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In this paper we present a continuous extension for longitudinal analysis settings of the recently proposed Covariate Balancing Propensity Score (CBPS) methodology. While extensions of the CBPS methodology to both marginal structural models and general treatment regimes have been proposed, these extensions have been kept separately. We propose to bring them together using the generalized method of moments to estimate inverse probability weights such that after weighting the association between time-varying covariates and the treatment is minimized. A simulation analysis confirms the correlation-breaking performance of the proposed technique. As an empirical application we look at the impact the gradual roll-out of Seguro Popular, a universal health insurance program, has had on the resources available for the provision of healthcare services in Mexico.

Objective. To investigate the degree in which answer patterns in the Household Water Insecurity Experiences Scale (HWISE) relate to scores aiming at identifying latent groups with different water insecurity levels in a nationwide representative sample of the Mexican population. Materials and methods. Based on data from the 2021 National Survey on Health and Nutrition (Estudio Nacional de Salud y Nutrición 2021, Ensanut 2021), sequence data representations, and a latent class analysis (LCA), in this article we estimate the likely misclassification errors of different cutoffs proposed for the HWISE scoring system. Results. The main results suggest that a 5-item subset of the HWISE may exhibit a more reliable and cost-effective behavior than the complete 12-item set for a 2-level partition of the sample. Conclusions. Our methodological approach provides new insights regarding the efficiency and likely errors in distinguishing between levels of water insecurity based on the Mexican chapter of the HWISE. [ABSTRACT FROM AUTHOR]