A Painful Reality Check? Examining the Accuracy of Subjective Survival Probabilities by Pain Interference and Depression Status

Abstract

Purpose

Pain and depression are linked to higher mortality risk and lower subjective survival probabilities (SSPs). We examine if SSPs for individuals with pain and depression match their actual lifespans.

Methods

Using data on 12,745 Health and Retirement Study respondents aged 57-89 in 2000 with follow-up through 2018, we assessed whether respondents’ SSPs were “correct,” “underestimated,” or “overestimated” relative to their lifespans. Adjusted multinomial logistic regressions predicted SSP accuracy based on pain interference, depression, and their interaction.

Results

Severe or interfering pain (i.e., high impact pain) was associated with a 25% higher risk of underestimating SSPs (RRR = 1.25, p = .04), and depression increased the risk by 49% (RRR = 1.49, p < .001). High impact pain and depression also corresponded with lower average SSPs and higher mortality risk.

Conclusion

High impact pain and depression increase the risk of underestimating longevity. Future research should explore the impact on health and financial decisions in older adults.

Keywords

longevity expectations subjective life expectancy physical functioning mortality

Introduction

The prevalence of chronic pain is increasing (Zimmer & Zajacova, 2018), as is the evidence for its harmful effects on mental and physical health. Pain has well-established associations with depression (Coyle, L.A & Atkinson, S., 2018; Duenas et al., 2016; Pincus & McCracken, 2013) and mortality risk (Grol-Prokopczyk, 2017; Smith et al., 2014; Vartiainen et al., 2022). More specifically, knee osteoarthritis, musculoskeletal, and back pain—among the most common types of chronic pain—have been shown to predict all-cause mortality even after controlling for potential confounders (Cleveland et al., 2019; Macfarlane et al., 2017; Roseen et al., 2021). While there are some published exceptions after accounting for pain severity or stratifying by gender (Roseen et al., 2021), the positive relationship between pain and mortality is largely stable.

These findings are consistent with prior work establishing that pain, and particularly pain that limits or is likely to limit daily activities, is associated with reductions in individuals’ subjectively perceived chances of living to advanced ages, or subjective survival probabilities (SSPs; Fennell et al., 2024). Intuitively, SSPs are negatively associated with mortality risk and, in fact, offer longevity predictions similar to those of population life tables (Bago d’Uva et al., 2020; Elder, 2013; Mirowsky, 1999; O’Connell, 2011; Perozek, 2008). Beyond their utility as a measure of mortality risk, individuals’ estimations of their own longevity matter for decisions that may ultimately lengthen or abbreviate their lives or influence their ability to live well. For example, low SSPs are associated with a lower likelihood of seeking preventative healthcare (e.g., cancer screenings) and abiding by public health recommendations (Biró, 2016; Celidoni et al., 2022; Picone et al., 2004). Additionally, individuals with low SSPs tend to retire and claim Social Security benefits earlier, and are less likely to invest in their retirements (Doerr & Schulte, 2012; Hurd et al., 2004; O’Brien et al., 2005).

In addition to pain, depression—a common co-morbidity—is also positively associated with mortality risk and has been shown to modulate subjective assessments of survival. A 2019 meta-analysis of 61 studies found that depression among older adults was positively predictive of both all-cause and cardiovascular mortality (Wei et al., 2019). Separate from this research establishing depression as a risk factor for mortality, previous work also supports a negative association between depression and SSPs (Chen et al., 2021; Fennell et al., 2024; Palgi et al., 2019; Papachristos & Verropoulou, 2020). In fact, not only do older adults with depression seem to report lower average SSPs than those without, but their SSPs are lower than longevity expectations calculated based on their demographic and health profiles (Papachristos et al., 2020). This evidence from Europe suggests that individuals with depression may be more prone to underestimating their longevity (Papachristos et al., 2020; Papachristos & Verropoulou, 2020). We suspect the same to be true in our US sample, not only for respondents with depression, but also among those experiencing pain.

The experience of chronic pain in the US is increasingly common, impacting 20% of the American adult population, including 31% of those aged 65 and older (Zelaya et al., 2020). While chronic pain is a leading cause of disability and a positive correlate of mortality (GBD 2016 Disease and Injury Incidence and Prevalence Collaborators, 2017; Grol-Prokopczyk, 2017), pain alone is rarely reported as a direct cause of death (Tennant, 2012). The unfortunate possibility of suicide notwithstanding (Van Orden & Conwell, 2011), individuals with pain and depression—who are otherwise unencumbered by additional life-threatening morbidities—may live reasonably long lives (Zimmer & Rubin, 2016). However, the expectation of an abbreviated life may actualize into shorter lifespans, or contribute to poor health through lower engagement in preventative healthcare (Biró, 2016; Celidoni et al., 2022). Older adults with pain who report limited perceptions of the future, or in this case, underestimated SSPs, are at risk for leading lives that will contribute to more years spent with disability and fewer financial resources to obtain proper care (Doerr & Schulte, 2012; Hurd et al., 2004; Laditka & Laditka, 2017; O’Brien et al., 2005). Our study examines the accuracy of SSPs among older adults with chronic pain to assess the validity of this large subpopulation’s often pessimistic future perceptions, and provide a basis for further work to improve chronic pain sufferers’ abilities to plan for the future.

We assess the simultaneous effects of pain and depression because the concurrent experience of both may have an even stronger negative influence on SSPs than would be expected from both factors independently. Ongoing pain may be a specific stressor about which depressed individuals ruminate and catastrophize (Quenstedt et al., 2021). This argument is exemplified by a recent study showing that older adults reported a significantly reduced desire to live to advanced ages when asked to imagine a hypothetical future with chronic pain (Skirbekk et al., 2021). It is possible that, in using their health to inform their estimations of their own longevity (Chen et al., 2021), individuals with pain and depression may estimate SSPs that are low, but accurate to their own lifespans. However, given the associations between pain, depression, and negative future perceptions, we suspect that individuals with both interfering pain and depression will be more likely to underestimate their lifespans than provide accurate or over-estimations.

