Table 1.
Ref. | Purpose | Design/method | Sample | Findings | Strengths/limitations/implications |
Chaufan et al[25] (2011) | To examine “upstream” social determinants of health in a Latino immigrant population focusing on T2DM risk and food environment | Mixed methods focus groups, Food store survey instrument California | Staff members (n = 6); clients (n = 15) | Poverty prevented Latino immigrants from access to adequate housing, high quality education, and culturally appropriate food choices | Community-based interviews elicited lived experience of Latino immigrant population Limitations: small sample size, nonrandomized, descriptive study Implications: Policy change to enable access to affordable culturally acceptable healthy foods |
Carbone et al[26] (2007) | To describe factors influencing Latino (Puerto Rican) diabetes self-management | Qualitative-focus groups Health center in Massachusetts, United States | Healthcare providers (n = 15) Patients (n = 37) | Cultural influence of family through traditional gender roles; financial restraints Social network (family and friends) source of strength; spirituality and faith important; discrepancy between healthcare provider and patient focus in self-management | Community-based limitations: Small sample size, qualitative study, not generalizable to all Latino populations Implications: account for inclusion of family and social networks in diabetes self-management education, need for increased cultural awareness by Healthcare provider of influencing factors on patient self-management |
Chaufan et al[27] (2011) | To gain understanding how food environments influence a low income immigrant population | Qualitative-focus groups | Staff (n = 6) Clients (n = 15) | Barriers to food access: transportation, language barriers, poverty, employment | Strengths: community based Limitations: small sample size, not generalizable Implications: Need for policy to increase food access for immigrant populations in lower socio-economic levels |
Tjia et al[28] (2008) | To gain understanding of barriers to medication adherence among older adults with T2DM | Qualitative-semi structured interviews | Adults over age 65 (n = 22) Female n = 16 | Older adults with T2DM had concerns about medication cost or medication burden but did not discuss with physician | Strengths: perspective of older adults Limitations: small sample size, not generalizable Implications: need for increased patient-physician communication; policy level for messages to encourage open patient-physician dialogue for medical treatment |
Denham et al[29] (2010) | To explore perceived patient barriers to diabetes education among healthcare providers | Cross-sectional survey | Healthcare providers from three practice settings: federally qualified healthcare centers; health departments; clinics (n = 182) | Perceived barriers: transportation; fewer diabetes educators and physicians; lack of insurance; education materials not screened for literacy levels nor cultural appropriateness | Strengths: look at perceived barriers to diabetes education among healthcare providers Limitations: small sample size, multiple sites, sample bias Implications: policy need to increase diabetes education reimbursement to providers for all individuals nationally; need to screen materials for literacy levels |
Heuer et al[30] (2006) | To describe Hispanic migrant farmworkers perceptions of diabetes | Qualitative phenomenological study | Migrant farmworkers (n = 12) Female n = 6 | Cultural/folk beliefs that diabetes was caused by stress or emotions | Strengths: focused on hispanic explanatory model of diabetes Limitations: small sample size, not generalizable Implications: need to tailor diabetes education to address culture and health beliefs |
Shigaki et al[31] (2010) | To examine how patients diagnosed with type 2 diabetes view role of nurses in disease management | Qualitative Semi-structured individual interviews | n = 13 Female n = 7 White n = 9 African American n = 3 Other n = 1 | Nurse viewed as a positive partner in disease management, patients prefer team-based medical care, open communication between healthcare providers and patient | Strengths: Patient perspective on nurse as partner in healthcare Limitation: small sample size; sample bias Implications: Nurse partner may provide link between patient and improving health outcomes in individuals with multiple co-morbidities including diabetes |
Fitzgerald et al[32] (2008) | To determine diabetes care perceptions in patient and provider | Quantitative cross sectional survey | Providers n = 71 Patients: n = 273 female 61% White 63% African American 33% | Patient and provider differences included patients with more positive attitudes family support, paying for diabetes care | Strengths: quantitative study using reliable and valid instrument Limitations: may not be representative for patients with no co-morbidities Implications: Importance of communication between patient and provider and not making assumptions about patients |
Zulkowski et al[33] (2005) | To examine differences between patient self-report and provider documentation in rural health center | Quantitative cross sectional survey; medical record review | n = 149 Female n = 86 | Statistical difference between patient and provider on medical management or diabetes knowledge; most patients had not been seen by diabetes educator or dietician | Strengths: good response rate (61%); limitations: race not documented Implications: Need for increased access to diabetes education in rural areas; need for open communication between patient and provider |
Ford et al[34] (2002) | To determine relationship between socio-economic status and race on diabetes management | Quantitative-random sampling by socio-economic status and race | n = 50 African American n = 21 Caucasian n = 29 Female n = 22 | Differences in perception of sense of loss by race but not socio-economic status | Strengths: Limitations: small sample size: not generalizable; underpowered (α = 0.34) Implications: Need for education to stress management to prevent disease related complications |
Mani et al[35] (2011) | To determine the influence of social networks on diabetes self-management | Cross-sectional survey | n = 154 Female n = 88 White n = 78 African American n = 61 | Diabetes concern increased when larger social network diagnosed with diabetes | Strengths: patient perspective of importance of social networks Limitations: not generalizable-sample from two inner city clinics, only English speaking, data skewed Implications: Important to consider the influence of social networks on diabetes self-management |
