.","lastName":"Deen","suffix":""},{"id":560845105,"identity":"ddc38203-8feb-423f-9f8f-b16016580210","order_by":4,"name":"Emily M. Lord","email":"","orcid":"","institution":"VA Boston Healthcare System","correspondingAuthor":false,"prefix":"","firstName":"Emily","middleName":"M.","lastName":"Lord","suffix":""},{"id":560845112,"identity":"d69a55a6-ccdf-4952-acbc-6a1013f7b88e","order_by":5,"name":"Sybil A. Murphy","email":"","orcid":"","institution":"VA Boston Healthcare System","correspondingAuthor":false,"prefix":"","firstName":"Sybil","middleName":"A.","lastName":"Murphy","suffix":""},{"id":560845117,"identity":"7a09b0f3-6858-41d8-a2f2-faf9754f7108","order_by":6,"name":"Yanping Li","email":"","orcid":"","institution":"VA Boston Healthcare System","correspondingAuthor":false,"prefix":"","firstName":"Yanping","middleName":"","lastName":"Li","suffix":""},{"id":560845124,"identity":"cdd1e96c-1161-4d3f-a266-8d249c6deac3","order_by":7,"name":"Yuk-Lam Ho","email":"","orcid":"","institution":"VA Boston Healthcare System","correspondingAuthor":false,"prefix":"","firstName":"Yuk-Lam","middleName":"","lastName":"Ho","suffix":""},{"id":560845126,"identity":"939452e2-b1a4-4b22-a031-9d17145f961e","order_by":8,"name":"Mary Pyatt","email":"","orcid":"","institution":"VA Boston Healthcare System","correspondingAuthor":false,"prefix":"","firstName":"Mary","middleName":"","lastName":"Pyatt","suffix":""},{"id":560845131,"identity":"44275266-4821-48d2-bd27-8856d53b4ba9","order_by":9,"name":"Sarah Wolfrum","email":"","orcid":"","institution":"VA Boston Healthcare System","correspondingAuthor":false,"prefix":"","firstName":"Sarah","middleName":"","lastName":"Wolfrum","suffix":""},{"id":560845138,"identity":"52f5d0c8-2390-4a29-bec3-7cfa7652c4ab","order_by":10,"name":"Xuan-Mai T. Nguyen","email":"","orcid":"","institution":"VA Boston Healthcare System","correspondingAuthor":false,"prefix":"","firstName":"Xuan-Mai","middleName":"T.","lastName":"Nguyen","suffix":""},{"id":560845141,"identity":"41913f90-d2cb-43c7-ae66-7ae7b6c09ad5","order_by":11,"name":"Lori Churby","email":"","orcid":"","institution":"VA Palo Alto Health Care System","correspondingAuthor":false,"prefix":"","firstName":"Lori","middleName":"","lastName":"Churby","suffix":""},{"id":560845143,"identity":"37dd9bba-f231-4cd9-b529-e86d7c7322ec","order_by":12,"name":"Anastasia Villafranca","email":"","orcid":"","institution":"VA Palo Alto Health Care System","correspondingAuthor":false,"prefix":"","firstName":"Anastasia","middleName":"","lastName":"Villafranca","suffix":""},{"id":560845147,"identity":"cfed99df-8d0c-4bbd-b0b8-7e0de0ec032f","order_by":13,"name":"Elena Nikolaev","email":"","orcid":"","institution":"VA Palo Alto Health Care System","correspondingAuthor":false,"prefix":"","firstName":"Elena","middleName":"","lastName":"Nikolaev","suffix":""},{"id":560845150,"identity":"c6f9eb00-383f-43da-a32b-39e8d0889617","order_by":14,"name":"Annie Franklin","email":"","orcid":"","institution":"VA Boston Healthcare System","correspondingAuthor":false,"prefix":"","firstName":"Annie","middleName":"","lastName":"Franklin","suffix":""},{"id":560845157,"identity":"ed0ff632-1ffc-4ef4-9f76-e4e4bffe0731","order_by":15,"name":"Kelly M. Harrington","email":"","orcid":"","institution":"VA Boston Healthcare System","correspondingAuthor":false,"prefix":"","firstName":"Kelly","middleName":"M.","lastName":"Harrington","suffix":""},{"id":560845163,"identity":"b0d53bd0-26f9-46b0-9395-96d16f223bbc","order_by":16,"name":"Ramin Pourali","email":"","orcid":"","institution":"VA Boston Healthcare System","correspondingAuthor":false,"prefix":"","firstName":"Ramin","middleName":"","lastName":"Pourali","suffix":""},{"id":560845164,"identity":"49671cdb-4ef4-4202-a7c3-955d70d511ad","order_by":17,"name":"Shahpoor Shayan","email":"","orcid":"","institution":"VA Boston Healthcare System","correspondingAuthor":false,"prefix":"","firstName":"Shahpoor","middleName":"","lastName":"Shayan","suffix":""},{"id":560845168,"identity":"0ac02365-09c8-40f0-a634-f4272d86a3b6","order_by":18,"name":"Luis E. Selva","email":"","orcid":"","institution":"VA Boston Healthcare System","correspondingAuthor":false,"prefix":"","firstName":"Luis","middleName":"E.","lastName":"Selva","suffix":""},{"id":560845174,"identity":"f89370de-7c10-4eeb-b845-488c9fb4e028","order_by":19,"name":"Saiju Pyarajan","email":"","orcid":"","institution":"VA Boston Healthcare System","correspondingAuthor":false,"prefix":"","firstName":"Saiju","middleName":"","lastName":"Pyarajan","suffix":""},{"id":560845178,"identity":"b10e726d-9939-49f7-8032-5807e65bd862","order_by":20,"name":"Kelly Cho","email":"","orcid":"","institution":"VA Boston Healthcare System","correspondingAuthor":false,"prefix":"","firstName":"Kelly","middleName":"","lastName":"Cho","suffix":""},{"id":560845180,"identity":"a692cbfa-201d-4976-ab93-9789aa66d80a","order_by":21,"name":"Jennifer Moser","email":"","orcid":"","institution":"Veterans Health Administration","correspondingAuthor":false,"prefix":"","firstName":"Jennifer","middleName":"","lastName":"Moser","suffix":""},{"id":560845181,"identity":"14d1a1fd-2c1f-4b04-a909-c4dc3d5583a3","order_by":22,"name":"Grant Huang","email":"","orcid":"","institution":"Veterans Health Administration","correspondingAuthor":false,"prefix":"","firstName":"Grant","middleName":"","lastName":"Huang","suffix":""},{"id":560845184,"identity":"6ca798fc-5b44-4a9a-95b9-07f45e5398be","order_by":23,"name":"Philip S. Tsao","email":"","orcid":"","institution":"VA Palo Alto Health Care System","correspondingAuthor":false,"prefix":"","firstName":"Philip","middleName":"S.","lastName":"Tsao","suffix":""},{"id":560845187,"identity":"7feaa13e-271f-4ac9-9413-510bb36e10c5","order_by":24,"name":"Sumitra Muralidhar","email":"","orcid":"","institution":"Veterans Health Administration","correspondingAuthor":false,"prefix":"","firstName":"Sumitra","middleName":"","lastName":"Muralidhar","suffix":""},{"id":560845189,"identity":"e4680cb4-af34-4971-a159-a8ae9cfcfcd5","order_by":25,"name":"J. Michael Gaziano","email":"","orcid":"","institution":"VA Boston Healthcare System","correspondingAuthor":false,"prefix":"","firstName":"J.","middleName":"Michael","lastName":"Gaziano","suffix":""}],"badges":[],"createdAt":"2025-12-03 17:23:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8272722/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8272722/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":98381280,"identity":"936570fc-5300-402d-9da6-a63534797faa","added_by":"auto","created_at":"2025-12-17 07:49:15","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":168190,"visible":true,"origin":"","legend":"","description":"","filename":"MVPMillionMilestoneFINAL.docx","url":"https://assets-eu.researchsquare.com/files/rs-8272722/v1/b4c6381c3ae1bec16ba6331c.docx"},{"id":98381277,"identity":"d618a85c-e8e5-43a7-be1e-353a1d18a018","added_by":"auto","created_at":"2025-12-17 07:49:14","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":36819,"visible":true,"origin":"","legend":"","description":"","filename":"b0842b574bb24ee195214039e4fd59ae.json","url":"https://assets-eu.researchsquare.com/files/rs-8272722/v1/c0b55f513c71434ef88a0ef1.json"},{"id":98440296,"identity":"d456b5f6-5728-453f-94f5-6d5134f4dc3f","added_by":"auto","created_at":"2025-12-17 17:03:41","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":157505,"visible":true,"origin":"","legend":"","description":"","filename":"b0842b574bb24ee195214039e4fd59ae1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8272722/v1/22a453ef64d134c16f03d7cd.xml"},{"id":98381281,"identity":"dfe70a2b-a1c8-459a-93a3-30f412f1def0","added_by":"auto","created_at":"2025-12-17 07:49:15","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":159164,"visible":true,"origin":"","legend":"","description":"","filename":"b0842b574bb24ee195214039e4fd59ae1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8272722/v1/b098b231785310759ad5b6d9.xml"},{"id":98381278,"identity":"cfb288f5-ad64-46ef-be86-975689fd69e0","added_by":"auto","created_at":"2025-12-17 07:49:15","extension":"html","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":171820,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8272722/v1/d94f18127290d770b8095f77.html"},{"id":102419763,"identity":"5891740e-a857-47e7-b9bc-38572d50a752","added_by":"auto","created_at":"2026-02-11 13:28:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1849108,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8272722/v1/ed5da32e-bef0-4122-af08-ab603ef2c99a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Overview of Recruitment Strategies to Reach the Million Milestone and Characterization of the Million Veteran Program Cohort","fulltext":[{"header":"Background","content":"\u003cp\u003eHealth care delivery has improved incrementally in recent decades, as initiatives to understand the relationship between genetics and health have amassed various resources for research. Of particular utility are large-scale population cohorts, such as the UK Biobank,(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) the Kaiser Permanente Research Program on Genes, Environment, and Health,(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) the Department of Veterans Affairs (VA) Million Veteran Program (MVP)(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) and the National Institutes of Health All of Us Research Program.(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) The success of these research programs depends in large part on their complex recruitment and enrollment methodologies, continued engagement with participants, and robust infrastructures for collecting, storing, and analyzing specimens and other data as resources for scientific endeavors.\u003c/p\u003e \u003cp\u003eIn 2006, the VA established a plan to initiate genetic health research for Veterans by developing a data resource that scientists could use to better understand the role of genes, health, lifestyle, and military experiences to provide personalized health care for Veterans. Following focus groups with Veterans to determine support for a genetic research program(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) and pilot work to establish the feasibility of enrolling Veterans into a large-scale genetic and health research program, the VA Office of Research and Development (ORD) began recruitment for MVP in 2011. A sample of at least one million Veterans was estimated to both sufficiently power genetic research studies and be generally representative of the larger VA user population.(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) MVP provides a unique and substantial scientific contribution to the fields of genetics, omics, and public health. As of this writing, the cohort represents approximately 11% of Veterans Health Administration (VHA) users, who consented to provide blood specimens for genetic testing, complete surveys, permit access to health records, and agree to be re-contacted about additional research opportunities. Research using MVP data has led to over 450 scientific publications and has initiated translational projects to improve clinical care in the VA healthcare system.(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eStandard MVP recruitment and enrollment efforts have been described previously.(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Between 2012 and 2019, the average annual enrollment was nearly 100,000 participants. With the onset of the COVID-19 pandemic in March 2020, MVP temporarily closed sites for in-person recruitment and enrollment activities and transitioned primarily to data collection activities (e.g., surveys) and piloting remote specimen collection. In addition to engaging Veterans through outreach efforts to increase return of the MVP Baseline and Lifestyle Surveys from participants,(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) the MVP COVID-19 Survey was developed and distributed to all eligible MVP participants to gather their COVID-19-specific health experiences.(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eIn-person recruitment and enrollment activities restarted at limited capacity at the end of 2020. A campaign was initiated in October 2022 to recruit the millionth Veteran participant by November 11th, 2023 (Veteran\u0026rsquo;s Day), referred to hereafter as the Million Milestone Campaign (MMC). Approximately 96,000 enrollments were needed within the timeframe to reach the objective, and on November 8th, 2023, MVP successfully reached the historic milestone. The objectives of this work are to: 1) describe the effectiveness of the strategies implemented during the MMC period and 2) characterize the cumulative MVP cohort through July 2024.