Vani Nimbal

Racial/Ethnic Differences in Gestational Diabetes Prevalence and Contribution of Common Risk Factors

BACKGROUND The White House, the American Heart Association, the Agency for Healthcare Research and Quality, and the National Heart, Lung and Blood Institute have all recently acknowledged the need to disaggregate Asian American subgroups to better understand this heterogeneous racial group. This study aims to assess racial/ethnic differences in relative contribution of risk factors of gestational diabetes mellitus (GDM) among Asian subgroups (Asian Indian, Chinese, Filipino, Japanese, Korean, and Vietnamese), Hispanics, non-Hispanic blacks, and non-Hispanic whites. METHODS Pregnant women in 2007-2012 were identified through California state birth certificate records and linked to the electronic health records in a large mixed-payer ambulatory care organisation in Northern California (n = 24 195). Relative risk and population attributable fraction (PAF) for specific racial/ethnic groups were calculated to assess the contributions of advanced maternal age, overweight/obesity (Centers for Disease Control and Prevention (CDC) standards and World Health Organization (WHO)/American Diabetes Association (ADA) body mass index cut-offs for Asians), family history of type 2 diabetes, and foreign-born status. RESULTS GDM was most prevalent among Asian Indians (19.3%). Relative risks were similar across all race/ethnic groups. Advanced maternal age had higher PAFs in non-Hispanic whites (22.5%) and Hispanics (22.7%). Meanwhile family history (Asian Indians 22.6%, Chinese 22.9%) and foreign-borne status (Chinese 40.2%, Filipinos 30.2%) had higher PAFs in Asian subgroups. Overweight/obesity was the most important GDM risk factor for non-Hispanic whites, Hispanics, Asian Indians, and Filipinos when the WHO/ADA cut-off points were applied. Advanced maternal age was the only risk factor studied that was modified by race/ethnicity, with non-Hispanic white and Hispanic women being more adversely affected than other racial/ethnic groups. CONCLUSIONS Overweight/obesity, advanced maternal age, family history of type 2 diabetes, and foreign-borne status are important risk factors for GDM. The relative contributions of these risk factors differ by race/ethnicity, mainly due to differences in population prevalence of these risk factors.

Patient and provider characteristics associated with colorectal, breast, and cervical cancer screening among Asian Americans.

Background: Routinely recommended screening for breast, cervical, and colorectal cancers can significantly reduce mortality from these types of cancer, yet screening is underutilized among Asians. Surveys rely on self-report and often are underpowered for analysis by Asian ethnicities. Electronic health records (EHR) include validated (as opposed to recall-based) rates of cancer screening. In this article, we seek to better understand cancer screening patterns in a population of insured Asian Americans. Methods: We calculated rates of compliance with cervical, breast, and colorectal cancer screening among Asians from an EHR population and compared them with non-Hispanic whites. We performed multivariable modeling to evaluate potential predictors (at the provider- and patient-level) of screening completion among Asian patients. Results: Aggregation of Asian subgroups masked heterogeneity in screening rates. Asian Indians and native Hawaiians and Pacific Islanders had the lowest rates of screening in our sample, well below that of non-Hispanic whites. In multivariable analyses, screening completion was negatively associated with patient–physician language discordance for mammography [OR, 0.81; 95% confidence interval (CI), 0.71–0.92] and colorectal cancer screening (OR, 0.79; CI, 0.72–0.87) and positively associated with patient–provider gender concordance for mammography (OR, 1.16; CI, 1.00–1.34) and cervical cancer screening (OR, 1.66; CI, 1.51–1.82). In addition, patient enrollment in online health services increased mammography (OR, 1.32; CI, 1.20–1.46) and cervical cancer screening (OR, 1.31; CI, 1.24–1.37). Conclusions: Language- and gender-concordant primary care providers and culturally tailored online health resources may help improve preventive cancer screening in Asian patient populations. Impact: This study demonstrates how the use of EHR data can inform investigations of primary prevention practices within the healthcare delivery setting. See all the articles in this CEBP Focus section, “Cancer in Asian and Pacific Islander Populations.” Cancer Epidemiol Biomarkers Prev; 23(11); 2208–17. ©2014 AACR.

Engaging South Asian women with type 2 diabetes in a culturally relevant exercise intervention: a randomized controlled trial.

Background We examined the efficacy of a culturally relevant exercise program in improving glycated hemoglobin (HbA1c) among South Asian women with type 2 diabetes, compared with usual care. Methods This was a randomized controlled 8-week pilot study of Bollywood dance among South Asian women with type 2 diabetes. The intervention consisted of 1 h Bollywood dance classes offered twice per week. The primary outcome was change in HbA1c. The effect of attendance on this outcome was also examined. Results The intervention group demonstrated a decrease in HbA1c from baseline (−0.18% (0.2%); p=0.018) compared with a non-significant increase in the usual care group (+0.03% (0.2%)); p value for difference between groups was 0.032. Participants attending at least 10 of 16 sessions had a statistically significant reduction in weight (−0.69 kg (0.76 kg)) compared with those attending fewer sessions (+0.86 kg (0.71 kg)). Conclusions These results support culturally relevant dance as a successful exercise intervention to promote HbA1c control, compared with usual care. Trial registration number NCT02061618.

