Effect of continuity of care on health-related quality of life in adult patients with hypertension: a cohort study in China
© The Author(s). 2016
Received: 27 February 2016
Accepted: 15 August 2016
Published: 28 November 2016
Continuity of care is widely considered a principle of primary care that decreases healthcare utilization and mortality. However, the effect of continuity of care on health-related quality of life (HRQoL) for adult patients with hypertension remains unclear.
To further evaluate the effect of continuity of care, we implemented a cohort study among hypertensive patients aged over 35 years (n = 1200) in six townships in Qianjiang District, Chongqing, China, between 2012 and 2014. The study ultimately included 1079 participants. The continuity of care index was calculated using claim-based longitudinal data obtained from hypertension follow-up service records. The baseline and endline survey-based data, tested by the SF-36 scale, were used to assess HRQoL. To control selection bias and examine the effect of continuity of care, a kernel-based propensity score matching difference-in-differences (DID) method was used. Additionally, descriptive statistics, chi-squared test, and Mann–Whitney nonparametric test were used to summarize characteristics, evaluate proportional differences, and analyze statistical differences, respectively.
Our results showed that patients in the high continuity of care group presented greater improvement in both Physical Component Summary (PCS, DID = 5.192 ± 1.970, p < 0.001) and Mental Component Summary (MCS, DID = 7.900 ± 1.815, p = 0.008) than those in the low continuity of care group. Moreover, patients in the high continuity of care group showed significant improvement in physical functioning, role-physical, general health, role-emotional, and mental health.
Our findings indicate that a long-term physician-patient relationship may improve HRQoL in patients with hypertension. However, more unified measurement tools are needed to evaluate continuity of care. Further studies should include more study settings.
KeywordsChina Continuity of care Hypertension Health-related quality of life Cohort study
Continuity of care is defined as a core attribute of primary care. However, considerable diversity exists in previous definitions. For instance, Saultz  defined continuity of care as a hierarchical concept that ranges from informational continuity and longitudinal continuity to interpersonal continuity. In his study, longitudinal continuity emphasized a familiar setting for patients to receive care that is easier for patients to access; interpersonal continuity was characterized by trust and a sense of responsibility. Nevertheless, Haggerty  combined longitudinal continuity and interpersonal continuity into one concept, called relational continuity. This study focuses on a long-term relationship for patients and physicians, without involving the sense of trust and responsibility. Additionally, this type of continuity of care is the same as the longitudinal continuity suggested by Saultz .
The effects of continuity of care have been debated in previous studies because some have concluded that continuity of care is associated with fewer hospitalizations, emergency department visits, and hospital admissions [3–7]; low pharmaceutical expenditures and healthcare expenses [5, 8–10]; decreased mortality rates [11, 12]; fewer duplicated medications ; improved medication adherence ; and patient satisfaction [15–18]. However, others insist that high continuity of care may lead to the purchase of more drugs overall and that the effects on HRQoL are unclear; thus, these should be further examined [19, 20].
Regarding the association between continuity of care and HRQoL, Hanninen  reported that diabetic patients who had been treated by the same general practitioner for at least 2 years seemed to have better mental and physical health, as well as less pain, than those who did not have a long-term physician-patient relationship. However, as pointed out by Hanninen , a causal relationship could not be established because many confounding factors were not controlled by the cross-sectional investigation. Meanwhile, Gulliford  implemented a cohort study for diabetic patients using random effect models adjusted for baseline value of outcome, age, sex, ethnicity, duration of diabetes, type of treatment, qualifications, housing tenure, and living alone. Similar to Gulliford’s study, we also attempted to reduce the confounding bias and find a causal relationship between continuity of care and HRQoL among patients with hypertension. In contrast to the study by Gulliford, a kernel-based propensity score matching DID analysis method was presently used to match the high continuity of care group and the low continuity of care group, yielding comparable treated and control groups of patients with hypertension.
