Research ArticleMethodology

Association of Patient-Centered Outcomes With Patient-Reported and ICD-9–Based Morbidity Measures

Elizabeth A. Bayliss, Jennifer L. Ellis, Jo Ann Shoup, Chan Zeng, Deanna B. McQuillan and John F. Steiner
The Annals of Family Medicine March 2012, 10 (2) 126-133; DOI: https://doi.org/10.1370/afm.1364

Abstract

PURPOSE Evaluating patient-centered care for complex patients requires morbidity measurement appropriate for use with a variety of clinical outcomes. We compared the contributions of self-reported morbidity and morbidity measured using administrative diagnosis data for both patient-reported outcomes and utilization outcomes.

METHODS Using a cohort of 961 persons aged 65 years or older with 3 or more medical conditions, we explored 9 health outcomes as a function of 4 independent variables representing different types of morbidity measures: International Classification of Diseases, Ninth Revision (ICD-9), a self-reported weighted count of conditions, and self-reported symptoms of depression and of anxiety. Outcomes varied from self-reported health status to utilization. Depending on the outcome measure, we used multivariate linear, negative binomial, or logistic regression, adjusting for demographic characteristics and length of enrollment to assess associations between dependent and all 4 independent variables.

RESULTS Higher morbidity measured by ICD-9 diagnoses was independently associated with less favorable levels of 7 of the 9 clinical outcomes. Higher self-reported disease burden was significantly associated with less favorable levels of 8 of the outcomes, controlling for the 3 other morbidity measures. Morbidity measured by diagnosis code was more strongly associated with higher utilization, whereas self-reported disease burden and emotional symptoms were more strongly associated with patient-reported outcomes.

CONCLUSIONS A comprehensive assessment of morbidity requires both subjective and objective measurement of disease burden as well as an assessment of emotional symptoms. Such multidimensional morbidity measurement is particularly relevant for research or quality assessments involving the delivery of patient-centered care to complex patient populations.

INTRODUCTION

Historically, assessment of health care quality has been quantified using disease-specific measures, such as targeted laboratory values or preventable hospitalizations.1–4 A more stringent and patient-centered standard, however, is to assess quality with the person, rather than the disease, as the unit of analysis.1,5–7 Doing so requires the use of patient-centered measures that express the net influence of all health conditions and their treatments on outcomes that are meaningful to patients. One example of such a measure is patient-reported outcomes that cross disease-specific boundaries (such as general health status or physical functioning).8 Assessing quality based on outcomes that matter to patients requires additional attention to measurement processes using these outcomes.9–12 Using patient-reported outcomes is particularly relevant when assessing process, content, and quality of health outcomes for persons with multiple interacting medical conditions, as well as for assessing multidimensional care interventions, such as implementations of the patient-centered medical home.13,14

The armamentarium of instruments available to quantify morbidity has expanded to meet this need and includes tools based on data from a variety of sources, including diagnosis codes, pharmacy data, chart review, and self-report.15–20 Morbidity measured with these instruments has been associated with a variety of global outcomes that range from mortality to hospitalization to cost of care to quality of life.16,18,20,21 Many of these morbidity scores have significant associations with more than one outcome.20,22,23

Ideally, the choice of instrument for a given investigation should be based on such construct validity. Even so, the choice of instrument is often based on convenience and/or available resources rather than demonstrated association with specific outcomes. For example, tools based on diagnosis codes from administrative data may be used to adjust for morbidity burden in investigations of quality-of-life outcomes or disease management behaviors—a method that may result in incomplete morbidity adjustment.24,25 In particular, assessing self-reported morbidity burden may be underutilized because collecting self-reported data can be resource-intensive.

Self-reported morbidity assessments may incorporate biopsychosocial constructs not captured by other measures of morbidity and, therefore, may be particularly relevant for assessments using patient-reported outcomes.20,26,27 In addition, there is evidence that any single morbidity measure may fail to capture the entire association between morbidity burden and a health outcome.28,29 Given the complexity of most health outcomes, using 2 or more measures that draw on different primary data sources may improve the ability to explain the role(s) of morbidity in predicting a variety of such outcomes.

