Health Trajectories in Nursing Science

1. Health Trajectories in Nursing Science Elizabeth C. Clipp, RN, PhD Professor and Associate Dean for Research Duke University School of Nursing

2. Overview • My background and context • Why a focus on health trajectories? • Health trajectories: Concepts • Health trajectories: Empirical Examples

3. Background • Master’s prepared nurse • NIMH Fellow -- Developmental Psychology • Glen Elder & Uri Bronfenbrenner (Mentors) • Applied a clinical lens to • Life span developmental psychology • Life Course Sociology • Large longitudinal data sets (Berkley Oakland, Terman, BLSA) • Dissertation: linking early loss events and late life functioning • Postdoctoral Fellow: Duke Aging Center • Caregiver longitudinal well-being study (predictors of institutionalization) • 20 years in Department Medicine / Geriatrics • Health Trajectories in Later Life • National Longitudinal Caregiving Study

4. During the last 4 years -- • Duke University School of Nursing (2001-present) • P20 Center (Trajectories of Aging & Care in Nursing Science 2001-2003) • P20 Center (Trajectories of Aging & Care in Nursing Science 2004-2007) • Hartford Interdisciplinary Research Center (longitudinal Pilots) • PhD Program (Trajectories of Chronic Illness and Care Systems) • ADR since 9/05

5. Duke University Medical Center – Durham NC

6. Duke School of Nursing Faculty and Staff

7. New Building -- Duke University School of Nursing Summer 2006

8. Health TrajectoriesIn Concept

9. What is a trajectory? • Dictionary: • Curve that a body describes in space; the path, progression, or line of development • Scientific Literature: • course of a dependent variable plotted over time • sequence of transitions • “Transitions give trajectories their distinctive shape and meaning (Elder)” • patterns of human functioning / symptoms (nursing) • Analytically: • longitudinal data incorporating at least 3 time points

10. Health Trajectories • Not a statistical approach • Rather, a way of thinking about • Health dynamics • Clinical phenomena of interest to nurses • Individual differences in health dynamics, specifically in clinical phenomena of interest to nurses • Exploiting longitudinal data in clinically relevant ways.

11. Health trajectories seek to identify patterns of clinically-relevant individual differences and to consider the significance of outliers • Individual Differences: Tendency of individual subjects to maintain the same relative rank on a specific characteristic as compared to the group • Outliers become interesting and clinically relevant – not a source of error

12. Health Trajectory Research “Splitting” Repeated measures on the same subjects over time (person centered) Stability and change patterns in clinical phenomena of interest to nurses Use clinical cut-points or software to identify clinically relevant subgroups Identify factors that differentiate trajectory patterns Based on trajectory patterns, interventions can be optimally targeted and timed. Useful approaches: LCA, HLM Useful software: Latent Gold, M-Plus Longitudinal Research “Lumping” Repeated measures on the same units of analysis over time (e.g., patients, providers, systems, counties). Many analytic approaches, with the more traditional approaches focusing on central tendency of the sample. Health trajectory approach ≠ traditional longitudinal approach

13. Origins • Alcohol abuse literature • Intake patterns: alcoholism dxs, abstinence • Education literature • Tracking students by competencies • Criminology literature • Understanding recidivism • Developmental Psychology • Life course and life span perspectives

14. Example: Trajectories of Behavior in Childhood leading to Various Adolescent Outcomes • 1,037 boys followed from age 6-15 with repeated measures of various external behaviors (aggression, opposition, hyperactivity) • 4 developmental trajectories identified: Chronic Problem, High-sporadic, Moderate-sporadic, no problem. • Results showed that boys who followed one trajectory for one behavior did not necessarily follow same trajectory for another type of behavior • Different behavioral trajectories led to different types of juvenile delinquency. • Chronic opposition trajectory led to covert delinquency (theft). • Chronic aggression trajectory led to overt delinquency (physical violence) and to the most serious acts. Trajectories of Boys’ Physical Aggression, Opposition, and Hyperactivity on the Path to Violent and Non-violent Juvenile Delinquency, Nagin D, Tremblay R, Child Development, Sept 1999.