Hypertension is one of the main chronic diseases in China . A 2014 report notes that the prevalence rate of hypertension is 29.6 % for persons aged 18 years or older . In 2009, free health services for hypertension patients aged over 35 years were included in the national public health program, named the National Essential Public Health Services Package (NEPHSP). This program establishes health records, screening, following-up, and systematic physical examinations for hypertensive patients in urban community health centers and rural township health centers . Moreover, the essence of having a family doctor lies on the principle of establishing a fixed relationship and continuity of care. The question that we aimed to answer is “Can a family doctor (representing continuity of care) improve HRQoL for patients with hypertension?” This question is important because the answer may influence the priority of basic public health reform that aims to sustain continuity of care and implement family doctor policies as compared to ensuring health service accessibility.
Therefore, to provide more empirical evidence of the effects of continuity of care and investigate its specific effects on HRQoL in China, we conducted this study to examine whether better continuity of care could improve HRQoL of hypertensive patients.
Setting and study sample
We selected Qianjiang District in Chongqing, China, as the study setting. The data analyzed herein were from the “Study on the Efficiency and Effectiveness of the Integrated Health Care Services in Rural China” funded by the China Medical Board, which was designed as a clustered randomized controlled trial in nearly 60 villages of six towns, with around 6000 chronic patients. Further details may be found in Tang’s paper .
To include patients with essential hypertension, the exclusion criteria for this study were as follows: (i) patients aged less than 35 years by December 2012, (ii) patients that were not registered as members of the NEPHSP program until January 2012. In the baseline investigation, conducted from July to August 2012, 1200 hypertensive patients were enrolled. The following patients were also excluded: (i) patients who did not use the stated address as their primary residence, (ii) those who received hypertension follow-up services less than three times a year, and (iii) those no longer requiring follow-up because of death. According to the above exclusion criteria, 121 patients were excluded, and 1079 patients were included in the study sample.
Given that hypertension patients were members of the NEPHSP program, these patients were followed up by physicians in the township health centers between January 2013 and December 2014. Ethical approval for this study was granted by the Ethics Committee of Tongji Medical College and Huazhong University of Science and Technology. All of the participants gave a written informed consent for participation in this study, provided consent before filling out the questionnaire, and consented to the publication of the data.
Measure of continuity of care
In order to depict continuity of care patterns, patients were asked regarding their experience while attending physician consultations and their responses were recorded by physicians in the township health centers. These accounts were kept in each patient’s individual health record. When they visited or were called by their physicians in the township health centers, the trained physicians asked them whether they consulted other doctors for treatment of hypertension during two follow-up periods. If so, the information about those experiences was also recorded in each patient’s individual health record, including the name of the medical institution, the date of the visit, and the doctor’s name (if possible). All of the 1200 patients’ individual health records were collected between January and February 2015.
Measure of HRQoL
Patient HRQoL was assessed using the Medical Outcome Study Short-Form 36-Item Health Survey (SF-36 Scale) developed by the RAND Corporation’s Health Insurance Experiment . The eight dimensions of HRQoL, the summary of physical quality of life (Physical Component Summary; PCS), and emotional quality of life (Mental Component Summary; MCS) were measured, as well as the reported health transition. The PCS and MCS were calculated by determining the mean average of all of the physically relevant questions (physical functioning, role-physical, body pain, and general health) and of all of the emotionally relevant items (vitality, social functioning, role-emotional and mental health), respectively [35, 36]. In the baseline and endline surveys, the data on HRQoL were collected through the SF-36 Scale.
In the baseline investigation, we used a self-designed questionnaire to collect patients’ socio-demographic characteristics (age, sex, education level, and marital outcome), as well as information on the duration (years) of hypertension.
Considering that continuity of care may have some association with health care provision capacity, the six towns were divided into two groups, a high capacity group and a low capacity group. This grouping was based on multiple considerations on their performance in the most recent 5 years and consulting from the leaders of the Health Bureau in Qianjiang District. Additionally, because of the project “Study on the Efficiency and Effectiveness of the Integrated Health Care Services in Rural China”, two of the six towns implemented both care integration and payment integration interventions, two of them implemented care integration only, and the final two did not apply any intervention. The latter served as the control groups. In this study, the intervention types were also considered. In the final model, the interaction of health care provision capacity and hypertension intervention type were included.
