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Table 1.  
Patient Characteristicsa
Patient Characteristicsa
Table 2.  
Comparison of Patient and Physician Reporting of Comorbid Conditions
Comparison of Patient and Physician Reporting of Comorbid Conditions
Table 3.  
Agreement (κ) of Comorbid Conditions Based on Patient Characteristicsa
Agreement (κ) of Comorbid Conditions Based on Patient Characteristicsa
Table 4.  
Odds Ratios (95% CIs) for Overall Agreement in Each Comorbid Conditiona
Odds Ratios (95% CIs) for Overall Agreement in Each Comorbid Conditiona
1.
Jani  AB, Hellman  S.  Early prostate cancer: clinical decision-making. Lancet. 2003;361(9362):1045-1053.PubMedArticle
2.
Albertsen  PC, Moore  DF, Shih  W, Lin  Y, Li  H, Lu-Yao  GL.  Impact of comorbidity on survival among men with localized prostate cancer. J Clin Oncol. 2011;29(10):1335-1341.PubMedArticle
3.
Siegel  R, DeSantis  C, Virgo  K,  et al.  Cancer treatment and survivorship statistics, 2012. CA Cancer J Clin. 2012;62(4):220-241.PubMedArticle
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American Cancer Society. What are the key statistics about prostate cancer? 2013. http://www.cancer.org/cancer/prostatecancer/detailedguide/prostate-cancer-key-statistics. Accessed April 5, 2014.
5.
Hall  WH, Jani  AB, Ryu  JK, Narayan  S, Vijayakumar  S.  The impact of age and comorbidity on survival outcomes and treatment patterns in prostate cancer. Prostate Cancer Prostatic Dis. 2005;8(1):22-30.PubMedArticle
6.
Lu-Yao  GL, Albertsen  P, Warren  J, Yao  SL.  Effect of age and surgical approach on complications and short-term mortality after radical prostatectomy: a population-based study. Urology. 1999;54(2):301-307.PubMedArticle
7.
Harlan  LC, Potosky  A, Gilliland  FD,  et al.  Factors associated with initial therapy for clinically localized prostate cancer: prostate cancer outcomes study. J Natl Cancer Inst. 2001;93(24):1864-1871.PubMedArticle
8.
Desch  CE, Penberthy  L, Newschaffer  CJ,  et al.  Factors that determine the treatment for local and regional prostate cancer. Med Care. 1996;34(2):152-162.PubMedArticle
9.
Yu  JB, Soulos  PR, Herrin  J,  et al.  Proton versus intensity-modulated radiotherapy for prostate cancer: patterns of care and early toxicity. J Natl Cancer Inst. 2013;105(1):25-32.PubMedArticle
10.
Institute of Medicine. IoM: Initial National Priorities for Comparative Effectiveness Research. Washington, DC: National Academies Press; 2009.
11.
Reeve  BB, Chen  RC, Moore  DT,  et al.  Impact of comorbidity on health-related quality of life after prostate cancer treatment: combined analysis of two prospective cohort studies. BJU Int. 2014;114(6b):E74-E81.PubMedArticle
12.
Katz  JN, Chang  LC, Sangha  O, Fossel  AH, Bates  DW.  Can comorbidity be measured by questionnaire rather than medical record review? Med Care. 1996;34(1):73-84.PubMedArticle
13.
Sinclair  S, Hammond  D, Goodman  S.  Sociodemographic differences in the comprehension of nutritional labels on food products. J Nutr Educ Behav. 2013;45(6):767-772.PubMedArticle
14.
von dem Knesebeck  O, Mnich  E, Daubmann  A,  et al.  Socioeconomic status and beliefs about depression, schizophrenia and eating disorders. Soc Psychiatry Psychiatr Epidemiol. 2013;48(5):775-782.PubMedArticle
15.
Charlson  ME, Pompei  P, Ales  KL, MacKenzie  CR.  A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373-383.PubMedArticle
16.
Extermann  M.  Measuring comorbidity in older cancer patients. Eur J Cancer. 2000;36(4):453-471.PubMedArticle
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Piccirillo  JF, Tierney  RM, Costas  I, Grove  L, Spitznagel  EL  Jr.  Prognostic importance of comorbidity in a hospital-based cancer registry. JAMA. 2004;291(20):2441-2447.PubMedArticle