The goal of this investigation was to explore the independent contributions of self-reported morbidity and a morbidity measure based on administrative diagnoses for 2 types of health outcomes that are particularly relevant to persons with multiple morbidities: 6 patient-reported outcomes and 3 utilization outcomes. We hypothesized that self-reported (subjective) morbidity would be particularly important in assessing patient-reported (subjective) health outcomes, whereas morbidity measurement based on diagnosis codes would be important in assessing health care utilization.

METHODS

Study Design and Population

The data for this cross-sectional investigation were collected as baseline information for a prospective cohort study that investigates predictors of a variety of health outcomes as a function of biopsychosocial factors in a population of persons with multiple morbidities. The cohort consisted of members of Kaiser Permanente Colorado (KPCO), an integrated, not-for-profit health maintenance organization, who were aged 65 years or older, enrolled for at least 1 year, and had 3 or more of a list of 10 common chronic medical conditions. We used an invitation letter to recruit a random sample within the eligibility criteria. The response rate to the original survey was 47%—likely a result of the length of the survey. Respondents were on average 1 year younger than nonrespondents; the 2 groups did not differ with regard to diagnosis-based morbidity level or sex. Because the investigation involved surveying participants on their processes of care, we excluded members who carried a diagnosis of dementia. The investigation was approved by the KPCO Institutional Review Board.

Measures and Analytic Variables

Our goal for this study was to explore each of a set of health outcomes as a function of 4 independent variables representing different types of morbidity measures: a widely used measure based on administrative data, a self-report of a weighted count of medical conditions, self-reported symptoms of depression, and self-reported symptoms of anxiety. We chose these variables based on (1) the common use of administrative data, such as diagnoses, to quantify morbidity; (2) our hypothesis that self-reported morbidity would additionally contribute to capturing complete morbidity burden in this patient population for these outcomes; and (3) recommendations that overall morbidity assessments incorporate mental as well as physical morbidity.30–32

For the diagnosis-based morbidity measure, we chose the Quan adaptation of the Charlson comorbidity index, which is adapted for use with the International Classification of Diseases, Ninth Edition (ICD-9) diagnostic codes and has construct validity in both primary care and hospital populations for multiple outcomes, including mortality, postoperative complications, length of stay, cost of hospitalization, cost of chronic disease care, and admission to skilled nursing facilities.15,22,23 It also has established associations with health-related quality-of-life outcomes, such as self-reported health and functional status.33–36 We included outpatient and inpatient diagnoses for the year before survey completion. For the self-report measure, we used a measure of disease burden we previously developed and validated against health status and physical functioning. It consists of a guided, open-ended count of conditions weighted by perceived limitation resulting from each condition and produces a summary score that represents a self-report of severity-adjusted disease burden.26,37 Depressive symptoms were assessed using the Patient Health Questionnaire 9-item depression screen (PHQ-9), and anxiety symptoms were measured using the Generalized Anxiety Disorder 7-item scale (GAD-7).38,39 We chose values of greater than 4 on the PHQ-9 and greater than 4 on the GAD-7 as being indicative of symptoms of each condition.40 These cut points are relatively low for these instruments and were not intended to serve as diagnostic criteria; rather, they were intended to account for emotional morbidity that could potentially confound measures of self-reported disease burden.

We chose our outcomes of interest to represent both subjective and objective health outcomes that are relevant to patients with multiple morbidities and could potentially be associated with high morbidity burden. In addition, we postulated that certain outcomes might be differentially associated with morbidity burden as calculated by administrative diagnosis data vs self-report.

We assessed 3 of the 6 patient-reported outcomes using the well-validated RAND 36 measure of health-related quality of life41: self-reported health status, and physical and mental well-being (physical component summary [PCS] and mental component summary [MCS] scores).36,42 We also assessed the domains of feeling overwhelmed by one’s medical conditions, experiencing financial constraints caused by health care costs, and level of general self-efficacy, concepts reported by patients to be a consequence of multi-morbidity.43 We measured these 3 concepts using an instrument we developed for the population of persons with multiple morbidities, which was previously validated against the outcomes of overall health status and physical functioning.44 All patient-reported outcomes were normed on a 100-point scale, with a higher score representing a better outcome (eg, better health, greater self-efficacy, fewer financial constraints). We assessed both self-reported morbidity and all patient-reported outcomes via telephone survey. Utilization outcomes included inpatient, outpatient, and emergency department utilization for the period of 10 months after the original survey.