A propensity score matching DID approach was used. This approach isolates the improvement in outcomes related to the high continuity of care group that exceeds changes over the same period in the low continuity of care group. In this study, patients with continuity of care index = 1 were classified into the high continuity of care group. This group represented the treated group. Other patients were classified into the low continuity of care group, that is, the control group.
Propensity score matching was used to match the high continuity of care group and the low continuity of care group, so that the treated and control groups were comparable to avoid selection bias related to several key factors. To create the propensity score, a logistic regression model was created with the high continuity of care patients (vs. the low continuity of care patients) as the dependent variable. The sociodemographic variables (age, sex, education level, and marital outcome) and duration of hypertension were included as independent variables, as well as the interaction of health care provision capacity and type of hypertension intervention collected from baseline investigations. This matching ensured that the patients in the high continuity of care group and in the low continuity of care group were comparable.
Kernel-based propensity score matching was used. Covariate imbalance before and after matching was checked with the absolute standardized difference of the means of the linear index of the propensity score in the treated and (matched) non-treated groups (Rubins’ B) and the ratio of treated to (matched) non-treated variances of the propensity score index (Rubin’s R).
The DID analysis includes the weights derived from the kernel-based propensity score matching [37–40]. We performed a balancing test of the difference in the means of the covariates between the control and treated groups in the baseline period to test whether the parallel trends in the baseline period was satisfied, as this is one of the key assumptions of a DID methodology.
Descriptive statistics were used to summarize the patients’ baseline characteristics. The chi-squared test was used to evaluate proportional differences in categorical variables. The Mann–Whitney nonparametric test was used for between-group differences.
All analyses were conducted using Stata 13.0. We set statistical significance at a two-tailed p < 0.05.
Patients’ characteristics in the high continuity of care group and low continuity of care group in the baseline period before matching (July–August 2012)
High continuity of care group
Low continuity of care group
Number of observations
Age (years), median (range)
Higher than primary educational level, n(%)
Duration of hypertension (years), median (range)
Health care provision capacity-high, n(%)
Hypertension intervention type
No intervention, n(%)
One intervention, n(%)
Two interventions, n(%)
Physical functioning, median (range)
Role-physical, median (range)
Body pain, median (range)
General health, median (range)
Vitality, median (range)
Social functioning, median (range)
Role-emotional, median (range)
Mental health, median (range)
Reported Health Transition, median (range)
PCS, median (range)
MCS, median (range)
Propensity score analysis
The associations between variables used to match with propensity score in logistic regression
95 % confidence Interval
Higher than primary educational level
Duration of hypertension
Type of township health center
After matching, the samples were considered sufficiently balanced: Rubins’ B = 10.3 and Rubin’s R = 0.89 . Besides, no major differences were observed regarding baseline characteristics for the high continuity of care group and the low continuity of care group for all p values with differences > 0.1. Therefore, variables were satisfactorily balanced.
DID analysis of HRQoL
Results of kernel-based propensity score matching DID models with HRQoL
High continuity of care group
Low continuity of care group
High continuity of care group
Low continuity of care group
Reported Health Transition
Physical component summary
Mental component summary
In the present study, we showed that continuity of care had a positive effect on HRQoL. Hypertensive patients who had been treated by the same physician for the past 2 years had better quality of life both physically and emotionally compared with those treated by several physicians. However, regarding the eight subscales of SF-36, patients reported positive effects of continuity of care on physical functioning, role-physical, general health, role-emotional as well as mental health; these effects were statistically significant. However, no significant differences were found on reported health transition.