18.
Kaplan  MH, Feinstein  AR.  The importance of classifying initial co-morbidity in evaluating the outcome of diabetes mellitus. J Chronic Dis. 1974;27(7-8):387-404.PubMedArticle
19.
Chen  RC, Royce  TJ, Extermann  M, Reeve  BB.  Impact of age and comorbidity on treatment and outcomes in elderly cancer patients. Semin Radiat Oncol. 2012;22(4):265-271.PubMedArticle
20.
Viera  AJ, Garrett  JM.  Understanding interobserver agreement: the kappa statistic. Fam Med. 2005;37(5):360-363.PubMed
21.
Landis  JR, Koch  GG.  The measurement of observer agreement for categorical data. Biometrics. 1977;33(1):159-174.PubMedArticle
22.
National Comprehensive Cancer Network. NCCN Guidelines for Patients: Prostate Cancer. Fort Washington, PA: National Comprehensive Cancer Network; 2013.
23.
Klabunde  CN, Reeve  BB, Harlan  LC, Davis  WW, Potosky  AL.  Do patients consistently report comorbid conditions over time? results from the prostate cancer outcomes study. Med Care. 2005;43(4):391-400.PubMedArticle
24.
Graff  JN, Mori  M, Li  H,  et al.  Predictors of overall and cancer-free survival of patients with localized prostate cancer treated with primary androgen suppression therapy: results from the prostate cancer outcomes study. J Urol. 2007;177(4):1307-1312.PubMedArticle
25.
Hoffman  RM, Barry  MJ, Stanford  JL, Hamilton  AS, Hunt  WC, Collins  MM.  Health outcomes in older men with localized prostate cancer: results from the Prostate Cancer Outcomes Study. Am J Med. 2006;119(5):418-425.PubMedArticle
26.
Yancik  R, Havlik  RJ, Wesley  MN,  et al.  Cancer and comorbidity in older patients: a descriptive profile. Ann Epidemiol. 1996;6(5):399-412.PubMedArticle
27.
Coebergh  JW, Janssen-Heijnen  ML, Post  PN, Razenberg  PP.  Serious co-morbidity among unselected cancer patients newly diagnosed in the southeastern part of The Netherlands in 1993-1996. J Clin Epidemiol. 1999;52(12):1131-1136.PubMedArticle
28.
Fowles  JB, Fowler  EJ, Craft  C.  Validation of claims diagnoses and self-reported conditions compared with medical records for selected chronic diseases. J Ambul Care Manage. 1998;21(1):24-34.PubMedArticle
29.
Skinner  KM, Miller  DR, Lincoln  E, Lee  A, Kazis  LE.  Concordance between respondent self-reports and medical records for chronic conditions: experience from the Veterans Health Study. J Ambul Care Manage. 2005;28(2):102-110.PubMedArticle
30.
Tisnado  DM, Adams  JL, Liu  H,  et al.  What is the concordance between the medical record and patient self-report as data sources for ambulatory care? Med Care. 2006;44(2):132-140.PubMedArticle
31.
Basch  E, Iasonos  A, McDonough  T,  et al.  Patient versus clinician symptom reporting using the National Cancer Institute Common Terminology Criteria for Adverse Events: results of a questionnaire-based study. Lancet Oncol. 2006;7(11):903-909.PubMedArticle
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Basch  E, Jia  X, Heller  G,  et al.  Adverse symptom event reporting by patients vs clinicians: relationships with clinical outcomes. J Natl Cancer Inst. 2009;101(23):1624-1632.PubMedArticle
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Janse  AJ, Gemke  RJ, Uiterwaal  CS, van der Tweel  I, Kimpen  JL, Sinnema  G.  Quality of life: patients and doctors don’t always agree: a meta-analysis. J Clin Epidemiol. 2004;57(7):653-661.PubMedArticle
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Martin  NE, Massey  L, Stowell  C,  et al.  Defining a standard set of patient-centered outcomes for men with localized prostate cancer. Eur Urol. 2015;67(3):460-467.PubMedArticle
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Original Investigation
February 16, 2017

