1  Data to Analyze

To answer the research question “Does obesity increase the risk of developing diabetes?” in the U.S. context, we do the following:

1.1 Choose a U.S. data source

  • Data source: National Health and Nutrition Examination Survey (NHANES) (Disease Control and Prevention 2021)
  • Availability: NHANES is a publicly available dataset that can be downloaded for free from the CDC website.
  • Design: Observational cross-sectional data. Hence, inferring causality is not a possibility or our objective here.

1.2 Confounder identification

Directed acyclic graph (DAG)

(Greenland, Pearl, and Robins 1999)

flowchart TB
  A[Obesity A] --> Y(Diabetes Y)
  L[Confounders C] --> Y
  L --> A

Hypothesized Directed acyclic graph drawn based on analyst’s best understanding of the literature

Exposure: Being obese

Outcome: Developing diabetes

Confounders: Demographic and lab variables

1.3 Structure of the data

flowchart LR
  D[NHANES 2013-14] --> demo[Demographic \nVariables \nand \nSample \nWeights]
  demo --> Age
  demo --> Sex
  demo --> Education
  demo --> r[Race or \nethnicity]
  demo --> m[Marital \nstatus]
  demo --> Income
  demo --> b[Birth place]
  demo --> sf[Survey \nfeatures: \nsampling \nweights, \nstrata, \ncluster]
  D --> bmi[Body \nMeasures]
  bmi --> Obesity
  D --> diq[Diabetes]
  diq --> Diabetes
  diq --> f[Family \nhistory of \ndiabetes]
  D --> smq[Smoking - \nCigarette Use]
  smq --> Smoking
  D --> dbq[Diet \nBehavior & \nNutrition]
  dbq --> Diet
  D --> paq[Physical \nActivity]
  paq --> p[Physical \nactivities]
  D --> huq[Hospital \nUtilization & \nAccess \nto Care]
  huq --> mm[Medical \naccess]
  D --> bpx[Blood \nPressure]
  bpx --> sbp[Systolic \nBlood \nPressure]
  bpx --> dbp[Diastolic \nBlood \nPressure]
  D --> bpq[Blood \nPressure & \nCholesterol]
  bpq --> hc[High \ncholesterol]
  D --> slq[Sleep \nDisorders]
  slq --> Sleep
  D --> biopro[Standard\n Biochemistry \nProfile]
  biopro --> u[Uric \nacid]
  biopro --> Protein
  biopro --> Bilirubin
  biopro --> Phosphorus
  biopro --> Sodium
  biopro --> Potassium
  biopro --> Globulin
  biopro --> Calcium
  D --> rxq[Prescription\n Medications -  \nICD-10-CM \ncodes]
  style D fill:#FFA500;
  style rxq fill:#00FF00;
  style biopro fill:#00FF00;
  style slq fill:#00FF00;
  style bpq fill:#00FF00;
  style bpx fill:#00FF00;
  style huq fill:#00FF00;
  style paq fill:#00FF00;
  style dbq fill:#00FF00;
  style smq fill:#00FF00;
  style diq fill:#00FF00;
  style bmi fill:#00FF00;
  style demo fill:#00FF00;

We do the same for the following cycles:

  • NHANES 2015-16
  • NHANES 2017-18

1.4 Identify measured and unmeasured variables in the data

Find variables capturing the following concepts in the data based on a hypothesized DAG.

Role Data Component Variables considered based on DAG
Outcome DIQ Have diabetes1
Exposure BMX Obese; BMI >= 30
Confounder (demographic) DEMO Age, Sex, Education, Race/ethnicity, Marital status, Annual household income, County of birth, Survey cycle year
(behaviour) SMQ, PAQ, SLQ, DBQ Smoking2, Vigorous work activity, Sleep3, Diet4
(health history / access) DIQ, HUQ Diabetes family history, Access to care5
(lab) BPX, BPQ, BIOPRO Blood pressure (systolic, diastolic6), Cholesterol, Uric acid, Total Protein, Total Bilirubin, Phosphorus, Sodium, Potassium, Globulin, Total Calcium
  • 14 demographic, behavioral, health history related variables
    • Mostly categorical
  • 11 lab variables
    • Mostly continuous

  1. combination of (a) Doctor told you have diabetes, (b) Taking insulin now, (c) Take diabetic pills to lower blood sugar.↩︎

  2. cigarette use (at least 100 cigarettes in life)↩︎

  3. Sleep hours/workdays↩︎

  4. How healthy is the diet↩︎

  5. Routine place to go for healthcare↩︎

  6. average of 4 measurements↩︎