Marital status and mortality in British women: a longitudinal study

Yin Bun Cheung


    Abstract
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Conclusion
 References
 
Background Most previous studies on marital status and mortality did not adjust for the effect of ‘marital selection’. Little research has been done about the relation between marital status and mortality in British women, with the exception of research on bereavement.

Methods Subjects consisted of women aged >=35 in a longitudinal study of a nationally representative sample. Marital status and covariates were enumerated at a baseline interview in 1984/85 and a follow-up interview in 1991/92. Death data up to May 1997 were obtained from the National Health Service Central Register. Cox regression was used to estimate hazard ratios (HR) for the single, divorced and widowed states in relation to the married state.

Results Having adjusted for age and martial selection factors, being single (HR = 1.45) was significantly associated with higher all-cause mortality. Being divorced and being widowed showed no excess mortality risk (each HR = 1.09).

Conclusions Being single was associated with higher mortality. A causal interpretation is plausible. Being divorced and being widowed were not associated with higher mortality.

Keywords Marital status, mortality, selection, British women

Accepted 5 August 1999


    Introduction
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Conclusion
 References
 
Despite much research since Durkheim's classic study on marriage and suicide,1 the association between marital status and mortality is not well understood. Most previous studies on this topic were based on the analysis of cross-sectional data. They failed to ascertain the direction of the (causal) relation. The ‘protection hypothesis' suggests that marital status has a causal impact on mortality. In contrast, the ‘selection hypothesis' maintains that health and health-related attributes determine marital status in the first place. The commonly observed excess mortality in the unmarried therefore reflects a selection bias. These two perspectives are not mutually exclusive. Recent studies have demonstrated that the probabilities of marriage among single people and divorce among those married are affected by people's health status.2–4 Behavioural factors, such as smoking and drinking, and socioeconomic background, as indicated by educational attainment and housing tenure, etc., may also play a role in determining the chance of getting married and divorced.5–9 Although not much is known about selection into widowhood, it has been hypothesized that assortative mating and the environment and lifestyle shared by spouses may confound the relation between widowhood and mortality.10 The death of a spouse is postulated to be indicative of some poor lifestyle factor or socioeconomic background, which may affect the survival chance of widow(er)s. Without appropriate adjustment for these marital selection factors, it is unsure whether marital status has any health impact.

The 1990s has seen several longitudinal studies on this issue. Using census-linked and population registry data, researchers in Sweden and Finland have shown an association between marital status and mortality after adjusting for socioeconomic factors that may have a selection effect.10–12 An American longitudinal study of the elderly also showed that marital status appeared to have a modest effect on health and mortality.13 However, these datasets did not allow adjustment for marital selection related to risk-taking behaviour and/or health status. In Britain there is considerable research interest about excess mortality during bereavement. Longitudinal studies of those widowed took place as early as in the 1950s.14 Some researchers have reviewed the bereavement literature.15–17 They pointed out considerable methodological problems in the literature, such as not having a proper control group and inadequate control for confounders. Two longitudinal studies of British men had the advantage of including subjects of all marital status and adjustment for behavioural, health and socioeconomic factors measured at baseline.18,19 They showed that unmarried men suffered higher mortality than their married counterparts. This association persisted after adjustment for various potential selection factors. Relatively little is known about British women, single and divorced women in particular.

In the last few decades, there was a worldwide trend of increasing mortality differentials by marital status.20 In Britain the relative mortality risk between unmarried and married women has also been on the increase.21 Marriage rate is decreasing and divorce rate is on the rise.22 Women tend to marry men older than themselves, and have a life expectancy at age 65 of about 4 years longer than that of men.23 This generates a demographic pressure of producing more widows than widowers. Given these demographic conditions, the impact of marital status on mortality in British women is a matter of public health significance. To address this public health issue and fill the gap in the literature, the present study focuses on the mortality differences of women of various marital status.


