A person-centered approach to characterizing longitudinal ambulatory impairment in Parkinson's disease

A person-centered approach to characterizing longitudinal ambulatory impairment in Parkinson's disease | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A person-centered approach to characterizing longitudinal ambulatory impairment in Parkinson's disease Farren Briggs, Douglas Gunzler, Steven Gunzler This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3778288/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Ambulatory impairment in Parkinson’s disease (PD) is common and complex, and poorly understood from the perspectives of those with PD. Gaining insights to the anticipated perceived trajectories and their drivers, will further facilitate patient-centered care. Latent class growth analysis, a person-centered mixture modelling approach, was applied to 16,863 people with PD stratified by early (N = 8612; 10 years) disease to discern clusters with similar longitudinal patterns of self-reported walking difficulty, measured by EuroQoL 5D-5L that is validated for use in PD. There were four clusters in early and mid-disease strata, with a fifth identified in later disease. Trajectories ranged from none to moderate mobility problems, with small clusters with severe problems. The percentage of subjects with moderate (early = 17.5%, mid = 26.4%, later = 32.5%) and severe (early = 3.8%, mid = 7.4%, later = 15.4%) mobility problems at baseline increased across disease duration groups. The trajectories tended to be stable with variability in moderate and severe groups. Across strata, clusters with moderate to severe problems were associated with more severe impairment, depression, anxiety, arthritis, higher BMI, lower income, and lower education, but no consistent race or gender differences. The findings reveal distinct longitudinal ambulatory patterns in PD based on a person-centered approach. Health sciences/Neurology/Neurological disorders/Movement disorders/Parkinsons disease Health sciences/Medical research/Epidemiology mobility impairment Parkinson’s disease latent class growth analysis patient reported outcome trajectories Figures Figure 1 Figure 2 Figure 3 Introduction Difficulty in walking is a common and visible impairment experienced by people with Parkinson's disease (PWP), and it is driven by a diverse collection of symptoms (e.g. start hesitation, shuffling gait, freezing, festination, propulsion, and difficulty in turning) 1 . It is also a prominent driver of lower quality of life (QoL) in Parkinson’s disease (PD) and it is associated with poor health outcomes, increased depressive symptoms, more frequent falls, loss of independence, decreased social participation, and greater interruptions of daily activities 1–5 . Unfortunately, there are substantial fluctuations in the severity of the underlying symptoms and in the accrual of neurological deficits, thus, ambulatory impairment appears heterogeneous and unpredictable in PWP 6 . This poses a significant challenge for successful patient-centered care, including tailoring clinical and rehabilitation care, prognostication, and developing long-term self-management strategies, as well as a challenge for defining robust endpoints in clinical and observational research. Patient-reported outcome (PRO) measures capture the lived experiences of patients, including meaningful and nuanced changes in health-related QoL, and over time they inherently reflect patients’ shifting priorities for daily living. There are several PD-specific PROs for mobility (i.e., MDS-UPDRS Part II); however, these instruments do not readily map to generic PROs which impedes comparisons with the general population and subpopulations where ambulatory impairment is also seemingly unpredictable (i.e., persons with multiple sclerosis). Also, it has been noted that the perceptions (and/or key health priorities) of PWP may evolve with their disease course 7, 8 ; e.g., in a qualitative study of functional mobility, the perceptions of people in the early-stages of PD were more aligned with neurologists while those in more advanced-stages were closer to physiotherapists 9 . Another important factor is the underlying heterogeneity in ambulatory impairment in PWP. Prior studies have only described relationships for the average change in measures of gait and walking speed. No study has yet described the likely intrinsic subgroups of PWP who exhibit similar longitudinal ambulatory trajectories over time, based in part on the combination and severity of underlying symptoms that evolve as PD progresses. Fortunately, latent class growth analysis (LCGA) is a data-driven approach that can identify these naturally occurring subgroups with distinct growth trajectories within a larger sample and it has been successfully used to discern distinct subgroups of PWP with similar longitudinal pain (measured by a generic PRO measure) trajectories 10–12 . Thus, several knowledge gaps may be addressed by leveraging LCGA to longitudinally model ambulatory impairment in PWP using a generic health-related QoL PRO, with considerations for disease duration. The objective of the current retrospective cohort study is to describe longitudinal ambulatory impairment trajectories in PWP leveraging self-reported information as captured by the European Quality of Life (EuroQoL) Questionnaire 5 level version (EQ-5D-5L) is a generic health-related QoL instrument that has construct validity in diverse populations and in PWP 13–16 . We hypothesize EQ-5D-5L mobility component will vary as a function of disease duration and that sociodemographic and clinical factors will be associated with assignment to distinct trajectories at each disease duration stage. We hope that by defining subgroups of PWP with shared perceived ambulatory impairment patterns, there is the potential to advance clinical/observational research and patient-centered care that can be readily compared to other populations. Methods Research ethics This secondary data analysis of de-identified data was deemed as non-human subject research by the institutional review boards at Case Western Reserve University and The MetroHealth System, Cleveland, Ohio. Study design Fox Insight ( https://foxinsight.michaeljfox.org ) is a virtual and ongoing longitudinal study of people aged 18 years or older, with and without PD, led by the Michael J. Fox Foundation 17 . It aims to facilitate discovery, validation, and reproducibility in PD PRO research, and includes several PROs, routine health and medical assessments, environmental exposure and healthcare preference questionnaires, with the option to provide biospecimens for genotyping 17 . The longitudinal data used were obtained from Fox Insight Data Exploration Network (Fox DEN) on 10/14/2021 and leveraged to construct a retrospective cohort of PWP who had completed the EQ-5D-5L at least once (for up-to-date information visit https://foxinsight-info.michaeljfox.org/insight/explore/insight.jsp ) 12 . Outcome EQ-5D-5L measures perspectives on five domains, including self-care, usual activities, pain/discomfort, anxiety/depression, and walking difficulty 18 . The outcome of interest was the longitudinal data for mobility component of the EQ-5D-5L, first deployed in 2017 and available under “Your Physical Experiences” in Fox DEN 17 . The mobility PRO is ordinal, measured on a 5-level Likert scale (0 = I have no problems in walking about, 1 = slight problems, 2 = moderate problems, 3 = severe problems, 4 = unable to walk about). There were 16,863 PD participants with EQ-5D-5L mobility data at baseline and ≥ 1 additional follow-up survey, and who had an indicator value for number of years with PD (early: 10 years [2070 PWP]). EQ-5D-5L may be completed at 6-month intervals; included subjects completed an average of 4.1 (SD = 2.1) surveys. There were 11,838 (70%), 8,557 (51%), 6,029 (36%), 4,257 (25%), 2,736 (16%), 1475 (9%) and 554 (3%) PWP with ≥ 3, ≥4, ≥ 5, ≥6, ≥ 7, ≥8, and ≥ 9 entries, respectively. Note that the decrease in sample size over time is not necessarily a matter of loss-to-follow-up (left censoring), but also right censoring, reflecting the ongoing recruitment of PWP. Only ≤ 9 observations per PWP were used for the stratified models for early and mid-disease duration, while ≤ 8 observations per PWP were used for the models for later disease duration to minimize data sparseness considering the total number of subjects endorsing each of the five mobility PRO categories at each follow-up time point. Consecutive responses for this PRO have high but incomplete concordance which mitigates concerns of redundancy and multicollinearity between in any two successive observations (Pearson correlation coefficient [PCC] = 0.59–0.82; similar patterns observed across disease duration strata early: PCC = 0.56–0.81, mid: PCC = 0.58–0.82, later: PCC = 0.53–0.80). It was important to stratify by disease duration as the accrual of ambulatory impairment in PWP is a function of disease function, and there are likely different rates at which impairment is accrued for a given length of disease, and lastly, perceptions of one’s disability may evolve with time 7–9 . Baseline variables As we have previously described, the baseline sociodemographic variables incorporated included age, gender, race/ethnicity (non-white vs. white), education (1 = Less than high school degree, 2 = High school degree, 3 = Some college, 4 = Associate’s degree, 5 = Bachelor’s degree, 6 = Master’s degree, 7 = Doctoral degree), employment (retired, full-time, part-time, or unemployed; retired was the reference category for employment dummy variables in the multivariable regression models), income (1= $ 100,000), and body mass index (BMI) 12 . Self-reported clinical factors were included based on their hypothesized relationships with ambulatory impairment in PWP, and included binary indicators about current depression, anxiety, arthritis, and back pain duration and limitations (from “Your Current Health”); poor balance (from “Brief Motor Screen”), experiences of OFF episodes (from “Impact of OFF Episodes”), work in the past week (from “Work-related Activity”), trouble getting out of bed, a car seat, or a deep chair, walking and balance problems and freezing up (from “Your Movement Experiences”) and walking activities, light, moderate and strenuous sport and recreational activities and muscle strength (from “Your Physical Activities”) 17 . Military veteran status, actively taking prescription PD medication, and EQ-5D-5L pain component (ordinal items: 0 = no pain, 4 = extreme pain) were also included. Statistical Analyses Descriptive statistics was completed for the entire sample and by disease duration strata. Kruskal-Wallis rank sum test and chi-square test assessed statistical significance in the comparison of continuous and categorical distributions across disease duration strata. LCGA allows for identifying meaningful clusters (or subgroups) within a larger study sample to examine longitudinal patterns over time 10–12 . We ( 1 ) performed LCGA to identify clusters of PWP based on longitudinal mobility trajectories (see path diagram in Fig. 1 ), and ( 2 ) evaluated measures that may associate with cluster membership. The clusters, also termed latent classes , identified by LCGA are not known (observed) a priori but are determined empirically 10 . A trajectory shape for each class is estimated (i.e. intercept and slopes), and individuals can be assigned to the latent class of the highest probability of membership, which can be graphically displayed to facilitate interpretation 19 . A common approach for a LCGA of an ordered-categorical outcomes is to assume that a normally distributed latent variable exists from which each level of the observed categories is derived when the latent variable exceeds specific thresholds 10 . For analytical purposes, we inferred a latent variable mobility* with four thresholds based on the observed data of five categories. That is, for each PWP at each time point, the mobility PRO = 0 if the value of mobility* is less than the first threshold ( \({\tau _1}\) ), the PRO = 1 if the value of mobility* is greater than the first threshold ( \({\tau _1}\) ) but less than the second threshold ( \({\tau _2}\) ), and so forth for increasing PRO responses (see Supplementary Methods for additional details). In the graphical displays, the threshold values for mobility* (which did not meaningfully vary over time) were denoted as \({\tau _1}\) , \({\tau _2}\) , \({\tau _3}\) and \({\tau _4}\) . Multivariable, multinomial logistic regression was used to determine if sociodemographic attributes influenced most likely cluster membership across strata (i.e. sociodemographic factors precede (lead to) cluster membership in a causal diagram). Odds ratios, 95% confidence intervals (CI) and p-values using z-tests for these multivariable models were reported. For other variables (military veteran status and clinical factors: depression, arthritis, anxiety, balance problems, pain, back pain duration and limitations, work in the past week, trouble getting out of bed, a car seat or a deep chair, freezing up, walking activities, light moderate and strenuous physical activities and muscle strength, OFF episodes, PD prescription medication), the directionality of the relationship with ambulatory impairment could not have been determined given the available data, therefore relationships between these variables and most likely cluster membership were evaluated using appropriate descriptive statistics (Kruskal-Wallis rank sum test or chi-square test) with a