Comparing physical and mental health across age gradients among older adults during the COVID-19 pandemic

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This study examined the prevalence and patterns of self-reported physical and mental health patterns among U.S. adults aged 60 and older, focusing on age groups differences optimal intervention timing, and modifiable predictors. Methods Using the Behavioral Risk Factor Surveillance System data from 2021–2023, we applied frequentist generalized ordered logistic and Bayesian ordinal logistic regressions to assess robustness across inferential frameworks. Results Older age was associated with progressively lower odds of reporting poor physical and mental health, with the 60–64 age group consistently reporting worse outcomes than older cohorts (65–69, 70–74, 75–79, and ≥ 80). While both domains followed similar age-related patterns, the magnitude of age associations was greater for mental health. Conclusion This study highlights “a tale of two age gradients” in older adulthood: ages 60–64 mark a critical intervention window, with elevated risks for both poor physical and mental health compared with older cohorts. Population Aging COVID-19 Mental Health Physical Function Health Disparities Figures Figure 1 Figure 2 Background Like many countries, the United States (U.S.) is experiencing a remarkable demographic shift toward an aging population, presenting profound challenges as well as important opportunities for reimagining its healthcare system ( 1 ). According to the 2023 U.S. Census Bureau projections, the number of Americans aged 60 years and older will rise from roughly 79 million in 2023 (about 23.9% of the total U.S. population) to about 98 million by 2050, an increase of 24 percent ( 2 ). This unprecedented demographic trend raises important concerns, as aging itself is a major risk factor for most health conditions ( 3 ), contributing to the high prevalence of chronic illnesses and the widespread decline in physical and mental health observed among older adults. It is well documented, and not surprising, that older adults face a high burden of physical and mental health conditions ( 4 , 5 ). Prior research also has illuminated the bidirectional interplay between physical and mental health ( 6 ), with evidence showing that changes in one domain can precede and influence subsequent changes in the other, .and examined their key social, behavioral, and clinical determinants ( 7 – 9 ). However, important knowledge gaps remain. First, despite evidence shows that the pandemic significantly worsened health outcomes for this population ( 10 , 11 ), limited research has examined age specific patterns in physical and mental health during and after the pandemic using large scale population based data. Second, few research has explicitly assessed whether certain age groups within older adulthood represent periods of heightened vulnerability, as opposed to assuming uniform or gradually slowing aging processes. Third, much of the existing literature relies on traditional age brackets (65–69, 75 − 74, 75–79, 85+) ( 12 , 13 ) that exclude adults aged 60–64, potentially obscuring heterogeneity within the older adult population. We focus on 2021 to 2023 period rather than 2020–2022 because 2020 was highly turbulent, whereas by 2021 the pandemic’s longer-term aftermath had begun, with disruptions persisting but conditions stabilizing enough to capture more reliable post-pandemic health patterns. Moreover, theoretically, we draw on life-course and gerontological sperspectives, which emphasize that aging is a cumulative process characterized by transitions across later life stages ( 14 – 16 ). These perspectives suggest that the early 60s mark the onset of “early old age.” Although adults aged 60–64 are often classified as a “pre-retirement” or “late midlife” group in public health and demography because of Medicare’s age 65 cutoff in the U.S., this categorization overlooks that health decline does not suddenly begin at 65. Instead, the transition is already underway during this earlier period, which may represent a critical window when preventive interventions can have disproportionate long-term effects on maintaining health and delaying morbidity. From a psychosocial perspective, socioemotional selectivity theory ( 17 , 18 ) further highlights the distinctiveness of this transition. As individuals enter their 60s, perceptions of future time horizons begin to narrow, which heightens the salience of emotionally meaningful goals and relationships. This shift can shape health behaviors, coping strategies, and mental well-being, potentially amplifying vulnerabilities during early old age while also fostering resilience in later old age. Thus, this study focuses on adults aged 60 and older, allowing us to explore health dynamics across key stages of the life course, capturing both the onset of age-related changes and their progression into advanced old age. Study Objectives This study has three primary objectives to examine the prevalence and patterns of self-reported physical and mental health conditions among U.S. adults aged 60 and older, and to identify the sociodemographic, behavioral, and clinical characteristics associated with these outcomes. By combining a life-course perspective with nationally representative longitudinal data, our work contributes to the literature by: ( 1 ) providing updated evidence on post-pandemic health patterns among older adults, ( 2 ) identifying critical transition points in later life that may serve as intervention targets, and ( 3 ) advancing a more nuanced age categorization framework to capture heterogeneity within older adulthood. Research Questions We address the following research questions: How do prevalence patterns of physical and mental health conditions vary across age groups of U.S. adults aged 60 and older, including ages associated with key late-life transitions, and how have these patterns changed from 2021 to 2023? Which sociodemographic (e.g., gender, race/ethnicity, education), health behavior (e.g., smoking, physical activity), and clinical characteristics (e.g., chronic disease burden) are associated with variations in both self-reported health outcomes? Hypotheses Guided by life-course and gerontological perspectives ( 14 – 16 ) and informed by socioemotional selectivity theory ( 17 , 18 ), we build on prior evidence ( 19 , 20 ) to propose the following hypotheses: H 1 : Self-reported physical and mental health will show different age-related patterns. H 1a (physical health): Self-reported physical health will generally decline with increasing age. H 1b (mental health): Self-reported mental health will remain stable or improve slightly with age. H 2 : Self-reported physical and mental health will differ across sociodemographic groups, with lower socioeconomic status associated with worse outcomes. H 3 : Negative health-related behaviors and clinical characteristics will be associated with poorer self-reported physical and mental health, while healthy behaviors will have protective but not fully compensatory effects. Methods Data Sources and Study Population We conducted a pooled cross‑sectional study using the 2021–2023 Behavioral Risk Factor Surveillance System (BRFSS) data, an annual, state‑based, random‑digit‑dial telephone survey coordinated by the Centers for Disease Control and Prevention (CDC). From an initial pool of 1,317,148 (2023: 433,323; 2022: 445,132; 2021:438,693) respondents across all 50 states, the District of Columbia, and U.S. territories. The survey methodology has been well described in the previous studies ( 21 , 22 ). We restricted the analytic sample to individuals aged 60 years and older (N = 401,411). We further excluded respondents with missing data on any of the study’s key variables (physical health days, mental health days, sociodemographic and health-related behavioral and clinical factors covariates), yielding a final unweighted sample of 399,696 individuals who were aged 60 years and older from 2021 to 2023. BRFSS employs complex, multistage sampling and provides design weights; all analyses incorporated the recommended weights, strata, and primary sampling units to produce nationally representative estimates. Variables Outcome Measures Our outcome variables are: 1) Physical health status in the past 30 days, derived from the question: “Now thinking about your physical health, which includes physical illness and injury, for how many days during the past 30 days was your physical health not good?”; and 2) Mental health status in the past 30 days, derived from the question: “Now thinking about your mental health, which includes stress, depression, and problems with emotions, for how many days during the past 30 days was your mental health not good?” Both measures have demonstrated reliability and validity as indicators of self-rated health and health-related quality of life. For example, the CDC has long endorsed these items as part of its Health-Related Quality of Life (HRQOL) surveillance system ( 23 ). Prior research has shown strong associations between these items and a wide range of clinical outcomes, including chronic disease burden, activity limitations, and mortality ( 24 ). These findings provide reassurance that the measures are both interpretable and robust indicators of health status in population-based surveys. Responses to both questions were categorized into three levels: 0 days (good health), 1–13 days (moderate health), and 14 or more days (poor health). These cut-points are widely used in literature and recommended by the CDC HRQOL guidelines. In particular, reporting ≥ 14 days of poor physical or mental health in the past month is a standard threshold for identifying substantial health-related quality-of-life impairment ( 25 , 26 ). Predictor Measures Our primary predictor is age group, categorized into five cohorts: 60–64, 65–69, 70–74, 75–79, and 80 years and older. Covariates This study controlled for two major domains of covariates. The first domain includes sociodemographic characteristics: gender, race/ethnicity, educational attainment, household income, residential setting (urban vs. rural), and health insurance coverage status (public, private, or uninsured). The second domain encompasses health-related behavioral, specifically overweight or obesity status, engagement in physical activity, and current smoking behavior. The third domain addressed disease burdens (clinical factors). Each disease burden variable was coded as 1 when the respondent reported at least one of the sub-conditions within that category and coded as 0 otherwise. The categories included functional physical limitations (difficulty walking or climbing stairs, dressing or bathing, or doing errands alone), sensory and cognitive impairments (difficulty seeing, hearing, or concentrating or remembering), cardiometabolic conditions (cardiometabolic or renal or urologic conditions), chronic inflammatory conditions (musculoskeletal or respiratory conditions), psychiatric disorders (mental health conditions), and malignant neoplasms (cancer). An overall comorbidity indicator was also created to reflect the number of the presence of these disease burdens. These covariates were selected based on extensive evidence linking sociodemographic disadvantage, unhealthy behaviors, and chronic health conditions to poorer physical and mental health outcomes among older adults. Specifically, obesity, physical inactivity, and smoking have been consistently linked to a greater risk of chronic conditions, functional decline, depression, and poorer self-reported health in this population ( 27 , 28 ). Statistical Analyses We conducted frequentist generalized ordered logistic regression analyses to assess whether the proportional odds assumption was held for both dependent variables. To assess potential multicollinearity among predictor variables, we calculated variance inflation factors (VIFs). All VIF values were below 4.61, with a mean VIF of 1.18, indicating that multicollinearity was not a serious concern. We then employed Bayesian ordinal logistic regressions to explore the associations between age groups and the two primary health outcomes, while adjusting for the full set of covariates described earlier, including sociodemographic characteristics and health-related behavioral and clinical factors. Models were fit using four Markov Chain Monte Carl (MCMC) chains, each with 2,000 iterations (1,000 warm-up and 1,000 sampling iterations), yielding 4,000 post-warmup draws per model. Posterior distributions were examined to estimate the direction and magnitude of associations. To evaluate the quality and convergence of the sampling process, we visually inspected trace plots and posterior histograms for selected parameters. All analyses were conducted using R version 4.3.2 (R Foundation for Statistical Computing, Vienna, Austria) with the “clm” and “brms” package. Statistical significance was evaluated at an alpha level of 0.05. Sensitivity Analysis We conducted two sets of sensitivity analyses. First, we introduced an interaction term between age group and gender to examine potential effect modification and identify any gender-specific patterns in the relationship. We found that the interaction between age group and gender reveals differing patterns in self-reported physical and mental health across age and sex (See Appendix). Specifically, older female, compared to those aged 60–64, were less likely to report poor physical and mental health. While females overall were more likely to report poor health compared to males, the gender gap narrows or even reverses in older age group (See Appendix-Table A-1). Second, to account for potential clustering by state, we estimated a multilevel model in which individual respondents were nested within states. The intra-class correlation coefficients were extremely low: 0.004 for physical health and 0.003 for mental health, indicating minimal variation across states. Results Sample Characteristics Among the 399,696 respondents of the final analytic sample, 63% reported good physical health (0 poor health days), 22% moderate (1–13 days), and 16% poor (≥ 14 days). For mental health, 72% reported good, 19% moderate, and 9% poor health. Most respondents were aged 60–74 years, and 52% were female. The majority identified as non-Hispanic White (86%), followed by Black (7%), Hispanic (3%) and other racial/ethnic groups. Approximately 71% of respondents had attended or graduated from college or a technical school. Over half (54%) reported an annual household income exceeding $ 50,000. Most lived in urban areas (84%) and had public health insurance coverage (73%). About 70% were classified as overweight, and 73% reported engaging in physical activity or exercise within the past 30 days. Regarding smoking behavior, 54% had never smoked, 36% were former smokers, and 10% were current smokers (daily or occasionally). About 25% of respondents had functional physical limitations, 24% had sensory and cognitive impairments, 36% had cardiometabolic condition, 58% had chronic inflammatory conditions, 17% had psychiatric disorders, and 16% had malignant neoplasms (cancer). On average, respondents had 1.6 comorbid conditions (SD = 1.4). The respondents were nearly evenly distributed across study years: 33% in 2021, 33% in 2022, and 34% in 2023 (see Table 1 ). Table 1 Descriptive Sample Statistics: Older Adults Living in the United States, 2021–2023 (N = 399,696) Measures Mean ± SD # (%) Outcome Physical Health Good health (0 days) 249,318 (62.38%) Moderate health (1–13 days) 88,350 (22.10%) Poor health (14 + days) 62,028 (15.52%) Mental Health Good health (0 days) 288,339 (72.14%) Moderate health (1–13 days) 75,462 (18.88%) Poor health (14 + days) 35,895 (8.98%) Predictors Age groups 60–64 91,223 (22.82%) 65–69 96,022 (24.02%) 70–74 87,342 (21.85%) 75–79 62,182 (15.56%) 80+ 62,927 (15.74%) Covariates Sociodemographic characteristics Gender Male 192,366 (48.13%) Female 207,330 (51.87%) Race/ethnicity White -non-Hispanic 342,281 (85.64%) Black-non-Hispanic 24,997 (6.25%) American Indian/Alaska Native 5,191 (1.30%) Asian-non-Hispanic 5,498 (1.38%) Native Hawaiian/Pacific Islander 776 (0.19%) Others Race/Multiracial 7,784 (1.95%) Hispanic 13,169 (3.29%) Educational attainment Did not graduate High School 17,274 (4.32%) Graduated High School/GED equivalent 97,196 (24.32%) Attended College or Technical School but did not finish? 