To test our central hypothesis, we benefit from a longitudinal dataset that has both subjective assessments of longevity along with follow-up mortality data for determining lifespan. With these data, we can compare respondents’ SSPs to their own lifespans, unlike previous studies that were limited by a lack of mortality follow-up data (Papachristos et al., 2020). To provide a more holistic picture of the interplay between pain, depression, SSPs, and mortality prior to testing the central hypothesis, we further examine both SSP reports and actual survival probabilities among individuals who vary by pain and depression status. We expect that individuals with both pain interference and depression will report the lowest SSPs relative to all other defined subgroups, and that this doubly encumbered subgroup will have a higher risk of dying at any given age during the study period.

Data

We used data from the Health and Retirement Study (HRS), a nationally representative longitudinal study of American adults aged 51 and over. As the HRS is a publicly available secondary dataset, it is exempt from ethics board review. Although the survey began in 1992, our sample includes community-dwelling, cohort eligible respondents interviewed in the 2000 wave, as there were inconsistencies in both skip patterns and question wording prior to that year. With follow-up mortality data through 2018, we are able to assess the accuracy of individuals’ SSPs provided that they were between the ages of 57–89 in 2000. Respondents who did not fall within this range were excluded for reasons related to an age-related skip pattern or target age assignments described in the next section. Of this sample, 90 respondents were missing values on three covariates (smoking status, marital status, or race). We excluded these respondents, yielding a final analytic sample of 12,745 individuals. The CONSORT diagram (Supplemental Figure 1) presents our sample selection process in more detail.

Outcome Measure

We calculated the accuracy of SSPs using the following two measures from the HRS survey:

1. Subjective survival probabilities (SSPs) HRS asked respondents aged 51-89, “what is the percent chance you will live to be x or more?” where the target age x was dictated by respondents’ age at interview: x = 75 for individuals aged 51-64, x = 80 for those aged 65-69, x = 85 for those 70–74, x = 90 for those 75–79, and x = 95 for those 80-84, and x = 100 for those 85–89. Respondents aged 51–56 were excluded from our sample because they were asked about living to a target age (x = 75) that was 19–25 years away. With 18 years’ worth of follow-up data, we would not have been able to assess the accuracy of their SSPs. Respondents provided a subjective percent chance of survival to x on a 0% (completely unlikely) to 100% (very likely) scale. Because older target ages represent increasingly exceptional longevity that may affect the accuracy of SSPs, we control for target age in the final model predicting SSP accuracy.

2. Survival status at target age Using the Cross-wave Tracker File provided by the HRS, we were able to evaluate the mortality status of respondents (i.e., whether they are alive or dead). For respondents in our analytic sample who passed away between 2000 and 2018, we calculated their exact age at death using data on the month and year of their birth and death. If the respondent had not died, we simply tagged that they were still living using a separate binary indicator. For individuals who died during the follow-up period, the HRS obtained information on date of death from the decedent’s family members. In the case of individuals who had dropped out of the study, mortality status was obtained either through linkage with the National Death Index (NDI; Sonnega et al., 2014; Weir, 2016) or via imputation informed by data on when the respondent was last known to be alive and when surveyors learned of the respondent’s passing (Tracker Final, Version 1.0 Data Description and Usage, 2024). Searches for death information via the NDI were only conducted for respondents whom the HRS research team was unable to contact for a request to re-interview in the most recent wave (Weir, 2016).

To assess SSP accuracy, we broke the 0%–100% distribution of SSPs into three categories. “Pessimistic” SSPs referred to those between 0%–49%, “optimistic” SSPs were between 51-100 %, and an “ambivalent” SSP was 50%. Approximately one-fourth of the total sample reported an SSP of 50% (n = 3369); if we had included this group in either the “pessimistic” or “optimistic” groups, we would risk skewing the results. Therefore, we coded individuals reporting this specific SSP as a separate category signifying ambivalence. With such a large group endorsing this midpoint, including them as a separate outcome category may provide insight into the factors related to indecision. From a statistical perspective, this approach also allows us to both retain our sample size and avoid skewing results related to under- and overestimating.

After dividing the distribution in three, we compared respondents’ SSP groupings to their mortality status at their target ages and organized respondents into one of four groups: those who provided (1) Correct SSPs, (2) Underestimated SSPs, (3) Overestimated SSPs, or (4) Ambivalent SSPs. For example, a respondent who reported having an 80% chance of living to age 75 or over would be deemed “correct” if they did, in fact, live to or beyond age 75. However, if that same respondent only lived until age 62, they were categorized as having an “overestimated” SSP. We also made a minor exception for individuals who nearly made it to their target age, but not quite: anyone who reported an optimistic SSP and passed away within two years prior to their target age were deemed “correct.” Analyses conducted with this adjustment are presented in this manuscript; models conducted without this adjustment yielded very similar results. Overall, this categorization allows “pessimistic” respondents to be coded as either “correct” or “underestimated” depending on their age of death; similarly, “optimistic” respondents may be coded as “correct” or “overestimated,” while “ambivalent” respondents are always coded as “ambivalent” regardless of their mortality status at their target ages.

To evaluate whether our findings are sensitive to this data-driven and conceptually simple categorization, we also present results from additional models that broaden the “ambivalent” category from those reporting exactly 50% to those reporting SSPs exclusively between 40%–60%, 30%–70%, or 20%–80%, inspired in part by cut points interpreted in prior work (Wu et al., 2014).

