Abstract
Objectives:
To measure the impact of patient-centered communication on mortality and hospitalization among patients with heart failure (HF).
Study Design:
This was a survey study of 6208 residents of 11 counties in Southeast Minnesota, USA, with incident HF [first-ever ICD-9 code 428 or ICD-10 code I50] between 1/1/2013 and 3/31/2016.
Methods:
Perceived patient-centered communication was assessed with the health care subscale of the Chronic Illness Resources Survey and measured as a composite score on three 5-points scales. We divided our cohort into tertiles and defined them: fair/poor (score < 12), good (score of 12 or 13) and excellent (score ≥ 14). The survey was returned by 2868 participants (response rate: 45%), and those with complete data were retained for analysis (N=2398). Cox and Andersen-Gill models were used to determine the association of patient-centered communication with death and hospitalization, respectively.
Results:
Among 2398 participants (median age 75 years, 54% men) 233 deaths and 1194 hospitalizations occurred after a mean (SD) follow-up of 1.3 (0.6) years. Compared to patients with fair/poor patient-centered communication, those with good (HR 0.70, 95% CI: 0.51 – 0.97) and excellent patient-centered communication (HR 0.70, 95% CI: 0.51 – 0.96) experienced lower risks of death after adjustment for various confounders (ptrend=0.020). Patient-centered communication was not associated with hospitalization.
Conclusions:
Among community patients living with HF, excellent and good patient-centered communication is associated with a reduced risk of death. Patient-centered communication can be easily assessed and consideration should be given toward implementation in the clinical practice.
Keywords: heart failure, mortality, hospitalization, communication, patient-centered
Précis:
Among community patients living with HF, excellent and good patient-centered communication was associated with a reduced risk of death.
INTRODUCTION
An estimated 6.5 million people live with heart failure (HF),1 and its prevalence is expected to further increase.2 Although the management of HF has improved, hospitalizations remain quite frequent and survival is 50% at 5 years.1, 3–5 Patients with HF are often elderly with multiple comorbidities, which account for a large proportion of hospitalizations.4, 6
Patient-centered communication is defined by acknowledgement of patients’ needs, preferences, and experiences. It provides opportunities for patients to participate in their care and supports the patient-provider relationship. In the last decades, there has been growing attention towards this construct,7, 8 and more efforts have been invested in teaching the basics of patient-centered communication to physicians in training9 with the hope that involving the patient in his/her care and in the medical decisions will foster disease self-management and ultimately improve outcomes.7, 8 Self-management is particularly important in HF, a chronic disease which requires a complex management including diet, weight monitoring and, when applicable, glycemic control and physical exercise.10 Although the effect of patient-centered communication on outcomes is conceptually plausible, to the best of our knowledge these associations have not been studied in HF.
To address this gap in knowledge, we aimed to determine if there is an association between patient-centered communication and outcomes (survival and hospitalizations) in a population-based community cohort of patients with HF.
METHODS
Study Setting and Design
The present study was conducted in a geographically defined area of Southeast Minnesota, which includes 11 counties (Dodge, Fillmore, Freeborn, Goodhue, Houston, Mower, Olmsted, Rice, Steele, Wabasha and Winona; 2010 US Census population: 491,684 residents). These counties are included in the Rochester Epidemiology Project (REP), which is a medical records linkage system that collects all health care utilization among residents of this area.11–14 This study was approved by Mayo Clinic and Olmsted Medical Center Institutional Review Boards.
Case Identification and Survey Administration
Residents at least 18 years old with a first-ever International Classification of Diseases, Ninth Revision (ICD-9) code 428 or ICD-10 code I50 for HF within the REP records of the 11-county area between January 1, 2013 and March 31, 2016 were identified. A survey was mailed to each identified patient in order to evaluate their perceived patient-centered communication, health literacy and other questions of interest. To increase the response rate, a mixed-mode (mail and phone) design was used to conduct the survey. Patients were mailed a packet with an introductory letter, the survey and a HIPPA form. Participants were given the option to complete the survey via mail or via phone. A second packet was mailed to the non-responders one month from the first mailing and a telephone contact was attempted with the remaining non-responders one month after the second packet was mailed.
