Added: Monica Dilley - Date: 27.09.2021 16:55 - Views: 29523 - Clicks: 8501
Try out PMC Labs and tell us what you think. Learn More. I use hazard regression methods to examine how the age difference between spouses affects their survival. In many countries, the age difference between spouses at marriage has remained relatively stable for several decades. In Denmark, men are, on average, about three years older than the women they marry. Most of the observed effects could not be explained satisfactorily until now, mainly because of methodological drawbacks and insufficiency of the data. The most common explanations refer to selection effects, caregiving in later life, and some positive psychological and sociological effects of having a younger spouse.
The present study extends earlier work by using longitudinal Danish register data that include the entire history of key demographic events of the whole population from onward. Controlling for confounding factors such as education and wealth, suggest that having a younger spouse is beneficial for men but detrimental for women, while having an older spouse is detrimental for both sexes.
In recent years, the search for a single determinant of lifespan, such as a single gene or the decline of a key body system, has been superseded by a new view Weinert and Timiras Lifespan is now seen as an outcome of complex processes with causes and consequences in all areas of life, in which different factors affect the individual lifespan simultaneously. Research focusing on nongenetic determinants of lifespan has suggested that socioeconomic status, education, and smoking and drinking behavior have a major impact on individual survival e.
Mortality of individuals is also affected by characteristics of their partnerships. Partnership, as a basic principle of human society, represents one of the closest relationships individuals experience during their lifetimes. Regarding predictors of their mortality, partners usually share many characteristics, such as household size, financial situation, of children, and quality of the relationship, but several factors might affect partners differently—for example, education and social status. A factor that might influence partners in different ways is the age gap between them.
To describe age dissimilarities between spouses, three different theoretical concepts have evolved over recent decades. The most common concept is homogamy or assortative mating , which ps that people, predisposed through cultural conditioning, seek out and marry others like themselves. One assumption is that a greater age gap is associated with a higher marital instability. A further prominent concept is marriage squeeze , which states that the supply and demand of partners forces the individuals to broaden or narrow the age range of acceptable partners. A third and less common concept is the double standard of aging , which assumes that men are generally less penalized for aging than women.
The age difference between spouses at marriage has remained relatively stable for several decades in many countries, a fact that was described by Klein as an almost historical pattern. An example for such a stable pattern is shown in Figure 1. It shows that, considering all marriages, Danish men are, on average, three years older at the time of their marriage than women. If only first marriages are considered, the gap between the sexes is a little smaller.
While the mean age at marriage increased by about six years during the twentieth century, especially since the end of the s, the age difference between the sexes increased only slowly in the first 50 years of the twentieth century and started to decrease again in the second half of the century. Today, the difference between the mean age at marriage of Danish men and women is only slightly smaller than it was at the beginning of the twentieth century.
At the same time, marriage behavior in Denmark changed dramatically in nearly all other aspects, especially because cohabitation without marriage and divorce became more widespread. In , the Danish Statistical Office counted divorces.
From then on, the of divorces increased steadily and reached its peak in with 15, registered divorces. This increase in the of divorces as an alternative to end a marriage is important because it reflects dramatic changes in the way marriages are dissolved. This proportion decreased with time. Generally, most marriages that are dissolved by the death of one of the spouses end by the death of the husband.
This is a universal pattern because men are not only older at the time of marriage but also die younger as compared with women Luy In the course of the twentieth century, Danish life expectancy increased for both sexes but rose more quickly for women. While the difference in life expectancy between the sexes at age 18 was about 2. Today, about two-thirds of all marriages that are dissolved by death end due to the death of the husband, and only one-third end by the death of the wife.
Studies considering the impact of age differences between the partners on their mortality are rare and relatively dated. The authors found a correlation between longevity and having a younger wife, which was the 13th highest among all 69 variables they studied in their analysis. The first study that considered the impact of an age gap on both sexes was conducted by Fox, Bulusu, and Kinlen They speculated that this pattern might be driven by the different characteristics of those who form these unusual partnerships.