Independent Variables

Pain Respondents were asked if they are “often troubled with pain.” If the respondent answered “no,” we coded them as experiencing no pain. If the respondent answered “yes,” they were asked two follow-up questions about pain intensity and interference: “How bad is the pain most of the time: mild, moderate or severe?” and “Does your pain make it difficult for you to do your usual activities such as household chores or work? (yes/no)” In accordance with prior work prioritizing the measurement of clinically relevant pain profiles (Zimmer & Zajacova, 2018), we used these three questions about pain presence, intensity, and activity interference to generate a three-category measure: (0) no reported pain, (1) mild or moderate pain that does not limit daily activities, and (2) severe pain or pain that limits daily activities. These categories are referred to as no pain, non-interfering mild or moderate pain (or “low impact pain”), and severe or interfering pain (or “high impact pain”), respectively.

Depression The eight-item Center for Epidemiologic Studies Depression (CESD-8) scale asks respondents if they had experienced eight self-reported symptoms in the past week, including low mood, motivation, energy level, sleep quality, and life satisfaction. In accordance with the established CESD-8 threshold (Dang et al., 2020), respondents who reported three or more symptoms were classified as having symptoms indicative of depression and were coded as 1. Individuals endorsing two or fewer symptoms were coded as 0. Although we refer to individuals as having depression or not throughout the manuscript, we acknowledge that endorsing symptoms in a survey does not equate to a clinical diagnosis. In Tables 1 –3, we use a categorical variable that combines pain presence, intensity, interference, and depression: (0) no pain and no depression, (1) no pain with depression, (2) non-interfering mild or moderate pain without depression, (3) non-interfering mild or moderate pain with depression, (4) severe or interfering pain without depression, and (5) severe or interfering pain with depression.

Table 1.

Sample Characteristics in Aggregate and Stratified by Pain and Depression Status for HRS Respondents.

	Total sample (N = 12,745)	No pain without depression (N = 7805)	No pain with depression (N = 1438)	Non-interfering pain without depression (N = 917)	Non-interfering pain with depression (N = 296)	Interfering or severe pain without depression (N = 1161)	Interfering or severe pain with depression (N = 1128)
	M(SD) or %	M(SD) or %	M(SD) or %	M(SD) or %	M(SD) or %	M(SD) or %	M(SD) or %
Pain interference and depression
No pain without depression	61.8%	100%	–	–	–	–	–
No pain with depression	11.0%	–	100%	–	–	–	–
Non-interfering pain without depression	7.2%	–	–	100%	–	–	–
Non-interfering pain with depression	2.2%	–	–	–	100%	–	–
Interfering pain without depression	9.0%	–	–	–	–	100%	–
Interfering pain with depression	8.8%	–	–	–	–	–	100%
Age at interview	68.5 (8.2)	68.3 (8.0)	69.4 (8.5)***	68.2 (8.3)	69.1 (8.9)	68 (8.2)	69.1 (8.7)*
Target age
x = 75	38.6%	38.7%	34.8%	39.8%	36.3%	42.8%	37.7%
x = 80	19.4%	20.1%	18.0%	19.9%	16.7%	18.1%*	17.8%
x = 85	16.6%	17.0%	17.2%	15.6%	17.1%	14.3%*	15.9%
x = 90	13.3%	13.1%	14.9%*	12.4%	14.6%	13.8%	12.7%
x = 95	8.4%	7.8%	10.6%***	8.6%	10.3%	7.2%	11.0%**
x = 100	3.7%	3.3%	4.4%**	3.7%	5.1%†	3.8%	4.9%*
Female	57.3%	52.8%	62.4%***	57.1%*	62.1%**	67.0%***	71.1%***
Race/ethnicity
White (ref)	85.9%	87.2%	79.3%	89.4%	83.0%	87.2%	81.7%
Black	7.5%	6.7%	11.3%***	6.0%	6.8%	6.8%	10.0%***
Other	1.8%	1.9%	2.2%	1.1%†	2.7%	1.9%	1.1%†
Hispanic	4.8%	4.2%	7.1%***	3.4%	7.5%**	4.1%	7.3%***
Education
Less than high school (ref)	25.8%	21.5%	35.3%	20.0%	35.7%	30.5%	41.6%
High school	33.4%	33.0%	33.7%***	34.9%	35.2%**	33.6%***	33.4%***
Some college	20.5%	21.4%	16.3%***	23.8%	18.2%***	21.5%**	16.5%***
College or above	20.3%	24.1%	14.6%***	21.3%	10.9%***	14.4%***	8.5%***
Marital status
Married/Partnered (ref)	63.7%	67.4%	50.4%	69.9%	53.9%	65.9%	49.4%
Separated/Divorced	11.4%	10.2%	13.6%***	9.1%	11.7%	12.5%†	17.8%***
Widowed	21.6%	18.9%	33.3%***	18.3%	32.2%***	18.2%	29.7%***
Never married	3.2%	3.4%	2.8%	2.7%	2.2%	3.4%	3.1%
Smoking status
Never smoker (ref)	40.3%	41.2%	38.9%	41.1%	39.7%	38.8%*	36.1%
Former smoker	45.5%	46.1%	42.1%	49.2%	42.1%	45.2%	44.0%
Current smoker	14.2%	12.7%	18.9%***	9.7%*	18.2%*	16.0%	19.9%***
Health
Heart disease	21.9%	18.3%	25.0%***	20.2%	30.0%***	27.4%***	36.9%***
Lung disease	7.7%	5.3%	10.1%***	6.8%	7.1%	10.4%***	19.5%***
Diabetes	13.2%	11.0%	16.5%***	12.0%	17.1%***	16.8%***	21.1%***
High blood pressure	45.9%	42.6%	50.2%***	44.4%	49.1%*	52.0%***	57.6%***
Ever had cancer	12.3%	11.5%	13.6%*	11.5%	14.6%	15.0%**	14.3%*
Ever had stroke	6.8%	5.2%	9.4%***	4.8%	9.0%**	8.5%***	14.0%***
SSP responses
Optimistic (>50%; ref)	47.2%	52.9%	40.0%	46.1%	29.4%	40.0%	29.6%
Pessimistic (<50%)	25.9%	19.9%	36.4%***	24.0%**	43.9%***	30.1%***	47.6%***
50%	26.9%	27.2%	23.6%	29.8%*	26.6%**	29.9%***	22.8%***
Outcome categories
Correct (ref)	51.4%	52.2%	49.2%	50.3%	50.9%	49.0%	51.4%
Overestimated	10.2%	10.4%	11.5%	9.0%	7.4%	9.0%	9.4%
Underestimated	11.6%	10.1%	15.8%***	10.8%	15.1%*	12.0%*	16.4%***
Ambivalent	26.9%	27.2%	23.6%	29.8%	26.6%	29.9%†	22.8%*
Died during follow-up	61.4%	57.2%	69.7%***	60.3%†	66.0%**	65.1%***	76.2%***

