The Prevalence of Type 2 Diabetes Mellitus in a Wisconsin Hmong Patient Population Kevin Koobmoov Thao MD Primary Care Research Fellow UW Department of.

The Prevalence of Type 2 Diabetes Mellitus in a Wisconsin Hmong Patient Population

Kevin Koobmoov Thao MDPrimary Care Research FellowUW Department of Family Medicine

Outline

•The Wisconsin Hmong •Diabetes in the Hmong population•Results•Next Steps

Population Totals

•33,791 Hmong living in Wisconsin according to 2000 US Census data - .63% of the states total population

•57.1% of the Hmong are under 18 years old and 2/3 are under 24

•Average size for Hmong family 6.4 persons

•This population has experienced a 100% growth since the 1990 census.

The Prevalence of Type 2 Diabetes Mellitus in a Wisconsin Hmong Patient Population

Purpose: To compare the prevalence of diabetes in the Hmong subpopulation of the University of Wisconsin Department of Family Medicine ambulatory care population to non-Hispanic white patients.

The UW Clinical-Public Health Data Exchange Pilot•Extraction of patient electronic medical

records of the UW DFM clinic population from years 2007-2009

•Outpatient visits to UW DFM clinics documented with the EpicCare© EMR

•Project Objective: To link patient electronic health records with public health databases to facilitate multidimensional investigations of population health.

Multi-Level Modeling and Data Mining of Disease Risk, Disparity, and Health Outcome Quality

Outcomes = Patient Factors +

ClinicianFactors +

ClinicFactors +

CommunityFactors

Asthma Age Age Location Census Block Group:

Diabetes Gender Gender Capabilities Poverty

CVD / CHF Race/ethnicity Certifications Processes Education level

Immunizations Co-morbidities Graduation   Built environment:

Obesity Medications date   Traffic

Hypertension Language Years of practice   Recreation / parks

Smoking Insurance   Safety / crime

Alcohol Urban / Rural     Psycho-demographics

A1c level Census Block Group     Restaurant mix

LDL     Fast food sales

HDL     Fresh fruit & vegetable sales / consumption

BP        Hospitalizations       Public Health Programs

Health Care -Process factors

(e.g, time to repeat follow-up)

Electronic Health Record & Hospitalization Data Census / ESRI Data + PH Information Systems

Study Data Selection

•EMR extract data contains demographic and health information on 192,201 unique ambulatory care patients

•2.5 million clinical encounters •Patient Confidentiality was protected by

removal of identifying information before extraction▫Name, exact birth date, SS#, exact

address, HIV diagnosis information, Medical Record Numbers

Population Selection by Race, Ethnicity and Language

Total patient Population192,201

Non-Hispanic White 157,526(82.0%)

Non-Hispanic Asian 5743 (2.99%)

Race/Ethnicity

Language

Hmong 611 (0.32%)

Non-Hispanic White 157,526(82.0%)

Hmong 611 (0.32%)

Comparison Group

Selected Group

Variable Definitions • Race/Ethnicity/Language: coded from the EMR fields • Age: Obtained from the EMR and categorized into

appropriate categories• Body Mass Index (BMI): calculated from the earliest weight

and height measurements in the patients record ▫ Then classified into categories▫ Normal weight (BMI<25)▫ Overweight (BMI 25-30)▫ Obese (BMI >30)▫ BMI Missing

• Health Insurance: Encoded from the EMR▫ Commercial, No Insurance, Workers Compensation ▫ Medicare▫ Medicaid

Type 2 Diabetes Diagnosis Variables• International Classification of Disease 9th Revision

(ICD-9) diagnosis codes▫250.x0 and 250.x2 where x can be variable

• Laboratory Values▫Fasting glucose >126 mg/dL x 2▫2 hour Glucose Tolerance Test > 200 x 2▫Random glucose > 200 x 2▫Hgb 1 Ac > 6.5%

• Medication list▫Medications listed under the classification “anti-

diabetes medication” in the EMR (excluding Metformin)

Type 2 Diabetes Diagnosis Algorithm

DM Type 2 was diagnosed if :1. Both the encounter and diagnosis fields were

consistent with diagnosis Or2. Either the encounter or diagnosis field indicated a

diagnosis and the diagnosis was confirmed by laboratory or medication list support of diabetes diagnosis

