Influences on Diabetes Self-Management Education Participation in a Low-Income, Spanish-Speaking, Latino Population

Testerman, Jill; Chase, Dian A.

doi:10.2337/ds16-0046

Cited by 20 publications

(22 citation statements)

References 21 publications

(23 reference statements)

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“…12 Inequities found in the social determinants of health add to the struggles of diabetes management including low-income, 13 inadequate housing, 14 behavioral health needs, 15 and lack of health insurance. 16 In addition, lack of resources/support for medications and doctor visits, 17 transportation, 4 day labor jobs/choice between work and doctor visits, 18 culture and language capabilities 19 are other barriers to diabetes self-management found among Mexican American populations. Community health workers are effective in delivering interventions that improve diabetes management by providing language interpretation; helping to address barriers to care including communication with providers; and advocating for their participants to receive culturally relevant healthcare services.…”

Section: Introductionmentioning

confidence: 99%

Improved diabetes control among low-income Mexican Americans through community-clinical interventions: results of an RCT

Reininger

Lee

Hessabi

et al. 2020

BMJ Open Diab Res Care

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IntroductionThis randomized controlled trial investigated community-clinical intervention strategies for a Mexican American population who had not demonstrated control of their diabetes. We tested a control program (Salud y Vida 1.0) supporting diabetes management versus an enhanced version (Salud y Vida 2.0) for reductions in HbA1c at 12 months.Research design and methodsAdults with uncontrolled diabetes (n=353) were enrolled if they had an HbA1c≥9.0% during a program or doctor’s visit between 6 and 36 months of their receipt of SyV 1.0 services, were patients at one of two clinics in local counties, and had an HbA1c≥8.0% at SyV 2.0 baseline enrollment. The control and intervention arms were coordinated by community health workers and the intervention arm included the control program enhanced with medication therapy management; behavioral health services; peer-led support groups; and additional community-based lifestyle programs also open to the family.ResultsAt 12 months, both study arms improved HbA1c (mean, (CI), Control (−0.47 (-0.74 to –0.20)) and intervention (−0.48 (-0.76 to –0.19)). The intervention group maintained HbA1c levels after month 6, whereas control group HbA1c levels slightly increased (adjusted mean from 9.83% at month 6%–9.90% at month 12). Also, HbA1c was examined by level of participant engagement. The high engagement group showed a decreasing trend over the study period, while control and lower engagement groups failed to maintain HbA1c levels at month 12.ConclusionsImproved HbA1c was found among a population that had not demonstrated diabetes management prior; however, mean HbA1c values were above clinical guideline recommendations. The randomized control trial findings provide additional evidence that extended time and intervention supports may be needed for populations experiencing inequities in social determinants of health.Trial registration numberNCT04035395.

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Section: Introductionmentioning

confidence: 99%

Improved diabetes control among low-income Mexican Americans through community-clinical interventions: results of an RCT

Reininger

Lee

Hessabi

et al. 2020

BMJ Open Diab Res Care

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“…The reason for this is unclear but might be due to the fact that some other factors (e.g. availability of transport to DSME centers especially in rural areas) might be more important determinants of the DSME participation in Florida [46,48,49]. Unfortunately, we did not have access to transportation data and therefore could not investigate this factor.…”

Section: Plos Onementioning

confidence: 98%

“…This again implies that even if DSME programs are available in rural Florida which had high diabetes prevalence, participation rate could be lower. It has been reported that health related program participation often depends on behavioral factors e.g., awareness and willingness to participate [49]. A New Jersey study also reported that DSME participation in certain counties did not always reflect DSME program availability [50].…”

Section: Plos Onementioning

confidence: 99%

“…Thus, DSME participation is affected by not only program availability but also by acceptability, accessibility and other factors [51,52]. The implication of this is that the low DSME participation rates observed in rural areas of Florida could be the result of complex interactions between cultural, psychological, environmental, economic, and human resource factors such as transportation [46,48], lack of specialists in rural areas, lack of diabetes educators [53], participants' literacy level, language barriers [54], lack of time, lack of childcare, participants' shame of illness, and participants lacking interest in their health [49].…”