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cem\u003eEthical and Eligibility Considerations\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe VA Central Institutional Review Board (IRB) approved the MVP protocol (#10\u0026ndash;02) in 2010 with continued oversight and ongoing annual review. At the onset of MVP in 2011, eligibility requirements to participate in MVP included VHA user status (i.e., the Veteran needed to have at least one visit at a VHA medical facility in the 12-month period leading up to enrolling). In January 2019, the VHA user status requirement was removed, thereby allowing any Veteran, regardless of their VHA use, to participate. A Veteran is defined as someone who has served in the active military, naval, or air service and was discharged under conditions other than dishonorable. The exclusion criteria included being incarcerated, and beingcognitively unable to provide consent. Compensation is not provided for participation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMMC Recruitment and Enrollment Infrastructure\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWith the goal of reaching the millionth participant by Veterans Day 2023, MVP developed the MMC using a variety of existing and novel strategies that ran between September 08, 2022, and November 8, 2023. Recruitment and enrollment activities were centrally managed by the MVP Boston, MA and Palo Alto, CA Coordinating Centers. The ORD MVP Program Office in Washington, DC, managed social media and advertising campaigns as well as general program oversight. The MVP Info Center located in Canandaigua, NY, answered incoming calls, conducted outbound calls, and assisted with administrative activities. The MVP local sites, located at VA medical facilities across the US, conducted standard recruitment and enrollment activities as part of the MMC. MVP Online, the online platform launched in 2019, provided Veterans with the options to enroll online, schedule in-person visits to complete enrollment in-person and/or provide a blood specimen, complete available surveys, request an at-home blood specimen kit, and follow their enrollment progress.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRecruitment Strategies to Reach the MMC\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe MMC employed a surround sound model, commonly used in marketing,(9)\u0026nbsp;where a combination of messages across various formats interact synergistically to achieve the desired outcome.\u003cu\u003e\u0026nbsp;\u003c/u\u003eThe MMC strategies included the following: 1) invitation mailings; 2) email campaigns; 3) recruitment calls; 4) invitations to enroll by mail; and 5) digital marketing. Veterans were able to enroll in-person, online, or by mail.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePaper Invitation Mailings.\u0026nbsp;\u003c/strong\u003eSince MVP\u0026rsquo;s inception, paper invitation mailings have been the primary strategy for recruitment and enrollment. Ongoing refinements to the mailing methodology have included modifications to the criteria for participant selection on the basis of VHA utilization frequency or distance to an MVP local site, the timing of mailings, the number of mailings, along with design enhancements and modifications. Standard MVP recruitment consists of up to three paper invitation mailings distributed to eligible Veterans. As part of the MMC, the volumes for those mailings were increased along with a fourth invitation sent to Veterans not yet enrolled. All four invitation mailings were distributed to Veterans (N=2,848,310)\u0026nbsp;on the basis of their previous invitation status and time since the last invitation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRecruitment Calls\u003c/strong\u003e\u003cem\u003e.\u0026nbsp;\u003c/em\u003eRecruitment calls were made to 161,558 Veterans\u0026nbsp;who had previous contact with MVP and upcoming VA visits.\u0026nbsp;As part of the MMC, the rate of standard outbound recruitment calls made to Veterans increased by approximately 12% compared with the previous year.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eEnroll by Mail Invitation\u003c/strong\u003e. A random sample of Veterans (N=100,000) who had shown interest in MVP but had not yet enrolled were sent an invitation to enroll by mail. The packet included a cover letter with instructions to review the enclosed MVP enrollment and consent documents, along with a postage-paid return envelope. The completed and returned documents were then reviewed to verify enrollment validity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEmail.\u0026nbsp;\u003c/strong\u003eDuring the MMC period, MVP sent out seven promotional emails. A total of\u0026nbsp;20,773,244\u0026nbsp;emails were sent to 5,532,594 Veterans who had valid email addresses available in their VA health records. The emails included general information about MVP, instructions on how to learn more about enrolling in MVP, and links to the MVP online platform. Key improvements made to the email distribution system before the MMC allowed us to contact more than 3 million Veterans within a 24-hour period.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDigital Marketing\u003c/strong\u003e. As part of the MMC, MVP launched a digital marketing strategy. Materials consisted of photos, videos, and calls to action directed at Veterans to learn more about MVP and how to enroll. A variety of digital platforms were used, including Google and Bing search advertisements, Facebook, Instagram, and Google Video and graphic advertisements that appear on websites, web applications, and social media, known as display ads.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAnalyses\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eEffectiveness was measured for each strategy except the digital marketing using the enrollment rate, which is the number of enrollees divided by the number of unique Veterans contacted through each strategy. Effectiveness was assessed for each enrollment modality and overall enrollment for the MMC recruitment strategies. Enrollment was attributed to the final recruitment strategy contact event before enrollment and presented as a proportion of the total enrollments up to July 2024.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMVP participants who enrolled but did not receive mailings, emails, recruitment calls, or enroll by mail invitations during the MMC may have learned about MVP at VA facilities, offsite MVP events, or engaged with MVP digital marketing campaigns. These enrollees were attributed to an \u0026ldquo;Other\u0026rdquo; strategy, and an enrollment rate is not reported because the number of unique Veterans contacted is unknown.\u0026nbsp;Measures of reach for the digital marketing strategy were reported as impressions (number of ads seen by the target audience) and click-through rate (CTR; the number of clicks divided by the total number of emails opened).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDescriptive statistics (n, %, mean, SD) were used to describe the demographic characteristics of the MVP enrollees prior to the MMC and those who enrolled during the MMC period. Comparisons were made to describe any significant differences between the enrollees before and after the MMC using standard mean differences (SMD; small: 0.2-0.5, medium or moderate: 0.5-0.8, large: \u0026gt;0.8).\u0026nbsp;(10)\u0026nbsp;SMDs were selected because, owing to the large sample sizes, \u003cem\u003ep\u003c/em\u003e-values from traditional \u003cem\u003et\u003c/em\u003e-tests for group differences are less meaningful.(11)\u0026nbsp; Descriptive statistics (n, %, mean, SD) were used to describe the demographics and health status outcomes of the cumulative MVP cohort by sex (biological sex: female, male). Comparisons of demographics were made to identify any differences between the MVP cohort and non-MVP VHA users via SMDs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAnalyses were conducted using the MVP Roster version 24.1 which includes Veterans enrolled through July 31, 2024. The MVP Roster is a research-ready dataset curated to reflect active MVP participants and includes self-report survey response data. Demographic data are matched and validated using the VA Corporate Data Warehouse (CDW)(12) and the Observational Medical Outcomes Partnership (OMOP).(13) All descriptive statistics were calculated using SAS Enterprise Guide 8.3 (SAS Institute Inc., Cary, NC, USA).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides an overview of the effectiveness (enrollment rates) of the recruitment strategies used during the 2022\u0026ndash;2023 MMC period. In total, 6,584,999 unique Veterans were contacted, resulting in an enrollment of 109,912 participants (1.7% enrollment rate). Among the enrollees, 60.7% enrolled in-person (n\u0026thinsp;=\u0026thinsp;66,748), 32.6% enrolled online (n\u0026thinsp;=\u0026thinsp;35,832), and 6.7% enrolled by mail (n\u0026thinsp;=\u0026thinsp;7,332). Paper invitation mailings contributed 14.6% to the total enrollment, recruitment calls contributed 20.1%, email campaigns accounted for 42.8%, invitations to enroll by mail contributed 7.1%, and other sources accounted for the remaining 15.4% of the total MVP enrollment during the MMC period. Among MVP participants who received a paper invitation mailing as their last recruitment contact, 16,062 enrolled, resulting in a 0.6% enrollment rate. Approximately two-thirds of these enrollments (n\u0026thinsp;=\u0026thinsp;10,673) occurred in-person, about one-third (n\u0026thinsp;=\u0026thinsp;5,381) were online, and fewer than 10 occurred by mail. Recruitment calls (n\u0026thinsp;=\u0026thinsp;161,558) to Veterans resulted in 22,069 enrollments (3.7% enrollment rate), with the majority enrolling in-person (n\u0026thinsp;=\u0026thinsp;21,503). Additionally, the invitation to enroll by mail was sent to 100,000 unique Veterans, resulting in 7,762 enrollments, yielding a 7.8% enrollment rate.\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 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffectiveness of the Million Milestone Campaign (MMC) Recruitment Strategies as Enrollment Rates by Enrollment Modality\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eEnrollment Count (n) and Rates (%) by Enrollment Modality \u003csup\u003e1\u003c/sup\u003e\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\u003eMMC Recruitment Strategy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eUnique Veterans Contacted\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eIn-Person\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eOnline\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eBy Mail\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eOverall Enrollment Rates (%)\u003c/b\u003e\u003csup\u003e\u003cb\u003e1\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eMVP Enrollment n (%)\u003c/b\u003e\u003csup\u003e\u003cb\u003e3\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\u003ePaper Invitation Mailings\u003c/b\u003e\u003csup\u003e\u003cb\u003e4\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,848,310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10,673 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5,381 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;10 (\u0026lt;\u0026thinsp;0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16,062 (14.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRecruitment Calls\u003c/b\u003e\u003csup\u003e\u003cb\u003e5\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e161,558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21,503 (13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e444 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e122 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22,069 (20.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInvitation to Enroll by Mail\u003c/b\u003e\u003csup\u003e\u003cb\u003e6\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e225 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e349 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7,188 (7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7,762 (7.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEmail Campaigns\u003c/b\u003e\u003csup\u003e\u003cb\u003e7\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,532,594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18,870 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28,115 (0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e46,999 (42.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOther\u003c/b\u003e\u003csup\u003e\u003cb\u003e8\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15,477 (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,543 (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17,020 (15.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal Unique Veterans Contacted\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e6,584,999\u003c/b\u003e\u003c/p\u003e \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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal (% of MVP Enrollments)\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 \u003cp\u003e\u003cb\u003e66,748 (60.7)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e35,832 (32.6)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e7,332 (6.7)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e109,912\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e1. Enrollment Rate (%): The percentage is calculated as the number of participants who enrolled having received their latest contact via the strategy divided by the number of unique Veterans contacted using the strategy.