†p < .10 *p < .05; **p < .01; ***p < .001. Note. “No pain without depression” served as the reference group for significance testing. Testing was conducted using linear, logistic, or multinomial logistic regressions depending on the predictor’s continuous, binary, or categorical nature

Table 2.

Linear Regression Predicting Subjective Survival Probabilities for Respondents Aged 57-89 (N = 12,745).

	Independent predictors			Composite variable
	b	B	SE	b	B	SE
Pain interference
No pain
Non-interfering pain	−4.38	−0.04***	0.92
Severe or interfering pain	−6.79	−0.08***	0.81
Depression	−7.68	−0.10***	0.77
Pain interference and depression
No pain without depression
No pain with depression				−7.44	−0.07***	0.99
Non-interfering mild/mod pain without depression				−3.90	−0.03***	1.02
Non-interfering mild/mod pain with depression				−13.44	−0.06***	1.92
Interfering or severe pain without depression				−6.84	−0.06***	0.97
Interfering or severe pain with depression				−14.33	−0.13***	1.17
Age at interview	0.39	0.10*	0.16	0.39	0.10*	0.16
Target age
x = 75 (ref)
x = 80	−8.27	−0.10***	1.19	−8.27	−0.10***	1.19
x = 85	−15.06	−0.18***	1.95	−15.04	−0.18***	1.95
x = 90	−28.92	−0.31***	2.69	−28.90	−0.31***	2.69
x = 95	−37.48	−0.33***	3.41	−37.45	−0.33***	3.42
x = 100	−43.36	−0.26***	4.32	−43.31	−0.26***	4.32
Female	1.98	0.03**	0.62	1.97	0.03**	0.62
Race/ethnicity
White (ref)
Black	6.23	0.05***	1.09	6.21	0.05***	1.09
Other	0.04	0.00	2.38	0.07	0.00	2.37
Hispanic	−4.28	−0.03**	1.48	−4.28	−0.03**	1.48
Education
Less than high school (ref)
High school	1.17	0.02	0.80	1.16	0.02	0.80
Some college	4.33	0.05***	0.87	4.33	0.05***	0.87
College and above	5.18	0.07***	0.86	5.17	0.07***	0.86
Marital status
Married/Partnered (ref)
Separated/Divorced	0.47	0.00	1.03	0.46	0.00	1.03
Widowed	−0.74	−0.01	0.79	−0.74	−0.01	0.79
Never married	−0.37	0.00	1.64	−0.37	0.00	1.64
Smoking status
Never smoker (ref)
Former smoker	0.53	0.01	0.60	0.52	0.01	0.60
Current smoker	−6.60	−0.07***	0.93	−6.59	−0.07***	0.93
Health conditions
Heart disease	−6.54	−0.09***	0.74	−6.53	−0.08***	0.74
Lung disease	−7.63	−0.06***	1.15	−7.66	−0.06***	1.15
Diabetes	−5.20	−0.06***	0.87	−5.20	−0.06***	0.87
High blood pressure	−3.78	−0.06***	0.57	−3.79	−0.06***	0.57
Ever had cancer	−3.95	−0.04***	0.85	−3.94	−0.04***	0.85
Ever had stroke	−2.43	−0.02*	1.23	−2.43	−0.02*	1.24
Constant	47.95	***	9.59	48.01	***	9.60

†p < .10 *p < .05; **p < .01; ***p < .001.

Table 3.

Cox Proportional Hazards Regression Predicting Hazard Ratios for Mortality Among Respondents Aged 57-89 in 2000 (N = 12,745).

	HR	95% confidence interval (CI)
	HR	Lower CI	Upper CI
Pain and depression
No pain without depression
No pain with depression	1.26***	1.16	1.37
Non-interfering mild or moderate pain without depression	1.11*	1.01	1.21
Non-interfering mild or moderate pain with depression	1.47***	1.26	1.71
Severe or interfering pain without depression	1.28***	1.17	1.40
Severe or interfering pain with depression	1.40***	1.25	1.56
Age at interview	1.11***	1.10	1.11
Female	0.73***	0.69	0.78
Race/ethnicity
White (ref)
Black	1.04	0.96	1.13
Other	0.85	0.66	1.10
Hispanic	0.84**	0.75	0.95
Education
Less than high school (ref)
High school	0.92†	0.86	0.99
Some college	0.86***	0.80	0.93
College and above	0.80***	0.74	0.87
Marital status
Married/Partnered (ref)
Separated/Divorced	1.20***	1.09	1.31
Widowed	1.11**	1.03	1.20
Never married	1.46***	1.24	1.72
Smoking status
Never smoker (ref)
Former smoker	1.23***	1.16	1.30
Current smoker	2.17***	1.96	2.42
Health conditions
Heart disease	1.34***	1.26	1.42
Lung disease	1.87***	1.70	2.06
Diabetes	1.60***	1.47	1.74
High blood pressure	1.22***	1.16	1.29
Ever had cancer	1.23***	1.14	1.33
Ever had stroke	1.45***	1.30	1.61

†p < .10 *p < .05; **p < .01; ***p < .001.