Statistical Analysis

We initially assessed bivariate correlations between the 2 morbidity measures and the outcomes of interest, followed by multivariate analyses using separate models for each outcome. For continuous outcomes (general health status, PCS score, and MCS score) we used multivariate linear regression to assess the outcome as a function of all 4 morbidity measures entered simultaneously into each model. Because of the large number of zero observations within the utilization outcomes, we dichotomized the outcomes of inpatient admissions and emergency department visits to any vs none and used logistic regression to assess these outcomes. A negative binomial model provided the best fit for the outcome of outpatient visits. The distribution of the outcomes of perceived financial constraints, feeling overwhelmed, and self-efficacy were highly skewed toward more favorable results. Because this analysis was exploratory, we opted to dichotomize at a low level of each variable (10th percentile), based on the premise that a lack of association between morbidity burden and the extreme levels of these outcome variables would preclude any further investigation of these particular outcomes. Logistic regression was also used to analyze these outcomes. All multivariate analyses were adjusted for age, sex, race, socioeconomic status, and years of enrollment.

Because the current investigation was conducted in a cohort developed for a separate study, we did not calculate sample size requirements for our outcomes. Our results present point estimates and confidence intervals for each independent association. The width of each confidence interval provides an indication of the precision of each estimate. Ranges for the independent variables vary and are listed with the results.

RESULTS

There were 961 participants in the cohort. A slight majority were female (55%), the mean age was 75 years, and they had an average of 7.9 self-reported chronic medical conditions. Two percent reported excellent health, 24% very good, 45% good, 23% fair, and 5% poor health. PCS scores were below average relative to national norms and MCS scores were approximately at the national average. Characteristics of the participant population are listed in Table 1.

View this table:
Table 1

Participant Characteristics (N = 961)

There were significant bivariate correlations between both diagnosis-based and disease burden scores for all outcomes. In multivariate analyses, higher morbidity as measured by ICD-9 diagnoses was significantly associated with less favorable levels of the outcome variables for all of the outcomes except for the MCS score and self-efficacy. In models that included the diagnosis-based morbidity score, as well as the presence of depressive and anxiety symptoms, higher self-reported disease burden was independently significantly associated with all of the outcomes except for emergency department admissions. Reported symptoms of anxiety were independently associated with the outcomes of emotional well-being, feeling overwhelmed by medical conditions, and outpatient utilization. Symptoms of depression were independently associated with all the patient-reported outcomes other than reported financial constraints, and with emergency department admissions (Tables 2 and 3).

View this table:
Table 2

Summary of Significant Associations Between Morbidity Measures and Patient-Reported Outcomes

View this table:
Table 3

Summary of Significant Associations Between Morbidity Measures and Utilization Outcomes

In general, higher utilization of outpatient and inpatient services was more strongly associated with higher morbidity as calculated by diagnosis code than with patient-reported burden, anxiety, or depressive symptoms (Table 3). In contrast, patient-reported outcomes were strongly and more frequently associated with the subjective predictors of patient-reported disease burden and emotional symptoms (Table 2).

DISCUSSION

Increasing and improving the delivery of patient-centered care requires systematic practice change (eg, the patient-centered medical home), increased awareness by clinicians, the support of policy makers, and active engagement of patients.45–47 Such improvements may be even more crucial for patients with multiple, complex health care needs.48–51 In all of these cases, success is measured by achieving health outcomes that matter to patients.52 We explored several such outcomes as a function of different measures of morbidity to determine the best methods of adjusting for morbidity in assessing these outcomes.

The independent contribution of self-reported morbidity to a selection of patient-reported and utilization outcomes suggests that a complete assessment of morbidity burden requires subjective and objective measures of morbidity, as well as assessments of mood. Overall, we found stronger associations between self-reported morbidity for patient-reported outcomes when compared with more objective outcomes of utilization. This finding suggests that assessing both self-reported morbidity and mood symptoms is particularly important in understanding the contribution of morbidity burden for investigations using patient-reported outcomes.