Patient-Centered Communication
Patient-centered communication was measured using the health care subscale of the Chronic Illness Resources Survey (CIRS).15, 16 This subscale is composed of three questions which evaluate shared decision-making, active listening, and efforts to ensure understanding: 1) Has your doctor involved you as an equal partner in making decisions about illness management strategies and goals? 2) Has your doctor or other health care advisor listened carefully to what you had to say about your illness? 3) Has your doctor or other health care provider thoroughly explained the results of tests you had done (e.g. cholesterol, blood pressure or other laboratory tests)? The response options included: not at all, a little, a moderate amount, quite a bit, a great deal, with points ranging from 1 to 5, respectively, for each response. The patient-centered communication score ranges from 3 to 15, where higher scores indicate better patient-centered communication. We divided our cohort into tertiles and defined them as: fair/poor (score< 12), good (score 12 – 13) and excellent (score ≥ 14). In order to be included in the analysis patients had to complete all 3 questions of the health care subscale of the CIRS. The Cronbach α for the three questions in our study was 0.82.
We also analyzed two questions included in our survey adopted from the National Health and Aging Trends study to evaluate whether family and/or friends attended health care visits with the patient and the reliance on family/friends in the management of care.17, 18 This was done since it reported that the presence of family and friends can influence the communication with the health care provider and confound the association.19 These questions were: 1) In the last year, did anyone (family, friend) sit in with you and your doctor during the visit? (response options: yes or no) and 2) People today are asked by their doctors and other health care providers to do many things to stay healthy or treat health problems – for example, manage medicines, get tests and lab work done, watch weight and blood pressure, or have yearly exams. How do you usually handle these things? (response options: handle mostly by self, handle together with family or close friends, family or close friends mostly handle these things, it varies). If the patient answered yes to the first question, we classified the patient as being accompanied to medical visits. For the second question, participants were categorized into patients who self-manage (response: handle mostly by self), co-manage (response: handle together with family or close friends) and delegate (response: family or close friends mostly handle these things or it varies).17
Other Patient Characteristics
Education, marital status and health literacy were collected from the survey. Health literacy was evaluated with a short screener composed of 3 validated questions: 1) How confident are you filling out forms by yourself? 2) How often do you have someone (like a family member, friend, hospital/clinic worker, or caregiver) help you read hospital materials? 3) How often do you have problems learning about your medical condition because of difficulty reading hospital materials?20, 21 Each question generates a score from 1 to 5, the sum of which gives a final score that could range from 3 to 15, with lower scores indicating inadequate health literacy.
Age, sex and Charlson comorbidity index,22, 23 were electronically obtained though the REP.
Outcomes
Participants were followed from recruitment through March 31, 2017 (mean follow up ± SD: 1.3 ± 0.6 years). Mortality was obtained through the REP, which collects information from death certificates from the state of Minnesota. Hospitalization was retrieved through the REP, which comprehensively collects this health care information for the area included in the study. In-hospital transfers were considered as one event.
Statistical Analysis
The response rate was calculated according to the guidelines of the American Association for Public Opinion Research (AAPOR) using formula 2, which is calculated dividing the number of complete and partial surveys by the total number of complete and partial surveys, refusals, non-contacts, others, and cases of unknown eligibility.24 Baseline characteristics and survey measures were presented as mean ± standard deviation or median (25th, 75th percentile) for continuous variables, as appropriate, and as frequency (percentage) for categorical variables. Baseline characteristics were compared among the patient-centered communication score using linear regression for continuous variables and the Wilcoxon-rank sum and Kruskal-Wallis test for categorical variables. Mortality was presented as cumulative incidence (1-Kaplan Meier), while cumulative mean number of hospitalizations by categories of patient-centered communication were plotted using a nonparametric estimator.25 Cox proportional hazards regression was used to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) for mortality, while Andersen-Gill modeling26 was used for hospitalizations to allow analysis of recurrent events. In addition, since standard survival models might produce biased estimates in the presence of competing risks, Fine-Gray competing risk models were used to analyze hospitalizations with death treated as a competing risk.27 The proportional hazards assumption was evaluated and found to be valid. Multivariable analyses adjusting for age, sex, marital status, educational attainment, Charlson comorbidity index and mode of survey completion (mail, phone) were conducted. Age, sex, educational attainment, and Charlson comorbidity index were compared between survey responders and non-responders. A level of significance of 0.05 was used for all analyses. All analyses were performed using SAS JMP version 14.1 and SAS version 9.4 (SAS Institute, Inc., Cary, NC).