Cases of inconsistency of ICD-9 codes within or between encounter and problem list fields were also addressed with another algorithm to determine type 2 diabetes diagnosis

Characteristics of Hmong and non-Hispanic white populationsCharacteristic Hmong Non-Hispanic WhiteNumber (percent of total) 611 157,526

Average age years 30.4 + 0.97 37.4 + .05

Age range in years (percentage of total)0-17 257 (42.6%) 30503 (19.4%)

18-54 227 (37.2%) 93374 (59.3%)

55-64 73 (12%) 17802 (11.3%)

65+ 54 (8.8%) 158747 (10%)

Total 611 157526

Sex % male 40.4% 45.7%

Characteristics of Hmong and non-Hispanic white populations cont.Characteristic Hmong Non-Hispanic WhiteMean BMI (kg/m2) 24.0 + .34 26.9 + .02

BMI Category (percent of total)Underweight and Normal Weight 159 (26.0%) 42236 (26.8%)

Overweight 106 (17.4%) 31599 (20.1%)

Obese 76 (12.4%) 32434 (20.6%)

BMI Missing 270 (44.2%) 51257 (32.5%)

Health Insurance (percent of total)Commercial/Workers comp/ No Insurance

153 (25.0%) 130606 (82.9%)

Medicaid 413 (67.6%) 9987 (6.3%)

Medicare 45 (7.36%) 16933 (10.8%)

UW DFM Data of Hmong Patients in Wisconsin

Census 2000 Data on the Hmong of Wisconsin

UW DFM Data of Hmong Patients in Wisconsin

Crude Diabetes Prevalence

Hmong Non-Hispanic White

Number with Diabetes

Diabetes Prevalence

Number with Diabetes

Diabetes Prevalence

χ^2 p-value

Odds Ratios

Total Study Population 41 6.7% 7590 4.8% 0.029

1.4

Adults (age >18) 41 11.6% 7583 6.0% <.001

2.1

Crude Diabetes PrevalenceHmong Non-Hispanic

White

Number with Diabetes

Diabetes Prevalence

Number with Diabetes Diabetes Prevalence

χ^2 p-value Odds Ratios

Age Range            

0-17 0 0.0% 7 0.0%

18-54 12 5.3% 2622 2.8% 0.02

1.9

55-64 19 26.0% 2176 12.2% <0.001

2.5

65+ 10 18.5% 2785 17.6% 0.856

1.1 BMI            

Normal weight 7 4.4% 369 0.9% <.001

5.2

Over weight 15 14.2% 1160 3.7% <.001

4.3

Obese 11 14.5% 3920 12.1% 0.524

1.2

Multivariable Logistic Regression Analysis

Non-Hispanic White (OR)

Hmong (OR) Wald χ^2 P value

Model 1 1.0 1.4 (1.0-2.0) 0.042

Model 2 1.0 1.7 (1.2-2.5) 0.003

OR is the odds ratio for diabetes (95% CI).Model 1 is adjusted for age, sex, and insuranceModel 2 is adjusted for age, sex, insurance and BMI

Limitations1. Selection Bias of the study population

UW DFM ambulatory care population size large, but non-random sample of Wisconsin residents. Questions of generalizability.

2. Selection Bias of the Hmong sampleLanguage field utilized for interpretive services. Unknown what proportion of Hmong are listing Hmong as language. Hmong ethnicity not an option for ethnicity coding.

3. Missing BMI data44.2% and 32.5% of records were missing height and weight data to calculate BMIBMI missing category was created and included in statistical analysis Models including and excluding BMI examined

Conclusion• This study supports previous study conclusions

that health care providers should be aware of the increase risk for diabetes in the Hmong population (Her 2005, McCarty 2005).

• Physicians should consider screening for glucose intolerance in the Hmong patient population starting at younger ages and lower BMI (McCarty 2005).

• Further population based research should be conducted to evaluate the prevalence of diabetes in the Wisconsin Hmong population.

Next Steps?

