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Barriers to Blood Glucose Monitoring in a Multiethnic Community

¹ Department of Medicine, Division of Endocrinology and Metabolism, University of Pittsburgh, Pittsburgh, Pennsylvania
² Department of Rural Health, University of Melbourne, Shepparton, Victoria, Australia

	ABSTRACT

TOP ABSTRACT INTRODUCTION RESEARCH DESIGN AND METHODS RESULTS CONCLUSIONS References

 
OBJECTIVE—We studied a multiethnic community to determine factors associated with blood glucose monitoring (BGM) and to determine the independent association between barriers to diabetes care and BGM.

RESEARCH DESIGN AND METHODS—A total of 323 participants (35.6% European, 32.2% Maori, and 32.2% Pacific Islander) from the South Auckland Diabetes Project (free of major complications by self-report) completed a qualitative survey to determine barriers to diabetes care. Five barriers to diabetes care categories were generated including internal psychological (self efficacy/health beliefs), external psychological (psychosocial environment), internal physical (comorbidities/side effects of treatment), external physical (finance/access to care), and educational (knowledge of diabetes/services) barriers.

RESULTS—Characteristics associated with BGM greater than or equal to twice weekly were female sex, HbA_1c >8%, higher diabetes knowledge scores, and insulin use. Multivariate analyses demonstrated that those reporting external physical barriers (OR 0.47, 95% CI 0.26–0.84), external psychological barriers (0.55, 0.30–1.0), and internal psychological barriers (0.56, 0.32–1.0) were less likely to perform BGM independent of ethnicity, insulin use, age, sex, diabetes knowledge, and glycemic control. Further multivariate analyses demonstrated that those reporting external physical barriers, particularly related to personal finance, were less likely to perform BGM.

CONCLUSIONS—These data demonstrate that patient-reported barriers to diabetes care are associated with BGM, particularly in relation to financial, psychosocial, and self-efficacy issues. Understanding these barriers and overcoming them within the context of the patient’s ethnic environment may lead to increased participation in self-care.

Abbreviations: BGM, blood glucose monitoring

	INTRODUCTION

TOP ABSTRACT INTRODUCTION RESEARCH DESIGN AND METHODS RESULTS CONCLUSIONS References

 
Clinical trials, including the Diabetes Control and Complications Trial (DCCT) (1) and the U.K. Prospective Diabetes Study (UKPDS) (2), have demonstrated that improving glycemic control can reduce the risk of microvascular complications in people with diabetes. Self-monitoring of blood glucose levels is an integral part of the intensive management required to optimize glucose control (3). Other factors instrumental to the success of achieving better glycemic control include psychosocial support (4), health beliefs (5), socioeconomic status (6), access to care (6), and type of care (7).

Previous studies have demonstrated lower use of self-monitoring in ethnic minorities (8,9), in those of lower socioeconomic status (6,8), and in those with lower diabetes knowledge (10). This is of concern as these groups are also more likely to suffer from the long-term consequences of diabetes (11).

Few data are available assessing the barriers to diabetes care that are most likely to impede self-management from the patient perspective, particularly in population-based cohorts. It was our objective in a multiethnic population-based cohort of patients with diabetes to determine factors associated with blood glucose monitoring (BGM) and to determine the independent association between barriers to diabetes care and BGM.

	RESEARCH DESIGN AND METHODS

TOP ABSTRACT INTRODUCTION RESEARCH DESIGN AND METHODS RESULTS CONCLUSIONS References

 
Inner-urban South Auckland includes some of the most disadvantaged communities in New Zealand, with high rates of unemployment and low income. The population consists of Europeans, Maori, Pacific Islanders, and smaller numbers of other ethnic groups (12). Polynesians (Maori and Pacific Islanders) experience high rates of gestational diabetes (13), diabetes (12), undiagnosed diabetes (14), and diabetes complications (15). General practitioners, who can refer patients to a hospital clinic or a community-based clinic, deliver much of the care to these patients.

