Evaluation of a diabetes management program in China demonstrated association of improved continuity of care with clinical outcomes

Journal of Clinical Epidemiology 61 (2008) 932e939

Evaluation of a diabetes management program in China demonstrated association of improved continuity of care with clinical outcomes Xiaolin Weia,*, Jan Barnsleyb,1, David Zakusb,c,2, Rhonda Cockerillb,3, Richard Glazierd,4, Xiaoming Sue,5 a

Nuffield Centre for International Health and Development, University of Leeds, 71e75 Clarendon Road, Leeds, West Yorks LS2 9PL, UK b Department of Health Policy, Management and Evaluation, 12 Queen’s Park Crescent West, 2nd Floor, University of Toronto, Canada c Centre for International Health, University of Toronto, Canada d University of Toronto, Inner City Health Research Unit, St. Michael’s Hospital, 30 Bond Street, Toronto, Ontario M5B 1W8, Canada e Department of Community Health and Women and Children’s Health, Shanghai Municipal Health Bureau, Room 235, 223 Han Kou Road, Shanghai, China 200002 Accepted 30 December 2007

Abstract Objective: The aim of the study was to evaluate a community-based diabetes management program in Shanghai, China and to examine the association between continuity of care and clinical outcomes. Study Design and Setting: The diabetes management program was implemented in downtown Shanghai. One hundred fifty-six patients participated in the intervention group and 182 patients were in the control group. Participants were elders without severe diabetic complications. Patient weight, body mass index, blood pressures, and fasting blood glucose were collected from outpatient records at baseline and the end of the study in both groups. Fructosamine level was measured to monitor glycemic control for patients in the intervention group. Continuity of care was measured based on our broad definition. Results: Improved patient health outcomes were observed in the diabetes management program: patients in the intervention group significantly reduced their weight, systolic blood pressure, and fasting blood glucose compared with those in the control group (P ! 0.05). In hierarchical regression models, continuity of care scales had a significant association with weight loss and fasting blood glucose reduction. Conclusion: This study suggested that continuity based on broad terms can act as an important management tool to improve the quality of primary care in similar urban settings. Ó 2008 Elsevier Inc. All rights reserved. Keywords: Diabetes management; Lifestyle change; Continuity of care; Outcome evaluation; Shanghai; China

1. Introduction Type 2 diabetes mellitus (DM) is a leading cause of micro and macro vascular complications and a major cause of premature death [1e4]. The World Health Organization has suggested that the developing world is facing a growing diabetes epidemic with potential effects as devastating as HIV/AIDS. It is estimated that the number of diabetes 1

Tel.: 416-978-1782; fax: 416-978-6177. Tel.: 416-978-1458; fax: 416-946-7910. 3 Tel.: 416-978 7721; fax: 416-978-7350. 4 Tel.: 416-864-6060 ext. 2574. 5 Tel.:/fax: þ8621-63290915. * Corresponding author. Tel: þ44-113-343-4865; fax: þ44-113-3436997. E-mail address: [email protected] (X. Wei) or jan.barnsley@ utoronto.ca (J. Barnsley) or [email protected] (D. Zakus) or rhonda. [email protected] (R. Cockerill) or [email protected] (R. Glazier) or [email protected] (X. Su). 2

0895-4356/08/$ e see front matter Ó 2008 Elsevier Inc. All rights reserved. doi: 10.1016/j.jclinepi.2007.12.013

patients will increase by 150% in the developing world and that at least 1 in 10 deaths among adults between 35 and 64 years of age can be attributable to diabetes [5]. China is experiencing an increasing number of type 2 DM cases with an estimate of 42.3 million patients by 2030 [5e7]. DM prevalence in the general population of Shanghai, the largest city in China, had increased steadily from 2.33% in 1994 to 4.76% in 1998 [8,9]. Lifestyle changes in dietary intake and self-management skills have proved to be an efficient way to manage diabetes, prevent complications, and maintain positive pharmaceutical effects [10,11]. However, the majority of interventions reported were focused on pharmaceutical treatments. There is a dearth of literature on the effect of an interactive patiente doctor relationship on diabetes management in developing countries [12]. This study evaluated a community-based diabetes management program, which aimed to promote lifestyle change