RESULTS
The survey was mailed to 6344 patients and 2868 patients responded (Response Rate: 45%). Responders and non-responders were similar in age (responders: mean ±SD 72±13; non-responders: mean ±SD 73±15; p=0.112), but responders were more likely to be male (p=0.027) and have more comorbidities (p<0.001). Among 2868 survey responders, 2398 had complete information for the characteristics of main interest and were retained for analysis. The median patient-centered communication score was 12, and the distribution of the score was skewed (Figure 1). There was no difference in the mean score among the 11 counties (p=0.281).
Figure 1.
Patient-Provider relationship distribution as measured by the health care subscale of the CIRS
Patients reporting excellent patient-centered communication were younger, had higher educational attainment, higher health literacy and were more likely to complete the survey via mail. Moreover, these participants were more likely to report self-managing their health (Table 1).
Table 1.
Baseline characteristics
| Characteristic | Total cohort (n=2398) |
Patient-centered communication | Ptrend | ||
|---|---|---|---|---|---|
| Fair/Poor (n=853) |
Good (n=686) |
Excellent (n=859) |
|||
| DEMOGRAPHICS | |||||
| Age, mean (SD) | 73.3 (12.6) | 75.4 (12.0) | 73.2 (13.1) | 71.4 (12.4) | <0.001 |
| Male sex | 1287 (53.7) | 478 (56.0) | 361 (52.6) | 448 (52.2) | 0.107 |
| Education | <0.001 | ||||
| Non high school graduate | 277 (11.6) | 113 (13.3) | 91 (13.3) | 73 (8.5) | |
| High school graduate | 845 (35.2) | 322 (37.8) | 238 (34.7) | 285 (33.2) | |
| Some college/college degree | 1005 (41.9) | 324 (38.0) | 296 (43.2) | 385 (44.8) | |
| Graduate school | 271 (11.3) | 94 (11.0) | 61 (8.9) | 116 (13.5) | |
| Married | 1412 (58.9) | 494 (57.9) | 403 (58.8) | 515 (60.0) | 0.391 |
| CLINICAL CHARACTERISTICS | |||||
| Charlson comorbidity index | 0.252 | ||||
| 0 | 213 (8.8) | 87 (10.2) | 48 (7.0) | 78 (9.1) | |
| 1–2 | 936 (39.0) | 340 (39.8) | 266 (38.8) | 330 (38.4) | |
| >=3 | 1249 (52.1) | 426 (49.9) | 372 (54.2) | 451 (52.5) | |
| MI1 | 384 (16.0) | 149 (17.5) | 106 (15.5) | 129 (15.0) | 0.167 |
| PVD2 | 858 (35.8) | 318 (37.3) | 248 (36.2) | 292 (34.0) | 0.156 |
| CVD3 | 339 (14.1) | 124 (14.5) | 95 (13.9) | 120 (14.0) | 0.737 |
| COPD4 | 766 (31.9) | 257 (30.1) | 227 (33.1) | 282 (32.8) | 0.232 |
| Renal disease | 559 (23.3) | 205 (24.0) | 158 (23.0) | 196 (22.8) | 0.552 |
| Diabetes | 805 (33.6) | 283 (33.2) | 258 (37.6) | 264 (30.7) | 0.282 |
| Cancer | 383 (16.0) | 120 (14.1) | 123 (17.9) | 140 (16.3) | 0.209 |
| SOCIO-BEHAVIORAL CHARACTERISTICS | |||||
| Survey completed by mail | 1511 (63.0) | 583 (68.4) | 415 (60.5) | 513 (59.7) | <0.001 |
| Health Literacy score,a median (25th, 75th percentile) | 12 (10–14) | 12 (10–14) | 12 (10–14) | 13 (11–15) | <0.001 |
| Accompanied to medical visits by family/friend | 1870 (78.8) | 642 (76.2) | 549 (81.1) | 679 (79.7) | 0.075 |
| Reliance on family/friends in the management of care: | 0.042 | ||||
| Self-manage | 1266 (53.3) | 453 (53.9) | 340 (49.8) | 473 (55.5) | |
| Co-manage | 801 (33.7) | 250 (29.7) | 265 (38.8) | 286 (33.5) | |
| Delegate | 310 (12.9) | 138 (16.2) | 78 (11.4) | 94 (10.9) | |
All values are presented as n (%) unless otherwise noted
Missing in 48 patients; 1MI: Myocardial Infarction, 2PVD: Peripheral Vascular Disease, 3CVD: Cardiovascular Disease, 4COPD: Chronic Obstructive Pulmonary Disease
After a mean ± SD follow up of 1.3 ± 0.6 years, 233 deaths occurred, equating to a mortality rate of 7% in the first year of follow-up. Patients with good and excellent patient-centered communication had a large reduced risk of death, when compared with patients with worse patient-centered communication (Figure 2). Adjustment for age, sex, marital status, Charlson comorbidity index, education and mode of survey completion, only slightly attenuated the association (Table 2).
