Deborah Taira Juarez

Diabetes Incidence Based on Linkages with Health Plans: The Multiethnic Cohort

OBJECTIVE Using the Hawaii component of the Multiethnic Cohort (MEC), we estimated diabetes incidence among Caucasians, Japanese Americans, and Native Hawaiians. RESEARCH DESIGN AND METHODS After excluding subjects who reported diabetes at baseline or had missing values, 93,860 cohort members were part of this analysis. New case subjects were identified through a follow-up questionnaire (1999–2000), a medication questionnaire (2003–2006), and linkage with two major health plans (2007). We computed age-standardized incidence rates and estimated hazard ratios (HRs) for ethnicity, BMI, education, and combined effects of these variables using Cox regression analysis. RESULTS After a total follow-up time of 1,119,224 person-years, 11,838 incident diabetic case subjects were identified with an annual incidence rate of 10.4 per 1,000 person-years. Native Hawaiians had the highest rate with 15.5, followed by Japanese Americans with 12.5, and Caucasians with 5.8 per 1,000 person-years; the adjusted HRs were 2.65 for Japanese Americans and 1.93 for Native Hawaiians. BMI was positively related to incidence in all ethnic groups. Compared with the lowest category, the respective HRs for BMIs of 22.0–24.9, 25.0–29.9, and ≥30.0 kg/m2 were 2.10, 4.12, and 9.48. However, the risk was highest for Japanese Americans and intermediate for Native Hawaiians in each BMI category. Educational achievement showed an inverse association with diabetes risk, but the protective effect was limited to Caucasians. CONCLUSIONS Within this multiethnic population, diabetes incidence was twofold higher in Japanese Americans and Native Hawaiians than in Caucasians. The significant interaction of ethnicity with BMI and education suggests ethnic differences in diabetes etiology.

Factors associated with poor glycemic control or wide glycemic variability among diabetes patients in Hawaii, 2006-2009.

Introduction Although glycemic control is known to reduce complications associated with diabetes, it is an elusive goal for many patients with diabetes. The objective of this study was to identify factors associated with sustained poor glycemic control, some glycemic variability, and wide glycemic variability among diabetes patients over 3 years. Methods This retrospective study was conducted among 2,970 diabetes patients with poor glycemic control (hemoglobin A1c [HbA1c] >9%) who were enrolled in a health plan in Hawaii in 2006. We conducted multivariable logistic regressions to examine factors related to sustained poor control, some glycemic variability, and wide glycemic variability during the next 3 years. Independent variables evaluated as possible predictors were age, sex, type of insurance coverage, morbidity, diabetes duration, history of cardiovascular disease, and number of medications. Results Longer duration of diabetes, being under age 35, and taking 15 or more medications were significantly associated with sustained poor glycemic control. Preferred provider organization and Medicare (vs health maintenance organization) enrollees and patients with high morbidity were less likely to have sustained poor glycemic control. Wide glycemic variability was significantly related to being younger than age 50, longer duration of diabetes, having coronary artery disease, and taking 5 to 9 medications per year. Conclusion Results indicate that duration of diabetes, age, number of medications, morbidity, and type of insurance coverage are risk factors for sustained poor glycemic control. Patients with these characteristics may need additional therapies and targeted interventions to improve glycemic control. Patients younger than age 50 and those with a history of coronary heart disease should be warned of the health risks of wide glycemic variability.

Significance of HbA1c and its measurement in the diagnosis of diabetes mellitus: US experience.

The 2014 American Diabetes Association guidelines denote four means of diagnosing diabetes. The first of these is a glycosylated hemoglobin (HbA1c) >6.5%. This literature review summarizes studies (n=47) in the USA examining the significance, strengths, and limitations of using HbA1c as a diagnostic tool for diabetes, relative to other available means. Due to the relatively recent adoption of HbA1c as a diabetes mellitus diagnostic tool, a hybrid systematic, truncated review of the literature was implemented. Based on these studies, we conclude that HbA1c screening for diabetes has been found to be convenient and effective in diagnosing diabetes. HbA1c screening is particularly helpful in community-based and acute care settings where tests requiring fasting are not practical. Using HbA1c to diagnose diabetes also has some limitations. For instance, HbA1c testing may underestimate the prevalence of diabetes, particularly among whites. Because this bias differs by racial group, prevalence and resulting estimates of health disparities based on HbA1c screening differ from those based on other methods of diagnosis. In addition, existing evidence suggests that HbA1c screening may not be valid in certain subgroups, such as children, women with gestational diabetes, patients with human immunodeficiency virus, and those with prediabetes. Further guidelines are needed to clarify the appropriate use of HbA1c screening in these populations.

