The Temporal Pattern of Different Predictors

Journal of Gerontology: SOCIAL SCIENCES 2004, Vol. 59B, No. 3, S181–S189

Copyright 2004 by The Gerontological Society of America

The Effect of the Duration of Follow-Up in Mortality Analysis: The Temporal Pattern of Different Predictors Bettina Meinow,1,3 Ingemar Ka˚reholt,2 Marti G. Parker,2 and Mats Thorslund1,2,3 2

1 Department of Social Work, Stockholm University, Sweden. Aging Research Center, Karolinska Institute & Stockholm University, Sweden. 3 Stockholm Gerontology Research Center, Stockholm, Sweden.

Objectives. This study presents a model of the mechanisms affecting how time since baseline affects the correlation between mortality and commonly used predictors. Methods. In 1986, 421 persons (aged 75 years or older) in a Swedish community were interviewed. Fifteen-year mortality rates were analyzed by using hazard regressions. Rather than using average risk over the whole follow-up time, this study looks at temporal differences in predictor strength. Results. All studied health variables, living conditions, and life satisfaction were much stronger predictors of mortality during the first 1 or 2 years of follow-up than during later years. Gender, social contacts, and mental status were about equally correlated to mortality throughout the period. Discussion. Of the presented mechanisms affecting predictive strength, results suggest the importance of the instability of predictors over time. Especially in old populations, predictors that can change rapidly (e.g., health) are strongest for the short term, revealing a lower average mortality risk for longer follow-ups. Rather stable variables (e.g., gender or social contacts) are not affected by the length of follow-up. When average risk is studied over a longer follow-up, insignificant results may hide significant effects during a part of the follow-up. These findings are relevant for studies that examine any kind of outcome after a follow-up.

P

ATTERNS of mortality can be seen as a measure of a population’s morbidity. All-cause mortality is a crude measure of health, but it has the advantages of being easily defined and often readily available. Mortality analyses are used to identify groups at risk for early death; these groups can then be targeted for interventions, and preventive measures can be developed. Mortality analyses are also used to make projections concerning future life expectancies. These projections, in turn, are used to design social policy and to estimate future needs for social security, medical care, and social services. Comparisons of mortality patterns are made in order to reveal differences between populations as well as changes over time. When interpreting mortality analyses, and especially when making comparisons across studies, we find it crucial to consider the dimensions that characterize data and that can influence results (Manton, 1990). Studies, and their results, differ in regards to the populations studied, the measurements and methods used, and aspects of the temporal dimension, such as frequency and point of time of measurement and follow-up time (Ljungquist, Berg, & Steen, 1995). In the case of mortality studies, or any study that examines baseline predictors of later outcomes, the temporal dimension refers to the role of the follow-up time after data collection on the strength of predictors. This study focuses on this temporal dimension in mortality analyses. Previous research on predictors of mortality has basically centered on the refinement of measures and statistical methods. Variables that have been identified as predictors for mortality despite varying constructs include age, gender, global self-rated

health (SRH; Idler & Benyamini, 1997, 1999; Manderbacka, Ka˚reholt, Martikainen, & Lundberg, 2003; Wolinsky & Tierney, 1998), social network (Berkman & Glass, 2000), health behaviors, activities of daily living (ADL), physical functional ability (Parker, Thorslund, & Nordstro¨m, 1992), heart or circulatory problems (Ka˚reholt, 2001), cognitive function (Bosworth, Schaie, & Willis, 1999), and sociodemographic variables such as education, income, employment status, marital status, race, and region of residence (Kallan, 1997; Kunst & Mackenbach, 1994; Liang et al., 2000; Martelin, Koskinen, & Valkonen, 1998). For a review of studies that assess predictors of mortality, see Miller and Weissert (2000). The statistical methods used in mortality analyses have developed from models that take into account only whether or not a person has died during the follow-up (e.g., logistic regression) to models that account for both if and when a person dies during the follow-up time (hazard regressions). Even though the follow-up time used in different studies varies considerably, comparisons of mortality predictors tend to ignore possible consequences of duration of follow-up. The vast majority of the literature on predictors of mortality does not present any theoretical considerations motivating the choice of a specific predictive period. Duration of follow-up is usually dependent on the availability of mortality data, rather than a selected part of the study design. However, when mortality is related to baseline information, relative mortality risks show the average correlation over the entire follow-up time. Thus, even if a variable significantly predicts mortality over a long followup, this does not necessarily mean that the mortality risk

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1. Duration Effects: ‘‘Real’’ Changes in the Strength of a Predictor Over Follow-Up Time

Figure 1. Mechanisms affecting the predictive strength of a variable over time.