Figure 2.
Mortality (A) and mean cumulative hospitalizations (B) by patient-centered communication
Table 2.
Hazard ratios (95% CI) for associations between patient-provider relationship and death and in patients with heart failure
| HR | 95% CI | P-trend | |
|---|---|---|---|
| Unadjusted | |||
| Fair/Poor | 1.00 | 0.002 | |
| Good | 0.72 | (0.53 – 0.99) | |
| Excellent | 0.61 | (0.45 – 0.84) | |
| Model 1a | |||
| Fair/Poor | 1.00 | 0.010 | |
| Good | 0.73 | (0.53 – 1.00) | |
| Excellent | 0.67 | (0.49 – 0.92) | |
| Model 2b | |||
| Fair/Poor | 1.00 | 0.020 | |
| Good | 0.70 | (0.51 – 0.97) | |
| Excellent | 0.70 | (0.51 – 0.96) |
Adjusted for age, sex, marital status, education, Charlson index and mode of survey completion
Adjusted for age, sex, marital status, education, Charlson index, mode of survey completion and health literacy
Further adjusting for being accompanied to medical visits or reliance on family and friends in managing health did not change the results. Moreover, adjusting for health literacy did not significantly change the point estimate nor the statistical significance (good HR: 0.70, 95%CI: 0.51 – 0.97; excellent HR: 0.70, 95%CI: 0.51 – 0.96; ptrend=0.020).
During follow-up, 1194 hospitalizations occurred among 633 people. Of these, 374 (59%) people had 1 hospitalization, 120 (19%) people had 2 hospitalizations and 139 (22%) people had 3 or more hospitalizations. There was no association between patient-centered communication and hospitalizations either before or after adjusting for age, sex, marital status, education, Charlson comorbidity index, mode of survey completion and health literacy (Figure 2 and Table 3). Results were similar when hospitalizations were analyzed with death treated as a competing risk (Table 3).
Table 3.
Hazard ratios (95% CI) for associations between patient-provider relationship and hospitalizations
| Cox Regression Model | Competing Risk Modela | |||||
|---|---|---|---|---|---|---|
| HR | 95% CI | P-trend | HR | 95% CI | P-trend | |
| Unadjusted | ||||||
| Fair/Poor | 1.00 | 0.237 | 1.00 | 0.159 | ||
| Good | 1.07 | (0.85 – 1.34) | 1.08 | (0.86 – 1.34) | ||
| Excellent | 1.14 | (0.92 – 1.42) | 1.17 | (0.94 – 1.45) | ||
| Model 1b | ||||||
| Fair/Poor | 1.00 | 0.523 | 1.00 | 0.398 | ||
| Good | 1.00 | (0.80 – 1.25) | 1.00 | (0.80 – 1.24) | ||
| Excellent | 1.07 | (0.86 – 1.34) | 1.10 | (0.88 – 1.36) | ||
| Model 2c | ||||||
| Fair/Poor | 1.00 | 0.433 | 1.00 | 0.363 | ||
| Good | 0.98 | (0.78 – 1.23) | 0.98 | (0.79 – 1.22) | ||
| Excellent | 1.09 | (0.88 – 1.36) | 1.11 | (0.89 – 1.37) | ||
Treating death as a competing risk
Adjusted for age, sex, marital status, education, Charlson index and mode of survey completion
Adjusted for age, sex, marital status, education, Charlson index, mode of survey completion and health literacy
In ancillary analyses we analyzed the association between patient-centered communication as a continuous score and death. For each increase in one unit of the score, patients had a 5% lower risk of mortality (HR: 0.95, 95% CI: 0.91–0.99; p=0.009). After adjusting for age, sex, marital status, education attainment, Charlson comorbidity index, mode of survey completion and health literacy the association was similar (HR: 0.95, 95% CI: 0.91–1.00; p=0.030).