These results confirm previous associations of patient-reported morbidity measurement instruments with quality of life and other patient-reported outcomes.20,27,53,54 Our investigation adds to this information by developing models that demonstrated the independent contribution of self-reported morbidity to a set of outcomes (including those that may be considered system centered as well as patient centered) while simultaneously accounting for diagnosis-based measurement of morbidity and depressive and anxiety symptoms. We observed slightly weaker independent associations between self-reported disease burden (compared with morbidity measured with diagnosis data) and the more objective outcomes of health care utilization. Previous investigations have noted varying independent contributions of self-reported functional and health status to predictions of health care costs.55–59 Our exploratory results using a more detailed self-report of disease burden in a relatively small sample suggest that it is also important to include self-assessment of morbidity when investigating health care utilization.

We investigated 3 well-validated and frequently used patient-reported outcomes and 3 newer patient-reported outcomes that we previously developed based on interviews with persons with multiple morbidities.43,44 The results add additional construct validation to our prior observations that the domains of financial constraints, feelings of being overwhelmed by medical conditions, and perceptions of self-efficacy are, in part, a function of morbidity burden.26,43

Even mild symptoms of anxiety and depression were significantly associated with selected outcomes independent of both self-reports and ICD-9 codes of morbidity that included diagnoses of depression and/or anxiety. These associations indicate the importance of assessing symptoms of mental well-being as part of completely quantifying morbidity rather than relying on administratively-reported diagnoses of depression or anxiety.

Depending on the research question, several of our morbidity measures could also be construed as patient-reported outcomes in themselves. This raises the question of where a self-reported morbidity measure (such as disease burden or mood assessment) ends and a patient-reported outcome begins. As with all outcomes, patient-reported outcomes are context specific. For example, in assessing the effectiveness of a new model of care designed to improve the physical well-being of persons with diabetes, obesity, and heart disease, our results suggest that it would be relevant to include morbidity measurements based on diagnosis data, a measure of self-reported morbidity, and a mood assessment. Doing so would more completely account for patient morbidity and permit a more accurate assessment of the effect of the care management intervention. In contrast, in assessing the effectiveness of a new model of care designed to decrease depressive symptoms for persons with depression and obesity, it may be more appropriate to adjust for morbidity measured by diagnosis data and self-reported burden, but not depressive symptoms—as depressive symptoms would be considered an outcome of interest.

There were several limitations to this investigation. It had a cross-sectional design and, as such, could not identify causal relationships between any of the morbidity measures and the outcomes that we studied. Conclusions are based on both the characteristics of the measures (the Quan adaptation of the Charlson comorbidity index based on ICD-9 codes) and the population (primarily white, aged 65 years and older, with health insurance). Because of skewed data, we chose low cut points for the outcomes when exploring associations between the measures of morbidity and the outcomes of financial constraints, feeling overwhelmed, and level of self-efficacy. Although we did find significant associations between the variables at these levels, these initial explorations will require more validation.

The goal of this investigation was to explore the potential contributions of specific independent variables with the selected outcomes rather than to develop complete predictive models for each outcome. We included symptoms of depression and anxiety as independent variables in our model, anticipating that there would be associations between mood symptoms and certain quality-of-life outcomes, as well as potential associations between mood symptoms and self-reported disease burden. Our findings that all of these measures have varying independent associations with patient-reported outcomes suggest it is important to include quantification of both self-reported morbidity and mood symptoms when accounting for morbidity in studies using patient-reported outcomes. Because we were not developing full predictive models, we also did not include the important domain of patient-level social factors as part of our model.13

In summary, our findings suggest that a complete assessment of morbidity requires both subjective and objective measurement of diseases and disease burden, as well as an assessment of emotional symptoms. Such multidimensional morbidity measurement is particularly relevant for research or quality assessments involving the delivery of patient-centered care to complex patient populations. In assessments involving these populations, overall morbidity burden may be an important independent predictor of health outcomes. Accurate measurement strategies to account for morbidity burden will become increasingly important as we develop new methods of evaluating patient-centered care delivery for complex patients.

Acknowledgments

We would like to thank Heather Nuanes, Quynh Le, Andrew Faber, and Stephanie Carwin for their patience, perseverance, and good humor in completing the telephone surveys.

Footnotes

  • Conflicts of interest: authors report none.

  • Funding support: This investigation was funded by the Agency for Healthcare Research and Quality (1R01 HS018404-01).