Some studies, as did ours, have attempted to establish a relationship between continuity of care and HRQoL. The results of the present study differed from those reported by Hanninen and Gulliford [21, 22]. We showed that good continuity of care could lead to better scoring in both PCS and MCS of the SF-36. A similar result was demonstrated by Hanninen, who found that good continuity of care was significantly associated with the better well-being dimensions of the SF-20. However, the continuity of care effects on the eight HRQoL dimensions observed in the present study differed from the findings of the other two studies in that we found positive effects on physical functioning, role-physical, general health, role-emotional as well as mental health. In contrast, Hanninen found positive effects on body pain and social functioning. Contrary to our findings and those by Hanninen, Gulliford did not find an association between continuity of care and SF-12 PCS or MCS.
For the reasons stated above, the results from these three studies are not directly comparable. First, the three studies focused on different medical conditions. Hanninen’s and Gulliford’s studies focused on patients with diabetes, whereas the present study focused on patients with hypertension. Second, the current study used the binary variable of whether hypertensive patients were treated by the same physician or different ones to measure continuity of care, which is similar to the method used by Hanninen but different to that used by Gulliford. The latter study was based on a new questionnaire designed to measure the experienced continuity of care for type 2 diabetes (Experienced Continuity of Care—Diabetes Mellitus [ECC-DM]). Therefore, to improve the comparability of different studies, more unified measurement tools are needed to evaluate continuity of care for specific medical conditions.
Most of the previous studies used claims data from health insurance reimbursement databases [3–6, 11, 42]. Moreover, the outcomes of these studies focused on health resource utilization (hospitalizations and emergency department visits) and healthcare expenses. As Bentler  demonstrated, claim-based continuity of care measures cannot reflect patient perceptions of continuous patient-provider relationships. Thus, he suggested that claim-based data should be incorporated with patient reports to fully evaluate continuity of care. We implemented a 2-year cohort study to obtain additional observational variables and reduce the confounding bias. Furthermore, selection bias is a significant problem when assessing the effect of continuity of care. Four related studies have considered this type of bias [13, 19, 22, 44], whereas the vast majority of previous studies have neglected it. In this study, selection bias was controlled via kernel-based propensity score matching.
Regarding the question of whether the basic public health care delivery system for patients with hypertension should be changed, the present research provided sound evidence. The present results showed that a long-term relationship between hypertensive patients and physicians could improve patient HRQoL. This shows the importance of family doctors for patients with hypertension. However, whether family doctors are of significance for patients with other conditions still needs further study. According to our knowledge, those who are willing to visit a fixed physician are more familiar with their physician and are more willing to accept their advice, resulting in better treatment compliance. The mediating effects among continuity of care, awareness, compliance, and outcomes (such as HRQoL, medical expenses, healthcare utilization) also need to be further tested.
This study is characterized by certain strengths. Instead of using claims data or cross-sectional investigation data separately, a cohort study was implemented. As a contribution of the follow-up service records, the recall bias was reduced and the calculation of continuity of care was ensured to be as accurate as possible. Additionally, propensity score matching was used to eliminate selection bias, which theoretically led to the reduction of the risks of baseline demographic distribution and other covariate differences. Furthermore, the use of a DID analysis minimized the potential differences for those patients in the high continuity of care and low continuity of care groups during the 2-year follow-up.
However, this current study also had certain limitations. First, as Robles  assumed, the use of health services may influence continuity of care. This variable was not used as a covariate to estimate the propensity score in this study because data collection proved to be difficult. Second, as classified by Jee , continuity of care was calculated primarily based on duration of provider relationship, density of visits, dispersion of providers, sequence of providers, and subjective estimates. In the present study, although the fixed physician and patient relationship was measured, it could not represent all dimensions of continuity of care. Third, the cohort study was implemented only in Qianjiang District, which does not represent all settings in China. Future studies should include more study settings to yield stronger evidence.
A long-term physician-patient relationship may improve the HRQoL of patients with hypertension. Patients with hypertension and good continuity of care presented significant improvements in physical quality of life, emotional quality of life, and some dimensions of HRQoL such as physical functioning, role-physical, general health, and role-emotional as well as mental health. Measurement tools that are more unified are needed to evaluate continuity of care in further studies.
