Prospective Analysis of Health and Mortality Risk in Veteran and Non-Veteran Participants in the Women's Health Initiative

Published:September 29, 2015DOI:https://doi.org/10.1016/j.whi.2015.08.006

      Abstract

      Background

      The health of postmenopausal women veterans is a neglected area of study. A stronger empirical evidence base is needed, and would inform the provision of health care for the nearly 1 million U.S. women veterans currently 50 years of age or older. To this end, the present work compares salient health outcomes and risk of all-cause mortality among veteran and non-veteran participants of the Women's Health Initiative (WHI).

      Methods

      This study features prospective analysis of long-term health outcomes and mortality risk (average follow-up, 8 years) among the 3,706 women veterans and 141,009 non-veterans who participated in the WHI Observational Study or Clinical Trials. Outcome measurements included confirmed incident cases of cardiovascular disease (CVD), cancer, diabetes, hip fractures, and all-cause mortality.

      Results

      We identified 17,968 cases of CVD, 19,152 cases of cancer, 18,718 cases of diabetes, 2,817 cases of hip fracture, and 13,747 deaths. In Cox regression models adjusted for age, sociodemographic variables, and health risk factors, veteran status was associated with significantly increased risk of all-cause mortality (hazard ratio [HR], 1.13; 95% CI, 1.03–1.23), but not with risk of CVD (HR, 1.00; 95% CI, 0.90–1.11), cancer (HR, 1.04; 95% CI, 0.95–1.14), hip fracture (HR, 1.16; 95% CI, 0.94–1.43), or diabetes (HR, 1.00; 95% CI, 0.89–1.1).

      Conclusions

      Women veterans' postmenopausal health, particularly risk for all-cause mortality, warrants further consideration. In particular, efforts to identify and address modifiable risk factors associated with all-cause mortality are needed.
      Over the past several decades, considerable empirical attention has been directed to the associations between military service, health, and mortality risk (
      • Kang H.K.
      • Bullman T.A.
      Mortality among U.S. veterans of the Persian Gulf War.
      ,
      • McLaughlin R.
      • Nielsen L.
      • Waller M.
      An evaluation of the effect of military service on mortality: Quantifying the healthy soldier effect.
      ), with a steadily increasing focus on women (
      • Cypel Y.
      • Kang H.
      Mortality patterns among women Vietnam-era veterans: Results of a retrospective cohort study.
      ,
      • Dalager N.A.
      • Kang H.K.
      • Thomas T.L.
      Cancer mortality patterns among women who served in the military: The Vietnam experience.
      ,
      • Kang H.K.
      • Cypel Y.
      • Kilbourne A.M.
      • Magruder K.M.
      • Serpi T.
      • Collins J.F.
      • Spiro 3rd, A.
      • et al.
      Mortality study of female US Vietnam era veterans, 1965–2010.
      ,
      • Thomas T.L.
      • Kang H.K.
      • Dalager N.A.
      Mortality among women Vietnam veterans, 1973–1987.
      ,
      • Vajdic C.M.
      • Stavrou E.P.
      • Ward R.L.
      • Falster M.O.
      • Pearson S.A.
      Minimal excess risk of cancer and reduced risk of death from cancer in Australian Department of Veterans’ Affairs clients: A record linkage study.
      ,
      • Waller M.
      • McGuire A.C.L.
      Changes over time in the “healthy soldier effect.”.
      ,
      • Yi S.W.
      Cancer incidence in Korean Vietnam veterans during 1992–2003: The Korean Veterans Health Study.
      ), whose representation in the armed forces has grown dramatically over this time period (
      U.S. Department of Veterans AffairsNational Center for Veteran Analysis and Statistics
      Table 6L:VetPop2011.
      ). Although this literature provides a strong foundation, it is limited in size and scope, with a predominant focus on women veterans during their early or midlife years (cf.
      • Dalager N.A.
      • Kang H.K.
      • Thomas T.L.
      Cancer mortality patterns among women who served in the military: The Vietnam experience.
      ). Given that there are nearly 1 million women veterans who are 50 years of age or older, increased research attention to health and mortality risk in women veterans' postmenopausal years is warranted.
      Prior population-based studies have consistently documented decreased risk of morbidity and all-cause mortality among veterans, including women, relative to the general population (
      • Cypel Y.
      • Kang H.
      Mortality patterns among women Vietnam-era veterans: Results of a retrospective cohort study.
      ,
      • Dalager N.A.
      • Kang H.K.
      • Thomas T.L.
      Cancer mortality patterns among women who served in the military: The Vietnam experience.
      ,
      • Kang H.K.
      • Cypel Y.
      • Kilbourne A.M.
      • Magruder K.M.
      • Serpi T.
      • Collins J.F.
      • Spiro 3rd, A.
      • et al.
      Mortality study of female US Vietnam era veterans, 1965–2010.
      ,
      • Thomas T.L.
      • Kang H.K.
      • Dalager N.A.
      Mortality among women Vietnam veterans, 1973–1987.
      ,
      • Vajdic C.M.
      • Stavrou E.P.
      • Ward R.L.
      • Falster M.O.
      • Pearson S.A.
      Minimal excess risk of cancer and reduced risk of death from cancer in Australian Department of Veterans’ Affairs clients: A record linkage study.
      ,
      • Waller M.
      • McGuire A.C.L.
      Changes over time in the “healthy soldier effect.”.
      ,
      • Yi S.W.
      Cancer incidence in Korean Vietnam veterans during 1992–2003: The Korean Veterans Health Study.
      ). This “healthy soldier effect,” typically documented in young to middle-aged veterans, is commonly ascribed to the health and fitness standards associated with military selection, as well as the increased commitment to physical fitness among military populations, and the continuous access to health care that military and veteran populations enjoy (see
      • Kang H.K.
      • Bullman T.A.
      Mortality among U.S. veterans of the Persian Gulf War.
      ,
      • McLaughlin R.
      • Nielsen L.
      • Waller M.
      An evaluation of the effect of military service on mortality: Quantifying the healthy soldier effect.
      ). A very limited literature examining health and mortality risk in older veterans suggests that the “healthy soldier effect” attenuates with time (
      • Liu X.
      • Engle C.
      • Kang H.
      • Cowan D.
      The effect of veteran status on mortality among older Americans and its pathways.
      ,
      • London A.S.
      • Wilmoth J.M.
      Military service and (dis)continuity in the life course: Evidence on disadvantage and mortality from the Health and Retirement Study and Study of Assets and Health among the Oldest-old.
      ,
      • Wilmoth J.M.
      • London A.S.
      • Parker W.M.
      Military service and men’s health trajectories in later life.
      ). Some research, in fact, characterizes a health “cross-over” effect among older veterans (i.e., age ≥70) who, despite many years of good health, evidence greater mortality risk and accelerated health decline, relative to non-veterans (
      • Liu X.
      • Engle C.
      • Kang H.
      • Cowan D.
      The effect of veteran status on mortality among older Americans and its pathways.
      ,
      • London A.S.
      • Wilmoth J.M.
      Military service and (dis)continuity in the life course: Evidence on disadvantage and mortality from the Health and Retirement Study and Study of Assets and Health among the Oldest-old.
      ).
      This health “cross-over” is thought to reflect the latent, cumulative, or synergistic effects of military health risks, high prevalence health risk behaviors (e.g., smoking), and the long-term health consequences of military-specific exposures (e.g., trauma from warzone deployment, military sexual trauma, combat;
      • London A.S.
      • Wilmoth J.M.
      Military service and (dis)continuity in the life course: Evidence on disadvantage and mortality from the Health and Retirement Study and Study of Assets and Health among the Oldest-old.
      ). Moreover, it provides a useful framework with which to conceptualize the possibility of a more distal association between military service—which typically concludes in early adulthood—and health in older adulthood (
      • London A.S.
      • Wilmoth J.M.
      Military service and (dis)continuity in the life course: Evidence on disadvantage and mortality from the Health and Retirement Study and Study of Assets and Health among the Oldest-old.
      ). Although this paradoxical effect would be expected to generalize to women, research evaluating the healthy soldier effect among older women veteran populations is all but nonexistent.
      Given the substantial (and growing) population of women veterans living in the U.S. today, research designed to characterize their postmenopausal health and mortality risks is warranted. The Clinical Trials and Observational Study of the Women's Health Initiative (WHI) program (
      The Women's Health Initiative Study Group
      Design of the Women's Health Initiative clinical trial and observational study.
      ) are well-positioned to address this literature gap. To this end, ours is the first study to evaluate whether women veteran participants in WHI (n = 3,706) have the same risk for key postmenopausal health conditions: cardiovascular disease (CVD), cancer, diabetes, hip fracture, and all-cause mortality, as the non-veteran participants (n = 141,009).