112,837 (28.23%) Graduated from College or Technical School 172,389 (43.13%) Income Less than $ 35,000 119,386 (29.87%) $ 35,000 to < $ 50,000 64,186 (16.06%) $ 50,000 to < $ 100,000 133,144 (33.31%) $ 100,000 to < $ 200,000 65,279 (16.33%) $ 200,000 or more 17,701 (4.43%) Residential setting Urban 335,146 (83.85%) Rural 64,550 (16.15%) Health insurance coverage Private 102,212 (25.57%) Public 291,954 (73.04%) Uninsured 5,530 (1.38%) Health-related behavioral and clinical characteristics Overweight Yes 118,792 (29.72%) No 280,904 (70.28%) Physical activity or exercise in the last 30 days Yes 107,186 (26.82%) No 292,510 (73.18%) Smoking behavior Current smoker - now smokes every day 30,007 (7.51%) Current smoker - now smoke some days 10,251 (2.56%) Former smoker 145,541 (36.41%) Never smoked 213,897 (53.51%) Functional physical limitations Yes 98,982 (24.76%) No 300,714 (75.24%) Sensory and cognitive impairments Yes 95,926 (24.00%) No 303,770 (76.00%) Cardiometabolic condition Yes 143,227 (35.83%) No 256,469 (64.17%) Chronic inflammatory conditions Yes 231,151 (57.83%) No 168,545 (42.17%) Psychiatric disorders Yes 68,892 (17.24%) No 330,804 (82.76%) Malignant neoplasms (Cancer) Yes 64,549 (16.15%) No 335,147 (83.85%) Overall comorbidity 1.6 ± 1.4 Year 2021 130,297 (32.60%) 2022 132,054 (33.04%) 2023 137,345 (34.36%) ***Insert Table 1 *** Frequentist Generalized Ordered Logistic Regressions Results Table 2 shows the frequentist generalized ordered logistic regression results for both self-reported health outcomes. Table 2 Generalized Ordered Logistic Regression Results: Associations between Age Groups and Self-Reported Health Outcomes Outcome Measures Self-reported physical health (OR, SE) Self-reported mental health (OR, SE) Predictors Age (Ref : 60–64) 65–69 0.81*** (0.01) 0.76*** (0.01) 70–74 0.72*** (0.01) 0.65*** (0.01) 75–79 0.65*** (0.01) 0.58*** (0.01) 80+ 0.53*** (0.01) 0.40*** (0.01) Covariates Sociodemographic characteristics Gender (Ref: Male) Female 1.03*** (0.01) 1.54*** (0.01) Race ( Ref: White -non-Hispanic) Black-non-Hispanic 0.92*** (0.02) 1.05** (0.02) American Indian/Alaska Native 1.05† (0.03) 1.20*** (0.03) Asian-non-Hispanic 0.85*** (0.03) 0.83*** (0.04) Native Hawaiian/Pacific Islander 0.87† (0.08) 1.17† (0.08) Others Race/Multiracial 1.05* (0.02) 1.08** (0.03) Hispanic 1.00 (0.02) 0.97 (0.02) Education ( Ref: Did not graduate High School) Graduate High School 0.97 (0.02) 1.02*** (0.02) Attended College or Technical School 1.05** (0.02) 1.12*** (0.02) Graduated from College or Technical School 1.11*** (0.02) 1.21*** (0.02) Income (Ref: Less than $ 35,000) $ 35,000 to < $ 50,000 0.88*** (0.01) 0.90*** (0.01) $ 50,000 to < $ 100,000 0.85*** (0.01) 0.82*** (0.01) $ 100,000 to < $ 200,000 0.79*** (0.01) 0.72*** (0.01) $ 200,000 or more 0.74*** (0.02) 0.66*** (0.02) Residential setting (Ref: Urban) Rural 0.97** (0.01) 0.87*** (0.01) Having health insurance coverage (Ref: Private) Public 1.15*** (0.01) 0.98† (0.01) Uninsured 1.05† (0.03) 1.16*** (0.03) Health-related behavioral and clinical characteristics Overweight (Ref: No) Yes 0.91*** (0.01) 0.85*** (0.01) Physical activity or exercise in the last 30 days (Ref: No) Yes 0.62*** (0.01) 0.87*** (0.01) Smoking behavior (Ref: Current smoker - now smokes every day) Current smoker - now smoke some days 1.13*** (0.02) 1.14*** (0.03) Former smoker 1.03† (0.01) 0.91*** (0.02) Never smoked 1.01 (0.01) 0.87*** (0.03) Functional physical limitations (Ref: No) 3.61*** (0.01) 1.65*** (0.02) Sensory and cognitive impairments (Ref: No) 1.34*** (0.01) 1.73*** (0.01) Cardiometabolic condition (Ref: No) 0.96*** (0.01) 0.95*** (0.01) Chronic inflammatory conditions (Ref: No) 1.24*** (0.01) 1.13*** (0.02) Psychiatric disorders (Ref: No) 1.23*** (0.01) 4.97*** (0.01) Malignant neoplasms (Cancer) (Ref: No) 0.87*** (0.01) 0.99 (0.01) Overall comorbidity 1.33*** (0.01) 1.09*** (0.01) Year (Ref: 2021) 2022 1.21*** (0.01) 1.06*** (0.01) 2023 1.31*** (0.01) 1.09*** (0.09) Threshold [1] Good | Moderate 2.72*** (0.02) 3.61*** (0.03) Threshold ( 50 ) Moderate | Poor 11.93*** (0.02) 19.16*** (0.03) † p < 0.1; *p < 0.05; **p < 0.01; ***p < 0.001 OR stands for Odds Ratio; SE stands for Standard Errors *** Insert Table 2 *** Self-Reported Physical Health Outcomes Holding all other covariates constant, older age groups had progressively lower odds of reporting worse physical health status compared to the cohort aged 60–64 years. Specifically, adults aged 65–69 had 0.81times the odds (OR = 0.81, p < 0.001), meaning 19% lower odds of reporting worse health compared to the cohort aged 60–64 years; for those aged 70–74, the odds were 0.72 times as high (OR = 0.72, p < 0.001), or 28% lower odds; adults aged 75–79 had 0.65 times the odds (OR = 0.65, p < 0.001), or 35% lower odds; and those aged 80 and older had 0.53 times the odds (OR = 0.53, p < 0.001), or 47% lower odds of reporting worse physical health status compared to adults aged 60–64. The predicted probabilities for each health status category for physical health are presented in Fig. 1 . The probability of reporting “Good” physical health increased from 63% among adults aged 60–64 to 76% among those aged 80 and older, while the probabilities of reporting “Moderate” or “Poor” physical health status decreased with age. ---------------------------------------------- Insert Fig. 1 ---------------------------------------------- Female respondents had slightly higher odds of reporting worse physical health than males, with a 3% increase in odds (OR = 1.03, p < 0.001). Higher income was associated with lower odds of reporting worse physical health. Compared to respondents who did not graduate from high school, those with higher levels of educational attainment had higher odds of reporting worse physical health. Living in a rural area was associated with 3% lower odds of reporting worse physical health compared to living in an urban area (OR = 0.97, p < 0.01). Respondents with public health insurance had 15% higher odds of reporting worse physical health compared to those with private insurance (OR = 1.15, p < 0.001). Being overweight was associated with lower odds of reporting worse physical health (OR = 0.91, p < 0.001). Engaging in physical activity or exercise in the past 30 days was associated with lower odds of reporting worse physical health (OR = 0.62, p < 0.001). Having functional physical limitations showed the strongest association (OR = 3.61, p < 0.001). Sensory and cognitive impairments (OR = 1.34, p < 0.001), chronic inflammatory conditions (OR = 1.24, p < 0.001), psychiatric disorders (OR = 1.23, p < 0.001), and overall comorbidity burden (OR = 1.23, p < 0.001) were all associated with higher odds of worse physical health. In contrast, having cardiometabolic conditions (OR = 0.96, p < 0.001) and malignant neoplasms or cancer (OR = 0.87, p < 0.001) were associated with lower odds of reporting worse physical health. Finally, compared to 2021, older adults surveyed in 2022 had 21% higher odds of reporting worse physical health (OR = 1.21, p < 0.001), and those surveyed in 2023 had 31% higher odds (OR = 1.31, p < 0.001). Self-Reported Mental Health Outcomes Holding all other covariates constant, older age groups had progressively lower odds of reporting worse mental health status compared to adults aged 60–64 years. Specifically, adults aged 65–69 had 0.76 times the odds (OR = 0.76, p < 0.001), meaning 24% lower odds of reporting worse health compared to the cohort aged 60–64 years; for those aged 70–74, the odds were 0.65 times as high (OR = 0.65, p < 0.001), or 35% lower odds; adults aged 75–79 had 0.58 times the odds (OR = 0.58, p < 0.001), or 42% lower odds, and those aged 80 and older had 0.40 times the odds (OR = 0.40, p < 0.001), or 60% lower odds of reporting worse mental health status compared to adults aged 60–64. The predicted probabilities for each health status category for mental health are presented in Fig. 2 . The probability of reporting “Good” mental health increased from 76% among adults aged 60–64 to 89% among those aged 80 and older, while the probabilities of reporting “Moderate” or “Poor” mental health status decreased with age. ---------------------------------------------- Insert Fig. 2 ---------------------------------------------- In addition, being female was associated with 1.54 times the odds, or 54% higher odds, of reporting worse mental health compared to males (OR = 1.54, p < 0.001). Higher income was consistently associated with lower odds of reporting worse mental health. However, compared to those who did not graduate from high school, higher educational attainment was associated with higher odds of reporting worse mental health. Living in a rural area was associated with 13% lower odds of reporting worse mental health compared to living in an urban area (OR = 0.87, p < 0.01). Uninsured respondents had 16% higher odds of reporting worse mental health compared to those with private insurance (OR = 1.16, p < 0.001). Being overweight was associated with lower odds of reporting worse mental health (OR = 0.85, p < 0.001). Engaging in physical activity or exercise in the past 30 days was also associated with lower odds of reporting worse mental health (OR = 0.87, p < 0.001). Compared to daily smokers, current smokers who smoked on some days had higher odds of reporting worse mental health (OR = 1.14, p < 0.001), whereas former smokers (OR = 0.91, p < 0.001) and never smokers (OR = 0.87, p < 0.001) had lower odds of reporting worse mental health. Having functional physical limitations (OR = 1.65, p < 0.001), sensory and cognitive impairments (OR = 1.73, p < 0.001), chronic inflammatory conditions (OR = 1.13, p < 0.001), psychiatric disorders (OR = 4.97, p < 0.001), and overall comorbidity burden (OR = 1.09, p < 0.001) were all associated with higher odds of worse mental health. In contrast, having cardiometabolic conditions (OR = 0.96, p < 0.001) was associated with lower odds of reporting worse mental health. Finally, compared to 2021, older adults surveyed in 2022 had 6% higher odds of reporting worse mental health (OR = 1.06, p < 0.001), and those surveyed in 2023 had 9% higher odds (OR = 1.09, p < 0.001). Bayesian Ordinal Logistic Regression Results As shown in Table 3 , results from the Bayesian ordinal logistic regression models were highly consistent with those obtained using frequentist generalized ordered logistic regression. Specifically, across both physical and mental health status outcomes, older adults were significantly less likely to report poor health compared to younger age groups. This was reflected in consistently negative posterior estimates for older age categories, indicating a protective association with increasing age. For example, adults aged 80 and above had the largest negative coefficients in both models. These findings align with those from the frequentist approach, indicating the robustness of the age-health association across analytic frameworks. Table 3 Bayesian Ordinal Logistic Regression Results: Associations between Age Groups and Self-Reported Health Outcomes Outcome Measures Self-reported physical health (β, CrI) Self-reported mental health (β, CrI) Predictors Age (Ref : 60–64) 65–69 -0.22 [-0.24, -0.20] -0.27 [-0.30, -0.25] 70–74 -0.33 [-0.35, -0.31] -0.43 [-0.45, -0.40] 75–79 -0.43 [-0.45, -0.41] -0.60 [-0.63, -0.58] 80+ -0.64 [-0.67, -0.62] -0.91 [-0.94, -0.88] Covariates Sociodemographic characteristics Gender (Ref: Male) Female 0.03 [ 0.01, 0.04 ] 0.43 [ 0.42, 0.45 ] Race ( Ref: White -non-Hispanic) Black-non-Hispanic -0.09 [-0.12, -0.06] 0.04 [0.01, 0.07] American Indian/Alaska Native 0.05 [-0.01, 0.10] 0.18 [0.12, 0.24] Asian-non-Hispanic -0.16 [-0.22, -0.10] -0.19 [-0.26, -0.12] Native Hawaiian/Pacific Islander -0.14 [-0.31, 0.02] 0.16 [0.00, 0.31] Others Race/Multiracial 0.05 [0.00, 0.10] 0.08 [0.03, 0.13] Hispanic -0.00 [-0.04, 0.04] -0.03 [-0.08, 0.01] Education ( Ref: Did not graduate High School) Graduate High School -0.03 [-0.06, 0.01] 0.02 [-0.02, 0.06] Attended College or Technical School 0.05 [0.01, 0.08] 0.11 [0.08, 0.15] Graduated from College or Technical School 0.10 [0.07, 0.14] 0.19 [0.15, 0.23] Income (Ref: Less than $ 35,000) $ 35,000 to < $ 50,000 -0.12 [-0.14, -0.10] -0.11 [-0.13, -0.09] $ 50,000 to < $ 100,000 -0.17 [-0.18, -0.15] -0.20 [-0.22, -0.18] $ 100,000 to < $ 200,000 -0.23 [-0.25, -0.20] -0.32 [-0.35, -0.30] $ 200,000 or more -0.30 [-0.33, -0.26] -0.42 [-0.46, -0.37] Residential setting (Ref: Urban) Rural -0.03 [-0.05, -0.01] -0.14 [-0.16, -0.12] Having health insurance coverage (Ref: Private) Public 0.14 [0.12, 0.16] -0.02 [-0.04, 0.00] Uninsured 0.05 [-0.01, 0.11] 0.15 [0.09, 0.21] Health-related behavioral and clinical characteristics Overweight (Ref: No) Yes -0.10 [-0.11, -0.08] -0.17 [-0.18, -0.15] Physical activity or exercise in the last 30 days (Ref: No) Yes -0.48 [-0.49, -0.46] -0.14 [-0.16, -0.13] Smoking behavior (Ref: Current smoker - now smokes every day) Current smoker - now smoke some days 0.12 [0.07, 0.16] 0.13 [0.08, 0.18] Former smoker 0.03 [-0.00, 0.05] -0.10 [-0.12, -0.07] Never smoked 0.01 [-0.02, 0.03] -0.14 [-0.17, -0.11] Functional physical limitations (Ref: No) 1.28 [1.27, 1.30] 0.50 [0.48, 0.52] Sensory and cognitive impairments (Ref: No) 0.29 [0.27, 0.31] 0.55 [0.53, 0.56] Cardiometabolic condition (Ref: No) -0.04 [-0.06, -0.02] -0.05 [-0.08, -0.03] Chronic inflammatory conditions (Ref: No) 0.21 [0.19, 0.23] 0.12 [0.10, 0.14] Psychiatric disorders (Ref: No) 0.21 [0.18, 0.23] 1.60 [1.58, 1.62] Malignant neoplasms (Cancer) (Ref: No) -0.14 [-0.16, -0.12] -0.01 [-0.04, 0.01] Overall comorbidity 0.29 [0.28, 0.30] 0.09 [0.08, 0.10] Year (Ref: 2021) 2022 0.19 [0.17, 0.21] 0.06 [0.04, 0.08] 2023 0.27 [0.25, 0.28] 0.09 [0.07, 0.11] Intercept-1: Good | Moderate and Poor 1.00 [0.95, 1.05] 1.28 [1.23, 1.34] Intercept-2: Good and Moderate | Poor 2.48 [2.43, 2.53] 2.95 [2.90,3.00] β stands for posterior mean of the distribution of the regression coefficient; CrI stands for 95% Credible Interval ***Insert Table 3 *** Figures A- 1 and A- 2 further illustrate the posterior distributions and convergence diagnostics for the age group coefficients in the physical and mental health models, respectively. In each figure, the left-hand panels display the posterior distributions of the regression coefficients for age groups (65–69, 70–74, 75–79, 80+), indicating the central tendency and uncertainty around each estimate. The right-hand panels show the corresponding trace plots across four MCMC chains, which demonstrate good chain mixing and stability over iterations. The narrow spread and absence of divergence in the trace plots confirm that the sampling process achieved excellent convergence (with R̂ ≈ 1.00), and that the posterior estimates are both stable and reliable. ---------------------------------------------- Insert Figure A- 1 ---------------------------------------------- ---------------------------------------------- Insert Figure A- 2 ---------------------------------------------- Reported Physical vs. Mental Health Our results suggest that adults aged 60–64 may represent a more vulnerable or transitional stage, exhibiting relatively higher odds of reporting poor physical and mental health, which partially support our H 1 . In contrast, both health outcomes appear to stabilize or even improve with increasing age, suggesting potential age-related resilience. Figure A-3 illustrates that mental health exhibits a steeper age gradient than physical health, with progressively lower odds of poor outcomes observed across successive age groups. In contrast, physical health shows a flatter age gradient, with smaller and less variable changes across age groups. The results also found that self-reported physical and mental health will differ across sociodemographic groups and health-related behaviors and clinical characteristics, which partially support our H 2 and H 3 . Additionally, time trends revealed a decline in perceived health status, with respondents in 2022 and 2023 reporting worse health outcomes compared to those surveyed in 2021. ---------------------------------------------- Insert Figure A-3 ---------------------------------------------- Discussion This study explored patterns of self-reported physical and mental health status among U.S. adults aged 60 and older, using nationally representative data from 2021 to 2023. Contrary to the common assumption and findings that advancing age inevitably leads to poorer health ( 29 ), our findings reveal a more nuanced picture: older adults in higher age brackets consistently reported lower odds of poor physical and mental health compared to the adults aged 60–64. Self-reported health outcomes also varied across sociodemographic