15. Why should nurse scientists take a trajectory approach? • Nurses focus on health, which is an fundamentally dynamic • Studies that examine serial measures or transitions provide important information about periods of stability, decline or recovery. • Examining trajectories permits the identification of factors that anticipate decline or enhance recovery. • Trajectories provide clues to who need interventions and when interventions are likely to be most effective.

16. Health TrajectoriesEarly empirical work Examples • Early work with the Terman Archive (1980s) Crude forms • EPESE data (1990s) 2a: Trajectory Delineation 2b: Trajectory Prediction • National Longitudinal Caregiver Study (2000-2001) Latent class analysis (M-Plus)

17. Example 1: Terman Archive (1922-1992) • 1500 school children in 1922 with high IQs were followed to study human development and its social and psychological correlates • Follow-ups 1928, 1936, and Q5 yrs to 1960. In 1940, 96% of the sample was still active. After 1960, follow-ups continued in 1972, 1977, 1982, 1986 and the last data collection was in the early 1990s. • 1991 Nurse lens: • 857 with coded health info from 1940 to 1986 (age 35-70s/80s) • What health trajectories describe these men from mid- to later life? What are the correlates of these trajectories?

18. Terman Archive (available data) • 3 items repeated at each of 8 waves 1945-1986: Self-rated health, energy/vitality, alcohol consumption • Pages of uncoded material in response to "describe health and health changes experienced since the last testing and how health influenced overall life” • Year of travel & recoding the archive • summary sheets, code development, physician ratings, reliability checks

19. Terman ArchiveTrajectories • Developed a typology of 5 temporal health patterns over a span of 45 years (1940-86) • Called these patterns “trajectories” • Trajectories relied on coding data based on nursing knowledge/ clinical experience (i.e., visual inspection of temporal patterns) • Trajectories strongly related to age, education, primary illnesses, alcohol use, energy and vitality

20. Simple Trajectory FormsA place to start Stability: “High Stable”, “Low Stable” Change: “Improving”, “Fluctuating”,“Declining”

21. Stable Good Health Stable Poor Health Terman Men Age ~34-76 8 Time Points 1945-1986 Linear Decline Decline at End of Life Decline and Recovery

22. Development of Ideas • Construction of crude physical health trajectories that related meaningfully to personal/psychosocial indicators (Terman Archive) • Clinical patterns vs. population-based research • Individual change vs. Group change • Person approach vs. Variable approach • Signal vs. noise

23. Trajectories of Health: Clipp, Elder & Pavalko. Behavior, Health and Aging, 1992. • “In thinking about older people: It has long been clear that for some, much of life is marked by sustained good health until the end of life, while for others, life is characterized by sudden or gradual declines in function, sometimes punctuated by intervals of complete or partial recovery. • These temporal patterns are multidimensional, dynamic, and result from a combination of factors including genetic endowment, active disease states, age-related changes, coping resources, life events, lifestyle patterns, and access to care. The complexity of these patterns accounts for why acute illnesses present and retreat and why chronic illnesses accumulate and interact to form intricate clinical profiles. • These temporal patterns can be described as health trajectories. • Nurses work to effectively intervene within these unfolding patterns of functioning and clinical symptoms - with the goal of positively reorienting problematic trajectories”.

24. Example 2 Trajectory Delineation and Prediction:Observations from the Duke EPESE

25. Trajectory DelineationStudy Aims • To empirically identify and describe 7-year trajectories of health among older men across 4 domains: • Depression • Self-rated health • Cognitive function • Physical function • To consider correlates and predictors of these trajectory patterns.

26. Data • Established Populations for Epidemiologic Studies of the Elderly (EPESE; Duke Site) • Longitudinal, with 3 detailed, face-to-face interviews in 1985, 1989, 1993 • 1,473 men age 65+ • Adjusted response rates, excluding mortality exceeded 90%

27. Four Health Domains Time Frame: 1986-1993 • Perceived Health (standard self-reports) • Functional Health (ADLs, IADLs) • Cognitive Functioning (SPMSQ) • Depression (CES-D) P1: 1,473 P2: 1,095 P3: 805