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e2. N\u0026thinsp;=\u0026thinsp;Total number of Veterans who received contact with a given recruitment strategy between September 8, 2022, and November 8, 2023. Strategies are not mutually exclusive\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e3. %: Proportion of total MVP enrollment between September 8, 2022, and July 31, 2024, attributed to the recruitment strategy.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e4. Paper Invitation Mailings: Living Veterans were eligible for a paper invitation if they had an address in the VA system, received care at or lived within 75 miles of an MVP/VA facility, and had not opted out of contact. Veterans are sent paper invitations up to 4 times before they are excluded from additional mailings.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e5. Recruitment Calls: Any living Veteran who had at least one paper invitation mailed to them and who had not already enrolled nor opted out of contact.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e6. Invitation to Enroll by Mail: Veterans were mailed a packet with enrollment information and consent documents.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e7. Email Campaigns: Any living Veteran who was not enrolled, not opted out of contact, and had a working email address\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e8. Other: Veterans who enrolled via any modality with no other contact from MVP via mailings, email, recruitment calls, or an enroll by mail invitation. These Veterans may have learned about MVP from MVP staff at VA facilities, at off-site MVP events, or engaged with MVP\u0026rsquo;s digital media campaigns (including emails not directly sent from MVP, newsletters and blogs) across federal and non-governmental affinity organizations.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAcross the seven promotional emails (n\u0026thinsp;=\u0026thinsp;20,773,244) to 5,532,594 Veterans, 44.0% (n\u0026thinsp;=\u0026thinsp;9,132,353) were opened, and the CTR was 21.6% (n\u0026thinsp;=\u0026thinsp;1,971,390). The email campaigns resulted in a total of 46,999 enrollments (0.8% enrollment rate); among these, 40.1% (n\u0026thinsp;=\u0026thinsp;18,870) enrolled in-person, 59.8% (n\u0026thinsp;=\u0026thinsp;28,115) enrolled online, and a few (n\u0026thinsp;=\u0026thinsp;14) enrolled by mail.\u003c/p\u003e \u003cp\u003eAmong the remaining enrollments (n\u0026thinsp;=\u0026thinsp;17,020, 15.6%) that were attributed to 'Other' strategies as their last contact, the majority (n\u0026thinsp;=\u0026thinsp;15,477; 91%) occurred in-person at local sites. Across all the digital marketing strategies employed, there were a total of 33,984,656 impressions. Facebook/Instagram (n\u0026thinsp;=\u0026thinsp;8,384,569), Google Video (n\u0026thinsp;=\u0026thinsp;14,128,699), and display ads (n\u0026thinsp;=\u0026thinsp;11,076,619) accounted for 98.8% of the ads viewed. These impressions resulted in 160,226 clicks to MVP Online, yielding an overall CTR of 0.47%. The CTRs varied across the three most used platforms: Facebook/Instagram ads had a 1.1% CTR (n\u0026thinsp;=\u0026thinsp;94,588), Google Video ads had a 0.11% CTR (n\u0026thinsp;=\u0026thinsp;15,678), and display ads had a 0.27% CTR (n\u0026thinsp;=\u0026thinsp;30,005).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides a comparison of MVP participants enrolled before and after the MMC. Among the 109,912 enrollees during the MMC, most were male (n\u0026thinsp;=\u0026thinsp;94,885, 86.5%) and 60.1% were born between 1945 and 1970. Nearly three-quarters were white (n\u0026thinsp;=\u0026thinsp;78,621, 71.5%), 13.5% (n\u0026thinsp;=\u0026thinsp;14,501) were Black, 1.3% (n\u0026thinsp;=\u0026thinsp;1,386) were Asian, 1.3% (n\u0026thinsp;=\u0026thinsp;1,443) were American Indian/Alaska Native/Native Hawaiian/Other Pacific Islander, 3.3% (n\u0026thinsp;=\u0026thinsp;3,577) were multiple races, and 8.3% (n\u0026thinsp;=\u0026thinsp;8,408) were of Latino/Hispanic ethnicity. Most (n\u0026thinsp;=\u0026thinsp;63,695, 68.0%) served in the 1990 or later service eras. Compared with the cohort of MVP enrollees prior to the MMC (n\u0026thinsp;=\u0026thinsp;906,672), those enrolled during the MMC period had similar distributions by sex (SMD: 0.10) and ethnicity (SMD: 0.0), and small differences in the age group born before 1930 (SMD: -0.22), race (SMD range: -0.12, 0.27), and service era (SMD range: -0.24, 0.37). Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e provides an overview of MVP demographic characteristics and Veterans\u0026rsquo; military service era for the total cohort (N\u0026thinsp;=\u0026thinsp;1,016,584) and is stratified by male (n\u0026thinsp;=\u0026thinsp;909,486; 89.5%) and female Veterans (n\u0026thinsp;=\u0026thinsp;106,707; 10.5%). The demographic characteristics of the total VHA population (N\u0026thinsp;=\u0026thinsp;10,984,668) are also reported for reference. Comparisons using SMDs between the MVP cohort with available VHA data (n\u0026thinsp;=\u0026thinsp;985,500) and the non-MVP VHA population (n\u0026thinsp;=\u0026thinsp;9,999,168) are presented. The VHA population included 93.1% male and 6.9% female Veterans, and there was a small difference (SMD\u0026thinsp;=\u0026thinsp;0.12) between the MVP and non-MVP VHA distributions by sex.\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 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic and Military Service for MMC MVP Enrollees (N\u0026thinsp;=\u0026thinsp;109,912) Compared with Prior MVP Enrollees (N\u0026thinsp;=\u0026thinsp;906,672)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal MMC Enrollees\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;109,912)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMVP Enrollees Prior to MMC\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;906,672)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMMC Enrollees Compared with Pre-MMC Enrollees\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003en (%)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003en (\u003c/b\u003e%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eSMD\u003c/b\u003e\u003csup\u003e\u003cb\u003e3\u003c/b\u003e\u003c/sup\u003e\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\u003eAge at Enrollment (years), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61.0 (15.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61.3 (14.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge at 2018 (Median across 2011\u0026ndash;2024), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56.5 (15.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63.9 (14.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex, %\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e94,885 (86.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e814,601 (89.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14,832 (13.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e91,875 (10.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBirth Year\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4,711 (4.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12,942 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1985-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4,993 (4.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27,070 (3.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1980-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7,098 (6.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35,655 (3.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1975-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6,705 (6.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30,713 (3.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1970-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8,562 (7.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39,512 (4.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1965-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11,007 (10.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54,346 (6.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1960-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12,554 (11.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e74,692(8.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1955-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12,740 (11.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e98,971 (10.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1950-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11,972 (10.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e122,040 (13.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1945-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17,805 (16.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e200,473 (22.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1940-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7,515 (6.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e92,192 (10.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1930-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,922 (3.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e89,002 (9.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e328 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29,064 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHispanic/Latino\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8,408 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e74,939 (8.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e93,417 (91.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e818,837 (91.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRaces\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e78,621 (71.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e657,454 (72.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14,501 (13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e157,766 (17.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,386 (1.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10,049 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmerican Indian/Alaska Native\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e702 (0.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,933 (0.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNative Hawaiian/Other Pacific Islander\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e741 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,568(0.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultiple\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,577 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44,539 (4.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eService Era\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeptember 2001 or later\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21,981 (23.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e122402 (14.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAugust 1990 to August 2001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41,714 (44.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e240591 (27.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMay 1975 to July 1990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24,837 (26.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e207912 (23.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAugust 1964 to April 1975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34,181 (36.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e414555 (47.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFebruary 1955 to July 1964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,722 (4.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79853 (9.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJuly 1950 to January 1955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,198 (1.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49743 (5.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJanuary 1947 to June 1950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e103 (0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6340 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecember 1941 to December 1946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e181 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20030 (2.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNovember 1941 or earlier\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24 (0.03%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e726 (0.08%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16,210 (14.