• Diabetes Prevention▫ Community Based

Participatory Research ▫ Increase physical activity ▫ Improve nutrition

• Diabetes Management▫ Clinical effectiveness trials

of culturally appropriate Diabetes education

▫ Improve diabetes self management education

More Epidemiology (miniSHOW?)Risk Factor Exploration

AcknowledgmentsMPH Program/Research Mentors

MPH Preceptor: Lawrence Hanrahan PhD Director of Public Health InformaticsChief EpidemiologistBureau of Health Information, Wisconsin Division of Public Health

Research Mentor: Brian Arndt MDFacultyUWSMPH Department of Family Medicine

MPH Capstone Committee Chair: John Frey MD ProfessorDepartment of Family MedicineHead of Community EngagementInstitute for Clinical and Translational ResearchUniversity of Wisconsin School of Medicine and Public Health

Public Health Informatics Specialist: Aman Tandias MSBureau of Health Information, Wisconsin Division of Public Health

Theresa Guilbert MDFaculty UWSMPH Department of Pediatrics

Barbara Duerst MS, RNMPH Associate Program DirectorUWSMPH

UW Department of Family Medicine MentorsThe work presented here was carried out while Kevin Thao

was a Primary Care Research Fellow supported by a National Research Service Award (T32HP10010) from the Health Resources and Services Administration to the University Of Wisconsin Department Of Family Medicine

Bruce Barrett MD, PhDDirector of the Primary Care Research FellowshipDepartment of Family Medicine

MaryBeth Plane PhDDirector of DFM Research Services Department of Family Medicine

Terry LittleUniversity Services Program Associate

Hmong/Madison Community Mentors

Fuechue ThaoPublic Health Clinic AideMadison Dane County Public Health

Susan Webb-Lukomski RN, BSNMadison Dane County Public Health

References• Culhane-Pera, K., Peterson, K. a, Crain, a L., Center, B. a, Lee, M., Her, B., et al. (2005). Group visits for Hmong

adults with type 2 diabetes mellitus: a pre-post analysis. Journal of health care for the poor and underserved, 16(2), 315-27. doi: 10.1353/hpu.2005.0030.

• Culhane-Pera, K. a, Her, C., & Her, B. (2007). "We are out of balance here": a Hmong cultural model of diabetes. Journal of immigrant and minority health / Center for Minority Public Health, 9(3), 179-90. doi: 10.1007/s10903-006-9029-3.

• Devlin, H., Roberts, M., Okaya, A., & Xiong, Y. M. (2006). Our lives were healthier before: focus groups with African American, American Indian, Hispanic/Latino, and Hmong people with diabetes. Health promotion practice, 7(1), 47-55. doi: 10.1177/1524839905275395.

• Franzen, L., & Smith, C. (2009a). Differences in stature , BMI , and dietary practices between US born and newly immigrated Hmong children q. Social Science & Medicine, 69(3), 442-450. Elsevier Ltd. doi: 10.1016/j.socscimed.2009.05.015.

• Franzen, L., & Smith, C. (2009b). Acculturation and environmental change impacts dietary habits among adult Hmong. Appetite, 52(1), 173-83. doi: 10.1016/j.appet.2008.09.012.

• Her, C., & Mundt, M. (2005). Risk prevalence for type 2 diabetes mellitus in adult Hmong in Wisconsin: a pilot study. WMJ : official publication of the State Medical Society of Wisconsin, 104(5), 70-7. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/16138520.

• Himes, J. H., Story, M., Czaplinski, K., & Dahlberg-Luby, E. (1992). Indications of early obesity in low-income Hmong children.pdf. American Journal of Diseases of Children, 146(1), 67-9.

• Koltyk, J. A. (1997). New Pioneers in the Heartland: Hmong Life in Wisconsin. Allyn & Bacon.• Mccarty, D. J. (2005). Glucose intolerance in Wisconsin ’ s Hmong population. Wisconsin Medical Journal, 104(5), 13-

15.• Stang, J., Kong, A., Story, M., Eisenberg, M. E., & Neumark-Sztainer, D. (2007). Food and weight-related patterns and

behaviors of Hmong adolescents. Journal of the American Dietetic Association, 107(6), 936-41. doi: 10.1016/j.jada.2007.03.003.