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2. Methods Key findings:  A newly developed questionnaire to assess continuity of care, defined as information exchange and goal alignment between patients and physicians, was used in a community-based diabetes management program that was established to improve the interaction between the providers and patients.  Improved patient outcomes (weight loss, blood pressure, and glucose control indicators) were identified in the diabetes management program. Patient weight loss and fructosamine reduction were associated with improved continuity of care measured by the questionnaire. What this adds to what was known:  Continuity of care is regarded an important element of primary care; however, no consistent linkage was identified in the literature between improved continuity and health outcomes, partially due to the incomplete measure of continuity.  In our study, patient improved health outcomes were associated with improved continuity of care, defined as information exchanges and goal alignment. What is the implication, what should change now:  This broader conceptualization of continuity of care might be useful when designing management strategies to improve the quality of community health care in Shanghai and similar urban settings.  This instrument should be tested in other primary care settings and with other health care providers. If the instrument proves generally acceptable, it could be adapted for assessing continuity of care for other chronic conditions.

and to improve continuity of care. Continuity of care based on agency theory was used as the theoretical framework in this study; it was defined as the degree to which health care activities are structured to increase information transfer and goal alignment between patients and doctors [13]. Information transfer refers to information exchange in both directions: (1) patient information offered to a clinician, such as the disease history, social-economic conditions, and personal preferences related to the treatment; and (2) treatment and health education information provided to the patient by the clinician. Goal alignment refers to actions that align the efforts of doctors with the needs and preferences of their patients [13]. We used the term continuity of care to reflect the extent to which doctors partnered with patients and hypothesized that it would be associated with health outcome improvements.

2.1. Setting and study population A diabetes management program was implemented by a Community Health Centre (CHC) located in downtown Shanghai. Another CHC with comparable characteristics but without a diabetes management program was selected as the control group. Sample size was determined by estimating the difference in the mediating variable, continuity of care, between patients in the intervention and control groups. A questionnaire to measure continuity of care was developed with items adopted from two questionnaires: the Primary Care Assessment Survey [14] and the Summary of Diabetes Self-care Activities Measure [15]. The questionnaire was validated to measure continuity of care as defined in agency theory; the validation results are reported in the accompanying paper. Based on 80% power to detect a significant difference of P 5 0.05, a moderate effect size of 0.3, and a 30% loss to follow up, 180 patients were required in each group [16,17]. Diabetes patients were identified in the intervention CHC through advertisements, whereas all eligible diabetes patients were selected from community health records in the control groups Eligibility criteria were men and women 40 years or older with a confirmed diagnosis of type 2 DM before the diabetes program start date (December, 2002) who identified a primary doctor as the regular source of diabetic care. Patients with severe diabetes complications (e.g., blindness or end stage renal failure), mental illness, receiving cancer chemotherapy or radiation within the past year, or in the aftermath of a severe stroke were excluded from this study. Patients in both the intervention and control groups were interviewed at the end of the study from September to November 2003. Figure 1 describes the 156 and 182 patients in the intervention and control CHCs, respectively. All 156 patients recruited in the intervention group had completed the activities of the community-based diabetes management program. The study obtained approval from ethics review committees at the University of Toronto and Shanghai Fudan University. All patients who agreed to participate were visited by community workers to explain the purpose of the study and to obtain informed consents. 2.2. The program The diabetes management program consisted of monthly patient club meetings with doctor facilitators during the period December 2002 to July 2003 and enhanced doctore patient interaction during routine visits. The first component of the patient education program was personalized diet therapy [18]. Aims of the diet therapy were reducing total saturated and trans fat intake, maintaining an appropriate total calorie intake, increasing fiber and vegetable consumption and controlling intake of oil and sweet fruits. The program provided detailed diet plans and defined a serving