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38,138 (4.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMulti-Service\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26,685 (24.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e202,488 (22.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e1. Total MVP participants enrolled N\u0026thinsp;=\u0026thinsp;109,912 during the Million Milestone Campaign (MMC): September 8, 2022, through July 31, 2024.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e2. Total MVP participants enrolled N\u0026thinsp;=\u0026thinsp;906,672 prior to the MMC: January 1, 2011, through September 7, 2022\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e3. SMD: Standard Mean Difference\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 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic and Military Service Era for MVP Participants (N\u0026thinsp;=\u0026thinsp;1,016,584) and Total VHA Users (N\u0026thinsp;=\u0026thinsp;10,984,668)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;909,486 (89.5%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;106,707 (10.5%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal MVP\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1,016,584\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal VHA\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;10,984,668\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMVP Compared with Non-MVP VHA Users SMD\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003en (%)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003en (%)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003en (%)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e%\u003c/b\u003e\u003csup\u003e\u003cb\u003e4\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge at Enrollment (years), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.5 (14.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.7 (13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e61.3 (14.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge at 2018 (Median across 2011\u0026ndash;2024), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.4 (14.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.7 (13.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e63.1 (15.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e64.4 (18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex, %\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e909,486 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90,9486 (89.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e93.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106,707 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e106,707 (10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBirth Year\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13,025 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,580 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17,653 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1985-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24,709 (2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7,333 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32,063 (3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1980-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32,110 (3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10,609 (9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42,753 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1975-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27,869 (3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9,524 (8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37,418 (3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1970-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37,365 (4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10,675 (10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48,074 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1965-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53,039 (5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12,278 (11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65,353 (6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1960-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72,003 (7.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15,192 (14.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e87,246 (8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1955-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96,334 (10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15,328 (14.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e111,711 (11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1950-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e123,044 (13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10,933 (10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e134,012 (13.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1945-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e212,706 (23.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,549 (5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e218,278 (21.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1940-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97,339 (10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,350 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e99,707 (9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1930-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91,226 (10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,685 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e92,924 (9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28,717 (3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e671 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29,392 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHispanic/Latino\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73,431 (8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9,915 (9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e83,347 (8.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e818,349 (91.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93,904 (90.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e912,254 (91.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e93.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRaces\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e670,513 (76.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65,561 (64.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e736,075 (75.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e79.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e146,283 (16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25,984 (25.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e172,267 (17.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,776 (0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e859 (0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5,635 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmerican Indian/Alaska Native\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9,984 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,451 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11,435 (1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNative Hawaiian/Other Pacific Islander\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,608 (0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e701 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5,309 (0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultiple\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41,103 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7,012 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48,116 (4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eService Era\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeptember 2001 or later\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e115,307 (13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28,788 (29.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e144,096 (15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAugust 1990 to August 2001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e225,264 (26.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55,801 (57.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e281,066 (29.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMay 1975 to July 1990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e198,520 (22.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33,145 (34.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e231,666 (24.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAugust 1964 to April 1975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e430,989 (49.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13,515 (14.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e444,507 (46.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFebruary 1955 to July 1964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81,542 (9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,749 (1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e83,291 (8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJuly 1950 to January 1955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49,342 (5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e823 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50,165 (5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJanuary 1947 to June 1950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,360 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6,425 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecember 1941 to December 1946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,360 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e462 (0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19,802 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNovember 1941 or earlier\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e731 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e750 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43,993 (4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9,968 (9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54,348 (5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMulti-Service\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e198,415 (21.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30,757 (28.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e229,173 (22.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e1. Total MVP participants N\u0026thinsp;=\u0026thinsp;1,016,584 as of July 2024\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e2. Total VHA Users N\u0026thinsp;=\u0026thinsp;10,984,668 as of July 2024 with VHA health records\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e3. MVP participants with VHA data (n\u0026thinsp;=\u0026thinsp;985,500) compared with Non-MVP VHA Users (n\u0026thinsp;=\u0026thinsp;9,999,168) using SMD (Standard Mean Difference)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e4. VHA population presented as percent only\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe mean age at the time of enrollment was 61.3 years (SD\u0026thinsp;=\u0026thinsp;14.5), with females being younger at an average age of 50.7 years (SD\u0026thinsp;=\u0026thinsp;13.5) compared to males at 62.5 years (SD\u0026thinsp;=\u0026thinsp;14.1). In the MVP cohort, most males (58.2%) were in the 5-year birth cohorts between 1940 and 1960, whereas more than half of the females (50.4%) were in the 1950 to 1970 birth cohorts. In contrast, the total VHA user population had a concentration of births within the 1930 to 1955 range (48.3%). The largest birth year group difference between MVP participants and non-MVP VHA users was among those born before 1930 (SMD = -0.30).\u003c/p\u003e \u003cp\u003eHispanic representation among MVP participants was 8.4% of the total cohort (8.2% of males and 9.6% of females), compared to 6.1% in the total VHA population (SMD\u0026thinsp;=\u0026thinsp;0.03). Among male MVP participants, 76.4% were White, 16.7% were Black, and 4.7% reported multiple races. For female MVP participants, 64.5% were White, 25.6% were Black, and 6.9% indicated multiple races. The total VHA users comprised 79.7% White, 16.7% Black, and 0.8% multiple races. Overall, racial group representation in the MVP cohort compared with non-MVP VHA users was similar (SMD range: -0.07 to 0.01).\u003c/p\u003e \u003cp\u003eNearly half of the male MVP participants (49.8%) served in the U.S. military between August 1964 and April 1975, whereas more than half of the female MVP participants (57.6%) served between August 1990 and August 2001. There were minor differences between the MVP and non-MVP VHA user groups regarding service era (SMD range: -0.29 to 0.17).