      Materials and Methods

       Overview of the WHI

      The WHI includes a set of three National Institutes of Health (NIH)-sponsored clinical trials and an observational study designed to identify factors associated with the development of heart disease, cancer, and fracture in postmenopausal women (within WHI menopause was defined as no vaginal bleeding for 6 months if ≥55, 12 months for 50- to 54-year-olds, prior hysterectomy, or use of postmenopausal hormones) who were aged 50 to 79 at WHI baseline, between 1993 and 1998 (
      The Women's Health Initiative Study Group
      Design of the Women's Health Initiative clinical trial and observational study.
      ; a comprehensive list of the investigators associated with the implementation of the WHI are presented in the acknowledgement presented in Supplementary Appendix A).
      Participants were recruited from 1993 to 1998 by 40 clinical centers around the country, which helped to ensure racial/ethnic, geographic, and sociodemographic diversity among the study participants. Original study endpoints for the observational study and clinical trials were in 2005. Extension studies are currently collecting follow-up data through 2015. The present work includes follow-up data through 2011, facilitating evaluation of long-term health outcomes over more than 20 consecutive years.
      Institutional review boards at all participating clinical centers reviewed and approved study procedures. All participants provided written informed consent at baseline and again at enrollment in the extension studies. Detailed accounts of the WHI recruitment procedures, study design, and methodology have been previously published (
      • Curb J.D.
      • McTiernan A.
      • Heckbert S.R.
      • Kooperberg C.
      • Stanford J.
      • Neveitt M.
      • Daugherty S.
      • et al.
      Outcomes ascertainment and adjudication methods in the Women’s Health Initiative.
      ,
      • Hays J.
      • Hunt J.
      • Hubbell F.A.
      • Anderson G.L.
      • Limacher M.
      • Allen C.
      • Roussouw J.E.
      The Women’s Health Initiative recruitment methods and results.
      ,
      The Women's Health Initiative Study Group
      Design of the Women's Health Initiative clinical trial and observational study.
      ).

       WHI Data Collection and Adjudication Procedures

      At baseline, participants completed self-report questionnaires designed to gather information related to WHI participants' sociodemographic, medical, and lifestyle characteristics. They also underwent a brief clinical examination that collected height, weight, and blood pressure measurements. WHI study follow-up involved completion of annual, mailed, follow-up questionnaires and regular physical examinations.
      Health conditions identified through these methods were confirmed via local (physicians from the local/regional WHI Clinical Centers who review participants medical record and study related medical documents to assign a diagnosis) and central adjudication (i.e., physicians at the WHI Clinical Coordinating Center and the NIH review and confirm the diagnosis). To minimize the potential for bias, local and central physician adjudicators were restricted in their access to participants' research record such that they were not exposed to any information that could result in unblinding (see
      • Curb J.D.
      • McTiernan A.
      • Heckbert S.R.
      • Kooperberg C.
      • Stanford J.
      • Neveitt M.
      • Daugherty S.
      • et al.
      Outcomes ascertainment and adjudication methods in the Women’s Health Initiative.
      ).

       Outcome Ascertainment

      Morbidity-related outcomes, including incident CVD, malignant cancer, diabetes, and hip fracture were identified by patient self-report via annual study follow-up questionnaires or detected during regularly scheduled medical examinations that were incorporated into the WHI follow-up procedures. All morbidity outcomes were adjudicated centrally, with the exception of diabetes, which was confirmed, centrally, whenever possible.

       Incident CVD

      Incident CVD was identified by patient self-report in annual, mailed follow-up questionnaires, or during the regularly scheduled medical examinations that were incorporated into the WHI follow-up procedures. CVD outcomes included cases with medically adjudicated diagnoses of coronary heart disease, stroke, congestive heart failure, angina, peripheral vascular disease, and coronary revascularization.

       Incident cancer

      Incident cancer was defined as incident cases of invasive or in situ cancers (except non-melanoma skin cancers), which were confirmed by local and central physician adjudication of pathology reports, and then coded according to Surveillance, Epidemiology, and End Results (SEER) standards of cancer classification, using the second edition of the International Classification of Diseases, Oncology (
      ,
      ). All confirmed new incident cases were classified as cancer outcomes. This included “second primary” cancer diagnoses, but not cancer recurrences or instances of premalignant disease.

       Incident diabetes

      Incident diabetes was defined by health care provider diagnosis of diabetes treated with antidiabetic medications (oral hypoglycemic agents or insulin; Curb;
      • de Boer I.H.
      • Tinker L.F.
      • Connelly S.
      • Curb J.D.
      • Howard B.V.
      • Kestenbaum B.
      • et al.
      Women’s Health Initiative Investigators
      Calcium plus vitamin D supplementation and the risk of incident diabetes in the Women’s Health Initiative.
      ,
      • Margolis K.L.
      • Lihong Q.
      • Brzyski R.
      • Bonds D.E.
      • Howard B.V.
      • Kempainen S.
      • et al.
      Women Health Initiative Investigators
      Validity of diabetes self-reports in the Women’s Health Initiative: Comparison with medication inventories and fasting glucose measurements.
      ). Data were gathered by patient self-report, and confirmed, when possible, by local and central adjudication, which included medical record review and laboratory data. Prior work confirms the accuracy of self-reported (treated) diabetes in WHI (
      • Margolis K.L.
      • Lihong Q.
      • Brzyski R.
      • Bonds D.E.
      • Howard B.V.
      • Kempainen S.
      • et al.
      Women Health Initiative Investigators
      Validity of diabetes self-reports in the Women’s Health Initiative: Comparison with medication inventories and fasting glucose measurements.
      ).

       Hip fractures

      Hip fractures included treated (inpatient or outpatient) events; fracture diagnoses were adjudicated (central review) and confirmed by examination of radiographic reports.

       All-cause mortality

      Deaths (all causes) were identified by a variety of sources including annual medical record review, obituary searches, as well as from information provided by WHI participants' proxy informants, and confirmed via the National Death Index Plus (
      • Bilgrad R.
      National Death Index user’s manual.
      ), the “gold standard” for ascertainment of mortality outcomes in epidemiologic studies (
      • Cowper D.C.
      • Kubal J.D.
      • Maynard C.
      • Hynes D.M.
      A primer and comparative review of major US mortality databases.
      ,
      • Doody M.M.
      • Hayes H.M.
      • Bilgrad R.
      Comparability of national death index plus and standard procedures for determining causes of death in epidemiologic studies.
      ,
      • Sohn M.W.
      • Arnold N.
      • Maynard C.
      • Hynes D.M.
      Accuracy and completeness of mortality data in the Department of Veterans Affairs.
      ). In addition, throughout the study observation period, routine efforts were made to match participants who were “lost to follow-up” to the National Death Index Plus, ensuring that data capture for all-cause mortality was as complete as possible.

       Variables

       Exposure (veteran status)

      Participants who at baseline responded affirmatively to the question: “Have you ever served in the armed forces?” were classified as “veterans,” all others as “non-veterans.”