Comparison of potentially preventable hospitalizations related to diabetes among Native Hawaiian, Chinese, Filipino, and Japanese elderly compared with whites, Hawai'i, December 2006-December 2010.

Introduction Approximately 25% of individuals aged 65 years or older in the United States have diabetes mellitus. Diabetes rates in this age group are higher for Asian American and Pacific Islanders (AA/PI) than for whites. We examined racial/ethnic differences in diabetes-related potentially preventable hospitalizations (DRPH) among people aged 65 years or older for Japanese, Chinese, Filipinos, Native Hawaiians, and whites. Methods Discharge data for hospitalizations in Hawai‘i for people aged 65 years or older from December 2006 through December 2010 were compared. Annual rates of DRPH by patient were calculated for each racial/ethnic group by sex. Rate ratios (RRs) were calculated relative to whites. Multivariable models controlling for insurer, comorbidity, diabetes prevalence, age, and residence location provided final adjusted rates and RRs. Results A total of 1,815 DRPH were seen from 1,515 unique individuals. Unadjusted RRs for DRPH by patient were less than1 in all AA/PI study groups compared with whites, but were highest among Native Hawaiians and Filipinos. In fully adjusted models accounting for higher diabetes prevalence in AA/PI groups, Native Hawaiian (adjusted rate ratio [aRR] = 1.59), Filipino (aRR = 2.26), and Japanese (aRR = 1.86) men retained significantly higher rates of diabetes-related potentially preventable hospitalizations than whites, as did Filipino women (aRR = 1.61). Conclusion Native Hawaiian, Filipino, and Japanese men and Filipino women aged 65 years or older have a higher risk than whites for DRPH. Health care providers and public health programs for elderly patients should consider effective programs to reduce potentially preventable hospitalizations among Native Hawaiian, Filipino, and Japanese men and Filipino women aged 65 years or older.

The Economic Value of a Wellness and Disease Prevention Program

The objective of this study was to determine the economic impact of the Hawaii Medical Service Associations health promotion/disease prevention program. A retrospective analysis of health risk, health claims, and cost was performed using a mixed model factorial design for the years 2002-2005 that compared program participants to nonparticipants. All analyses were adjusted for preexisting observed differences based on sex, age, baseline morbidity, and health care costs between participants and nonparticipants using propensity score matching method and/or covariates as appropriate. After analyzing data from more than 166,000 HMSA members over a 4-year period, participants were found to incur consistently lower costs. Predictive modeling of upward cost trajectories relative to actual health care costs for participants and risk-matched nonparticipants indicated a savings of

Prevalence of Heart Disease and Its Risk Factors Related to Age in Asians, Pacific Islanders, and Whites in Hawai'i

350 per participant per year. Those who participated in additional wellness programming demonstrated additional cost savings. This study illustrates the economic value of a comprehensive health promotion program.

Failure to Reach Target Glycated A1c Levels Among Patients with Diabetes Who Are Adherent to Their Antidiabetic Medication

Objective. To examine disparities in disease prevalence related to age and race/ethnicity. Study design. Retrospective observational study. Methods. Eligible population included enrollees with largest insurer in Hawai‘i. Chronic diseases were identified from claims data (1999–2009) based on algorithms including diagnostic codes and pharmaceutical utilization. Relative risk of heart disease and its risk factors were calculated for Native Hawaiians and Asian sub-groups by age. Results. Prevalence of heart disease and its risk factors differed substantially by age and race/ethnicity. Native Hawaiians and Filipinos had higher rates of hypertension and diabetes; Asians had highest rates of hyperlipidemia. Whites had the lowest prevalence of risk factors yet their risk of heart disease equaled other groups. Conclusion. Prevalence curves began diverging at age 30 for risk factors and age 40 for heart disease. This suggests approaches to reduce the burden of disease for vulnerable groups need to begin in early adulthood if not sooner.