Bonferroni correction for multiple testing. Statistical significance was defined by a two-tailed α = 0.05 (except when conducting the Bonferroni adjustment). LCGA was done using MPlus v8.6, and the MPlusAutomation package automated estimation and interpretation 20, 21 . R program in the R studio environment was used for data management, graphical displays, and other statistical analyses. Data Availability The Fox Insight Study data are available to others through the Fox DEN ( https://foxden.michaeljfox.org/ ). The data used in this study is available from the authors to qualified researchers with Fox Insight Data Use approval ( https://foxden.michaeljfox.org/insight/register/ ). Please contact the corresponding author for more information. Results Descriptive Analyses The study population (Table 1 ) had an average age of 65.7 years (SD = 9.5) and the majority (51%) were in the earliest stages of their disease (< 3 years from onset) at baseline. Forty six percent was female and 97% were white. Consistent with prior research 22 , a higher percentage of PWP had moderate to severe ambulatory impairment at baseline in those with longer PD disease duration. By disease duration strata, PWP did differ on most attributes, except for gender, OFF episodes, and light and moderate sport/recreational activities. The comparisons in Table 1 emphasizes that the study population differed by disease duration, therefore, underscoring the importance of modelling trajectories stratified by disease duration. Table 1 Baseline Characteristics of Fox Insight Parkinson’s Disease Study Population * Overall Disease Duration (N; %) Early 10 years P † N 16863 100% 8612 51.1% 6181 36.7% 2070 12.3% EQ-5D-5L: Mobility Impairment (%) < 0.001 None 5683 33.7% 3622 42.1% 1754 28.4% 307 14.8% Slight 6902 40.9% 3495 40.6% 2636 42.7% 771 37.2% Moderate 3313 19.7% 1240 14.4% 1401 22.7% 672 32.5% Severe 873 5.2% 233 2.7% 357 5.8% 283 13.7% Not able to walk 86 0.5% 19 0.2% 30 0.5% 37 1.8% Sociodemographic attributes Body mass index (mean (SD)) 26.55 (5.12) 26.74 (5.18) 26.39 (5.07) 26.22 (5.03) < 0.001 Education (mean (SD)) 4.8 (1.53) 4.82 (1.50) 4.82 (1.54) 4.72 (1.58) 0.032 Age (mean (SD)) 65.74 (9.17) 64.7 (9.51) 66.68 (8.81) 67.28 (8.21) < 0.001 Gender = Female (%) 7577 (46.0) 3922 (46.5) 2740 (45.5) 915 (45.3) 0.400 Race = Non-White (%) 439 (2.7) 205 (2.4) 182 (3.0) 52 (2.6) 0.091 Employment (%) < 0.001 Full 3226 19.7% 2227 26.5% 867 14.5% 132 6.6% < 0.001 Part-time 1307 8.0% 778 9.3% 426 7.1% 103 5.1% < 0.001 Retired 11055 67.5% 4986 59.4% 4400 73.5% 1669 83.2% < 0.001 Unemployed 794 4.8% 398 4.7% 295 4.9% 101 5.0% 0.806 Clinical factors Veteran (%) 2434 14.8% 1218 14.5% 904 15.0% 312 15.5% 0.406 OFF Episodes = Yes (%) 353 45.4% 140 33.9% 164 56.9% 49 63.6% < 0.001 Current Medication for PD = Yes (%) 14717 90.4% 7009 83.8% 5780 97.2% 1928 97.7% < 0.001 Current Depression = Yes (%) 3725 25.5% 1880 25.3% 1318 24.7% 527 28.9% 0.001 Current Anxiety = Yes (%) 4211 28.9% 2122 28.6% 1511 28.3% 578 31.8% 0.014 Current Arthritis = Yes (%) 5938 40.7% 2971 40.0% 2172 40.8% 795 43.6% 0.018 Balance Poor = Yes (%) 434 49.7% 282 45.5% 116 57.7% 36 67.9% < 0.001 Current Back Pain = Yes (%) 5132 34.7% 2400 1.9% 1971 36.4% 761 41.0% < 0.001 Back Pain Limit Activities = Yes (%) 3633 70.8% 1637 68.2% 1421 72.1% 575 75.6% < 0.001 Work-related Activity = Yes (%) 6120 39.9% 3680 46.0% 1963 35.5% 477 26.2% < 0.001 Pain (%) < 0.001 None 4646 27.6% 2737 31.8% 1505 24.4% 404 19.6% Slight 7082 42.0% 3713 43.1% 2565 41.6% 804 39.0% Moderate 4270 25.4% 1855 21.6% 1736 28.1% 679 32.9% Severe 752 4.5% 275 3.2% 324 5.3% 153 7.4% Extreme 92 0.5% 27 0.3% 41 0.7% 24 1.2% Trouble Getting out of bed, a care, or a deep chair (%) < 0.001 Normal 5037 33.0% 3292 41.2% 1501 27.4% 244 13.4% Slight 6821 44.7% 3488 43.7% 2575 47.1% 758 41.8% Mild 2278 14.9% 904 11.3% 913 16.7% 461 25.4% Moderate 939 6.1% 257 3.2% 398 7.3% 284 15.6% Severe 199 1.3% 46 0.6% 85 1.6% 68 3.7% Problems with Balance and Walking < 0.001 Normal 5206 34.1% 3459 43.3% 1514 27.7% 233 12.8% Slight 6639 43.5% 3425 42.9% 2541 46.4% 673 37.1% Mild 1603 10.5% 561 7.0% 671 12.3% 371 20.4% Moderate 1563 10.2% 488 6.1% 644 11.8% 431 23.7% Severe 263 1.7% 54 0.7% 102 1.9% 107 5.9% Suddenly stop or freeze when walking < 0.001 Normal 11177 73.2% 6656 83.3% 3760 68.7% 761 41.9% Slight 2329 15.2% 903 11.3% 981 17.9% 445 24.5% Mild 902 5.9% 256 3.2% 394 7.2% 252 13.9% Moderate 643 4.2% 128 1.6% 245 4.5% 270 14.9% Severe 223 1.5% 44 0.6% 92 1.7% 87 4.8% Walking Activities < 0.001 Never 1270 8.3% 590 7.4% 479 8.7% 201 11.0% Seldom 3074 20.0% 1501 18.7% 1126 20.4% 447 24.5% Sometimes 3899 25.4% 1985 24.8% 1428 25.8% 486 26.6% Often 7125 46.4% 3938 49.1% 2493 45.1% 694 38.0% Light sport and recreational activities 0.237 Never 9771 63.7% 5162 64.5% 3439 62.4% 1170 64.1% Seldom 2913 19.0% 1464 18.3% 1098 19.9% 351 19.2% Sometimes 1814 11.8% 947 11.8% 661 12.0% 206 11.3% Often 836 5.5% 429 5.4% 310 5.6% 97 5.3% Moderate sport and recreational activities 0.105 Never 11547 75.4% 5965 74.8% 4161 75.5% 1421 77.9% Seldom 1916 12.5% 1004 12.6% 697 12.6% 215 11.8% Sometimes 1313 8.6% 711 8.9% 461 8.4% 141 7.7% Often 529 3.5% 290 3.6% 192 3.5% 47 2.6% Strenuous sport and recreational activities < 0.001 Never 9538 62.3% 4690 58.8% 3571 64.8% 1277 70.1% Seldom 2067 13.5% 1120 14.0% 731 13.3% 216 11.9% Sometimes 2293 15.0% 1328 16.6% 737 13.4% 228 12.5% Often 1411 9.2% 842 10.6% 468 8.5% 101 5.5% Muscle strength < 0.001 Never 5414 35.3% 2728 34.1% 1958 35.5% 728 39.8% Seldom 3907 25.5% 1988 24.9% 1464 26.5% 455 24.9% Sometimes 4130 26.9% 2203 27.5% 1478 26.8% 449 24.6% Often 1888 12.3% 1078 13.5% 615 11.2% 195 10.7% * Mean ± standard deviation for continuous measures and number of subjects in each category for discrete measures with p-values reported from Kushall-Wallis and chi-square tests where appropriate. † P < 0.05 is considered statistically significant Average Trajectory Using the Single Cluster Solution When considering only a single cluster solution (the overall average trajectory), PWP had on average reported having slight ambulatory problems (starting above the first threshold \({\tau _1}\) but below the second threshold \({\tau _2}\) which corresponds to moderate problems) in each duration strata (Fig. 2 ). In the later disease duration stratum, the trajectory was closer to the \({\tau _2}\) ; thus, PWP in this stratum had higher mobility impairment on average. These single solution trajectories did not change substantially over time in review of the confidence intervals, though in the early disease stratum there was a small negative linear (Estimate= -0.078, Standard Error [SE] = 0.021, p < 0.001) and positive quadratic effect (Estimate = 0.016, SE = 0.007, p = 0.02); in the mid-disease stratum there was a small negative linear effect (Estimate= -0.067, SE = 0.024, p = 0.005); and there were no significant slope effects in the late disease stratum. Number of Clusters by Disease Duration Using LCGA, four latent classes best described perceived longitudinal ambulatory impairment patterns across the early and mid-disease duration strata, while five latent classes best described impairment in the later disease duration stratum. Across models, these solutions achieved a near minimum (< 1% decrease after in adding an additional class) for BIC, aBIC, AIC and AICC values (Supplementary Table 1) and were a near maximum entropy. Similarly, the interpretability of the classes supported these solutions across each of the stratified models. Description of Clusters (subgroups) The average mobility trajectories for each cluster within each disease duration strata are displayed in Fig. 3 . In the early disease stratum, we labeled the four clusters as: no ambulatory impairment (Class 1: 37.8% [of participants]), slight impairment (Class 2: 40.7%), moderate impairment (Class 3: 17.5%) and severe impairment with variability (Class 4: 3.8%) (Fig. 3 .A). In the mid disease stratum, four subgroups were similarly described (Fig. 3 .B. In the later disease (> 10 years) stratum, we labeled Class 1 through Class 4 similarly, with Class 5 (2.8%) labeled as extreme impairment with variability as it was above \({\tau _4}\) (Fig. 3 .C). The percentage of subjects in the moderate and severe subgroups increased with disease duration (Fig. 3 ). In contrast to the single cluster solutions in Fig. 2 , that exhibit no change to slight improvement across strata, upon inspection of the individual trajectories per strata in Fig. 3 , the slight to moderate impairment trajectories (Classes 1 & 2) are stable with time, will those in the moderate to extreme impairment trajectories (Classes 3 to 5) continue to accrue impairment with time – this emphasizes the importance of examining mobility in the distinct cluster/subgroups of PWP rather than in the overall study population as an average trajectory. Cluster membership characteristics Results from the multivariable multinomial logistic regression models with the least impaired cluster (Class 1) as the reference are presented in Table 2 . Females were less likely to be in clusters with greater ambulatory impairment in PWP with mid-disease, but trending but mostly non-significant relationships in the other strata. On average, older age, higher BMI, lower education, lower income, and being unemployed versus retired were largely associated with increased assignment to clusters with high impairment across disease duration strata. Also, those employed had less impairment compared to retirees. There was also no evidence to suggest differences between white and non-white PWP in cluster membership (although there is an imbalance in the distribution by race in the study population - see Table 1 ). Table 2 Multinomial Regression Results for each disease duration strata with the least impaired (Class 1) cluster as the reference category. Attribute Early 10 years Odds Ratio (95% CI) P Odds Ratio (95% CI) P Odds Ratio (95% CI) P Class 2 vs Class 1 Gender = Female 0.93 (0.83, 1.04) 0.2 0.83 (0.71, 0.96) 0.012 0.86 (0.64, 1.16) 0.3 BMI 1.07 (1.06, 1.08) < 0.001 1.05 (1.03, 1.07) < 0.001 1.07 (1.04, 1.11) < 0.001 Race = Non-White 1.06 (0.74, 1.52) 0.8 1.6 (1.02, 2.50) 0.04 0.72 (0.31, 1.64) 0.4 Education 0.95 (0.92, 0.99) 0.017 0.99 (0.94, 1.04) 0.7 0.97 (0.88, 1.07) 0.6 Income 0.87 (0.84, 0.91) < 0.001 0.9 (0.86, 0.95) < 0.001 0.89 (0.80, 0.99) 0.025 Full Employment vs Retired 0.94 (0.81, 1.09) 0.4 1.04 (0.84, 1.30) 0.7 0.78 (0.47, 1.28) 0.3 Part Employment vs Retired 0.97 (0.80, 1.17) 0.8 1.16 (0.88, 1.53) 0.3 0.81 (0.45, 1.47) 0.5 Unemployment vs Retired 1.56 (1.15, 2.11) 0.004 2.11 (1.41, 3.16) 0.9 Age 1 (1.00, 1.01) 0.3 1 (0.99, 1.01) 0.4 0.99 (0.97, 1.01) 0.3 Class 3 vs Class 1 Gender = Female 0.86 (0.75, 1.00) 0.047 0.76 (0.65, 0.90) 0.001 0.79 (0.58, 1.07) 0.13 BMI 1.12 (1.11, 1.14) < 0.001 1.12 (1.10, 1.14) < 0.001 1.09 (1.06, 1.13) < 0.001 Race = Non-White 1.35 (0.85, 2.15) 0.2 1.49 (0.89, 2.48) 0.13 0.48 (0.18, 1.22) 0.12 Education 0.91 (0.87, 0.96) < 0.001 0.96 (0.91, 1.02) 0.2 0.99 (0.89, 1.09) 0.8 Income 0.78 (0.74, 0.82) < 0.001 0.8 (0.76, 0.85) < 0.001 0.8 (0.72, 0.89) < 0.001 Full Employment vs Retired 0.69 (0.56, 0.85) < 0.001 0.72 (0.55, 0.94) 0.017 0.48 (0.27, 0.86) 0.013 Part Employment vs Retired 0.71 (0.54, 0.92) 0.011 0.94 (0.68, 1.31) 0.7 0.71 (0.38, 1.34) 0.3 Unemployment vs Retired 1.85 (1.30, 2.65) < 0.001 1.86 (1.19, 2.93) 0.007 1.58 (0.73, 3.43) 0.2 Age 1.02 (1.01, 1.03) < 0.001 1.02 (1.01, 1.04) < 0.001 1.02 (1.00, 1.04) 0.13 Class 4 vs Class 1 Gender = Female 0.95 (0.73, 1.23) 0.7 0.71 (0.55, 0.91) 0.007 0.57 (0.39, 0.85) 0.005 BMI 1.15 (1.13, 1.18) < 0.001 1.14 (1.11, 1.17) < 0.001 1.12 (1.07, 1.16) < 0.001 Race = Non-White 1.44 (0.59, 3.51) 0.4 1.83 (0.90, 3.76) 0.1 1.11 (0.39, 3.20) 0.8 Education 0.84 (0.77, 0.91) < 0.001 0.93 (0.85, 1.01) 0.066 1.04 (0.92, 1.19) 0.5 Income 0.74 (0.68, 0.81) < 0.001 0.71 (0.66, 0.77) < 0.001 0.69 (0.60, 0.79) < 0.001 Full Employment vs Retired 0.37 (0.23, 0.62) < 0.001 0.55 (0.32, 0.94) 0.029 0.11 (0.02, 0.46) 0.003 Part Employment vs Retired 0.32 (0.16, 0.64) 0.001 0.48 (0.24, 0.98) 0.043 0.23 (0.06, 0.80) 0.021 Unemployment vs Retired 3.06 (1.78, 5.26) < 0.001 3.86 (2.14, 6.97) < 0.001 3.17 (1.33, 7.57) 0.009 Age 1.07 (1.05, 1.09) < 0.001 1.07 (1.06, 1.09) < 0.001 1.05 (1.02, 1.07) < 0.001 Class 5 vs Class 1 Gender = Female No fifth cluster No fifth cluster 0.88 (0.46, 1.66) 0.7 BMI 1.13 (1.06, 1.20) < 0.001 Race = Non-White 2.18 (0.43, 11.1) 0.3 Education 0.83 (0.67, 1.03) 0.087 Income 0.88 (0.70, 1.10) 0.3 Full Employment vs Retired 0 (0.00, 0.00) < 0.001 Part Employment vs Retired 0.72 (0.16, 3.29) 0.7 Unemployment vs Retired 2.05 (0.39, 10.7) 0.4 Age 1.1 (1.05, 1.14) < 0.001 Descriptive statistics are reported for sociodemographic and clinical variables in Supplementary Tables 2–4. In brief, in the early disease stratum, the higher impairment classes include a higher percentage of PWP on prescribed PD medications, with a greater prevalence of depression, anxiety and arthritis. The higher classes also reported more impairment in balance, back pain problems, walking impairment, pain and trouble getting out of bed and less work-related activity, sport and recreational activities (light, moderate and strenuous) and muscle strength. Class 3 had a higher proportion of PWP with current depression and anxiety than Class 4, while Class 4 had more physical impairment than Class 3. These trends were similar in the mid disease stratum, except Class 4 had higher percentages of current depression and anxiety than Class 3. There were also no differences in the percentage on PD medication (given the Bonferroni correction). The later duration stratum continued similar trends as the mid disease stratum, except there were no differences in the proportion of veterans or PWP with balance impairment across clusters. Discussion Ambulatory impairment is common in PWP, with a heterogenous presentation that negatively impacts QoL 1, 2 . Little is known about how PWP experience their difficulty in walking, much less over time, and by disease duration. Studies that have analyzed mobility in PWP have done so in aggregate, and resultantly fail to observe intrinsic and meaningful variation in subgroup ambulatory patterns – which is highly relevant for PROs. The analysis of subgroup mobility PRO trajectories in PWP is essential for understanding the progression of ambulatory impairment and may facilitate optimizing treatment plans, self-management strategies (i.e. exercise regimen), prognostication, and efforts to develop robust endpoints for clinical and observational research. Here we leveraged a readily accessible and broadly used health-related QoL instrument to identify and characterize subgroups of PWP with similar perceived ambulatory impairment trajectories over time and stratified by disease duration. PWP at the early and mid-disease stages of PD were clustered into four trajectories with > 65% having no to slight and stable impairment, and > 20% having moderate to severe trajectories that were increasing over time. PWP at the later stage of PD were clustered into five trajectories, including 2.8% in an extremely impaired subgroup – in general, ~ 50% had at modest slight and stable impairment while the other ~ 50% had moderate to extreme impairment that increased with time. There were also significant associations with trajectory membership for multiple sociodemographic and clinical attributes, which offers insights to drivers and correlates of heterogeneity in ambulatory impairment. Collectively, the findings may be leveraged to identify PWP at risk for greater sustained ambulatory impairment and may be utilized in patient-centered care approaches to advance care management and shared decision making. The multivariable models provided new insights into mobility impairment in PWP. For example, despite comparing multiple facets of PD presentation, it has been unclear to extent to which there may be gender differences in motor functioning, mobility, and health-related quality of life 23, 24 . As evident from the multinomial models where we adjusted for likely confounders, we observed females were less like to be in the more impaired clusters in those with mid-disease. There were an underrepresentation of females in Class 3 vs Class 1 during the earliest stage of PD and Class 4 vs Class 1 during the later stage of PD, which highlights that there is a non-linear relationship between sex and mobility over the disease course – which, in part, may explain the unclear patterns previously observed by others 23, 24 . The relationships for employed were as one would speculate, with part/full-time employed PWP being less burdened with high impairment compared to retirees across disease duration strata, while unemployed PWP (which would include those on disability) were more much likely to be in clusters with more severe impairment compared to retirees. Lower income was consistently associated with higher impairment and consistent with prior findings 25 . This effect was irrespective of disease course which illustrated how profound social inequities can impact PD outcomes. Another social determinant of health, higher education, has been inversed associated with white matter hyperintensities and lower MDS-UPDRS scores independent of nigrostriatal dopaminergic denervation in PWP 26 . Here, we observed higher education having a protective effect in relation to perceived mobility impairment only during at the earliest stage of PD, and merits further investigation into the relationship of resilience and PD progression. Another key observation that requires further inquiry, are the patterns observed for race. We did not observe substantial differences in longitudinal mobility impairment between white and non-white PWP when not adjusting and when adjusting for other social determinants of health. This lack of a longitudinal difference is intriguing considering cross-sectional racial difference observed for other health-related quality of life measures 27 . Our observation may be driven by the modest non-white subset in the current data, or that we were able to adjust for key socioeconomic variables (i.e. education, employment, and income) – others have observed that adjusting for income and education mitigated racial differences in PD severity models 25 . Thus, considering socioeconomic conditions are downstream of race in a causal diagram, subsequent work should explore causal mediation analyses to determine the extent to which social inequities drive racial differences in PD. In our post-hoc analyses, we noted that PWP with poorer mental health, higher burden of pain, and being a veteran were associated with a higher burden of ambulatory impairment – this may inform care conversations related to PD management and prognostication by aiding efforts to identify PWP most vulnerable for long-term adverse outcomes in functional mobility. These findings offer new perspectives on the longitudinal mobility experiences of PWP, from a person-centered framework. Understanding the anticipated trajectories PWP will experience will facilitate the development of tailored care/treatment strategies and allow for greater allocation of resources particularly for those with sudden increases in impairment and for those with moderate to extreme impairment that does not decrease with time. The findings also have great potential for developing novel endpoints for clinical and observational research. It would also be important to determine the underlying symptomatology for individual clusters and the extent to which these symptoms are preventable, treatable, or l-3,4-dihydroxyphenylalanine (Levodopa) responsive. It would also be information to focus on PWP who mobility impairment remained low and explore what risk and care strategies may have contributed to these favorable trends. Lastly, more granular baseline data such as subdivision of PWP into heterogeneous PD subtypes (i.e. tremor-dominant versus PIGD) and incorporating genetic and biomarker data may allow better prediction of mobility trajectories as experienced by PWP. There are several strengths in the current study, including the large sample size, the application of LCGA to discern subgroups, the opportunity to stratify models by disease duration, the availability of longitudinal EQ-5D-5L data, and the extensive baseline information. There are a few limitations to acknowledge, the first is the study population was comprised of PWP who were digitally literate and therefore it may not represent the cognitively impaired or other marginalized subpopulations. There was also an underrepresentation of Non-White PWP in the data, therefore these results might have limited generalizability to Non-White populations. This study also assumes that all LCGA model assumptions were met in this PD sample for valid inference under the special considerations in which the latent variable mobility* was used as the outcome 28 . We did perform more robust inference in case there is a violation of model parametric assumptions and included quadratic terms in our models in case the trajectory of mobility impairment is non-linear. A key limitation is that in our chosen solutions, there were some clusters of a small cell size, and the entropy values and a few of the posterior probability of membership averages were < 70%. In summary, LCGA uncovered multiple distinct ambulatory impairment trajectories and distinct subgroups of PWP based on their experiences with difficulties in walking. This is consistent with our prior work on pain perceptions, emphasizing the need the account for longitudinal heterogeneity in PD symptomatology, the need to factor in disease duration, and the power of PRO for facilitating these discoveries 12 . We hope that this work can serve as a framework for characterizing other complex PD impairments, as well as impairment in other chronic disorders, which may subsequently optimize patient care and facilitate the discovery of modifiable risk factors for symptom exacerbation by serving as robust phenotypes for clinical and observational research. Declarations Author Contribution FB and DG conceptualized the study. FB and DG were responsible for data management. All authors reviewed analytical framework. DG conducted statistical analyses. FB and DG drafted the manuscript. SG guided interpretations. All authors reviewed the manuscript. Acknowledgments Financial support for this study was provided by a grant from The Michael J. Fox Foundation for Parkinson’s Research MJFF-020155. The funding agreement ensured the authors’ independence in designing the study, interpreting the data, writing, and publishing the report. The Fox Insight Study (FI) is funded by The Michael J. Fox Foundation for Parkinson’s Research. We would like to thank the Parkinson’s community for participating in this study to make this research possible. Competing Interests DDG has received grant support from NIH and the Michael J Fox Foundation and reports a book royalty agreement with Taylor & Francis Publishing. FBSB has received grant support from NIH and the Michael J Fox Foundation. FBSB has been a paid consultant for Michael J. Fox Foundation and has received speaker honorarium from Sanofi. SG has received grant support from the NIH and the Michael J Fox Foundation; he has participated in clinical studies funded by the Parkinson Foundation, Biogen, UCB, and Amneal. References Rahman S, Griffin HJ, Quinn NP, Jahanshahi M. Quality of life in Parkinson's disease: the relative importance of the symptoms. Mov Disord 2008;23:1428–1434. Wu P-L, Lee M, Huang T-T. Effectiveness of physical activity on patients with depression and Parkinson's disease: A systematic review. PloS one 2017;12:e0181515-e0181515. Criminger C, Swank C. Impact of dual-tasking on mobility tasks in Parkinson's disease as described through 2D kinematic analysis. Aging Clin Exp Res 2020;32:835–840. Shulman LM, Gruber-Baldini AL, Anderson KE, Vaughan CG, Reich SG, Fishman PS, et al. The evolution of disability in Parkinson disease. Mov Disord 2008;23:790–796. Hammarlund CS, Andersson K, Andersson M, Nilsson MH, Hagell P. The significance of walking from the perspective of people with Parkinson's disease. J Parkinsons Dis 2014;4:657–663. Wilson J, Alcock L, Yarnall AJ, Lord S, Lawson RA, Morris R, et al. Gait progression over 6 years in Parkinson’s disease: Effects of age, medication, and pathology. Frontiers in aging neuroscience 2020;12:577435. Schenkman M, Cutson TM, Zhu CW, Whetten-Goldstein K. A longitudinal evaluation of patients' perceptions of Parkinson's disease. Gerontologist 2002;42:790–798. Knutsson I, Rydstrom H, Reimer J, Nyberg P, Hagell P. Interpretation of response categories in patient-reported rating scales: a controlled study among people with Parkinson's disease. Health Qual Life Outcomes 2010;8:61. Bouca-Machado R, Goncalves N, Lousada I, Patriarca MA, Costa P, Nunes R, et al. Patients and Health Professional's Perspective of Functional Mobility in Parkinson's Disease. Front Neurol 2020;11:575811. Gunzler DD, Perzynski AT, Carle AC. Structural Equation Modeling for Health and Medicine. 2021. Gunzler DD, Morris N, Perzynski A, Ontaneda D, Briggs F, Miller D, et al. Heterogeneous depression trajectories in multiple sclerosis patients. Multiple Sclerosis and Related Disorders 2016;9:163–169. Gunzler DD, Gunzler SA, Briggs FBS. Heterogeneous pain trajectories in persons with Parkinson's disease. Parkinsonism & Related Disorders 2022;102:42–50. Schrag A, Selai C, Jahanshahi M, Quinn NP. The EQ-5D–a generic quality of life measure-is a useful instrument to measure quality of life in patients with Parkinson's disease. J Neurol Neurosurg Psychiatry 2000;69:67–73. Dams J, Klotsche J, Bornschein B, Reese JP, Balzer-Geldsetzer M, Winter Y, et al. Mapping the EQ-5D index by UPDRS and PDQ-8 in patients with Parkinson's disease. Health Qual Life Outcomes 2013;11:35. Alvarado-Bolaños A, Cervantes-Arriaga A, Rodríguez-Violante M, Llorens-Arenas R, Calderón-Fajardo H, Millán-Cepeda R, et al. Convergent validation of EQ-5D-5L in patients with Parkinson's disease. Journal of the Neurological Sciences 2015;358:53–57. Xin Y, McIntosh E. Assessment of the construct validity and responsiveness of preference-based quality of life measures in people with Parkinson’s: a systematic review. Quality of Life Research 2017;26:1–23. Smolensky L, Amondikar N, Crawford K, Neu S, Kopil CM, Daeschler M, et al. Fox Insight collects online, longitudinal patient-reported outcomes and genetic data on Parkinson's disease. Sci Data 2020;7:67. Oppe M, Devlin NJ, van Hout B, Krabbe PF, de Charro F. A program of methodological research to arrive at the new international EQ-5D-5L valuation protocol. Value Health 2014;17:445–453. Muthén B, Shedden K. Finite mixture modeling with mixture outcomes using the EM algorithm. Biometrics 1999;55:463–469. Muthén LK, Muthén BO. Mplus. The comprehensive modelling program for applied researchers: user’s guide 2012;5. Hallquist MN, Wiley JF. MplusAutomation: An R Package for Facilitating Large-Scale Latent Variable Analyses in M plus. Structural equation modeling: a multidisciplinary journal 2018;25:621–638. Tai Y-C, Lin C-H. An overview of pain in Parkinson's disease. Clinical Parkinsonism & Related Disorders 2020;2:1–8. Picillo M, Nicoletti A, Fetoni V, Garavaglia B, Barone P, Pellecchia MT. The relevance of gender in Parkinson's disease: a review. J Neurol 2017;264:1583–1607. Crispino P, Gino M, Barbagelata E, Ciarambino T, Politi C, Ambrosino I, et al. Gender Differences and Quality of Life in Parkinson's Disease. Int J Environ Res Public Health 2020;18. Hemming JP, Gruber-Baldini AL, Anderson KE, Fishman PS, Reich SG, Weiner WJ, et al. Racial and socioeconomic disparities in parkinsonism. Arch Neurol 2011;68:498–503. Kotagal V, Bohnen NI, Muller ML, Koeppe RA, Frey KA, Langa KM, et al. Educational attainment and motor burden in Parkinson's disease. Mov Disord 2015;30:1143–1147. Di Luca DG, Luo S, Liu H, Cohn M, Davis TL, Ramirez-Zamora A, et al. Racial and Ethnic Differences in Health-Related Quality of Life for Individuals With Parkinson Disease Across Centers of Excellence. Neurology 2023;100:e2170-e2181. Bauer DJ. Observations on the use of growth mixture models in psychological research. Multivariate Behavioral Research 2007;42:757–786. Additional Declarations Competing interest reported. DG has received grant support from NIH and the Michael J Fox Foundation and reports a book royalty agreement with Taylor & Francis Publishing. FB has received grant support from NIH and the Michael J Fox Foundation. FB has been a paid consultant for Michael J. Fox Foundation and has received speaker honorarium from Sanofi. SG has received grant support from the NIH and the Michael J Fox Foundation; he has participated in clinical studies funded by the Parkinson Foundation, Biogen, UCB, and Amneal. Supplementary Files MobilityLCGASuppMethods.docx Supp.Tables.