       Covariates

      Covariates were variables, measured at WHI baseline, that were a priori thought to vary by veteran status and/or confer risk for the outcomes of interest (
      • Bass E.
      • French D.D.
      • Bradham D.D.
      • Rubenstein L.Z.
      Risk-adjusted mortality rates of elderly veterans with hip fractures.
      ,
      • Cummings S.R.
      • Nevitt M.C.
      • Browner W.S.
      • Stone K.
      • Fox K.M.
      • Ensrud K.E.
      • Vogt T.M.
      • et al.
      Risk factors for hip fracture in white women. Study of Osteoporotic Fractures Research Group.
      ,
      • Hopper J.L.
      • Seeman E.
      The bone density of female twins discordant for tobacco use.
      ,
      • Mendis S.
      The contribution of the Framingham Heart Study to the prevention of cardiovascular disease: A global perspective.
      ,
      U.S. Department of Health and Human Services
      Smoking and health: A national status report.
      ,
      • van Melle L.P.
      • De Jonge P.
      • Spijkerman T.A.
      • Tijssen J.G.P.
      • Ormel J.
      • van Veldhuisen D.J.
      • van den Berg M.P.
      • et al.
      Prognostic association of depression following myocardial infarction with mortality and cardiovascular events: A meta-analysis.
      ,
      • Zhang Y.
      Cardiovascular diseases in American women.
      ). Demographic characteristics, including age, race/ethnicity, education and cigarette smoking were assessed by self-report at study baseline. Body mass index and hypertension were measured clinically at baseline, and depressive symptomology was assessed by self-report at study baseline using the 8-item Burnam scale (
      • Burnam M.A.
      • Wells K.B.
      • Leake B.
      • Landsverk J.
      Development of a brief screening instrument for detecting depressive disorders.
      ) derived from the Center for Epidemiologic Studies Depression Scale (
      • Radloff L.S.
      The CES-D scale: A self-report depression scale for research in the general population.
      ). A noteworthy difference of the Burnam Scale, relative to other common screening measures for depression, is that the scoring algorithm dictates that individual items on the Burnam Scale are logistically weighted such that the algorithm yields a composite score with values that can range from 0 to 1, with a cutpoint of 0.06 (
      • Burnam M.A.
      • Wells K.B.
      • Leake B.
      • Landsverk J.
      Development of a brief screening instrument for detecting depressive disorders.
      ) used to signify presence of current depression.
      In addition, several variables that conferred specific risk to postmenopausal bone health or fracture risk were used exclusively in models evaluating hip fracture outcomes. These included self-reported baseline measures of physical activity, measured in metabolic minutes, parental hip fracture, personal history of fracture at 55 years or older, alcohol use (drinks per week), physical functioning (Rand SF-36 Physical Functioning Scale;
      • Bohannon R.W.
      • DePasquale L.
      Physical Functioning Scale of the Short-Form (SF) 36: Internal consistency and validity with older adults.
      ), comorbid medical conditions, osteoporosis, current use of bisphosphonates, corticosteroids, and psychoactive medications, which may impact bone health and/or contribute to fall risk), current or prior use of hormone replacement therapy, and calcium and vitamin D intake (
      • Robbins J.
      • Aragaki A.K.
      • Kooperberg C.
      • Watts N.
      • Wactawski-Wende J.
      • Jackson R.D.
      • Cauley J.
      • et al.
      Factors associated with 5-year risk of hip fracture in postmenopausal women.
      ,
      • Williams A.R.
      • Weiss N.S.
      • Ure C.L.
      • Ballard J.
      • Daling J.R.
      Effect of weight, smoking and estrogen use on the risk of hip and forearm fractures in postmenopausal women.
      ). Self-reported health (
      • Ware J.E.
      • Sherbourne C.D.
      The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection.
      ) and self-reported prior hip fracture were covariates used exclusively in mortality outcome models.

       Other descriptors

      Baseline prevalence of self-reported CVD, cancer, diabetes, and hip fracture were included as descriptive variables and to subset cohort risk sets for analysis of respective incident events.

       Statistical Analysis

      Descriptive statistics were used to characterize all participants with respect to demographic characteristics, baseline health risks, baseline prevalent CVD, diabetes, cancer, and hip fracture. Formal tests of significance were not conducted for descriptive analyses. The large study population would be expected to produce many significant associations, potentially encouraging undue emphasis on statistically significant—rather than clinically meaningful—differences.
      Cox proportional hazards models estimated the association between veteran status and incident CVD, cancer, diabetes, hip fracture, and all-cause mortality in models that were sequentially adjusted for baseline age (continuous; model 1), and then additionally for sociodemographic factors (e.g., race/ethnicity, education), mental health (i.e., depression) and health-related (i.e., hypertension, body mass index) confounders associated with all outcomes (model 2).
      Hip fracture models included all covariates in model 2 and were then further adjusted for several additional variables with specific relevance to bone health or fracture risk (model 3). These included baseline alcohol use, psychoactive medication, self-reported health, number of chronic conditions, physical functioning, bisphosphonate use, corticosteroid use, parental hip fracture, other fracture after age 55, total baseline calcium and vitamin D intake, and hormone therapy use. Mortality models included all covariates in model 2, and were then further adjusted for alcohol use, physical activity, self-reported health, baseline prevalent number of chronic conditions, and hip fracture (model 4).
      Kaplan–Meier estimates were used to graphically view the annualized incidence of each disease outcome by veteran status. The proportionality assumption was examined graphically and through evaluation of interaction terms between veteran status and time on health outcomes. The interaction of veteran status and age on health outcomes was also examined. As neither of these interaction terms was significant, they were omitted from the final models. Exclusions to each outcome model (CVD, cancer, diabetes, hip fracture, all-cause mortality) were made owing to participants' missing covariate values or baseline prevalent disease for each model's specific outcome and participants were censored at either their last follow-up visit known to be without the outcome of interest or death, whichever was first. Sensitivity analyses, applied to all models, used multiple imputations to address subject loss associated with missing covariate values.
      All statistical tests were two-sided. Analyses were conducted in SAS version 9.3 for Windows (SAS Inc., Cary, NC) and STATA version 10 for Windows (STATA Inc., College Station, TX).