The South Auckland Diabetes Project
Between April 1992 and August 1995, a locally recruited multiethnic team from the South Auckland Diabetes Project visited 27,419 households in inner-urban South Auckland of which 25,039 (91%) participated in surveys (16). Concurrently, a survey was administered to known diabetic patients (16). Presence of known diabetes for each patient was confirmed by comparison with general practitioners, diabetes clinics, and diabetes research databases (17). A total of 1,739 diabetic subjects of European, Maori, or Pacific Islander descent were identified. A survey was administered to these subjects asking for demographic and diabetes history information including presence of complications, self-care behaviors, barriers to diabetes care, and diabetes knowledge.

BGM was determined by asking participants how they tested their blood glucose level and how often they tested per month. Diabetes knowledge was assessed by four open-ended questions as has been previously described (18): 1) What is diabetes? 2) What symptoms can you get with a high sugar level? 3) What damage can diabetes cause? and 4) How is diabetes treated? Type 1 diabetes was defined as age at diagnosis <20 years and exclusive treatment with insulin. To determine barriers to diabetes care, participants responded to three standardized open-ended questions enumerating whether and why they were worried about their diabetes, how they would improve local diabetes services, and what they believed prevented themselves or others from properly taking care of their diabetes. Each barrier to diabetes care response was then given a code to facilitate analyses (a maximum of three responses was coded). Validation of the survey is described elsewhere (16). Briefly, a detailed qualitative study among 50 diabetic subjects and diabetes care workers generated 30 barrier groups. The 458 individual codes generated from the household survey were then mapped onto the 30 slightly amended barrier codes. The mapping was undertaken requiring unanimous agreement among three coders (a physician, an anthropologist, and a psychologist). Again by unanimous agreement, these response codes were then grouped into five barrier categories described in Table 1.

Because having severe complications may influence self-care, subsequent analyses included 323 participants free of major complications (laser treatment, blindness due to diabetes, foot amputations or ulcers, kidney failure, and acute cardiovascular events, e.g., stroke or acute myocardial infarction) by self-report.

Lab methods
Glycated hemoglobin was measured using cation exchange high performance liquid chromatography (Biorad Diamat; Biorad) (upper limit of reference range 6.2%).

Analyses
BGM twice per week and each of the five barriers to diabetes care categories were analyzed as dichotomous variables (yes/no or presence/absence, respectively). Univariate associations were conducted using ANOVA, generalized linear models, or ². Adjustment of discrete variables for ethnicity was undertaken using Mantel Haentzel ². Five separate logistic regression models were used to examine the independent association between each barrier to diabetes care category and the outcome variable, self-monitoring blood glucose at least twice per week. The barrier, insulin therapy (yes/no), age in tertiles (18 to 43, 43 to 68, and >68 years), sex (female versus male), HbA_1c (8 vs. >8%), and diabetes knowledge score in tertiles (7 to 35.7%, 35.7 to 64.4%, and >64.4%) were forced into the model as explanatory variables. Dummy variables were created for the three ethnic groups with Europeans as the reference. Socioeconomic status was determined using the Elley-Irving Socio-economic Scale (19). Data were analyzed using SAS version 8 (SAS Institute, Cary, NC). Univariate results are presented by ethnic group. P values 0.05 were considered significant. The local ethics committees approved all surveys.

	RESULTS

TOP ABSTRACT INTRODUCTION RESEARCH DESIGN AND METHODS RESULTS CONCLUSIONS References

 
Study population
Characteristics of the 323 people with diabetes and no significant complications are shown in Table 2. Ethnic groups were significantly different in age, proportion with secondary education, diabetes knowledge scores, BGM daily and twice weekly, and glycemic control. Interestingly, despite these differences, there were no significant differences between ethnic groups in the frequency of self-reported barriers to diabetes care.

View larger version (20K): [in this window] [in a new window]   Figure 1— Multiple logistic regression results for BGM at least twice weekly. All models were adjusted for ethnicity, age, sex, insulin treatment, diabetes knowledge, and insulin treatment. Diabetes knowledge and age were divided into tertiles for entry into the model. Glycemic control was dichotomized as 8 vs. >8%). Insulin treatment was entered as "yes" or "no," and ethnicity was adjusted using three dummy variables with Europeans as the reference. *The model used to assess the association between knowledge barriers and BGM did not include diabetes knowledge score. X indicates the OR, and the line represents the 95% CI.