groups and across health-related behavioral and clinical characteristics, including functional physical limitations, sensory and cognitive impairments, cardiometabolic conditions, chronic inflammatory conditions, psychiatric disorders, cancer, and overall comorbidity burden. Comparing the two domains reveals a “tale of two age gradients,” physical health showed a gradual and steady pattern of improvement with age, while the magnitude of age associations is consistently greater for mental health than for physical health. These patterns likely reflect both survivorship effects and cohort effects, where healthier individuals are more likely to reach older ages ( 30 ). This contrast suggests that mental health may be more sensitive to transitional challenges in early older adulthood ( 31 ). It may also reflect that adults aged 65 and older demonstrate greater resilience in later life, particularly in maintaining physical health. Survivorship bias may have played a pivotal role in shaping the observed patterns, as individuals who reach advanced old age (80+) are often a healthier and more resilient subset of their birth cohort, which also echo socioemotional selectivity theory ( 17 , 18 ). Consequently, the patterns we found may underestimate the true extent of health decline in the broader population of older adults. In addition, the possibility of cohort effects warrants careful consideration. Adults aged 80 and older represent a distinct generation compared to those in the 60–64 age group, shaped by different historical exposures, socialization processes, and life-course experiences. These generational differences may influence how individuals perceive, interpret, and report both physical and mental health, particularly in the context of self-rated measures. These factors add complexity to how age-related patterns should be interpreted, reminding us that differences across groups may reflect not only biological aging but also survivorship and cohort-specific experiences. Our findings provided actionable insights into the timing of aging-related health interventions. Specifically, across both physical and mental health domains, with adults aged 60–64 as the reference group, we identified a consistent decrease in the odds of reporting worse physical and mental health with increasing age. This pattern remained consistent across all five age groups examined, suggesting that this subgroup represents a critical window for targeted support. Traditional age grouping often excludes adults 60–64 when characterizing the older population, which may mask important health differences with later life, particularly among adults aged 60–64 who often experience significant physical and mental health risks ( 32 ). Adults in their early 60s often face emerging health challenges, for instance, memory and other cognitive problems that do not meet the threshold for dementia were relatively common in community-dwelling adults in this age group ( 33 ). A recent multi-omics study has identified two major periods of biological transition, occurring around ages 44 and 60, each marked by widespread molecular and functional changes. The later transition, occurring around age 60, is particularly relevant to this study, as it coincides with significant shifts in immune regulation, carbohydrate metabolism, and other age-associated pathways ( 34 ). Importantly, mental health challenges also tend to emerge during this transitional period. Interventions targeting this subgroup should focus on screening for and addressing early-onset psychological distress, depression, or anxiety alongside physical health screenings, to promote healthier aging patterns and better support individuals as they navigate this vulnerable period. Beyond age domain, our study confirmed that sociodemographic characteristics, health behaviors, and clinical factors were significant and expected predictors of health patterns for older adults, in line with prior literature ( 9 , 35 , 36 ). Our findings revealed that females were more likely to report poorer physical and mental health. This pattern aligns with previous research indicating that females tend to experience a higher burden of chronic conditions and psychological distress in later life ( 37 , 38 ). Given these findings, gender-sensitive health interventions, particularly those targeting mental health and chronic disease management, are essential to ensuring equitable support for older adults. One promising approach is demonstrated by as Pinazo-Hernandis, Sales ( 39 ), who introduced a group-based integrative reminiscence program led by a psychologist. In this intervention, participants shared and reflected life experiences to reinforce a sense of meaning, identity continuity, and positive emotions, which ultimately reduced loneliness and depressive symptoms among older women during COVID-19 social isolation. Additionally, higher household income was consistently protective for both health outcomes, with a clear gradient indicating lower odds of reporting worse health at each higher income level, aligned with the previous findings ( 40 ). However, inconsistent with the prevailing body of research ( 41 ), a somewhat unexpected finding was that higher educational attainment was associated with higher odds of reporting worse mental health. This counterintuitive result may reflect greater awareness and willingness to report mental health difficulties among highly educated older adults or unmeasured confounders related to stress and psychosocial factors in this group. For example, Belo, Navarro-Pardo ( 42 ) found that suggest that older adults with higher education levels may be more attuned to their mental health status and more likely to report issues such as depression and anxiety. Our findings also highlighted the importance of modifiable health behaviors. Specifically, physical inactivity and smoking were significantly associated with worse self-reported physical and mental health, consistent with prior literature ( 43 , 44 ). Promoting physical activity, especially in early older adulthood, not only improves fitness but also mitigates age-related health decline. As such, behavior-based interventions targeting exercise and smoking cessation remain essential for enhancing health status among older adults. Consistent with prior research ( 45 , 46 ), our findings suggest that conditions affecting functional capacity, cognitive functioning, and psychological well-being, as well as comorbidity were more strongly associated with self-reported physical and mental health than conditions defined primarily by biomedical diagnoses. This highlights the cumulative and multifaceted challenges faced by older adults with complex health needs. Interestingly, cardiometabolic conditions and cancer were associated with lower odds of reporting worse physical health, and cardiometabolic conditions were also associated with lower odds of worse mental health, which may reflect greater healthcare engagement, effective disease management, or survivorship and reporting effects among those living with these conditions. From an coping strategy perspective, these findings support evidence-based strategies such as comprehensive geriatric assessment, functional rehabilitation programs, integrated behavioral health services, and multimorbidity focused care models that prioritize functional status and mental health alongside disease management ( 47 ). In addition, structured chronic disease management programs and survivorship care models for cardiometabolic conditions and cancer may help sustain health perceptions through regular monitoring, coordinated care, and patient self-management support ( 48 ). Therefore, our findings emphasize the need for targeted, multi-approach interventions that recognize the distinct aging patterns of physical and mental health, reflecting our “tale of two age gradients” Such interventions should prioritize adults aged 60–64, adopt gender sensitive approaches, focus on modifiable health behaviors, and integrate functional and mental health assessments with objective measures to guide personalized care, while ensuring equity across gender and socioeconomic groups. Limitation We acknowledge several limitations of our study. First, the pooled cross-sectional analytic design precludes the ability to establish causality or directionality between predictors and health outcomes. Second, a critical consideration is the potential impact of survivorship bias, which has been recognized as a methodological challenge in aging research ( 49 ), particularly in studies that rely on self-reported health data across older age cohorts. Individuals with poorer health are more likely to experience earlier mortality, institutionalization, or survey attrition, leading to their underrepresentation in older age groups. As a result, those who remain in the sample, especially in pooled cross-sectional data, may represent a healthier and more resilient subset of the population, particularly among those aged 65 and older. This bias could partially explain our finding that older adults reported lower odds of poor physical and mental health relative to the 60–64 age group. Third, the years of data we selected (2021–2023) overlap with both acute and recovery phases of the COVID-19 pandemic. Pandemic-related disruptions to healthcare, social isolation, infection risk, and economic stress may have disproportionately affected the health and well-being of older adults, particularly those aged 60–64 who were more likely to remain in the workforce and thus at higher risk of exposure. These contextual factors may have influenced the patterns we identified, and some differences across age groups may reflect pandemic-specific stressors in addition to aging processes. Fourth, as noted, the use of self-reported data for both physical and mental health introduces the potential for recall bias, reporting bias. Fifth, while we examined year effects, these capture population-level shifts rather than true longitudinal trends within individuals. Sixth, due to data limitations, specifically the absence of these measures in all survey years, we were unable to include social engagement variables such as social support and loneliness in our models. Next, adults aged 55 to 59 were not included in the study because this age group is commonly categorized as middle aged. However, excluding this group may have limited our ability to capture additional insights related to transitional aging stages. Finally, we were unable to examine age and health status in greater detail, as the BRFSS data were limited to aggregated categories for both variables. Conclusion We identified that a tale of two age gradients in aging related health, in which both physical and mental health show progressively lower odds of poor outcomes across older age groups, while the strength of age associations were consistently greater for mental health than for physical health. These findings challenge assumptions of uniform, linear deterioration and highlight the need for tailored interventions that address the unique patterns of each health domain particularly the elevated risk observed among adults aged 60 to 64. Interventions initiated in the early 60s, especially those that simultaneously target both physical and mental health risks, may have the greatest impact on preventing long-term decline, enhancing well-being, and reducing future healthcare burdens. Declarations Acknowledgments Not applicable. Declaration of conflicting interest The authors declare that there are no conflicts of interest. Funding statement This study did not receive funding. Ethical approval and informed consent statements The study used identified, publicly available data. Institutional Review Board approval and informed consent were therefore not required. Human Ethics and Consent to Participate declarations Not applicable. Data availability statement The data used in this study are publicly available from the Behavioral Risk Factor Surveillance System (BRFSS) through the Centers for Disease Control and Prevention at https://www.cdc.gov/brfss/index.html Author Contribution JMP conceptualized and designed the study, assisted with statistical analyses, interpreted the results, drafted the manuscript, and critically revised the manuscript for important intellectual content. 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Jt Comm J Qual Patient Saf. 2005;31(5):249–57. Additional Declarations No competing interests reported. Supplementary Files Appendix.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 04 Mar, 2026 Reviewers invited by journal 24 Feb, 2026 Editor assigned by journal 23 Feb, 2026 Editor invited by journal 02 Feb, 2026 Submission checks completed at journal 30 Jan, 2026 First submitted to journal 30 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8688597","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":596580931,"identity":"765f4543-6b80-4cb6-81a8-b6359d24b397","order_by":0,"name":"Jae Man Park","email":"","orcid":"","institution":"The University of Texas Health Science Center at Houston","correspondingAuthor":false,"prefix":"","firstName":"Jae","middleName":"Man","lastName":"Park","suffix":""},{"id":596580932,"identity":"06ea7e8b-622b-4619-882b-df620cf4e82e","order_by":1,"name":"Jialing Zhu","email":"","orcid":"","institution":"Georgetown University","correspondingAuthor":false,"prefix":"","firstName":"Jialing","middleName":"","lastName":"Zhu","suffix":""},{"id":596580933,"identity":"489b5dbf-f950-440b-874d-07a37f9221f9","order_by":2,"name":"Xiao Li","email":"","orcid":"","institution":"University of Nebraska at Kearney","correspondingAuthor":false,"prefix":"","firstName":"Xiao","middleName":"","lastName":"Li","suffix":""},{"id":596580935,"identity":"65eb8971-c933-44e1-ae05-871289d239ef","order_by":3,"name":"Sugy Choi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFUlEQVRIiWNgGAWjYBAC+QYQWXOAgY0dxCogQovBARB5DKiFB8QyIEYLiGBsAiqXSCBWi0T6NQnGhjtyfJJvj0nzGNjlybv3HnzAUHHPrgGHFvkZOWVALc+M2aTz0oBakosNz5xLNmA4U5yMSwvDjZw0oJbDiW3SOWZALcyJG2fkmEkwtiUk43QZXIvkGZCWepAW8x/4taQfk2BsAmqR4AFpOZw4XyLHjAGoxQ6n98+8YbZIOHbYmI0nx9hyjsHxxA08Z4wlEs4kJODSIt+e/vDGh5rDcvLtZwxvvKmoTpzf3mP44UNFgj1OhzHwmEhADWSRANt7AEgARRIbcGphf/wBymIGM+ShSvHYMgpGwSgYBSMMAAC7nVjmJyOUZAAAAABJRU5ErkJggg==","orcid":"","institution":"New York University","correspondingAuthor":true,"prefix":"","firstName":"Sugy","middleName":"","lastName":"Choi","suffix":""}],"badges":[],"createdAt":"2026-01-24 18:23:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8688597/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8688597/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103516456,"identity":"6b51ff9e-3cae-44bb-acaa-d440dcfffd03","added_by":"auto","created_at":"2026-02-26 14:28:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":61255,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePredicated Probabilities for Self-Reported Physical Health by Age Group\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8688597/v1/9651296a1b283f88f39af65b.png"},{"id":103516420,"identity":"2fa533a1-b2bd-4605-9bf3-03e56ea9495b","added_by":"auto","created_at":"2026-02-26 14:28:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":59033,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePredicated Probabilities for Self-Reported Mental Health by Age Group\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8688597/v1/b9343894df0dc716035ff119.png"},{"id":104397613,"identity":"e44f2520-a1aa-4d09-8e81-5c345d688c04","added_by":"auto","created_at":"2026-03-11 11:52:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2460053,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8688597/v1/f9614390-540f-4c9c-b20e-93c917bb98d2.pdf"},{"id":103516193,"identity":"fcccd4b2-82af-4eda-94f6-90aedcd40d42","added_by":"auto","created_at":"2026-02-26 14:27:17","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":543118,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-8688597/v1/8fe340a69df6096883b45aba.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparing physical and mental health across age gradients among older adults during the COVID-19 pandemic","fulltext":[{"header":"Background","content":"\u003cp\u003eLike many countries, the United States (U.S.) is experiencing a remarkable demographic shift toward an aging population, presenting profound challenges as well as important opportunities for reimagining its healthcare system (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). According to the 2023 U.S. Census Bureau projections, the number of Americans aged 60 years and older will rise from roughly 79\u0026nbsp;million in 2023 (about 23.9% of the total U.S. population) to about 98\u0026nbsp;million by 2050, an increase of 24 percent (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). This unprecedented demographic trend raises important concerns, as aging itself is a major risk factor for most health conditions (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), contributing to the high prevalence of chronic illnesses and the widespread decline in physical and mental health observed among older adults.