      Results

       Participant Characteristics

      Approximately 3% (n = 3,706) of the 144,715 participants were veterans. Women veterans were older than non-veterans (mean age at WHI baseline, 67.1 vs. 63.2; Table 1). Moreover, the age distribution was quite different between groups, with nearly 50% of veteran participants versus 22% of non-veteran participants, being aged 70 to 79 at WHI baseline. Relative to non-veterans, women veteran participants were also more likely to be Caucasian (87.1% of veterans vs. 82.4% of non-veterans), to have completed college (46.8% of veterans vs. 39.5% of non-veterans), to smoke (or have smoked) cigarettes, and to have diagnosed hypertension, osteoporosis, and diminished physical functioning at baseline relative to their non-veteran peers (Table 1).
      Table 1Participant Characteristics at WHI Baseline
      VariableVeteran (n = 3,706)Non-Veteran (n = 141,009)
      Demographics
      Age (y)
      Age is included as a linear variable (years).
      (mean ± SD)
      67.1 ± 8.063.3 ± 7.2
       50–59, n (%)782 (21.1)46,072 (32.7)
       60–69, n (%)1,077 (29.1)64,328 (45.6)
       70–79, n (%)1,847 (49.8)30,609 (21.7)
       Missing, n (%)00
      Race/ethnicity, n (%)
       White3,229 (87.1)116,161 (82.4)
       Black or African American263 (7.1)12,736 (9.0)
       Asian or Pacific Islander45 (1.2)3,957 (2.8)
       Hispanic/Latino85 (2.3)5,532 (3.9)
       American Indian or Alaskan Native24 (0.7)597 (0.4)
       Other47 (1.3)1,656 (1.2)
       Missing13 (0.4)370 (0.3)
      Education, n (%)
       College degree or higher1,733 (46.8)55,735 (39.5)
       Missing14 (0.4)915 (0.7)
      Self-reported health, n (%)
       Excellent630 (17)23,972 (17)
       Very good1,594 (43)57,814 (41)
       Good1,112 (30)44,981 (32)
       Fair323 (8.7)11,704 (8.3)
       Poor22 (0.6)1,128 (0 .7)
       Missing25 (0.7)1,410 (1.0)
      Physical activity (MET min/week), n (%)
       <100781 (21.1)31,045 (22.0)
       100–499973 (26.3)38,885 (27.6)
       500–1,2001,093 (29.5)40,263 (28.6)
       >1,200850 (22.9)30,417 (21.6)
       Missing9 (0.2)399 (0.3)
      Comorbidities
       Count, mean ± SD2.02 ± 1.11.80 ± 1.4
       Missing, n (%)595 (16.1)19,966 (14.2)
      Depression
       Depressive symptoms, mean ± SD (range
      § The Burnam Scale for Depression uses a scoring algorithm that permits scores from 0 to 1 with a cutpoint of 0.06 signifying probable current depression.
      )
      0.04 ± 0.12 (0–0.94)0.04 ± 0.13 (0–0.96)
       Missing, mean ± SD89 ± 2.43,485 ± 2.5
      Body mass index (kg/m2), n (%)
       Overweight (25.0–29.9)1,319 (35.6)48,285 (34.2)
       Obese (>30)1,062 (28.7)41,764 (29.6)
       Missing39 (1.1)1,292 (0.9)
      Hypertension, n (%)1,361 (36.7)47,471 (33.7)
       Missing38 (1.0)1,161 (0.8)
      Smoking, n (%)
       Never1,625 (43.9)71,190 (50.5)
       Past1,687 (45.5)58,543 (41.5)
       Current317 (8.6)9,469 (6.7)
      Pack-years,
      Age is included as a linear variable (years).
      mean ± SD
      12.6 ± 20.49.4 ± 17.5
       Missing, n (%)180 (4.9)5,127 (3.6)
      Alcohol use
       Drinks per week, mean ± SD2.52 ± 5.02.38 ± 4.9
       Missing, n (%)11 (0.3)249 (0.2)
      Physical function, n (%)
       Score > 901,123 (30.3)51,534 (36.6)
       Missing82 (2.2)2,571 (1.8)
      Parent broke hip > age 40, n (%)1,286 (34.7)51,667 (36.6)
       Missing341 (9.2)11,264 (8.0)
      Fracture ≥ age 55, n (%)3 (0.1)62 (0.0)
       Missing139 (3.8)2,580 (1.8)
      Osteoporosis, n (%)348 (9.4)10,795 (7.7)
       Missing44 (1.2)1,810 (1.3)
      Total baseline calcium intake (mg)
       <600686 (18.5)30,053 (21.3)
       600–1,2001,401 (37.8)53,032 (37.6)
       >1,2001,610 (43.4)57,715 (40.9)
       Missing9 (0.2)209 (0.2)
      Total baseline vitamin D supplement use (IU), n (%)
       <4003,042 (82.1)118,645 (84.1)
       400–600561 (15.1)18,501 (13.1)
       ≥60094 (2.5)3,654 (2.6)
       Missing9 (0.2)209 (0.2)
      Medication use,
      Missing data on medication use are not available; participants were asked to endorse use, and absence of data point signals nonuse of medications.
      n (%)
       Psychoactive,
      Psychoactive medication use includes antipsychotic, antiepileptic, anxiolytic, hypnotic and antidepressant drug use, for smoking we included pack-years, taking into account the number of years smoked and the average number of cigarettes smoked daily, in the model.
      current use
      448 (12.1)17,522 (14)
       Bisphosphonates, current use78 (2.1)2,879 (2.0)
       Corticosteroid, current use44 (1.2)995 (0.7)
       Hormone therapy use
      Never1,625 (43.9)61,228 (43.4)
      Past721 (19.5)22,127 (15.7)
      Current1,356 (36.6)57,530 (40.8)
      Missing4 (0.1)124 (0.1)
      WHI component,
      Participants could enroll in more than one study component, including more than one of the WHI-related trials. As such, sums of the percent of participants across the WHI components exceed 100%.
      n (%)
       Hormone Therapy Trial647 (17.5)22,707 (16.1)
       Diet Modification Trial935 (25.2)39,628 (28.1)
       Calcium and Vitamin D Trial764 (20.6)30,049 (21.3)
       Observational Study2,291 (61.8)85,034 (60.3)
      Abbreviations: MET, metabolic equivalent; SD, standard deviation; WHI, Women’s Health Initiative.
      Note. All variables presented are included as study covariates.
      Age is included as a linear variable (years).
      Missing data on medication use are not available; participants were asked to endorse use, and absence of data point signals nonuse of medications.
      Psychoactive medication use includes antipsychotic, antiepileptic, anxiolytic, hypnotic and antidepressant drug use, for smoking we included pack-years, taking into account the number of years smoked and the average number of cigarettes smoked daily, in the model.
      § The Burnam Scale for Depression uses a scoring algorithm that permits scores from 0 to 1 with a cutpoint of 0.06 signifying probable current depression.
      Participants could enroll in more than one study component, including more than one of the WHI-related trials. As such, sums of the percent of participants across the WHI components exceed 100%.
      Age-stratified analyses revealed that among younger participants, aged 50 to 59 and 60 to 69 at WHI baseline (reflecting participant ages between the years 1993 and 1998), women veterans had lower baseline prevalence of CVD and cancer than non-veterans. However, this difference reversed for participants aged 70 or older (Table 2). Patterns of baseline prevalence for diabetes and hip fracture were similar, although less pronounced.
      Table 2Self-Reported Baseline Prevalence of Disease by Veteran Status and Age Decade
      VariableVeteran (n = 3,706)Non-Veteran (n = 141,009)
      n%n%
      CVD
       All79821.924,67917.7
       50–591062.95,6654.1
       60–692266.211,5068.3
       ≥7046612.87,5085.4
      Cancer
       All47613.013,4379.6
       50–59641.73,4912.5
       60–691223.36,0494.3
       ≥702907.93,8972.8
      Diabetes
       All2155.88,4096.0
       50–59240.72,1141.5
       60–69802.24,1482.9
       ≥701113.02,1471.5
      Hip fracture
       All290.87510.5
       50–590190.0
       60–6920.13080.2
       ≥70270.74240.3
      Abbreviation: CVD, cardiovascular disease.