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 21 Mar, 2024 Reviews received at journal 18 Mar, 2024 Reviewers agreed at journal 08 Mar, 2024 Reviews received at journal 16 Feb, 2024 Reviewers agreed at journal 06 Feb, 2024 Reviewers invited by journal 05 Jan, 2024 Editor assigned by journal 05 Jan, 2024 Editor invited by journal 23 Dec, 2023 Submission checks completed at journal 23 Dec, 2023 First submitted to journal 19 Dec, 2023 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-3778288","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":263566205,"identity":"154d63e4-305e-4260-b074-09939578eca1","order_by":0,"name":"Farren Briggs","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtElEQVRIiWNgGAWjYBAC9hk8zA8SKhh4GBgYGxgY2IjQwnODh83gwRkStTBIPmyDcYnSIt17wCBx3jYZ/mmHGxg+lB0mQovMuYQHidtu80jcTmxgnHGOCC32EjkGBiAtDEAtzLxtxNgC1CKROOc2jzxIy1/itTTc5jEAaWEkSovMuTSDhGO3eQyBWg72nEsnQot07+GHP2pu28vdTn/44EeZNWEtKOAAiepHwSgYBaNgFOACAFM/PfRx1y/wAAAAAElFTkSuQmCC","orcid":"","institution":"University of Miami","correspondingAuthor":true,"prefix":"","firstName":"Farren","middleName":"","lastName":"Briggs","suffix":""},{"id":263566206,"identity":"ef871b2b-74ed-4cbc-b645-5e04608ade0e","order_by":1,"name":"Douglas Gunzler","email":"","orcid":"","institution":"Case Western Reserve University","correspondingAuthor":false,"prefix":"","firstName":"Douglas","middleName":"","lastName":"Gunzler","suffix":""},{"id":263566207,"identity":"0ce496f9-761a-4e57-b96e-df5c7fe18dd7","order_by":2,"name":"Steven Gunzler","email":"","orcid":"","institution":"University Hospitals Cleveland Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Steven","middleName":"","lastName":"Gunzler","suffix":""}],"badges":[],"createdAt":"2023-12-19 19:14:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3778288/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3778288/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49071365,"identity":"dd6f5e99-b9ee-42e9-858a-40b2e214f3fe","added_by":"auto","created_at":"2024-01-02 17:09:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":41898,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMobility mixture model. \u003c/strong\u003eIntercept and Mobility\u003csub\u003e1\u003c/sub\u003e = baseline mobility; Slope = linear rate of change; Quadratic = quadratic rate of change; Class = categorical latent variable. Mobility\u003csub\u003e2\u003c/sub\u003e, …, Mobility\u003csub\u003eM \u003c/sub\u003eare varying follow-up scores across 4.5 years for each subject.\u0026nbsp;\u003c/p\u003e","description":"","filename":"Figure1.JCN.png","url":"https://assets-eu.researchsquare.com/files/rs-3778288/v1/7d875488eb55a43d186b3c9d.png"},{"id":49071363,"identity":"83c434f2-a142-487f-84ce-7268071fab3b","added_by":"auto","created_at":"2024-01-02 17:09:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":74739,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eQuadratic regression of mobility. \u003c/strong\u003eShaded region represents a 95% Confidence Interval. The y-axis shows mobility* which is the normally distributed latent variable analytically inferred from the mobility ordered-categorical outcome. Thresholds (i.e. \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;τ \u003csub\u003e1\u003c/sub\u003e and τ\u003csub\u003e2\u003c/sub\u003e \u0026nbsp;\u0026nbsp;) are the values for mobility* for which the mobility ordered-categorical outcome crosses categories. Thus, a mobility* value above τ\u003csub\u003e1 \u003c/sub\u003e\u0026nbsp;\u0026nbsp;and below \u0026nbsp;\u0026nbsp;\u0026nbsp;τ\u003csub\u003e2\u003c/sub\u003e would signify that mobility is in response category one.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3778288/v1/764ec2f9db6701b5b11cd2b8.png"},{"id":49072463,"identity":"17c5b8ae-5e6b-4304-9547-c0c10b06529d","added_by":"auto","created_at":"2024-01-02 17:17:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":255846,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAverage within-class trajectories across disease duration using quadratic regression. \u003c/strong\u003eShaded region in each plot represents a 95% Confidence Interval.\u003c/p\u003e\n\u003cp\u003eThe y-axis shows mobility* which is the normally distributed latent variable analytically inferred from the mobility ordered-categorical outcome. Thresholds (i.e. \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;τ\u003csub\u003e1\u003c/sub\u003e, τ\u003csub\u003e2\u003c/sub\u003e \u0026nbsp;, τ\u003csub\u003e3\u003c/sub\u003e \u0026nbsp;\u0026nbsp;and τ\u003csub\u003e4\u003c/sub\u003e \u0026nbsp;\u0026nbsp;) are the values for mobility* for which the mobility ordered-categorical outcome crosses categories. Thus, the mobility response is one if the value of mobility* is greater than the first threshold, but less than the second threshold, the mobility response is two if the value of mobility* is greater than the second threshold, but less than the third threshold, the mobility response is three if the value of mobility* is greater than the third threshold, but less than the fourth threshold and the mobility response is four if the value of mobility* is greater than the fourth threshold.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3778288/v1/90f7e4892369925f414b6ebe.png"},{"id":49073100,"identity":"c7a4ce00-0286-45f7-806d-cc0111228734","added_by":"auto","created_at":"2024-01-02 17:25:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":820423,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3778288/v1/7fe04618-59bf-4e20-ac8a-7a71c7e2e333.pdf"},{"id":49071364,"identity":"63fe6756-6bb0-489d-a488-13cdf52b5ebd","added_by":"auto","created_at":"2024-01-02 17:09:30","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":26212,"visible":true,"origin":"","legend":"","description":"","filename":"MobilityLCGASuppMethods.docx","url":"https://assets-eu.researchsquare.com/files/rs-3778288/v1/c75d9376655b98f94236c5e4.docx"},{"id":49071366,"identity":"8d6e3c5e-11d2-42a7-aa9d-b8b340af0764","added_by":"auto","created_at":"2024-01-02 17:09:31","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":86790,"visible":true,"origin":"","legend":"","description":"","filename":"Supp.Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-3778288/v1/3cec0b17a5e03e354126a1ee.docx"}],"financialInterests":"Competing interest reported. DG has received grant support from NIH and the Michael J Fox Foundation and reports a book royalty agreement with Taylor \u0026 Francis Publishing. FB has received grant support from NIH and the Michael J Fox Foundation. FB has been a paid consultant for Michael J. Fox Foundation and has received speaker honorarium from Sanofi. SG has received grant support from the NIH and the Michael J Fox Foundation; he has participated in clinical studies funded by the Parkinson Foundation, Biogen, UCB, and Amneal.","formattedTitle":"A person-centered approach to characterizing longitudinal ambulatory impairment in Parkinson's disease","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDifficulty in walking is a common and visible impairment experienced by people with Parkinson's disease (PWP), and it is driven by a diverse collection of symptoms (e.g. start hesitation, shuffling gait, freezing, festination, propulsion, and difficulty in turning) \u003csup\u003e1\u003c/sup\u003e. It is also a prominent driver of lower quality of life (QoL) in Parkinson\u0026rsquo;s disease (PD) and it is associated with poor health outcomes, increased depressive symptoms, more frequent falls, loss of independence, decreased social participation, and greater interruptions of daily activities\u003csup\u003e1\u0026ndash;5\u003c/sup\u003e. Unfortunately, there are substantial fluctuations in the severity of the underlying symptoms and in the accrual of neurological deficits, thus, ambulatory impairment appears heterogeneous and unpredictable in PWP \u003csup\u003e6\u003c/sup\u003e. This poses a significant challenge for successful patient-centered care, including tailoring clinical and rehabilitation care, prognostication, and developing long-term self-management strategies, as well as a challenge for defining robust endpoints in clinical and observational research.\u003c/p\u003e \u003cp\u003ePatient-reported outcome (PRO) measures capture the lived experiences of patients, including meaningful and nuanced changes in health-related QoL, and over time they inherently reflect patients\u0026rsquo; shifting priorities for daily living. There are several PD-specific PROs for mobility (i.e., MDS-UPDRS Part II); however, these instruments do not readily map to generic PROs which impedes comparisons with the general population and subpopulations where ambulatory impairment is also seemingly unpredictable (i.e., persons with multiple sclerosis). Also, it has been noted that the perceptions (and/or key health priorities) of PWP may evolve with their disease course \u003csup\u003e7, 8\u003c/sup\u003e; e.g., in a qualitative study of functional mobility, the perceptions of people in the early-stages of PD were more aligned with neurologists while those in more advanced-stages were closer to physiotherapists \u003csup\u003e9\u003c/sup\u003e. Another important factor is the underlying heterogeneity in ambulatory impairment in PWP. Prior studies have only described relationships for the \u003cem\u003eaverage\u003c/em\u003e change in measures of gait and walking speed. No study has yet described the likely intrinsic subgroups of PWP who exhibit similar longitudinal ambulatory trajectories over time, based in part on the combination and severity of underlying symptoms that evolve as PD progresses. Fortunately, latent class growth analysis (LCGA) is a data-driven approach that can identify these naturally occurring subgroups with distinct growth trajectories within a larger sample and it has been successfully used to discern distinct subgroups of PWP with similar longitudinal pain (measured by a generic PRO measure) trajectories \u003csup\u003e10\u0026ndash;12\u003c/sup\u003e. Thus, several knowledge gaps may be addressed by leveraging LCGA to longitudinally model ambulatory impairment in PWP using a generic health-related QoL PRO, with considerations for disease duration.\u003c/p\u003e \u003cp\u003eThe objective of the current retrospective cohort study is to describe longitudinal ambulatory impairment trajectories in PWP leveraging self-reported information as captured by the European Quality of Life (EuroQoL) Questionnaire 5 level version (EQ-5D-5L) is a generic health-related QoL instrument that has construct validity in diverse populations and in PWP \u003csup\u003e13\u0026ndash;16\u003c/sup\u003e. We hypothesize EQ-5D-5L mobility component will vary as a function of disease duration and that sociodemographic and clinical factors will be associated with assignment to distinct trajectories at each disease duration stage. We hope that by defining subgroups of PWP with shared perceived ambulatory impairment patterns, there is the potential to advance clinical/observational research and patient-centered care that can be readily compared to other populations.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eResearch ethics\u003c/h2\u003e \u003cp\u003eThis secondary data analysis of de-identified data was deemed as non-human subject research by the institutional review boards at Case Western Reserve University and The MetroHealth System, Cleveland, Ohio.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eFox Insight (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://foxinsight.michaeljfox.org\u003c/span\u003e\u003cspan address=\"https://foxinsight.michaeljfox.