28. Trajectory Delineation • 7-year health domains (measures) • Self-rated health (standard single-item) • Depression (CES-D) • Cognitive function (SPMSQ) • Physical function (ADLs/IADLs) • Distribution of measures examined; clinically-relevant cut points selected • Patterns of stability and change in the indicators assessed across the 7-year interval

29. Observations (con’t) Based on the Terman work (informed by clinical observation), we looked for and again found that five trajectories summarized the 7-year health histories: High Stable Low Stable Improving Declining Fluctuating

30. Observations • Means analysis -- we found relative stability in in the four health domains (depression, self-reported health, cognitive functioning, and physical functioning). • In other words -- “High Stable” trajectories within all four health domains most commonly characterized the EPESE men. • However, group means masked substantial variability, as shown by Z-scores; other 4 trajectories were observed fairly evenly.

31. Observations (con’t) After eliminating “High Stable” men • Men with lower self-rated health tended to demonstrate higher levels of depression • Most of the men (65%) with low levels of physical functioning also had low levels of cognitive functioning • Began to think about the interrelationships among clinical trajectories

32. Conclusions • Most community-dwelling older men enjoy high levels of health over time (high self-ratings, little or no evidence of depression or ADL challenge, and high levels of cognitive functioning). • However, this “high stable” group drives measures of central tendency and masks several clinically meaningful patterns of stability and change among many other men.

33. Trajectory PredictionThinking about Health Trajectories as Outcomes • What are the demographic and health history predictors of 7-year health trajectories? • Self-Related Health • Depression • Cognitive Functioning • Physical Functioning

34. Predictor Pool(3 Variable sets) • 15 demographic and social indicators • 9 medical and health service use indicators • 4 baseline indicators of each health domain

35. Demographic and Social Predictors Age, Race, Education, Income Urban/Rural Residence Marital, Working, Veteran Status Freq. of Church Attendance Perceived Adequacy of Finances Number of Negative Life Events 4 Dimensions of Social Support

36. Medical and Health Service Use Predictors Diagnosis of Stroke, Heart Attack, Diabetes, Hypertension Chronic Illness Severity Score # Physician Visits / mo & yr Neglect Going to Doctor When Need to Go Current Smoker

37. Analytic Strategies • Bivariate Means Analysis (ANOVAs) • Logistic Regressions with “High Stable” Men as the Reference Group

38. Predictors Odds Ratios Low Stable Improving Fluctuating Declining N=83 N=90 N=76 N=68 Age (4 categories) .91 * ** 2.60 Race (black=1) * 0.32 Years of Educ. (4 categories) .89 * Urban vs. Rural (Rural =1) 0.44 ** Chronic Illness Sev. Score (3 categories ) 2.14 1.70 1.70 ** * * Neglect Going to Doctor (0-1) 1.61 2.50 * *** No. Doctor Visits (1-4+) 1.12 1.09 1.20 * * *** Current Smoker (0-1) 2.70 ** Freq. of Church Attendance (4 categories) .78 * Perceived Adeq., Finances (3 categories) 0.54 .52 * * Cognitive Status (4 categories) .51 ** Depression (4 categories) 1.16 1.30 1.17 * 1.40 *** * *** A Also controlling on Income, Marital and Working statuses, Veteran status and Service Connected Disability, History of Stroke, Diabetes, Heart Attack, Hypertension, No. Neg. Life Events, Amount of Social Support Given, Amount of Social Support Received, No. People Interact With, Perceived Adequacy of Social Support, and Functional Impairment. * p<.05, ** p<.01, *** p<.001 Table 2 Significant Baseline Predictors of SELF-RATED HEALTH Trajectories Using Logistic Regressiona , with “High Stable” (N=284) as reference group