Comparison of Patient Report and Medical Records of ComorbiditiesResults From a Population-Based Cohort of Patients With Prostate Cancer

Author Affiliations
  • 1Department of Radiation Oncology, the University of North Carolina at Chapel Hill, Chapel Hill
  • 2Lineberger Comprehensive Cancer Center, the University of North Carolina at Chapel Hill, Chapel Hill
  • 3Department of Health Policy and Management, the University of North Carolina at Chapel Hill, Chapel Hill
  • 4Cecil G. Sheps Center for Health Services Research, the University of North Carolina at Chapel Hill, Chapel Hill
JAMA Oncol. Published online February 16, 2017. doi:10.1001/jamaoncol.2016.6744
Key Points

Question  Do medical records and patient reports on the presence or absence of comorbid conditions agree in patients with newly diagnosed prostate cancer?

Findings  In this population-based and diverse cohort of 881 patients, comorbidities were prevalent, and patient reports and medical records for most medical conditions agreed in more than 90% of patients.

Meaning  Patient reporting provides information similar to medical record abstraction and may be a less costly method for assessing comorbid conditions for observational comparative effectiveness research.

Abstract

Importance  The comorbid conditions of patients with cancer affect treatment decisions, which in turn affect survival and health-related quality-of-life outcomes. Comparative effectiveness research studies must account for these conditions via medical record abstraction or patient report.

Objective  To examine the agreement between medical records and patient reports in assessing comorbidities.

Design, Setting, and Participants  Patient-reported information and medical records were prospectively collected as part of the North Carolina Prostate Cancer Comparative Effectiveness & Survivorship Study, a population-based cohort of 881 patients with newly diagnosed localized prostate cancer enrolled in the North Carolina Central Cancer Registry from January 1, 2011, through June 30, 2013. The presence or absence of 20 medical conditions was compared based on patient report vs abstraction of medical records.

Main Outcomes and Measures  Agreement between patient reports and medical records for each condition was assessed using the κ statistic. Subgroup analyses examined differences in κ statistics based on age, race, marital status, educational level, and income. Logistic regression models for each condition examined factors associated with higher agreement.

Results  A total of 881 patients participated in the study (median age, 65 years; age range, 41-80 years; 633 white [71.9%]). In 16 of 20 conditions, there was agreement between patient reports and medical records for more than 90% of patients; agreement was lowest for hyperlipidemia (68%; κ = 0.36) and arthritis (66%; κ = 0.14). On multivariable analysis, older age (≥70 years old) was significantly associated with lower agreement for myocardial infarction (odds ratio [OR], 0.31; 95% CI, 0.12-0.80), cerebrovascular disease (OR, 0.10; 95% CI, 0.01-0.78), coronary artery disease (OR, 0.37; 95% CI, 0.20-0.67), arrhythmia (OR, 0.44; 95% CI, 0.25-0.79), and kidney disease (OR, 0.18; 95% CI, 0.06-0.52). Race and educational level were not significantly associated with κ in 18 of 19 modeled conditions.

Conclusions and Relevance  Overall, patient reporting provides information similar to medical record abstraction without significant differences by patient race or educational level. Use of patient reports, which are less costly than medical record audits, is a reasonable approach for observational comparative effectiveness research.

Introduction

Prostate cancer treatment decision making and patient outcomes are greatly affected by patient baseline comorbid conditions.1,2 Because the median age of diagnosis of prostate cancer in the United States is 67 years,3 many patients have other medical conditions concomitantly, such as diabetes, hypertension, cardiovascular disease, and cerebrovascular disease.4,5 Radical prostatectomy is a more likely treatment given to younger patients with fewer comorbidities, whereas radiotherapy and conservative management (hormone therapy or no treatment) are more likely given to older patients and those with more comorbidities.3,68 Another study9 suggests that patients with fewer comorbidities are more likely to travel long distances to receive treatment at large-volume academic centers.

Comparative effectiveness of patient outcomes among different localized prostate cancer treatment options is one of the highest-priority research areas according to the Institute of Medicine.10 Because a patient’s comorbid conditions heavily influence treatment selection and directly affect survival and health-related quality-of-life outcomes,1,2,11 observational comparative effectiveness research studies must account for these conditions. A central methodologic issue is whether to collect comorbidity data using medical record abstraction or patient report to maximize data quality while minimizing cost of data collection. Medical record collection and abstraction depend on the scrupulousness of the documenting health care professional, require an abstractor with sufficient medical training, and are more costly to perform.12 On the other hand, patient report relies on each patient accurately knowing his or her medical history, which may be dependent in part on his or her health literacy. For observational comparative effectiveness research, it is not clear that a current criterion standard exists, and each source of information (patient vs medical records) has the potential to overreport and underreport comorbid medical conditions.