Models were further adjusted for socioeconomic status with no change in the estimates for the models. When subjects with type 1 diabetes were removed from the analyses, the ORs were slightly attenuated for internal and external psychological barriers (0.57–0.60 and 0.55–0.57, respectively); however, the findings remained unchanged for external physical barriers.

	CONCLUSIONS

TOP ABSTRACT INTRODUCTION RESEARCH DESIGN AND METHODS RESULTS CONCLUSIONS References

 
In a multiethnic population-based cohort of patients with diabetes, our findings demonstrate that those reporting personal barriers to diabetes care, particularly those relating to finance and access (external physical barriers), community and family support (external psychological barriers), and self efficacy, motivation, and health beliefs (internal psychological barriers) were less likely to perform BGM twice weekly. These relationships existed despite patient age, sex, level of diabetes knowledge, ethnic group, or glycemic control. Whereas previous studies have focused on clinic attendees, managed care settings (6), or aggregate data (8) to demonstrate associations between barriers to care and self-care, our data add to currently available evidence based on reports from a population-based sample.

The strongest relationship was between the "external physical" barriers to diabetes care and BGM and provides evidence that the manner in which diabetes care is structured, delivered, and financed may affect the ability of patients to care for their diabetes. As personal finance was independently associated with BGM after adjusting for other barriers and ethnicity, insulin use, age, sex, glycemic control, and diabetes knowledge, particular attention should be paid to this aspect of diabetes care, with self-management recommendations made by health care providers. Previous work in this population found finances to be particularly problematic for the Maori and Pacific Islanders, whereas costs of care were associated with stopping both self-monitoring and insulin therapy (20). We have previously shown that Maori and Pacific Islanders, when asked about barriers to care, ranked barriers regarding physical access and personal costs of care much higher than those of European descent (16,20). As >66% of this population subgroup was unemployed (18), this finding is not surprising. Cost of self-care as a barrier was demonstrated by Karter et al. (6), where higher out-of-pocket costs were associated with poor (less than daily) adherence to self-monitoring. Similarly, a cross-sectional study of the diabetes database in Tayside, Scotland, found a lower use of test strips in the less affluent (21). These results are in contrast to those of Harris et al. (9), where socioeconomic status was not associated with self-monitoring. However, in that report, the effect of income on self-monitoring was different in Mexicans (8), indicating that socioeconomic factors may have differential effects across ethnic groups.

There was also a borderline association between external psychological barriers to diabetes care and BGM. These barriers include external support systems such as family, cultural appropriateness, satisfaction with care, and public awareness of diabetes. Studies have shown that social (22) and familial support (4) can affect self-care. Additionally, cultural sensitivity from the health care providers, including use of interpreters, development of community-based programs, and adapting educational materials to the ethnic group concerned (5), may increase participation in self-management practices.

Borderline associations between internal psychological barriers to diabetes care and BGM were also found. These barriers include health beliefs, motivation and self-efficacy, and emotional issues. Although logical, little quantitative evidence has previously existed linking psychological status with self-care. Aalto et al. (23) found that self-efficacy was positively associated with BGM in patients with type 1 diabetes. Glasgow et al. (24) found that an intervention targeting social learning variables, particularly problem-solving skills and self-efficacy, increased the frequency of BGM in patients with type 2 diabetes. Thus, interventions aimed at improving self-efficacy may prove beneficial for increasing participation in BGM.

We did not find an association between internal physical barriers (comorbid conditions, side effects of medication, and pain associated with blood glucose monitoring) and BGM. This may be due to the selection of the study population, where those included in the analyses were complication-free by definition and therefore not as severely ill as those already damaged by diabetes. The study was also possibly too small to identify a relationship between BGM and side effects of medication (e.g., insulin therapy) or self-monitoring. The chance of finding such events in a cross sectional study would be low.

That we did not find an association between BGM and those reporting that they knew too little about diabetes and diabetes care services was not surprising. The importance of actual diabetes knowledge rather than perceived need was reflected by the continued relationship between the diabetes knowledge score and BGM after adjustment for barriers to care. Peveler et al. (10) found a similar pattern where those higher diabetes knowledge scores were correlated with frequency of blood glucose testing.

