ࡱ> uwrst LbjbjVV Z<<> h h 4hG<Lq`D_______$'bd8``+`/// PY/_//H&K`)IHouse198834Social relationships and health 3Journal34Social relationships and health 3House,J.S.Landis,K.R.Umberson,D.1988/7/29AnimalsepidemiologyHealthHealth StatusHumansmortalityphysiologyProspective StudiesRiskRisk FactorsSocial BehaviorSocial IsolationNot in File540545Science2414865
Department of Epidemiology, University of Michigan, Ann Arbor 48109
PM:3399889Science1House200133Social isolation kills, but how and why? 1Journal33Social isolation kills, but how and why? 1House,J.S.2001/3Coronary DiseaseHealth PromotionHealth StatusHumansModels,PsychologicalmortalitypsychologyRiskSocial Control,InformalSocial IsolationSocial SupportNot in File273274Psychosom.Med.632PM:11292275Psychosom.Med.1(House, 2001; House, Landis & Umberson, 1988) and mortality from a range of clinical conditions across adulthood  ADDIN REFMGR.CITE Kaplan200635Marital status and longevity in the United States population 1Journal35Marital status and longevity in the United States population 1Kaplan,R.M.Kronick,R.G.2006/9AdultAgedAged,80 and overepidemiologyFemaleHealthHealth SurveysHumansLogistic ModelsLongevityMaleMarital StatusMarriageMiddle AgedmortalityRisk FactorsSpousesstatistics & numerical dataUnited StatesVital StatisticsWomenNot in File760765J Epidemiol.Community Health609
University of California, Los Angeles, PO Box 951772, Room 31-293-C, CHS UCLA Los Angeles, CA 90025, USA. rmkaplan@ucla.edu
PM:16905719J Epidemiol.Community Health1(Kaplan & Kronick, 2006), particularly conditions relating to cardiovascular disease  ADDIN REFMGR.CITE Lett200582Social support and coronary heart disease: epidemiologic evidence and implications for treatment 2Journal82Social support and coronary heart disease: epidemiologic evidence and implications for treatment 2Lett,H.S.Blumenthal,J.A.Babyak,M.A.Strauman,T.J.Robins,C.Sherwood,A.2005/11ComorbidityCoronary DiseaseDepressive DisorderdiagnosisepidemiologyFemaleHealth BehaviorHumansMalemethodsNorth CarolinaOutcome Assessment (Health Care)physiopathologyPrognosisProspective StudiesPsychological TheoryRiskRisk FactorsSocial ClassSocial SupportTerminology as TopicNot in File869878Psychosom.Med.676
Department of Psychiatry and Behavioral Science, Duke University Medical Center, Durham, North Carolina 27710, USA. heather.lett@alumni.duke.edu
PM:16314591Psychosom.Med.1Brummett20019Characteristics of socially isolated patients with coronary artery disease who are at elevated risk for mortalityJournal9Characteristics of socially isolated patients with coronary artery disease who are at elevated risk for mortalityBrummett,B.H.Barefoot,J.C.Siegler,I.C.Clapp-Channing,N.E.Lytle,B.L.Bosworth,H.B.Williams,R.B.,Jr.Mark,D.B.2001/3AgedCoronary Artery DiseaseCoronary DiseaseepidemiologyFemaleFollow-Up StudiesHumansMaleMiddle AgedmortalityNorth CarolinaPrognosisProspective StudiesPsychiatric Status Rating ScalespsychologyRiskSeverity of Illness IndexSocial IsolationSocial SupportNot in File267272Psychosom.Med.632
Behavioral Medicine Research Center, Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, North Carolina 27710, USA. brummett@acpub.duke.edu
PM:11292274Psychosom.Med.1(Brummett, Barefoot, Siegler, Clapp-Channing, Lytle, Bosworth et al., 2001; Lett, Blumenthal, Babyak, Strauman, Robins & Sherwood, 2005). This has been clearly demonstrated in the literature examining the relationship between marital status and health outcomes  ADDIN REFMGR.CITE Kiecolt-Glaser20015Marriage and health: his and hers 5Journal5Marriage and health: his and hers 5Kiecolt-Glaser,J.K.Newton,T.L.2001/7DepressionDivorceFemaleHealth StatusHostilityHumansMaleMarriagePain MeasurementpsychologyRisk FactorsSex Factorsstatistics & numerical dataStress,PsychologicalWomenWomen's HealthNot in File472503Psychol.Bull.1274
Department of Psychiatry, Ohio State University College of Medicine, 1670 Upham Drive, Columbus, Ohio 43210, USA. kiecolt-glaser.1@osu.edu
PM:11439708Psychol.Bull.1(Kiecolt-Glaser & Newton, 2001). All of the various unmarried states (being single never married, being separated/divorced and being widowed) have been associated with elevated mortality risks  ADDIN REFMGR.CITE Johnson200067Marital status and mortality: the national longitudinal mortality study 1Journal67Marital status and mortality: the national longitudinal mortality study 1Johnson,N.J.Backlund,E.Sorlie,P.D.Loveless,C.A.2000/5Age DistributionAgedCardiovascular DiseasesCause of DeathConfounding Factors (Epidemiology)epidemiologyFemaleHumansIncidenceLongitudinal StudiesMaleMarital StatusmethodsMiddle AgedmortalityProportional Hazards ModelsProspective StudiesRegistriesRiskRisk AssessmentRisk FactorsSex DistributionSocioeconomic Factorsstatistics & numerical datatrendsUnited StatesWomenNot in File224238Ann.Epidemiol.104
Demographic Statistical Methods Division, U.S. Bureau of the Census, Washington, DC 20233, USA
PM:10854957Ann.Epidemiol.1Manzoli200737Marital status and mortality in the elderly: a systematic review and meta-analysis 2Journal37Marital status and mortality in the elderly: a systematic review and meta-analysis 2Manzoli,L.Villari,P.Pirone,M.Boccia,A.2007/1AgedCohort StudiesepidemiologyFemaleHealthHumansItalyMaleMarital StatusMarriagemortalityResearch DesignRiskRisk Factorsstatistics & numerical dataNot in File7794Soc.Sci.Med.641
Section of Epidemiology and Public Health, University G. d'Annunzio of Chieti, Italy. lmanzoli@post.harvard.edu
PM:17011690Soc.Sci.Med.1Ikeda200738Marital status and mortality among Japanese men and women: the Japan Collaborative Cohort Study 1Journal38Marital status and mortality among Japanese men and women: the Japan Collaborative Cohort Study 1Ikeda,A.Iso,H.Toyoshima,H.Fujino,Y.Mizoue,T.Yoshimura,T.Inaba,Y.Tamakoshi,A.2007AdultAgedCardiovascular DiseasesCohort StudiesDivorceepidemiologyFemaleHealthHumansJapanLife StyleMaleMarital StatusMarriageMiddle AgedmortalityNeoplasmsProspective StudiesQuestionnairesRespiratory Tract DiseasesRiskRisk AssessmentRisk FactorsSocial Supportstatistics & numerical dataWidowhoodWomenNot in File73BMC Public Health7147
Department of Public Health Medicine, Doctoral Program in Social and Environmental Medicine, Graduate School of Comprehensive Human Sciences, University of Tsukuba, Tennoudai, Tsukuba-shi, Ibaraki, Japan. ai-ikeda@umin.net <ai-ikeda@umin.net>
PM:17484786BMC Public Health1(Ikeda, Iso, Toyoshima, Fujino, Mizoue, Yoshimura et al. 2007; Johnson, Backlund, Sorlie & Loveless, 2000; Manzoli, Villari, Pirone & Boccia, 2007). The two main explanations that have been proposed in accounting for these observations are social selection and social causation theory  ADDIN REFMGR.CITE Joung199850A longitudinal study of health selection in marital transitions 17Journal50A longitudinal study of health selection in marital transitions 17Joung,I.M.van de Mheen,H.D.Stronks,K.van Poppel,F.W.Mackenbach,J.P.1998/2AdolescentAdultAgedanalysisChronic DiseaseDivorceepidemiologyFemaleFollow-Up StudiesHealthHealth StatusHumansLife Change EventsLongitudinal StudiesMaleMarital StatusMarriageMiddle AgedNetherlandsProportional Hazards ModelsRiskSingle PersonWidowhoodNot in File425435Soc.Sci.Med.463
Department of Public Health, Erasmus University Rotterdam, The Netherlands