The purpose of this study was to compare patient reports and medical records in assessing comorbidity in a population-based cohort of patients with newly diagnosed prostate cancer. We quantify the level of agreement between these 2 sources on common comorbid conditions and assess factors associated with agreement. Given that socioeconomic status is associated with health literacy,13,14 we hypothesized that patients who were nonwhite and those with lower educational levels would have lower agreement between patient reports and medical records.

Methods
Data Collection

The North Carolina Prostate Cancer Comparative Effectiveness & Survivorship Study (NC ProCESS) is a prospective, population-based cohort of patients with newly diagnosed localized prostate cancer enrolled throughout North Carolina in collaboration with the Rapid Case Ascertainment system of the North Carolina Central Cancer Registry. The Rapid Case Ascertainment system proactively identified patients with newly diagnosed prostate cancer from all 100 North Carolina counties from January 1, 2011, through June 30, 2013. Names of patients, pathologic and diagnostic information, and physician names and addresses were sent weekly to the Rapid Case Ascertainment staff by tumor registrars at local hospitals. The patients were then approached by the NC ProCESS staff for study participation; 1419 (57.2%) of 2480 eligible patients agreed to enroll. All patients were enrolled before treatment and followed up prospectively to collect data from medical records and patient-reported outcomes. For each patient, primary care physician and urologist records, as well as radiation oncologist records if consulted, were obtained. Individual-level sociodemographic information (race, marital status, educational level, income) was collected by patient report. This study was approved by the University of North Carolina Institutional Review Board, including consent to obtain records. Written informed consent was obtained from all patients, and data were deidentified for analysis.

Outcome Measures

This study included 881 patients enrolled in the NC ProCESS. Data were collected on the following 20 comorbid conditions: myocardial infarction, congestive heart failure, peripheral vascular disease, cerebrovascular disease, chronic obstructive pulmonary disease, peptic ulcer disease, liver disease, diabetes, kidney disease, other cancers, human immunodeficiency virus or AIDS, coronary artery disease, arrhythmia, clotting disorders, hypertension, hyperlipidemia, inflammatory bowel disease, asthma, anemia and other blood disorders, and arthritis. These conditions are included in the most commonly used comorbidity indexes in cancer research, including the Charlson Comorbidity Index,15,16 Adult Comorbidity Evaluation Index,17 Index of Co-Existent Diseases,16 and Kaplan-Feinstein Comorbidity Index.18,19 The conditions were assessed by patient report via telephone survey (“Have you ever been told by a doctor or other health professional that you have [comorbid condition]?”) and by medical record abstraction at the time of study enrollment, which was always before treatment. Telephone surveys were conducted by the Carolina Survey Research Laboratory at the University of North Carolina, and staff followed the above script to elicit comorbidity information. In addition, medical records were abstracted for the presence of these comorbidities.

Statistical Analysis

We describe the presence of each condition based on patient report, medical record abstraction, both, or neither; κ statistics were used to quantify the level of agreement between patient report and medical records. The κ statistic is a way of reporting agreement between 2 information sources by providing a quantitative measurement of the interobserver agreement magnitude, accounting for chance agreement.20 This is standardized to fit a scale of −1 to 1; 0 represents the amount of expected agreement by chance alone, whereas 1 is perfect agreement.20 Data from patients who did not respond to the telephone survey (11 of 881 patients [1.2%]) or reported that they were unsure whether they had a condition (affecting ≥1 comorbid conditions in 15 of 881 patients [1.7%]) were excluded from analysis. Landis and Koch thresholds were used to classify agreement levels as poor or slight (<0.20), fair (≥0.20 to <0.40), moderate (≥0.40 to <0.60), substantial (≥0.60 to <0.80), or almost perfect (≥0.80).21 We performed subgroup analyses to determine whether the κ statistic varied by age, race, educational level, income, or cancer aggressiveness (defined by prostate cancer risk group).22 We then performed logistic regression to assess covariates associated with agreement between patient report and medical records, with separate models for each comorbidity. The subgroup and multivariable analyses inform our understanding of whether certain subgroups of patients with prostate cancer have higher or lower agreement in terms of the presence of comorbid conditions from the 2 data sources. All statistical analyses were performed using SAS statistical software, version 9.4 (SAS Institute Inc).