This project proceeded under the help of local administrative authorities: Qianjiang Health Bureau and Medical Insurance Department of Qianjiang Human Resource and Social Security Bureau. Besides, thanks very much for Dr. Tianan Yang who helped modify and revise the paper.
This project was funded by the China Medical Board (11-069) and National Natural Science Foundation of China (71403092) and China Postdoctoral Science Foundation (2014 M562035).
Availability of data and materials
We do not feel that it is appropriate to publish the raw data used to perform this study. The data can be available from the authors upon request.
Conceived and designed the experiments: Ting Ye, Liang Zhang. Performed the experiment: Xiaowei Sun, Wenxi Tang, Yan Zhang, Yudong Miao. Analyzed the data: Ting Ye, Xiaowei Sun, Yudong Miao. Contributed reagents/ materials/ analysis tools: Ting Ye, Yan Zhang. Wrote the paper: Ting Ye, Liang Zhang. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Consent for publication
Ethics approval and consent to participate
All of the research methods and investigation tools in this study were approved by the Ethics Committee of Tongji Medical College, Huazhong University of Science and Technology (IORG No: IORG0003571). All of the participants gave a written informed consent for participation in this study, provided consent before filling out the questionnaire, and consented to the publication of the data.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Saultz JW. Defining and measuring interpersonal continuity of care. Ann Fam Med. 2003;1(3):134–43.View ArticlePubMedPubMed CentralGoogle Scholar
- Haggerty JL, Reid RJ, Freeman GK, Starfield BH, Adair CE, McKendry R. Continuity of care: a multidisciplinary review. BMJ. 2003;327(7425):1219–21.View ArticlePubMedPubMed CentralGoogle Scholar
- Romaire MA, Haber SG, Wensky SG, McCall N. Primary care and specialty providers: an assessment of continuity of care, utilization, and expenditures. Med Care. 2014;52(12):1042–9.PubMedGoogle Scholar
- Cheng SH, Chen CC, Hou YF. A longitudinal examination of continuity of care and avoidable hospitalization: evidence from a universal coverage health care system. Arch Intern Med. 2010;170(18):1671–7.View ArticlePubMedGoogle Scholar
- Hong JS, Kang HC, Kim J. Continuity of care for elderly patients with diabetes mellitus, hypertension, asthma, and chronic obstructive pulmonary disease in Korea. J Korean Med Sci. 2010;25(9):1259–71.View ArticlePubMedPubMed CentralGoogle Scholar
- Cheng SH, Hou YF, Chen CC. Does continuity of care matter in a health care system that lacks referral arrangements? Health Policy Plann. 2011;26(2):157–62.View ArticleGoogle Scholar
- Cho KH, Lee SG, Jun B, Jung BY, Kim JH, Park EC. Effects of continuity of care on hospital admission in patients with type 2 diabetes: analysis of nationwide insurance data. BMC Health Serv Res. 2015;15:107.View ArticlePubMedPubMed CentralGoogle Scholar
- Chen CC, Chen SH. Better continuity of care reduces costs for diabetic patients. Am J Manag Care. 2011;17(6):420–7.PubMedGoogle Scholar
- Marchinko S, Clarke D. The Wellness Planner: empowerment, quality of life, and continuity of care in mental illness. Arch Psychiatr Nurs. 2011;25(4):284–93.View ArticlePubMedGoogle Scholar