    Methods
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Conclusion
 References
 
The Health and Lifestyle Survey (HALS) is a longitudinal study of a nationwide probability sample. The baseline survey (HALS1) was carried out in 1984/85. A follow-up survey (HALS2) took place in 1991/92. The survey collected a wide range of health and lifestyle data from 9003 adults in England, Wales and Scotland. The majority of the respondents are flagged on the National Health Service (NHS) Central Register. Details of the survey have been reported elsewhere.24,25

The present study included female respondents aged >=35 in HALS1. Death data from HALS1 to May 1997 were available for the analysis, giving a follow-up period of about 12.3 years. Among 3497 eligible respondents, 73 were not flagged on the NHS Central Register. Of the 73, 15 participated in HALS2. These 15 subjects were included in the analysis and were censored at the time of HALS2; the other 58 were excluded in the main analysis. Also excluded were 61 subjects who did not give all the relevant information for the covariate adjustment. The main analysis involved 3378 respondents.

Legal marital status and cohabitation were enumerated in both HALS1 and HALS2. In women born prior to the 1940s cohabitation was rare.26 A negligible proportion (1%) of the subjects reported themselves as cohabiting in HALS1 or HALS2. This study only considered legal marital status. Marital status was created as a time-varying independent variable. Responses in HALS2 were used to update it. Age at beginning of a marital spell was also made time-dependent accordingly. For brevity, in this paper ‘single’ refers to ‘never married’, and ‘divorced’ includes ‘separated’ women.

Five variables measured in HALS1 were taken as indicators of the health, socioeconomic and behavioural aspects of marital selection. Recent longitudinal studies have shown that self-reported health is powerful in predicting both physiological health and mortality.25,27 This variable was coded as ‘excellent’, ‘good’, ‘fair’, or ‘poor’. Adult height is related to health and nutrition during paediatric years,28 and to perceived physical attractiveness.29 Some studies have demonstrated its usefulness in predicting mortality.30,31 These properties make adult height a useful indicator of marital selection. The HALS interviews included an item on self-reported height. In addition, a home visit was made by a nurse who carried out physiological measurements, including height. The height variable was based on the measured height if available (80% of the subjects), and self-reported height otherwise (20%). Among the 80% of subjects who had both data available, intra-class correlation between the measured and self-reported values were 0.82. Highest education attainment was categorized as ‘below O Level’, ‘O Level or equivalent’, and ‘A Level or above’. In the survey this variable was also strongly associated with housing tenure and social class. Smoking represents one aspect of an unhealthy lifestyle, and is closely related to other risk-taking behaviour.32 The subjects were classified as ‘non-smoker’, ‘ex-smoker’, <=20 cigarettes/day’, and ‘>20 cigarettes/day’. Drinking was based on several questions about drinking pattern and a diary recording alcohol consumption in the last 7 days. The subjects were classified as ‘non-drinker’, ‘ex-drinker’, ‘light drinker’ or ‘moderate/heavy drinker’. The boundary of light and moderate/heavy drinkers was 14 alcohol units per week, which was the Health Education Authority's recommendation for sensible drinking.33 Since only seven subjects drank at the harmful level (>35 units), all subjects consuming over 14 units were grouped into the moderate/heavy category. The differentiation between ex-smokers/ex-drinkers and non-smokers/non-drinker was needed because some people may stop smoking and drinking as a result of health problems.34 They are not comparable to people who never smoke or drink.

The primary concern of the present study was all-cause mortality. Causes of death were coded according to the Ninth Revision of the International Classification of Diseases (ICD). For subsidiary analysis, the causes were classified into three broad groups: cardiovascular diseases (CVD) (ICD codes 390– 458), neoplasms (ICD codes 140–239) and other diseases (non-CVD/non-cancer).

Descriptive statistics about non-response, marital transition, number of deaths, etc., were presented to facilitate interpretation of subsequent regression analysis. The main analysis was based on Cox regression models. One set of analysis adjusted for a second order polynomial of age; another set adjusted for the selection factors in addition. A statistical test proposed by Grambsch and Therneau, which was based on scaled Schoenfeld residuals, was used to assess the proportional hazards assumption.35 If a global test showed non-proportionality, the covariate-wise residuals were examined. For the analysis of all-cause mortality, a probability value of <=5% was taken as statistically significant. It was appreciated that the sample size was not ideal for the subsidiary, cause-specific analysis. The point estimates related to cause-specific mortality were interpreted in a tentative manner regardless of statistical significance. Sensitivity analyses were carried out to check how (not) using time-varying covariates according to HALS2 data could affect the main research findings.