Results

A total of 881 patients were analyzed (median age, 65 years; age range, 41-80 years). This cohort was sociodemographically diverse, with 633 white participants (71.9%) and 248 nonwhite participants (28.1%), 281 (31.9%) with high school education or less, and 322 (36.5%) with household income of $40 000 or less. Baseline characteristics are given in Table 1.

Table 2 gives the frequency of patient and physician reporting of each comorbid condition, level of agreement, and κ statistics. We found agreement between patient reports and medical records in more than 90% of patients for all conditions, except coronary artery disease (87%), arrhythmia (86%), hypertension (85%), hyperlipidemia (68%), and arthritis (66%). When assessed using the κ statistic, agreement was substantial or almost perfect for myocardial infarction (κ = 0.62), cerebrovascular disease (κ = 0.72), diabetes (κ = 0.90), human immunodeficiency virus or AIDS (κ = 1.00), and hypertension (κ = 0.68), whereas agreement was lower for the other conditions. When there was disagreement, both scenarios (medical records indicating condition not reported by patients and patient indicating presence of condition not indicated in medical records) were observed. For cerebrovascular disease, chronic obstructive pulmonary disease, diabetes, coronary artery disease, and hypertension, medical records were more likely to report the condition than patients, whereas for myocardial infarction, congestive heart failure, peptic ulcer disease, kidney disease, other cancers, arrhythmia, clotting disorders, hyperlipidemia, asthma, anemia and other blood disorders, and arthritis, patients were more likely to indicate the condition.

Subgroup analysis for the κ statistic based on age, race, marital status, educational level, income, and prostate cancer risk groups for each comorbidity is given in Table 3. The κ statistics differed by patient race in 7 conditions: white patients had higher κ statistics in chronic obstructive pulmonary disease, liver disease, cancers other than prostate, and coronary artery disease, whereas nonwhite patients had higher κ statistics in congestive heart failure, clotting disorders, and inflammatory bowel disease. The κ statistics also differed by educational level in 4 conditions: patients with a high school education or less had higher κ statistics in kidney disease, clotting disorders, and anemia and other blood disorders, whereas those with more than a high school education had a higher κ statistic in inflammatory bowel disease.

In multivariable logistic regression (Table 4), older age was associated with lower overall agreement in multiple conditions: older than 70 years compared with younger than 60 years was associated with lower overall agreement for myocardial infarction (odds ratio [OR], 0.31; 95% CI, 0.12-0.80), cerebrovascular disease (OR, 0.10; 95% CI, 0.01-0.78), kidney disease (OR, 0.18; 95% CI, 0.06-0.52), coronary artery disease (OR, 0.37; 95% CI, 0.20-0.67), and arrhythmia (OR, 0.44; 95% CI, 0.25-0.79); age between 60 and 69 years compared with younger than 60 years was associated with lower overall agreement for cerebrovascular disease (OR, 0.11; 95% CI, 0.01-0.85), kidney disease (OR, 0.33; 95% CI, 0.12-0.91), and coronary artery disease (OR, 0.55; 95% CI, 0.31-0.96). Nonwhite race was associated with lower overall agreement for kidney disease (OR, 0.21; 95% CI, 0.10-0.43). A high school education or less compared with more education was associated with higher overall agreement for anemia and other blood disorders (OR, 2.57; 95% CI, 1.24-5.33).

Discussion

In this study of 881 patients with prostate cancer from a diverse, population-based cohort, we examined the agreement between patient report and medical record documentation for 20 comorbid conditions commonly included in comorbidity indexes used in cancer research. In 16 of these conditions, we found agreement between patient report and medical record documentation in more than 90% of patients, whereas agreement was notably lower for hyperlipidemia and arthritis. On multivariable analysis, older age was significantly associated with lower overall agreement for multiple cardiovascular conditions and kidney disease. Our hypothesis that nonwhite race and lower educational level would be associated with lower patient vs physician report agreement was mostly incorrect.

Accurate assessment of comorbidities is important because prostate cancer treatment decision making is directly affected by a patient’s baseline comorbidity status.1,2 In the United States, younger and healthier patients commonly undergo prostatectomy, whereas older patients and those with more comorbid conditions receive radiation or conservative management.19 Comparative effectiveness research for localized prostate cancer treatment options is a high-priority research area10; given clinical patient selection into different treatment groups, observational studies must accurately account for a patient’s comorbidities to reach valid conclusions. Because a patient’s comorbid conditions directly affect survival2 and quality of life,11 a better understanding of how to assess comorbidity information addresses one of the most important methodologic issues in comparative effectiveness research.