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Intervention CHC

Control CHC

200 patients enrolled

256 patients enrolled

156 patients completed program activities

44 patients dropped out

25 moved out

11 hospitalized

All 156 patients identified one regular source of care and completed the questionnaire

228 patients were reached for questionnaire interview

8 lost to follow-up

182 patients identified one regular source of care and completed the questionnaire

46 patients did not identify a regular source of care and were excluded

Fig. 1. Number of patient participants in the intervention and control CHCs.

amount in an easily measurable way so patients could choose a menu or create their own plans. The second component of the patient education program focused on selfmanagement and communication skills with doctors. It was believed that enhanced patientedoctor communication was crucial to the success of the diet therapy because (1) the doctor needed to obtain extensive medical and personal information from the patient to make a suitable personal diet plan and (2) the patient needed continuous contacts with the doctor to adjust his/her diet therapy. All activities were conducted by Shanghai family doctors most of whom have completed a 3-year medical college education program after high school. Patients were organized into a patient club in their own communities for monthly education sessions. Each session consisted of a family doctor’s lecture followed by discussion of patient diet plans and self-management activities for the month. Session themes addressed five key health education issues: relevance, individualization, feedback, reinforcement, and facilitation [19]. Before the program started, family doctors in the intervention CHC received four intensive training workshops on (1) techniques and skills in patient diet management and (2) patient centered care. 2.3. Continuity of care The instrument measuring continuity of care adopted subscales from the Primary Care Assessment Survey [14,20] and the Summary of Diabetes Self-Care Management

Activity Measures [15]. These subscales corresponded to our two domains of continuity of care based on agency theory: information transfer and goal alignment. Satisfactory results were reported on the assumptions of Likert-scaling items, reliability, validity, completeness, and score distribution (see the accompanying tool development paper). Analysis of variance showed statistically significant differences between the two groups on all scales except the scale of ‘‘trust’’ (P ! 0.001). No significant differences were observed between the two groups regarding the frequencies of diabetic foot check and retinopathy referrals (Table 1). 2.4. Clinical parameters We collected information on weight, height, blood pressure (systolic and diastolic), fasting blood glucose (FBG), and fructosamine level in the intervention group at baseline (December 2002). The same information was collected at the end of the study in September 2003. All laboratory and physical examinations were done by family doctors or laboratory technicians in the CHCs or their clinics. Plasma glucose was measured in the morning after at least eight hours of fasting. The WHO’s 1999 diabetes diagnosis standards were applied to both groups [21]. Participants in the intervention CHC had their fructosamine level measured in December 2002, and in February, April, and July 2003. Fructosamine test measures the concentration of glycated serum proteins and serves as a marker to monitor blood glucose in the past 2 weeks. Although not widely

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Table 1 Statistical descriptions of scale scores for patients in intervention and control CHCs Domains

Scales

Groups

Information transfer

Contextual knowledge of the patient

Interventional Control CHC Total Interventional Control CHC Total Interventional Control CHC Total Interventional Control CHC Total Interventional Control CHC Total Interventional Control CHC Total

Diabetes counselling

Personal communication

Goal alignment

Physician examination

Interpersonal treatment

Trust

Process of care (number of checks per year) Foot checkb Retinopathy referralb a b

CHC

CHC

CHC

CHC

CHC

CHC

Intervention CHC Control CHC Intervention CHC Control CHC

N

Mean

SD

Significancea

156 180 336 156 178 334 155 180 335 141 180 321 156 179 335 153 175 328

55.82 45.63 50.36 59.35 42.65 50.45 66.83 59.10 62.68 65.48 56.94 60.70 68.08 60.71 64.14 73.65 72.41 72.99

16.71 12.99 15.66 19.75 17.63 20.41 13.33 10.26 12.38 16.38 12.81 15.08 12.41 9.73 11.64 9.12 9.65 9.41