\u003c/p\u003e \u003cp\u003eIt is well documented, and not surprising, that older adults face a high burden of physical and mental health conditions (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Prior research also has illuminated the bidirectional interplay between physical and mental health (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), with evidence showing that changes in one domain can precede and influence subsequent changes in the other, .and examined their key social, behavioral, and clinical determinants (\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, important knowledge gaps remain. First, despite evidence shows that the pandemic significantly worsened health outcomes for this population (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), limited research has examined age specific patterns in physical and mental health during and after the pandemic using large scale population based data. Second, few research has explicitly assessed whether certain age groups within older adulthood represent periods of heightened vulnerability, as opposed to assuming uniform or gradually slowing aging processes. Third, much of the existing literature relies on traditional age brackets (65\u0026ndash;69, 75\u0026thinsp;\u0026minus;\u0026thinsp;74, 75\u0026ndash;79, 85+) (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) that exclude adults aged 60\u0026ndash;64, potentially obscuring heterogeneity within the older adult population. We focus on 2021 to 2023 period rather than 2020\u0026ndash;2022 because 2020 was highly turbulent, whereas by 2021 the pandemic\u0026rsquo;s longer-term aftermath had begun, with disruptions persisting but conditions stabilizing enough to capture more reliable post-pandemic health patterns. Moreover, theoretically, we draw on life-course and gerontological sperspectives, which emphasize that aging is a cumulative process characterized by transitions across later life stages (\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). These perspectives suggest that the early 60s mark the onset of \u0026ldquo;early old age.\u0026rdquo; Although adults aged 60\u0026ndash;64 are often classified as a \u0026ldquo;pre-retirement\u0026rdquo; or \u0026ldquo;late midlife\u0026rdquo; group in public health and demography because of Medicare\u0026rsquo;s age 65 cutoff in the U.S., this categorization overlooks that health decline does not suddenly begin at 65. Instead, the transition is already underway during this earlier period, which may represent a critical window when preventive interventions can have disproportionate long-term effects on maintaining health and delaying morbidity. From a psychosocial perspective, socioemotional selectivity theory (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) further highlights the distinctiveness of this transition. As individuals enter their 60s, perceptions of future time horizons begin to narrow, which heightens the salience of emotionally meaningful goals and relationships. This shift can shape health behaviors, coping strategies, and mental well-being, potentially amplifying vulnerabilities during early old age while also fostering resilience in later old age. Thus, this study focuses on adults aged 60 and older, allowing us to explore health dynamics across key stages of the life course, capturing both the onset of age-related changes and their progression into advanced old age.\u003c/p\u003e\n\u003ch3\u003eStudy Objectives\u003c/h3\u003e\n\u003cp\u003eThis study has three primary objectives to examine the prevalence and patterns of self-reported physical and mental health conditions among U.S. adults aged 60 and older, and to identify the sociodemographic, behavioral, and clinical characteristics associated with these outcomes. By combining a life-course perspective with nationally representative longitudinal data, our work contributes to the literature by: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) providing updated evidence on post-pandemic health patterns among older adults, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) identifying critical transition points in later life that may serve as intervention targets, and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) advancing a more nuanced age categorization framework to capture heterogeneity within older adulthood.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eResearch Questions\u003c/h2\u003e \u003cp\u003eWe address the following research questions:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eHow do prevalence patterns of physical and mental health conditions vary across age groups of U.S. adults aged 60 and older, including ages associated with key late-life transitions, and how have these patterns changed from 2021 to 2023?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eWhich sociodemographic (e.g., gender, race/ethnicity, education), health behavior (e.g., smoking, physical activity), and clinical characteristics (e.g., chronic disease burden) are associated with variations in both self-reported health outcomes?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eHypotheses\u003c/h3\u003e\n\u003cp\u003eGuided by life-course and gerontological perspectives (\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) and informed by socioemotional selectivity theory (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), we build on prior evidence (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) to propose the following hypotheses:\u003c/p\u003e \u003cp\u003eH\u003csub\u003e1\u003c/sub\u003e: Self-reported physical and mental health will show different age-related patterns.\u003c/p\u003e \u003cp\u003eH\u003csub\u003e1a\u003c/sub\u003e (physical health): Self-reported physical health will generally decline with increasing age.\u003c/p\u003e \u003cp\u003eH\u003csub\u003e1b\u003c/sub\u003e (mental health): Self-reported mental health will remain stable or improve slightly with age.\u003c/p\u003e \u003cp\u003eH\u003csub\u003e2\u003c/sub\u003e: Self-reported physical and mental health will differ across sociodemographic groups, with lower socioeconomic status associated with worse outcomes.\u003c/p\u003e \u003cp\u003eH\u003csub\u003e3\u003c/sub\u003e: Negative health-related behaviors and clinical characteristics will be associated with poorer self-reported physical and mental health, while healthy behaviors will have protective but not fully compensatory effects.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData Sources and Study Population\u003c/h2\u003e \u003cp\u003eWe conducted a pooled cross‑sectional study using the 2021\u0026ndash;2023 Behavioral Risk Factor Surveillance System (BRFSS) data, an annual, state‑based, random‑digit‑dial telephone survey coordinated by the Centers for Disease Control and Prevention (CDC). From an initial pool of 1,317,148 (2023: 433,323; 2022: 445,132; 2021:438,693) respondents across all 50 states, the District of Columbia, and U.S. territories. The survey methodology has been well described in the previous studies (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). We restricted the analytic sample to individuals aged 60 years and older (N\u0026thinsp;=\u0026thinsp;401,411). We further excluded respondents with missing data on any of the study\u0026rsquo;s key variables (physical health days, mental health days, sociodemographic and health-related behavioral and clinical factors covariates), yielding a final unweighted sample of 399,696 individuals who were aged 60 years and older from 2021 to 2023. BRFSS employs complex, multistage sampling and provides design weights; all analyses incorporated the recommended weights, strata, and primary sampling units to produce nationally representative estimates.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eVariables\u003c/h3\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eOutcome Measures\u003c/h2\u003e \u003cp\u003eOur outcome variables are: 1) Physical health status in the past 30 days, derived from the question: \u0026ldquo;Now thinking about your physical health, which includes physical illness and injury, for how many days during the past 30 days was your physical health not good?\u0026rdquo;; and 2) Mental health status in the past 30 days, derived from the question: \u0026ldquo;Now thinking about your mental health, which includes stress, depression, and problems with emotions, for how many days during the past 30 days was your mental health not good?\u0026rdquo; Both measures have demonstrated reliability and validity as indicators of self-rated health and health-related quality of life. For example, the CDC has long endorsed these items as part of its Health-Related Quality of Life (HRQOL) surveillance system (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Prior research has shown strong associations between these items and a wide range of clinical outcomes, including chronic disease burden, activity limitations, and mortality (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). These findings provide reassurance that the measures are both interpretable and robust indicators of health status in population-based surveys.\u003c/p\u003e \u003cp\u003eResponses to both questions were categorized into three levels: 0 days (good health), 1\u0026ndash;13 days (moderate health), and 14 or more days (poor health). These cut-points are widely used in literature and recommended by the CDC HRQOL guidelines. In particular, reporting\u0026thinsp;\u0026ge;\u0026thinsp;14 days of poor physical or mental health in the past month is a standard threshold for identifying substantial health-related quality-of-life impairment (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePredictor Measures\u003c/h3\u003e\n\u003cp\u003eOur primary predictor is age group, categorized into five cohorts: 60\u0026ndash;64, 65\u0026ndash;69, 70\u0026ndash;74, 75\u0026ndash;79, and 80 years and older.\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eThis study controlled for two major domains of covariates. The first domain includes sociodemographic characteristics: gender, race/ethnicity, educational attainment, household income, residential setting (urban vs. rural), and health insurance coverage status (public, private, or uninsured). The second domain encompasses health-related behavioral, specifically overweight or obesity status, engagement in physical activity, and current smoking behavior. The third domain addressed disease burdens (clinical factors). Each disease burden variable was coded as 1 when the respondent reported at least one of the sub-conditions within that category and coded as 0 otherwise. The categories included functional physical limitations (difficulty walking or climbing stairs, dressing or bathing, or doing errands alone), sensory and cognitive impairments (difficulty seeing, hearing, or concentrating or remembering), cardiometabolic conditions (cardiometabolic or renal or urologic conditions), chronic inflammatory conditions (musculoskeletal or respiratory conditions), psychiatric disorders (mental health conditions), and malignant neoplasms (cancer). An overall comorbidity indicator was also created to reflect the number of the presence of these disease burdens. These covariates were selected based on extensive evidence linking sociodemographic disadvantage, unhealthy behaviors, and chronic health conditions to poorer physical and mental health outcomes among older adults. Specifically, obesity, physical inactivity, and smoking have been consistently linked to a greater risk of chronic conditions, functional decline, depression, and poorer self-reported health in this population (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analyses\u003c/h2\u003e \u003cp\u003eWe conducted frequentist generalized ordered logistic regression analyses to assess whether the proportional odds assumption was held for both dependent variables. To assess potential multicollinearity among predictor variables, we calculated variance inflation factors (VIFs). All VIF values were below 4.61, with a mean VIF of 1.18, indicating that multicollinearity was not a serious concern.\u003c/p\u003e \u003cp\u003eWe then employed Bayesian ordinal logistic regressions to explore the associations between age groups and the two primary health outcomes, while adjusting for the full set of covariates described earlier, including sociodemographic characteristics and health-related behavioral and clinical factors. Models were fit using four Markov Chain Monte Carl (MCMC) chains, each with 2,000 iterations (1,000 warm-up and 1,000 sampling iterations), yielding 4,000 post-warmup draws per model. Posterior distributions were examined to estimate the direction and magnitude of associations. To evaluate the quality and convergence of the sampling process, we visually inspected trace plots and posterior histograms for selected parameters.\u003c/p\u003e \u003cp\u003eAll analyses were conducted using R version 4.3.2 (R Foundation for Statistical Computing, Vienna, Austria) with the \u0026ldquo;clm\u0026rdquo; and \u0026ldquo;brms\u0026rdquo; package. Statistical significance was evaluated at an alpha level of 0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity Analysis\u003c/h2\u003e \u003cp\u003eWe conducted two sets of sensitivity analyses. First, we introduced an interaction term between age group and gender to examine potential effect modification and identify any gender-specific patterns in the relationship. We found that the interaction between age group and gender reveals differing patterns in self-reported physical and mental health across age and sex (See Appendix). Specifically, older female, compared to those aged 60\u0026ndash;64, were less likely to report poor physical and mental health. While females overall were more likely to report poor health compared to males, the gender gap narrows or even reverses in older age group (See Appendix-Table A-1). Second, to account for potential clustering by state, we estimated a multilevel model in which individual respondents were nested within states. The intra-class correlation coefficients were extremely low: 0.004 for physical health and 0.003 for mental health, indicating minimal variation across states.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSample Characteristics\u003c/h2\u003e \u003cp\u003eAmong the 399,696 respondents of the final analytic sample, 63% reported good physical health (0 poor health days), 22% moderate (1\u0026ndash;13 days), and 16% poor (\u0026ge;\u0026thinsp;14 days). For mental health, 72% reported good, 19% moderate, and 9% poor health. Most respondents were aged 60\u0026ndash;74 years, and 52% were female. The majority identified as non-Hispanic White (86%), followed by Black (7%), Hispanic (3%) and other racial/ethnic groups.