PM:9460823Soc.Sci.Med.1(Joung, van de Mheen, Stronks, van Poppel & Mackenbach, 1998). Although these are non-mutually exclusive explanations with respect to marriage and health, social selection usually refers to the selection of healthier individuals into marriage and unhealthy persons into unmarried states whereas social causation refers to the social/economic resources, sometimes referred to as the protective or social support consequences of marriage, and better health behaviours (this can also be a selection effect through assortive mating) that can accompany the married state and promote health and the harmful consequences of bereavement or marital dissolution experienced by widowed persons and the separated or divorced. Although social selection and social causation represent contrasting accounts at an ultimate level of explanation  ADDIN REFMGR.CITE Tinbergen196378On Aims and Methods in Ethology.Journal78On Aims and Methods in Ethology.Tinbergen,N1963methodsNot in File410433Zeitschrift für Tierpsychologie20Zeitschrift für Tierpsychologie1(Tinbergen, 1963), the proximate biobehavioural mechanisms are likely to be shared in social selection and social causation e.g. health behaviour, psychological distress, stress-related pathophysiological responses. A range of studies have demonstrated that behavioural  ADDIN REFMGR.CITE Umberson199264Gender, marital status and the social control of health behavior 4Journal64Gender, marital status and the social control of health behavior 4Umberson,D.1992/4AdultanalysisFemaleGender IdentityHealthHealth BehaviorHumansInterviews as TopicMaleMarital StatusMarriageMiddle AgedModels,PsychologicalmortalitypsychologySocial Control,InformalSpousesTexasUnited StatesWomenNot in File907917Soc.Sci.Med.348
Department of Sociology, University of Texas, Austin 78712
PM:1604380Soc.Sci.Med.1Molloy200847Social networks and partner stress as predictors of adherence to medication, rehabilitation attendance, and quality of life following acute coronary syndrome 2Journal47Social networks and partner stress as predictors of adherence to medication, rehabilitation attendance, and quality of life following acute coronary syndrome 2Molloy,G.J.Perkins-Porras,L.Strike,P.C.Steptoe,A.2008/1Acute Coronary SyndromeAgeddrug therapyEnglandepidemiologyFemaleHealthHospitalizationHumansMaleMiddle AgedMorbiditymortalityPatient CompliancepsychologyQuality of LifeQuestionnairesRehabilitation CentersRiskSexual PartnersSocial SupportStress,PsychologicalutilizationNot in File5258Health Psychol.271
Department of Epidemiology and Public Health, University College London, United Kingdom. g.molloy@ucl.ac.uk
PM:18230014Health Psychol.1(Molloy, Perkins-Porras, Strike & Steptoe, 2008; Umberson, 1992), psychological distress  ADDIN REFMGR.CITE Kessler198277Marital-Status and Depression - the Importance of Coping Resources 15Journal77Marital-Status and Depression - the Importance of Coping Resources 15Kessler,R.C.Essex,M.1982DepressionMarital StatusNorth CarolinaNot in File484507Social Forces6120037-7732
LAWRENCE UNIV,APPLETON,WI 54911
ISI:A1982PQ62700008Social Forces1Umberson199276Widowhood and Depression - Explaining Long-Term Gender Differences in Vulnerability 13Journal76Widowhood and Depression - Explaining Long-Term Gender Differences in Vulnerability 13Umberson,D.Wortman,C.B.Kessler,R.C.1992/3DepressionFamilyLIFEMarital StatusROLESSEX-DIFFERENCESSTRESSWidowhoodWomenNot in File1024Journal of Health and Social Behavior3310022-1465
UNIV MICHIGAN,INST SOCIAL RES,ANN ARBOR,MI 48109 SUNY STONY BROOK,SOCIAL HLTH PHD PROGRAM,STONY BROOK,NY 11794
ISI:A1992HK84100002Journal of Health and Social Behavior1(Kessler & Essex, 1982; Umberson, Wortman & Kessler, 1992) and pathophysiological mechanisms  ADDIN REFMGR.CITE Uchino200657Social support and health: a review of physiological processes potentially underlying links to disease outcomes 9Journal57Social support and health: a review of physiological processes potentially underlying links to disease outcomes 9Uchino,B.N.2006/8Cardiovascular SystemHealthHumansImmune SystemInflammationMorbiditymortalityNeurosecretory SystemsphysiologyphysiopathologypsychologyPsychophysiologySocial SupportNot in File377387J Behav.Med.294
Department of Psychology and Health Psychology Program, University of Utah, 380 S. 1530 E., Rm. 502, Salt Lake City, 84112 Utah, USA. bert.uchino@psych.utah.edu
PM:16758315J Behav.Med.1(Uchino, 2006) that can influence morbidity and mortality from cardiovascular disease (CVD) are associated with various states of social isolation. In comparison with the other leading causes of mortality (e.g. cancer, respiratory conditions, infectious disease and external causes) theoretical models linking marital status with processes that are known to directly influence CVD mortality have been more completely outlined and tested e.g. cardiovascular reactivity  ADDIN REFMGR.CITE Kiecolt-Glaser20015Marriage and health: his and hers 5Journal5Marriage and health: his and hers 5Kiecolt-Glaser,J.K.Newton,T.L.2001/7DepressionDivorceFemaleHealth StatusHostilityHumansMaleMarriagePain MeasurementpsychologyRisk FactorsSex Factorsstatistics & numerical dataStress,PsychologicalWomenWomen's HealthNot in File472503Psychol.Bull.1274
Department of Psychiatry, Ohio State University College of Medicine, 1670 Upham Drive, Columbus, Ohio 43210, USA. kiecolt-glaser.1@osu.edu
PM:11439708Psychol.Bull.1(Kiecolt-Glaser & Newton, 2001). However there are few reliable estimates and comparisons of the extent to which these mechanisms can potentially explain the association between each of the unmarried states and risk of CVD mortality. The present study uniquely addresses this gap in this literature. This type of analysis is required to move our understanding of marriage and its role in the pathogenesis of CVD forward, as there are potentially differing mechanisms, which may be more or less important in the various unmarried states  ADDIN REFMGR.CITE Kiecolt-Glaser20015Marriage and health: his and hers 5Journal5Marriage and health: his and hers 5Kiecolt-Glaser,J.K.Newton,T.L.2001/7DepressionDivorceFemaleHealth StatusHostilityHumansMaleMarriagePain MeasurementpsychologyRisk FactorsSex Factorsstatistics & numerical dataStress,PsychologicalWomenWomen's HealthNot in File472503Psychol.Bull.1274
Department of Psychiatry, Ohio State University College of Medicine, 1670 Upham Drive, Columbus, Ohio 43210, USA. kiecolt-glaser.1@osu.edu
PM:11439708Psychol.Bull.1(Kiecolt-Glaser & Newton, 2001). Although these three classes of mechanisms are clearly interdependent, a comparison of the separate explanatory power of these could inform what intervention strategies might be most effective in reducing the risk associated with being unmarried i.e. behaviour change, psychotherapy and biomedical intervention. One of the strongest recurrent findings in this literature on marital status and health has been the presence of gender differences in the relationship between marital status and health outcomes (Umberson, 1992). Being married is associated with greater protection for men compared to women, therefore gender stratified analysis have become commonplace in much of this work (Kaplan & Kronick, 2006;  ADDIN REFMGR.CITE Scafato200889Marital and cohabitation status as predictors of mortality: A 10-year follow-up of an Italian elderly cohort 1Journal89Marital and cohabitation status as predictors of mortality: A 10-year follow-up of an Italian elderly cohort 1Scafato,E.Galluzzo,L.Gandin,C.Ghirini,S.Baldereschi,M.Capurso,A.Maggi,S.Farchi,G.For The Ilsa Working Group2008/11AgedAgingDIFFERENTIALSepidemiologyHealthHealth PromotionItalyLongitudinal StudiesMarital StatusmortalityRiskWomenNot in File14561464Soc.Sci.Med.679