Additional Covariates We also included sociodemographic and health controls in our analyses as these may influence both SSPs and mortality risk. Sociodemographic variables included age, gender, educational attainment (less than high school, high school, some college, and college or above), and marital status (married/partnered, separated/divorced, widowed, and never married). Health status variables included smoking status and six self-reported chronic health conditions (heart disease, lung disease, diabetes, high blood pressure, cancer, and stroke). For each condition, if the respondent reported ever or currently having the disease, they were coded as 1; if not, they were coded as 0. Smoking status was controlled as a 3-category variable: (0) Never, (1) Former, and (2) Current smoker.

Analytic Strategy

We began by describing sample characteristics for the full sample and across pain interference and depression status categories (Table 1). For our first analysis, we used an adjusted OLS regression to identify which group (defined by pain and depression status) reported the lowest chances of survival to their target ages (Table 2). Specifically, we used the six-category variable that combines pain presence, intensity, interference, and depression status as the primary predictor. Using this variable rather than an interaction term between pain and depression allowed us to report the specific SSP averages for each subgroup, controlling for relevant demographic, socioeconomic, health behavior, and health status covariates.

Our second analysis examined whether subgroups defined by pain and depression status had increased risk of death during the study period using a Cox proportional hazards model (Table 3). For this analysis, the time variable was the difference between respondents’ “start” and “exit” age (either their age at death or age at last interview), ranging from 0 to 18 years. The failure indicator was death (coded as 1). In preparation for analysis, the data were weighted using respondent-level weights (see below for more information) and were set up to utilize the multiply imputed values for variables previously described. The Cox proportional hazards model yielded hazard ratios quantifying the risk of death while controlling for all covariates named in the measures section. We additionally generated a Kaplan-Meier plot (Figure 1) to visualize survival probabilities throughout the study period by pain-depression group, allowing us to test our hypothesis that respondents with interfering pain and depression would have a higher risk of dying at any given time than respondents without.

Third, we estimated the relative risk of underestimated, overestimated, or ambivalent survival probabilities relative to correctly estimated SSPs using an adjusted multinomial logistic regression. The primary relationships of interest were between SSP accuracy, pain interference, and depression. We assessed both the main effects of pain composite variable and depression as well as their interaction to test whether there is a synergistic effect of experiencing both. In supplementary materials (Table S1), we also included models stratified by target age as SSP accuracy may differ as a function of the exceptionality of living to the assigned x. For example, respondents may be rightfully more pessimistic about surviving to 100 than they are about surviving to age 75, and such pessimism may be less so related to pain or depression as respondents reach more advanced ages. Due to small cell sizes among the x = 100 group, we analyzed the x = 95 and x = 100 respondents in the same model.

To improve confidence in our analysis, we also conducted a sensitivity check that imposed more conservative cut points of the 0%–100% SSP scale (Table S2). Our initial categorization of the outcome variable essentially simplified the SSP question to “Do you think you will live to x or older?”: “yes” (51%–100%), “no” (0%–49%), or “maybe” (50%). However, respondents who reported a 51% chance of living to their target age are clearly not as confidently optimistic as respondents reporting 100%. Therefore, for this sensitivity check, we widened the “ambivalent” category to include individuals reporting a 31%–69% chance of living to x. This range was informed by Wu et al. (2014) in which the authors claimed that respondents’ reports of SSPs at or exceeding 70% indicated that their perceived chances of living to their target age were “probable” (70%), “very probable” (80%), “almost certain” (90%), and “certain” (100%). On the other hand, SSPs under 30% signified respondents’ perception that there was only a slight or no chance of living to x. Therefore, for our main sensitivity analysis, we considered individuals who reported a 0%–30% chance as “pessimistic,” and individuals reporting 70%–100% as “optimistic.” We also conducted additional robustness checks with ambivalence categories ranging from 40%–60% and 20%–80% to offer a gradient of adjustment along the SSP continuum. We assessed whether these differential categorizations yield similar model results to our original specification.

Our analyses were conducted using Stata 18 and weighted to generate estimates that were representative of the US population. Respondent-level weights adjusted for nonresponse bias, complex survey design, and poststratification adjustments. More specifically, they controlled for the HRS’s purposeful oversampling of Black Americans, Hispanics, and Floridians as well as the different sampling frame used for recruiting participants for an “oldest old” subsample (Lee et al., 2021).

Results

We first document sample characteristics in aggregate and over groups defined by respondents’ pain interference and depression status. From our analytic sample—comprised of 12,745 adults aged 57-89—27% reported that they were troubled with pain, and 65% of those individuals reported severe pain and/or pain-related interference with daily activities. Approximately 22% of the total sample reported three or more depressive symptoms in the prior week. Of respondents with suspected depression, 39.4% reported severe or interfering pain, 10.3% had non-interfering mild or moderate pain, and 50.2% reported no pain.