differences between different categories of a variable remain constant throughout the study period. Temporal variation in the correlation is hidden, and the true strength of a correlation at any given time is not revealed. A few studies at least comment on the possible effect of duration of follow-up on the correlation of predictors to mortality (Hessler, Pazaki, Madsen, & Blake, 1990; Strawbridge & Wallhagen, 1999). One hypothesis is that longer follow-up times lead to an underestimation of a variable’s actual strength as a predictor (Ferraro & Kelley-Moore, 2001). However, in their review of 42 studies on SRH and mortality, Idler and Benyamini (1997, 1999) found a significant independent effect of global SRH even in studies with follow-up periods from 2 to 28 years. More recent studies that explore SRH and mortality show differences for different lengths of follow-up depending on gender, the concept of SRH used, and whether the models controlled for other factors (Benyamini, Blumstein, Lusky, & Modan, 2003; Deeg & Kriegsman, 2003). Data from the Berlin Aging Study analyzed with Cox regression indicated that the effects of psychological risk factors did not diminish over time (Maier & Smith, 1999). Ljungquist, Berg, and Steen (1996) studied how the identification of determinants of survival depends on the length of the predictive period. They used logistic regression to compare mortality 5 years after baseline, 10 years after baseline, 15 years after baseline, and 20 years after baseline. They found different effects of duration, depending on the studied variable as well as the age and gender of the individual. However, their approach does not reveal the effect of covariates during the follow-up period, that is, how the predictor strength changes between baseline and Year 5, between Years 5 and 10, and so forth. Obviously, there is a lack of consensus in the literature about the effects of the follow-up time on predictor strength in mortality analyses, as well as little empirical investigation. Figure 1 depicts five conceivable mechanisms affecting the predictive strength of a variable over time. Conceptually, a distinction can be made between ‘‘real changes’’ in the strength of a predictor over time (duration, age, period, and cohort effects) and other effects that change the ‘‘statistical’’ correlation between risk factor and mortality (instability of predictors and selective mortality).

An understanding of causal relations between variables should take into account that the causal relationship itself may change over time (Blossfeld & Rohwer, 1997). There are several possible shapes of predictor strength over time. First, a predictor itself may have temporal primacy; that is, its ability to predict mortality decreases with a longer time between measurement and mortality. This applies to acute health conditions, such as heart attack. During the acute phase, mortality risk is high, but if the individual survives, mortality risk may then diminish over time. Spousal bereavement has also been shown to lead to a higher risk during the first months after the loss, with declining risk afterward (Bowling, 1987; Lichtenstein, Gatz, & Berg, 1998). Second, a variable might involve an increasing risk over time with mainly a long-term effect on mortality (e.g., smoking or diabetes). The effect may start almost immediately when entering a certain status and then increase gradually, or there could be a longer time lag. Third, variables may also have a rather constant correlation to mortality over time, given no status change during the follow-up period (e.g., gender or health problems).

2. Age Effects: Different Strength of Predictors at Different Ages The effect of a predictor on mortality may change with age. For example, the effects of femur fractures may be more serious at older ages.

3. Period and Cohort Effects: Secular Changes in Medical Care and Lifestyle Changes Over long periods of time, it may be possible to detect period and cohort effects that are due to developments in medical care and changing lifestyles.

4. Instability of Predictors: Unknown Changes in the Variable Value Over Time The correlation between mortality and associated variables is commonly studied from baseline over a specific follow-up time. The true effect of a variable, such as ADL, is the predictor strength of ADL before anyone in the sample has changed his or her ADL. However, there are few variables that remain constant throughout a longer study period (e.g., gender) or that change in a predictable rate (e.g., age). Especially in old populations, health variables, as well as civil status, may change rapidly over the studied period. As a consequence, statistically, the predictive strength of these variables may appear to decrease over time in a similar way, as for variables with a temporal primacy effect as described herein. However, in these cases, it is not due to an actual ‘‘weakening’’ of the predictive strength but to the decreased accuracy of the baseline data.

5. Selective Mortality Selective mortality is a general constraint for research on advanced old age and may also result in changes in relative mortality risks over time (Hertzog, 1996). The occurrence of early deaths mainly among the least robust persons with specific characteristics may leave a relatively high rate of hardy

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persons among the surviving elderly population during later parts of the follow-up (Markides, 1989). Thus, the sample may change fundamentally when ‘‘early deaths’’ occur, leaving a hardier subsample for which predictors are less predictive. The selection process may even produce mortality crossovers, as evident for the difference between racial and ethnic groups in the American population (Kunitz & Levy, 1989). Selective mortality tends to decrease the difference in relative mortality risk between those being exposed to a risk factor (e.g., health problems) and those who are not. The predictive strengths of variables are affected by all of the aforementioned mechanisms. As a further challenge, these mechanisms are themselves interrelated; for example, time and age are interdependent. This study is a secondary analysis of baseline data from 1986 and mortality data from the subsequent 15 years. The project focuses on temporal differences in predictor strength at intervals after baseline rather than studying the average mortality risk for variables over the entire period. From the resulting patterns, inferences can be made concerning the effects of the various mechanisms, in particular the instability of predictors.