39. Table 3 Significant Baseline Predictors of DEPRESSION Trajectories Using Logistic Regressiona , with “High Stable” (N=338) as reference group 1.07 * 1.10 ** .43 * 1.50 * 2.10 *** .78 ** 1.86 ** 1.18 ** .67 ** .65 * .70 * Predictors Odds Ratios Improving Fluctuating Declining Low Stable N=94 N=91 N=87 N=68 * Age (4 categories) .31 Currently working (0-1) * Veteran (0-1) 1.80 Neglect Going to Doctor (0-1) 2.50 *** Current Smoker (0-1) 2.00 * Freq. of Church Attendance (4 categories) No. Negative Life Events (3 categories) 2.00 ** No. People Interact With (4 categories) 1.01 * Social Support Received (4 categories) * Per. Avail. of Soc. Support (3 categories) .62 Self-Rated Health (4 categories) .29 *** a Also controlling on Race, Education, Income, Marital Status, Rural/Urban Residence, Service Connected Disability, History of Stroke, Diabetes, Heart Attack, Hypertension, Chronic Illness Severity Score, No. Physician Visits, Perceived Adequacy of Financial Resources, Amount of Social Support Given, Cognitive Status, and Functional Impairment. * p<.05, ** p<.01, *** p<.001

40. Table 4 Significant Baseline Predictors of COGNITIVE FUNCTION Trajectories Using Logistic Regressiona , with “High Stable” (N=300) as reference group 1.11 1.13 ** *** 2.30 3.20 2.00 4.74 * ** * *** .77 .87 .88 .68 *** ** *** *** 1.01 * .79 .75 * * 1.15 * 1.01 * 2.90 * Predictors Odds Ratios Improving Fluctuating Declining Low Stable N=87 N=73 N=141 N=156 Age (4 categories) Race (black=1) Years of Educ. (4 categories) Income (4 categories) Freq. of Church Attendance (4 categories) Social Support Received (4 categories) Self-Rated Health (4 categories) Functional Status (3 categories) a Also controlling on Marital and Working Statuses, Rural/Urban Residence, Veteran Status, Service Connected Disability, History of Stroke, Diabetes, Heart Attack, Hypertension, Chronic Illness Severity Score, Neglect Own Health, No. Physician Visits, Current Smoker, Perceived Adequacy of Financial Resources, Amount of Social Support Given, No. People Interact With, Adequacy of Social Support, No. Negative Life Events, and Depression. b * p<.05, ** p<.01, *** p<.001

41. Table 5 Significant Baseline Predictors of PHYSICAL FUNCTION Trajectories Using Logistic Regressiona , with “High Stable” (N=500) as reference group 1.10 1.10 1.30 * *** *** .36 * .65 * 7.00 * 2.10 * .81 .91 .58 * * *** 1.20 1.38 * ** .63 ** 1.90 ** 1.20 * Predictors Odds Ratios Improving Fluctuating Declining Low Stable N=29 N=32 N=120 N=45 Age (4 categories) Race (black=1) .28 * Currently married (0-1) Veteran (0-1) Dx: Stroke (0-1) Perceived Adeq., Finances (3 categories) Social Support Given (4 categories) Social Support Received (4 categories) Self-Rated Health (4 categories) Cognitive Status (4 categories) Depression (4 categories) a Also controlling on Education, Income, Working Status, Rural/Urban Residence, Service Connected Disability, History of Diabetes, Heart Attack, Hypertension, Chronic Illness Severity Score, Neglect Own Health, No. Physician Visits, Current Smoker, Freq. of Church Attendance, Perceived Adequacy of Social Support, No. of People Interact With, No. Negative Life Events, Cognitive Impairment, and Self-Rated Health. * p<.05, ** p<.01, *** p<.001

42. Table 1a: Summary of ANOVAs Health Domains 1985 Baseline Predictors Self-Rated Depression Cognitive Physical Health Function Function A. Demographic & Social Age (4 categories) *** *** *** Race (black=1) ** ** *** *** Years of Educ. (4 categories) *** *** *** *** Income (4 categories) *** *** *** *** Urban vs. Rural (Rural=1) ** * Currently married (0-1) ** *** *** Currently working (0-1) * *** * *** Veteran (0-1) *** *** Freq. of Church Attendance (4 categories) *** *** *** *** Perceived Adeq., Finances (3 categories) *** *** *** *** No. Negative Life Events (3 categories) *** *** * * No. People Interact with (4 categories) * ** Social Support Received (4 categories) * * Per. Avail. of Social Support (3 categories) *** *** *** Social Support Given (4 categories) * ** *** *** * ** *** Predictor significantly discriminates among trajectories in that domain at .05 , .01 ,or .001 levels