Song et al[36] (2012) | To examine social networks among Korean Americans diagnosed with type 2 diabetes | Cross-sectional survey from a larger study | n = 83 Female n = 35 Married n = 73 | Gender differences in source of support, men sought spouse, women had higher unmet needs for social support than men, self-efficacy negatively associated with unmet social support, education level strong predictor of self-care activities, unmet needs for social support negatively associated with diabetes self-care | Strengths: patient perception of social support Limitations: small homogenous sample; not generalizable Implications: need to recognize the role of gender in determining social support among Korean Americans |
Kollannoor-Samuel et al[37] (2011) | To identify influence of social determinants of health on FPG and HbA1c among low income Latinos | Cross-sectional | n = 211 Female n = 155 Puerto Rican n = 171 Unemployed n = 178 Spanish speaking only n = 138 | Lower socio-economic status had higher FPG and HbA1c levels; better long term glycemic control when insured; increased physical activity associated with lower FPG and HbA1c levels | Strengths: sample size; part of larger RCT Limitations: large number of female participants-not generalizable or transferable to other Latino populations; unknown length of time since diagnosis Implications: Importance of physical activity for improved glycemic control; need for health insurance, increase in education to improve glycemic outcomes over the long term |
Chiu et al[38] (2011) | To determine how gender influences functional limitations with type 2 diabetes | Secondary data analysis | n = 1619 Female n = 861 | Psychosocial factors mediator between biological and exercise factors | Strengths: consistent with previous research Limitations: participant self report; no causal pathway Implications: Need to incorporate gender and psychosocial factors in diabetes treatment plan |
Iida et al[39] (2010) | Examine role of spousal support for individual diagnosed with type 2 diabetes | Mixed methods-longitudinal Computer diary survey; individual interviews | n = 129 married couples Caucasian 75%; African American 23.6% | Physical symptoms increased spousal support; women gave higher level of support when spouse worried about diabetes; less spousal support when negative emotional effect from previous day | Strengths: sample size Longitudinal study, diary Limitations: unable to determine causal effect Implications: Need to measure and include spousal support in diabetes self-management plan |
Mayberry et al[40] (2005) | To assess determinants of glycemic monitoring by primary care provider among medicaid beneficiaries | Retrospective cohort study | n = 3321 African American (2025) 61%; White (1296) 39%; Female 74.6%; Urban 49.6%; rural 50.4% | African Americans diagnosed at younger age; frequency of physician visits and medication prescription strong predictor of meeting ADA recommendation guideline for HbA1c monitoring; low monitoring of HbA1c and FPG among medicaid beneficiaries with type 2 diabetes, no significance between Black and White Medicaid beneficiaries for diabetes monitoring | Strengths: provides insight into determinants of glucose monitoring in Medicaid beneficiaries Limitations: Secondary data analysis; inability to determine patient-physician relationship Implications: Need to establish ADA evidence guidance protocol in office practice settings |
Ford et al[41] (2005) | To determine estimates of obesity and diabetes weight information from healthcare providers in 100 United States metropolitan service areas | Cross sectional survey: 2000 Behavioral Risk Factor Surveillance System | n =81917 individuals with BMI data Sample size varied due to missing data | Highest prevalence of obesity Appalachian region; OR of being diagnosed with diabetes 3.5 higher in Charleston WV than Santa Fe NM; weight loss or maintenance discussed with physician 11.7%-34.6% | Strength: study at community level Limitation: secondary data analysis, missing data; self report Implications: Policy level- need to examine determinants of obesity and diabetes at local metropolitan service areas. Healthcare providers need to include discussion of weight management for individuals diagnosed with obesity or diabetes |
Adams et al[42] (2008) | To examine medication adherence as a factor of glycemic control based on race | Secondary data analysis newly diagnosed patients prescribed oral medications | n = 1806 Black n = 467 White n = 1339 | Black patients higher A1c at diagnosis; insufficient evidence to determine medication adherence by race | Strengths: limitations: unable to determine causal effect; potential overestimation of medication adherence based on data; implications: Need at policy level to provide screening for earlier diagnosis of Black and female patients |
Aikens et al[43] (2005) | To examine the relationship of patient-provider communication on diabetes self-management and outcomes | Cross-sectional Telephone survey | n = 736 White 51% Black 20% Hispanic 11.9% Female 31.6% | Primary care provider main manager of diabetes (70.9%) General patient-provider communication related to improved quality of life; diabetes specific patient-provider communication related to glycemic control | Strengths: sample across three health systems; limitations: cross-sectional study; self-report; implications: need for open patient-provider communication to enhance problem solving, need to tailor management plan to individual patient |
Paris et al[44] (2001) | To determine if determinants of type 2 diabetes were present in personnel entering the military | Cross-sectional secondary data analysis | Diagnosed with diabetes n = 419 Not diagnosed with diabetes n = 627 | Enlisted military rank as socioeconomic measure for diabetes diagnosis higher in lower rank, minorities higher level of diabetes; educational not significant variable | Strengths: adequate power Limitations: no causal effect; potential for misclassification of diabetes diagnosis in database; implications: need for focus on policy level for increased physical activity and body mass index monitoring |
FPG: Fasting plasma glucose; HbA1c: Glycosylated hemoglobin A1c; BMI: Body mass index; T2DM: Type 2 diabetes mellitus; RCT: Randomized control trial; ADA: American diabetes Association.