Population Health and Health Determinants Unit, National Centre for Epidemiology, Surveillance and Health Promotion (CNESPS), Istituto Superiore di Sanita (ISS), Via Giano della Bella 34, 00161 Roma, Italy
PM:18675500Soc.Sci.Med.1Scafato, Galluzzo, Gandin, Ghirini, Baldereschi, Capurso et al. 2008). Various explanations have been proposed for observed gender differences in the marriage-health relationship, namely gender differences in the social control of health behaviour, with women being more likely to control others health behaviour (Umberson, 1992) and the qualitative differences between men and womens support networks, with men more likely to rely on wife or partner as the main source of support, whereas women may have several close confidants. The extent of the differences between the intermediate processes between marital status and CVD mortality in men and women has not to date been well charaterised. Therefore the analysis also aimed to examine gender differences in CVD mortality and potential intermediate mechanisms according to marital status. We analysed data from the Scottish Health Survey (The Scottish Government Statistics, 2008) to address the following questions:(i) How much of the association between marital status and cardiovascular mortality can be explained by behavioural, psychological distress and metabolic dysregulation (ii) Does the relative contribution of behavioural, psychological distress and metabolic processes vary across the marital status categories i.e. being single never married, being separated/divorced and being widowed. In this study we eliminated individuals with previously clinically diagnosed CVD in order to assess the relationship between marital status and cardiovascular mortality in a population that were free from clinically confirmed CVD at baseline. Methods Sample The Scottish Health Survey (SHS) is a periodic survey (typically every 3-5 years) that draws a nationally representative sample of the general population living in households. The sample was drawn using multistage stratified probability sampling with postcode sectors selected at the first stage and household addresses selected at the second stage. Different samples were drawn for each survey. The present analyses combined data from the 1995, 1998 and 2003 SHS in adults aged 35 yrs and older. The overall response rate ranged between 60-76% for the different survey years (The Scottish Government Statistics, 2008). Participants gave full informed consent to participate in the study and ethical approval was obtained from the London Research Ethics Council. Out of a total of 16,144 we excluded 1094 participants (7%) with a previous clinical history of CVD or cancer. There were 1151 participants with incomplete data (7%), therefore there was complete data available for 13,889 participants. This sample comprised the dataset for the present analysis. Baseline assessment Survey interviewers visited eligible households and collected data on demographics and health behaviours (physical activity, smoking, alcohol intake). There were 6 possible categories for marital status: 1. married, 2. co-habiting, 3. widowed, 4. divorced, 5. separated or 6 single and never married. For the purpose of this study 4 marital status categories were created namely 1. Married/co-habiting, 2. Single, never married, 3. Separated/divorced and 4. Widowed. On a separate visit nurses collected information on medical history, and took anthropometry variables (height, weight, waist circumference) from consenting adults. Detailed information on the survey method can be found elsewhere  ADDIN REFMGR.CITE The Scottish Government Statistics.200866Scottish Health Survey PublicationsBook, Whole66Scottish Health Survey PublicationsThe Scottish Government Statistics.2008HealthNot in File2008/6/6http://www.scotland.gov.uk/Topics/Statistics/Browse/Health/scottish-health-survey/Publications 2(The Scottish Government Statistics., 2008). Predictor and outcome variables Current mental health was assessed from the 12 item version of the General Health Questionnaire (GHQ-12), which is a measure of psychological distress devised for population studies. The questionnaire comprises twelve questions, asking informants about their general level of happiness, experience of depressive and anxiety symptoms, and sleep disturbance over the last four weeks. Interpretation of the answers is based on a four point response scale scored using a bimodal method (symptom present: 'not at all' = 0, 'same as usual' = 0, 'more than usual' = 1 and 'much more than usual' = 1). The GHQ-12 is a highly validated instrument and has been strongly associated with various psychological disorders such as depression and anxiety  ADDIN REFMGR.CITE Goldberg19977The validity of two versions of the GHQ in the WHO study of mental illness in general health care 7Journal7The validity of two versions of the GHQ in the WHO study of mental illness in general health care 7Goldberg,D.P.Gater,R.Sartorius,N.Ustun,T.B.Piccinelli,M.Gureje,O.Rutter,C.1997/1FemaleHealthHealth Status IndicatorsHumansLondonLongitudinal StudiesMaleMental HealthmethodsPsychiatric Status Rating ScalesPsychometricsReproducibility of ResultsRoc CurveSampling StudiesSensitivity and SpecificitystandardsTranslatingWorkWorld HealthWorld Health OrganizationNot in File191197Psychol.Med.271
Institute of Psychiatry, London
PM:9122299Psychol.Med.1(Goldberg, Gater, Sartorius, Ustun, Piccinelli, Gureje et al. 1997). We used a score of (4 to define possible caseness of psychological distress according to studies validating the GHQ-12 against standardised psychiatric interviews  ADDIN REFMGR.CITE Goldberg19977The validity of two versions of the GHQ in the WHO study of mental illness in general health care 7Journal7The validity of two versions of the GHQ in the WHO study of mental illness in general health care 7Goldberg,D.P.Gater,R.Sartorius,N.Ustun,T.B.Piccinelli,M.Gureje,O.Rutter,C.1997/1FemaleHealthHealth Status IndicatorsHumansLondonLongitudinal StudiesMaleMental HealthmethodsPsychiatric Status Rating ScalesPsychometricsReproducibility of ResultsRoc CurveSampling StudiesSensitivity and SpecificitystandardsTranslatingWorkWorld HealthWorld Health OrganizationNot in File191197Psychol.Med.271
Institute of Psychiatry, London