0.00

156 180 156 178

0.93 0.26 0.92 0.54

5.45 1.36 2.49 1.96

0.13

0.00

0.00

0.00

0.00

0.23

0.12

Significance tests are based on F Scores in ANOVA analysis unless showed otherwise. P ! 0.05 is considered as statistically significant. T-test is employed to test the frequencies of certain cares.

used, the fructosamine test was reported as accurate, inexpensive, and applicable for assessing blood glucose during the past 2 weeks [22,23]. Patients in the control group were asked to provide their outpatient charts containing information on clinical visits and laboratory tests during the past 18 months. Information on weight, height, fasting blood glucose, and blood pressures were retrospectively collected from the control group outpatient charts for two points of time: December 2002 and September 2003. Two 3-month window periods (November 2002 to January 2003 and August to October 2003) were allowed for recording laboratory tests. Fructosamine was not measured in the control group due to the equipment limitation. 2.5. Analysis Independent and paired t-tests and analysis of variance were used to compare the level of continuity of care and clinical parameters. Hierarchical regression models were used to analyze the association between continuity of care and improved health outcomes. Six regression models were tested in which the dependent variables were weight loss and changes in body mass index (BMI), FBG, fructosamine level, and systolic and diastolic blood pressures. Each regression model had the same independent variables entered in two blocks. The first block contained background variables such as age, gender, years with diabetes, education, income, number of people at home, and the number of DM comorbidities and complications. The second block contained those continuity of care scales with significant

differences between the intervention and control groups, that is, contextual knowledge of the patient, diabetes counselling, personal communication, physician examination, and interpersonal treatment. The intervention marker was not included as an independent variable because it was highly correlated with the continuity of care scales, which may cause collinearity in the regression models.

3. Results Participants were elders with an average age above 67 years. About two thirds were females. Both intervention and control CHCs share similar provider and socioeconomic characteristics (Table 2). We compared the demographic characteristics of the 44 patients who dropped out of the program, the 156 patients who completed the program, and the 182 patients in the control group. No statistically significant differences were found among the three groups based on t-tests and chi-squared tests (P O 0.05). The clinical parameters of diabetes patients in both groups are presented at baseline and after 9 months of program implementation in Table 3. At baseline, patients in both groups had similar clinical parameters except that patients in the intervention group were significantly heavier (P ! 0.05). After 9 months, FBG and systolic blood pressure were significantly lower for patients in the intervention group compared to the control group (P ! 0.05). Similar levels of weight, BMI, and diastolic blood pressure were observed in both groups (P O 0.05).

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Table 2 Patient characteristics in intervention and control CHCs Intervention CHC (n 5 156)

Control CHC (n 5 182)

Mean age Years with diabetes Age range

67.84 6 9.65 8.45 6 5.93 40e88

69.35 6 9.75 8.95 6 6.39 41e90

Sex Male Female

51 (32.7) 105 (67.3)

58 (31.9) 124 (68.1)

Ethnicity Han Hui Other

150 (96.1) 5 (3.2) 1 (0.6)

173 (95.1) 6 (3.3) 3 (1.6)

Mean education years

8.85 6 4.61

8.80 6 4.99

Monthly income (RMB)a !500 500e999 1,000e1,999 O2,000 Retired

11 98 38 9 126

(7.1) (62.8) (24.4) (5.8) (80.7)

12 121 43 6 148

(6.6) (66.5) (23.6) (3.2) (81.3)

Marital status Married Divorced Widowed Single

108 4 40 3

(69.7) (2.6) (25.8) (1.9)

128 3 48 3

(70.3) (1.6) (26.4) (1.6)

Number of family members Rangednumber of family members Mean number of diseases

3.25 6 1.73 (1e10) 2.30 6 1.65

Comorbidities Hypertension Renal diseases Heart disease Others

86 6 14 30

(55.1) (3.8) (9) (19.2)

3.29 6 2.01 1e11 2.45 6 1.71 101 5 17 28

(55.5) (2.7) (9.3) (15.4)

Health insurance

142 (91)

168 (92.3)

Regular source of care With one doctor With a group of doctors

115 (73.7) 41 (26.3)

136 (74.7) 46 (25.3)

Values are numbers (percentage) unless otherwise stated. a 1 USD 5 8.27 RMB.