In the s, two studies provided further insights into this topic. Both studies used the same data and generally supported earlier findings. They conceded that regarding larger age gaps should be interpreted with caution, mainly due to insufficient data. Because the direction of the observed effects were about the same, Foster et al. The first possible explanation, that healthier or more active individuals are selected by younger men or women, was already mentioned by Fox et al.
Such individuals would have lived longer whomever they married because physical vitality and health usually coincides with an increased longevity. Another possible outcome of selection is that physical needs are better taken care of in later life for persons married to younger spouses. The second possible explanation refers to spousal interaction. It is speculated that there might be something psychologically, sociologically, or physiologically beneficial about a relationship with a younger spouse.
This explanation directly refers to psychological determinants of mortality such as social and interpersonal influences, happiness, self-concept, and social status. The major drawbacks of all these studies are that their data were limited to five-year age groups, that the authors did not include any information about additional variables such as duration of the marriage , and that they were limited to married couples.
The missing information on the duration of the marriage could lead to a selection bias because it is uncertain whether the marriages in the samples were of sufficient duration to allow for any effects on mortality. Foster et al. In two more-recent publications, historical data were used to identify a mortality pattern by the age of a spouse. Williams and Durm basically replicated the of the studies mentioned earlier, but their study also faced the same limitations.
Kemkes-Grottenthaler used a set of 2, family-related entries dating from to from two neighboring parishes in Germany. She showed that the mortality differentials were not only determined by the age gap itself but were also affected by several covariates, such as socioeconomic status and reproductive output. Regarding socioeconomic status, she found that age heterogamy was much more prevalent in upper classes.
In contrast, the reviews of Berardo et al. However, although findings are mixed, research indicates that confounding factors like socioeconomic status are of critical importance for the analysis of the mortality differentials attributable to the age gap between spouses. In sum, research found that having a younger spouse is beneficial, while having an older spouse is detrimental for the survival chances of the target person. The most common explanations refer to health selection effects, caregiving in later life, and some positive psychological and sociological effects.
In this section, I develop some hypotheses about the relationship between the spousal age gap and the risk of dying. limitations are addressed by using detailed Danish register data in a time-dependent framework using hazard regression. For men, the findings regarding the age gap to the spouse are relatively consistent: namely, that male mortality increases when the wife is older and decreases when the wife is younger. research also indicated that mortality by the age gap to the spouse differs between the sexes, but none of the authors proposed reasons for this effect Kemkes-Grottenthaler ; Williams and Durm The most common explanations of mortality differences by age gap to the spouse—health selection, caregiving in later life, and positive psychological effects of having a younger spouse—do not suggest large differences between the sexes.
Thus, I hypothesize a similar pattern for women: namely, that the chance of dying increases when the husband is older and decreases when the husband is younger. I also hypothesize that the duration of marriage has an impact on the mortality differentials by the age gap to the spouse. studies speculated that marriages should be of sufficient duration to allow for any effects on mortality. This reasoning suggests that the mortality advantage of individuals who are younger than their spouses should not be observable in marriages of short duration. In addition, I analyze the impact of socioeconomic status.
research e. Generally, more highly educated persons and individuals with greater wealth are known to experience lower mortality, but no study has analyzed whether these socioeconomic variables might have an impact on the survival differentials by the age gap to the spouse. If the frequency of age heterogamy differs by social class, it could partially explain these survival differentials.
research has argued that social norms and cultural background can explain the mortality differentials. Although Denmark is known to be a very homogeneous country, it is likely that social norms may differ between Danish and non-Danish as well as between rural and urban areas. Thus, I hypothesize that mortality by age gap to the spouse might differ by place of residence and by citizenship of the target person. Denmark is among the countries with the most sophisticated administration systems worldwide Eurostat All persons living in Denmark have a personal identification that is ased at birth or at the time of immigration.
This personal identification was a crucial part of the Population Registration Act, which introduced a computerized Central Population Register. This register serves as the source register for almost all major administrative systems in Denmark, which means that most registers can be linked by using the personal identification . Today, many different authorities maintain about 2, public personal registers on almost all aspects of life. While the majority of these registers are administrative, a small proportion can be used for statistical or research purposes.