PM:9122299Psychol.Med.1(Goldberg et al., 1997). Existing hypertension and diabetes was confirmed from self reported doctors diagnosis, which is generally considered as being reliable  ADDIN REFMGR.CITE Colditz198679Validation of questionnaire information on risk factors and disease outcomes in a prospective cohort study of women 62Journal79Validation of questionnaire information on risk factors and disease outcomes in a prospective cohort study of women 62Colditz,G.A.Martin,P.Stampfer,M.J.Willett,W.C.Sampson,L.Rosner,B.Hennekens,C.H.Speizer,F.E.1986/5AdultbloodBlood PressureCardiovascular DiseasesCholesterolCohort StudiesEpidemiologic MethodsepidemiologyFemaleFollow-Up StudiesFractures,BoneHumansHypertensionMedical RecordsMiddle AgedMorbidityNeoplasmsQuestionnairesRiskRisk FactorsUnited StatesWomenNot in File894900Am.J Epidemiol.1235PM:3962971Am.J Epidemiol.1(Colditz, Martin, Stampfer, Willett, Sampson, Rosner et al. 1986). Obesity was defined as body mass index ( 30 kg/m2. Health behaviours were measured using self report questionnaires. Physical activity questions inquired about participation in the four weeks prior to the interview. Frequency, duration, and intensity of participation was assessed across three domains of activity: leisure time sports (e.g., cycling, swimming, running, aerobics, dancing, and ball sports such as football and tennis), walking for any purpose, and domestic physical activity (e.g., heavy housework, home improvement activities, manual and gardening work). Health behaviours were treated as categorical variables: physical activity was categorised into five groups according to frequency of any activity lasting at least 30 minutes (reference group no activity, <1/wk, 1-2 /wk, 3-4/wk, (5/wk); smoking was categorised into five groups (reference group never smoked, past smokers, current smokers <10 cigarettes/day, 10-20 cigarettes/day, >20 cigarettes/day); alcohol intake was quantified in units per week (1 unit = half pint beer, a small glass of wine, or a measure of spirits) and categorised into sex specific tertiles with the highest tertile representing hazardous levels (14+ units for women/ 21+ units for men). The main outcome was cardiovascular mortality and all cause mortality. This information was obtained from a patient-based database of CVD hospital admissions and deaths (Information Services Division [ISD] Scotland) that was linked to the surveys. The ISD database has demonstrated 94% accuracy and 99% completeness when samples of computerized CVD records from the Scottish national database were compared with the original patient case notes. Classification of the underlying cause of death was obtained from the General Registrar Office for Scotland and was based on information collected from the death certificate together with any additional information provided subsequently by the certifying doctor. Mortality from cardiovascular causes was coded according to ϲ Classification of Diseases - Version 9 (ICD-9) (390-459) and ICD-10 (I01-I99). Data on CVD hospital admissions were available between 1980 and September 2006 that allowed us to exclude 846 participants with existing CVD at baseline. Statistical analysis Logistic regression was used to examine associations between marital status categories and behavioural, psychological distress and metabolic risk factors. These models included adjustments for age. Cox proportional hazards models were used with months as the time scale to estimate the risk of cardiovascular and all cause mortality according to marital status. For participants who survived the data were censored to September 2006. The proportional hazards assumption was examined by comparing the cumulative hazard plots grouped on exposure, although no appreciable violations were noted. In the basic multivariate model we adjusted for potential confounders including age (continuous score) and socioeconomic group using the Registrar General Classification (categories: I/II professional/intermediate, III skilled non-manual/ skilled manual, IV/V part-skilled/unskilled) as these two variables have known relationships to marital status e.g. the widowed are much older and the unmarried are more likely to be in lower socio-economic groups. In order to test the extent to which behavioural, psychological distress and metabolic dysregulation accounted for the association between marital status and CVD mortality, we grouped together CVD risk factors considered to potentially explain the association on an a priori basis. This included behavioural factors (physical activity, smoking, alcohol, treated as categorical variables), psychological distress (GHQ-12 treated as a continuous score), a metabolic dysregulation factor (body mass index, the presence of hypertension and diabetes, treated as categorical variables). We separately added these risk factors, one set at a time, into the basic model. Finally we performed a fully adjusted analysis that included all of the factors simultaneously. The proportion of CVD risk reduction explained by each set of factors was computed as follows: (HRbasic model HRadjusted)/ HRbasic model 1) ( 100. We used ANOVA tests to examine continuous variables across the marital categories. All analyses were performed using SPSS (version 14) and all tests of statistical significance were based on two-sided probability. Results The mean age of the sample was 52.3 years (SD: 11.8, range 35-95) and 56.1% were female. There were a total of 892 deaths, 258 (28.9%) were due to CVD and 353 (39.6%) were due to cancer over an average of 7.2 years of follow up. Coronary heart disease accounted for 65.1%, cerebrovascular diseases 26.7%, and aortic aneurysm 4.3%, of all cardiovascular deaths. At baseline, 65% of participants were married/co-habiting, 11 % were single, never married, 14.4% were separated/divorced and 9.5% were widowed. As shown in table 1 there were significant age differences across the 4 marital categories for men and women (p<0.01). Table 1 therefore present age adjusted logistic regression models that examine the associations between marital status and behavioural, psychological distress and metabolic risk factors. There was no association between being unmarried and being physically inactive for men and women. All unmarried categories for men and women were significantly more likely to smoke than married individuals. Separated/divorced and widowed men were more likely to engage in hazardous drinking. All unmarried categories for men and women were significantly more likely to experience psychological distress (GHQ ( 4). Separated/divorced men were more likely to have a diagnosis of hypertension. Single, never married men and women were more likely to have diabetes. Further details are provided in Table 1. Table 2 demonstrates the gender stratified hazard ratios for all cause mortality and cardiovascular mortality in the unmarried versus the married groups. An age adjusted test for the interaction between marital status and gender showed that there was a significant interaction for all cause mortality (p= 0.025), however this was not observed for CVD mortality. All unmarried states were associated with a significantly higher risk of all cause mortality with the exception of separated/divorced women. All unmarried states were associated with a higher risk of cardiovascular mortality with the exception of widowed women. In general, the risk of death was higher for cardiovascular causes, especially in the case of single or widowed men, and separated/divorced women. In sensitivity analysis we restricted the analysis to participants who were greater than 50 and less than 80 given that a primary cause of CVD mortality is most typical of this age group. We found that the overall pattern of results did not change. For example in single, never married men (N=3,160, 124 CVD deaths) the hazard ratio for CVD mortality was 2.97 (95% CI 1.85-4.78) and for single, never married women (N=4,213, 93 CVD deaths) hazard ratio was 2.23 (1.17-4.24). Table 3 presents the gender stratified analysis for the marital status and cardiovascular mortality with separate adjustments