• University of Wisconsin and Applied Population Laboratory. (2002). Wisconsin ’ s Hmong Population.

Thank You, Questions?

“The ability to ask the right question is more than half the battle of finding the answer.”

Thomas J. Watson

Other Unexplored Risk Factors

Obesity Risk Factors Related to Environmental Change

Obesity

Poor Dietary Habits

Physical Inactivity

Heart Disease

Diabetes

Cancer

Obesity Risk Factors Related to Environmental Change

Obesity

Poor Dietary Habits

Physical Inactivity

Heart Disease

Diabetes

Cancer

http://kcortiz.photoshelter.com/gallery-image/FORCED-REBELLION-HMONG-CIA-VETERANS-OF-THE-SECRET-WAR/G0000ddMEaqXj9SU/I0000iOTLjb2km_w

Diabetes in 5 minutes to the Hmong• One Type • Death Sentence• Risk Factors

▫ America ▫ Weather▫ Anguish/Loss of

Home▫ Obesity

Poor diet and physical inactivity

• Adverse Health Outcomes

• Herbs/Nothing

Chronic disease of insulin (kua fajsiv)

Two types Risk Factors

AgeEthnicityObesity

○ Poor diet and physical inactivity

Adverse Health Outcomes

Treatments

Limitations Continued1. Selection Bias of the study population

2009 BRFSS reported 80.8% of Americans had primary care providers and 81.65% were seen for routine health check up in the last two years

The Wisconsin Family Health Survey, 2001-2005 indicates 92% of surveyed Wisconsin residents had a place of routine health care and 87% of Wisconsin Asians reported having a place for routine healthcare

Unknown – Primary care utilization patterns of Hmong in WisconsinDiabetes screening rates of Hmong in Wisconsin primary care clinics

2. Selection Bias of the Hmong sampleUnknown – Proportion of Hmong patients utilizing interpretive servicesPotential surname based analysis method possible, but not validated

3. Missing BMI data

Patient Race and Ethnicity Breakdown

Race Frequency PercentMissing 2260 1.18%American Indian or Alaska Native 1761 0.92%Asian 5743 2.99%Black or African American 7584 3.95%Native Hawaiian or Other Pacific Islander 245 0.13%White 165700 86.21%Patient Refuses to Answer 1379 0.72%Unknown 7529 3.92%Total 192201 100.00%

Ethnicity Frequency PercentMissing 2953 1.54%Hispanic/Latino 7858 4.09%Not Hispanic or Latino 171758 89.36%Patient Refuses to Answer 1050 0.55%Unknown 8582 4.47%Total 192201 100.00%

Diabetes Diagnosis AlgorithmCriteria Patient Count Prevalence

diabetes_p 9,788 5.09%

diabetes_e 10,452 5.44%

diabetes_p or diabetes_e 11,483 5.97%

diabetes 9,804 5.10%

diabetes1_p 678 0.35%

diabetes1_e 737 0.38%

diabetes1_p or diabetes1_e 828 0.43%

diabetes1 740 0.39%

diabetes2_p 8,975 4.67%

diabetes2_e 9,673 5.03%

diabetes2_p or diabetes2_e 10,605 5.52%

diabetes2 9,034 4.70%

Cases where the patient has ICD 9 codes for both type 1 and type 2For patients with both diabetes type 1 and type 2 ICD 9 codes, determine

which is the most likely one to be correct using the following algorithm.Use 250.0x only (omit ICD 9 codes for diabetes complications)• Rationale: Some users may not have realized the diabetes complications

have type-specific codes. Therefore the codes for diabetes complications are not reliable in resolving conflict between types.

Look at the latest 3 entries only, using encounter date for encounter dx and entry date for problem list dx.

• Rationale: Data entry errors decrease over time as users become more familiar with the system. Therefore we can expect the later entries to be more reliable.

Patients may have been initially misdiagnosed and the diagnosis was later corrected.

Take the majority of the latest 3 entries. If there is only one entry, then use that entry's dx. If there are two entries and they are of different types:if the dates are different, take the more recent oneif the dates are the same, leave the type unspecified