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is a virtual and ongoing longitudinal study of people aged 18 years or older, with and without PD, led by the Michael J. Fox Foundation \u003csup\u003e17\u003c/sup\u003e. It aims to facilitate discovery, validation, and reproducibility in PD PRO research, and includes several PROs, routine health and medical assessments, environmental exposure and healthcare preference questionnaires, with the option to provide biospecimens for genotyping \u003csup\u003e17\u003c/sup\u003e. The longitudinal data used were obtained from Fox Insight Data Exploration Network (Fox DEN) on 10/14/2021 and leveraged to construct a retrospective cohort of PWP who had completed the EQ-5D-5L at least once (for up-to-date information visit \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://foxinsight-info.michaeljfox.org/insight/explore/insight.jsp\u003c/span\u003e\u003cspan address=\"https://foxinsight-info.michaeljfox.org/insight/explore/insight.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) \u003csup\u003e12\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eOutcome\u003c/h2\u003e \u003cp\u003eEQ-5D-5L measures perspectives on five domains, including self-care, usual activities, pain/discomfort, anxiety/depression, and walking difficulty \u003csup\u003e18\u003c/sup\u003e. The outcome of interest was the longitudinal data for mobility component of the EQ-5D-5L, first deployed in 2017 and available under \u0026ldquo;Your Physical Experiences\u0026rdquo; in Fox DEN \u003csup\u003e17\u003c/sup\u003e. The mobility PRO is ordinal, measured on a 5-level Likert scale (0\u0026thinsp;=\u0026thinsp;I have no problems in walking about, 1\u0026thinsp;=\u0026thinsp;slight problems, 2\u0026thinsp;=\u0026thinsp;moderate problems, 3\u0026thinsp;=\u0026thinsp;severe problems, 4\u0026thinsp;=\u0026thinsp;unable to walk about). There were 16,863 PD participants with EQ-5D-5L mobility data at baseline and \u0026ge;\u0026thinsp;1 additional follow-up survey, and who had an indicator value for number of years with PD (early: \u0026lt;3 years [N\u0026thinsp;=\u0026thinsp;8612 PWP], mid: 3\u0026ndash;10 years [N\u0026thinsp;=\u0026thinsp;6181 PWP], later: \u0026gt;10 years [2070 PWP]). EQ-5D-5L may be completed at 6-month intervals; included subjects completed an average of 4.1 (SD\u0026thinsp;=\u0026thinsp;2.1) surveys. There were 11,838 (70%), 8,557 (51%), 6,029 (36%), 4,257 (25%), 2,736 (16%), 1475 (9%) and 554 (3%) PWP with \u0026ge;\u0026thinsp;3, \u0026ge;4, \u0026ge;\u0026thinsp;5, \u0026ge;6, \u0026ge;\u0026thinsp;7, \u0026ge;8, and \u0026ge;\u0026thinsp;9 entries, respectively. Note that the decrease in sample size over time is not necessarily a matter of loss-to-follow-up (left censoring), but also right censoring, reflecting the ongoing recruitment of PWP.\u003c/p\u003e \u003cp\u003eOnly\u0026thinsp;\u0026le;\u0026thinsp;9 observations per PWP were used for the stratified models for early and mid-disease duration, while\u0026thinsp;\u0026le;\u0026thinsp;8 observations per PWP were used for the models for later disease duration to minimize data sparseness considering the total number of subjects endorsing each of the five mobility PRO categories at each follow-up time point. Consecutive responses for this PRO have high but incomplete concordance which mitigates concerns of redundancy and multicollinearity between in any two successive observations (Pearson correlation coefficient [PCC]\u0026thinsp;=\u0026thinsp;0.59\u0026ndash;0.82; similar patterns observed across disease duration strata early: PCC\u0026thinsp;=\u0026thinsp;0.56\u0026ndash;0.81, mid: PCC\u0026thinsp;=\u0026thinsp;0.58\u0026ndash;0.82, later: PCC\u0026thinsp;=\u0026thinsp;0.53\u0026ndash;0.80). It was important to stratify by disease duration as the accrual of ambulatory impairment in PWP is a function of disease function, and there are likely different rates at which impairment is accrued for a given length of disease, and lastly, perceptions of one\u0026rsquo;s disability may evolve with time \u003csup\u003e7\u0026ndash;9\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eBaseline variables\u003c/h2\u003e \u003cp\u003eAs we have previously described, the baseline sociodemographic variables incorporated included age, gender, race/ethnicity (non-white vs. white), education (1\u0026thinsp;=\u0026thinsp;Less than high school degree, 2\u0026thinsp;=\u0026thinsp;High school degree, 3\u0026thinsp;=\u0026thinsp;Some college, 4\u0026thinsp;=\u0026thinsp;Associate\u0026rsquo;s degree, 5\u0026thinsp;=\u0026thinsp;Bachelor\u0026rsquo;s degree, 6\u0026thinsp;=\u0026thinsp;Master\u0026rsquo;s degree, 7\u0026thinsp;=\u0026thinsp;Doctoral degree), employment (retired, full-time, part-time, or unemployed; retired was the reference category for employment dummy variables in the multivariable regression models), income (1= \u0026lt;\u003cspan\u003e$\u003c/span\u003e20,000, 2=\u003cspan\u003e$\u003c/span\u003e20,000-\u003cspan\u003e$\u003c/span\u003e34,999, 3=\u003cspan\u003e$\u003c/span\u003e35,000-\u003cspan\u003e$\u003c/span\u003e49,999, 4=\u003cspan\u003e$\u003c/span\u003e50,000-\u003cspan\u003e$\u003c/span\u003e74,999, 5=\u003cspan\u003e$\u003c/span\u003e75,000-\u003cspan\u003e$\u003c/span\u003e99,999, 6= \u0026gt;\u003cspan\u003e$\u003c/span\u003e100,000), and body mass index (BMI) \u003csup\u003e12\u003c/sup\u003e. Self-reported clinical factors were included based on their hypothesized relationships with ambulatory impairment in PWP, and included binary indicators about current depression, anxiety, arthritis, and back pain duration and limitations (from \u0026ldquo;Your Current Health\u0026rdquo;); poor balance (from \u0026ldquo;Brief Motor Screen\u0026rdquo;), experiences of OFF episodes (from \u0026ldquo;Impact of OFF Episodes\u0026rdquo;), work in the past week (from \u0026ldquo;Work-related Activity\u0026rdquo;), trouble getting out of bed, a car seat, or a deep chair, walking and balance problems and freezing up (from \u0026ldquo;Your Movement Experiences\u0026rdquo;) and walking activities, light, moderate and strenuous sport and recreational activities and muscle strength (from \u0026ldquo;Your Physical Activities\u0026rdquo;)\u003csup\u003e17\u003c/sup\u003e. Military veteran status, actively taking prescription PD medication, and EQ-5D-5L pain component (ordinal items: 0\u0026thinsp;=\u0026thinsp;no pain, 4\u0026thinsp;=\u0026thinsp;extreme pain) were also included.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analyses\u003c/h2\u003e \u003cp\u003eDescriptive statistics was completed for the entire sample and by disease duration strata. Kruskal-Wallis rank sum test and chi-square test assessed statistical significance in the comparison of continuous and categorical distributions across disease duration strata. LCGA allows for identifying meaningful clusters (or subgroups) within a larger study sample to examine longitudinal patterns over time \u003csup\u003e10\u0026ndash;12\u003c/sup\u003e. We (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) performed LCGA to identify clusters of PWP based on longitudinal mobility trajectories (see path diagram in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), and (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) evaluated measures that may associate with cluster membership. The clusters, also termed \u003cem\u003elatent classes\u003c/em\u003e, identified by LCGA are not known (observed) a priori but are determined empirically \u003csup\u003e10\u003c/sup\u003e. A trajectory shape for each class is estimated (i.e. intercept and slopes), and individuals can be assigned to the latent class of the highest probability of membership, which can be graphically displayed to facilitate interpretation \u003csup\u003e19\u003c/sup\u003e. A common approach for a LCGA of an ordered-categorical outcomes is to assume that a normally distributed latent variable exists from which each level of the observed categories is derived when the latent variable exceeds specific thresholds \u003csup\u003e10\u003c/sup\u003e. For analytical purposes, we inferred a latent variable mobility* with four thresholds based on the observed data of five categories. That is, for each PWP at each time point, the mobility PRO\u0026thinsp;=\u0026thinsp;0 if the value of mobility* is less than the first threshold (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\tau _1}\\)\u003c/span\u003e\u003c/span\u003e), the PRO\u0026thinsp;=\u0026thinsp;1 if the value of mobility* is greater than the first threshold (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\tau _1}\\)\u003c/span\u003e\u003c/span\u003e) but less than the second threshold (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\tau _2}\\)\u003c/span\u003e\u003c/span\u003e), and so forth for increasing PRO responses (see Supplementary Methods for additional details). In the graphical displays, the threshold values for mobility* (which did not meaningfully vary over time) were denoted as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\tau _1}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\tau _2}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\tau _3}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\tau _4}\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMultivariable, multinomial logistic regression was used to determine if sociodemographic attributes influenced most likely cluster membership across strata (i.e. sociodemographic factors precede (lead to) cluster membership in a causal diagram). Odds ratios, 95% confidence intervals (CI) and p-values using z-tests for these multivariable models were reported. For other variables (military veteran status and clinical factors: depression, arthritis, anxiety, balance problems, pain, back pain duration and limitations, work in the past week, trouble getting out of bed, a car seat or a deep chair, freezing up, walking activities, light moderate and strenuous physical activities and muscle strength, OFF episodes, PD prescription medication), the directionality of the relationship with ambulatory impairment could not have been determined given the available data, therefore relationships between these variables and most likely cluster membership were evaluated using appropriate descriptive statistics (Kruskal-Wallis rank sum test or chi-square test) with a Bonferroni correction for multiple testing.\u003c/p\u003e \u003cp\u003eStatistical significance was defined by a two-tailed α\u0026thinsp;=\u0026thinsp;0.05 (except when conducting the Bonferroni adjustment). LCGA was done using MPlus v8.6, and the MPlusAutomation package automated estimation and interpretation \u003csup\u003e20, 21\u003c/sup\u003e. R program in the R studio environment was used for data management, graphical displays, and other statistical analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData Availability\u003c/h2\u003e \u003cp\u003eThe Fox Insight Study data are available to others through the Fox DEN (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://foxden.michaeljfox.org/\u003c/span\u003e\u003cspan address=\"https://foxden.michaeljfox.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The data used in this study is available from the authors to qualified researchers with Fox Insight Data Use approval (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://foxden.michaeljfox.org/insight/register/\u003c/span\u003e\u003cspan address=\"https://foxden.michaeljfox.org/insight/register/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Please contact the corresponding author for more information.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive Analyses\u003c/h2\u003e \u003cp\u003eThe study population (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) had an average age of 65.7 years (SD\u0026thinsp;=\u0026thinsp;9.5) and the majority (51%) were in the earliest stages of their disease (\u0026lt;\u0026thinsp;3 years from onset) at baseline. Forty six percent was female and 97% were white. Consistent with prior research \u003csup\u003e22\u003c/sup\u003e, a higher percentage of PWP had moderate to severe ambulatory impairment at baseline in those with longer PD disease duration. By disease duration strata, PWP did differ on most attributes, except for gender, OFF episodes, and light and moderate sport/recreational activities. The comparisons in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e emphasizes that the study population differed by disease duration, therefore, underscoring the importance of modelling trajectories stratified by disease duration.\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\u003eBaseline Characteristics of Fox Insight Parkinson\u0026rsquo;s Disease Study Population *\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c10\" namest=\"c4\"\u003e \u003cp\u003eDisease Duration\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e(N; %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eEarly\u0026thinsp;\u0026lt;\u0026thinsp;3 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eMid 3\u0026ndash;10 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eLater\u0026thinsp;\u0026gt;\u0026thinsp;10 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eP \u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e36.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEQ-5D-5L: Mobility Impairment (%)\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e14.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e42.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e37.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e32.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot able to walk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSociodemographic attributes\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(5.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(5.