for health behaviours, psychological distress and metabolic factors. All unmarried groups had significantly higher risk of CV mortality relative to the married/co-habiting with the exception of widowed women in age and SES adjusted models. Table 3 present the details of the adjusted analysis for health behaviours (physical activity, smoking and alcohol), psychological distress and metabolic dysregulation (hypertension, diabetes and BMI). As there was not a significant association between being widowed and CVD mortality for women we did not investigate potential intermediate mechanisms in any more detail. The results show that inclusion of the health behaviour data in the models was associated with attenuation in the strength of the relationship between marital status and CVD mortality for all categories of unmarried status for men, but only in the separated/divorced category for women. It is clear that inclusion of health behaviour data is associated with much greater attenuation in the observed relationship between being separated or divorced and CVD mortality than the two other unmarried categories. Including psychological distress in the models was associated with attenuation in the observed relationship between being unmarried and CVD mortality with attenuation ranging from 2.8% for single, never married women to 10.3% for separated/divorced women and between 5.2% for widowed men and 8.8% for separated/divorced men. Finally including metabolic dysregulation variables (presence of hypertension, diabetes and BMI) in the models was associated with a between 3.2% and 16% attenuation for women and between a 4.4% and 8.8% attenuation in the observed relationship between being unmarried and CVD mortality for unmarried men. Discussion The present data once again demonstrated the increased cardiovascular mortality risks for unmarried men and women. The associations observed in the present data were largely concordant with two recent population studies from the US  ADDIN REFMGR.CITE Kaplan200635Marital status and longevity in the United States population 1Journal35Marital status and longevity in the United States population 1Kaplan,R.M.Kronick,R.G.2006/9AdultAgedAged,80 and overepidemiologyFemaleHealthHealth SurveysHumansLogistic ModelsLongevityMaleMarital StatusMarriageMiddle AgedmortalityRisk FactorsSpousesstatistics & numerical dataUnited StatesVital StatisticsWomenNot in File760765J Epidemiol.Community Health609
University of California, Los Angeles, PO Box 951772, Room 31-293-C, CHS UCLA Los Angeles, CA 90025, USA. rmkaplan@ucla.edu
PM:16905719J Epidemiol.Community Health1(Kaplan & Kronick, 2006) and Japan  ADDIN REFMGR.CITE Ikeda200738Marital status and mortality among Japanese men and women: the Japan Collaborative Cohort Study 1Journal38Marital status and mortality among Japanese men and women: the Japan Collaborative Cohort Study 1Ikeda,A.Iso,H.Toyoshima,H.Fujino,Y.Mizoue,T.Yoshimura,T.Inaba,Y.Tamakoshi,A.2007AdultAgedCardiovascular DiseasesCohort StudiesDivorceepidemiologyFemaleHealthHumansJapanLife StyleMaleMarital StatusMarriageMiddle AgedmortalityNeoplasmsProspective StudiesQuestionnairesRespiratory Tract DiseasesRiskRisk AssessmentRisk FactorsSocial Supportstatistics & numerical dataWidowhoodWomenNot in File73BMC Public Health7147
Department of Public Health Medicine, Doctoral Program in Social and Environmental Medicine, Graduate School of Comprehensive Human Sciences, University of Tsukuba, Tennoudai, Tsukuba-shi, Ibaraki, Japan. ai-ikeda@umin.net <ai-ikeda@umin.net>
PM:17484786BMC Public Health1(Ikeda et al., 2007) and several older studies  ADDIN REFMGR.CITE Ben-Shlomo19931Magnitude and causes of mortality differences between married and unmarried menJournal1Magnitude and causes of mortality differences between married and unmarried menBen-Shlomo,Y.Smith,G.D.Shipley,M.Marmot,M.G.1993/6AdultAge FactorsAgedAlcohol DrinkingCause of DeathCohort StudiesDivorceepidemiologyFollow-Up StudiesGovernmentHealthHumansLondonMaleMarital StatusMarriageMiddle AgedmortalityOccupational DiseasesRiskRisk FactorsSingle PersonSmokingSocial ClassWorkNot in File200205J Epidemiol.Community Health473
Department of Epidemiology and Public Health, University College and Middlesex School of Medicine, University College, London
PM:8350032J Epidemiol.Community Health1Johnson200067Marital status and mortality: the national longitudinal mortality study 1Journal67Marital status and mortality: the national longitudinal mortality study 1Johnson,N.J.Backlund,E.Sorlie,P.D.Loveless,C.A.2000/5Age DistributionAgedCardiovascular DiseasesCause of DeathConfounding Factors (Epidemiology)epidemiologyFemaleHumansIncidenceLongitudinal StudiesMaleMarital StatusmethodsMiddle AgedmortalityProportional Hazards ModelsProspective StudiesRegistriesRiskRisk AssessmentRisk FactorsSex DistributionSocioeconomic Factorsstatistics & numerical datatrendsUnited StatesWomenNot in File224238Ann.Epidemiol.104
Demographic Statistical Methods Division, U.S. Bureau of the Census, Washington, DC 20233, USA
PM:10854957Ann.Epidemiol.1Ebrahim199583Marital status, change in marital status, and mortality in middle-aged British men 6Journal83Marital status, change in marital status, and mortality in middle-aged British men 6Ebrahim,S.Wannamethee,G.McCallum,A.Walker,M.Shaper,A.G.1995/10/15AdultAgedbloodBlood PressureBody Mass IndexCardiovascular DiseasesCause of DeathCholesterolCohort StudiesConfidence IntervalsConfounding Factors (Epidemiology)EmploymentEnglandepidemiologyGreat BritainHealthHumansLife StyleLondonMaleMarital StatusMarriageMiddle AgedmortalityNeoplasmsProspective StudiesRiskRisk FactorsSmokingSocial ClassSocial SupportSocioeconomic FactorsNot in File834842Am.J Epidemiol.1428
Department of Public Health, Royal Free Hospital School of Medicine, University of London, England
PM:7572960Am.J Epidemiol.1(Ben-Shlomo, Smith, Shipley & Marmot, 1993; Ebrahim, Wannamethee, McCallum, Walker & Shaper, 1995; Johnson et al., 2000), suggesting that these relationships are highly robust across time and place. The unique contribution of the present study was to focus on the extent to which health behaviours, psychological distress and metabolic dysregulation can account for the association between the various unmarried categories and cardiovascular mortality risk. The present analyses show that between 16% and 39% of the variance in the observed relationships between being unmarried and CVD mortality can be accounted for by these variables. Health behaviour data emerged as being particularly important in explaining the observed relationship between being unmarried and CVD mortality among men. This is consistent with the social control hypothesis of marital relationships that argues that women are more likely to regulate mens health behaviour in marital relationship (Umberson, 1992). However the findings clearly indicate that the explanatory power of health behaviour data varies greatly depending on the unmarried category for both men and women. In men health behaviour data has a relatively lower explanatory value for the CVD mortality risk associated with being single, never married and with being widowed compared with the risk associated with being separated or divorced and in women health behaviour data is only of value in explaining the CVD risk associated with being separated or divorced. Health