METHODS

Data The study originates in an interview survey of communitybased elderly persons (n ¼ 421) that was conducted in 1986 in Tierp, a rural community in central Sweden. Every eighth person aged 75 to 84 years (n ¼ 161) was randomly selected from the population register, and all persons aged 85 and older (n ¼ 260) were selected for interview. As a way to adjust for this sampling procedure in analyses, the younger respondents were given a weight eight times of the older age group. Nonresponse was 6% in the younger group and 3% in the older group. District nurses carried out the interviews. Questions included ADLs and instrumental ADLs (IADLs), mental health, physical health and health symptoms, drug use, housing, and social contacts. For a further description of the study, see Thorslund and Wa¨rneryd (1990). Proxy interviews were conducted for persons with poor cognition or very poor health. In total there were 1.7% proxy interviews and 8.8% proxy-assisted interviews. The socioeconomic distribution is rather homogenous in this study population. Former farmers, blue-collar workers, and lower white-collar workers are the dominating groups. Mortality was followed from the day of the interview in 1986 until the end of January 2001, when 94% of the sample was deceased. The Swedish system of a personal identification number and population registers facilitated the collection of date of death for all the deceased.

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tion of single symptom and drug items; nor was there information available on specific illnesses or symptom severity. Independent variables are based both on self-report and the nurses’ evaluations. We collapsed response categories when some of the categories had a low response, and when analysis showed that alternatives had a similar correlation to mortality.

Sociodemographic Variables Age. —We treat age as a time-varying covariate and increment it each calendar year. On a logarithmic scale, mortality rises linearly. Therefore, we give age linear representation in a multiplicative model, as proposed by Kohler and Kohler (2000). Cohabitation. —We use this variable to distinguish between those living alone and those living with someone.

Self-Reported Health Variables and Drug Use Somatic symptom index. —We based this index on a list of self-reported somatic symptoms related to mortality: chest pain, back pain, stomach pain, constipation, joint pain, rheumatism, bronchial problems, breathlessness, swollen legs, loss of appetite, insomnia, frequent tiredness, urination difficulties, and skin problems (e.g., eczema or sores). SRH. —Global SRH was a single item with four possible responses: ‘‘very healthy,’’ ‘‘fairly ‘‘healthy,’’ ‘‘somewhat sick,’’ and ‘‘very sick.’’ We dichotomized the variable between the ‘‘healthy’’ and ‘‘sick’’ responses. Mobility index. —The mobility index refers to self-reported ability to rise from an armless kitchen chair, get in and out of bed, walk indoors, and walk outdoors. Response alternatives ranged from ‘‘yes, manage alone without difficulties’’ (¼ 0) to ‘‘no, cannot manage; need someone’s help’’ (¼3). The total score of 12 was classified into three groups: those with no difficulties (0), some difficulties (1–4), and many difficulties (5–12). ADL index. —We based the ADL index on the self-reported ability to eat, go to the toilet, dress and undress oneself, get in and out of bed, wash oneself, and take a bath or shower.

Variables Used in the Analysis

IADL index. —We based this index on the self-reported ability to prepare food, make the bed, and do the house cleaning. A previous study based on the same material showed that these three activities were representative for all IADL activities (Norstro¨m & Thorslund, 1991). In order to control for ability rather than actual practice, each IADL question, such as ‘‘Do you usually prepare food?’’ was followed by the question ‘‘Would you be able to prepare food if necessary?’’ A positive answer to either question was considered as reported ability.

We chose predictor domains that have been shown to predict mortality in other studies (e.g., Miller & Weissert, 2000). We then tested the available variables in these domains (e.g., demographic variables, health, drug use, and cohabitation) for their predictive ability in the data. We utilized all variables showing predictive strength, with a short or long follow-up, in the analysis. However, the sample size did not allow examina-

Number of types of drugs. —We classified the drugs respondents reported taking into 11 groups: heart medicine, diuretics, blood pressure medication, diabetes, pain, sulfa drugs and penicillin, sleeping medicine and psychopharmaceuticals, decongestants, vitamins, eye drops, and others. The variable is the number of drug classifications, that is, 0 to 11.

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Social Variables Social contacts. —We based the social contacts index on questions about participation in organizations and having someone to talk to about personal matters (other than spouse). We made this into a dichotomous variable: those who neither participated in organizations nor had someone to talk with and those who did. Life satisfaction. —We determined life satisfaction on the basis of the question, roughly translated, ‘‘How happy are you with life in general?’’ There were five possible responses: ‘‘very happy,’’ ‘‘fairly happy,’’ ‘‘so-so,’’ ‘‘could be better,’’ and ‘‘unhappy.’’ In analyses, we dichotomized the variable into the categories ‘‘very happy’’ and ‘‘less than very happy.’’ Nurse’s evaluations. —The nurse made several health checks (pulse, blood pressure, presence of leg sores, edema, etc.). On the basis of these checks and her observations during the interview, she made global assessments of mental status, ADL, and IADL. We transformed these variables into dichotomous variables.