    Results
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Conclusion
 References
 
A small proportion (3%, n = 119) of the 3497 eligible subjects were excluded from the analysis due to missing values or failure to flag on the NHS Central Register. The figures were 3% (n = 67) among the married, 4% (n = 8) among the single, 3% (n = 7) among the divorced, and 6% (n = 37) among the widows.

Table 1Go shows the 3378 subjects' follow-up status and marital status in HALS2 by marital status in HALS1. Among the survivors, the probabilities of non-response in HALS2 were 29%, 31%, 39% and 33% for the married, single, divorce, and widowed, respectively. Only 263 subjects reported a change in marital status. Marital transition during the 7-year period was uncommon among women single or widowed in HALS1. Among women married in HALS1, transition was mainly to widowhood. Among the divorced, transition was mainly to marriage. In the survival analysis below, marital status was a time-varying variable updated if a subject participated in HALS2 and reported a change in marital status. Otherwise, HALS1 marital status was used throughout.


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Table 1 Frequency distribution of marital status and follow-up status in (Health and Lifestyle Survey) HALS2, by marital status in HALS1
 
Table 2Go shows the survival time in person-years (rounded to integer) and number of deaths. Most (71%) of the survival time was spent in the married state; a small proportion (5%) was spend in the single state. There were 658 deaths during the study period. About half were due to CVD (51% of which were deaths from ischaemic heart disease); about one-quarter were due to neoplasms (only one case of benign neoplasm).


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Table 2 Survival time (person-year) and number of deaths by marital status
 
Marital status was associated with age. At the beginning of the spells of marital status, the mean age (SD) of married, single, divorced and widowed women were 52 (12), 59 (15), 50 (10) and 69 (11), respectively. The mean height in cm (SD) of married, single, divorced and widowed women were 160.7 (6.8), 160.1 (7.5), 160.2 (6.1) and 158.4 (6.9), respectively. Table 3Go shows the percentage distribution of survival time by marital status and the categorical selection factors. In general, the single state was associated with a favourable profile. It had higher percentage distribution in excellent or good health, in A Level education or above, and in non-smoker and non-drinker categories. In contrast, the divorced state had an unfavourable profile. It was more associated with fair or poor health, smoking and drinking.


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Table 3 Percentage distribution (column %) of survival time according to categorical covariates by marital statusa
 
Table 4Go shows the force of mortality of being single, divorced and widowed in relation to being married, adjusted for a second order polynomial for age effect. For all-cause mortality, the HR for the single, divorced and widowed were 1.24, 1.29 and 1.13, respectively (each P > 0.05). With regard to cardiovascular mortality, all three HR were very close to unity (HR between 1.01 and 1.08; each P > 0.05). Single and divorced women had higher relative mortality due to neoplasms (HR = 1.36 and 1.48; each P > 0.05). The figure for widows was close to unity (HR = 0.95; P > 0.05). For other causes of death, the single, divorced and widowed had HR of 1.64, 1.33 and 1.41, respectively (each P > 0.05).


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Table 4 Hazard ratios (95% CI) for marital status relative to the married, adjusted for age and age2
 
Table 5Go shows the results of Cox regression models with multiple adjustment for selection factors and age effect. Self-reported health was significantly associated with all-cause mortality, CVD mortality and non-CVD/non-cancer mortality (P < 0.05). The ordinal grades appeared to show a dose-response relation. Height was not significantly associated with any cause of death. Adding a quadratic term did not change this lack of association (details not shown). Although statistically insignificant, there was an educational gradient in all-cause and CVD mortality. There was an obvious excess mortality among ex-smokers and current smokers, regardless of cause of death. The association between heavy smoking (>=20 cigarettes/day) and mortality due to neoplasms was particularly strong (HR = 3.12; P < 0.05). Ex-drinkers had elevated mortality from CVD (HR = 1.64; P < 0.05) and, to a lesser extent, overall mortality (HR = 1.34; P > 0.05).