behaviours emerged as a particularly important variable in understanding the cardiovascular mortality risk associated with being separated/divorced for both men and women. Health behaviour explained 33% of the association observed between being separated/divorced and cardiovascular mortality in men and 21% of this association in women suggesting that poor health behaviour has significant explanatory power for understanding differences in health outcomes in this group. The observed key role of health behaviour in accounting for cardiovascular mortality is in line with a previous population study in the Netherlands examining self-rated health  ADDIN REFMGR.CITE Joung199850A longitudinal study of health selection in marital transitions 17Journal50A longitudinal study of health selection in marital transitions 17Joung,I.M.van de Mheen,H.D.Stronks,K.van Poppel,F.W.Mackenbach,J.P.1998/2AdolescentAdultAgedanalysisChronic DiseaseDivorceepidemiologyFemaleFollow-Up StudiesHealthHealth StatusHumansLife Change EventsLongitudinal StudiesMaleMarital StatusMarriageMiddle AgedNetherlandsProportional Hazards ModelsRiskSingle PersonWidowhoodNot in File425435Soc.Sci.Med.463
Department of Public Health, Erasmus University Rotterdam, The Netherlands
PM:9460823Soc.Sci.Med.1Joung199551Health behaviours explain part of the differences in self reported health associated with partner/marital status in The Netherlands 18Journal51Health behaviours explain part of the differences in self reported health associated with partner/marital status in The Netherlands 18Joung,I.M.Stronks,K.van de,Mheen H.Mackenbach,J.P.1995/10AdultAgedAlcohol DrinkingBody Mass IndexCoffeeCohort StudiesDietepidemiologyExerciseFemaleHealthHealth BehaviorHealth StatusHumansMaleMarital StatusMiddle AgedNetherlandsProspective StudiesSmokingSocial SupportWomenNot in File482488J Epidemiol.Community Health495
Department of Public Health, Erasmus University, Rotterdam, The Netherlands
PM:7499990J Epidemiol.Community Health1(Joung, Stronks, van de & Mackenbach, 1995). The results also confirm that a large part of the association between being unmarried and mortality can not be explained by the key health behaviours, aspects of psychological distress and metabolic dysregulation as measured in this study. In women in particular the data suggests that the increased CVD mortality risk associated with being single never married is not accounted for by health behaviour or emotional distress by any appreciable amount. As prior evidence supports direct influences of marriage on a range of biological markers  ADDIN REFMGR.CITE Kiecolt-Glaser20015Marriage and health: his and hers 5Journal5Marriage and health: his and hers 5Kiecolt-Glaser,J.K.Newton,T.L.2001/7DepressionDivorceFemaleHealth StatusHostilityHumansMaleMarriagePain MeasurementpsychologyRisk FactorsSex Factorsstatistics & numerical dataStress,PsychologicalWomenWomen's HealthNot in File472503Psychol.Bull.1274
Department of Psychiatry, Ohio State University College of Medicine, 1670 Upham Drive, Columbus, Ohio 43210, USA. kiecolt-glaser.1@osu.edu
PM:11439708Psychol.Bull.1(Kiecolt-Glaser & Newton, 2001), such as immune and endocrine processes, it is possible that these direct processes may be relatively more important for women than men when compared with indirect processes through health behaviour and psychological distress in accounting for the elevated CVD risk associated with being a single, never married women. The present data would provide some support for this contention. However, it is important to note that we did not assess other potentially important psychobiological indicators, such as inflammatory markers, hemodyamic and autonomic nervous system functioning, and lipid profiles, which have known relationships with other psychosocial variables and CVD outcomes. In addition there are some limitations to the statistical power of the analysis for women in particular, as there are a low number of events for some unmarried categories e.g. single, never married women, which can limit the reliability of the estimates. The lack of association between being separated/divorced and all cause mortality and being widowed and cardiovascular mortality in women in the present study supports the findings of a previous study that found no association between being separated/divorced and widowed and all cause mortality in women  ADDIN REFMGR.CITE Cheung200073Marital status and mortality in British women: a longitudinal study 2Journal73Marital status and mortality in British women: a longitudinal study 2Cheung,Y.B.2000/2AdultAgedBereavementCardiovascular DiseasesCause of DeathepidemiologyFemaleGreat BritainHealthHumansLongitudinal StudiesMarital StatusmethodsMiddle AgedmortalityNeoplasmsProportional Hazards ModelsRiskSurvival AnalysisWomenWomen's HealthNot in File9399Int.J Epidemiol.291
Institute for Human Services Research, Kowloon, Hong Kong. ybcheung@vol.net
PM:10750609Int.J Epidemiol.1(Cheung, 2000). This finding is also compatible with the argument in this literature that women benefit less from the presence of a marital relationship and that marital disruption is more damaging for men than women  ADDIN REFMGR.CITE Kiecolt-Glaser20015Marriage and health: his and hers 5Journal5Marriage and health: his and hers 5Kiecolt-Glaser,J.K.Newton,T.L.2001/7DepressionDivorceFemaleHealth StatusHostilityHumansMaleMarriagePain MeasurementpsychologyRisk FactorsSex Factorsstatistics & numerical dataStress,PsychologicalWomenWomen's HealthNot in File472503Psychol.Bull.1274
Department of Psychiatry, Ohio State University College of Medicine, 1670 Upham Drive, Columbus, Ohio 43210, USA. kiecolt-glaser.1@osu.edu
PM:11439708Psychol.Bull.1(Kiecolt-Glaser & Newton, 2001). The higher risk of cardiovascular disease in men and women compared with non-cardiovascular death in the present data suggesting that marriage might may be particularly related to mechanisms specifically affecting CVD risk. One key set of behaviours that may be relevant and have been shown to be related to marital status and relationship quality are secondary prevention behaviours following the onset of a condition related to CVD e.g. cardiac rehabilitation attendance  ADDIN REFMGR.CITE Molloy200888Marital status and cardiac rehabilitation attendance: a meta-analysis 1Journal88Marital status and cardiac rehabilitation attendance: a meta-analysis 1Molloy,G.J.Hamer,M.Randall,G.Chida,Y.2008/9/15epidemiologyHealthHEART-DISEASELondonMarital StatusmethodsOdds RatioNot in FileEur J Cardiovasc Prev Rehabil
Psychobiology Group, Department of Epidemiology and Public Health, University College London, London, UK
PM:18800004Eur J Cardiovasc Prev Rehabil1(Molloy, Hamer, Randall & Chida, 2008) and medication adherence  ADDIN REFMGR.CITE Molloy200847Social networks and partner stress as predictors of adherence to medication, rehabilitation attendance, and quality of life following acute coronary syndrome 2Journal47Social networks and partner stress as predictors of adherence to medication, rehabilitation attendance, and quality of life following acute coronary syndrome 2Molloy,G.J.Perkins-Porras,L.Strike,P.C.Steptoe,A.2008/1Acute Coronary SyndromeAgeddrug therapyEnglandepidemiologyFemaleHealthHospitalizationHumansMaleMiddle AgedMorbiditymortalityPatient CompliancepsychologyQuality of LifeQuestionnairesRehabilitation CentersRiskSexual PartnersSocial SupportStress,PsychologicalutilizationNot in File5258Health Psychol.271