Comparative effectiveness of early versus delayed metformin in the treatment of type 2 diabetes

AIM The purpose of this study was to evaluate the effectiveness of early versus delayed initiation of metformin in type 2 diabetes. METHODS We identified 2925 new users of metformin with type 2 diabetes between 2005 and 2012 in the electronic health records of an integrated health system in Northern California. Patients were matched 1:1 on the propensity for receiving early treatment (defined as ≤6 months from first evidence of diabetes). We evaluated the effectiveness of early versus delayed metformin treatment on intermediate clinical outcomes indicated by changes in hemoglobin A1c (HbA1c) and body mass index (BMI), as well as the incidence of therapy intensification (addition or substitution of a second antihyperglycemic agent). RESULTS A total of 2144 propensity-score matched patients were included in the early or delayed treatment group (n=1072, in each). Early treatment was associated with significantly larger decreases in HbA1c (-0.36%; 95% confidence intervals [CI]: -0.44 to -0.27%; P<0.001) and BMI (-0.46 kg/m(2); 95% CI: -0.64 to -0.29 kg/m(2); P<0.001) relative to delayed treatment. Patients receiving early treatment also had a greater likelihood of attaining an HbA1c<7% (<53 mmol/mol) (odds ratio: 2.00; 95% CI: 1.63-2.45; P<0.001) and a reduced risk of therapy intensification (hazard ratio: 0.72; 95% CI: 0.61-0.85; P<0.001). CONCLUSIONS Treatment with metformin earlier in the course of type 2 diabetes is associated with better glycemic control, more pronounced weight reduction, and a lower risk for therapy intensification than delayed treatment. Antihyperglycemic therapy should be initiated early after diagnosis to achieve optimal outcomes.

Dyslipidemia in Special Ethnic Populations

This article reviews racial/ethnic differences in dyslipidemia-prevalence of dyslipidemia, its relation to coronary heart disease (CHD) and stroke mortality rates, response to lipid-lowering agents, and lifestyle modification. Asian Indians, Filipinos, and Hispanics are at higher risk for dyslipidemia, which is consistent with the higher CHD mortality rates in these groups. Statins may have greater efficacy for Asians, but the data are mixed. Lifestyle modifications are recommended. Culturally-tailored prevention and intervention should be provided to the minority populations with elevated risk for dyslipidemia and considerably more research is needed to determine the best approaches to helping specific subgroups.

Estimating generic drug use with electronic health records data from a health care delivery system: Implications for quality improvement and research

BACKGROUND Generic drug use in the outpatient setting is typically measured with adjudicated pharmacy claims; however, not all delivery systems have access to these data for their clinical populations. OBJECTIVE To develop an algorithm to estimate generic drug use in an outpatient setting using electronic health records (EHR) data. METHODS Twenty-five therapeutic classes were chosen with the potential for low generic use that were prescribed to managed care beneficiaries in a health care system in Northern California. An algorithm was developed to estimate generic drug use based on medication names and dispense-as-written requests from electronic prescriptions, as well as information on generic availability at the time the prescriptions were written. The algorithm was used to quantify a generic utilization rate (GUR) across therapeutic classes and was validated by comparing the estimated GUR to the true GUR, using pharmacy claims corresponding to prescriptions in the same patient cohort. RESULTS Among managed care beneficiaries, 104,859 prescriptions were identified for drugs in the therapeutic classes of interest with corresponding pharmacy claims. The algorithm estimated a GUR of 73.7% across 25 unique classes. The actual GUR based on pharmacy claims was 73.1%. Sensitivity (97%) and specificity (89%) of the algorithm were high, and total percentage of agreement was 95%. CONCLUSIONS An algorithm that estimates generic drug use performed well in a population of managed care beneficiaries. Health care delivery systems may apply methods described in this article to quantify generic drug use in their ambulatory populations for quality improvement and research initiatives, particularly when pharmacy claims are unavailable. DISCLOSURES This study was funded by a grant from the U.S. Food and Drug Administration in cooperative agreement with the Johns Hopkins School of Medicine and the Palo Alto Medical Foundation Research Institute (1U01FD005267-01). Romanelli has received research grant support from Pfizer and Janssen Scientific Affairs. Authors have no other conflicts to disclose. Romanelli and Segal contributed the study concept and design. Nimbal took the lead in data collection, assisted by Romanelli. All authors were involved with data interpretation and revision of the manuscript. The manuscript was written by Romanelli and Nimbal.

Provider and Patient Determinants of Generic Levothyroxine Prescribing: An Electronic Health Records–Based Study

Background: Despite the availability of generic levothyroxine products for more than a decade, uptake of these products is poor. Objective: We sought to evaluate determinants of generic prescribing of levothyroxine. Methods: In a cross-sectional analysis of electronic health records data between 2010 and 2013, we identified adult patients with a levothyroxine prescription from a primary-care physician (PCP) or endocrinologist. We used mixed-effect logistic regression models with random intercepts for prescribing provider to examine predictors of generic levothyroxine prescribing. Models include patient, prescription, and provider fixed-effect covariates. Odds ratios (ORs) and 95% CIs were generated. Between-provider random variation was quantified by the intraclass correlation coefficient (ICC). Results: Study patients (n = 63 838) were clustered among 941 prescribing providers within 25 ambulatory care clinics. The overall prevalence of generic prescribing of levothyroxine was 73%. In the multivariable mixed-effect model, patients were significantly less likely to receive generic levothyroxine from an endocrinologist than a PCP (OR = 0.43; 95% CI = 0.33-0.55; P < 0.001). Women were less likely to receive generic levothyroxine than men from endocrinologists (OR = 0.68; 95% CI = 0.59-0.78; P < 0.001) but not from PCPs. Between-provider variation in generic prescribing was 18.3% in the absence of fixed-effect covariates and could be explained marginally by patient, prescription, and provider factors (ICC = 15.9%). Conclusions: Generic levothyroxine prescribing differed by PCPs and endocrinologists. Residual variation in generic prescribing, after accounting for measurable factors, indicates the need for provider interventions or patient education aimed at improving levothyroxine generic uptake.