Analysis We used proportional hazards regressions with piecewise constant baseline intensity to analyze mortality for the 1986– 2001 period. This multiple regression method accounts for both the number and the timing of the deaths in the studied population (Blossfeld, Hammerle, & Mayer, 1989; Blossfeld & Rohwer, 1995). That is, the analysis considers whether a person dies during the follow-up period as well as how long each person survives after the baseline interview in 1986. The dependent variable is death risk per day. When analyzing the temporal pattern of the correlation of predictors to mortality, we assume that, in reality, mortality risk differences between categories of a variable (e.g., differences between those who manage all six ADL activities alone and those who need help with one or more activities) change gradually during the follow-up period. This means that, for example, a model where the differences are allowed to change linearly through discrete time spans would be realistic. In a first step, we analyzed mortality risks at 1-year intervals. From these analyses, however, it became evident that differences between time periods are most obvious when we compare the first year, and for some variables the first 2 years, with later years (not shown). Therefore, in order to highlight variations in mortality risk differences between categories over time as clearly as possible, the models used here assume discrete changes of mortality risk at certain points of time after baseline, even if a gradual change over time would be more realistic. We have divided the follow-up period into two discrete periods: the first year and Years 2 to 15 after the interview or, alternatively, the first 2 years and Years 3 to 15 after the interview, depending on which cutoff point between time periods reflected the largest differences in mortality risks for the categories of the independent variables.

RESULTS Table 1 describes the study population. Numbers and proportions of all independent variables included in analysis

are shown for the two age groups of 75 to 84 years and 85 years or older as well as for the whole study population. The sample consists of a total of 421 persons; 161 (38.2%) were aged 75 to 84 and 260 (61.8%) were aged 85 or older. The mean age for the younger group is 79.0 years; for the older group it is 88.0 years. Because the sample is community based, there are few age differences in most health and subjective items, whereas larger differences were found for function variables and cohabitation. Of the sample, 54% are women (47% in the younger group and 59% in the older group). Table 2 presents relative mortality risks for two time periods: Columns 2 and 3 contain mortality risks for the first year compared with Years 2 to 15, and columns 5 and 6 show mortality risks for the first 2 years compared with Years 3 to 15. The variables in columns 2 and 3 showed the largest risk differences between categories of predictor variables when the first year was compared with Years 2 to 15. For variables shown in columns 5 and 6, corresponding differences were largest between the first two years and Years 3 to 15. If the effect of a variable was considered to be basically constant over time, relative mortality risks are shown for both alternatives. In comparison, relative mortality risks for the whole 15-year follow-up period (as obtained from most other studies) are presented in the last column of Table 2. All analyses are controlled for age (as described in the Methods) and gender. Two comparisons of relative mortality risks can be made on the basis of Table 2: First, the relative mortality risk for different categories of a predictor can be compared. Second, the strength of predictors for different time periods after baseline can be compared. Obviously, the somatic symptom index has the strongest correlation with mortality. Mortality risk increases considerably and significantly with the number of symptoms during the first year as well as in later years. A relative risk of nearly 11 for persons with 1 or 2 symptoms in the second column of Table 2 means that they had an almost 11 times higher mortality risk ( p ¼ .03) than the reference category, those without symptoms, during the first year. Among persons with 3 to 6 symptoms, the corresponding relative mortality risk was 13 times higher; for those with 7–10 symptoms it was 27 times higher than for those who report no symptoms. Likewise, consequences of duration of follow-up on the strength of predictors over time are most evident for the somatic symptom index. Differences in relative risks between persons with different values of predictor variables are larger the first year than during Years 2 to 15. For example, the relative mortality risk among persons with 7– 10 symptoms in 1986 was 27 times higher than among those without any symptoms during the first year after the collection of baseline data. In contrast, as we can see in the third column of Table 2, the relative risk for the same category is only three times higher during Years 2 to 15 after baseline. This corresponds to 19% deaths (8 of 42 persons) during the first year after data collection and 81% (34 of 42 persons) during Years 2 to 15 among persons with 7–10 symptoms. The p values for the differences of the relative mortality risk between the first year and later years for the somatic symptom index range from .045 to .077. Even though differences between the two time periods are not significant in all cases, we find a similar pattern for the types of drugs, global SRH, life satisfaction, mobility index,

FOLLOW-UP TIME IN MORTALITY ANALYSIS

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Table 1. Description of the Study Population Age in 1986 Variable

Alla

75–84

85þ

No. of deaths during: Year 1 of follow-up Year 2 of follow-up Year 3–15 of follow-up Survived the whole follow-up Average time at risk (days)

