Health Programme Evaluation by Propensity Score Matching: Accounting for Treatment Intensity and Health Externalities with an Application to Brazil

Most of the literature on health programme evaluation has estimated average programme impacts relying on either: (i) data on the presence or absence of an intervention in a particular locality, or (ii) data on individual participation in the health programme. By estimating an average health impact which is independent of the programme’s population coverage, the empirical approaches of these studies overlook the important fact that public health interventions create externalities whose magnitude depends crucially on the number of covered individuals in a locality. The main contributions of this paper are to suggest and apply an empirical approach for the impact evaluation of public health interventions which also takes into account treatment externalities, when non-experimental, routine data are available. The proposed framework involves the computation of average treatment effects by a propensity score matching-difference-in-differences estimator adapted to the case of multiple treatments, jointly evaluating the impact of different programme coverage levels. The methods are used to conduct an impact evaluation of the Family Health Programme (Programa Saude da Familia—PSF), the broadest health programme ever launched in Brazil, on adult and child health. I find that exposure to higher PSF coverage levels leads to improvements in individual health outcomes, with relatively small effects for adults but larger estimated impacts for children.