As shown in the left-most column of Table 1, the analytic sample was majority female (57%), Non-Hispanic White (86%), and married (64%), with an average age of 68.5 years (SD = 8.2) in 2000. One-fifth of respondents (20.3%) had a college education or more. Roughly 60% reported being either a former or current smoker, and past or present chronic health conditions were self-reported at the following rates: heart disease (21.9%), lung disease (7.7%), diabetes (13.2%), high blood pressure (45.9%), cancer (12.3%), and stroke (6.8%). Of the total sample, 61.4% died during the study period. With respect to SSPs, 47.2% of the total sample reported SSPs higher than 50%, 25.9% reported SSPs lower than 50%, and 26.9% reported an SSP of exactly 50%. After comparing respondents’ SSP projections to subsequent mortality data, 51.4% reported SSPs that were accurate to their own lifespans, 26.9% reported an ambivalent 50% chance of survival to x, 11.6% underestimated their SSPs, and 10.2% overestimated them.

Table 1 also highlights the significant differences in these sample characteristics when stratified into subgroups defined by pain and depression status. When compared to respondents without pain or depression, individuals with either pain interference, depression, or both, were significantly more likely to be female and more likely to die within the observation period (2000-2020). With the exception of individuals reporting neither depression nor interfering pain, all other subgroups were less educated, more likely to be current smokers, and more likely to have at least four of the six measured chronic health conditions when compared to people with no pain or depression. Individuals in the three subgroups defined (in part) by having depression were more likely to underestimate their lifespans relative to people without pain or depression; of individuals without depression, only those with severe and/or interfering pain were more likely to underestimate their chances of survival relative to the reference group (p < .05).

We then conducted an OLS regression to examine how the adjusted SSPs compare across subgroups defined by pain and depression status (Table 2). We found that respondents who reported both severe or interfering pain and depression reported the lowest SSPs of any group. The mean SSP of this group was 14.3 percentage points lower than for individuals with no pain or depression (p < .001). Following pairwise comparisons of the marginal means from this model, it is clear that subgroups defined by depression have significantly lower SSPs than those without depression within each pain type / status (all p < .001).

This pattern is also observed in Figure 2, which uses fully adjusted Model 2 results to show mean SSPs across groups. After calculating pairwise comparisons between group means, we see that each group with depression reported a significantly lower SSP than the comparable group without depression. Whether pain was non-interfering or interfering seemed to have little effect on SSPs among respondents with depression, for example, the average SSP for depressed individuals with interfering pain (45.1%; 95% CI [43.0, 47.3]) did not differ significantly from that of those with non-interfering pain and depression (46.0%; 95% CI [43.0, 47.3]).

Results from the analysis predicting risk of death among respondents over the 18-year study period suggest that both pain and depression correspond with significantly elevated mortality risk (Table 3). However, subgroups defined by depression yielded larger hazard ratios than those that were not. Adjusting for all other covariates and relative to those with neither pain nor depression, subgroups of people with depression had the following associated elevations in mortality risk: Individuals who had depression but were pain-free (HR = 1.26, p < .001), non-interfering mild or moderate pain with depression (HR = 1.47, p < .001), and severe/interfering pain with depression (HR = 1.40, p < .001). For subgroups of respondents without depression, our model yielded the following mortality risks relative to those with no reported pain or depression: non-interfering mild or moderate pain with no depression (HR = 1.11, p > .05), and severe/interfering pain with no depression (HR = 1.28, p < .001).

Figure 1 is a Kaplan-Meier plot showing the adjusted survival curves for each group defined by pain and depression status. Unsurprisingly, respondents with the best survival probabilities throughout the study period are those with no pain or depression, whereas individuals with both severe or interfering pain and depression had the worst survival outcomes. For example, the survival probability of those who reported no pain or depression in 2000 was 0.75 in 2010, but only about 0.55 for those reporting both severe/interfering pain and depression that same year.

Next, we conducted a multinomial logistic regression to determine the relative risk of reporting underestimated, overestimated, and ambivalent SSPs (Table 4). All results should be interpreted net of covariates and relative to the base outcome: reporting a “correct” SSP. We found that, while the experience of non-interfering mild or moderate pain was not significantly associated with SSP accuracy, experiencing severe or activity-interfering pain was associated with a 25% higher risk of underestimating SSPs (RRR = 1.25, p = .04). Severe/interfering pain did not modulate the risk of reporting an overestimated (RRR = .88, p = .34) or ambivalent SSP (RRR = 1.15, p = .09). Individuals with depression had a 49% higher risk of underestimating (RRR = 1.49, p < .001) and a 15% lower risk of being ambivalent (RRR = 0.85, p = .04). Neither interaction term, indicating the experience of both pain and depression, significantly modulated the risk of any reporting outcome.

Table 4.

Multinomial Logistic Regression Predicting Accuracy of Subjective Survival Probabilities for Respondents Aged 57-89 (N = 12,745).