\u003c/p\u003e \u003cp\u003eApproximately 71% of respondents had attended or graduated from college or a technical school. Over half (54%) reported an annual household income exceeding \u003cspan\u003e$\u003c/span\u003e50,000. Most lived in urban areas (84%) and had public health insurance coverage (73%). About 70% were classified as overweight, and 73% reported engaging in physical activity or exercise within the past 30 days. Regarding smoking behavior, 54% had never smoked, 36% were former smokers, and 10% were current smokers (daily or occasionally). About 25% of respondents had functional physical limitations, 24% had sensory and cognitive impairments, 36% had cardiometabolic condition, 58% had chronic inflammatory conditions, 17% had psychiatric disorders, and 16% had malignant neoplasms (cancer). On average, respondents had 1.6 comorbid conditions (SD\u0026thinsp;=\u0026thinsp;1.4). The respondents were nearly evenly distributed across study years: 33% in 2021, 33% in 2022, and 34% in 2023 (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Sample Statistics: Older Adults Living in the United States, 2021\u0026ndash;2023 (N\u0026thinsp;=\u0026thinsp;399,696)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeasures\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003cp\u003e# (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical Health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood health (0 days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e249,318 (62.38%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate health (1\u0026ndash;13 days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88,350 (22.10%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor health (14\u0026thinsp;+\u0026thinsp;days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62,028 (15.52%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMental Health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood health (0 days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e288,339 (72.14%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate health (1\u0026ndash;13 days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75,462 (18.88%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor health (14\u0026thinsp;+\u0026thinsp;days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35,895 (8.98%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePredictors\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge groups\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91,223 (22.82%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e65\u0026ndash;69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96,022 (24.02%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70\u0026ndash;74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87,342 (21.85%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75\u0026ndash;79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62,182 (15.56%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e80+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62,927 (15.74%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCovariates\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSociodemographic characteristics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e192,366 (48.13%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e207,330 (51.87%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace/ethnicity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite -non-Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e342,281 (85.64%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack-non-Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24,997 (6.25%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmerican Indian/Alaska Native\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,191 (1.30%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian-non-Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,498 (1.38%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNative Hawaiian/Pacific Islander\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e776 (0.19%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers Race/Multiracial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,784 (1.95%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13,169 (3.29%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational attainment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDid not graduate High School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17,274 (4.32%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGraduated High School/GED equivalent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97,196 (24.32%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttended College or Technical School but did not finish?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e112,837 (28.23%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGraduated from College or Technical School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e172,389 (43.13%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIncome\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than \u003cspan\u003e$\u003c/span\u003e35,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e119,386 (29.87%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e35,000 to \u0026lt; \u003cspan\u003e$\u003c/span\u003e50,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64,186 (16.06%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e50,000 to \u0026lt; \u003cspan\u003e$\u003c/span\u003e100,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e133,144 (33.31%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e100,000 to \u0026lt; \u003cspan\u003e$\u003c/span\u003e200,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65,279 (16.33%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e200,000 or more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17,701 (4.43%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResidential setting\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e335,146 (83.85%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64,550 (16.15%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHealth insurance coverage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102,212 (25.57%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e291,954 (73.04%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUninsured\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,530 (1.38%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHealth-related behavioral and clinical characteristics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOverweight\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e118,792 (29.72%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e280,904 (70.28%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePhysical activity or exercise in the last 30 days\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e107,186 (26.82%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e292,510 (73.18%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking behavior\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoker - now smokes every day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30,007 (7.51%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoker - now smoke some days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10,251 (2.56%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e145,541 (36.41%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever smoked\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e213,897 (53.51%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFunctional physical limitations\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98,982 (24.76%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e300,714 (75.24%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSensory and cognitive impairments\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95,926 (24.00%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e303,770 (76.00%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCardiometabolic condition\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e143,227 (35.83%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e256,469 (64.17%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChronic inflammatory conditions\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e231,151 (57.83%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e168,545 (42.17%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePsychiatric disorders\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68,892 (17.24%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e330,804 (82.76%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMalignant neoplasms (Cancer)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64,549 (16.15%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e335,147 (83.85%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOverall comorbidity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYear\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e130,297 (32.60%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e132,054 (33.04%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e137,345 (34.36%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e***Insert Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e***\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eFrequentist Generalized Ordered Logistic Regressions Results\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the frequentist generalized ordered logistic regression results for both self-reported health outcomes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneralized Ordered Logistic Regression Results: Associations between Age Groups and Self-Reported Health Outcomes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eOutcome Measures\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelf-reported\u003c/p\u003e \u003cp\u003ephysical health\u003c/p\u003e \u003cp\u003e(OR, SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSelf-reported\u003c/p\u003e \u003cp\u003emental health\u003c/p\u003e \u003cp\u003e(OR, SE)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003ePredictors\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (Ref\u003c/b\u003e: 60\u0026ndash;64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e65\u0026ndash;69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.81***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.76***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70\u0026ndash;74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.72***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.65***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75\u0026ndash;79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.65***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.58***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e80+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.53***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.40***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCovariates\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSociodemographic characteristics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e (Ref: Male)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.03***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.54***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace (\u003c/b\u003eRef: White -non-Hispanic)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack-non-Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.92***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.02)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.05**\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.02)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmerican Indian/Alaska Native\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.05\u0026dagger;\u003c/p\u003e \u003cp\u003e(0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.20***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.03)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian-non-Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.85***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.03)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.83***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.04)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNative Hawaiian/Pacific Islander\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.87\u0026dagger;\u003c/p\u003e \u003cp\u003e(0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.17\u0026dagger;\u003c/p\u003e \u003cp\u003e(0.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers Race/Multiracial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.05*\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.02)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.08**\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.03)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003cp\u003e(0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003cp\u003e(0.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation (\u003c/b\u003eRef: Did not graduate High School)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGraduate High School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003cp\u003e(0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.02***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.02)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttended College or Technical School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.05**\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.02)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.12***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.02)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGraduated from College or Technical School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.11***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.02)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.21***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.02)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIncome\u003c/b\u003e (Ref: Less than \u003cspan\u003e$\u003c/span\u003e35,000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e35,000 to \u0026lt; \u003cspan\u003e$\u003c/span\u003e50,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.88***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.90***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e50,000 to \u0026lt; \u003cspan\u003e$\u003c/span\u003e100,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.85***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.82***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e100,000 to \u0026lt; \u003cspan\u003e$\u003c/span\u003e200,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.79***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.72***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e200,000 or more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.74***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.02)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.66***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.02)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResidential setting\u003c/b\u003e (Ref: Urban)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.97**\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.87***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHaving health insurance coverage\u003c/b\u003e (Ref: Private)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.15***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98\u0026dagger;\u003c/p\u003e \u003cp\u003e(0.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUninsured\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.05\u0026dagger;\u003c/p\u003e \u003cp\u003e(0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.16***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.03)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHealth-related behavioral and clinical characteristics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOverweight\u003c/b\u003e (Ref: No)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.91***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.85***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePhysical activity or exercise in the last 30 days\u003c/b\u003e (Ref: No)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.62***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.87***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking behavior\u003c/b\u003e (Ref: Current smoker - now smokes every day)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoker - now smoke some days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.13***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.02)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.14***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.03)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.03\u0026dagger;\u003c/p\u003e \u003cp\u003e(0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.91***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.02)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever smoked\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003cp\u003e(0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.87***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.03)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFunctional physical limitations (Ref: No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e3.61***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.65***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.02)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensory and cognitive impairments (Ref: No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.34***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.73***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiometabolic condition (Ref: No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.96***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.95***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic inflammatory conditions (Ref: No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.24***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.13***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.02)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePsychiatric disorders (Ref: No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.23***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e4.97***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalignant neoplasms (Cancer) (Ref: No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.87***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003cp\u003e(0.