36 55 303 27

(8.6) (13.1) (72.0) (6.4)

5 18 116 22

(3.1) (11.2) (72.0) (13.7)

31 37 187 5

(11.9) (14.2) (71.9) (1.9)

2,132

2,669

1,799

Gender Male Female

192 (45.6) 229 (54.4)

85 (52.8) 76 (47.2)

107 (41.2) 153 (58.9)

Cohabitation (men) Living with one or others Living alone

112 (58.3) 80 (41.7)

59 (69.4) 26 (30.6)

53 (49.5) 54 (50.5)

Cohabitation (women) Living with one or others Living alone

86 (37.6) 143 (62.5)

35 (46.1) 41 (53.9)

51 (33.3) 102 (66.7)

Somatic symptom index 0 symptoms 1–2 symptoms 3–6 symptoms 7–10 symptoms

66 138 171 42

21 60 62 16

45 78 109 26

Global SRH Very healthy–fairly healthy Somewhat–very sick

389 (93.1) 29 (7.0)

149 (93.7) 10 (6.3)

240 (92.7) 19 (7.3)

Mobility index No difficulties Some difficulties Many difficulties

193 (45.9) 185 (43.9) 43 (10.2)

102 (63.4) 53 (32.9) 6 (3.7)

91 (35.0) 132 (50.8) 37 (14.2)

ADL index Manages all 6 activities alone Needs help with 1þ activities

251 (59.6) 170 (40.4)

125 (77.6) 36 (22.4)

126 (48.5) 134 (51.5)

IADL index Manages all 3 activities alone Needs help with 1þ activities

147 (34.9) 274 (65.1)

81 (50.3) 80 (49.7)

66 (25.4) 194 (74.6)

No. of types of drugs 0 1–3 4–7

78 (18.6) 273 (64.9) 70 (16.6)

35 (21.7) 101 (62.7) 25 (15.5)

43 (16.5) 172 (66.2) 45 (17.3)

Life satisfaction Very happy Fairly happy, so-so, or unhappy

196 (47.1) 220 (52.9)

73 (46.2) 85 (53.8)

123 (47.7) 135 (52.3)

Social contacts Has social contacts Has no social contacts

355 (84.5) 65 (15.5)

144 (90.0) 16 (10.0)

211 (81.2) 49 (18.9)

ADL Independent Dependent

302 (72.1) 117 (27.9)

132 (82.5) 28 (17.5)

170 (65.6) 89 (34.4)

IADL Independent Dependent

186 (44.5) 232 (55.5)

99 (61.9) 61 (38.1)

87 (33.7) 171 (66.3)

Mental status Healthy Somewhat or very sick

375 (89.5) 44 (10.5)

138 (86.3) 22 (13.8)

237 (91.5) 22 (8.5)

Sociodemographic variables

Self-reported health variables and drug use (15.9) (33.1) (41.0) (10.1)

(13.2) (37.7) (39.0) (10.1)

(17.4) (30.2) (42.3) (10.1)

Social variables

Nurse’s evaluations

Notes: Percentages are given parenthetically. For the entire study population (All), n ¼ 421; for those aged 75–84, n ¼ 161; for those aged 85 or older, n ¼ 260. SRH ¼ self-rated health; ADL ¼ activities of daily living; IADL ¼ instrumental ADL. a Not weighted. In all other analyses, the younger respondents were given a weight that was eight times that of the older age group.

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Table 2. Comparison of Mortality Risk During the First 1 or 2 Years and Later Years Variable

Year 1

Years 2–15

Year 1 vs. Years 2–15: p

Year 1 þ 2

Years 3–15

Years 1 þ 2 vs. Years 3–15: p

Entire Time (Avg.)

2.08 (.157)a

Demographic variables Gender (ref. female) Men

1.89 (.000)

.864

2.04 (.016)

1.89 (.000)

.770

1.89 (.000)

Cohabitation—men (ref. living with someone) Living alone 0.36 (.066)

0.84 (.402)

.155

—b

—

—

0.80 (.274)

Cohabitation—women (ref. living with someone) Living alone 2.54 (.152)

1.28 (.274)

.314

—

—

—

1.31 (.223)

Somatic symptom index (ref. 0 symptoms) 1–2 symptoms 10.89 (.033) 3–6 symptoms 13.41 (.015) 7–10 symptoms 27.33 (.004)

1.10 (.657) 1.50 (.060) 3.29 (.000)

.045 .045 .077

— — —

— — —

— — —

1.15 (.510) 1.57 (.035) 3.42 (.000)

Global SRH (ref. very or fairly healthy) Somewhat/very sick 4.26 (.045)

2.19 (.007)

.389

—

—

—

2.33 (.002)

— —

— —

— —

2.39 (.000) 4.49 (.000)