Department of Epidemiology and Public Health, University College London, United Kingdom. g.molloy@ucl.ac.uk
PM:18230014Health Psychol.1(Molloy et al., 2008), however such measures were not available in the present study. As we have acknowledged in the introduction the three classes of mechanism that are examined in this analysis are highly interdependent. For example it is clear that hazardous drinking can be a risk factor for the development of subsequent psychological distress and obesity or diabetes. This can make statistical models with simultaneous adjustment of interdependent processes very difficult to interpret and we would encourage researchers in this area to consider the issue of over-adjustment in statistical models that attempt to identify important intermediate mechanisms in the marital status-health relationship, which can be obscured if analysis are not driven by a specific research question that is theoretically informed by conceptual models that consider the interdependencies of intermediate processes. Future work should also consider in more detail the interactions between these processes and marital status. There are several limitations to the current study which should be acknowledged. As data on marital status was collected at one time point, we were unable to look at the influence of marital transitions  ADDIN REFMGR.CITE Ebrahim199583Marital status, change in marital status, and mortality in middle-aged British men 6Journal83Marital status, change in marital status, and mortality in middle-aged British men 6Ebrahim,S.Wannamethee,G.McCallum,A.Walker,M.Shaper,A.G.1995/10/15AdultAgedbloodBlood PressureBody Mass IndexCardiovascular DiseasesCause of DeathCholesterolCohort StudiesConfidence IntervalsConfounding Factors (Epidemiology)EmploymentEnglandepidemiologyGreat BritainHealthHumansLife StyleLondonMaleMarital StatusMarriageMiddle AgedmortalityNeoplasmsProspective StudiesRiskRisk FactorsSmokingSocial ClassSocial SupportSocioeconomic FactorsNot in File834842Am.J Epidemiol.1428
Department of Public Health, Royal Free Hospital School of Medicine, University of London, England
PM:7572960Am.J Epidemiol.1(Ebrahim et al., 1995) on cardiovascular mortality or the influence of CVD events on marital transitions. This would have allowed a more conclusive analysis about issues relating to health selection and social causation. In relation to this we did not have information on the number of years married or the time since or number of separation(s)/divorce(s) or bereavement(s). This information is important in that it represents time since and intensity of exposure to a protective or deleterious social conditions  ADDIN REFMGR.CITE Zhang200685Marital history and the burden of cardiovascular disease in midlife 13Journal85Marital history and the burden of cardiovascular disease in midlife 13Zhang,Z.M.2006/4ADULTScardiovascular diseasecumulative disadvantageDivorceEXPLANATIONSFamilyHealthLIFE-COURSEmarital historyMarital StatusMarriagemidlifemortalityRetirementSELECTIONTRANSITIONSNot in File266270Gerontologist4620016-9013
Bowling Green State Univ, Dept Sociol, Bowling Green, OH 43403 USA Bowling Green State Univ, Ctr Family & Demog Res, Bowling Green, OH 43403 USA
ISI:000236643900013Gerontologist1(Zhang, 2006). The data set did not have any measures of marital quality for the married participants. Several studies have shown that the quality of the marital relationship can contribute to increased risk for CVD  ADDIN REFMGR.CITE De Vogli R.200768Negative aspects of close relationships and heart disease 3Journal68Negative aspects of close relationships and heart disease 3De Vogli R.Chandola,T.Marmot,M.G.2007/10/8CholesterolCohort StudiesComorbidityCoronary DiseaseDepressionEmploymentEnglandepidemiologyExerciseFemaleFollow-Up StudiesHealthHealth BehaviorHumansHypertensionInterpersonal RelationsLondonMaleMarital StatusmethodsObesityProspective StudiesRiskSex FactorsSmokingSocial ClassSocial SupportSocioeconomic FactorsWomenWorkNot in File19511957Arch.Intern.Med.16718
Department of Epidemiology and Public Health, University College London, 1-19 Torrington Place, London, England. r.devogli@ucl.ac.uk
PM:17923594Arch.Intern.Med.1Eaker200728Marital status, marital strain, and risk of coronary heart disease or total mortality: the Framingham Offspring Study 4Journal28Marital status, marital strain, and risk of coronary heart disease or total mortality: the Framingham Offspring Study 4Eaker,E.D.Sullivan,L.M.Kelly-Hayes,M.D'Agostino,R.B.,Sr.Benjamin,E.J.2007/7AdolescentAdultAgedbloodBlood PressureBody Mass IndexCause of DeathCholesterolcomplicationsConflict (Psychology)Coronary DiseaseEmotionsepidemiologyFemaleHumansIncidenceMaleMarital StatusMarriageMiddle AgedmortalityMultivariate AnalysisPrognosispsychologyRiskRisk FactorsSex FactorsStress,PsychologicalUnited StatesNot in File509513Psychosom.Med.696
Eaker Epidemiology Enterprises, LLC, Gaithersburg, MD 20882, USA. eakerepi@tznet.com
PM:17634565Psychosom.Med.1(De Vogli, Chandola & Marmot, 2007; Eaker, Sullivan, Kelly-Hayes, D'Agostino, Sr. & Benjamin, 2007). Several of the measures, including smoking and physical activity, were assessed by self-report at one time point only, which precludes a formal mediation analysis as the temporal relationship between marital status and intermediate mechanisms cannot be established. More precise and repeated assessment of these variables would have allowed for a more formal and compelling mediational analysis. More generally the study is also subject to the usual limitations of survey methodology e.g. certain groups may be underrepresented (the homeless, prisoners, psychiatric hospital residents), and while the data linkage process has been validated it remains imperfect e.g. deaths that happen outside of the UK and that are not registered will not be detected. Finally the distinction between marriage and co-habitation status was not investigated in the present analysis  ADDIN REFMGR.CITE Scafato200889Marital and cohabitation status as predictors of mortality: A 10-year follow-up of an Italian elderly cohort 1Journal89Marital and cohabitation status as predictors of mortality: A 10-year follow-up of an Italian elderly cohort 1Scafato,E.Galluzzo,L.Gandin,C.Ghirini,S.Baldereschi,M.Capurso,A.Maggi,S.Farchi,G.For The Ilsa Working Group2008/11AgedAgingDIFFERENTIALSepidemiologyHealthHealth PromotionItalyLongitudinal StudiesMarital StatusmortalityRiskWomenNot in File14561464Soc.Sci.Med.679
Population Health and Health Determinants Unit, National Centre for Epidemiology, Surveillance and Health Promotion (CNESPS), Istituto Superiore di Sanita (ISS), Via Giano della Bella 34, 00161 Roma, Italy