       Main Outcomes

      Table 3 presents number of events, person-years, annualized incidence, and results of Cox proportional models, sequentially adjusted for age, sociodemographic variables, and health risk factors for each outcome of interest. In fully adjusted models (model 2), veteran status was not associated with risk of CVD (HR, 1.0; 95% CI, 0.90–1.11), total cancer (HR, 1.04; 95% CI, 0.95–1.14), or diabetes (HR, 1.00; 95% CI, 0.89–1.12).
      Table 3Association of Veteran Status and Key Health Outcomes
      VariableNo. of EventsPerson-YearsAnnualized %Primary Analyses, HR (95% CI)Sensitivity Analyses, HR (95% CI), Imputation for Missing Covariates
      CVD
       V60927,2652.23Model 1: 1.00 (0.90–1.11)
       NV17,3591,063,6711.63Model 2: 1.00 (0.90–1.11)
      Cancer
       V58027,4452.11Model 1: 1.09 (0.99–1.20)
       NV18,5721,065,5661.74Model 2: 1.04 (0.95–1.14)
      Diabetes
       V46125,3401.82Model 1: 0.98 (0.87–1.11)
       NV18,257973,4821.89Model 2: 1.00 (0.89–1.12)
      Hip Fracture
       V14328,5820.50Model 1: 1.24 (1.01–1.53)Model 1: 1.24 (1.01–1.53)
       NV2,6741,105,5420.24Model 2: 1.18 (0.96–1.45)Model 2: 1.20 (1.02–1.43)
      Model 3: 1.16 (0.94–1.43)Model 3: 1.18 (0.99–1.40)
      All-cause mortality
       V57328,8301.99Model 1: 1.20 (1.09–1.31)Model 1: 1.20 (1.09–1.31)
       NV13,1741,110,3731.19Model 2: 1.16 (1.05–1.27)Model 2: 1.13 (1.04–1.23)
      Model 4: 1.13 (1.03–1.25)Model 4: 1.12 (1.02–1.22)
      Abbreviations: CVD, cardiovascular disease; HR, hazard ratio; NV, non-veterans; V, veterans.
      Note. Bolded text indicates significant outcome models, specifically those with confidence intervals that do not include 1. Non-veterans are the reference group; HR < 1 indicate that veterans are less likely than non-veterans to have that condition. Annualized percentage is defined as cases per 100 person-years.
      Model 1 is adjusted for age. Model 2 is adjusted for age, race/ethnicity, education, Women’s Health Initiative (WHI) component and, for clinical trials participants, randomization assignment, smoking, body mass index, hypertension, and depression. Model 3 is adjusted for all variables in model 2 plus alcohol, psychoactive medication, self-reported health, number of chronic conditions, physical activity, physical functioning, bisphosphonates, corticosteroids, parental hip fracture, other fracture after age 55, total baseline calcium and vitamin D intake, and hormone therapy use. Model 4 is adjusted for all variables in model 2 plus alcohol use, physical activity, self-reported health, baseline prevalent number of chronic conditions, and hip fracture.
      Age-adjusted models (model 1) suggested a slightly increased risk of hip fracture among veteran women relative to non-veteran controls (Table 3); however, this association was not robust to further adjustment for sociodemographic and health risk factors (model 2; HR, 1.16; 95% CI, 0.94–1.43) or in models that accounted for confounders specific to bone health or fracture risk (model 3; HR, 1.16; 95% CI, 0.94–1.43; Table 3).
      Relative to non-veteran controls, veteran women had a significantly increased risk of all-cause mortality in age-adjusted analyses (model 1). Findings were robust in fully adjusted models that accounted for age, sociodemographic variables, and health risk factors along with alcohol use, self-reported health, chronic health conditions and prior hip fracture (model 4; HR, 1.13; 95% CI, 1.03–1.25).

       Sensitivity Analyses

      Loss of participants owing to missing values on salient covariates was substantial, and particularly problematic in the hip fracture models, which featured a large number of covariates (Figure 1; Supplementary Appendix B). To address this, we conducted sensitivity analyses using multiple imputations for missing values. For all outcomes, imputation-based results agreed almost entirely, in terms of significance and direction, with our main outcomes. Results of significant models (i.e., hip fracture, all-cause mortality) are presented in Table 3.
      Figure thumbnail gr1
      Figure 1Cohort composition. *Clinical trial participants could enroll in more than one of the three trials; therefore, the size of the total clinical trial study population is smaller than the sum of the specific study population for each of the constituent trials.