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall comorbidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.33***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.09***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYear (Ref: 2021)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.21***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.06***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.31***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.09***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.09)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThreshold [1] Good | Moderate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2.72***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.02)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e3.61***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.03)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThreshold (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e) Moderate | Poor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e11.93***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.02)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e19.16***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.03)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026dagger;\u003c/b\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.1; *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003eOR stands for Odds Ratio; SE stands for Standard Errors\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*** Insert Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e***\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSelf-Reported Physical Health Outcomes\u003c/h2\u003e \u003cp\u003eHolding all other covariates constant, older age groups had progressively lower odds of reporting worse physical health status compared to the cohort aged 60\u0026ndash;64 years. Specifically, adults aged 65\u0026ndash;69 had 0.81times the odds (OR\u0026thinsp;=\u0026thinsp;0.81, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), meaning 19% lower odds of reporting worse health compared to the cohort aged 60\u0026ndash;64 years; for those aged 70\u0026ndash;74, the odds were 0.72 times as high (OR\u0026thinsp;=\u0026thinsp;0.72, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), or 28% lower odds; adults aged 75\u0026ndash;79 had 0.65 times the odds (OR\u0026thinsp;=\u0026thinsp;0.65, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), or 35% lower odds; and those aged 80 and older had 0.53 times the odds (OR\u0026thinsp;=\u0026thinsp;0.53, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), or 47% lower odds of reporting worse physical health status compared to adults aged 60\u0026ndash;64.\u003c/p\u003e \u003cp\u003eThe predicted probabilities for each health status category for physical health are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The probability of reporting \u0026ldquo;Good\u0026rdquo; physical health increased from 63% among adults aged 60\u0026ndash;64 to 76% among those aged 80 and older, while the probabilities of reporting \u0026ldquo;Moderate\u0026rdquo; or \u0026ldquo;Poor\u0026rdquo; physical health status decreased with age.\u003c/p\u003e \u003cp\u003e----------------------------------------------\u003c/p\u003e \u003cp\u003eInsert Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e \u003cp\u003e----------------------------------------------\u003c/p\u003e \u003cp\u003eFemale respondents had slightly higher odds of reporting worse physical health than males, with a 3% increase in odds (OR\u0026thinsp;=\u0026thinsp;1.03, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Higher income was associated with lower odds of reporting worse physical health. Compared to respondents who did not graduate from high school, those with higher levels of educational attainment had higher odds of reporting worse physical health. Living in a rural area was associated with 3% lower odds of reporting worse physical health compared to living in an urban area (OR\u0026thinsp;=\u0026thinsp;0.97, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Respondents with public health insurance had 15% higher odds of reporting worse physical health compared to those with private insurance (OR\u0026thinsp;=\u0026thinsp;1.15, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eBeing overweight was associated with lower odds of reporting worse physical health (OR\u0026thinsp;=\u0026thinsp;0.91, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Engaging in physical activity or exercise in the past 30 days was associated with lower odds of reporting worse physical health (OR\u0026thinsp;=\u0026thinsp;0.62, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eHaving functional physical limitations showed the strongest association (OR\u0026thinsp;=\u0026thinsp;3.61, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Sensory and cognitive impairments (OR\u0026thinsp;=\u0026thinsp;1.34, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), chronic inflammatory conditions (OR\u0026thinsp;=\u0026thinsp;1.24, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), psychiatric disorders (OR\u0026thinsp;=\u0026thinsp;1.23, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and overall comorbidity burden (OR\u0026thinsp;=\u0026thinsp;1.23, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were all associated with higher odds of worse physical health. In contrast, having cardiometabolic conditions (OR\u0026thinsp;=\u0026thinsp;0.96, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and malignant neoplasms or cancer (OR\u0026thinsp;=\u0026thinsp;0.87, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were associated with lower odds of reporting worse physical health.\u003c/p\u003e \u003cp\u003eFinally, compared to 2021, older adults surveyed in 2022 had 21% higher odds of reporting worse physical health (OR\u0026thinsp;=\u0026thinsp;1.21, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and those surveyed in 2023 had 31% higher odds (OR\u0026thinsp;=\u0026thinsp;1.31, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eSelf-Reported Mental Health Outcomes\u003c/h2\u003e \u003cp\u003eHolding all other covariates constant, older age groups had progressively lower odds of reporting worse mental health status compared to adults aged 60\u0026ndash;64 years. Specifically, adults aged 65\u0026ndash;69 had 0.76 times the odds (OR\u0026thinsp;=\u0026thinsp;0.76, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), meaning 24% lower odds of reporting worse health compared to the cohort aged 60\u0026ndash;64 years; for those aged 70\u0026ndash;74, the odds were 0.65 times as high (OR\u0026thinsp;=\u0026thinsp;0.65, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), or 35% lower odds; adults aged 75\u0026ndash;79 had 0.58 times the odds (OR\u0026thinsp;=\u0026thinsp;0.58, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), or 42% lower odds, and those aged 80 and older had 0.40 times the odds (OR\u0026thinsp;=\u0026thinsp;0.40, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), or 60% lower odds of reporting worse mental health status compared to adults aged 60\u0026ndash;64.\u003c/p\u003e \u003cp\u003eThe predicted probabilities for each health status category for mental health are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The probability of reporting \u0026ldquo;Good\u0026rdquo; mental health increased from 76% among adults aged 60\u0026ndash;64 to 89% among those aged 80 and older, while the probabilities of reporting \u0026ldquo;Moderate\u0026rdquo; or \u0026ldquo;Poor\u0026rdquo; mental health status decreased with age.\u003c/p\u003e \u003cp\u003e----------------------------------------------\u003c/p\u003e \u003cp\u003eInsert Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003c/p\u003e \u003cp\u003e----------------------------------------------\u003c/p\u003e \u003cp\u003eIn addition, being female was associated with 1.54 times the odds, or 54% higher odds, of reporting worse mental health compared to males (OR\u0026thinsp;=\u0026thinsp;1.54, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Higher income was consistently associated with lower odds of reporting worse mental health. However, compared to those who did not graduate from high school, higher educational attainment was associated with higher odds of reporting worse mental health. Living in a rural area was associated with 13% lower odds of reporting worse mental health compared to living in an urban area (OR\u0026thinsp;=\u0026thinsp;0.87, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Uninsured respondents had 16% higher odds of reporting worse mental health compared to those with private insurance (OR\u0026thinsp;=\u0026thinsp;1.16, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eBeing overweight was associated with lower odds of reporting worse mental health (OR\u0026thinsp;=\u0026thinsp;0.85, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Engaging in physical activity or exercise in the past 30 days was also associated with lower odds of reporting worse mental health (OR\u0026thinsp;=\u0026thinsp;0.87, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Compared to daily smokers, current smokers who smoked on some days had higher odds of reporting worse mental health (OR\u0026thinsp;=\u0026thinsp;1.14, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas former smokers (OR\u0026thinsp;=\u0026thinsp;0.91, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and never smokers (OR\u0026thinsp;=\u0026thinsp;0.87, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) had lower odds of reporting worse mental health.\u003c/p\u003e \u003cp\u003eHaving functional physical limitations (OR\u0026thinsp;=\u0026thinsp;1.65, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), sensory and cognitive impairments (OR\u0026thinsp;=\u0026thinsp;1.73, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), chronic inflammatory conditions (OR\u0026thinsp;=\u0026thinsp;1.13, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), psychiatric disorders (OR\u0026thinsp;=\u0026thinsp;4.97, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and overall comorbidity burden (OR\u0026thinsp;=\u0026thinsp;1.09, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were all associated with higher odds of worse mental health. In contrast, having cardiometabolic conditions (OR\u0026thinsp;=\u0026thinsp;0.96, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) was associated with lower odds of reporting worse mental health.\u003c/p\u003e \u003cp\u003eFinally, compared to 2021, older adults surveyed in 2022 had 6% higher odds of reporting worse mental health (OR\u0026thinsp;=\u0026thinsp;1.06, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and those surveyed in 2023 had 9% higher odds (OR\u0026thinsp;=\u0026thinsp;1.09, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eBayesian Ordinal Logistic Regression Results\u003c/h2\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, results from the Bayesian ordinal logistic regression models were highly consistent with those obtained using frequentist generalized ordered logistic regression. Specifically, across both physical and mental health status outcomes, older adults were significantly less likely to report poor health compared to younger age groups. This was reflected in consistently negative posterior estimates for older age categories, indicating a protective association with increasing age. For example, adults aged 80 and above had the largest negative coefficients in both models. These findings align with those from the frequentist approach, indicating the robustness of the age-health association across analytic frameworks.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBayesian Ordinal Logistic Regression Results: Associations between Age Groups and Self-Reported Health Outcomes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eOutcome Measures\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelf-reported\u003c/p\u003e \u003cp\u003ephysical health\u003c/p\u003e \u003cp\u003e(β, CrI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSelf-reported\u003c/p\u003e \u003cp\u003emental health\u003c/p\u003e \u003cp\u003e(β, CrI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003ePredictors\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (Ref\u003c/b\u003e: 60\u0026ndash;64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e65\u0026ndash;69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.22\u003c/p\u003e \u003cp\u003e[-0.24, -0.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.27\u003c/p\u003e \u003cp\u003e[-0.30, -0.25]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70\u0026ndash;74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.33\u003c/p\u003e \u003cp\u003e[-0.35, -0.31]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.43\u003c/p\u003e \u003cp\u003e[-0.45, -0.40]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75\u0026ndash;79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.43\u003c/p\u003e \u003cp\u003e[-0.45, -0.41]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.60\u003c/p\u003e \u003cp\u003e[-0.63, -0.58]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e80+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.64\u003c/p\u003e \u003cp\u003e[-0.67, -0.62]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.91\u003c/p\u003e \u003cp\u003e[-0.94, -0.88]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCovariates\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSociodemographic characteristics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e (Ref: Male)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003cp\u003e\u003cb\u003e[\u003c/b\u003e0.01, 0.04\u003cb\u003e]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003cp\u003e\u003cb\u003e[\u003c/b\u003e0.42, 0.45\u003cb\u003e]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace (\u003c/b\u003eRef: White -non-Hispanic)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack-non-Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003cp\u003e[-0.12, -0.06]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003cp\u003e[0.01, 0.07]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmerican Indian/Alaska Native\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003cp\u003e[-0.01, 0.10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003cp\u003e[0.12, 0.24]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian-non-Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.16\u003c/p\u003e \u003cp\u003e[-0.22, -0.10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.19\u003c/p\u003e \u003cp\u003e[-0.26, -0.12]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNative Hawaiian/Pacific Islander\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.14\u003c/p\u003e \u003cp\u003e[-0.31, 0.02]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003cp\u003e[0.00, 0.31]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers Race/Multiracial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003cp\u003e[0.00, 0.10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003cp\u003e[0.03, 0.13]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00\u003c/p\u003e \u003cp\u003e[-0.04, 0.04]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003cp\u003e[-0.08, 0.01]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation (\u003c/b\u003eRef: Did not graduate High School)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGraduate High School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003cp\u003e[-0.06, 0.01]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003cp\u003e[-0.02, 0.06]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttended College or Technical School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003cp\u003e[0.01, 0.08]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003cp\u003e[0.08, 0.15]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGraduated from College or Technical School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003cp\u003e[0.07, 0.14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003cp\u003e[0.15, 0.23]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIncome\u003c/b\u003e (Ref: Less than \u003cspan\u003e$\u003c/span\u003e35,000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e35,000 to \u0026lt; \u003cspan\u003e$\u003c/span\u003e50,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.12\u003c/p\u003e \u003cp\u003e[-0.14, -0.10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003cp\u003e[-0.13, -0.09]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e50,000 to \u0026lt; \u003cspan\u003e$\u003c/span\u003e100,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.17\u003c/p\u003e \u003cp\u003e[-0.18, -0.15]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.20\u003c/p\u003e \u003cp\u003e[-0.22, -0.18]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e100,000 to \u0026lt; \u003cspan\u003e$\u003c/span\u003e200,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.23\u003c/p\u003e \u003cp\u003e[-0.25, -0.