In order to investigate whether these results could be influenced by measurement bias (in particular, straightlining, whereby responders rush through the survey and choose the same answer for all questions), we retested the association between patient-centered communication and outcomes after excluding those with the highest score from the analysis (score =15, n=651). The association between patient-centered communication and death using fair/poor as the reference was similar after adjustment for age, sex, marital status, education attainment, Charlson comorbidity index, mode of survey completion and health literacy [good HR: 0.72, 95% CI: 0.52 – 0.99; excellent HR: 0.65, 95% CI: 0.37 – 1.14; p trend = 0.027], while there was no association with hospitalization before or after adjustment.
DISCUSSION
In the present study we demonstrated that among patients with HF in the community, patients with excellent and good patient-centered communication with the provider are associated with nearly a 30% lower risk of death, independently of age, sex, marital status, education, Charlson comorbidity index, mode of survey completion and health literacy, while no association was found with hospitalization.
We relied on the health care subscale of the CIRS to measure perceived patient-centered communication.15, 16 The CIRS is a validated instrument to evaluate different resources needed by patients for self-management. This tool has been used by others to evaluate patients with different diseases,28 but to our knowledge the validity of the CIRS has yet to be studied in HF. The health care subscale of the CIRS is composed of 3 questions, making this instrument appealing and easy to implement clinically. This set of questions evaluates shared decision making, active listening, and understanding, which are all components of patient-centered communication as described by Epstein and Street.29, 30 Those with fair/poor and good patient-centered communication are older, have lower educational attainment, lower health literacy and are more dependent on family and friends in the management of care when compared with those who reported excellent patient-centered communication. Therefore, this suggests that patients who reported fair/poor and good patient-centered communication have distinct needs compared to those who experience excellent patient-centered communication with their provider.
It has been reported that communication needs of patients with HF are not always met by providers.31 Ineffective communication has been described as a possible explanation for worse outcomes among patients with limited health literacy.32 In fact, different studies have shown that an inadequate level of health literacy leads to an increased risk of mortality.33–35 and hospitalization.35 Indeed, in our cohort, patients who report fair/poor patient-centered communication have lower health literacy. Therefore, our data highlight the need to further study patient-centered communication and the nature of its association with outcomes.36 Finally, we cannot exclude the possibility that a lower score in the perceived patient-centered communication could be due to distress related to a more severe disease. In this case, patients who experienced fair/poor patient-centered communication would be at increased risk of death because of the severity of the underlying condition. Although this is possible, our cohort of patients is comprised of incident cases with no differences in numbers of comorbidities between the tertiles, making this hypothesis less probable.
Perceived patient-centered communication was not associated with hospitalizations. Patients with HF are elderly and the decision to hospitalize a patient is driven by many factors including residing in a nursing home, presence of an in-home nurse and social support. Therefore, this finding may reflect residual confounding. Thus, further studies are needed to understand this finding.
Limitations, Strengths and Clinical Implications
As with any research using surveys, we cannot rule out non-responder bias. However, our response rate is in line with other national surveys, as rates have been steadily declining over the past few decades.37, 38 For example, the median state response rate was 62% for Behavioral Risk Factor Surveillance System (BRFSS) in 1997 and has declined to 49% in 2014.38 Furthermore, a larger proportion of survey participants had a higher education level,39 however we would expect this to bias our results towards the null. In our study, indeed we found a significant association between patient-centered communication and mortality, thus the true association may even be stronger. However, we did not find a significant association between patient-centered communication and hospitalizations, and we cannot rule out that this may be because of the larger proportion of responders with a higher education level.
Measurement bias must also be considered whereby patients who have the maximum score may not have completed the survey honestly (straight-lining). As excluding patients with the highest score did not change the point estimate, we believe that measurement bias is limited. To the best of our knowledge, there are no standardized cutpoints in the literature to define adequate patient-centered communication as measured by the healthcare subscale of the CIRS, therefore we divided our cohort into tertiles. Since the distribution is skewed towards higher values those in the fair/poor tertile comprise a rather large spectrum of different patient-centered communication. The population studied includes mostly Caucasian individuals, thus replication in other populations is needed to confirm our findings. Finally, as in any observational study, we cannot rule out the effect of residual confounding.