One rather curious finding was the positive association between BGM and HbA_1c, where those with the higher HbA_1c levels were more likely to perform BGM. Similar findings were reported by Harris et al. (9,25). This finding should not imply that less monitoring is associated with better glycemic control. It may be that these subjects were aware of their elevated glucose levels and were taking steps to improve glycemic control, thus increasing their frequency of self-monitoring. Additionally, providers may have recommended an increased frequency of monitoring due to concern over elevated glucose levels. However, due to the cross-sectional nature of these analyses, no cause-effect relationship can be determined.

Limitations
There are certain limitations to these analyses that should be considered when interpreting results. The barriers to diabetes care questions were open-ended and prone to under-reporting (26). Additionally, because only three responses were coded, it is possible that barriers may have been under-recorded. However, it was felt that issues reported by individuals were likely to be those most important to the study participants, and external validity was provided by the highest number of answers being obtained from those with the greatest number of barriers: Maori and Pacific Islanders (16). It is therefore likely that they have expressed their major concerns. Additionally, multilingual teams were used to overcome language barriers that may have been inherent in administration of an open-ended survey to a multiethnic population. (16). Regarding validity of the barriers survey, three coders were used to develop the barriers to diabetes care categories. Unanimous agreement was required for the code, and no adjudication was necessary (16), indicating high inter-rater reliability. The external validity and reproducibility of the barriers has been previously described (16).

Although the response rate for clinical exams was moderate, participants were not significantly different from nonparticipants, thus results are not likely affected by the response rate. There was a 1- to 2-year time lag between the assessment of barriers and physical exams as a result of the funding process. While associations between glycemic control and barriers to diabetes care may be over- or underestimated because of this, it is unlikely that barriers changed over time. This study did not collect data on provider practice patterns or patient adherence to provider recommendations, thus it is difficult to assess which (if any) patients should be monitoring more regularly.

These analyses include patients with both type 1 and type 2 diabetes, as BGM is recommended for all patients with diabetes. Although there is stronger evidence supporting its role for type 1 diabetic patients (3), we felt it was important to include all patients in these analyses. However, we relaxed the criteria from daily monitoring to twice weekly monitoring to account for the large majority of subjects having type 2 diabetes. When the analysis was repeated excluding type 1 diabetic subjects, there was little change in the associations observed, demonstrating that inclusion of type 1 diabetic subjects in the analyses had little impact on the overall interpretation of results.

While the evidence supporting the utility of BGM and long-term glycemic control is not sufficiently strong, there is evidence to support patients monitoring their blood glucose to avoid hyper- and hypoglycemic episodes (3). Additionally, evidence suggests that processes involved in intensive therapy do not substantially interfere with patients’ quality of life (27,28). These data have quantified, from a patient perspective, what barriers to BGM are important. Overcoming barriers to diabetes care to enhance participation in self-management requires a range of strategies targeted at individual need. Substantial efforts aimed at identifying individual barriers to care using clinically relevant tools (29) should be developed to overcome these barriers and to improve self-care and outcomes. Due to the cross-sectional nature of studies addressing barriers to diabetes care and self-monitoring, future research should include a randomized clinical trial addressing methods to overcome all or some of these barriers to diabetes care while encouraging self-care practices.

	Acknowledgments

 
We are grateful to the Health Research Council, the Auckland Medical Research Foundation for their financial support, and to the members of the South Auckland Diabetes Project for their hard work.

	Footnotes

 
Address correspondence and reprint requests to Professor David Simmons, Department of Rural Health, University of Melbourne, P.O. Box 6500, Shepparton, Victoria 3632, Australia. E-mail: dsimmons{at}unimelb.edu.au.

Received for publication 17 August 2001, and accepted in revised form 3 July 2002.

A table elsewhere in this issue shows conventional and Système International (SI) units and conversion factors for many substances.

	References

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This Article Abstract Full Text (PDF) Purchase Article View Shopping Cart Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Request Permissions Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Zgibor, J. C. Articles by Simmons, D. Search for Related Content PubMed PubMed Citation Articles by Zgibor, J. C. Articles by Simmons, D. Social Bookmarking                What's this?