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.32\u003c/p\u003e \u003cp\u003e[-0.35, -0.30]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e200,000 or more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.30\u003c/p\u003e \u003cp\u003e[-0.33, -0.26]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.42\u003c/p\u003e \u003cp\u003e[-0.46, -0.37]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResidential setting\u003c/b\u003e (Ref: Urban)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003cp\u003e[-0.05, -0.01]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.14\u003c/p\u003e \u003cp\u003e[-0.16, -0.12]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHaving health insurance coverage\u003c/b\u003e (Ref: Private)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003cp\u003e[0.12, 0.16]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003cp\u003e[-0.04, 0.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUninsured\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003cp\u003e[-0.01, 0.11]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003cp\u003e[0.09, 0.21]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHealth-related behavioral and clinical characteristics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOverweight\u003c/b\u003e (Ref: No)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003cp\u003e[-0.11, -0.08]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.17\u003c/p\u003e \u003cp\u003e[-0.18, -0.15]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePhysical activity or exercise in the last 30 days\u003c/b\u003e (Ref: No)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.48\u003c/p\u003e \u003cp\u003e[-0.49, -0.46]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.14\u003c/p\u003e \u003cp\u003e[-0.16, -0.13]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking behavior\u003c/b\u003e (Ref: Current smoker - now smokes every day)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoker - now smoke some days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003cp\u003e[0.07, 0.16]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003cp\u003e[0.08, 0.18]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003cp\u003e[-0.00, 0.05]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003cp\u003e[-0.12, -0.07]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever smoked\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003cp\u003e[-0.02, 0.03]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.14\u003c/p\u003e \u003cp\u003e[-0.17, -0.11]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFunctional physical limitations\u003c/b\u003e (Ref: No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003cp\u003e[1.27, 1.30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003cp\u003e[0.48, 0.52]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSensory and cognitive impairments\u003c/b\u003e (Ref: No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003cp\u003e[0.27, 0.31]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003cp\u003e[0.53, 0.56]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCardiometabolic condition\u003c/b\u003e (Ref: No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003cp\u003e[-0.06, -0.02]\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003cp\u003e[-0.08, -0.03]\u003c/p\u003e\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChronic inflammatory conditions\u003c/b\u003e (Ref: No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003cp\u003e[0.19, 0.23]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003cp\u003e[0.10, 0.14]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePsychiatric disorders\u003c/b\u003e (Ref: No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003cp\u003e[0.18, 0.23]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.60\u003c/p\u003e \u003cp\u003e[1.58, 1.62]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMalignant neoplasms (Cancer)\u003c/b\u003e (Ref: No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.14\u003c/p\u003e \u003cp\u003e[-0.16, -0.12]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003cp\u003e[-0.04, 0.01]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOverall comorbidity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003cp\u003e[0.28, 0.30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003cp\u003e[0.08, 0.10]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYear (Ref: 2021)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003cp\u003e[0.17, 0.21]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003cp\u003e[0.04, 0.08]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003cp\u003e[0.25, 0.28]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003cp\u003e[0.07, 0.11]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept-1: Good | Moderate and Poor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003cp\u003e[0.95, 1.05]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003cp\u003e[1.23, 1.34]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept-2: Good and Moderate | Poor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.48\u003c/p\u003e \u003cp\u003e[2.43, 2.53]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.95\u003c/p\u003e \u003cp\u003e[2.90,3.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eβ stands for posterior mean of the distribution of the regression coefficient;\u003c/p\u003e \u003cp\u003eCrI stands for 95% Credible Interval\u003c/p\u003e\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e***Insert Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e***\u003c/p\u003e \u003cp\u003eFigures A-\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and A-\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e further illustrate the posterior distributions and convergence diagnostics for the age group coefficients in the physical and mental health models, respectively. In each figure, the left-hand panels display the posterior distributions of the regression coefficients for age groups (65\u0026ndash;69, 70\u0026ndash;74, 75\u0026ndash;79, 80+), indicating the central tendency and uncertainty around each estimate. The right-hand panels show the corresponding trace plots across four MCMC chains, which demonstrate good chain mixing and stability over iterations. The narrow spread and absence of divergence in the trace plots confirm that the sampling process achieved excellent convergence (with R̂ \u0026asymp; 1.00), and that the posterior estimates are both stable and reliable.\u003c/p\u003e \u003cp\u003e----------------------------------------------\u003c/p\u003e \u003cp\u003eInsert Figure A-\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e \u003cp\u003e----------------------------------------------\u003c/p\u003e \u003cp\u003e----------------------------------------------\u003c/p\u003e \u003cp\u003eInsert Figure A-\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003c/p\u003e \u003cp\u003e----------------------------------------------\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eReported Physical vs. Mental Health\u003c/h2\u003e \u003cp\u003eOur results suggest that adults aged 60\u0026ndash;64 may represent a more vulnerable or transitional stage, exhibiting relatively higher odds of reporting poor physical and mental health, which partially support our H\u003csub\u003e1\u003c/sub\u003e. In contrast, both health outcomes appear to stabilize or even improve with increasing age, suggesting potential age-related resilience. Figure A-3 illustrates that mental health exhibits a steeper age gradient than physical health, with progressively lower odds of poor outcomes observed across successive age groups. In contrast, physical health shows a flatter age gradient, with smaller and less variable changes across age groups. The results also found that self-reported physical and mental health will differ across sociodemographic groups and health-related behaviors and clinical characteristics, which partially support our H\u003csub\u003e2\u003c/sub\u003e and H\u003csub\u003e3\u003c/sub\u003e. Additionally, time trends revealed a decline in perceived health status, with respondents in 2022 and 2023 reporting worse health outcomes compared to those surveyed in 2021.\u003c/p\u003e \u003cp\u003e----------------------------------------------\u003c/p\u003e \u003cp\u003eInsert Figure A-3\u003c/p\u003e \u003cp\u003e----------------------------------------------\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study explored patterns of self-reported physical and mental health status among U.S. adults aged 60 and older, using nationally representative data from 2021 to 2023. Contrary to the common assumption and findings that advancing age inevitably leads to poorer health (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e), our findings reveal a more nuanced picture: older adults in higher age brackets consistently reported lower odds of poor physical and mental health compared to the adults aged 60\u0026ndash;64. Self-reported health outcomes also varied across sociodemographic groups and across health-related behavioral and clinical characteristics, including functional physical limitations, sensory and cognitive impairments, cardiometabolic conditions, chronic inflammatory conditions, psychiatric disorders, cancer, and overall comorbidity burden.\u003c/p\u003e \u003cp\u003eComparing the two domains reveals a \u0026ldquo;tale of two age gradients,\u0026rdquo; physical health showed a gradual and steady pattern of improvement with age, while the magnitude of age associations is consistently greater for mental health than for physical health. These patterns likely reflect both survivorship effects and cohort effects, where healthier individuals are more likely to reach older ages (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). This contrast suggests that mental health may be more sensitive to transitional challenges in early older adulthood (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). It may also reflect that adults aged 65 and older demonstrate greater resilience in later life, particularly in maintaining physical health. Survivorship bias may have played a pivotal role in shaping the observed patterns, as individuals who reach advanced old age (80+) are often a healthier and more resilient subset of their birth cohort, which also echo socioemotional selectivity theory (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Consequently, the patterns we found may underestimate the true extent of health decline in the broader population of older adults. In addition, the possibility of cohort effects warrants careful consideration. Adults aged 80 and older represent a distinct generation compared to those in the 60\u0026ndash;64 age group, shaped by different historical exposures, socialization processes, and life-course experiences. These generational differences may influence how individuals perceive, interpret, and report both physical and mental health, particularly in the context of self-rated measures. These factors add complexity to how age-related patterns should be interpreted, reminding us that differences across groups may reflect not only biological aging but also survivorship and cohort-specific experiences.\u003c/p\u003e \u003cp\u003eOur findings provided actionable insights into the timing of aging-related health interventions. Specifically, across both physical and mental health domains, with adults aged 60\u0026ndash;64 as the reference group, we identified a consistent decrease in the odds of reporting worse physical and mental health with increasing age. This pattern remained consistent across all five age groups examined, suggesting that this subgroup represents a critical window for targeted support. Traditional age grouping often excludes adults 60\u0026ndash;64 when characterizing the older population, which may mask important health differences with later life, particularly among adults aged 60\u0026ndash;64 who often experience significant physical and mental health risks (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdults in their early 60s often face emerging health challenges, for instance, memory and other cognitive problems that do not meet the threshold for dementia were relatively common in community-dwelling adults in this age group (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). A recent multi-omics study has identified two major periods of biological transition, occurring around ages 44 and 60, each marked by widespread molecular and functional changes. The later transition, occurring around age 60, is particularly relevant to this study, as it coincides with significant shifts in immune regulation, carbohydrate metabolism, and other age-associated pathways (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eImportantly, mental health challenges also tend to emerge during this transitional period. Interventions targeting this subgroup should focus on screening for and addressing early-onset psychological distress, depression, or anxiety alongside physical health screenings, to promote healthier aging patterns and better support individuals as they navigate this vulnerable period.\u003c/p\u003e \u003cp\u003eBeyond age domain, our study confirmed that sociodemographic characteristics, health behaviors, and clinical factors were significant and expected predictors of health patterns for older adults, in line with prior literature (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Our findings revealed that females were more likely to report poorer physical and mental health. This pattern aligns with previous research indicating that females tend to experience a higher burden of chronic conditions and psychological distress in later life (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Given these findings, gender-sensitive health interventions, particularly those targeting mental health and chronic disease management, are essential to ensuring equitable support for older adults. One promising approach is demonstrated by as Pinazo-Hernandis, Sales (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e), who introduced a group-based integrative reminiscence program led by a psychologist. In this intervention, participants shared and reflected life experiences to reinforce a sense of meaning, identity continuity, and positive emotions, which ultimately reduced loneliness and depressive symptoms among older women during COVID-19 social isolation.\u003c/p\u003e \u003cp\u003eAdditionally, higher household income was consistently protective for both health outcomes, with a clear gradient indicating lower odds of reporting worse health at each higher income level, aligned with the previous findings (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). However, inconsistent with the prevailing body of research (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e), a somewhat unexpected finding was that higher educational attainment was associated with higher odds of reporting worse mental health. This counterintuitive result may reflect greater awareness and willingness to report mental health difficulties among highly educated older adults or unmeasured confounders related to stress and psychosocial factors in this group. For example, Belo, Navarro-Pardo (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e) found that suggest that older adults with higher education levels may be more attuned to their mental health status and more likely to report issues such as depression and anxiety.