This study has several important strengths. This is a population-based community study made possible by the REP,11–13 which enables comprehensively retrieving data on comorbidities and outcomes in a large geographically defined region of Southeast Minnesota. Additionally, the sample size of the present study is quite large and we utilized validated instruments to evaluate patient-centered communication and health literacy. The three questions from the health care subscale of the CIRS constitute an efficient approach to assess perceived patient-centered communication, which could be easily implemented clinically to identify patients who have communication barriers with care providers and to customize a tailored intervention. In fact, according to the Expanded Chronic Care model, improved outcomes may be a consequence of an effective interaction with the health care provider.40 Finally, this study evaluates the communication between the health care provider and the patient as perceived by the patient with HF, but we do not have information on how the health care provider perceives the same interaction. It would be of interest in future studies to understand the level of concordance in the evaluation of patient-centered communication by the doctor-patient dyad.
CONCLUSION
Among community patients with HF, better patient-centered communication was associated with increased survival. Patient-centered communication can be easily assessed and consideration should be given toward implementation in the clinical practice.
Take Away Points:
Excellent and good patient-centered communication, as measured by a survey, and was found to be associated with an increased risk of death among a community cohort of patients with heart failure.
Patients with excellent patient-centered communication were younger and had higher education attainment than those with good and fair/poor communication
Patients with excellent and good patient-centered communication had reduced risk of death after adjustment for confounders
Patient-centered communication can be easily assessed and consideration should be given toward implementation in the clinical practice
ACKNOWLEDGMENTS
We thank Ellen Koepsell, RN for her study support and Deborah Strain for her manuscript submission assistance.
Funding:
This work was supported by grants from the National Institute on Aging (R01 AG034676), National Heart, Lung and Blood Institute (R01 HL120859) and the Patient Centered Outcomes Research Institute - PCORI (CDRN-1501-26638). The funding sources played no role in the design, methods, subject recruitment, data collection, analysis or preparation of the paper.
Footnotes
Publisher's Disclaimer: “This is the pre-publication version of a manuscript that has been accepted for publication in The American Journal of Managed Care (AJMC). This version does not include post-acceptance editing and formatting. The editors and publisher of AJMC are not responsible for the content or presentation of the prepublication version of the manuscript or any version that a third party derives from it. Readers who wish to access the definitive published version of this manuscript and any ancillary material related to it (eg, correspondence, corrections, editorials, etc) should go to www.ajmc.com or to the print issue in which the article appears. Those who cite this manuscript should cite the published version, as it is the official version of record.”
Declaration of Interest: Declarations of interest: none.
REFERENCES
- 1.Benjamin EJ, Blaha MJ, Chiuve SE, Cushman M, Das SR, Deo R, de Ferranti SD, Floyd J, Fornage M, Gillespie C, et al. Heart Disease and Stroke Statistics-2017 Update: A Report From the American Heart Association. Circulation. 2017;135(10):e146–e603. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Heidenreich PA, Albert NM, Allen LA, Bluemke DA, Butler J, Fonarow GC, Ikonomidis JS, Khavjou O, Konstam MA, Maddox TM, et al. Forecasting the impact of heart failure in the United States: a policy statement from the American Heart Association. Circ Heart Fail. 2013;6(3):606–619. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Gerber Y, Weston SA, Redfield MM, Chamberlain AM, Manemann SM, Jiang R, Killian JM, Roger VL. A contemporary appraisal of the heart failure epidemic in Olmsted County, Minnesota, 2000 to 2010. JAMA Intern Med. 2015;175(6):996–1004. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Roger VL. The changing landscape of heart failure hospitalizations. J Am Coll Cardiol. 2013;61(12):1268–1270. [DOI] [PubMed] [Google Scholar]