Section: Plos Onementioning

confidence: 99%

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Geographic disparities and temporal changes of diabetes prevalence and diabetes self-management education program participation in Florida

et al. 2021

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Background Although Diabetes Self-Management Education (DSME) programs are recommended to help reduce the burden of diabetes and diabetes-related complications, Florida is one of the states with the lowest DSME participation rates. Moreover, there is evidence of geographic disparities of not only DSME participation rates but the burden of diabetes as well. Understanding these disparities is critical for guiding control programs geared at improving participation rates and diabetes outcomes. Therefore, the objectives of this study were to: (a) investigate geographic disparities of diabetes prevalence and DSME participation rates; and (b) identify predictors of the observed disparities in DSME participation rates. Methods Behavioral Risk Factor Surveillance System (BRFSS) data for 2007 and 2010 were obtained from the Florida Department of Health. Age-adjusted diabetes prevalence and DSME participation rates were computed at the county level and their geographic distributions visualized using choropleth maps. Significant changes in diabetes prevalence and DSME participation rates between 2007 and 2010 were assessed and counties showing significant changes were mapped. Clusters of high diabetes prevalence before and after adjusting for common risk factors and DSME participation rates were identified, using Tango’s flexible spatial scan statistics, and their geographic distribution displayed in maps. Determinants of the geographic distribution of DSME participation rates and predictors of the identified high rate clusters were identified using ordinary least squares and logistic regression models, respectively. Results County level age-adjusted diabetes prevalence varied from 4.7% to 17.8% while DSME participation rates varied from 26.6% to 81.2%. There were significant (p≤0.05) increases in both overall age-adjusted diabetes prevalence and DSME participation rates from 2007 to 2010 with diabetes prevalence increasing from 7.7% in 2007 to 8.6% in 2010 while DSME participation rates increased from 51.4% in 2007 to 55.1% in 2010. Generally, DSME participation rates decreased in rural areas while they increased in urban areas. High prevalence clusters of diabetes (both adjusted and unadjusted) were identified in northern and central Florida, while clusters of high DSME participation rates were identified in central Florida. Rural counties and those with high proportion of Hispanics tended to have low DSME participation rates. Conclusions The findings confirm that geographic disparities in both diabetes prevalence and DSME participation rates exist. Specific attention is required to address these disparities especially in areas that have high diabetes prevalence but low DSME participation rates. Study findings are useful for guiding resource allocation geared at reducing disparities and improving diabetes outcomes.

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“…However, some studies have found that certain patient-related factors associated with attendance include program length, logistic barriers such as transportation, 5 full-time work, 3 and shame associated with diagnosis. 6 In addition to the high diabetes diagnosis rates among socioeconomically disadvantaged individuals, diabetes health outcomes in this population remains worse than their counterparts with higher education or income. 7 , 8 Multiple studies show that disadvantaged populations benefit from DSMES in achieving glycemic control; however, attrition rates remain high among this population causing them not to receive the recommended hours of DSMES.…”

Section: Introductionmentioning

confidence: 99%

Diabetes Self-Management Education and Support: Referral and Attendance at a Patient-Centered Medical Home

Hassan

Curtis

Kerver

et al. 2020

J Prim Care Community Health

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Background Although evidence shows that diabetes self-management education and support (DSMES) is an effective tool to help individuals with type 2 diabetes (T2DM) improve their health outcomes, there remains a large number of individuals not attending DSMES. Understanding how frequently patients receive referrals to DSMES and the number of DSMES hours they receive is important to determine, as well as patients’ health outcomes of utilizing DSMES. This will help us understand patterns of utilization and the outcomes that occur when such a valuable resource is utilized. Methods Secondary data analysis was conducted of patient electronic medical records at a primary healthcare federally qualified clinic and 2 area hospitals. We identified 105 adult patients with a new T2DM diagnosis with at least 2 A1c lab results 3 to 12 months apart during the study period. Results Only 53.5% were referred to DSMES. Out of those who were referred, 66% received no DSMES, 17% received 1-hour assessment, 4% received partial DSMES, and 13% received 8 or more hours. Linear regression of percent change in A1c and number of DSMES hours received, revealed that receiving 1 ( P = .001) or 8 or more hours of DSMES ( P = .022) had a significant negative relationship with the percent difference in A1c compared to the group who received no DSMES. Patients who had an hour of assessment had a similar percent reduction in A1c to those who had partial DSMES. Conclusion Referral rates and enrollment in DSMES remain low. Those who enrolled often dropped out after the one-hour assessment session. Results suggest making the one-hour assessment session more educationally comprehensive or longer to retain patients. Improving the DSMES referral process and further investing physicians’ decisions on whether to refer or not refer patients to DSMES are key for future studies.

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Influences on Diabetes Self-Management Education Participation in a Low-Income, Spanish-Speaking, Latino Population

Cited by 20 publications

References 21 publications

Improved diabetes control among low-income Mexican Americans through community-clinical interventions: results of an RCT

Improved diabetes control among low-income Mexican Americans through community-clinical interventions: results of an RCT

Geographic disparities and temporal changes of diabetes prevalence and diabetes self-management education program participation in Florida

Diabetes Self-Management Education and Support: Referral and Attendance at a Patient-Centered Medical Home

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