      Discussion

      Among the 144,715 postmenopausal WHI participants included in our study, veteran status conferred a consistent risk to all-cause mortality that was independent of age. Veteran status was not associated with risk for morbidity outcomes; however, the low incidence of hip fracture and total cancer cases among study participants may have limited power to detect small, but important, differences. Moreover, the nature and direction of these outcomes suggest a need for further investigation of women veterans' postmenopausal bone health and cancer risk. All findings were robust to adjustment for sociodemographic and health risk factors and held in sensitivity analyses with more inclusive cohorts.
      Some key results of the present study seem to contrast with much of the prior literature on the “healthy soldier effect” (
      • Cypel Y.
      • Kang H.
      Mortality patterns among women Vietnam-era veterans: Results of a retrospective cohort study.
      ,
      • Dalager N.A.
      • Kang H.K.
      • Thomas T.L.
      Cancer mortality patterns among women who served in the military: The Vietnam experience.
      ,
      • Kang H.K.
      • Cypel Y.
      • Kilbourne A.M.
      • Magruder K.M.
      • Serpi T.
      • Collins J.F.
      • Spiro 3rd, A.
      • et al.
      Mortality study of female US Vietnam era veterans, 1965–2010.
      ,
      • MacFarlane G.J.
      • Biggs A.M.
      • Maconochie N.
      • Hotopf M.
      • Doyle P.
      • Lunt M.
      Incidence of cancer among UK Gulf war veterans: Cohort study.
      ,
      • MacIntyre N.R.
      • Mitchell R.E.
      • Oberman A.
      • Harlan W.R.
      • Graybiel A.
      • Johnson E.
      Longevity in military pilots: 37-year follow-up of the Navy’s 1,000 Aviators.
      ,
      • McBride D.
      • Cox B.
      • Broughton J.
      • Tong D.
      The mortality and cancer experience on New Zealand Vietnam war veterans: A cohort study.
      ,
      • McLaughlin R.
      • Nielsen L.
      • Waller M.
      An evaluation of the effect of military service on mortality: Quantifying the healthy soldier effect.
      ,
      • Thomas T.L.
      • Kang H.K.
      • Dalager N.A.
      Mortality among women Vietnam veterans, 1973–1987.
      ,
      • Vajdic C.M.
      • Stavrou E.P.
      • Ward R.L.
      • Falster M.O.
      • Pearson S.A.
      Minimal excess risk of cancer and reduced risk of death from cancer in Australian Department of Veterans’ Affairs clients: A record linkage study.
      ,
      • Waller M.
      • McGuire A.C.L.
      Changes over time in the “healthy soldier effect.”.
      ,
      • Yi S.W.
      Cancer incidence in Korean Vietnam veterans during 1992–2003: The Korean Veterans Health Study.
      ). Indeed, several prior studies suggest similar, but more commonly deficient, risk of cancer-related morbidity or mortality among women veterans relative to the general public (
      • Dalager N.A.
      • Kang H.K.
      • Thomas T.L.
      Cancer mortality patterns among women who served in the military: The Vietnam experience.
      ,
      • Vajdic C.M.
      • Stavrou E.P.
      • Ward R.L.
      • Falster M.O.
      • Pearson S.A.
      Minimal excess risk of cancer and reduced risk of death from cancer in Australian Department of Veterans’ Affairs clients: A record linkage study.
      ).
      • Thomas T.L.
      • Kang H.K.
      • Dalager N.A.
      Mortality among women Vietnam veterans, 1973–1987.
      found lower than expected rates of all-cause mortality among 4,600 women veterans deployed to Vietnam relative to the general population (standardized mortality ratios [SMR], 0.82; 95% CI, 0.69–0.97) over a 13-year observation period. Similarly, a 40-year follow-up study of approximately 12,000 women who served during Vietnam found decreased risk of all-cause mortality among deployed women who served in theater relative to the general population, reporting SMRs of 0.85 (95% CI, 0.80–0.91;
      • Kang H.K.
      • Cypel Y.
      • Kilbourne A.M.
      • Magruder K.M.
      • Serpi T.
      • Collins J.F.
      • Spiro 3rd, A.
      • et al.
      Mortality study of female US Vietnam era veterans, 1965–2010.
      ).
      Unique aspects of the WHI study methodology and population may offer some important clues regarding the discrepancy of our findings from those of the broader literature. First, we compared mortality risk in veteran and non-veteran women who were health-eligible for inclusion in the clinical trials and/or observational studies of WHI. This likely attenuated the healthy soldier bias (
      • Bross I.D.
      • Bross N.S.
      Do atomic veterans have excess cancer? New results correcting for the healthy soldier bias.
      ) that may be present in studies that benchmark health risks in veterans—who are selected into military service in part based on their good health—against the general population, for whom no such selection bias is present.
      Second, 50% of women veteran participants, relative to about 22% of non-veteran participants, were aged 70 or older at study enrollment. Outcome models were age adjusted; nevertheless, the asymmetric age distribution of the study population may suggest that results disproportionately reflect mortality risk among the large group of older women veterans in WHI. In this light, findings may be consistent with prior literature, which implies that the healthy soldier effect may attenuate over time. Indeed, per the healthy soldier effect, heightened mortality risk would be expected among older women veterans relative to their non-veteran peers—who should have experienced a period of heightened risk an earlier age. Underscoring this point, several prior studies characterize a veteran/non-veteran health cross-over in older adulthood (>age 70;
      • Wilmoth J.M.
      • London A.S.
      • Parker W.M.
      Military service and men’s health trajectories in later life.
      ,
      • London A.S.
      • Wilmoth J.M.
      Military service and (dis)continuity in the life course: Evidence on disadvantage and mortality from the Health and Retirement Study and Study of Assets and Health among the Oldest-old.
      ). Our study was not specifically designed to compare health trajectories throughout the postmenopausal period, and we cannot confirm the presence or absence of such an effect with our present analyses. However, we note that the pattern of baseline disease prevalence (Table 2) may prefigure the true presence of a cross-over effect, offering some important context in which to interpret the observed pattern of mortality risk in veterans. Further confirmatory research, including population-based studies designed to prospectively examine the possibility of such a veteran/non-veteran health cross-over, would deepen our understanding of women veterans’ postmenopausal health trajectories.
      In addition, given that the largest subgroup of veteran participants, those aged 70 to 79 at baseline, are age consistent with military service during World War II (
      • Washington D.L.
      • Bean-Mayberry B.
      • Hamilton A.B.
      • Cordasco K.M.
      • Yano E.M.
      Women veterans’ healthcare delivery preferences and use by military service era: Findings from the National Survey of Women Veterans.
      )—a time when women's military occupational roles and associated occupational health exposures were quite distinct from those of subsequent generations of military women (
      • Treadwell M.E.
      The United States Army in World War II. Special studies: The Women’s Army Corps.
      )—it may be important to consider that military service era, independently or synergistically with the effects of age, may also contribute to variability in health and mortality risk.
      Further, salient differences in health risk and health risk behaviors may have contributed to women veterans' heightened risk of all-cause mortality. For example, although models adjusted for cigarette smoking, this may not have fully attenuated the effects of veteran women's greater prevalence and longer duration (pack-years) of smoking. Finally, although WHI offers no contextual information about women's prior military service, given the age and likely period of military service for WHI participants, it is reasonable to assume that many veteran women were exposed to military-specific occupational hazards (i.e., deployment overseas, warzone exposure, military sexual trauma), which would also be expected to contribute to heightened mortality risk.
      Limitations include our study's observational design, which precludes causal inferences about the association of military service and women's long-term health. Participants do not represent a population-based sample, which may limit the generalizability of our findings. However, study participants were recruited nationally and reflect the considerable racial, ethnic, geographic, and socioeconomic diversity of the general U.S. population. In addition, generalizability may be limited because nearly one-half of the women veteran participants in WHI are age consistent with military service during World War II, and the military occupational roles and health-related exposures of these women may be quite different from those of women from subsequent military generations. Further, veteran status was determined by participants' self-report, but not confirmed with review of military records. However, we note that WHI's method of assessing veteran status by self-report is consistent with strategies used in several other large-scale observational studies (e.g.,
      • Hoerster K.D.
      • Lehavot K.
      • Simpson T.
      • McFall M.
      • Reiber G.
      • Nelson K.M.
      Health and health behavior differences: U.S. military, veteran, and civilian men.
      ,
      • Koepsell T.
      • Reiber G.
      • Simmons K.W.
      Behavioral risk factors and use of preventive services among veterans in Washington State.
      ). Because WHI offers no contextual information about participants' military service, we are unable to evaluate how factors such as military generation (era served), duration of service, occupation or role within the military, warzone deployment, exposure to military sexual trauma, or other military occupational health risks might impact variability in veteran participants' health or mortality risk. Finally, data on lifetime prevalence of mental health conditions are not available within WHI and mental health outcomes were not a focus of the WHI clinical trials or observational study. Although variability in veterans' and non-veterans’ prevalence and severity of mental health problems (e.g., traumatic stress disorders) would be expected, and may contribute to differential morbidity and mortality risk, we are unable to account for these factors within the present study. Despite these limitations, our study has many strengths, which include the use of a large, racially, ethnically, and geographically diverse group of postmenopausal women, a strong rate of participant retention, the inclusion of variables on many known health confounders, and prospective attainment of adjudicated health outcomes, in which women veterans and non-veterans were identically followed, over a multi-decade observational follow-up period.

       Implications for Practice and/or Policy

      Our findings offer several important implications pertinent to clinical health care practice and policy for women veterans. First, this study highlights heightened postmenopausal risk of all-cause mortality among women veterans relative to their non-veteran peers, illuminating the potential salience of prior military service as a factor in determining women's life-long health. As such, this work may help to increase awareness of the unique health care needs of older, postmenopausal women veterans among the health care providers who care for this population of women.
      Second, these findings underscore the importance of efforts to identify and address modifiable health and mortality risk factors among women veterans. Our descriptive findings (Table 2) related to older (age ≥70) women veteran's heightened baseline prevalence of cancer and CVD may offer an important clue in this regard. Moreover, the nature and direction of our cancer outcome models (Table 3) may suggest that further study of women veterans' postmenopausal risk for cancer would be valuable and informative. Epidemiological research on the incidence and prevalence of specific types of cancer among women veterans is quite scant. However, the recent HealthViews study (
      • Kang H.K.
      • Cypel Y.
      • Kilbourne A.M.
      • Magruder K.M.
      • Serpi T.
      • Collins J.F.
      • Spiro 3rd, A.
      • et al.
      Mortality study of female US Vietnam era veterans, 1965–2010.
      ) suggests a heightened prevalence of brain and pancreatic cancers among women who served as military nurses in the Vietnam theater, and
      • Zhu K.
      • Devesa S.S.
      • Wu H.
      • Zahm S.H.
      • Ismail J.
      • Anderson W.F.
      • McGlynn K.A.
      • et al.
      Cancer Incidence in the U.S. Military Population: Comparison with Rates from the SEER Program.
      found significantly heightened risk for breast cancer among contemporary military and veteran populations. These may represent logical and important “next steps” of inquiry related to women veterans' cancer risk.
      Third, women veterans' 9% baseline prevalence of osteoporosis (Table 1), coupled with the nature and direction of the hip fracture outcomes, suggest that increased attention to matters of bone health among postmenopausal women veterans may be important. Specifically, these findings underscore the importance of screening for osteoporosis, evaluating fall risk, and identifying (and intervening with) other factors that may contribute to fracture risk among postmenopausal women veterans. Finally, given the heightened baseline prevalence of both smoking and hypertension—factors that may impact bone health and fracture risk—further investigation of these factors as potential mediators of veteran women's risk for hip fractures is also warranted.
      In conclusion, this study is among the first large scale efforts to investigate health and mortality risks among postmenopausal women veterans. While our study population may represent a select group of women veterans whose postmenopausal health trajectories may not be fully generalizable, this work provides a much needed empirical foundation for the study of postmenopausal health and mortality risk in women veterans. It is our hope that this work will encourage further research efforts that will further deepen our understanding of this unique population of women.