- 5.Roger VL, Weston SA, Redfield MM, Hellermann-Homan JP, Killian J, Yawn BP, Jacobsen SJ. Trends in heart failure incidence and survival in a community-based population. JAMA. 2004;292(3):344–350. [DOI] [PubMed] [Google Scholar]
- 6.Dunlay SM, Redfield MM, Weston SA, Therneau TM, Hall Long K, Shah ND, Roger VL. Hospitalizations after heart failure diagnosis a community perspective. J Am Coll Cardiol. 2009;54(18):1695–1702. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Street RL Jr., Spears E, Madrid S, Mazor KM. Cancer Survivors’ Experiences with Breakdowns in Patient-Centered Communication. Psychooncology. 2018;28(2):423–429. [DOI] [PubMed] [Google Scholar]
- 8.Naughton CA. Patient-Centered Communication. Pharmacy (Basel). 2018;6(1):pii: E18. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Wild D, Nawaz H, Ullah S, Via C, Vance W, Petraro P. Teaching residents to put patients first: creation and evaluation of a comprehensive curriculum in patient-centered communication. BMC Med Educ. 2018;18(1):266. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Yancy CW, Jessup M, Bozkurt B, Butler J, Casey DE Jr., Drazner MH, Fonarow GC, Geraci SA, Horwich T, Januzzi JL, et al. 2013 ACCF/AHA guideline for the management of heart failure: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2013;62(16):e147–239. [DOI] [PubMed] [Google Scholar]
- 11.St Sauver JL, Grossardt BR, Yawn BP, Melton LJ 3rd, Pankratz JJ, Brue SM, Rocca WA. Data resource profile: the Rochester Epidemiology Project (REP) medical records-linkage system. Int J Epidemiol. 2012;41(6):1614–1624. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.St Sauver JL, Grossardt BR, Leibson CL, Yawn BP, Melton LJ 3rd, Rocca WA. Generalizability of epidemiological findings and public health decisions: an illustration from the Rochester Epidemiology Project. Mayo Clin Proc. 2012;87(2):151–160. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Rocca WA, Yawn BP, St Sauver JL, Grossardt BR, Melton LJ, 3rd. History of the Rochester Epidemiology Project: half a century of medical records linkage in a US population. Mayo Clin Proc. 2012;87(12):1202–1213. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Rocca WA, Grossardt BR, Brue SM, Bock-Goodner CM, Chamberlain AM, Wilson PM, Finney Rutten LJ, St Sauver JL. Data Resource Profile: Expansion of the Rochester Epidemiology Project medical records-linkage system (E-REP). Int J Epidemiol. 2018;47(2):368–368j. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Glasgow RE, Toobert DJ, Barrera M Jr., Strycker LA. The Chronic Illness Resources Survey: cross-validation and sensitivity to intervention. Health Educ Res. 2005;20(4):402–409. [DOI] [PubMed] [Google Scholar]
- 16.Glasgow RE, Strycker LA, Toobert DJ, Eakin E. A social-ecologic approach to assessing support for disease self-management: the Chronic Illness Resources Survey. J Behav Med. 2000;23(6):559–583. [DOI] [PubMed] [Google Scholar]
- 17.Wolff JL, Boyd CM. A Look at Person- and Family-Centered Care Among Older Adults: Results from a National Survey [corrected]. J Gen Intern Med. 2015;30(10):1497–1504. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Wolff JL, Boyd CM, Gitlin LN, Bruce ML, Roter DL. Going it together: persistence of older adults’ accompaniment to physician visits by a family companion. J Am Geriatr Soc. 2012;60(1):106–112. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Troy E, Doltani D, Harmon D. The role of a companion attending consultations with the patient. A systematic review. Ir J Med Sci. 2018;188(3):743–750. [DOI] [PubMed] [Google Scholar]
- 20.Chew LD, Bradley KA, Boyko EJ. Brief questions to identify patients with inadequate health literacy. Fam Med. 2004;36(8):588–594. [PubMed] [Google Scholar]