1.50 (.014) 3.07 (.002)

.055 .439

1.68 (.000) 3.63 (.000)

ADL index (ref. manage all 6 activities alone) Needs help with 1þ activities —

—

—

2.45 (.000)

1.46 (.033)

.043

1.70 (.001)

IADL index (ref. manage all 3 activities alone) Needs help with 1þ activities —

—

—

2.05 (.001)

1.38 (.039)

.077

1.50 (.005)

4.70 (.024) 11.88 (.001)

1.40 (.077) 2.68 (.000)

.089 .057

— —

— —

— —

1.44 (.052) 2.83 (.000)

—

—

—

2.05 (.000)

1.36 (.044)

.009

1.53 (.003)

1.75 (.008)

.916

1.59 (.092)

1.75 (.006)

.661

1.72 (.007)

Self-reported health variables and drug use

Mobility index (ref. no difficulties) Some difficulties Many difficulties

No. of types of drugs (ref. 0) 1–3 4–7 Social variables Life satisfaction (ref. very happy) Fairly happy, so-so, or unhappy

Social contacts (ref. has social contacts) Has no social contacts 1.61 (.487) Nurse’s evaluations ADL (ref. independent) Dependent

—

—

—

2.24 (.002)

1.35 (.147)

.104

1.56 (.011)

IADL (ref. independent) Dependent

—

—

—

1.87 (.006)

1.54 (.007)

.424

1.61 (.001)

1.46 (.639)

1.54 (.051)

.950

1.54 (.044)

1.51 (.087)

.865

1.54 (.044)

Mental status (ref. healthy) Somewhat/very sick

Notes: Table comparison is controlled for age (given linear representation) and gender. Entire Time (Avg:) ¼ average for the entire follow-up time, Years 1– 15. SRH ¼ self-rated health; ADL ¼ activities of daily living; IADL ¼ instrumental ADL. a The p values for differences to the reference category are presented parenthetically. b The variables showing the largest differences in mortality risks between the first year and Years 2–15 are shown in columns 2 and 3, whereas those variables showing the largest differences between the first 2 years and Years 3–15 are in columns and 5 and 6. Variables that were basically constant over time are shown in all columns.

ADL, IADL, and nurses’ evaluations of ADL and IADL. The effects of these variables are larger during the first part of the follow-up period than during the second. Still, there is clearly an effect also during Years 2 to 15 or 3 to 15, respectively. Even if the relative risk differences between categories for most variables are larger for the first part of the follow-up than during later years, results are not more often significant the first year or the first 2 years, respectively. This is because there are fewer deaths and less time at risk during the first part of the followup—simply because the first period is only 1 or 2 years long and the second is 14 or 13 years long. This results in larger standard errors and p values during the first period. The last column in Table 2 shows the relative mortality risks for the independent variables for the entire 15-year follow-up time, when the temporal pattern during the follow-up is not

taken into account. This is similar to what is most commonly done in studies analyzing mortality: Results show the average correlation between predictor and mortality over the entire period. These relative mortality risks are quite similar to the risks for the second, longer time periods shown for Years 2 to 15 (column 3) and Years 3 to 15 (column 6). In contrast to health variables and life satisfaction, the variables of gender, social contacts, and mental status have a similar relation to mortality throughout the whole study period. For example, persons who report having social contacts have a lower mortality risk throughout the whole study period compared with those reporting no social contacts. All analyses were done for men and women separately, and age was controlled for (not shown). However, the only variable showing different patterns for men and women was

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‘‘cohabitation.’’ As we can see in Table 2, during the first year, the mortality risk is 64% lower among men living alone than among those living together with someone, whereas this is the reverse for women. Further analysis (results not shown) indicated that men living alone in their own homes are the healthy representatives of the age group 75 and older. In contrast, it seems to be more common for women to live alone in spite of health indicators that are predictive of mortality.

