This seems to be a classic case of a survival model with time-varying covariates where you are interested in the time between some reference time and the time of the event. If you only have annual data, that's probably best analyzed with a discrete-time survival model.
A discrete-time survival model can be implemented as a binomial regression model on a data set like yours with one row per time period per individual. You need to have a column for the time elapsed since the reference time. For example, if these are employees of a corporation, that might be the time since hire. Also, if the event can happen at most once per individual, then an individual doesn't provide data about event risk at times after the event. So no data rows should be included for an individual after the event time.
You then perform a binomial regression with the elapsed time included as a predictor, perhaps flexibly modeled with a regression spline. This page outlines the principles based on logistic regression, and provides some references. This web page has links to some classic presentations by Singer and Willett. A complementary log-log link (instead of a logit link) is more aligned with the Cox type of proportional hazard survival analysis, as described on this page and its links.
One warning: make sure that any predictors in your model are in fact predictors of the event. With this type of data, it's possible for a covariate's value during the time period of the event to be a result of the event's occurrence during the time period rather than a cause of the event. Think carefully about whether your data are at risk of that type of misinterpretation.