- 21.Chew LD, Griffin JM, Partin MR, Noorbaloochi S, Grill JP, Snyder A, Bradley KA, Nugent SM, Baines AD, Vanryn M. Validation of screening questions for limited health literacy in a large VA outpatient population. J Gen Intern Med. 2008;23(5):561–566. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.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. [DOI] [PubMed] [Google Scholar]
- 23.Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol. 1992;45(6):613–619. [DOI] [PubMed] [Google Scholar]
- 24.The American Association for Public Opinion Research (AAPOR). Standard Definitions: Final Dispositions of Case Codes and Outcome Rates for Surveys. 2016. 9th edition Accessed at: https://www.aapor.org/AAPOR_Main/media/publications/Standard-Definitions20169theditionfinal.pdf. on 12/13/19 [Google Scholar]
- 25.Nelson WB. Recurrent events, data analysis for product repairs, disease recurrences, and other applications. ASA-SIAM Series on Statistics and Applied Probability, 2003, Schenectady, New York. [Google Scholar]
- 26.Andersen PK, Gill RD. Cox’s regression model for counting processes : A large sample study. Annals of Stat. 1982;101100–1120. [Google Scholar]
- 27.Fine JP, Gray RJ. A Proportional Hazards Model for the Subdistribution of a Competing Risk. Journal of the American Statistical Association. 1999;94(446):496–509. [Google Scholar]
- 28.Mann EG, Harrison MB, LeFort S, VanDenKerkhof EG. What Are the Barriers and Facilitators for the Self-Management of Chronic Pain with and without Neuropathic Characteristics? Pain Manag Nurs. 2017;18(5):295–308. [DOI] [PubMed] [Google Scholar]
- 29.Street RL Jr., Elwyn G, Epstein RM. Patient preferences and healthcare outcomes: an ecological perspective. Expert Rev Pharmacoecon Outcomes Res. 2012;12(2):167–180. [DOI] [PubMed] [Google Scholar]
- 30.Epstein RM, Franks P, Fiscella K, Shields CG, Meldrum SC, Kravitz RL, Duberstein PR. Measuring patient-centered communication in patient-physician consultations: theoretical and practical issues. Soc Sci Med. 2005;61(7):1516–1528. [DOI] [PubMed] [Google Scholar]
- 31.Harding R, Selman L, Beynon T, Hodson F, Coady E, Read C, Walton M, Gibbs L, Higginson IJ. Meeting the communication and information needs of chronic heart failure patients. J Pain Symptom Manage. 2008;36(2):149–156. [DOI] [PubMed] [Google Scholar]
- 32.Paasche-Orlow MK, Wolf MS. The causal pathways linking health literacy to health outcomes. Am J Health Behav. 2007;31 Suppl 1 S19–26. [DOI] [PubMed] [Google Scholar]
- 33.Peterson PN, Shetterly SM, Clarke CL, Bekelman DB, Chan PS, Allen LA, Matlock DD, Magid DJ, Masoudi FA. Health literacy and outcomes among patients with heart failure. JAMA. 2011;305(16):1695–1701. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.McNaughton CD, Cawthon C, Kripalani S, Liu D, Storrow AB, Roumie CL. Health literacy and mortality: a cohort study of patients hospitalized for acute heart failure. J Am Heart Assoc. 2015;4(5):Pii: e001799. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Fabbri M, Yost K, Finney Rutten LJ, Manemann SM, Boyd CM, Jensen D, Weston SA, Jiang R, Roger VL. Health Literacy and Outcomes in Patients With Heart Failure: A Prospective Community Study. Mayo Clin Proc. 2018;93(1):9–15. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Horstman MJ, Stewart DE, Naik AD. Improving patients’ postdischarge communication: making every word count. Circulation. 2014;130(13):1091–1094. [DOI] [PubMed] [Google Scholar]
- 37.Czajka JL, Beyler A. Declining response rates in federal surveys: trends and implications (background paper). Mathematica, 2016. Accessed at: https://www.mathematica-mpr.com/our-publications-and-findings/publications/declining-response-rates-in-federal-surveys-trends-and-implications-background-paper on March 27, 2020: Mathematica Policy Research. [Google Scholar]
- 38.Groves R, Fowler FJ, Couper M, Lepkowski J, Singer E, Tourangeau R. Survey Methodology, 2nd Edition : John Wiley & Sons, Inc; Hoboken, NJ; 2009. [Google Scholar]
- 39.Simsek I, Manemann SM, Yost KJ, Chamberlain AM, Fabbri M, Jiang R, Weston SA, Roger VL. Participation Bias in a Survey of Community Patients with Heart Failure. Mayo Clin Proc. 2020 (In press). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.The MacColl Institute. The Care Model: Expanded Chronic Care Model. http://www.improvingchroniccare.org/index.php?p=ICIC_Expanded&s=156. Accessed March 27, 2020.