Does Pay for Performance Improve Cardiovascular Care in a “Real-World” Setting?

The objectives of this study were to describe patient characteristics and types of medications taken by those with poor glycemic control (A1c>7%) despite being adherent to antidiabetic medications. This is a retrospective analysis of administrative data from adult patients with diabetes enrolled in a large health plan in Hawaii (n=21,267 observations for 11,013 individuals) and adherent to their antidiabetic medications. Multivariable logistic regressions were estimated to determine characteristics and types of medications associated with poor glycemic control. Separate models were estimated to examine category of medication (insulin only, 1 oral medication, multiple oral medications, both oral medications and insulin) and specific therapeutic class of oral antidiabetic medications. Despite being adherent to their medications, 56.1% of patients had poor glycemic control. Compared to patients taking combination sulfonylureas, patients had a higher odds of having A1c>7% for all other oral diabetic medications, with odds ratios ranging from OR=2.07 for sulfonylureas alone to OR=1.33 for combination DPP-4 inhibitors. More than half of patients in this study had poor A1c control despite being adherent to their medications. This suggests that physicians, pharmacists, and other providers may need to monitor treatment regimens more carefully, encourage healthy behaviors, and intensify pharmacological treatment as needed.

Risk-Adjusted In-Hospital Mortality Models for Congestive Heart Failure and Acute Myocardial Infarction: Value of Clinical Laboratory Data and Race/Ethnicity.

The objective was to investigate the impact of a pay-for-performance program (P4P) on quality care and outcomes among cardiovascular disease (CVD) patients. Claims data were used to identify CVD patients in a commercial plan in 1999-2006. Multivariate analyses were employed to examine the impact of P4P on quality care (lipid monitoring and treatment) and quality care on outcomes (new coronary events, hospitalizations, and lipid control). Patients who were treated by physicians participating in P4P were more likely to receive quality care than patients who were not. Patients who received quality care were less likely to have new coronary events (odds ratio [OR] = 0.80; 95% confidence interval [CI] = 0.69-0.92), be hospitalized (OR = 0.76; 95% CI = 0.69-0.83), or have uncontrolled lipids (OR = 0.67; 95% CI = 0.61-0.73) than patients who did not. A P4P program was associated with increased lipid monitoring and treatment. Receipt of this quality care was associated with improved lipid control and reduced likelihood of new coronary events and hospitalizations.

Heart failure patients receiving ACEIs/ARBs were less likely to be hospitalized or to use emergency care in the following year.

OBJECTIVE To examine the impact of key laboratory and race/ethnicity data on the prediction of in-hospital mortality for congestive heart failure (CHF) and acute myocardial infarction (AMI). DATA SOURCES Hawaii adult hospitalizations database between 2009 and 2011, linked to laboratory database. STUDY DESIGN Cross-sectional design was employed to develop risk-adjusted in-hospital mortality models among patients with CHF (n = 5,718) and AMI (n = 5,703). DATA COLLECTION/EXTRACTION METHODS Results of 25 selected laboratory tests were requested from hospitals and laboratories across the state and mapped according to Logical Observation Identifiers Names and Codes standards. The laboratory data were linked to administrative data for each discharge of interest from an all-payer database, and a Master Patient Identifier was used to link patient-level encounter data across hospitals statewide. PRINCIPAL FINDINGS Adding a simple three-level summary measure based on the number of abnormal laboratory data observed to hospital administrative claims data significantly improved the model prediction for inpatient mortality compared with a baseline risk model using administrative data that adjusted only for age, gender, and risk of mortality (determined using 3Ms All Patient Refined Diagnosis Related Groups classification). The addition of race/ethnicity also improved the model. CONCLUSIONS The results of this study support the incorporation of a simple summary measure of laboratory data and race/ethnicity information to improve predictions of in-hospital mortality from CHF and AMI. Laboratory data provide objective evidence of a patients condition and therefore are accurate determinants of a patients risk of mortality. Adding race/ethnicity information helps further explain the differences in in-hospital mortality.