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e provides a health profile of the MVP cohort by sex, including various health characteristics and VHA health care utilization patterns. General health rated as good or better was reported by 61.9% of males, 66.2% of females, and 61.9% overall. The Patient Health Questionnaire-2 (PHQ-2) identified 11.7% of MVP participants as screening positive for depression, with lower endorsement by males (11.1%, n\u0026thinsp;=\u0026thinsp;46,295) than females (17.6%,n\u0026thinsp;=\u0026thinsp;7,203). Less than a third of males (32%, n\u0026thinsp;=\u0026thinsp;290,669) and a small proportion of females (15.4%, n\u0026thinsp;=\u0026thinsp;16,476) had three or more comorbidities, as calculated using the weighted Charlson Comorbidity Index. Overall and for both males and females, the mean BMI (kg/m\u003csup\u003e2\u003c/sup\u003e) was near or above the obese range (29.8 [SD\u0026thinsp;=\u0026thinsp;5.8], 29.8 [SD\u0026thinsp;=\u0026thinsp;5.7], and 30.2 [SD\u0026thinsp;=\u0026thinsp;6.6], respectively).\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 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMVP Health Profile and VHA Care Utilization\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;909,486 (89.5%)\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;106,707 (10.5%)\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal MVP\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1,016,584\u003c/p\u003e \u003cp\u003eN (%)\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\u003eGeneral Health\u003c/b\u003e\u003csup\u003e\u003cb\u003e1\u003c/b\u003e\u003c/sup\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26,547 (5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,880 (5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29,427 (5.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery Good\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e108,707 (20.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12,283 (22.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e120,991 (20.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e194,715 (36.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21,062 (38.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e215,779 (36.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e155,549 (29.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14,928 (27.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e170,477 (28.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48,748 (9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,601 (6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52,350 (8.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDepression\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46,295 (11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7,203 (17.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53,501 (11.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCharlson Comorbidity Index\u003c/b\u003e\u003csup\u003e3\u003c/sup\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58,094 (54.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e347,720 (38.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e406,122 (40)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 or 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32,148 (30.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e271,097 (29.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e303,305 (29.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;=3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16,465 (15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e290,669 (32.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e307,157 (30.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003csup\u003e4\u003c/sup\u003e (kg/m\u003csup\u003e2\u003c/sup\u003e), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29.8 (5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.2 (6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.8 (5.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDietary DASH score\u003c/b\u003e\u003csup\u003e5\u003c/sup\u003e mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.6 (5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.2 (5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.8 (5.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePhysical Activity\u003c/b\u003e\u003csup\u003e6\u003c/sup\u003e (MET hour/week), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.5 (5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 (5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.5 (5.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking Status\u003c/b\u003e\u003csup\u003e\u003cb\u003e7\u003c/b\u003e\u003c/sup\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e217,933 (26.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21,568 (22.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e239,521 (25.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e390,079 (47.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31,686 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e421,835 (45.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e221,922 (26.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41,897 (44.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e263,841 (28.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDrinking Status\u003c/b\u003e\u003csup\u003e8\u003c/sup\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e189,995 (54.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16,911 (55.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e206,909 (54.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e134,244 (38.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10,058 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e144,303 (37.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26,158 (7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,259 (10.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29,417 (7.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePercent of Care at VA\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61,603 (11.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,890 (10.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67,494 (11.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e103,636 (19.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8,234 (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e111,871 (19.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u0026ndash;50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38,047 (7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,353 (6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41,400 (7.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e51\u0026ndash;75%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35,773 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,628 (6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39,402 (6.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e76\u0026ndash;99%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e88,741 (16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11,043 (20.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99,785 (17.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e202,687 (38.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22,626 (41.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e225,313 (38.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e1. General Health was assessed using a single item \u0026ldquo;VR12 Q1- In general, would you say your health is:\u0026rdquo; with response options from the Veterans RAND 12 item Health Survey (VR-12). N missing\u0026thinsp;=\u0026thinsp;427,560.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e2. PHQ_2: Patient Health Questionnaire (PHQ-2): 2-item measure to assess a patient\u0026rsquo;s mental health. A score greater than 3 indicated depression.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e3. Charlson Comorbidity Index considers the number of and seriousness of comorbid conditions or diseases.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e4. BMI: n missing\u0026thinsp;=\u0026thinsp;14,693.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e5. Dietary DASH Score: Range 7\u0026ndash;35; higher score indicates a diet agrees more with the DASH diet guidelines.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e6. MET: metabolic equivalent. The American Heart Association guidelines suggest 150 minutes or 8.3 MET hours/week as an optimal physical activity level.\u003csup\u003e24\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e7. Smoking Status: n missing\u0026thinsp;=\u0026thinsp;91,387.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e8. Drinking Status: n missing\u0026thinsp;=\u0026thinsp;635,955.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDifferences in health behaviors between male and female Veterans were observed. Adherence to a preventive Dietary Approaches to Stop Hypertension (DASH) diet, with a maximum score of 35, was 23.8 (SD\u0026thinsp;=\u0026thinsp;5.2) overall; 23.6 (SD\u0026thinsp;=\u0026thinsp;5.2) for males and 25.2 (SD\u0026thinsp;=\u0026thinsp;5.2) for females. Females in the cohort reported more physical activity per week (5.0 MET hours/week, SD\u0026thinsp;=\u0026thinsp;5.7) compared with males (4.5 MET hours/week, SD\u0026thinsp;=\u0026thinsp;5.4). A higher proportion of females were current smokers (n\u0026thinsp;=\u0026thinsp;41,897, 44%) compared with males (n\u0026thinsp;=\u0026thinsp;221,922, 26.7%) and current drinkers (n\u0026thinsp;=\u0026thinsp;3,259, 10.8% vs. n\u0026thinsp;=\u0026thinsp;26,158, 7.5%). Overall, more than a third (n\u0026thinsp;=\u0026thinsp;225,313, 38.5%) of the MVP cohort relied on the VA for 100% of their care, with a higher proportion of female Veterans utilizing VA care (n\u0026thinsp;=\u0026thinsp;22,626, 41.3%) than males (n\u0026thinsp;=\u0026thinsp;202,687, 38.2%).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present work summarizes the recruitment strategies and enrollment outcomes of the MMC to achieve the million milestone for MVP, as well as the characteristics of the overall cohort as of July 2024. Reaching the million milestone is a significant and historic marker for scientific research, particularly in genetics and health. As of July 2024, the cohort includes a large and diverse sample of Veterans (n\u0026thinsp;=\u0026thinsp;1,016,584) enrolled in MVP, representing almost one-tenth of the entire VHA population, with minor demographic differences compared with non-MVP VHA users.\u003c/p\u003e \u003cp\u003eDuring the 2022\u0026ndash;2023 MMC period, approximately 6.5\u0026nbsp;million VHA Veterans were contacted, resulting in nearly 110,000 enrollments and a 1.7% overall MVP enrollment rate. The variety of strategies implemented during the MMC demonstrates the effectiveness of a surround sound model that used a combination of approaches to achieve an outcome and helped overcome the recruitment barriers experienced following the COVID-19 pandemic.\u003c/p\u003e \u003cp\u003ePaper invitation mailings accounted for approximately 15% of enrollments during the MMC. Importantly many Veterans who received paper invitations also received recruitment calls. Although recruitment calls are time- and resource-intensive, making contacting every Veteran who received a paper invitation impractical, they had the highest effectiveness rate (14%) among all strategies used in the MMC. Taken together, the combination of paper invitations and recruitment calls accounted for 35% of enrollments during the MMC.\u003c/p\u003e \u003cp\u003eDuring the 2022\u0026ndash;2023 MMC period, MVP used mass email invitations as a standard recruitment strategy for the first time, which proved to be a powerful recruitment tool, accounting for 42.8% of enrollments. Prior to the MMC, online enrollments made up approximately 20% of annual enrollees. This proportion increased to about one-third of enrollments during the MMC period, likely due to the multiple mass email invitations distributed because of technological enhancements realized during that timeframe. Although the enrollment rates from email campaigns alone were lower than those from the combined strategy of paper invitations and recruitment calls, the low effort required to send emails to a large audience with email addresses and the rapid response time made emails an effective tool. MVP operational reports show that approximately 95% of online enrollments occurred within one or two days of a mass email invitation, highlighting the importance of this strategy for efficient enrollments.\u003c/p\u003e \u003cp\u003eInvitations to enroll by mail accounted for just over 7% of overall enrollments, with a high enrollment rate within this strategy (about 8%). Enrolling by mail is an important recruitment method, particularly for reaching populations who do not have internet access, prefer not to use an online process, or are unable to enroll in person owing to geographic constraints or limited transportation options. This strategy has been utilized in other population health studies(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) with completion rates and costs similar to those of in-person enrollment and collection.