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Purpose of research . Conditional scenario forecast is typically constructed for a single index of health usually infant mortality and cannot reveal the heterogeneous, sometimes opposite, response of different child-health indexes to social infrastructure development. This paper creates a multi-outcome conditional forecast of four child-health indexes under alternative infrastructure trajectories and assesses how far recorded indexes are fit for monitoring the results of such policy . Materials and methods. A balanced panel of fourteen regions of Uzbekistan over 2012–2024 (one hundred and eighty-two observations) is used. Four outcomes infant mortality, proportion of underweight children, incidence of bacterial dysentery and acute respiratory infections are modelled jointly by a system of externally unrelated regressions with regional fixed effects and logarithmic dependent variables. Infrastructure is presented by three composite indexes medical, utility, and educational. Scenario trajectories are specified as index increments calibrated to historical rates, and forecast uncertainty is estimated using the Monte Carlo simulation method. Results. A unified infrastructure-driven acceleration shifts the four indexes in different directions. By 2030, under the forced scenario, infant mortality decreases by 4.2 percent, the proportion of underweight children by 13.3 percent and dysentery incidence by 9.6 percent, while the reported incidence of acute respiratory infections does not decrease, but increases by 1.4 percent. The cause is the positive association of the medical index with the recorded respiratory morbidity, reflecting fuller ascertainment and offsetting the weak protective effect of educational infrastructure . Conclusion. A single-outcome forecast yields a falsely homogeneous picture of improvement. A multi-outcome forecast shows that some child-health indexes are structurally unresponsive to infrastructure development in recorded terms, calling for caution in using reported morbidity as a measure of progress and for well-targeted regional policy.
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Мамбетсапаев Курбанияз Айниязович (2026) studied this question.
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