After 9 months, participants in the intervention group significantly reduced their body weight by an average of 2.08 kg (P ! 0.05) compared to a nonsignificant reduction in the control group (Table 4). The systolic and diastolic blood pressures were also significantly reduced in the intervention group (P ! 0.05) but not in the control group. Average FBG declined at 0.56 mmol/l in the intervention group and increased but not significantly in the control group. Diabetes patients in the intervention group showed a continuous decrease in fructosamine levels (Fig. 2). Compared to patients in the control group, patients in the intervention group had significantly reduced their weight, systolic blood pressure, and FBG (P ! 0.05). No significant collinearities were found in the hierarchical regression models. Of the six regression models, only two models, with the dependent variables weight loss and FBG reduction, had an overall significant R2 indicating that

Table 3 Clinical and metabolic characteristics (6 SD) of the study population (n 5 338) at baseline and after 9 months

Number of subjects

Intervention group

Control group

156

182

P

Baseline Weight (kg) 65.57 6 15.42 62.39 6 12.78 BMI (kg/m2) 25.04 6 5.53 24.32 6 6.49 Systolic blood pressure (mmHg) 137.43 6 17.34 138.01 6 18.56 Diastolic blood pressure (mmHg) 83.89 6 10.93 83.56 6 11.45 Fasting blood glucose (mmol/l) 7.38 6 2.87 7.77 6 3.37 Fructosamine level (mmol/l) 2.22 6 0.97 NA

0.039 0.278 0.769 0.788 0.259

After 9 months Weight (kg) 63.49 6 11.07 61.84 6 11.31 23.92 6 3.60 24.07 6 4.09 BMI (kg/m2) Systolic blood pressure (mmHg) 132.96 6 16.65 136.59 6 15.87 Diastolic blood pressure (mmHg) 81.14 6 9.02 82.04 6 10.34 Fasting blood glucose (mmol/l) 6.82 6 1.47 8.11 6 2.29 Fructosamine level (mmol/l) 1.99 6 1.17 NA

0.177 0.722 0.041 0.398 0.000

the independent variables significantly contributed to the variance accounted for in each dependent variable. Overall, independent variables accounted for 32% and 29% of the variance (represented by the total R2) in weight loss and FBG reduction, respectively. The background variables did not significantly contribute to either model. The, R2 changed significantly only after continuity of care scales were entered into each model (R2 changes were 0.211 and 0.149, respectively, P ! 0.01), which indicated that patient weight loss and FBG reduction had a significant association with continuity scales (Table 5).

4. Discussion The diabetes management program was associated with patient weight loss, glycemic control, and blood pressure management. Patients in both intervention and control groups had similar levels of FBG and blood pressures at baseline. After 9 months, patients in the intervention group significantly reduced their weight, BMI, FBG, fructosamine level, and blood pressures, whereas patients in the control group had no significant changes. Weight loss is an effective factor to prevent and control diabetes, substantially reducing multiple cardiovascular risk factors in diabetes patients [24,25]. We identified a 2 kg reduction of body weight in patients with a BMI slightly higher than 25 within 1 year that was similar to what has been reported in other studies [26,27]. Improved blood pressure control is crucial to reduce the risk of diabetes complications [28,29]. One study has found that each 10 mmHg decrease in mean systolic blood pressure was associated with a reduction of 12% in complications related to diabetes, 15% in mortality rates and 11% for myocardial infarction [30]. Our study found that participants in the diabetes management program reduced systolic blood pressure significantly by an average of 4.47 mmHg. Even

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Table 4 Comparison of mean reductions (baseline minus 9 months later) 6 SD and 95% confidence interval (95% CI) in clinical and metabolic parameters in the intervention and control groups

