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.
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
2. Example: Policy Impact Model
Conceptual Equation:
antibiotic_rateit = β₀ + β₁policyit + γXit + αi + λt + εit
antibiotic_rateit = β₀ + β₁policyit + γXit + αi + λt + εit
- 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)
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.
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
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
Conclusion
A well-designed health econometric model integrates strong controls and credible identification strategies.
No comments:
Post a Comment