      Acknowledgments

      The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the Department of Veterans Affairs. Drs. Marcia Stefanick and Andrea LaCroix had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
      The Women's Health Initiative (WHI) program is funded by the  National Heart, Lung, and Blood Institute , National Institutes of Health , U.S. Department of Health and Human Services through contracts HHSN268201100046C, HHSN268201100001C, HHSN268201100002C, HHSN268201100003C, HHSN268201100004C, and HHSN271201100004C.
      Dr. Weitlauf's contribution to this work was supported in part by the Department of Veterans Affairs, Office of Research and Development, Health Services Research and Development Division, Career Development Award [CD206035 2 to J.W.]. To the extent that the funding agencies associated with this project played no role (beyond financial support) in the development or execution of the study described, all investigators (authors) are independent from the sources of study funding.

      Supplementary Data

      References

        • Bass E.
        • French D.D.
        • Bradham D.D.
        • Rubenstein L.Z.
        Risk-adjusted mortality rates of elderly veterans with hip fractures.
        Annals of Epidemiology. 2007; 17: 514-519https://doi.org/10.1016/j.annepidem.2006.12.004
        • Bilgrad R.
        National Death Index user’s manual.
        National Center for Health Statistics, Centers for Disease Control and Prevention, Hyattsville, MD1997
        • Bohannon R.W.
        • DePasquale L.
        Physical Functioning Scale of the Short-Form (SF) 36: Internal consistency and validity with older adults.
        Journal of Geriatric Physical Therapy. 2010; 33: 16-18https://doi.org/10.1097/JPT.0b013e3181d0735e
        • Bross I.D.
        • Bross N.S.
        Do atomic veterans have excess cancer? New results correcting for the healthy soldier bias.
        American Journal of Epidemiology. 1987; 126: 1042-1050
        • Burnam M.A.
        • Wells K.B.
        • Leake B.
        • Landsverk J.
        Development of a brief screening instrument for detecting depressive disorders.
        Medical Care. 1988; 26: 775-789
        • Cowper D.C.
        • Kubal J.D.
        • Maynard C.
        • Hynes D.M.
        A primer and comparative review of major US mortality databases.
        Annals of Epidemiology. 2002; 12: 462-468https://doi.org/10.1016/S1047-2797(01)00285-X
        • Cummings S.R.
        • Nevitt M.C.
        • Browner W.S.
        • Stone K.
        • Fox K.M.
        • Ensrud K.E.
        • Vogt T.M.
        • et al.
        Risk factors for hip fracture in white women. Study of Osteoporotic Fractures Research Group.
        New England Journal of Medicine. 1995; 332: 767-773https://doi.org/10.1056/NEJM199503233321202
      1. Cunningham J. Ries L. Hankey B. Seiffert J. Lyles B. Shambaugh E. Van Holten V. The SEER program code manual. revised ed. Cancer Statistics Branch, Surveillance Program, Division of Cancer Prevention and Control, National Cancer Institute, Bethesda, MD1992
        • Curb J.D.
        • McTiernan A.
        • Heckbert S.R.
        • Kooperberg C.
        • Stanford J.
        • Neveitt M.
        • Daugherty S.
        • et al.
        Outcomes ascertainment and adjudication methods in the Women’s Health Initiative.
        Annals of Epidemiology. 2003; : S122-S128https://doi.org/10.1016/S1047-2797(03)00048-6
        • Cypel Y.
        • Kang H.
        Mortality patterns among women Vietnam-era veterans: Results of a retrospective cohort study.
        Annals of Epidemiology. 2008; 18: 244-252https://doi.org/10.1016/j.annepidem.2007.11.009
        • Dalager N.A.
        • Kang H.K.
        • Thomas T.L.
        Cancer mortality patterns among women who served in the military: The Vietnam experience.
        Journal of Occupational and Environmental Medicine. 1995; 37: 298-305
        • de Boer I.H.
        • Tinker L.F.
        • Connelly S.
        • Curb J.D.
        • Howard B.V.
        • Kestenbaum B.
        • et al.
        • Women’s Health Initiative Investigators
        Calcium plus vitamin D supplementation and the risk of incident diabetes in the Women’s Health Initiative.
        Diabetes Care. 2008; 31: 701-707https://doi.org/10.2337/dc07-1829
        • Doody M.M.
        • Hayes H.M.
        • Bilgrad R.
        Comparability of national death index plus and standard procedures for determining causes of death in epidemiologic studies.
        Annals of Epidemiology. 2001; 11: 46-50https://doi.org/10.1016/S1047-2797(00)00177-0
        • Hays J.
        • Hunt J.
        • Hubbell F.A.
        • Anderson G.L.
        • Limacher M.
        • Allen C.
        • Roussouw J.E.
        The Women’s Health Initiative recruitment methods and results.
        Annals of Epidemiology. 2003; 13: S18-S77https://doi.org/10.1016/S1047-2797(03)00042-5
        • Hoerster K.D.
        • Lehavot K.
        • Simpson T.
        • McFall M.
        • Reiber G.
        • Nelson K.M.
        Health and health behavior differences: U.S. military, veteran, and civilian men.
        American Journal of Preventive Medicine. 2012; 43: 483-489https://doi.org/10.1016/j.amepre.2012.07.029
        • Hopper J.L.
        • Seeman E.
        The bone density of female twins discordant for tobacco use.
        New England Journal of Medicine. 1994; 330: 387-392https://doi.org/10.1056/NEJM199402103300603
        • Kang H.K.
        • Bullman T.A.
        Mortality among U.S. veterans of the Persian Gulf War.
        New England Journal of Medicine. 1996; 335: 1498-1504
        • Kang H.K.
        • Cypel Y.
        • Kilbourne A.M.
        • Magruder K.M.
        • Serpi T.
        • Collins J.F.
        • Spiro 3rd, A.
        • et al.
        Mortality study of female US Vietnam era veterans, 1965–2010.
        American Journal of Epidemiology. 2014; 179: 721-730https://doi.org/10.1093/aje/kwt319
        • Koepsell T.
        • Reiber G.
        • Simmons K.W.
        Behavioral risk factors and use of preventive services among veterans in Washington State.
        Preventive Medicine. 2002; 35: 557-562https://doi.org/10.1006/pmed.2002.1121
        • Liu X.
        • Engle C.
        • Kang H.
        • Cowan D.
        The effect of veteran status on mortality among older Americans and its pathways.
        Population Research and Policy Review. 2005; 24: 573-592https://doi.org/10.1007/s11113-005-5056-3
        • London A.S.
        • Wilmoth J.M.
        Military service and (dis)continuity in the life course: Evidence on disadvantage and mortality from the Health and Retirement Study and Study of Assets and Health among the Oldest-old.
        Research on Aging. 2010; 28: 135-159
        • MacFarlane G.J.
        • Biggs A.M.
        • Maconochie N.
        • Hotopf M.
        • Doyle P.
        • Lunt M.
        Incidence of cancer among UK Gulf war veterans: Cohort study.
        British Medical Journal. 2003; 327https://doi.org/10.1136/bmj.327.7428.1373
        • MacIntyre N.R.
        • Mitchell R.E.
        • Oberman A.
        • Harlan W.R.
        • Graybiel A.
        • Johnson E.
        Longevity in military pilots: 37-year follow-up of the Navy’s 1,000 Aviators.
        Aviation, Space, and Environmental Medicine. 1978; 49: 1120-1122
        • Margolis K.L.
        • Lihong Q.
        • Brzyski R.
        • Bonds D.E.
        • Howard B.V.
        • Kempainen S.
        • et al.
        • Women Health Initiative Investigators
        Validity of diabetes self-reports in the Women’s Health Initiative: Comparison with medication inventories and fasting glucose measurements.
        Clinical Trials. 2008; 5: 240-247https://doi.org/10.1177/1740774508091749
        • McBride D.
        • Cox B.
        • Broughton J.
        • Tong D.
        The mortality and cancer experience on New Zealand Vietnam war veterans: A cohort study.
        BMJ Open, 3,. 2013; : e003379https://doi.org/10.1136/bmjopen-2013-003379
        • McLaughlin R.
        • Nielsen L.
        • Waller M.
        An evaluation of the effect of military service on mortality: Quantifying the healthy soldier effect.
        Annals of Epidemiology. 2008; 18: 928-936https://doi.org/10.1016/j.annepidem.2008.09.002
        • Mendis S.
        The contribution of the Framingham Heart Study to the prevention of cardiovascular disease: A global perspective.
        Progressive Cardiovascular Disease. 2010; 53: 10-14https://doi.org/10.1016/j.pcad.2010.01.001
        • Radloff L.S.
        The CES-D scale: A self-report depression scale for research in the general population.
        Applied Psychological Measurements. 1977; 1: 385-401
        • Robbins J.
        • Aragaki A.K.
        • Kooperberg C.
        • Watts N.
        • Wactawski-Wende J.
        • Jackson R.D.
        • Cauley J.
        • et al.
        Factors associated with 5-year risk of hip fracture in postmenopausal women.
        Journal of the American Medical Association. 2007; 298: 2389-2398https://doi.org/10.1097/01.ogx.0000310358.61870.8c
        • Sohn M.W.
        • Arnold N.
        • Maynard C.
        • Hynes D.M.
        Accuracy and completeness of mortality data in the Department of Veterans Affairs.
        Population Health Metrics. 2006; 4: 2https://doi.org/10.1186/1478-7954-4-2
        • The Women's Health Initiative Study Group
        Design of the Women's Health Initiative clinical trial and observational study.
        Controlled Clinical Trials. 1998; 19: 61-109https://doi.org/10.1016/S0197-2456(97)00078-0
        • Thomas T.L.
        • Kang H.K.
        • Dalager N.A.
        Mortality among women Vietnam veterans, 1973–1987.
        American Journal of Epidemiology. 1991; 134: 973-980
        • Treadwell M.E.
        The United States Army in World War II. Special studies: The Women’s Army Corps.
        Center of Military History, U.S. Army, Washington DC1954 (Library of Congress Catalog Card Number 53-61563 First Printed 1954-CMH Pub 11–8)
        • U.S. Department of Health and Human Services
        Smoking and health: A national status report.
        U.S. Government Printing Office, Washington, DC1986 (1986. DHHS publication PHS 87–8396)
        • U.S. Department of Veterans Affairs
        • National Center for Veteran Analysis and Statistics
        Table 6L:VetPop2011.
        2011 (Available:) (Accessed September 4, 2015)
        • Vajdic C.M.
        • Stavrou E.P.
        • Ward R.L.
        • Falster M.O.
        • Pearson S.A.
        Minimal excess risk of cancer and reduced risk of death from cancer in Australian Department of Veterans’ Affairs clients: A record linkage study.
        Australian and New Zealand Journal of Public Health. 2014; 38: 30-34https://doi.org/10.1111/1753-6405.12168
      2. Van Holten P.C. Van Holten V. Muir C. International classification of diseases for oncology. 2nd ed. World Health Organization, Geneva1990
        • van Melle L.P.
        • De Jonge P.
        • Spijkerman T.A.
        • Tijssen J.G.P.
        • Ormel J.
        • van Veldhuisen D.J.
        • van den Berg M.P.
        • et al.
        Prognostic association of depression following myocardial infarction with mortality and cardiovascular events: A meta-analysis.
        Psychosomatic Medicine. 2004; 66: 814-822
        • Ware J.E.
        • Sherbourne C.D.
        The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection.
        Medical Care. 1992; 30: 473-483
        • Waller M.
        • McGuire A.C.L.
        Changes over time in the “healthy soldier effect.”.
        Population Health Metrics. 2011; 9: 7https://doi.org/10.1186/1478-7954-9-7
        • Washington D.L.
        • Bean-Mayberry B.
        • Hamilton A.B.
        • Cordasco K.M.
        • Yano E.M.
        Women veterans’ healthcare delivery preferences and use by military service era: Findings from the National Survey of Women Veterans.
        Journal of General Internal Medicine. 2013; 28: S571-S576https://doi.org/10.1007/s11606-012-2323-y
        • Williams A.R.
        • Weiss N.S.
        • Ure C.L.
        • Ballard J.
        • Daling J.R.
        Effect of weight, smoking and estrogen use on the risk of hip and forearm fractures in postmenopausal women.
        Obstetrics and Gynecology. 1982; 60: 695-699
        • Wilmoth J.M.
        • London A.S.
        • Parker W.M.
        Military service and men’s health trajectories in later life.
        Journals of Gerontology, Series B, Psychological Sciences and Social Sciences. 2010; 65B: 744-755https://doi.org/10.1093/geronb/gbq072
        • Yi S.W.
        Cancer incidence in Korean Vietnam veterans during 1992–2003: The Korean Veterans Health Study.
        Journal of Preventive Medicine and Public Health. 2013; 46: 309-318https://doi.org/10.3961/jpmph.2013.46.6.309
        • Zhang Y.
        Cardiovascular diseases in American women.
        Nutrition, Metabolism and Cardiovascular Disease. 2010; 20: 386-393https://doi.org/10.1016/j.numecd.2010.02.001
        • Zhu K.
        • Devesa S.S.
        • Wu H.
        • Zahm S.H.
        • Ismail J.
        • Anderson W.F.
        • McGlynn K.A.
        • et al.
        Cancer Incidence in the U.S. Military Population: Comparison with Rates from the SEER Program.
        Cancer Epidemiology, Biomarkers, & Prevention. 2009; 18: 1740-1745