Weight (Kg) BMI (kg/m2) Systolic blood pressure (mmHg) Diastolic blood pressure (mmHg) Fasting blood glucose (mmol/l) Fructosamine level (mmol/l)

Intervention group (n 5 156)

Control group (n 5 182)

Mean reductions

95% CI

Mean reductions

95% CI

2.08 6 6.39 1.12 6 4.86 4.47 6 13.50 2.75 6 7.65 0.56 6 2.49 0.23 6 1.06

1.08e3.08 0.36e1.88 2.35e6.58 1.55e3.95 0.17e0.95 0.06e0.39

0.55 6 7.08 0.25 6 5.53 1.42 6 11.32 1.52 6 10.78 0.34 6 2.84 NA

0.48 0.55 0.23 0.05 0.75

though control of blood pressure was relatively satisfactory in both the intervention and control groups, a UK prospective study noted that any reduction of systolic blood pressure higher than 120 mmHg could significantly reduce diabetes complication risks [30]. Diabetes patients in the intervention group had an FBG higher than the WHO’s diagnostic marker (FBG 5 7 mmol/l) at baseline, but was reduced to less than the control target recommended by the 2003 Canadian Diabetes Practice Guidelines (6e7 mmol/l) by the end of the study [4]. The significant reduction of fructosamine level further supported good glycemic control in the intervention group. Evidence shows that FBG is directly related to the development of cardiovascular diseases and other complications in diabetes [31]. Our results regarding FBG control, plus evidence of weight loss and systolic blood pressure reduction, suggested that the diabetes management program may reduce diabetes patients’ risks of cardiovascular diseases. We did not use glycated hemoglobin A1C tests because they were relatively expensive and beyond the research budget of the CHCs. Another interesting finding was our identification of an association between continuity of care measured by our questionnaire with weight loss and FBG reduction which supported our hypothesis that the diabetes management program can improve continuity of care and, thereby, contribute to improved patient outcomes. However, we did not

Fructosamine Level (mmol/L)

2.50

2.00

1.50

1.00

0.50 Mean 0.00 Dec. 2002

Feb. 2003

Apr. 2003

Jul. 2003

Fig. 2. Monitoring of fructosamine level of patients in the intervention group (n 5 156).

to to to to to

P 1.58 1.05 3.07 3.09 0.07

0.038 0.124 0.025 0.234 0.002

identify an association between continuity of care scales and several other clinical parameters: BMI, fructosamine level, systolic and diastolic blood pressure. One explanation was that some of the parameters were already within normal range or nearly under control in the intervention group at baseline, for example, the BMI’s cut off point is 25, the baseline average BMI was 25.04 with 63.2% under 25; the recommended fructosamine level for diabetes diagnosis is below 2.6 mmol/l; 79.5% patients at baseline were within this range. Most lifestyle change programs are developed and implemented in resource intensive settings, preventing their replication in developing countries. This program focused on improving the continuity of care with activities that were already included in routine care; thus, it has the potential to be integrated into the CHC’s routine work. Shanghai has undergone a community health reform in which doctors of the CHCs are trained as family doctors to provide primary care and act as the first health contact of patients. Family doctors in Shanghai CHCs also have half of their time devoted to public health service including frequent home visits and communication with patients living in their communities [32]. Though our diabetes management program contained intensive provider and patient training at the beginning, the need for patient training declined over time, with it being given only during the normal patiente physician interactions which happened in the CHCs or during the family doctor’s home visits. Maintaining a healthy diet became the key issue in the later stage. Therefore, the intervention CHC gradually moved its focus from intensive healthy diet education to improving communications between patients and doctors, which did not require large additional resource inputs. Family doctors in the intervention CHC provided more feedback on patient dietary plans, more information on health education and treated patients more holistically. This participatory approach has the potential to benefit patients treated for other health problems. The control CHC plans to implement this program and the Shanghai Health Bureau has shown interest in disseminating it to other CHCs in the city. In other settings, which have a shortage of family physicians or consider intense interactions with patients too costly, frequent communication with patients could be done by physician assistants or nurses.