\u003c/p\u003e \u003cp\u003eOther strategies accounted for more than 15% of enrollments, with most (approximately 91%) of these enrollments occurring in person. Although it is not possible to discern the exact source that motivated these Veterans to participate, we assume that they likely learned about the program from MVP at a local site or through engagement with digital media because they did not receive contact via the other recruitment strategies during the MMC timeframe. The digital marketing strategy effectively promoted MVP by leveraging advertising across digital domains. An integrative literature review on the use of social media for participant recruitment in research revealed that just over half (51%) of the studies examined employed social media alongside other recruitment methods.(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) Research indicates that social media has the advantage of reaching specific populations, such as individuals with certain medical conditions, those with low incomes, and racial and ethnic groups that are historically underrepresented in research.(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) Digital recruitment strategies for the MMC including mass emails, social media, and online advertising campaigns, enabled engagement with Veterans who may not frequently use VHA services, or who live in rural areas, and do not typically respond to printed mailings, or generally may not participate in research.\u003c/p\u003e \u003cp\u003eUsing novel and existing recruitment methods for the MMC extended reach beyond standard MVP mailing and in-person recruitment efforts. A systematic review and meta-analysis of recruitment strategies in large population biobank cohort studies(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) found that participation rates were not significantly tied to a particular recruitment strategy. The MMC strategies described here demonstrated the success of the surround sound model in enrolling close to 110,000 Veterans to reach the million milestone. These participants included Veterans who were not previously enrolled through MVP\u0026rsquo;s traditional recruitment efforts at local sites, as well as those who may have been hesitant to volunteer or had limited resources to engage in the research program. In the future, MVP aims to expand as broadly as possible to provide enrollment opportunities for all Veterans, such as through continued mass emails or remote enrollment locations, regardless of their geographic location or access to technology. This work represents the first characterization of the MVP cohort with more than a million participants, including comparisons between those who enrolled before the MMC and those who enrolled during the MMC, and between the cumulative MVP cohort and the non-MVP VHA user population. Overall, only minor differences were identified in the pre-MMC and MMC enrollee comparisons and between the cumulative MVP cohort and non-MVP VHA users in terms of sex, birth year, race, ethnicity, and service era. Most participants in the MVP cohort were male. However, the female participants make up the largest sample of any VA research study to date (approximately 107,000), which is significant given that female Veterans constitute the fastest-growing population of VHA users.(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) The female MVP participants were slightly younger and represented more diverse racial and ethnic groups. Additionally, more than 425,000 MVP participants reported military service 1990 and later, which comprises the fastest growing service eras.(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) The data resources developed from MVP offer opportunities for future population-level research to focus on a variety of understudied Veteran experiences, such as toxic exposures, sex-specific health needs, and rurality.\u003c/p\u003e \u003cp\u003eAmong the MVP cohort, most survey respondents reported good or better health, which aligns with findings from a 2024 report by the Survey of Enrollees indicating that 62% of Veterans using the VA healthcare system reported good, very good, or excellent health.(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) However, health records revealed that most MVP participants in the cohort had at least one comorbidity and a mean BMI in the overweight range. Self-report survey responses revealed that female participants are more physically active and follow a diet more closely aligned with the heart-healthy DASH dietary pattern. Paradoxically, a greater proportion of female MVP participants reported being current smokers and drinkers. This discrepancy could be due to the older average age of male Veterans compared to female Veterans. Importantly, nearly 55% of the MVP cohort utilize VHA for most or all of their healthcare needs. This closely matches the Survey of Enrollees, which reported that 46% of more than 8\u0026nbsp;million Veterans either had no healthcare needs outside the VA or did not use non-VA healthcare services.(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) Understanding the health profile of the MVP sample has significant implications for the future development of VHA resources and policies.\u003c/p\u003e \u003cp\u003eThere are important limitations to consider when interpreting the effectiveness of the recruitment strategies and the overall cohort description. For example, the recruitment efforts during the MMC were not mutually exclusive, since Veterans could have been contacted through more than one strategy before enrolling. Attributing enrollment to the recruitment strategy associated with the last recorded contact with a Veteran may be imprecise due to the concurrent use of multiple recruitment strategies. As noted, enrollment rates from other strategies cannot be calculated since the denominator of unique Veterans contacted was unknown. Despite these limitations, the surround sound method of broadcasting MVP through multiple channels may have influenced the decision to participate. While this work does not account for the number and combinations of contact strategies used, future efforts will assess their effects on overall MVP enrollment.\u003c/p\u003e \u003cp\u003eAlthough MVP participants are generally comparable to the broader VHA population, MVP uses a convenience sampling approach. Ongoing work aims to better understand how representative the MVP sample is of the overall VHA population across various health domains. MVP's integration within the VHA as a learning healthcare system, along with its linkages to health records and other data sources, facilitates investigations that can lead to advancements specific to Veteran health, directly translating into improvements in clinical care. Additionally, ongoing pilot work is exploring the feasibility and capability of returning clinically actionable genetic findings to MVP participants.(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e)\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe MMC successfully reached its target of one million MVP participants by employing a variety of recruitment strategies. Ongoing MVP recruitment efforts aim to ensure representation of Veteran subpopulations to support research that addresses the evolving healthcare needs of all Veterans. The substantial number of MVP participants and the resulting curated health and genetic data provide a novel and historic resource, significantly benefiting scientific research and supporting clinical care improvements within the VHA healthcare system.(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) Continued investigations using MVP data can lead to discoveries about Veteran-specific health experiences, including mental health conditions, toxic exposures, and general population health concerns related to aging, cardiometabolic diseases, and cancer. Owing to the generous participation and ongoing engagement of Veterans in MVP, researchers throughout the VA and approved academic collaborators continue to produce a large body of research using the MVP data repository.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDASH:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eDietary\u0026nbsp;Approaches\u0026nbsp;to\u0026nbsp;Stop\u0026nbsp;Hypertension\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMMC\u003c/em\u003e:\u0026nbsp;\u003c/strong\u003eMillion Milestone Campaign\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMVP:\u003c/em\u003e\u003c/strong\u003e Million Veteran Program\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eORD:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eOffice of Research and Development\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eVA\u003c/em\u003e\u003c/strong\u003e: Department of Veterans Affairs\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eVHA\u003c/em\u003e\u003c/strong\u003e: Veterans Health Administration\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was conducted according to the guidelines of the Declaration of Helsinki and approved by the Department of Veteran Affairs Central IRB (protocol code MVP001, approved in 2010).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe MVP protocol is approved by the Department of Veterans Affairs VA Central Institutional Review Board (#10\u0026ndash;02). All participants provide consent to participate in MVP.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMVP participants consent to their information being used for scientific publications and that they will not be individually identified.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs described in detail in Gaziano\u0026rsquo;s MVP introduction paper(3) and MVP website [https://mvp. va.gov], access to MVP data and/or samples is governed by the scope of MVP informed consent and VA policies and requires scientific review by appropriate VA review committees. Data are currently available to VA investigators and other approved partners with plans for expanding to non-VA investigators in the future. Enquiries can be directed to the MVP Program Office [[email protected]]. For accessing the resource, a consortium approach is strongly encouraged; collaborators from university affiliates and other organizations working with VA investigators are encouraged [https://www.research.va.gov/MVP/research.cfm].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research is based on data from the VA Million Veteran Program supported by award MVP#000 from the Department of Veterans Affairs. This publication does not represent the views of the Department of Veterans Affairs or the U.S. government. The authors have no financial disclosures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSBW: Conceptualization, Methodology, Investigation, Resources, Writing - Original Draft, Writing - Review \u0026amp; Editing, Supervision\u003c/p\u003e\n\u003cp\u003eARW: Writing - Original Draft, Writing - Review \u0026amp; Editing, Project Administration\u003c/p\u003e\n\u003cp\u003eJVB: Conceptualization, Methodology, Writing - Original Draft, Writing - Review \u0026amp; Editing\u003c/p\u003e\n\u003cp\u003eJED: Conceptualization, Methodology, Resources, Writing - Review \u0026amp; Editing\u003c/p\u003e\n\u003cp\u003eEML: Software, Formal Analysis, Resources, Data Curation, Writing - Review \u0026amp; Editing\u003c/p\u003e\n\u003cp\u003eSAM: Software, Formal Analysis, Resources, Data Curation, Writing - Review \u0026amp; Editing\u003c/p\u003e\n\u003cp\u003eYL: Software, Formal Analysis, Resources, Data Curation, Writing - Review \u0026amp; Editing\u003c/p\u003e\n\u003cp\u003eYH: Resources, Data Curation, Writing - Review \u0026amp; Editing, Supervision\u003c/p\u003e\n\u003cp\u003eMP: Software, Formal Analysis, Resources, Data Curation, Writing - Review \u0026amp; Editing\u003c/p\u003e\n\u003cp\u003eSW: Software, Formal Analysis, Resources, Data Curation, Writing - Review \u0026amp; Editing\u003c/p\u003e\n\u003cp\u003eXTN: Conceptualization, Writing - Review \u0026amp; Editing, Supervision\u003c/p\u003e\n\u003cp\u003eLC: Resources, Writing - Review \u0026amp; Editing\u003c/p\u003e\n\u003cp\u003eAV: Resources, Writing - Review \u0026amp; Editing\u003c/p\u003e\n\u003cp\u003eEN: Resources, Writing - Review \u0026amp; Editing\u003c/p\u003e\n\u003cp\u003eAF: Resources, Writing - Review \u0026amp; Editing\u003c/p\u003e\n\u003cp\u003eKMH: Conceptualization, Methodology, Writing - Review \u0026amp; Editing\u003c/p\u003e\n\u003cp\u003eRP: Resources, Writing - Review \u0026amp; Editing\u003c/p\u003e\n\u003cp\u003eSS: Resources, Writing - Review \u0026amp; Editing\u003c/p\u003e\n\u003cp\u003eLES: Resources, Writing - Review \u0026amp; Editing\u003c/p\u003e\n\u003cp\u003eSP: Resources, Writing - Review \u0026amp; Editing\u003c/p\u003e\n\u003cp\u003eKC: Conceptualization, Writing - Review \u0026amp; Editing, Supervision\u003c/p\u003e\n\u003cp\u003eJM: Resources, Writing - Review \u0026amp; Editing\u003c/p\u003e\n\u003cp\u003eGH: Resources, Writing - Review \u0026amp; Editing\u003c/p\u003e\n\u003cp\u003ePST: Conceptualization, Resources, Writing - Review \u0026amp; Editing, Supervision\u003c/p\u003e\n\u003cp\u003eSM: Conceptualization, Resources, Writing - Review \u0026amp; Editing, Supervision\u003c/p\u003e\n\u003cp\u003eJMG: Conceptualization, Resources, Writing - Review \u0026amp; Editing, Supervision\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the Veteran participants for their generous contributions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors would also like to acknowledge the following:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMVP Program Office\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e-\u0026nbsp;\u003c/strong\u003eSumitra Muralidhar, Ph.D., Program Director US Department of Veterans Affairs, 810 Vermont Avenue NW, Washington, DC 20420\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e-\u0026nbsp;\u003c/strong\u003eJennifer Moser, Ph.D., Associate Director, Scientific Programs US Department of Veterans Affairs, 810 Vermont Avenue NW, Washington, DC 20420\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e-\u0026nbsp;\u003c/strong\u003eJennifer E. Deen, B.S., Associate Director, Cohort \u0026amp; Public Relations US Department of Veterans Affairs, 810 Vermont Avenue NW, Washington, DC 20420\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMVP Executive Committee\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e- Co-Chair: Philip S. Tsao, Ph.D. VA Palo Alto Health Care System, 3801 Miranda Avenue, Palo Alto, CA 94304\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Co-Chair: Sumitra Muralidhar, Ph.D. US Department of Veterans Affairs, 810 Vermont Avenue NW, Washington, DC 20420\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- J. Michael Gaziano, M.D., M.P.H. VA Boston Healthcare System, 150 S. Huntington Avenue, Boston, MA 02130\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Elizabeth Hauser, Ph.D. Durham VA Medical Center, 508 Fulton Street, Durham, NC 27705\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e-\u0026nbsp;\u003c/strong\u003eAmy Kilbourne, Ph.D., M.P.H. VA HSR\u0026amp;D, 2215 Fuller Road, Ann Arbor, MI 48105\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Michael Matheny, M.D., M.S., M.P.H. VA Tennessee Valley Healthcare System, 1310 24th Ave. South, Nashville, TN 37212\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Dave Oslin, M.D. Philadelphia VA Medical Center, 3900 Woodland Avenue, Philadelphia, PA 19104\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Deepak Voora, MD Durham VA Medical Center, 508 Fulton Street, Durham, NC 27705\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMVP Co-Principal Investigators\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e-\u0026nbsp;\u003c/strong\u003eJ. Michael Gaziano, M.D., M.P.H. VA Boston Healthcare System, 150 S. Huntington Avenue, Boston, MA 02130\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e-\u0026nbsp;\u003c/strong\u003ePhilip S. Tsao, Ph.D. VA Palo Alto Health Care System, 3801 Miranda Avenue, Palo Alto, CA 94304\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMVP Core Operations\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e- Jessica V. Brewer, M.P.H., Director, MVP Cohort Operations VA Boston Healthcare System, 150 S. Huntington Avenue, Boston, MA 02130\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Mary T. Brophy M.D., M.P.H., Director, VA Central Biorepository VA Boston Healthcare System, 150 S. Huntington Avenue, Boston, MA 02130\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Kelly Cho, M.P.H, Ph.D., Director, MVP Phenomics VA Boston Healthcare System, 150 S. Huntington Avenue, Boston, MA 02130\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e-\u0026nbsp;\u003c/strong\u003eLori Churby, B.S., Director, MVP Regulatory Affairs VA Palo Alto Health Care System, 3801 Miranda Avenue, Palo Alto, CA 94304\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Scott L. DuVall, Ph.D., Director, VA Informatics and Computing Infrastructure (VINCI) VA Salt Lake City Health Care System, 500 Foothill Drive, Salt Lake City, UT 84148\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Saiju Pyarajan Ph.D., Director, Data and Computational Sciences VA Boston Healthcare System, 150 S. Huntington Avenue, Boston, MA 02130\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Robert Ringer, Pharm.D., Director, VA Albuquerque Central Biorepository New Mexico VA Health Care System, 1501 San Pedro Drive SE, Albuquerque, NM 87108\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Luis E. Selva, Ph.D., Director, MVP Biorepository Coordination VA Boston Healthcare System, 150 S. Huntington Avenue, Boston, MA 02130\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Shahpoor (Alex) Shayan, M.S., Director, MVP PRE Informatics VA Boston Healthcare System, 150 S. Huntington Avenue, Boston, MA 02130\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Brady Stephens, M.S., Principal Investigator, MVP Information Center Canandaigua VA Medical Center, 400 Fort Hill Avenue, Canandaigua, NY 14424\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Stacey B. Whitbourne, Ph.D., Director, MVP Cohort Development and Management VA Boston Healthcare System, 150 S. Huntington Avenue, Boston, MA 02130\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMVP Local Site Investigators\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e- Samuel Aguayo, M.D., Phoenix VA Health Care System, 650 E. Indian School Road, Phoenix, AZ 85012\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Sunil Ahuja, M.D., South Texas Veterans Health Care System, 7400 Merton Minter Boulevard, San Antonio, TX 78229\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Kathrina Alexander, M.D., Veterans Health Care System of the Ozarks, 1100 North College Avenue, Fayetteville, AR 72703\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Harshitha Kota, M.D., Columbia VA Health Care System, Garners Ferry Road, Columbia, SC 29209\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Prakash Balasubramanian, M.D., William S. Middleton Memorial Veterans Hospital, 2500 Overlook Terrace, Madison, WI 53705\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Zuhair Ballas, M.D., Iowa City VA Health Care System, 601 Highway 6 West, Iowa City, IA 52246-2208\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Jean Beckham, Ph.D., Durham VA Medical Center, 508 Fulton Street, Durham, NC 27705\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Sujata Bhushan, M.D., VA North Texas Health Care System, 4500 S. Lancaster Road, Dallas, TX 75216\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Edward Boyko, M.D., VA Puget Sound Health Care System, 1660 S. Columbian Way, Seattle, WA 98108-1597\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- David Cohen, M.D., Portland VA Medical Center, 3710 SW U.S. Veterans Hospital Road, Portland, OR 97239\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Louis Dellitalia, M.D., Birmingham VA Medical Center, 700 S. 19th Street, Birmingham AL 35233\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Mona Brake, M.D., Robert J. Dole VA Medical Center, 5500 East Kellogg Drive, Wichita, KS 67218-1607\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Joseph Fayad, M.D., VA Southern Nevada Healthcare System, 6900 North Pecos Road, North Las Vegas, NV 89086\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Daryl Fujii, Ph.D., VA Pacific Islands Health Care System, 459 Patterson Rd, Honolulu, HI 96819\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Saib Gappy, M.D., John D. Dingell VA Medical Center, 4646 John R Street, Detroit, MI 48201\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Frank Gesek, Ph.D., White River Junction VA Medical Center, 163 Veterans Drive, White River Junction, VT 05009\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Jennifer Greco, M.D., Sioux Falls VA Health Care System, 2501 W 22nd Street, Sioux Falls, SD 57105\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Michael Godschalk, M.D., Richmond VA Medical Center, 1201 Broad Rock Blvd., Richmond, VA 23249\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Todd W. Gress, M.D., Ph.D., Hershel \u0026ldquo;Woody\u0026rdquo; Williams VA Medical Center, 1540 Spring Valley Drive, Huntington, WV 25704\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Samir Gupta, M.D., M.S.C.S., VA San Diego Healthcare System, 3350 La Jolla Village Drive, San Diego, CA 92161\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Salvador Gutierrez, M.D., Edward Hines, Jr. VA Medical Center, 5000 South 5th Avenue, Hines, IL 60141\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- John Harley, M.D., Ph.D., Cincinnati VA Medical Center, 3200 Vine Street, Cincinnati, OH 45220\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Tze Shien Lo, M.D., Fargo VA Health Care System, 2101 N. Elm, Fargo, ND 58102\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Mark Hamner, M.D., Ralph H. Johnson VA Medical Center, 109 Bee Street, Mental Health Research, Charleston, SC 29401\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Adriana Hung, M.D., M.P.H., VA Tennessee Valley Healthcare System, 1310 24th Avenue, South Nashville, TN 37212\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Robin Hurley, M.D., W.G. (Bill) Hefner VA Medical Center, 1601 Brenner Ave, Salisbury, NC 28144\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Radhika Manhapra, M.D., Hampton VA Medical Center, 100 Emancipation Drive, Hampton, VA 23667\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Frank Jacono, M.D., VA Northeast Ohio Healthcare System, 10701 East Boulevard, Cleveland, OH 44106\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Darshana Jhala, M.D., Philadelphia VA Medical Center, 3900 Woodland Avenue, Philadelphia, PA 19104\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Scott Kinlay, M.B.B.S., Ph.D., VA Boston Healthcare System, 150 S. Huntington Avenue, Boston, MA 02130\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Gerald Wayne Dryden, Jr., M.D., Ph.D., Louisville VA Medical Center, 800 Zorn Avenue, Louisville, KY 40206\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Brooks Robey, M.D., Southeast Louisiana Veterans Health Care System, 2400 Canal Street, New Orleans, LA 70119\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Peter Liang, M.D., M.P.H., VA New York Harbor Healthcare System, 423 East 23rd Street, New York, NY 10010\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Suthat Liangpunsakul, M.D., M.P.H., Richard Roudebush VA Medical Center, 1481 West 10th Street, Indianapolis, IN 46202\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Jack Lichy, M.D., Ph.D., Washington DC VA Medical Center, 50 Irving St, Washington, D. C. 20422\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- C. Scott Mahan, M.D., Charles George VA Medical Center, 1100 Tunnel Road, Asheville, NC 28805\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Ronnie Marrache, M.D., VA Maine Healthcare System, 1 VA Center, Augusta, ME 04330\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Stephen Mastorides, M.D., James A. Haley Veterans\u0026rsquo; Hospital, 13000 Bruce B. Downs Blvd, Tampa, FL 33612\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Amneet S. Rai, Pharm.D., VA Sierra Nevada Health Care System, 975 Kirman Avenue, Reno, NV 89502\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Kristin Mattocks, Ph.D., M.P.H., Central Western Massachusetts Healthcare System, 421 North Main Street, Leeds, MA 01053\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Adriana Martin, M.D., Ph.D., Southern Arizona VA Health Care System, 3601 S 6th Avenue, Tucson, AZ 85723\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Jonathan Moorman, M.D., Ph.D., James H. Quillen VA Medical Center, Corner of Lamont \u0026amp; Veterans Way, Mountain Home, TN 37684\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Timothy Morgan, M.D., VA Long Beach Healthcare System, 5901 East 7th Street Long Beach, CA 90822\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Maureen Murdoch, M.D., M.P.H., Minneapolis VA Health Care System, One Veterans Drive, Minneapolis, MN 55417\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- James Norton, Ph.D., VA Health Care Upstate New York, 113 Holland Avenue, Albany, NY 12208\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Olaoluwa Okusaga, M.D., Michael E. DeBakey VA Medical Center, 2002 Holcombe Blvd, Houston, TX 77030\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Kris Ann Oursler, M.D., Salem VA Medical Center, 1970 Roanoke Blvd, Salem, VA 24153\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Elizabeth S. Bast, M.D., M.P.H., Miami VA Health Care System, 1201 NW 16th Street, 11 GRC, Miami FL 