  • Previous presentation: Portions of this material were presented at the North American Primary Care Research Group annual meeting, Banff, Alberta, Canada, November, 2011.

  • Received for publication September 13, 2011.
  • Revision received December 16, 2011.
  • Accepted for publication December 30, 2011.

References

  1. ↵
    1. Nolan T,
    2. Berwick DM
    . All-or-none measurement raises the bar on performance. JAMA. 2006;295(10):1168–1170.
    OpenUrlCrossRefPubMed
    1. Shekelle PG,
    2. MacLean CH,
    3. Morton SC,
    4. Wenger NS
    . Assessing care of vulnerable elders: methods for developing quality indicators. Ann Intern Med. 2001;135(8 Pt 2):647–652.
    OpenUrlCrossRefPubMed
    1. Elixhauser A,
    2. Pancholi M,
    3. Clancy CM
    . Using the AHRQ quality indicators to improve health care quality. Jt Comm J Qual Patient Saf. 2005;31(9):533–538.
    OpenUrlPubMed
  4. ↵
    1. Shortell SM,
    2. Gillies R,
    3. Siddique J,
    4. et al
    . Improving chronic illness care: a longitudinal cohort analysis of large physician organizations. Med Care. 2009;47(9):932–939.
    OpenUrlCrossRefPubMed
  5. ↵
    1. Berwick DM
    . Measuring physicians’ quality and performance: adrift on Lake Wobegon. JAMA. 2009;302(22):2485–2486.
    OpenUrlCrossRefPubMed
    1. Werner RM,
    2. Greenfield S,
    3. Fung C,
    4. Turner BJ
    . Measuring quality of care in patients with multiple clinical conditions: summary of a conference conducted by the Society of General Internal Medicine. J Gen Intern Med. 2007;22(8):1206–1211.
    OpenUrlCrossRefPubMed
  7. ↵
    1. Glasgow RE,
    2. Peeples M,
    3. Skovlund SE
    . Where is the patient in diabetes performance measures? The case for including patient-centered and self-management measures. Diabetes Care. 2008;31(5): 1046–1050.
    OpenUrlAbstract/FREE Full Text
  8. ↵
    1. Stewart AL,
    2. Greenfield S,
    3. Hays RD,
    4. et al
    . Functional status and well-being of patients with chronic conditions. Results from the Medical Outcomes Study. JAMA. 1989;262(7):907–913.
    OpenUrlCrossRefPubMed
  9. ↵
    1. Mandelblatt JS,
    2. Bierman AS,
    3. Gold K,
    4. et al
    . Constructs of burden of illness in older patients with breast cancer: a comparison of measurement methods. Health Serv Res. 2001;36(6 Pt 1):1085–1107. Corrected in Health Serv Res. 2007;42(5):2088. [Maserejan N corrected to Maserejian N].
    OpenUrlPubMed
    1. Perruccio AV,
    2. Katz JN,
    3. Losina E
    . Health burden in chronic disease: multimorbidity is associated with self-rated health more than medical comorbidity alone. J Clin Epidemiol. 2012;65(1):100–106.
    OpenUrlCrossRefPubMed
    1. Fortin M,
    2. Lapointe L,
    3. Hudon C,
    4. Vanasse A,
    5. Ntetu AL,
    6. Maltais D
    . Multimorbidity and quality of life in primary care: a systematic review. Health Qual Life Outcomes. 2004;2:51.
    OpenUrlCrossRefPubMed
  12. ↵
    1. Safford MM,
    2. Allison JJ,
    3. Kiefe CI
    . Patient complexity: more than comorbidity. the vector model of complexity. J Gen Intern Med. 2007;22(Suppl 3):382–390.
    OpenUrlPubMed
  13. ↵
    1. Rosen AK,
    2. Reid R,
    3. Broemeling AM,
    4. Rakovski CC
    . Applying a risk-adjustment framework to primary care: can we improve on existing measures? Ann Fam Med. 2003;1(1):44–51.
    OpenUrlAbstract/FREE Full Text
  14. ↵
    1. Peek CJ,
    2. Baird MA,
    3. Coleman E
    . Primary care for patient complexity, not only disease. Fam Syst Health. 2009;27(4):287–302.