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Table 5 Hazard ratios (95% CI) for marital status relative to the married, with multiple adjustment for all variables shown and age and age2
 
Having simultaneously adjusted for selection factors and age, the HR of all-cause mortality for being single increased upward to 1.45 (P < 0.05); both HR of being divorced and widowed decreased downward to 1.09 (P > 0.05). All the marital groups had HR of CVD mortality close to unity (between 0.86 and 1.18). The single and divorced had slightly elevated mortality due to neoplasms (HR = 1.33 and 1.31; P > 0.05) but the widowed did not (HR = 0.91; P > 0.05). With regard to mortality of non-CVD/non-cancer causes, the relative hazards for the single, divorced and widowed were 1.88 (P < 0.05), 1.14 (P > 0.05) and 1.39 (P > 0.05), respectively. The proportional hazards assumption of each model was tested. The assumption did not hold in the models for mortality from neoplasms and non-CVD/non-cancer causes (each P < 0.05). Covariate-wise tests showed that in the neoplasm model the effect of widowhood appeared to vary over time (P < 0.05). In the non-CVD/non-cancer model the effects of poor self-reported health and O Level education appeared to change with time (each P < 0.05). All other variables in the two models behaved in a constant way over time. Figure 1Go plots the scaled Schoenfeld residuals for the effect of widowhood on neoplasm mortality against survival time in years, together with a locally weighted regression smooth of the residuals.35 In the first half of survival time widowhood appeared to be more hazardous than expected, while in the second half it became ‘protective’ against death from neoplasms.



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Figure 1 Scaled Schoenfeld residuals for the effect of widowhood on neoplasm mortality plotted against survival time in years. A horizontal reference line of zero and a locally weighted regression smooth (span = 0.8) were included

 
Two sets of sensitivity analyses were carried out to check the influence on the models in Table 5Go of (not) updating the regressors by using HALS2 data. The first set used marital status in HALS1 instead of the time-varying marital status variable. The second set modified the health and behavioural selection variables (i.e. self-reported health, height, smoking and drinking) of the 263 subjects who reported a marital change in HALS2 as time-varying covariates according to HALS2 values. The results were very similar to those reported in Table 5Go (details not shown).


    Discussion
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Conclusion
 References
 
The Health and Lifestyle Survey provided prospective data with sufficient baseline variables to investigate the association between marital status and mortality. Most previous studies were based on cross-sectional data and were unable to remove the confounding effect of marital selection. The survey also offered good statistical power to demonstrate even a small relative risk in all-cause mortality, though it is not as good for analysis of cause-specific mortality. Previous analysis had established the representativeness of the HALS1 sample.24 In the present study, the number of cases excluded because of failure to flag on the NHS Central Registry or missing values was small. The marital groups had a similar probability of being excluded from the analysis (3–4%), except the widowed who had a higher probability (6%). In any case the impact would be small due to the small number of excluded cases.

Marital status was updated by using information from HALS2. This reduced the classification errors that some previous studies were unable to avoid.13,18 Sensitivity analysis showed that, in this particular data set, updating marital status or not did not make any material impact. About one-third of the surviving subjects did not participate in HALS2 and their marital status could not be updated. Official statistics showed that people who had not married by their early thirties were unlikely to marry at all.36 Remarriage at advanced age was also rare.36 Therefore the single and widowed subjects had entered a stable marital career by HALS1. From the mid-1980s to mid-1990s the median age at divorce was around 34,37 which was much lower than the average age of the married subjects (i.e. 52). As such, one would expect that the subjects married in HALS1 had relatively higher probability of transition to widowhood than to divorce. The findings shown in Table 1Go agreed with these general demographic patterns, so the non-response in HALS2 would cause little problem in the classification of the single and widowed states. Had they been interviewed in HALS2, some married people would have reported as widowed and, to a lesser extent, divorced; some of those divorced would have reported as (re)married. This misclassification of exposure might result in some underestimation of the HR.

Since changes in health and risk-taking behaviour are expected to be dependent upon marital status,18,38,39 it is unreasonable to use HALS2 data to update the health and behavioural variables among those not reporting a change in marital status. It is arguable whether they should be updated among the 263 subjects who reported a change in marital status, because of the uncertainty of the (causal) relation between changes in marital status, health status and behavioural pattern. Sensitivity analysis with the four variables updated gave virtually the same results. This is not surprising because only 30 deaths were affected by the updating. Educational attainment is usually achieved by young adulthood and is stable among the middle-aged and the elderly so the issue did not concern this variable.