33125\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Robert Zwolak, M.D., Ph.D., Manchester VA Medical Center, 718 Smyth Road, Manchester, NH 03104\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Seema Joshi, MD, FACP, ABOIM, VA Eastern Kansas Health Care System, 4101 S 4th Street Trafficway, Leavenworth, KS 66048\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Michael Rauchman, M.D., St. Louis VA Health Care System, 915 North Grand Blvd, St. Louis, MO 63106\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Paul Dougherty, D.C., Syracuse VA Medical Center, 800 Irving Avenue, Syracuse, NY 13210\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Satish Sharma, M.D., Providence VA Medical Center, 830 Chalkstone Avenue, Providence, RI 02908\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- River Smith, Ph.D., Eastern Oklahoma VA Health Care System, 1011 Honor Heights Drive, Muskogee, OK 74401\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Peruvemba Sriram, M.D., N. FL/S. GA Veterans Health System, 1601 SW Archer Road, Gainesville, FL 32608\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Patrick Strollo, Jr., M.D., VA Pittsburgh Health Care System, University Drive, Pittsburgh, PA 15240\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Neeraj Tandon, M.D., Overton Brooks VA Medical Center, 510 East Stoner Ave, Shreveport, LA 71101\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Philip Tsao, Ph.D., VA Palo Alto Health Care System, 3801 Miranda Avenue, Palo Alto, CA 94304-1290\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Gerardo Villarreal, M.D., New Mexico VA Health Care System, 1501 San Pedro Drive, S.E. Albuquerque, NM 87108\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Michael K. Ong, M.D., Ph.D., VA Greater Los Angeles Health Care System, 11301 Wilshire Blvd, Los Angeles, CA 90073\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Jessica Walsh, M.D., VA Salt Lake City Health Care System, 500 Foothill Drive, Salt Lake City, UT 84148\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- John Wells, Ph.D., Edith Nourse Rogers Memorial Veterans Hospital, 200 Springs Road, Bedford, MA 01730\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Jeffrey Whittle, M.D., M.P.H., Clement J. Zablocki VA Medical Center, 5000 West National Avenue, Milwaukee, WI 53295\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Mary Whooley, M.D., San Francisco VA Health Care System, 4150 Clement Street, San Francisco, CA 94121\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Daniel J. Hogan, M.D., Bay Pines VA Healthcare System, 10,000 Bay Pines Blvd Bay Pines, FL 33744\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Peter Wilson, M.D., Atlanta VA Medical Center, 1670 Clairmont Road, Decatur, GA 30033\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Junzhe Xu, M.D., VA Western New York Healthcare System, 3495 Bailey Avenue, Buffalo, NY 14215-1199\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Shing Shing Yeh, Ph.D., M.D., Northport VA Medical Center, 79 Middleville Road, Northport, NY 11768\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Robyn Chase, D.O., Northern Arizona VA Health Care System, 500 Highway 89 North Prescott, AZ 86313\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Eknath Naik, M.D., Ph.D., West Palm Beach VA Medical Center, 7305 North Military Trail West Palm Beach, FL 33410\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Ismene Petrakis, M.D., VA Connecticut Healthcare System 950 Campbell Avenue West Haven, CT 06516\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Andrew Yen, M.D., VA Northern California Health Care System 10535 Hospital Way, Mather, CA 95655\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, et al. UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age. PLOS Med. 2015 Mar 31;12(3):e1001779. \u003c/li\u003e\n\u003cli\u003eHedderson MM, Ferrara A, Avalos LA, Van den Eeden SK, Gunderson EP, Li DK, et al. The Kaiser Permanente Northern California research program on genes, environment, and health (RPGEH) pregnancy cohort: study design, methodology and baseline characteristics. BMC Pregnancy Childbirth. 2016 Nov 29;16(1):381. \u003c/li\u003e\n\u003cli\u003eGaziano JM, Concato J, Brophy M, Fiore L, Pyarajan S, Breeling J, et al. Million Veteran Program: A mega-biobank to study genetic influences on health and disease. J Clin Epidemiol. 2016 Feb 1;70:214\u0026ndash;23. \u003c/li\u003e\n\u003cli\u003eAll of Us Research Program Investigators, Denny JC, Rutter JL, Goldstein DB, Philippakis A, Smoller JW, et al. The \u0026ldquo;All of Us\u0026rdquo; Research Program. N Engl J Med. 2019 Aug 15;381(7):668\u0026ndash;76. \u003c/li\u003e\n\u003cli\u003eKaufman D, Murphy J, Erby L, Hudson K, Scott J. Veterans\u0026rsquo; attitudes regarding a database for genomic research. Genet Med. 2009 May 1;11(5):329\u0026ndash;37. \u003c/li\u003e\n\u003cli\u003eScience Corner | Veterans Affairs [Internet]. [cited 2025 Sep 10]. Available from: https://www.mvp.va.gov/pwa/science\u003c/li\u003e\n\u003cli\u003eNguyen XMT, Whitbourne SB, Li Y, Quaden RM, Song RJ, Nguyen HNA, et al. Data Resource Profile: Self-reported data in the Million Veteran Program: survey development and insights from the first 850 736 participants. Int J Epidemiol. 2023 Feb 1;52(1):e1\u0026ndash;17. \u003c/li\u003e\n\u003cli\u003eWhitbourne SB, Nguyen XMT, Song RJ, Lord E, Lyden M, Harrington KM, et al. Million Veteran Program\u0026rsquo;s response to COVID-19: Survey development and preliminary findings. PloS One. 2022;17(4):e0266381. \u003c/li\u003e\n\u003cli\u003eBriggs R, Stipp H. How Internet Advertising Works. In: Webvertising: The Ultimate Internet Advertising Guide [Internet]. Wiesbaden: Vieweg+Teubner Verlag; 2000 [cited 2025 Jul 1]. p. 99\u0026ndash;128. Available from: https://doi.org/10.1007/978-3-322-86793-3_11\u003c/li\u003e\n\u003cli\u003eAndrade C. Mean Difference, Standardized Mean Difference (SMD), and Their Use in Meta-Analysis: As Simple as It Gets. J Clin Psychiatry. 2020 Sep 22;81(5):20f13681. \u003c/li\u003e\n\u003cli\u003eSullivan GM, Feinn R. Using Effect Size\u0026mdash;or Why the P Value Is Not Enough. J Grad Med Educ. 2012 Sep;4(3):279\u0026ndash;82. \u003c/li\u003e\n\u003cli\u003eCorporate Data Warehouse (CDW) [Internet]. 2023 [cited 2025 Jul 1]. Available from: https://www.hsrd.research.va.gov/for_researchers/cdw.cfm\u003c/li\u003e\n\u003cli\u003eData Standardization \u0026ndash; OHDSI [Internet]. [cited 2025 Jul 1]. Available from: https://www.ohdsi.org/data-standardization/\u003c/li\u003e\n\u003cli\u003eKoenig MR, Wesselink AK, Kuriyama AS, Chaiyasarikul A, Hatch EE, Wise LA. Feasibility of mail-based biospecimen collection in an online preconception cohort study. Front Reprod Health [Internet]. 2023 Jan 9 [cited 2025 Jul 1];4. Available from: https://www.frontiersin.org/journals/reproductive-health/articles/10.3389/frph.2022.1052231/full\u003c/li\u003e\n\u003cli\u003eDarko EM, Kleib M, Olson J. Social Media Use for Research Participant Recruitment: Integrative Literature Review. J Med Internet Res. 2022 Aug 4;24(8):e38015. \u003c/li\u003e\n\u003cli\u003eTopolovec-Vranic J, Natarajan K. The Use of Social Media in Recruitment for Medical Research Studies: A Scoping Review. J Med Internet Res. 2016 Nov 7;18(11):e5698. \u003c/li\u003e\n\u003cli\u003eBaldyga K, Iloputaife I, Taffet G, LaGanke N, Manor B, Lipsitz LA, et al. Comparison of targeted web-based advertising versus traditional methods for recruiting older adults into clinical trials. J Am Geriatr Soc. 2025;73(1):182\u0026ndash;92. \u003c/li\u003e\n\u003cli\u003eVan Zon SKR, Scholtens S, Reijneveld SA, Smidt N, B\u0026uuml;ltmann U. Active recruitment and limited participant-load related to high participation in large population-based biobank studies. J Clin Epidemiol. 2016 Oct;78:52\u0026ndash;62. \u003c/li\u003e\n\u003cli\u003eGoldstein KM, Pace R, Dancu C, Raman SR, Bridges-Curry Z, Klimek-Johnson P, et al. An Evidence Map of the Women Veterans\u0026rsquo; Health Literature, 2016 to 2023: A Systematic Review. JAMA Netw Open. 2025 Apr 22;8(4):e256372. \u003c/li\u003e\n\u003cli\u003eVeterans Health Administration, Office of Policy and Planning [Internet]. [cited 2025 Aug 26]. National Center for Veterans Analysis and Statistics. Available from: https://www.va.gov/vetdata/Veteran_Population.asp\u003c/li\u003e\n\u003cli\u003eDepartment of Veterans Affairs. 2024 Survey of Veteran Enrollees\u0026rsquo; Health and use of Health Care. Washington, D.C.: Office of Strategic Planning and Analysis; 2024 Jan. \u003c/li\u003e\n\u003cli\u003eVassy JL, Brunette CA, Yi T, Harrison A, Cardellino MP, Assimes TL, et al. Design and pilot results from the Million Veteran Program Return Of Actionable Results (MVP-ROAR) Study. Am Heart J. 2024 Oct;276:99\u0026ndash;109. \u003c/li\u003e\n\u003cli\u003eMontgomery RB, Lynch JA, Brown J, Maxwell KN, Kabilovic N, Stoll K, et al. Remote delivery of cancer genetic testing in veterans with metastatic prostate cancer: A Million Veteran Program study. J Clin Oncol. 2024 Jun 1;42(16_suppl):1541\u0026ndash;1541. \u003c/li\u003e\n\u003cli\u003eKaminsky LA, Montoye AHK. Physical Activity and Health: What Is the Best Dose? \u003cem\u003eJ Am Heart Assoc\u003c/em\u003e. 2014;3(5). doi:10.1161/jaha.114.001430\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Veterans, Genetics, Health, Research, Population study","lastPublishedDoi":"10.21203/rs.3.rs-8272722/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8272722/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe Department of Veterans Affairs (VA) Million Veteran Program (MVP) began in 2011 with the goal of recruiting at least one million Veterans to participate in a large population genetic and health research program. The Million Milestone Campaign (MMC) was implemented between 2022 and 2023 to reach pre-pandemic recruitment rates and the millionth enrollee by Veterans Day of 2023. The objectives are to describe and evaluate the effectiveness of MMC recruitment strategies and to characterize the cumulative MVP cohort.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eDuring the MMC, multiple recruitment strategies, including paper invitation mailings, recruitment calls, mass emails, enroll by mail invitations, and digital marketing, were implemented using a \u0026ldquo;surround sound\u0026rdquo; model. Strategy effectiveness was assessed via enrollment rates for each enrollment modality from September 2022 through July 2024. Email and digital media reach were measured by click-through rates and ads viewed. Standard mean differences were used to compare the demographics of enrollees during the MMC with those of enrollees prior, and the cumulative MVP cohort with those of non-MVP VA users.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eMore than 6.5\u0026nbsp;million Veterans were contacted and 109,912 enrolled during the MMC. Enrollment rates from recruitment calls (13.7%) were highest, followed by invitations to enroll by mail (7.8%), email (0.8%), and paper invitations (0.6%). Emails yielded the most enrollments (n\u0026thinsp;=\u0026thinsp;46,999, 43% of total enrollments), followed by recruitment calls (n\u0026thinsp;=\u0026thinsp;22,069, 20%), paper invitations (n\u0026thinsp;=\u0026thinsp;16,062, 14.6%), and invitations to enroll by mail (n\u0026thinsp;=\u0026thinsp;7,762,7.1%). There were small differences in demographics between those enrolled during the MMC and prior enrollees (n\u0026thinsp;=\u0026thinsp;906,672); likewise, the cumulative cohort (N\u0026thinsp;=\u0026thinsp;1,016,584) of largely male participants resembles the non-MVP VA user population.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIn the year leading to the culmination of one million MVP participants, the multi-strategy MMC was effective in recruiting nearly 110,000 Veterans and informing ongoing recruitment. Combined strategies used in a surround sound model of recruitment yielded enrollments that aligned with pre-pandemic rates and accumulated one-tenth the largest cohort of Veterans in VA research history. MVP\u0026rsquo;s integration within the VHA as a learning health care system, along with linkages to health records and other data sources, is a resource for investigators to improve Veteran health care with precision medicine.\u003c/p\u003e","manuscriptTitle":"Overview of Recruitment Strategies to Reach the Million Milestone and Characterization of the Million Veteran Program Cohort","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-17 07:49:10","doi":"10.21203/rs.3.rs-8272722/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e4660196-562d-47b9-b590-b68053fc5e9c","owner":[],"postedDate":"December 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-11T13:27:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-17 07:49:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8272722","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8272722","identity":"rs-8272722","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}