- De Maeseneer JM, De Prins L, Gosset C, Heyerick J. Provider continuity in family medicine: does it make a difference for total health care costs? Ann Fam Med. 2003;1(3):144–8.View ArticlePubMedPubMed CentralGoogle Scholar
- Hoertel N, Limosin F, Leleu H. Poor longitudinal continuity of care is associated with an increased mortality rate among patients with mental disorders: results from the French National Health Insurance Reimbursement Database. Eur Psychiatry. 2014;29(6):358–64.View ArticlePubMedGoogle Scholar
- Shin DW, Cho J, Yang HK, Park JH, Lee H, Kim H, Oh J, Hwang S, Cho B, Guallar E. Impact of continuity of care on mortality and health care costs: a nationwide cohort study in Korea. Ann Fam Med. 2014;12(6):534–41.View ArticlePubMedPubMed CentralGoogle Scholar
- Cheng SH, Chen CC. Effects of continuity of care on medication duplication among the elderly. Med Care. 2014;52(2):149–56.View ArticlePubMedGoogle Scholar
- Chen CC, Tseng CH, Cheng SH. Continuity of care, medication adherence, and health care outcomes among patients with newly diagnosed type 2 diabetes: a longitudinal analysis. Med Care. 2013;51(3):231–7.View ArticlePubMedGoogle Scholar
- Hjortdahl P, Laerum E. Continuity of care in general practice: effect on patient satisfaction. BMJ. 1992;304(6837):1287–90.View ArticlePubMedPubMed CentralGoogle Scholar
- Saultz JW, Albedaiwi W. Interpersonal continuity of care and patient satisfaction: a critical review. Ann Fam Med. 2004;2(5):445–51.View ArticlePubMedPubMed CentralGoogle Scholar
- Beattie P, Dowda M, Turner C, Michener L, Nelson R. Longitudinal continuity of care is associated with high patient satisfaction with physical therapy. Phys Ther. 2005;85(10):1046–52.PubMedGoogle Scholar
- Weyrauch KF. Does continuity of care increase HMO patients’ satisfaction with physician performance? J Am Board Fam Pract. 1996;9(1):31–6.PubMedGoogle Scholar
- Robles S, Anderson GF. Continuity of care and its effect on prescription drug use among Medicare beneficiaries with hypertension. Med Care. 2011;49(5):516–21.View ArticlePubMedGoogle Scholar
- Panattoni L, Stone A, Chung S, Tai-Seale M. Patients report better satisfaction with part-time primary care physicians, despite less continuity of care and access. J Gen Intern Med. 2015;30(3):327–33.View ArticlePubMedGoogle Scholar
- Hanninen J, Takala J, Keinanen-Kiukaanniemi S. Good continuity of care may improve quality of life in Type 2 diabetes. Diabetes Res Clin Pract. 2001;51(1):21–7.View ArticlePubMedGoogle Scholar
- Gulliford MC, Naithani S, Morgan M. Continuity of care and intermediate outcomes of type 2 diabetes mellitus. Fam Pract. 2007;24(3):245–51.View ArticlePubMedGoogle Scholar
- He J. Hypertension in China: a large and increasing public health challenge. J Hypertens. 2016;34(1):29–31.View ArticlePubMedGoogle Scholar
- Wang J, Zhang L, Wang F, Liu L, Wang H. Prevalence, awareness, treatment, and control of hypertension in China: results from a national survey. Am Journal Hypertens. 2014;27(11):1355–61.View ArticleGoogle Scholar
- Ministry of Health, PRC. Opinions on promoting the essential public health services gradually equalization. 2009. Available: http://www.sda.gov.cn/WS01/CL0611/41413.html. Accessed 10 Jul 2009.