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(5.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e26.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(5.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(1.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(9.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(9.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e66.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(8.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e67.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(8.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u0026thinsp;=\u0026thinsp;Female (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(46.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(46.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(45.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(45.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.400\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u0026thinsp;=\u0026thinsp;Non-White (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment (%)\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFull\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePart-time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRetired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e73.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e83.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.806\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical factors\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVeteran (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e15.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.406\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOFF Episodes\u0026thinsp;=\u0026thinsp;Yes (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e56.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e63.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent Medication for PD\u0026thinsp;=\u0026thinsp;Yes (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e83.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e97.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e97.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent Depression\u0026thinsp;=\u0026thinsp;Yes (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e28.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent Anxiety\u0026thinsp;=\u0026thinsp;Yes (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e31.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent Arthritis\u0026thinsp;=\u0026thinsp;Yes (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e40.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e43.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBalance Poor\u0026thinsp;=\u0026thinsp;Yes (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e57.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e67.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent Back Pain\u0026thinsp;=\u0026thinsp;Yes (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e36.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e41.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBack Pain Limit Activities\u0026thinsp;=\u0026thinsp;Yes (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e72.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e75.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWork-related Activity\u0026thinsp;=\u0026thinsp;Yes (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e26.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePain (%)\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e19.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e41.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e39.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e32.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtreme\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrouble Getting out of bed, a care, or a deep chair (%)\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e47.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e41.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e25.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e15.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProblems with Balance and Walking\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e46.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e37.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e20.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e23.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSuddenly stop or freeze when walking\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e83.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e68.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e41.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e24.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e14.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWalking Activities\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeldom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e24.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSometimes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e26.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOften\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e45.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e38.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLight sport and recreational activities\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.237\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e62.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e64.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeldom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e19.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSometimes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOften\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate sport and recreational activities\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e74.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e75.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e77.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeldom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSometimes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOften\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStrenuous sport and recreational activities\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e64.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e70.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeldom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSometimes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOften\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMuscle strength\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e39.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeldom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e26.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e24.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSometimes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e26.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e24.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOften\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e* Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation for continuous measures and number of subjects in each category for discrete measures with p-values reported from Kushall-Wallis and chi-square tests where appropriate.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u0026dagger; P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 is considered statistically significant\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAverage Trajectory Using the Single Cluster Solution\u003c/h2\u003e \u003cp\u003eWhen considering only a single cluster solution (the overall average trajectory), PWP had on average reported having slight ambulatory problems (starting above the first threshold \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\tau _1}\\)\u003c/span\u003e\u003c/span\u003e but below the second threshold \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\tau _2}\\)\u003c/span\u003e\u003c/span\u003ewhich corresponds to moderate problems) in each duration strata (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In the later disease duration stratum, the trajectory was closer to the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\tau _2}\\)\u003c/span\u003e\u003c/span\u003e; thus, PWP in this stratum had higher mobility impairment on average. These single solution trajectories did not change substantially over time in review of the confidence intervals, though in the early disease stratum there was a small negative linear (Estimate= -0.078, Standard Error [SE]\u0026thinsp;=\u0026thinsp;0.021, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and positive quadratic effect (Estimate\u0026thinsp;=\u0026thinsp;0.016, SE\u0026thinsp;=\u0026thinsp;0.007, p\u0026thinsp;=\u0026thinsp;0.02); in the mid-disease stratum there was a small negative linear effect (Estimate= -0.067, SE\u0026thinsp;=\u0026thinsp;0.024, p\u0026thinsp;=\u0026thinsp;0.005); and there were no significant slope effects in the late disease stratum.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eNumber of Clusters by Disease Duration\u003c/h2\u003e \u003cp\u003eUsing LCGA, four latent classes best described perceived longitudinal ambulatory impairment patterns across the early and mid-disease duration strata, while five latent classes best described impairment in the later disease duration stratum. Across models, these solutions achieved a near minimum (\u0026lt;\u0026thinsp;1% decrease after in adding an additional class) for BIC, aBIC, AIC and AICC values (Supplementary Table\u0026nbsp;1) and were a near maximum entropy. Similarly, the interpretability of the classes supported these solutions across each of the stratified models.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDescription of Clusters (subgroups)\u003c/h2\u003e \u003cp\u003eThe average mobility trajectories for each cluster within each disease duration strata are displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. In the early disease stratum, we labeled the four clusters as: no ambulatory impairment (Class 1: 37.8% [of participants]), slight impairment (Class 2: 40.7%), moderate impairment (Class 3: 17.5%) and severe impairment with variability (Class 4: 3.8%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.A). In the mid disease stratum, four subgroups were similarly described (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.B. In the later disease (\u0026gt;\u0026thinsp;10 years) stratum, we labeled Class 1 through Class 4 similarly, with Class 5 (2.8%) labeled as extreme impairment with variability as it was above \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\tau _4}\\)\u003c/span\u003e\u003c/span\u003e(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.C). The percentage of subjects in the moderate and severe subgroups increased with disease duration (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In contrast to the single cluster solutions in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, that exhibit no change to slight improvement across strata, upon inspection of the individual trajectories per strata in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the slight to moderate impairment trajectories (Classes 1 \u0026amp; 2) are stable with time, will those in the moderate to extreme impairment trajectories (Classes 3 to 5) continue to accrue impairment with time \u0026ndash; this emphasizes the importance of examining mobility in the distinct cluster/subgroups of PWP rather than in the overall study population as an average trajectory.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCluster membership characteristics\u003c/h2\u003e \u003cp\u003eResults from the multivariable multinomial logistic regression models with the least impaired cluster (Class 1) as the reference are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Females were less likely to be in clusters with greater ambulatory impairment in PWP with mid-disease, but trending but mostly non-significant relationships in the other strata. On average, older age, higher BMI, lower education, lower income, and being unemployed versus retired were largely associated with increased assignment to clusters with high impairment across disease duration strata. Also, those employed had less impairment compared to retirees. There was also no evidence to suggest differences between white and non-white PWP in cluster membership (although there is an imbalance in the distribution by race in the study population - 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=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultinomial Regression Results for each disease duration strata with the least impaired (Class 1) cluster as the reference category.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAttribute\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eEarly\u0026thinsp;\u0026lt;\u0026thinsp;3 years\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMid 3\u0026ndash;10 years\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eLater\u0026thinsp;\u0026gt;\u0026thinsp;10 years\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOdds Ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOdds Ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOdds Ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP\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\u003eClass 2 vs Class 1\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u0026thinsp;=\u0026thinsp;Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.93 (0.83, 1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.83 (0.71, 0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.86 (0.64, 1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.07 (1.06, 1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.05 (1.03, 1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.07 (1.04, 1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u0026thinsp;=\u0026thinsp;Non-White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.06 (0.74, 1.