Our study is novel because, to our knowledge, no prior studies have directly compared patient and physician report of comorbidities specifically in patients with prostate cancer. Both sources of information are used in different prostate cancer studies to account for comorbidities,2327 yet we do not know whether patient reports and medical records provide the same information. A report23 from the Prostate Cancer Outcomes Study, which in a population-based cohort of patients enrolled in 1994 collected patient-reported comorbidity information serially, found that patient reports of comorbid conditions were consistent over time; however, the study did not provide detailed data on how patient reports compared with medical records. Another study published by Katz et al12 in 1996 of 170 hospitalized patients compared patient reports and hospital records. For conditions studied that overlap with our study, the κ statistics were similar to our findings for myocardial infarction, congestive heart failure, peripheral vascular disease, cerebrovascular disease, chronic obstructive pulmonary disease, and peptic ulcer disease, whereas they differed by more than 0.20 for diabetes, kidney disease, and other cancers.12 We also found several other studies2830 that examined concordance between medical records and patient reports. However, these studies did not specifically include patients with cancer, were conducted in settings that were not real world (inpatient, Veterans Affairs ambulatory care, or patients with specific health plans), and did not examine whether agreement differed by patient subgroups.

Given the lack of prior data, results from this study provide important information on a central methodologic issue in observational comparative effectiveness research: to accurately account for potential differences in patient characteristics across comparison groups. Assessing comorbid medical conditions using patient report is a less expensive option than medical record abstraction. Our results suggest that patient report provides information similar to medical records for most comorbidities studied, and there is no consistent pattern of higher or lower agreement by race or educational level, but there was a lower κ statistic in older patients for several, mostly cardiovascular, conditions, which are especially likely to affect older individuals. One potential explanation is that older patients are more likely to have more comorbidities,5 and as the number of comorbidities increases, precision between medical record and patient report decreases. In addition, older patients may have lower health literacy, which may have contributed to the lower agreement. However, additional research is needed to further examine measurement of cardiovascular comorbidities in older patients with cancer. Because the NC ProCESS obtains longer-term follow-up in these patients, we will be able to assess whether patient report, medical records, or both best predict survival and quality-of-life outcomes.

There is an established literature on patient-reported outcomes that has consistently found discordance between patient report vs physician report in cancer treatment–related adverse effects and health-related quality of life.3133 Patients tend to report these symptoms earlier and more frequently than do physicians,32 and this research has led to a common acceptance of patient-reported outcomes as a valid measure in clinical trials. Thematically, these prior studies3133 provide a framework for interpreting results from this study: indeed, there are modest discrepancies between patient and physician reporting of comorbid conditions. In this study, we obtained and abstracted medical records from the primary care physician and cancer specialists because the specialists need to accurately collect comorbidity information for treatment decision making and the primary care physician would be expected to know and document all medical conditions in their patients. Doing so, we found agreement between patient report and medical record documentation in more than 90% of patients. These results support the recent standard set defined by an international group of experts regarding data that should be collected for all patients with localized prostate cancer, including comorbidities using patient report instead of medical record abstraction.34

Strengths and Limitations

This study contains multiple methodologic strengths. The NC ProCESS cohort is large and population based, and the rich diversity of enrolled patients allowed for subgroup analyses to examine agreement by age, race, educational level, and other factors. The cohort is modern, with all patients enrolled from 2011 to 2013. Enrollment of patients from the community setting also provides information that is reflective and generalizable to real-world patients and medical care. In addition, patient-reported comorbidity information and medical records were obtained before prostate cancer treatment, which avoids potential confounding from conditions developed after (or potentially because of) treatment. On the other hand, in our literature search, we found no existing data on what constitutes a meaningful difference in κ statistic. In Table 3 (subgroup analysis), we highlighted differences of 0.20 or greater to help the reader more easily identify subgroups with sizable differences in agreement,21 and the actual values are provided to allow readers to make their own conclusions.

