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A Good Econometric Model for Health Research

A strong econometric model in health research combines a clear causal or predictive question with an appropriate regression structure and data design. Below is a practical framework with examples applicable in Stata or any statistical software.

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1. Common Econometric Models for Health

Health economics typically focuses on:

• Health outcomes: hospitalization, mortality, disease prevalence
• Healthcare costs: total expenditure, drug costs
• Policy effects: insurance schemes, vaccination programs

Common model types:

• OLS: continuous outcomes
• Logit/Probit: binary outcomes
• Difference-in-Differences: policy evaluation
• Count models: Poisson / Negative Binomial

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2. Example: Policy Impact Model

Conceptual Equation:

antibiotic_rate_it = β₀ + β₁policy_it + γX_it + α_i + λ_t + ε_it

• i = clinic, t = time
• policy = treatment indicator
• X = controls
• αᵢ = fixed effects
• λₜ = time effects

Stata Code

reghdfe antibiotic_rate i.policy age male chronic_conditions, ///
         absorb(clinic_id month) vce(cluster clinic_id)

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Example Output

Variable	Coef.	Std. Err.	P>|t|
policy	-2.10	0.65	0.001
age	-0.04	0.01	0.002
male	0.30	0.12	0.012
chronic_cond	1.80	0.35	0.000

Interpretation: The policy reduces antibiotic prescriptions by about 2.1 per 1,000 visits.

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3. Health Insurance & Hospitalization (Kenya Example)

Research Question: Does insurance increase hospitalization?

Binary Model

Pr(Y = 1 | X) = G(β₀ + β₁insurance + β₂X + ε)

Stata Code

probit hospitalized i.insurance age c.age#c.age i.sex ///
        i.education ln_income chronic_illness ///
        i.rural distance_km, nolog

margins, dydx(insurance)
marginsplot

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Count Model

log(E[N | X]) = β₀ + β₁insurance + β₂X

Stata Code

nbreg n_admissions i.insurance age c.age#c.age i.sex ///
      i.education ln_income chronic_illness ///
      i.rural distance_km, irr

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Conclusion

A well-designed health econometric model integrates strong controls and credible identification strategies.