DISCUSSION This study examined the temporal pattern of the predictive validity of various measures during a 15-year follow-up period. The effect of a number of predictors on mortality during the first 1 or 2 years after the interview was compared with effects for the rest of the 15-year follow-up period. Especially highly unstable health variables were found to be much stronger predictors of mortality during the first or first 2 years of followup than during later years. This trend is not detected when mortality is analyzed over longer follow-up periods only. Drawing on the conceptual framework presented in Figure 1, we find that the observed decrease of the predictive strength of some variables may be caused by duration effects, age effects, period and cohort effects, instability of predictors, selective mortality, or a combination of all five. This study did not include any specific diseases; the information on symptoms and drugs was prevalence and not incidence. For the variables available in this study, it is reasonable to assume that there is no duration effect; that is, the variables have a rather constant correlation to mortality, given no changes in variable values during the follow-up period. In order to investigate a possible age effect on the changes in the correlation between independent variables and mortality over time, we repeated analyses, adding an interaction term between age and each of the different predictor variables (not shown). However, obtained differences in predictive strength between time periods did not change substantially, suggesting that the considerable decrease of the predictor strength over time is not due to an age effect. Thus, mortality risk differences between categories of the predictor variables, such as those with few somatic symptoms compared with those with many, appear to change only slightly with age. Although period and cohort effects can be expected, they would only be manifested in rather large samples and over very long follow-up times. The decrease in the correlation to mortality over time could also be due to selective mortality. For example, the somatic symptom index is a crude measure of severity. Even though symptoms not predictive of mortality were excluded, among the remaining symptoms there could have been great variation in severity. Within the category of 7 to 10 symptoms, for example, it is probable that those persons with the most severe symptoms die during the first years. The remaining persons may represent a group with many but less severe symptoms, that is, with a weaker correlation to mortality. Therefore the predictive strength for the somatic symptom index could become weaker after the first year. However, the 15-year follow-up time is a rather short time span to cover the progression of selective mortality. Gender differences in mortality appear to decline at high ages (Markides, 1989). The results from the present sample, however,

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indicate only a slight, nonsignificant decrease in mortality risk differences between the two sexes. Based on gender differences in morality risk over time, selective mortality does not appear to play a major role during this follow-up period. A third possible explanation for decreasing mortality risk differences between categories of variables over time is instability of predictors during follow-up. When baseline predictors are used, respondents with good health at baseline are assumed to be in good health throughout the follow-up period (Strawbridge & Wallhagen, 1999). The true effect of a variable, such as ADL, is the predictive strength of ADL before anyone in the sample has changed their ADL. However, the proportion of persons whose status changes after baseline increases during the follow-up period and the baseline information gets less and less accurate as time passes. Therefore, the correlation between baseline status and mortality is strongest shortly after baseline and weakens during the follow-up. As a consequence, health variables that are especially unstable will have the strongest correlation on a short follow-up. SRH also showed a significant correlation throughout the study period, with a stronger correlation during the first year. The effect shown for the first period is most probably closer to the true effect of the independent variables because health assessed at baseline has not changed as much during the first year than during later years. The smaller effects during the rest of the follow-up are closer to what is obtained from other studies that do not take temporal differences into account but reveal the average mortality risks for the whole follow-up period. The variables that have the largest differences in mortality risk between the first and second part of the follow-up are probably those that change most rapidly over time. For some variables, the difference in mortality risk between the two time periods is largest when the first year is compared with Years 2 to 15, and for some variables it is largest when the first 2 years are compared with Years 3 to 15. This pattern could either be from random variation or because variables are subject to change at different rates. The effects of gender, social contacts, and a nurse’s global assessment of mental status were found to remain basically constant. One explanation for this could be that these variables either do not change at all (e.g., gender), or they do not change as rapidly as health indicators. In these cases, the correlation to mortality would remain basically constant during the 15-year follow-up. The social contact measure available is a very rough indicator of a person’s social ties, distinguishing between rather isolated persons who neither participate in organizations nor have a confidant and those who do. This measure of social contacts is likely to remain more stable over time than a measure that was sensitive to numbers of contacts or changes in social support. Antonucci’s (1991) extensive research on what she terms ‘‘convoys of social support’’ has shown that social relationships tend to be relatively stable over time, constituting a cumulative influence on the individual and affecting well-being in old age. The nurse’s global assessment of mental health was the third variable that showed a similar correlation to mortality during the 15-year period. This observed mental status is unlikely to change rapidly in a community-based sample. If the mental status of all persons is assumed to deteriorate on average at a similar rate, the relative mortality risk difference between the two groups would remain approximately constant.