- Tang W, Sun X, Zhang Y, Ye T, Zhang L. How to build and evaluate an integrated health care system for chronic patients: study design of a clustered randomised controlled trial in rural China. Int J Integr Care. 2015;15:e7.View ArticleGoogle Scholar
- Bice TW, Boxerman SB. A quantitative measure of continuity of care. Med Care. 1977;15(4):347–9.View ArticlePubMedGoogle Scholar
- Smedby O, Eklund G, Eriksson EA, Smedby B. Measures of continuity of care. A register-based correlation study. Med Care. 1986;24(6):511–8.View ArticlePubMedGoogle Scholar
- Ribka JP. Building systems to measure continuity of care. Nurs Case Manag. 1998;3(4):151–4.PubMedGoogle Scholar
- Gulliford MC, Naithani S, Morgan M. Measuring continuity of care in diabetes mellitus: an experience-based measure. Ann Fam Med. 2006;4(6):548–55.View ArticlePubMedPubMed CentralGoogle Scholar
- Uijen AA, Heinst CW, Schellevis FG, van den Bosch WJ, van de Laar FA, Terwee CB, Schers HJ. Measurement properties of questionnaires measuring continuity of care: a systematic review. PLoS One. 2012;7(7):e42256.View ArticlePubMedPubMed CentralGoogle Scholar
- Roos LL, Roos NP, Gilbert P, Nicol JP. Continuity of care: does it contribute to quality of care? Med Care. 1980;18(2):174–84.View ArticlePubMedGoogle Scholar
- Christakis DA, Mell L, Wright JA, Davis R, Connell FA. The association between greater continuity of care and timely measles-mumps-rubella vaccination. Am J Public Health. 2000;90(6):962–5.View ArticlePubMedPubMed CentralGoogle Scholar
- Ware JE. SF-36 health survey manual and interpretation guide. Boston: New England Medical Center; 1993. p. 107.Google Scholar
- Ware JJ, Kosinski M, Bayliss MS, McHorney CA, Rogers WH, Raczek A. Comparison of methods for the scoring and statistical analysis of SF-36 health profile and summary measures: summary of results from the Medical Outcomes Study. Med Care. 1995;33(4 Suppl):S264–79.Google Scholar
- Ware JJ, Gandek B. Overview of the SF-36 Health Survey and the International Quality of Life Assessment (IQOLA) Project. J Clin Epidemiol. 1998;51(11):903–12.View ArticlePubMedGoogle Scholar
- Gebel M, Vossemer J. The impact of employment transitions on health in Germany. A difference-in-differences propensity score matching approach. Soc Sci Med. 2014;108:128–36.View ArticlePubMedGoogle Scholar
- Rosenthal MB, Sinaiko AD, Eastman D, Chapman B, Partridge G. Impact of the Rochester Medical Home Initiative on Primary Care Practices, Quality, Utilization, and Costs. Med Care. 2015;53(11):967–73.PubMedGoogle Scholar
- Sari N, Osman M. The effects of patient education programs on medication use among asthma and COPD patients: a propensity score matching with a difference-in-difference regression approach. BMC Health Serv Res. 2015;15:332.View ArticlePubMedPubMed CentralGoogle Scholar
- Maluccio JA, Palermo T, Kadiyala S, Rawat R. Improving Health-Related Quality of Life among People Living with HIV: Results from an Impact Evaluation of a Food Assistance Program in Uganda. PLoS One. 2015;10(8):e135879.View ArticleGoogle Scholar
- Rubin DB. Using Propensity Scores to Help Design Observational Studies: Application to the Tobacco Litigation. Health Serv Outcome Res Methodol. 2001;2:169–88.Google Scholar
- Chu HY, Chen CC, Cheng SH. Continuity of care, potentially inappropriate medication, and health care outcomes among the elderly: evidence from a longitudinal analysis in Taiwan. Med Care. 2012;50(11):1002–9.View ArticlePubMedGoogle Scholar
- Bentler SE, Morgan RO, Virnig BA, Wolinsky FD. Do claims-based continuity of care measures reflect the patient perspective? Med Care Res Rev. 2014;71(2):156–73.View ArticlePubMedGoogle Scholar
- Wasson JH, Sauvigne AE, Mogielnicki RP, Frey WG, Sox CH, Gaudette C, Rockwell A. Continuity of outpatient medical care in elderly men. A randomized trial. JAMA. 1984;252(17):2413–17.View ArticlePubMedGoogle Scholar
- Jee SH, Cabana MD. Indices for continuity of care: A systematic review of the literature. Med Care Res Rev. 2006;63(2):158–88.View ArticlePubMedGoogle Scholar