Generally, the Danish registers are considered a source of detailed and exact information with a very low percentage of missing data. For this study, individual-level data from five different registers are linked with one another through the personal identification . An overview of registers that are used for this analysis is shown in Table 1.
The register extract I use here covers the period between and The information from the Register of Deaths and the Migration Register are given on a daily basis, meaning that the exact day of the event is known. The variables personal identification of the partner , wealth , municipality of residence , and citizenship were coded as time-varying covariates. The covariate age gap to the spouse is also time-varying but was computed from existing variables.
The variable sex is a time-constant covariate by nature, while education was assumed to be time-constant despite its inherently time-varying nature. My data set includes only people aged 50 and over. At these advanced ages, education is unlikely to change, so this approach should give approximately the same .
The remaining variables, marital status , date of migration , and type of migration , as well as date of birth and date of death , were used to define the time periods under risk. The base population of my analysis is all married people aged 50 years and older living in Denmark between January 1, , and December 31, There are three ways for individuals to enter the study: 1 being married and 50 years old or older on January 1, ; 2 being married and becoming 50 years old between January 2, , and December 31, ; and 3 immigrating to Denmark between January 1, , and December 31, , and being married, and being 50 years or older.
There are five possible ways to exit the study: 1 dying between January 1, , and December 31, ; 2 divorcing between January 1, , and December 31, ; 3 becoming widowed between January 1, , and December 31, ; 4 being alive on December 31, ; and 5 emigrating from Denmark between January 1, , and December 31, Hazard regression, also called event-history analysis or survival analysis, represents the most suitable analytical framework for studying the time-to-failure distribution of events of individuals over their life course.
The general proportional hazards regression model is expressed by. Since the failure event in our analysis is the death of the individual, the baseline hazard of our model h 0 t is age, measured as time since the 50th birthday. It is assumed to follow a Gompertz distribution, defined as. The Gompertz distribution, proposed by Benjamin Gompertz in , has been widely used by demographers to model human mortality data. The exponentially increasing hazard of the Gompertz distribution is a useful approximation for ages between 30 and For younger ages, mortality tends to differ from the exponential curve due to infant and accident mortality.
For advanced ages, the increase in the risk of death tends to decelerate so that the Gompertz model overestimates mortality at these ages Thatcher, Kannisto, and Vaupel I assume that the impact of this deceleration on my is negligible because the of married people over age 95 is extremely low. The research plan was to test whether the age difference between the spouses affected both sexes in the same way. Therefore, all regression models were calculated for females and males separately. It should be noted that the male and female models do not necessarily include the same individuals.
If both spouses are aged 50 or older, a couple is included in all models. If only the husband is 50 years or older, a couple is included only in the male models. Correspondingly, a couple is only included in the female models if the wife is 50 years or older and the husband is 49 years or younger. In total, 1,, married individuals aged 50 and older are included in the data set; , of them are male, , female. The distribution of all persons in the data set by age gap to the spouse is presented in Figure 2. It shows that most men are between two and three years older than their wives, while most women are two years younger than their husbands.
Table 2 gives descriptive information on all covariates. It shows the distribution of time at risk measured in days for men and women. In total, I observed 3, million person-days for men and 2, million person-days for women. The proportion of missing information is highest for duration of marriage. This is because the date of marriage is unknown for all couples who married before January 1, A large of missing values is also found for the variables highest achieved education and highest achieved education of the spouse , with the proportion missing data increasing for older cohorts.
I find no indication that this effect influenced the outcome of the regression models. In the following paragraphs, I present the of four estimated hazard regression models. For men, the relative risk of dying by the age gap to the spouse and the standard errors of the fourth model are shown in Figure 3. The corresponding for women are shown in Figure 4.
Both figures consist of four separate curves showing the relative risk of dying by age gap to the spouse. The reference category, represented by a dotted vertical line, includes all persons who are less than one year younger or older than their spouses. The part of each curve to the left of the reference category relates to individuals with older spouses, the right part relates to individuals with younger spouses.Older women married couple
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