    OpenUrlCrossRefPubMed
  15. ↵
    1. Quan H,
    2. Sundararajan V,
    3. Halfon P,
    4. et al
    . Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care. 2005;43(11):1130–1139.
    OpenUrlCrossRefPubMed
  16. ↵
    1. Klabunde CN,
    2. Warren JL,
    3. Legler JM
    . Assessing comorbidity using claims data: an overview. Med Care. 2002;40(8 (Suppl):IV-26–IV-35.
    OpenUrlPubMed
    1. Fishman PA,
    2. Goodman MJ,
    3. Hornbrook MC,
    4. Meenan RT,
    5. Bachman DJ,
    6. O’Keeffe Rosetti MC
    . Risk adjustment using automated ambulatory pharmacy data: the RxRisk model. Med Care. 2003;41(1):84–99.
    OpenUrlCrossRefPubMed
  18. ↵
    1. Charlson ME,
    2. Pompei P,
    3. Ales KL,
    4. MacKenzie CR
    . A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373–383.
    OpenUrlCrossRefPubMed
    1. Linn BS,
    2. Linn MW,
    3. Gurel L
    . Cumulative illness rating scale. J Am Geriatr Soc. 1968;16(5):622–626.
    OpenUrlPubMed
  20. ↵
    1. Sangha O,
    2. Stucki G,
    3. Liang MH,
    4. Fossel AH,
    5. Katz JN
    . The Self-Administered Comorbidity Questionnaire: a new method to assess comorbidity for clinical and health services research. Arthritis Rheum. 2003;49(2):156–163.
    OpenUrlCrossRefPubMed
  21. ↵
    1. Charlson M,
    2. Szatrowski TP,
    3. Peterson J,
    4. Gold J
    . Validation of a combined comorbidity index. J Clin Epidemiol. 1994;47(11):1245–1251.
    OpenUrlCrossRefPubMed
  22. ↵
    1. Deyo RA,
    2. Cherkin DC,
    3. Ciol MA
    . Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol. 1992;45(6):613–619.
    OpenUrlCrossRefPubMed
  23. ↵
    1. Charlson ME,
    2. Charlson RE,
    3. Peterson JC,
    4. Marinopoulos SS,
    5. Briggs WM,
    6. Hollenberg JP
    . The Charlson comorbidity index is adapted to predict costs of chronic disease in primary care patients. J Clin Epidemiol. 2008;61(12):1234–1240.
    OpenUrlCrossRefPubMed
  24. ↵
    1. Hornbrook MC,
    2. Goodman MJ
    . Chronic disease, functional health status, and demographics: a multi-dimensional approach to risk adjustment. Health Serv Res. 1996;31(3):283–307.
    OpenUrlPubMed
  25. ↵
    1. Bayliss EA,
    2. Blatchford PJ,
    3. Newcomer SR,
    4. Steiner JF,
    5. Fairclough DL
    . The effect of incident cancer, depression and pulmonary disease exacerbations on type 2 diabetes control. J Gen Intern Med. 2011; 26(6):575–581.
    OpenUrlCrossRefPubMed
  26. ↵
    1. Bayliss EA,
    2. Ellis JL,
    3. Steiner JF
    . Seniors’ self-reported multimorbidity captured biopsychosocial factors not incorporated into two other data-based morbidity measures. [pmid:18757178.]. J Clin Epidemiol. 2009;62(5):550–557, e1.
    OpenUrlCrossRefPubMed
  27. ↵
    1. Crabtree HL,
    2. Gray CS,
    3. Hildreth AJ,
    4. O’Connell JE,
    5. Brown J
    . The Comorbidity Symptom Scale: a combined disease inventory and assessment of symptom severity. J Am Geriatr Soc. 2000;48(12): 1674–1678.
    OpenUrlPubMed
  28. ↵
    1. Lash TL,
    2. Thwin SS,
    3. Horton NJ,
    4. Guadagnoli E,
    5. Silliman RA
    . Multiple informants: a new method to assess breast cancer patients’ comorbidity. Am J Epidemiol. 2003;157(3):249–257.
    OpenUrlAbstract/FREE Full Text
  29. ↵
    1. Lash TL,
    2. Mor V,
    3. Wieland D,
    4. Ferrucci L,
    5. Satariano WA,
    6. Silliman RA
    . Methodology, design, and analytic techniques to address measurement of comorbid disease. J Gerontol A Biol Sci Med Sci. 2007;62(3): 281–285.