\u003c/p\u003e \u003cp\u003eOur findings also highlighted the importance of modifiable health behaviors. Specifically, physical inactivity and smoking were significantly associated with worse self-reported physical and mental health, consistent with prior literature (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). Promoting physical activity, especially in early older adulthood, not only improves fitness but also mitigates age-related health decline. As such, behavior-based interventions targeting exercise and smoking cessation remain essential for enhancing health status among older adults.\u003c/p\u003e \u003cp\u003eConsistent with prior research (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e), our findings suggest that conditions affecting functional capacity, cognitive functioning, and psychological well-being, as well as comorbidity were more strongly associated with self-reported physical and mental health than conditions defined primarily by biomedical diagnoses. This highlights the cumulative and multifaceted challenges faced by older adults with complex health needs. Interestingly, cardiometabolic conditions and cancer were associated with lower odds of reporting worse physical health, and cardiometabolic conditions were also associated with lower odds of worse mental health, which may reflect greater healthcare engagement, effective disease management, or survivorship and reporting effects among those living with these conditions. From an coping strategy perspective, these findings support evidence-based strategies such as comprehensive geriatric assessment, functional rehabilitation programs, integrated behavioral health services, and multimorbidity focused care models that prioritize functional status and mental health alongside disease management (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). In addition, structured chronic disease management programs and survivorship care models for cardiometabolic conditions and cancer may help sustain health perceptions through regular monitoring, coordinated care, and patient self-management support (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTherefore, our findings emphasize the need for targeted, multi-approach interventions that recognize the distinct aging patterns of physical and mental health, reflecting our \u0026ldquo;tale of two age gradients\u0026rdquo; Such interventions should prioritize adults aged 60\u0026ndash;64, adopt gender sensitive approaches, focus on modifiable health behaviors, and integrate functional and mental health assessments with objective measures to guide personalized care, while ensuring equity across gender and socioeconomic groups.\u003c/p\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eLimitation\u003c/h2\u003e \u003cp\u003eWe acknowledge several limitations of our study. First, the pooled cross-sectional analytic design precludes the ability to establish causality or directionality between predictors and health outcomes. Second, a critical consideration is the potential impact of survivorship bias, which has been recognized as a methodological challenge in aging research (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e), particularly in studies that rely on self-reported health data across older age cohorts. Individuals with poorer health are more likely to experience earlier mortality, institutionalization, or survey attrition, leading to their underrepresentation in older age groups. As a result, those who remain in the sample, especially in pooled cross-sectional data, may represent a healthier and more resilient subset of the population, particularly among those aged 65 and older. This bias could partially explain our finding that older adults reported lower odds of poor physical and mental health relative to the 60\u0026ndash;64 age group. Third, the years of data we selected (2021\u0026ndash;2023) overlap with both acute and recovery phases of the COVID-19 pandemic. Pandemic-related disruptions to healthcare, social isolation, infection risk, and economic stress may have disproportionately affected the health and well-being of older adults, particularly those aged 60\u0026ndash;64 who were more likely to remain in the workforce and thus at higher risk of exposure. These contextual factors may have influenced the patterns we identified, and some differences across age groups may reflect pandemic-specific stressors in addition to aging processes. Fourth, as noted, the use of self-reported data for both physical and mental health introduces the potential for recall bias, reporting bias. Fifth, while we examined year effects, these capture population-level shifts rather than true longitudinal trends within individuals. Sixth, due to data limitations, specifically the absence of these measures in all survey years, we were unable to include social engagement variables such as social support and loneliness in our models. Next, adults aged 55 to 59 were not included in the study because this age group is commonly categorized as middle aged. However, excluding this group may have limited our ability to capture additional insights related to transitional aging stages. Finally, we were unable to examine age and health status in greater detail, as the BRFSS data were limited to aggregated categories for both variables.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe identified that a tale of two age gradients in aging related health, in which both physical and mental health show progressively lower odds of poor outcomes across older age groups, while the strength of age associations were consistently greater for mental health than for physical health. These findings challenge assumptions of uniform, linear deterioration and highlight the need for tailored interventions that address the unique patterns of each health domain particularly the elevated risk observed among adults aged 60 to 64. Interventions initiated in the early 60s, especially those that simultaneously target both physical and mental health risks, may have the greatest impact on preventing long-term decline, enhancing well-being, and reducing future healthcare burdens.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of conflicting interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not receive funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval and informed consent statements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study used identified, publicly available data. Institutional Review Board approval and informed consent were therefore not required.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this study are publicly available from the Behavioral Risk Factor Surveillance System (BRFSS) through the Centers for Disease Control and Prevention at\u003c/p\u003e\n\u003cp\u003ehttps://www.cdc.gov/brfss/index.html\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJMP conceptualized and designed the study, assisted with statistical analyses, interpreted the results, drafted the manuscript, and critically revised the manuscript for important intellectual content. JZ contributed to data curation, performed statistical analyses, interpreted findings, and participated in drafting and revising the manuscript. XL conceptualized and designed the study, assisted in the interpretation of the data, and critically revised the manuscript for methodological accuracy and clarity. SC conceptualized and supervised the overall study, guided the statistical analysis and interpretation of data, critically revised the manuscript, and oversaw the project. All authors reviewed, provided substantial intellectual input, and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJones CH, Dolsten M. Healthcare on the brink: Navigating the challenges of an aging society in the United States. npj Aging. 2024;10(1):22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUSCB. 2023 national population projections datasets. 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BMC Public Health. 2022;22(1):1560.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoswell EK, Brown MJ, Donelle L, Yell N, Farrell T, Hung P, et al. Geographic disparities in unpaid caregiving. J Rural Health. 2025;41(2):e70039.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoriarty DG, Zack MM, Kobau R. The Centers for Disease Control and Prevention's Healthy Days Measures\u0026ndash;Population tracking of perceived physical and mental health over time. Health Qual Life Outcomes. 2003;1(1):37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZack MM, Moriarty DG, Stroup DF, Ford ES, Mokdad AH. Worsening trends in adult health-related quality of life and self-rated health\u0026mdash;United States, 1993\u0026ndash;2001. Public Health Rep. 2004;119(5):493\u0026ndash;505.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaylor VR. Measuring healthy days: Population assessment of health-related quality of life. In: Prevention CfDCa, editor. 2000. pp. 1\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndresen EM, Catlin TK, Wyrwich KW, Jackson-Thompson J. Retest reliability of surveillance questions on health related quality of life. J Epidemiol Community Health. 2003;57(5):339\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJylh\u0026auml; M. What is self-rated health and why does it predict mortality? Towards a unified conceptual model. Soc Sci Med. 2009;69(3):307\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFan Y, He D. Self-rated health, socioeconomic status and all-cause mortality in Chinese middle-aged and elderly adults. Sci Rep. 2022;12(1):9309.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArnhold T, Szenkur\u0026ouml;k V, Weber D. Mapping inequalities in the health of older adults around the world: Heterogeneities in cognitive and physical functioning. 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J Epidemioloy Community Health. 2023;77(11):744\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar R, Dear KB, Christensen H, Ilschner S, Jorm AF, Meslin C, et al. Prevalence of mild cognitive impairment in 60-to 64-year-old community-dwelling individuals: The personality and total health through life 60\u0026thinsp;+\u0026thinsp;Study. Dement Geriatr Cogn Disord. 2005;19(2\u0026ndash;3):67\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShen X, Wang C, Zhou X, Zhou W, Hornburg D, Wu S, et al. Nonlinear dynamics of multi-omics profiles during human aging. Nat Aging. 2024;4:1619\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrakash K, Stenholm S, Kyr\u0026ouml;nlahti S, Kulmala J, Tanjung K, Nosraty L, et al. Sociodemographic and work-related determinants of self-rated health trajectories: A collaborative meta-analysis of cohort studies from Europe and the US. Sci Rep. 2025;15(1):5394.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi X, Zhu J, Park JM, Mitchell J. Unravelling the determinants of life expectancy during and after the COVID-19 pandemic: A qualitative comparative analysis. J Glob Health. 2025;15:04126.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrfila F, Ferrer M, Lamarca R, Tebe C, Domingo-Salvany A, Alonso J. Gender differences in health-related quality of life among the elderly: The role of objective functional capacity and chronic conditions. Soc Sci Med. 2006;63(9):2367\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOtten D, Heller A, Schmidt P, Beutel ME, Br\u0026auml;hler E. Gender differences in the prevalence of mental distress in East and West Germany over time: A hierarchical age-period-cohort analysis, 2006\u0026ndash;2021. Soc Psychiatry Psychiatr Epidemiol. 2024;59(2):315\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePinazo-Hernandis S, Sales A, Martinez D. Older women\u0026rsquo;s loneliness and depression decreased by a reminiscence program in times of COVID-19. Front Psychol. 2022;13:802925.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThornton M, Bowers K. Poverty in older adulthood: A health and social crisis. Online J Issues Nurs. 2024;29(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNiemeyer H, Bieda A, Michalak J, Schneider S, Margraf J. Education and mental health: Do psychosocial resources matter? SSM Popul Health. 2019;7:100392.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBelo P, Navarro-Pardo E, Pocinho R, Carrana P, Margarido C. Relationship between mental health and the education level in elderly people: mediation of leisure attitude. 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The impacts of multimorbidity trajectories and patterns on functional limitations over time in middle-aged and older adults. Arch Gerontol Geriatr. 2025;137:105919.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeakamela KP, Mashaba RG, Ntimana CB, Kabudula CW, Sodi T. Multimorbidity management: A scoping review o finterventions and health outcomes. Int J Environ Res Public Health. 2025;22(5):770.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbbas S, Fadem S, Dhaliwal K, Hemler J, Hudson S, Ferrante J, et al. Advancing shared care by improving communication and coordination of cancer patients with cardiometabolic comorbidities. Annals Family Med. 2025;23:7703. ((Supplement 1)).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuller LA, Turner JA. Sample selection bias due to differential mortality: A supplementary measure of old-age poverty. 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Jt Comm J Qual Patient Saf. 2005;31(5):249\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Population Aging, COVID-19, Mental Health, Physical Function, Health Disparities","lastPublishedDoi":"10.21203/rs.3.rs-8688597/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8688597/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eWhile aging brings inevitable health decline, few studies have examined how social and behavioral factors shape physical and mental health trajectories in older adulthood. This study examined the prevalence and patterns of self-reported physical and mental health patterns among U.S. adults aged 60 and older, focusing on age groups differences optimal intervention timing, and modifiable predictors.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eUsing the Behavioral Risk Factor Surveillance System data from 2021\u0026ndash;2023, we applied frequentist generalized ordered logistic and Bayesian ordinal logistic regressions to assess robustness across inferential frameworks.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOlder age was associated with progressively lower odds of reporting poor physical and mental health, with the 60\u0026ndash;64 age group consistently reporting worse outcomes than older cohorts (65\u0026ndash;69, 70\u0026ndash;74, 75\u0026ndash;79, and \u0026ge;\u0026thinsp;80). While both domains followed similar age-related patterns, the magnitude of age associations was greater for mental health.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study highlights \u0026ldquo;a tale of two age gradients\u0026rdquo; in older adulthood: ages 60\u0026ndash;64 mark a critical intervention window, with elevated risks for both poor physical and mental health compared with older cohorts.\u003c/p\u003e","manuscriptTitle":"Comparing physical and mental health across age gradients among older adults during the COVID-19 pandemic","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-26 14:10:14","doi":"10.21203/rs.3.rs-8688597/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"146619582887794851101896520190461677928","date":"2026-03-04T15:18:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-24T14:12:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-23T08:58:16+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-02T08:05:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-30T22:08:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Geriatrics","date":"2026-01-30T22:02:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7b8aa384-aed7-4e5d-8b55-c19d01904103","owner":[],"postedDate":"February 26th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-26T14:10:14+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-26 14:10:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8688597","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8688597","identity":"rs-8688597","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}