      Biography

      Julie C. Weitlauf, PhD, is Director of the Women's Mental Health and Aging Core of the VISN 21 MIRECC at the VA Palo Alto Health Care System, and Clinical Associate Professor (Affiliated) of Psychiatry and Behavioral Sciences at Stanford.

      Biography

      Andrea Z. LaCroix, PhD, is Professor and Chief of Epidemiology and Director of the Women's Health Center of Excellence within the Department of Family Medicine and Public Health at the University of California, San Diego.

      Biography

      Chloe E. Bird, PhD, is Senior Sociologist at RAND, with particular expertise in women's cardiovascular health. She also serves as Professor within the Pardee RAND Graduate School, and as Editor-in-Chief of Women's Health Issues.

      Biography

      Nancy F. Woods, PhD, RN, FAAN, is Professor of Biobehavioral Nursing, and Dean Emeritus of the University of Washington School of Nursing.

      Biography

      Donna L. Washington, MD, MPH, is Director of the Office of Health Equity/QUERI Partnered Evaluation Center, Greater Los Angeles VA Health Care System, and Professor of Medicine, at the University of California, Los Angeles, David Geffen School of Medicine.

      Biography

      Jodie G. Katon, PhD, MS, is Health Sciences Research Specialist within the VA Puget Sound Health Care System and Senior Epidemiologic Consultant within the VA Office of Patient Care, Women's Health Services.

      Biography

      Michael J. LaMonte, PhD, MPH, is Research Associate Professor of Epidemiology within the Department of Epidemiology and Environmental Health of the School of Public Health and Health Professions of the State University of New York at Buffalo.

      Biography

      Mary K. Goldstein, MD, MS in Health Services Research, is Director of the Palo Alto Geriatrics Research Education and Clinical Center, VA Palo Alto Health Care System, and Professor of Medicine, Center for Primary Care and Outcomes Research at Stanford University School of Medicine.

      Biography

      Shari S. Bassuk, ScD, is an epidemiologist with the Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School.

      Biography

      Gloria E. Sarto, MD, PhD, is Professor Emeritus of Obstetrics and Gynecology at the University of Wisconsin-Madison, has served on several boards/committees within the Institute of Medicine, and was involved in the development of NIH's Office of Women's Health.

      Biography

      Marcia L. Stefanick, PhD, is Professor of Medicine (Stanford Prevention Research Center), and of Obstetrics & Gynecology at Stanford University School of Medicine. She is also Director of the Stanford Women and Sex Differences in Medicine (WSDM) Center.