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.6 (1.02, 2.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.72 (0.31, 1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95 (0.92, 0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.99 (0.94, 1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.97 (0.88, 1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.87 (0.84, 0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9 (0.86, 0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.89 (0.80, 0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFull Employment vs Retired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.94 (0.81, 1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.04 (0.84, 1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.78 (0.47, 1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePart Employment vs Retired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97 (0.80, 1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.16 (0.88, 1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.81 (0.45, 1.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployment vs Retired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.56 (1.15, 2.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.11 (1.41, 3.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.99 (0.45, 2.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.00, 1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (0.99, 1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.99 (0.97, 1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClass 3 vs Class 1\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u0026thinsp;=\u0026thinsp;Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.86 (0.75, 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.76 (0.65, 0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.79 (0.58, 1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.12 (1.11, 1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.12 (1.10, 1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.09 (1.06, 1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u0026thinsp;=\u0026thinsp;Non-White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.35 (0.85, 2.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.49 (0.89, 2.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.48 (0.18, 1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.91 (0.87, 0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.96 (0.91, 1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.99 (0.89, 1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.78 (0.74, 0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8 (0.76, 0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.8 (0.72, 0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFull Employment vs Retired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.69 (0.56, 0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.72 (0.55, 0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.48 (0.27, 0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePart Employment vs Retired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.71 (0.54, 0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.94 (0.68, 1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.71 (0.38, 1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployment vs Retired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.85 (1.30, 2.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.86 (1.19, 2.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.58 (0.73, 3.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02 (1.01, 1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.02 (1.01, 1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.02 (1.00, 1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClass 4 vs Class 1\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u0026thinsp;=\u0026thinsp;Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95 (0.73, 1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.71 (0.55, 0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.57 (0.39, 0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.15 (1.13, 1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.14 (1.11, 1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.12 (1.07, 1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u0026thinsp;=\u0026thinsp;Non-White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.44 (0.59, 3.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.83 (0.90, 3.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.11 (0.39, 3.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.84 (0.77, 0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.93 (0.85, 1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.04 (0.92, 1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.74 (0.68, 0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.71 (0.66, 0.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.69 (0.60, 0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFull Employment vs Retired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.37 (0.23, 0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.55 (0.32, 0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.11 (0.02, 0.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePart Employment vs Retired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.32 (0.16, 0.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.48 (0.24, 0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.23 (0.06, 0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployment vs Retired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.06 (1.78, 5.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.86 (2.14, 6.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.17 (1.33, 7.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.07 (1.05, 1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.07 (1.06, 1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.05 (1.02, 1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClass 5 vs Class 1\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u0026thinsp;=\u0026thinsp;Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo fifth cluster\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo fifth cluster\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.88 (0.46, 1.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.13 (1.06, 1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u0026thinsp;=\u0026thinsp;Non-White\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.18 (0.43, 11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.83 (0.67, 1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.88 (0.70, 1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFull Employment vs Retired\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0 (0.00, 0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePart Employment vs Retired\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.72 (0.16, 3.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployment vs Retired\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.05 (0.39, 10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.1 (1.05, 1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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\u003eDescriptive statistics are reported for sociodemographic and clinical variables in Supplementary Tables\u0026nbsp;2\u0026ndash;4. In brief, in the early disease stratum, the higher impairment classes include a higher percentage of PWP on prescribed PD medications, with a greater prevalence of depression, anxiety and arthritis. The higher classes also reported more impairment in balance, back pain problems, walking impairment, pain and trouble getting out of bed and less work-related activity, sport and recreational activities (light, moderate and strenuous) and muscle strength. Class 3 had a higher proportion of PWP with current depression and anxiety than Class 4, while Class 4 had more physical impairment than Class 3. These trends were similar in the mid disease stratum, except Class 4 had higher percentages of current depression and anxiety than Class 3. There were also no differences in the percentage on PD medication (given the Bonferroni correction). The later duration stratum continued similar trends as the mid disease stratum, except there were no differences in the proportion of veterans or PWP with balance impairment across clusters.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAmbulatory impairment is common in PWP, with a heterogenous presentation that negatively impacts QoL \u003csup\u003e1, 2\u003c/sup\u003e. Little is known about how PWP experience their difficulty in walking, much less over time, and by disease duration. Studies that have analyzed mobility in PWP have done so in aggregate, and resultantly fail to observe intrinsic and meaningful variation in subgroup ambulatory patterns \u0026ndash; which is highly relevant for PROs. The analysis of subgroup mobility PRO trajectories in PWP is essential for understanding the progression of ambulatory impairment and may facilitate optimizing treatment plans, self-management strategies (i.e. exercise regimen), prognostication, and efforts to develop robust endpoints for clinical and observational research. Here we leveraged a readily accessible and broadly used health-related QoL instrument to identify and characterize subgroups of PWP with similar perceived ambulatory impairment trajectories over time and stratified by disease duration. PWP at the early and mid-disease stages of PD were clustered into four trajectories with \u0026gt;\u0026thinsp;65% having no to slight and stable impairment, and \u0026gt;\u0026thinsp;20% having moderate to severe trajectories that were increasing over time. PWP at the later stage of PD were clustered into five trajectories, including 2.8% in an extremely impaired subgroup \u0026ndash; in general, ~\u0026thinsp;50% had at modest slight and stable impairment while the other\u0026thinsp;~\u0026thinsp;50% had moderate to extreme impairment that increased with time. There were also significant associations with trajectory membership for multiple sociodemographic and clinical attributes, which offers insights to drivers and correlates of heterogeneity in ambulatory impairment. Collectively, the findings may be leveraged to identify PWP at risk for greater sustained ambulatory impairment and may be utilized in patient-centered care approaches to advance care management and shared decision making.\u003c/p\u003e \u003cp\u003eThe multivariable models provided new insights into mobility impairment in PWP. For example, despite comparing multiple facets of PD presentation, it has been unclear to extent to which there may be gender differences in motor functioning, mobility, and health-related quality of life \u003csup\u003e23, 24\u003c/sup\u003e. As evident from the multinomial models where we adjusted for likely confounders, we observed females were less like to be in the more impaired clusters in those with mid-disease. There were an underrepresentation of females in Class 3 vs Class 1 during the earliest stage of PD and Class 4 vs Class 1 during the later stage of PD, which highlights that there is a non-linear relationship between sex and mobility over the disease course \u0026ndash; which, in part, may explain the unclear patterns previously observed by others \u003csup\u003e23, 24\u003c/sup\u003e. The relationships for employed were as one would speculate, with part/full-time employed PWP being less burdened with high impairment compared to retirees across disease duration strata, while unemployed PWP (which would include those on disability) were more much likely to be in clusters with more severe impairment compared to retirees. Lower income was consistently associated with higher impairment and consistent with prior findings \u003csup\u003e25\u003c/sup\u003e. This effect was irrespective of disease course which illustrated how profound social inequities can impact PD outcomes. Another social determinant of health, higher education, has been inversed associated with white matter hyperintensities and lower MDS-UPDRS scores independent of nigrostriatal dopaminergic denervation in PWP \u003csup\u003e26\u003c/sup\u003e. Here, we observed higher education having a protective effect in relation to perceived mobility impairment only during at the earliest stage of PD, and merits further investigation into the relationship of resilience and PD progression. Another key observation that requires further inquiry, are the patterns observed for race. We did not observe substantial differences in longitudinal mobility impairment between white and non-white PWP when not adjusting and when adjusting for other social determinants of health. This lack of a longitudinal difference is intriguing considering cross-sectional racial difference observed for other health-related quality of life measures \u003csup\u003e27\u003c/sup\u003e. Our observation may be driven by the modest non-white subset in the current data, or that we were able to adjust for key socioeconomic variables (i.e. education, employment, and income) \u0026ndash; others have observed that adjusting for income and education mitigated racial differences in PD severity models \u003csup\u003e25\u003c/sup\u003e. Thus, considering socioeconomic conditions are downstream of race in a causal diagram, subsequent work should explore causal mediation analyses to determine the extent to which social inequities drive racial differences in PD. In our post-hoc analyses, we noted that PWP with poorer mental health, higher burden of pain, and being a veteran were associated with a higher burden of ambulatory impairment \u0026ndash; this may inform care conversations related to PD management and prognostication by aiding efforts to identify PWP most vulnerable for long-term adverse outcomes in functional mobility.