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Few studies have examined temporal changes in mortality predictors. Our results concerning health variables are in line with the speculations of Ferraro and Kelley-Moore (2001) that long follow-up periods tend to result in weaker effects of baseline SRH. Our results concerning mental status and social contacts are in line with analyses of data by Maier and Smith (1999) from the Berlin Aging study that suggested that effects of psychological risk factors did not change over time. Using a follow-up time that ranged from 3 to 6 years, they also found that the correlation between well-being and mortality did not change over time, which conflicts with our findings of change for life satisfaction. However, the two constructs were quite different. This study shows that duration of follow-up may explain some of the differences found in predictors and predictor strength when one is comparing mortality studies. Drawing on the model presented in Figure 1, we assumed that it is unlikely that the variables available in this study would have duration effects. Age effects can be controlled for and did not change the results substantially. Therefore, the statistical decrease in predictive strength of a variable is most likely the result of instability of predictors and, perhaps to some extent, selective mortality. To be able to truly disentangle the effects presented in Figure 1, frequent panel waves (e.g., yearly or monthly) or event history data over a very long time span would be required. The longer the interval between panel waves, the more uncertainty there will be regarding the exact point in time when an individual changed status (e.g., health) affecting the correlation to mortality. Indeed, an event-oriented design that records incidence provides the most complete data possible on changes that may occur at any point in time (Blossfeld & Rohwer, 1997). For the impact of the different effects to be examined further, future studies with event-oriented data or panel data with close intervals would support time-dependent covariates that could be compared with the results by using baseline data and date of death only. Analyzing the effect of selective mortality would additionally require a statistical model that accounts for unobserved heterogeneity (Ka˚reholt, 2000; Lillard, Brien, & Waite, 1995). However, many researchers have to rely on existing secondary data for their analyses and have no control over length of follow-up or number of intervals between waves. Baseline cross-sectional data in combination with date of death is a widely used study design. Given this data structure, what is the ideal cutoff for certain variables predicting mortality? From this study, it is not possible to define ideal time frames for mortality analyses of specific variables. Our results suggest, however, that follow-up times in very old populations should be short, that is, 1 or 2 years, for variables that can be expected to change rapidly, particularly function, health, and probably also care utilization indicators. For variables that either do not change at all (gender) or change more slowly (e.g., social contacts or mental status), the predictive strength is less affected by the length of the follow-up time. These variables may be as good predictors of mortality in the long run as in the short run, and the time frame of the follow-up may be of less importance. A limitation of the present study is the relatively small study population. Therefore, not all the differences in relative mortality risks between the earlier and later part of the follow-up period

are statistically significant. However, the temporal pattern of mortality risk differences between categories of a variable, that is, weaker predictive strength with longer follow-up, is similar for those variables that can change rapidly. A larger sample and more sensitive health indicators would have allowed us to compare predictor strength for more than two time periods and to more fully describe the temporal pattern of predictor strength for different kinds of variables, that is, variables that change at different rates or have a temporally varying impact on mortality. The study includes only community-based elderly people. Because of the Swedish policy of helping people remain in their homes as long as possible, the sample includes many frail elderly people. Still, a sample that had included institutionalized persons probably would have shown higher mortality levels in general. When only baseline cross-sectional data and date of death are available, modeling the interaction between time from baseline and different predictors is the best solution to investigate the temporal pattern of predictors and to consider the true effect of the independent variables. When only the average mortality risk is studied for variables over a long follow-up period, insignificant results may hide significant effects during a part of the follow-up. ACKNOWLEDGMENTS This research was supported by Grant V96 166 from the Vardal Foundation for Health Care Sciences and Allergy Research. Address correspondence to Bettina Meinow, Stockholm Gerontology Research Center, P.O. Box 6401, SE-113 82 Stockholm, Sweden. E-mail: [email protected] REFERENCES Antonucci, T. C. (1991). Attachment, social support, and coping with negative life events in mature adulthood. In E. M. Cummings, A. L. Greene, & K. H. Karraker (Eds.), Life-span developmental psychology (pp. 261–276). Hillsdale, NJ: Erlbaum. Benyamini, Y., Blumstein, T., Lusky, A., & Modan, B. (2003). Gender differences in the self rated health-mortality association: Is it poor self-rated health that predicts mortality or excellent self-rated health that predicts survival? The Gerontologist, 43, 396–405; discussion 372–395. Berkman, L. F., & Glass, T. (2000). Social integration, social networks, social support, and health. In L. Berkman & I. Kawachi (Eds.), Social epidemiology (pp. 137–173). New York: Oxford University Press. Blossfeld, H.-P., Hammerle, A., & Mayer, K. U. (1989). Event history analysis. Hillsdale, NJ: Erlbaum. Blossfeld, H.-P., & Rohwer, G. (1995). Techniques of event history modeling. Mahwah, NJ: Erlbaum. Blossfeld, H. P., & Rohwer, G. (1997). Causal inference, time and observation plans in the social sciences. Quality & Quantity, 31, 361–384. Bosworth, H. B., Schaie, K. W., & Willis, S. L. (1999). Cognitive and sociodemographic risk factors for mortality in the Seattle Longitudinal Study. Journal of Gerontology: Psychological Sciences, 54B, P273–P282. Bowling, A. (1987). Mortality after bereavement: A review of the literature on survival periods and factors affecting survival. Social Science & Medicine, 24, 117–124. Deeg, D. J., & Kriegsman, D. M. (2003). Concepts of self-rated health: Specifying the gender difference in mortality risk. The Gerontologist, 43, 376–386; discussion 372–375. Ferraro, K. F., & Kelley-Moore, J. A. (2001). Self-rated health and mortality among Black and White adults: Examining the dynamic evaluation thesis. Journal of Gerontology: Social Sciences, 56B, S195–S205. Hertzog, C. (1996). Research design in studies of aging and cognition. In J. E. Birren & K. W. Schaie (Eds.), Handbook of the psychology of aging (4th ed., pp. 24–35). San Diego, CA: Academic Press.

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