One limitation of the study is that we did not examine whether different sources of medical records (academic vs community, primary care physician vs specialist) differed in information accuracy. The issue of accuracy in comorbidity reporting is difficult to assess; when medical records and patient reports differ, it is unclear who is right. Conditions listed in the medical record may be incorrect if they are assumptions based on a patient’s medication list or errors that continue to propagate over time. Another limitation of the study is that we were unable to compare calculated comorbidity scores (eg, Charlson Comorbidity Index) between medical records and patient report, which are commonly used in comparative effectiveness studies, because of a lack of certain specific details required for score calculation. However, because there was overall high agreement in most comorbid conditions examined, we hypothesize that calculated comorbidity scores will be similar between medical records and patient report, which will need to be confirmed in future studies.

Conclusions

In a population-based cohort of patients with newly diagnosed prostate cancer, agreement between patient and physician reporting occurred in more than 90% of patients for 16 of 20 assessed comorbid conditions. This is the first large-scale study, to our knowledge, to specifically examine information source in comorbidity reporting, a central issue in observational comparative effectiveness research. Overall, patient reporting provides information similar to medical record abstraction, without significant differences by patient race or educational level. However, assessment of cardiovascular conditions in older patients requires further study.

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Article Information

Corresponding Author: Ronald C. Chen, MD, MPH, Department of Radiation Oncology, University of North Carolina at Chapel Hill, Campus Box 7512, 101 Manning Dr, Chapel Hill, NC 27599 (ronald_chen@med.unc.edu).

Accepted for Publication: November 16, 2016.

Published Online: February 16, 2017. doi:10.1001/jamaoncol.2016.6744

Author Contributions: Dr Chen had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Ye, Godley, Chen.
Acquisition, analysis, or interpretation of data: Ye, Moon, Carpenter, Reeve, Usinger, Green, Spearman, Sheets, Pearlstein, Lucero, Waddle, Chen.
Drafting of the manuscript: Ye, Carpenter, Usinger, Green, Spearman, Sheets, Pearlstein, Lucero, Chen.
Critical revision of the manuscript for important intellectual content: Ye, Moon, Carpenter, Reeve, Usinger, Waddle, Godley, Chen.
Statistical analysis: Ye, Green.
Obtained funding: Chen.
Administrative, technical, or material support: Carpenter, Usinger, Spearman, Sheets, Lucero, Waddle, Godley.

Study supervision: Chen.

Conflict of Interest Disclosures: None reported.

Funding/Support: This research was funded by contract HHSA29020050040I from the Agency for Healthcare Research and Quality, US Department of Health and Human Services, as part of the DEcIDE (Developing Evidence to Inform Decisions about Effectiveness) program.

Role of the Funder/Sponsor: The funding source had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and the decision to submit the manuscript for publication.

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Jani  AB, Hellman  S.  Early prostate cancer: clinical decision-making. Lancet. 2003;361(9362):1045-1053.PubMedArticle
2.
Albertsen  PC, Moore  DF, Shih  W, Lin  Y, Li  H, Lu-Yao  GL.  Impact of comorbidity on survival among men with localized prostate cancer. J Clin Oncol. 2011;29(10):1335-1341.PubMedArticle
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Hall  WH, Jani  AB, Ryu  JK, Narayan  S, Vijayakumar  S.  The impact of age and comorbidity on survival outcomes and treatment patterns in prostate cancer. Prostate Cancer Prostatic Dis. 2005;8(1):22-30.PubMedArticle
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Lu-Yao  GL, Albertsen  P, Warren  J, Yao  SL.  Effect of age and surgical approach on complications and short-term mortality after radical prostatectomy: a population-based study. Urology. 1999;54(2):301-307.PubMedArticle
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Harlan  LC, Potosky  A, Gilliland  FD,  et al.  Factors associated with initial therapy for clinically localized prostate cancer: prostate cancer outcomes study. J Natl Cancer Inst. 2001;93(24):1864-1871.PubMedArticle
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Yu  JB, Soulos  PR, Herrin  J,  et al.  Proton versus intensity-modulated radiotherapy for prostate cancer: patterns of care and early toxicity. J Natl Cancer Inst. 2013;105(1):25-32.PubMedArticle
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Reeve  BB, Chen  RC, Moore  DT,  et al.  Impact of comorbidity on health-related quality of life after prostate cancer treatment: combined analysis of two prospective cohort studies. BJU Int. 2014;114(6b):E74-E81.PubMedArticle
12.
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