43. * ** *** Predictor significantly discriminates among trajectories in that domain at .05 , .01 ,or .001 levels Table 1b: Summary of ANOVAs Health Domains 1985 Baseline Predictors Self-Rated Depression Cognitive Physical Health Function Function B. Medical & Health Services Use * ** * Service connected disability (0-1) * * *** Dx: Stroke (0-1) *** Dx: Diabetes (0-1) *** ** Dx: Heart Attack (0-1) ** ** Dx: Hypertension (0-1) *** * Chronic Illness Sev. Score (3 categories) *** *** Neglect Going to Doctor (0-1) *** ** No. Doctor Visits (1-4+) ** Current Smoker (0-1) C. Baseline Domain Measures Self-Rated Health (4 categories) NA *** *** *** Depression (4 categories) *** NA *** *** Cognitive Status (4 categories) ** *** NA *** NA Functional Impairment (3 categories) * **

44. Findings (7-year window) What at baseline predicts a vulnerability trajectory of self-rated health? • In contrast with the men having high stable ratings of self-rated health, men with low stable ratings were more likely, at baseline, to be white, to live in urban areas, to have higher chronic illness severity scores, to smoke regularly, to have symptoms of depression, and to perceive their finances as inadequate for meeting their needs. • In contrast with high stable men, men with low stable trajectories of self-rated health report significantly more visits to the doctor, yet also are more likely to report that they neglect going when they need to go. • Overall: Older men who, over the 7-year trajectory window, rate their health consistently low are the highest users of health services but, at the same time, report high levels of unmet need.

45. Findings (7-year window) What at baseline predicts a vulnerability trajectory of depression? • Two key predictors of depression trajectories: # of negative life events that the men experienced at baseline, and baseline measures of health service need. • For every category increase in number of negative life events experienced at baseline, the odds of exhibiting an increasing or stable high depression symptom trajectory rise nearly two fold. • The predictor “Neglect going to the doctor when I need to go” is positively associated with the odds of exhibiting increasing, fluctuating, or stable high depression trajectories.

46. Findings (7-year window) What at baseline predicts a vulnerability trajectory of cognitive functioning? • The literature suggests that higher levels of cognitive impairment are associated with increased receipt of assistance. We found this to be true, but only among men with low stable trajectories of cognitive function. These men need assistance and they are more likely to receive it. • However, among older men whose cognitive functioning is changing -- either in patterns of decline, improvement, or fluctuation – these men receive no more social support on average than men with high functioning trajectories. This begs the question, “how long do symptoms of cognitive impairment typically exist before support is mobilized?

47. Findings (7-year window) What predicts a vulnerability trajectory of physical functioning? • In comparison to physically high functioning men, men with low stable physical functioning were more likely to be older, white, with histories of stroke, giving less but receiving greater amounts of social support, more cognitively impaired and more depressed. • However, men with greater levels of physical dependency who undoubtedly need support do appear to be receiving it. “Overall, we are encouraged by the precision gained in using trajectories rather than measures of central tendency to chart health processes over time” – GSA 2003.

48. EPESE Data -- after thoughts… • Most community-dwelling older people are functioning well across health domains. But many others are not - yet their vulnerabilities (illness pathways) are masked in traditional analytic approaches. • Nurse scientists need to consider both traditional (central tendency) and trajectory approaches (identification of clinically-meaningful groups) when measuring clinical phenomena over time. • The clinically relevant categories can be identified best by nurse scientists. • Future studies need to focus on the interaction of multiple health trajectories within patients. This will allow nurses to design interventions that build on one’s strengths while targeting areas of vulnerability.

49. Example 3National Longitudinal Caregiving Study • Funded by the VA HSR&D National Nurses’ Research Initiative (1997-2001) • Goal: to examine comprehensively the informal disease burden on families caring for elderly with dementing disorders

50. National Longitudinal Caregiver Study (NLCS) • 4-year longitudinal study (4 annual surveys) • 2,278 informal primary caregivers at baseline • Patients are elderly (60+) veterans followed in the VA Hospital system, nationwide • Identified using VA administrative databases (formal diagnoses -- ICD-9 codes for AD or VAD • Mail surveys in 1998 (baseline), 1999, 2000, 2001 • Unit of analysis is the caregiver, but data include caregiver and patient information