    OpenUrlAbstract/FREE Full Text
  30. ↵
    1. Sherbourne CD,
    2. Sturm R,
    3. Wells KB
    . What outcomes matter to patients? J Gen Intern Med. 1999;14(6):357–363.
    OpenUrlCrossRefPubMed
    1. Prince M,
    2. Patel V,
    3. Saxena S,
    4. et al
    . No health without mental health. Lancet. 2007;370(9590):859–877.
    OpenUrlCrossRefPubMed
  32. ↵
    1. Lyness JM,
    2. Niculescu A,
    3. Tu X,
    4. Reynolds CF III.,
    5. Caine ED
    . The relationship of medical comorbidity and depression in older, primary care patients. Psychosomatics. 2006;47(5):435–439.
    OpenUrlCrossRefPubMed
  33. ↵
    1. Di Bari M,
    2. Virgillo A,
    3. Matteuzzi D,
    4. et al
    . Predictive validity of measures of comorbidity in older community dwellers: the Insufficienza Cardiaca negli Anziani Residenti a Dicomano Study. J Am Geriatr Soc. 2006;54(2):210–216.
    OpenUrlCrossRefPubMed
    1. Heller DA,
    2. Ahern FM,
    3. Pringle KE,
    4. Brown TV
    . Among older adults, the responsiveness of self-rated health to changes in Charlson comorbidity was moderated by age and baseline comorbidity. J Clin Epidemiol. 2009;62(2):177–187.
    OpenUrlCrossRefPubMed
    1. Salaffi F,
    2. Carotti M,
    3. Stancati A,
    4. Grassi W
    . Health-related quality of life in older adults with symptomatic hip and knee osteoarthritis: a comparison with matched healthy controls. Aging Clin Exp Res. 2005;17(4):255–263.
    OpenUrlCrossRefPubMed
  36. ↵
    1. Andresen EM,
    2. Bowley N,
    3. Rothenberg BM,
    4. Panzer R,
    5. Katz P
    . Test-retest performance of a mailed version of the Medical Outcomes Study 36-Item Short-Form Health Survey among older adults. Med Care. 1996;34(12):1165–1170.
    OpenUrlCrossRefPubMed
  37. ↵
    1. Bayliss EA,
    2. Ellis JL,
    3. Steiner JF
    . Subjective assessments of comorbidity correlate with quality of life health outcomes: initial validation of a comorbidity assessment instrument. Health Qual Life Outcomes. 2005;3:51.
    OpenUrlCrossRefPubMed
  38. ↵
    1. Kroenke K,
    2. Spitzer RL,
    3. Williams JB
    . The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606–613.
    OpenUrlCrossRefPubMed
  39. ↵
    1. Spitzer RL,
    2. Kroenke K,
    3. Williams JB,
    4. Löwe B
    . A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med. 2006;166(10):1092–1097.
    OpenUrlCrossRefPubMed
  40. ↵
    1. Kroenke K,
    2. Spitzer RL,
    3. Williams JB,
    4. Löwe B
    . The Patient Health Questionnaire Somatic, Anxiety, and Depressive Symptom Scales: a systematic review. Gen Hosp Psychiatry. 2010;32(4):345–359.
    OpenUrlCrossRefPubMed
  41. ↵
    Health RAND. MOS 36-item short form survey instrument. http://www.rand.org/health/surveys_tools/mos/mos_core_36item.html. 6-20-2011. 10-1-2009.
  42. ↵
    1. Ware JE,
    2. Kosinski M
    . SF-36 Physical and Mental Health Summary Scales: A Manual for Users of Version 1. 2nd ed. Lincoln, RI: Quality-Metric; 2001.
  43. ↵
    1. Bayliss EA,
    2. Steiner JF,
    3. Fernald DH,
    4. Crane LA,
    5. Main DS
    . Descriptions of barriers to self-care by persons with comorbid chronic diseases. Ann Fam Med. 2003;1(1):15–21.
    OpenUrlAbstract/FREE Full Text
  44. ↵
    1. Bayliss EA,
    2. Ellis JL,
    3. Steiner JF
    . Barriers to self-management and quality-of-life outcomes in seniors with multimorbidities. [PMID 17893380.]. Ann Fam Med. 2007;5(5):395–402.
