1️⃣ Why Use Logistic Regression in NIS?

The National Inpatient Sample (NIS) contains millions of hospitalization records across the United States. When the outcome is binary (e.g., mortality: yes/no, complication: yes/no), logistic regression is the preferred statistical method. It helps researchers determine whether an exposure independently predicts an outcome while controlling for confounders such as age, sex, comorbidities, and hospital characteristics.

2️⃣ Defining Exposure, Outcome, and Covariates

In NIS regression analysis, you must clearly define your primary exposure (e.g., diabetes), outcome (e.g., inpatient mortality), and covariates (e.g., age, gender, hypertension, hospital teaching status). Including relevant covariates in a multivariable model helps adjust for confounding and improves the validity of your findings. Weighted analysis is also necessary to generate nationally representative estimates.

3️⃣ Interpreting Odds Ratios and Confidence Intervals

The results of logistic regression are presented as Odds Ratios (OR) or Adjusted Odds Ratios (aOR). An OR >1 suggests increased odds of the outcome, while OR <1 suggests decreased odds. The 95% Confidence Interval (CI) tells us about precision, and if it does not cross 1, the result is statistically significant (usually alongside p < 0.05).

4️⃣ Example for Better Understanding

Suppose we study whether obesity predicts inpatient mortality in patients admitted with acute myocardial infarction using the NIS. After adjusting for age, gender, diabetes, and hospital factors, we find an aOR of 1.25 (95% CI: 1.10–1.42, p < 0.01). This means obese patients have 25% higher odds of inpatient mortality compared to non-obese patients, after controlling for other variables.

This is how regression helps identify independent predictors in large national databases.