The impact of marital status on mortality, if any, may consist of two components. One is the impact of being in a marital state; another is the short-term impact of transition into a marital state. Previous studies has found evidence of a short-term impact of transition from marriage to divorce and widowhood.10,19,40 The present study was not designed to capture the effect of a marital transition. The results were interpreted as the effect of being in a marital status.

The single state was associated with a more favourable health, educational and behavioural background; the divorced state was associated with an unfavourable background. The primary concern of the present study is all-cause mortality. Adjusting for the effect of age but not other variables, there was little evidence to suggest any excess all-cause mortality among the unmarried groups. Adjustment for the selection factors demonstrated a statistically significant excess mortality for the single (HR = 1.45). The adjustment made the estimates for the divorced and widowed closer to the null value (each HR = 1.09). As previously mentioned, the HR might have been somewhat underestimated but not overestimated. Having considered the issues of sample representativeness, marital selection effect, and classification errors, it seems that being single did have a causal impact on mortality in British women aged over 35. There was no evidence to suggest that being divorced and widowed would cause an excess mortality. This is similar to the finding of a nationally representative study of British men, which showed that with partial and full statistical adjustment the HR for the single was 1.6 and 1.4, respectively (each P < 0.05).19 In either case of statistical adjustment divorced and widowed men did not appear to suffer higher all-cause mortality (HR between 1.1–1.2; P > 0.05).

Keeping in mind the reduced sample size and the play of chance resulting from multiple testing, the marital patterns in cause-specific mortality are interpreted in a more tentative manner. None of the unmarried states was associated with higher CVD mortality. It supports previous research that being a widow did not obviously increase the chance of having a ‘broken heart’.40,41 None of the unmarried groups had a general increase in all three types of mortality. Being single or widowed were more associated with non-CVD/non-cancer deaths than with CVD or neoplasm deaths. Being divorced was more associated with death from neoplasms than with other types of death. Some researchers have questioned the validity of the hypothesis of general susceptibility.14,42 The findings here lead to a speculation that different unmarried status has different psychosomatic and behavioural implications, which may have specific rather than general effects on health.

Grambsch and Therneau's test showed that the proportional hazards assumption was valid for the all-cause and CVD mortality models. The assumption for the other two cause-specific mortality models was rejected. Non-proportionality in the neoplasm model was related to widowhood; non-proportionality in the non-CVD/non-cancer model was not related to marital status at all. A plot of residuals against survival time suggested that the impact of widowhood on neoplasm mortality was hazardous in the short run but appeared to be ‘protective’ in the long run. That there may be a short-term increase in mortality during bereavement is not surprising. It has been demonstrated and discussed in the literature.10,14,40,43 The long-term ‘protective’ effect is likely to be spurious. As time went by more married women became widows. If becoming widowed was hazardous but being widowed was not, the increase over time in misclassification of marital status would produce a seemingly protective effect for widowhood. A recent American study has also discussed this problem.13 This problem means that the HR for the widowed might be underestimated, and highlights the importance of developing better systems to capture marital changes.


    Conclusion
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Conclusion
 References
 
The decline in the marriage system is increasing the proportion of single and divorced people. An ageing population and a sex difference in life expectancy between males and females will continue to drive the prevalence of widowhood. The present study has shown that the single appeared to suffer excess all-cause mortality. Being divorced and being widowed were not associated with higher mortality, but the possibility of underestimation could not be excluded. More studies are needed to confirm whether the relation between being single and higher mortality is truly causal, and to investigate the long-term and short-term impact of divorce and widowhood. Improvements in data capture systems that allow continuous or frequent updates of marital status are highly desirable.


    Acknowledgments
 
The author thanks Professor BD Cox, University of Cambridge, and The Data Archive, University of Essex, for making The Health and Lifestyle Survey data available; and Mr J Thorburn for comments on an earlier version of the manuscript.


    Notes
 
Institute for Human Services Research, PO Box 73815, Kowloon Central Post Office, Hong Kong. E-mail: ybcheung{at}vol.net


    References
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Conclusion
 References
 
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