\u003c/p\u003e \u003cp\u003eThese findings offer new perspectives on the longitudinal mobility experiences of PWP, from a person-centered framework. Understanding the anticipated trajectories PWP will experience will facilitate the development of tailored care/treatment strategies and allow for greater allocation of resources\u003c/p\u003e \u003cp\u003eparticularly for those with sudden increases in impairment and for those with moderate to extreme impairment that does not decrease with time. The findings also have great potential for developing novel endpoints for clinical and observational research. It would also be important to determine the underlying symptomatology for individual clusters and the extent to which these symptoms are preventable, treatable, or l-3,4-dihydroxyphenylalanine (Levodopa) responsive. It would also be information to focus on PWP who mobility impairment remained low and explore what risk and care strategies may have contributed to these favorable trends. Lastly, more granular baseline data such as subdivision of PWP into heterogeneous PD subtypes (i.e. tremor-dominant versus PIGD) and incorporating genetic and biomarker data may allow better prediction of mobility trajectories as experienced by PWP.\u003c/p\u003e \u003cp\u003eThere are several strengths in the current study, including the large sample size, the application of LCGA to discern subgroups, the opportunity to stratify models by disease duration, the availability of longitudinal EQ-5D-5L data, and the extensive baseline information. There are a few limitations to acknowledge, the first is the study population was comprised of PWP who were digitally literate and therefore it may not represent the cognitively impaired or other marginalized subpopulations. There was also an underrepresentation of Non-White PWP in the data, therefore these results might have limited generalizability to Non-White populations. This study also assumes that all LCGA model assumptions were met in this PD sample for valid inference under the special considerations in which the latent variable mobility* was used as the outcome \u003csup\u003e28\u003c/sup\u003e. We did perform more robust inference in case there is a violation of model parametric assumptions and included quadratic terms in our models in case the trajectory of mobility impairment is non-linear. A key limitation is that in our chosen solutions, there were some clusters of a small cell size, and the entropy values and a few of the posterior probability of membership averages were \u0026lt;\u0026thinsp;70%.\u003c/p\u003e \u003cp\u003eIn summary, LCGA uncovered multiple distinct ambulatory impairment trajectories and distinct subgroups of PWP based on their experiences with difficulties in walking. This is consistent with our prior work on pain perceptions, emphasizing the need the account for longitudinal heterogeneity in PD symptomatology, the need to factor in disease duration, and the power of PRO for facilitating these discoveries \u003csup\u003e12\u003c/sup\u003e. We hope that this work can serve as a framework for characterizing other complex PD impairments, as well as impairment in other chronic disorders, which may subsequently optimize patient care and facilitate the discovery of modifiable risk factors for symptom exacerbation by serving as robust phenotypes for clinical and observational research.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eFB and DG conceptualized the study. FB and DG were responsible for data management. All authors reviewed analytical framework. DG conducted statistical analyses. FB and DG drafted the manuscript. SG guided interpretations. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eFinancial support for this study was provided by a grant from The Michael J. Fox Foundation for Parkinson\u0026rsquo;s Research MJFF-020155. The funding agreement ensured the authors\u0026rsquo; independence in designing the study, interpreting the data, writing, and publishing the report. The Fox Insight Study (FI) is funded by The Michael J. Fox Foundation for Parkinson\u0026rsquo;s Research. We would like to thank the Parkinson\u0026rsquo;s community for participating in this study to make this research possible.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDDG has received grant support from NIH and the Michael J Fox Foundation and reports a book royalty agreement with Taylor \u0026amp; Francis Publishing. FBSB has received grant support from NIH and the Michael J Fox Foundation. FBSB has been a paid consultant for Michael J. Fox Foundation and has received speaker honorarium from Sanofi. SG has received grant support from the NIH and the Michael J Fox Foundation; he has participated in clinical studies funded by the Parkinson Foundation, Biogen, UCB, and Amneal.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRahman S, Griffin HJ, Quinn NP, Jahanshahi M. Quality of life in Parkinson's disease: the relative importance of the symptoms. Mov Disord 2008;23:1428\u0026ndash;1434.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu P-L, Lee M, Huang T-T. Effectiveness of physical activity on patients with depression and Parkinson's disease: A systematic review. PloS one 2017;12:e0181515-e0181515.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCriminger C, Swank C. Impact of dual-tasking on mobility tasks in Parkinson's disease as described through 2D kinematic analysis. 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A longitudinal evaluation of patients' perceptions of Parkinson's disease. Gerontologist 2002;42:790\u0026ndash;798.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKnutsson I, Rydstrom H, Reimer J, Nyberg P, Hagell P. Interpretation of response categories in patient-reported rating scales: a controlled study among people with Parkinson's disease. Health Qual Life Outcomes 2010;8:61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBouca-Machado R, Goncalves N, Lousada I, Patriarca MA, Costa P, Nunes R, et al. Patients and Health Professional's Perspective of Functional Mobility in Parkinson's Disease. Front Neurol 2020;11:575811.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGunzler DD, Perzynski AT, Carle AC. Structural Equation Modeling for Health and Medicine. 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGunzler DD, Morris N, Perzynski A, Ontaneda D, Briggs F, Miller D, et al. 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Health Qual Life Outcomes 2013;11:35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlvarado-Bola\u0026ntilde;os A, Cervantes-Arriaga A, Rodr\u0026iacute;guez-Violante M, Llorens-Arenas R, Calder\u0026oacute;n-Fajardo H, Mill\u0026aacute;n-Cepeda R, et al. Convergent validation of EQ-5D-5L in patients with Parkinson's disease. Journal of the Neurological Sciences 2015;358:53\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXin Y, McIntosh E. Assessment of the construct validity and responsiveness of preference-based quality of life measures in people with Parkinson\u0026rsquo;s: a systematic review. Quality of Life Research 2017;26:1\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmolensky L, Amondikar N, Crawford K, Neu S, Kopil CM, Daeschler M, et al. Fox Insight collects online, longitudinal patient-reported outcomes and genetic data on Parkinson's disease. Sci Data 2020;7:67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOppe M, Devlin NJ, van Hout B, Krabbe PF, de Charro F. A program of methodological research to arrive at the new international EQ-5D-5L valuation protocol. Value Health 2014;17:445\u0026ndash;453.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuth\u0026eacute;n B, Shedden K. Finite mixture modeling with mixture outcomes using the EM algorithm. Biometrics 1999;55:463\u0026ndash;469.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuth\u0026eacute;n LK, Muth\u0026eacute;n BO. Mplus. \u003cem\u003eThe comprehensive modelling program for applied researchers: user\u0026rsquo;s guide\u003c/em\u003e 2012;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHallquist MN, Wiley JF. MplusAutomation: An R Package for Facilitating Large-Scale Latent Variable Analyses in M plus. 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Arch Neurol 2011;68:498\u0026ndash;503.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKotagal V, Bohnen NI, Muller ML, Koeppe RA, Frey KA, Langa KM, et al. Educational attainment and motor burden in Parkinson's disease. Mov Disord 2015;30:1143\u0026ndash;1147.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDi Luca DG, Luo S, Liu H, Cohn M, Davis TL, Ramirez-Zamora A, et al. Racial and Ethnic Differences in Health-Related Quality of Life for Individuals With Parkinson Disease Across Centers of Excellence. Neurology 2023;100:e2170-e2181.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBauer DJ. Observations on the use of growth mixture models in psychological research. Multivariate Behavioral Research 2007;42:757\u0026ndash;786.\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":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"mobility impairment, Parkinson’s disease, latent class growth analysis, patient reported outcome, trajectories","lastPublishedDoi":"10.21203/rs.3.rs-3778288/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3778288/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAmbulatory impairment in Parkinson\u0026rsquo;s disease (PD) is common and complex, and poorly understood from the perspectives of those with PD. Gaining insights to the anticipated perceived trajectories and their drivers, will further facilitate patient-centered care. Latent class growth analysis, a person-centered mixture modelling approach, was applied to 16,863 people with PD stratified by early (N\u0026thinsp;=\u0026thinsp;8612; \u0026lt;3 years), mid (N\u0026thinsp;=\u0026thinsp;6181; 3\u0026ndash;10 years) and later (N\u0026thinsp;=\u0026thinsp;2070; \u0026gt;10 years) disease to discern clusters with similar longitudinal patterns of self-reported walking difficulty, measured by EuroQoL 5D-5L that is validated for use in PD. There were four clusters in early and mid-disease strata, with a fifth identified in later disease. Trajectories ranged from none to moderate mobility problems, with small clusters with severe problems. The percentage of subjects with moderate (early\u0026thinsp;=\u0026thinsp;17.5%, mid\u0026thinsp;=\u0026thinsp;26.4%, later\u0026thinsp;=\u0026thinsp;32.5%) and severe (early\u0026thinsp;=\u0026thinsp;3.8%, mid\u0026thinsp;=\u0026thinsp;7.4%, later\u0026thinsp;=\u0026thinsp;15.4%) mobility problems at baseline increased across disease duration groups. The trajectories tended to be stable with variability in moderate and severe groups. Across strata, clusters with moderate to severe problems were associated with more severe impairment, depression, anxiety, arthritis, higher BMI, lower income, and lower education, but no consistent race or gender differences. The findings reveal distinct longitudinal ambulatory patterns in PD based on a person-centered approach.\u003c/p\u003e","manuscriptTitle":"A person-centered approach to characterizing longitudinal ambulatory impairment in Parkinson's disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-02 17:09:26","doi":"10.21203/rs.3.rs-3778288/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-03-21T10:02:14+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-03-18T15:30:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"43a04e4a-7101-4651-b4b8-cc2f37f49db1","date":"2024-03-08T13:31:38+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-02-16T11:48:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"76af5ac7-68b0-4980-b284-aad0c6dc0073","date":"2024-02-06T16:35:19+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-01-05T14:38:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-01-05T14:34:44+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2023-12-23T09:17:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-12-23T09:13:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2023-12-19T18:58:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5f2be10b-d636-404b-86ab-dd9003a5e98b","owner":[],"postedDate":"January 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":27797823,"name":"Health sciences/Neurology/Neurological disorders/Movement disorders/Parkinsons disease"},{"id":27797824,"name":"Health sciences/Medical research/Epidemiology"}],"tags":[],"updatedAt":"2024-05-14T10:16:44+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-02 17:09:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3778288","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3778288","identity":"rs-3778288","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}