	Underestimated (n = 1488)			Ambivalent (n = 3349)			Overestimated (n = 1287)
	RRR	95% CI		RRR	95% CI		RRR	95% CI
	RRR	Lower CI	Upper CI	RRR	Lower CI	Upper CI	RRR	Lower CI	Upper CI
Pain
No pain (ref)
Non-interfering mild or moderate pain	1.14	0.89	1.47	1.15	0.97	1.37	0.93	0.70	1.23
Interfering or severe pain	1.25*	1.01	1.55	1.15†	0.98	1.36	0.89	0.70	1.13
Depression	1.49***	1.23	1.81	0.85*	0.72	1.00	0.97	0.78	1.20
Non-interfering pain * depression	0.81	0.50	1.30	0.95	0.65	1.41	0.69	0.38	1.23
Interfering or severe pain * depression	0.84	0.60	1.17	0.82	0.62	1.09	0.86	0.58	1.28
Age at interview	1.02	0.98	1.06	1.02	1.00	1.05	0.96†	0.93	1.00
Target age
x = 75 (ref)
x = 80	1.48**	1.12	1.96	1.26*	1.03	1.55	1.88***	1.37	2.58
x = 85	1.77*	1.13	2.77	1.34†	0.96	1.85	3.46***	2.11	5.68
x = 90	1.96*	1.05	3.69	1.19	0.76	1.87	4.71***	2.35	9.42
x = 95	1.01	0.44	2.29	0.76	0.42	1.35	4.20**	1.75	10.08
x = 100	0.38†	0.13	1.09	0.37**	0.18	0.77	6.06**	2.05	17.98
Female	1.00	0.87	1.15	0.95	0.86	1.05	0.73***	0.63	0.85
Race/ethnicity
White (ref)
Black	0.84	0.67	1.05	0.75**	0.63	0.89	1.49***	1.21	1.84
Other	1.40	0.89	2.21	1.34	0.93	1.92	1.50†	0.93	2.40
Hispanic	1.68***	1.32	2.14	0.81†	0.65	1.01	0.87	0.62	1.22
Education
Less than high school (ref)
High school	0.85*	0.72	1.00	0.98	0.87	1.11	0.89	0.74	1.06
Some college	0.67***	0.55	0.82	0.90	0.78	1.03	0.87	0.71	1.06
College or above	0.58***	0.47	0.71	0.73***	0.63	0.85	0.85	0.69	1.04
Marital status
Married/Partnered (ref)
Separated/Divorced	0.91	0.72	1.15	0.95	0.80	1.13	1.23†	0.97	1.54
Widowed	0.94	0.79	1.12	1.16*	1.02	1.32	1.16	0.96	1.40
Never married	0.95	0.64	1.39	1.17	0.88	1.57	1.95***	1.36	2.81
Smoking status
Never smoker (ref)
Former smoker	0.79**	0.69	0.92	0.98	0.89	1.09	1.15†	0.99	1.35
Current smoker	1.18	0.96	1.45	1.37***	1.18	1.59	1.91***	1.55	2.36
Health conditions
Heart disease	1.07	0.91	1.26	1.03	0.92	1.16	1.09	0.92	1.29
Lung disease	0.61***	0.46	0.80	0.91	0.76	1.09	1.08	0.85	1.38
Diabetes	1.00	0.82	1.21	1.16*	1.01	1.33	1.55***	1.29	1.87
High blood pressure	1.18*	1.03	1.34	1.11*	1.01	1.23	1.12†	0.98	1.29
Ever had cancer	0.94	0.78	1.14	1.03	0.90	1.19	1.13	0.93	1.37
Ever had stroke	0.67**	0.51	0.89	0.65***	0.53	0.79	1.12	0.87	1.43
Constant	0.06*	0.01	0.62	0.10**	0.02	0.52	0.92	0.08	11.13

†p < .10 *p < .05; **p < .01; ***p < .001. Note. Base outcome is “correct” (n = 6621).

When stratified by target age, high impact pain and depression seem to be associated with pessimistic SSPs at x = 75, 80, and 85, even if non-significantly (Table S1). However, there is variability in how pain and depression interact in these age-stratified models. While there were no significant interactions in the larger model (Table 4), low impact pain and depression significantly reduced the risk of overestimation in the x = 75 model (RRR = 0.31, p < .05), and increased the risk of overestimation in the x = 80 model (RRR = 6.71, p < .01). Further, the interaction between high impact pain and depression reduced the risk of underestimation in the x = 85 model (RRR = 0.33, p < .01). In the models restricted to x = 90 and x = 95 / 100, there were no significant interactions; high impact pain was associated with lower risk of overestimation for x = 90 (RRR = 0.51, p < .05), and depression lowered the risk of ambivalence in the oldest group (RRR = 0.57, p < .01).

Results from the sensitivity checks (Table S2) employed more conservative categorizations of the outcome variable. These three tests broadened the “ambivalent” category to include respondents reporting SSPs of 40–60%, of 30–70%, and of 20–80%, respectively. We place the most emphasis on findings from the 30%–70% model as these cut points have been supported by prior work (Wu et al., 2014); other specifications were estimated as robustness checks along the SSP range. The 30%–70% model results were nearly identical to those shown in Table 4. Severe or interfering pain (RRR = 1.32, p < .05) and depression (RRR = 1.67, p < .001) were both independently associated with higher risks of SSP underestimation, and depression was also associated with a lower risk of ambivalence (RRR = 0.85, p < .05). Similar to the primary model, neither interaction term was significantly associated with any SSP accuracy outcome. The model testing 40 and 60% cut points similarly found independent relationships between pain, depression, and SSP underestimation, but did not find depression to be associated with ambivalence. The most conservative model (with the ambivalent category ranging from 20%–80%) only replicated the depression-related findings from the main specification; pain was not significantly associated with SSP accuracy.

Discussion

The present study used unique data with both subjective survival expectations and follow-up information on mortality to examine the effects of pain and depression on the accuracy of SSPs, both independently and synergistically. Considering the disruptions caused by activity-interfering pain and the pessimistic outlook that comes with pain-related depression, we hypothesized that individuals with both severe/interfering pain and depression would both report the lowest SSPs and be more likely to die, and die younger, during the study period. We also expected that individuals with severe/interfering pain and/or depression would underestimate their chances of living to a given target age for the same reasons.

As expected, individuals doubly encumbered by both severe or interfering pain and depression report the lowest SSPs, on average. However, the difference in mean predicted SSPs for depressed respondents with and without this high impact pain was not significant. As we reconcile our findings with prior work suggesting that depression itself interferes with daily activities by way of poor psychological wellbeing and lack of motivation (Edwards et al., 2011), it is clear that depression, not pain interference, is the stronger contributing factor in reducing individuals’ SSPs. This does not mean that experienced pain plays no role in influencing peoples’ perceptions of their lifespans. In line with Fennell et al. (2024), reporting any pain, regardless of depression status, was associated with lower average SSPs.

