The Effects of Trigger Events on Changes in Children's Health Insurance Coverage. F. Effects of Trigger Events


We have seen that transitions in health insurance coverage among children are often preceded by changes in their parents' employment, AFDC participation, family income, or family composition, although the frequency of these events varies by the type of transition. While this gives us a measure of the potential role of these events in effecting the transitions that we observe, it is quite possible for transitions to be frequently preceded by particular events but for these same events to be followed only infrequently by transitions. It might be the case, for example, that there are important mediating factors that must be present if a transition in health insurance coverage is to be produced by a particular trigger event. If these factors are not captured in our survey data, we cannot identify them and measure their impact, but the nature of the relationship between possible trigger events and transitions may suggest their presence.

To provide a measure of the effects of possible trigger events on the occurrence of transitions we examined the frequency of transitions as a function of the prior occurrence of these events. To do so, we defined selected changes occurring between months m-1 and m as potential trigger events and then estimated the relationship between these events and the likelihood that a transition in health insurance coverage was recorded over the next four months. We did this separately for each of the four types of coverage, with the outcomes of interest in each case being transitions to any of the other three types of coverage versus no transition. We present our findings in two forms: first, as the results of a logistic regression of transition outcomes on the full set of possible trigger events and, second, as estimates of the frequency of each type of transition among the subset of children experiencing a given event. The regression results give us a measure of the relative importance of individual events in predicting transitions while the conditional frequencies tell us in a more intuitive form how often transitions actually occurred after events that the regression analysis identified as the strongest predictors.

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