    OpenUrlAbstract/FREE Full Text
  45. ↵
    1. Epstein RM,
    2. Fiscella K,
    3. Lesser CS,
    4. Stange KC
    . Why the nation needs a policy push on patient-centered health care. Health Aff (Millwood). 2010;29(8):1489–1495.
    OpenUrlAbstract/FREE Full Text
    1. Epstein RM,
    2. Peters E
    . Beyond information: exploring patients’ preferences. JAMA. 2009;302(2):195–197.
    OpenUrlCrossRefPubMed
  47. ↵
    1. Boivin A,
    2. Green J,
    3. van der Meulen J,
    4. Légaré F,
    5. Nolte E
    . Why consider patients’ preferences? A discourse analysis of clinical practice guideline developers. Med Care. 2009;47(8):908–915.
    OpenUrlCrossRefPubMed
  48. ↵
    1. Clarke JL,
    2. Meiris DC
    . Building bridges: integrative solutions for managing complex comorbid conditions. Am J Med Qual. 2007;22 (2 Suppl):5S–16S.
    OpenUrlFREE Full Text
    1. Parekh AK,
    2. Barton MB
    . The challenge of multiple comorbidity for the US health care system. JAMA. 2010;303(13):1303–1304.
    OpenUrlCrossRefPubMed
    1. Parekh AK,
    2. Goodman RA,
    3. Gordon C,
    4. Koh HK
    HHS Interagency Workgroup on Multiple Chronic Conditions. Managing multiple chronic conditions: a strategic framework for improving health outcomes and quality of life. Public Health Rep. 2011;126(4):460–471.
    OpenUrlPubMed
  51. ↵
    US Department of Health and Human Services. Multiple chronic conditions--a strategic framework: optimum health and quality of life for individuals with multiple chronic conditions. Washington, DC: US Dept Health and Human Services; 2010.
  52. ↵
    1. Berwick DM
    . What ‘patient-centered’ should mean: confessions of an extremist. Health Aff (Millwood). 2009;28(4):w555-w565.
    OpenUrlAbstract/FREE Full Text
  53. ↵
    1. Byles JE,
    2. D’Este C,
    3. Parkinson L,
    4. O’Connell R,
    5. Treloar C
    . Single index of multimorbidity did not predict multiple outcomes. J Clin Epidemiol. 2005;58(10):997–1005.
    OpenUrlCrossRefPubMed
  54. ↵
    1. Tooth L,
    2. Hockey R,
    3. Byles J,
    4. Dobson A
    . Weighted multimorbidity indexes predicted mortality, health service use, and health-related quality of life in older women. J Clin Epidemiol. 2008;61(2):151–159.
    OpenUrlCrossRefPubMed
  55. ↵
    1. Fleishman JA,
    2. Cohen JW,
    3. Manning WG,
    4. Kosinski M
    . Using the SF-12 health status measure to improve predictions of medical expenditures. Med Care. 2006;44(5 Suppl):I54–I63.
    OpenUrlPubMed
    1. DeSalvo KB,
    2. Jones TM,
    3. Peabody J,
    4. et al
    . Health care expenditure prediction with a single item, self-rated health measure. Med Care. 2009;47(4):440–447.
    OpenUrlCrossRefPubMed
    1. Sibley LM,
    2. Moineddin R,
    3. Agha MM,
    4. Glazier RH
    . Risk adjustment using administrative data-based and survey-derived methods for explaining physician utilization. Med Care. 2010;48(2):175–182.
    OpenUrlCrossRefPubMed
    1. Stam PJ,
    2. van Vliet RC,
    3. van de Ven WP
    . Diagnostic, pharmacy-based, and self-reported health measures in risk equalization models. Med Care. 2010;48(5):448–457.
    OpenUrlCrossRefPubMed
  59. ↵
    1. Pietz K,
    2. Ashton CM,
    3. McDonell M,
    4. Wray NP
    . Predicting healthcare costs in a population of veterans affairs beneficiaries using diagnosis-based risk adjustment and self-reported health status. Med Care. 2004;42(10):1027–1035.
    OpenUrlCrossRefPubMed
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