A somewhat similar pattern emerged when we estimated risk of death for subgroups defined by pain and depression over the study period. Relative to individuals with no pain or depression, significantly larger percentages of respondents in subgroups defined by depression died during the study period (Table 1), and similarly, had steeper survival curves regardless of pain status (Table 3). That said, the experience of pain with or without depression also significantly increased respondents’ risk of death over the study period. Therefore, this work supports the growing literature on the respective links between pain and depression on mortality (Glei & Weinstein, 2023; Macfarlane et al., 2017; Wei et al., 2019).

The primary aim of this paper was to assess whether the combined experience of depression and high impact pain was associated with reports of inappropriately low longevity expectations in older adults. Unexpectedly, we found no evidence for the interaction between pain and depression in the larger model, but severe or interfering pain and depression did independently increase the risk of SSP underestimation in our sample. This underestimation may lead to a myriad of potential negative consequences related to financial and physical wellbeing. Individuals who anticipate shorter lifespans tend to retire earlier and claim Social Security benefits younger (Doerr & Schulte, 2012; Hurd et al., 2004), increasing the likelihood that they may prematurely exhaust retirement savings. Underestimation of one’s lifespan may also lend itself to choices that contribute to poorer health and lower quality of life: people with lower SSPs report lower likelihoods of seeking preventative healthcare and less willingness to abide by public health guidelines (Biró, 2016; Celidoni et al., 2022; Picone et al., 2004). The tendency for individuals with activity-interfering pain or depression to underestimate their lifespans may also increase their need for unplanned, effortful, and expensive caregiving as they age beyond their expectations.

Our findings should be considered alongside data and analytic limitations. First, the HRS asks about the probability of living to a specific target age, not about the age at which a respondent believes they will die. With data on subjective life expectancy in years, a comparison between this subjective measure and the respondents’ objective age at death would have been more easily made. Lacking this measure, we conducted sensitivity analyses with different categorizations of the outcome variable; reassuringly, findings were very similar to our main results. Other notable limitations include that our analyses are only salient to individuals aged 57-89, and that we only used measures of pain, depression, and SSPs from 2000. The experience of pain and depression as well as subjective assessments of one’s own lifespan are subject to change over time (Palloni & Novak, 2017; Schneider et al., 2012). Our study design did not allow us to model possible causal associations (perhaps reciprocal ones) between pain and depression. Future work may attend to this issue with thoughtfulness about the ordering of pain and depression.

Future research should further investigate the mechanisms underlying the possible relationship between underestimated SSPs and abbreviated lifespans, including among (but not limited to) people with pain and/or depression. Structural equation modeling and the exploitation of longitudinal data may help elucidate whether pessimistic reports of low SSPs increase mortality risk at younger ages through poor health behaviors. Specific to pain, it may be clinically important to assess whether SSPs predict engagement with certain pain treatments. For example, it may be that individuals with high SSPs are more likely to engage in pain treatments that are uncomfortable and time-consuming in the short-term, but offer positive long-term outcomes (e.g., physical therapy), whereas individuals with low SSPs may prefer treatments that provide immediate relief without considering negative long-term consequences (e.g., opioid therapy). Additionally, in our target age-stratified models, there was mixed and inconsistent evidence for the significant interplay between pain and depression on SSP accuracy. As respondents age and, thus, estimate their chances of survival to more advanced ages, it is not clear how individuals take these factors into account. Future work should continue to examine the factors individuals consider when estimating their chances of living to exceptional ages.

In conclusion, the present study highlights that both severe/activity-interfering pain and depression are independently associated with the underestimation of subjective assessments of longevity. Our primary sensitivity test (that imposed SSP cut points at 30 and 70%) supported these findings. We present this study as further evidence of the negative psychological effects of high impact pain and depression. Subpopulations of older adults with interfering pain and/or depression may be at risk for inadequate preparation for retirement and lack of engagement in preventative healthcare as a consequence of underestimating their own lifespans. Further investigation of the consequences of inaccurate SSP assessments specifically among older adults with high impact pain and/or depression is warranted, as they represent large and vulnerable groups that may be amenable to intervention. Within the healthcare setting, curiosity around patient’s subjective assessments of their own longevity, as well as their own aging, may be helpful in monitoring psychological and functional experiences of pain, depression, or other conditions. In addition to being a signal of adaptive health and finance-related behaviors, a high SSP is also likely a positive indicator that an individual is faring well in their everyday life.

Supplemental Material

Supplemental Material - A Painful Reality Check? Examining the Accuracy of Subjective Survival Probabilities by Pain Interference and Depression Status

Supplemental Material for A Painful Reality Check? Examining the Accuracy of Subjective Survival Probabilities by Pain Interference and Depression Status by Gillian Fennell, Theresa Andrasfay, Hanna Grol-Prokopczyk, and Jennifer Ailshire in Journal of Aging and Health

Footnotes

Authors’ Note

Gillian Fennell has since moved from the University of Southern California to the Department of Rheumatology at Boston University Chobanian and Avedisian School of Medicine.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Institute on Aging of the National Institutes of Health: [R01AG065351] to H.G.P and [T32AG000037] to G.F. It is further supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases: [T32AR080623] to G.F. The Health and Retirement Study is sponsored by the National Institute on Aging grant number NIA [U01-AG009740] and is conducted by the University of Michigan. The content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

ORCID iDs

Gillian Fennell

Theresa Andrasfay

Hanna Grol-Prokopczyk

Jennifer Ailshire

Supplemental Material

Supplemental material for this article is available online

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