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Table 5 Results from hierarchical regression models to test the contribution of continuity of care variables to changes in health outcomes Fasting blood glucose reduction (n 5 102)

Weight loss (n 5 118) Betaa

P

Beta

P

0.397 0.034 0.081 0.085 0.038 0.020 0.057

0.000 0.734 0.459 0.478 0.736 0.838 0.568

0.394 0.073 0.064 0.083 0.043 0.008 0.075

0.000 0.497 0.579 0.499 0.721 0.937 0.481

Block 2: continuity variables Contextual knowledge of patients Diabetes counselling Personal communication Physical examination Interpersonal treatment Trust

0.079 0.379 0.094 0.032 0.341 0.245

0.617 0.007 0.589 0.793 0.041 0.031

0.056 0.374 0.073 0.031 0.378 0.215

0.736 0.014 0.695 0.810 0.034 0.088

Model fits R2 in block 1 R2 change Total R2

0.109 0.211 0.320

0.172 0.001 0.002

0.142 0.149 0.292

0.070 0.019 0.008

Block 1: background variables Age Years with DM Gender Education Income Number of persons living together Number of complications and comorbidities

a

Beta is the partial correlation coefficient that reflects the weight associated with standardized scores of the variables.

Three design limitations in this study should be kept in mind. First, the control group was identified in the middle of the study and clinical data were collected retrospectively. This was because we were requested to evaluate the program by the Shanghai Municipal Health Bureau in the middle of the program. The continuity of care questionnaire was not available at baseline so a preeafter comparison of continuity scales was not available. The research was subject to internal validity threats of history and contamination effects [33]. A strict selection process was used to ensure that the intervention and control groups were comparable. Indepth interviews with providers identified that (1) the two groups of patients were similar in terms of other chronic management programs; (2) there was no self-implemented diabetes management program in the control group; (3) there were no community events affecting patient health outcomes and diabetes treatment unevenly in the two groups; and (4) the SARS outbreak, which happened in the middle of the program, did not affect the two CHC catchment areas differently. The unexpected SARS outbreak in China caused the program to stop group activities for two months, but patients reported that they continued with their planned meals. Second, patients of this study were not selected randomly but participated on a voluntary basis. We only included the 156 patients who completed all program activities in the analytical model. Thus, the results cannot be easily extrapolated to other settings. However, the interviews with doctors indicated that the participants in this research were representative of diabetes patients living in Shanghai communities. Third, we were unable to blind the interviewers to the site in which the diabetes intervention was implemented.

5. Conclusion The value of the community-based diabetes management program rested in using continuity of care as an explicit management mechanism to promote primary health care. Furthermore, this study demonstrated that continuity of care, as measured by our questionnaire, was associated with improved patient outcomes. This suggested that this broader conceptualization of continuity of care might be useful when designing management strategies to improve the quality of community health care in Shanghai and similar urban settings. Acknowledgments Opinions in this paper reflect only those of the authors. The authors would like to express their deepest thanks to colleagues in Shanghai: Drs Wei Sun, Kejun Yang and Jianxia Ge. We are also grateful to Dr. Donaldson for allowing us to use her definition of continuity of care and to Dr. Safran for giving us approval to adapt her PCAS questionnaire for this study. References [1] Mousley M. Diabetes and its effect on wound healing and patient care. Nurs Times 2003;99(42):3e4. 70. [2] Wee HL, Ho HK, Li SC. Public awareness of diabetes mellitus in Singapore. Singapore Med J 2002;43:128e34. [3] Sinclair AJ. Diabetes in the elderly: a perspective from the United Kingdom. Clin Geriatr Med 1999;15:225e37. [4] Hux JE, Booth GL, Slaughter PM, Laupacis A. Diabetes in Ontario. Toronto: Institute for Clinical Evaluative Sciences; 2003.

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