PM:18675500Soc.Sci.Med.1(Scafato, Galluzzo, Gandin, Ghirini, Baldereschi, Capurso et al. 2008). While this is an important related issue it was viewed to be beyond the scope of the current research questions. There are however several notable strengths to the present study including the large, community-based representative sample that excluded those with clinically confirmed CVD at baseline. The prospective and retrospective data linkage to National Health Service databases in Scotland represents a unique resource to examine the relationship between key psychosocial variables such as marital status and subsequent health outcomes, while controlling for variables related to previous clinical diagnoses. The analysis presents for the first time precise estimates of the extent to which key behavioural, emotional and metabolic variables can partly explain the observed relationship between marital status categories and cardiovascular mortality for men and women. This work adds to the growing and increasingly influential body of evidence demonstrating the key role of structural social network phenomena such as marital relationships in understanding health behaviours and disease at the population level  ADDIN REFMGR.CITE Christakis200710The spread of obesity in a large social network over 32 years 10Journal10The spread of obesity in a large social network over 32 years 10Christakis,N.A.Fowler,J.H.2007/7/26AdultanalysisAttitudeBody Mass IndexCausalityFemaleFriendsHealthHumansLogistic ModelsLongitudinal StudiesMalemethodsObesityPrevalencepsychologySmokingSocial BehaviorSocial SupportSociology,MedicalNot in File370379N.Engl.J Med.3574
Department of Health Care Policy, Harvard Medical School, Boston, MA 02115, USA. christakis@hcp.med.harvard.edu
PM:17652652N.Engl.J Med.1Christakis200887The collective dynamics of smoking in a large social network 2Journal87The collective dynamics of smoking in a large social network 2Christakis,N.A.Fowler,J.H.2008/5/22AdultAgedAlgorithmsepidemiologyFemaleFriendsHealthHumansLogistic ModelsLongitudinal StudiesMalemethodsMiddle AgedPrevalencepsychologySmokingSmoking CessationSocial BehaviorSocial SupportSociology,MedicalUnited StatesNot in File22492258N.Engl.J Med.35821
Department of Health Care Policy, Harvard Medical School, Boston, MA 02115, USA. christakis@hcp.med.harvard.edu
PM:18499567N.Engl.J Med.1Iwashyna200326Marriage, widowhood, and health-care use 26Journal26Marriage, widowhood, and health-care use 26Iwashyna,T.J.Christakis,N.A.2003/12AgedAged,80 and overCardiovascular DiseasesDecision MakingepidemiologyFemaleHealthHealth Services for the AgedHip FracturesHospitalizationHospitalsHumansIncidenceLength of StayLinear ModelsMaleMarital StatusMarriageMedicareNeoplasmsPatient Acceptance of Health CarePatient ReadmissionPennsylvaniaQuality of Health CareSpousesstandardsstatistics & numerical datatherapyUnited StatesutilizationWidowhoodNot in File21372147Soc.Sci.Med.5711
Department of Medicine, Hospital of the University of Pennsylvania, Pennsylvania, PA, USA. iwashyna@alumni.princeton.edu
PM:14512244Soc.Sci.Med.1(Christakis & Fowler, 2007; Christakis & Fowler, 2008; Iwashyna & Christakis, 2003). The present findings can guide future work attempting to unravel the key proximate mechanisms that can explain the relationship between marital status and CVD morbidity and mortality. In terms of practical application the findings would support approaches that emphasis health behaviour change in those that may be at risk of conditions related to CVD because of their martial status, particularly men, as these variables appear to have the greatest explanatory power in accounting for the marriage-CVD relationship. Table 1. Age adjusted logistic regression models for marital status and behaviour, psychological distress and metabolic dysregulation in healthy participants stratified by gender ----------------------------------------------------------- Behavioural Distress Metabolic Physical Current Hazardous Psychological Obesity Hypertension Inactivity Smoker Alcohol Distress (BMI >30) N OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) ---------------------------------------------------------- Men Married 4272 1.00 1.00 1.00 1.00 1.00 1.00 Single 788 0.92 (0.79 1.08) 1.77 (1.51 2.07) 1.06 (0.90 1.25) 1.50 (1.22 1.86) 1.06 (0.88 1.27) 1.18 (0.98 1.43) Sep/Div 746 1.11 (0.95 1.31) 2.70 (2.35 3.24) 1.76 (1.50 2.07) 2.45 (2.02 2.97) 0.73 (0.60 0.90) 1.35 (1.22 1.63) Widowed 295 0.99 (0.77 1.27) 1.59 (1.22 2.08) 1.37 (1.05 1.80) 2.05 (1.49 2.84) 0.84 (0.62 -1.14) 0.96 (0.74 1.26) Women Married 4757 1.00 1.00 1.00 1.00 1.00 1.00 Single 743 1.13 (0.96 1.33) 1.41 (1.19 1.67) 1.01 (0.70 1.43) 1.29 (1.05 1.59) 1.15 (0.95 1.39) 1.06 (0.87 1.28) Sep/Div 1258 1.05 (0.92 1.20) 2.45 (2.16 2.79) 1.15 (0.88 1.51) 2.35 (2.03 2.72) 1.07 (0.92 1.24) 1.06 (0.90 1.24) Widowed 1030 1.09 (0.94 1.27) 2.03 (1.73 2.39) 0.56 (0.34 0.92) 1.77 (1.46 2.15) 1.09 (0.92 1.30) 0.93 (0.79 1.09) --------------------------------------------------------------- ..Table 1 continued. Diabetes Age (Mean SD) N OR (95% CI) Men Married 4272 1.00 51.82 (11.25) Single 788 1.48 (1.00 2.19) 48.94 (11.30) Sep/Div 746 1.15 (0.74 1.78) 50.09 (9.93) Widowed 295 1.10 (0.67 1.81) 66.07 (11.66) Women Married 4757 1.00 51.19 (10.85) Single 743 1.83 (1.24 2.70) 50.46 (12.59) Sep/Div 1258 1.18 (0.79 1.74) 48.91 (9.87) Widowed 1030 0.97 (0.67 1.39)) 65.73 (10.48) --- Participants with previous hospitalisation for CVD excluded from all analyses. Table 2. Age adjusted Cox regression models for marital status and mortality in healthy participants stratified by gender All cause death CVD death N Deaths HR (95% CI) Deaths HR (95% CI) Men Married 4272 231 1.00 66 1.00 Single 788 91 2.52 (1.97 3.21) 31 3.02 (1.97 4.63) Sep/Div 746 84 2.25 (1.75 2.90) 23 2.04 (1.25 3.34) Widowed 295 65 1.83 (1.37 2.45) 25 2.51 (1.53 4.10) Women Married 4757 182 1.00 40 1.00 Single 743 53 1.66 (1.22 2.26) 15 1.99 (1.10 3.62) Sep/Div 1258 54 1.28 (0.94 1.73) 23 2.59 (1.55 4.33) Widowed 1030 132 1.38 (1.08 1.76) 35 1.37 (0.84 2.23) Participants with previous hospitalisation for CVD excluded from all analyses. Table 3 Adjusted analyses for the association between marital status and CVD death (% attenuation in relationships by adjustments)1. Model 1 Model 2 Model 3 Model 4 Fully adjusted Deaths/N HR (95% CI) HR (95% CI) HR (95% CI) HR (95% CI) HR (95% CI) Men Married 66/ 4272 1.00 1.00 1.00 1.00 1.00 Single 31/ 788 2.71 (1.76 4.17) 2.55 (1.65 3.93) 2.70 (1.75 4.16) 2.56 (1.66 3.97) 2.44 (1.57 3.78) (15%) (9.4%) (8.1%) (8.8%) (15.8%) Sep/Div 23/ 746 1.91 (1.17 3.14) 1.61 (0.97 2.68) 1.83 (1.11 3.01) 1.87 (1.13 3.08) 1.56 (0.93 2.63) (13%) (33%) (8.8%) (4.4%) (38.5%) Widowed 25/ 295 2.34 (1.43 3.83) 2.17 (1.33 3.56) 2.27 (1.39 3.72) 2.27 (1.38 3.73) 2.11 (1.29 3.47) (11%) (12.7%) (5.2%) (5.2%) (17.2%) Women Married 40/ 4757 1.00 1.00 1.00 1.00 1.00 Single 15/ 743 2.06 (1.13 3.76) 2.06 (1.13 3.75) 2.03 (1.11 3.71) 1.83 (1.00 3.37) 1.84 (1.00 3.38) (no attenuation) (no attenuation) (2.8%) (16%) (17.2%) Sep/Div 23/ 1258 2.55 (1.52 4.28) 2.22 (1.32 3.73) 2.39 (1.42 4.02) 2.50 (1.50 4.20) 2.13 (1.26 3.61) (3%) (21.2%) (10.3) (3.2%) (27.1%) Widowed 35/ 1030 1.35 (0.83 2.20) 1.24 (0.76 2.03) 1.28 (0.78 2.10) 1.35 (0.82 2.20) 1.20 (0.73 1.97) (not applicable) (not applicable) (not applicable) (not applicable) (not applicable) Model 1 adjusted for age, SES Model 2 adjusted for age, SES + health behaviours (physical activity, smoking, alcohol) Model 3 adjusted for age, SES + distress (GHQ-12) Model 4 adjusted for age, SES + metabolic dysregulation (doctor diagnosed hypertension, diabetes, and BMI). 1The proportion of CVD risk reduction explained by each set of factors was computed as follows: (HRbasic model HRadjusted)/ HRbasic model 1) ( 100  ADDIN REFMGR.REFLIST REFERENCES Ben-Shlomo,Y., Smith,G.D., Shipley,M., & Marmot,M.G. 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