Using care days to identify opportunities and manage funds under a IV-E Waiver
This post describes how care days serve as a strategic quantity when deciding how to manage capped funds under the Title IV-E waiver. The post summarizes Fred Wulczyn’s January 24, 2014 webinar “Care Days, Waivers, CQI, and Intervention Design.” If you missed that session, or you are looking for a refresher to prepare for Part 2 of this series, read on below.
Care days—the number of days children spend in foster care—are at the core of planning for, implementing, and monitoring foster care interventions, especially in the context of a Title IV-E waiver. In this post, we review how understanding your system’s care day usage (i.e., which children use how many care days and when) is the key to (1) calculating your waiver allocation, (2) identifying opportunities for reducing care day use, (3) strategizing about which opportunities call for which kinds of interventions, and (4) assessing the potential return on investment in those interventions.
1. Care days are essential to calculating the amount of your waiver allotment and its purchasing power.
The IV-E waiver provides a state with a capped allocation based on that state’s historical expenditures. The ultimate allocation is the result of a calculation that takes into account the following:
- Expenditures = Price x Quantity. How much your system spends on foster care (expenditures) is a function of how much that collection of services costs per day (price) and the number of care days for which it is provided (quantity).
- Quantity = Admissions x Duration. The number of care days a system uses (quantity) is a function of how many children are placed in care (admissions) and how long they stay in care (duration).
- Price depends on the level and intensity of care. How much foster care services cost (price) depends on the makeup of the system’s service array (e.g., in-home services, family foster care, residential care, etc.).
The interventions your state proposes to implement under the waiver are expected to influence formulas noted above. For example, prevention efforts are intended to affect admissions by reducing the number of children who enter care; permanency efforts are intended to affect duration by reducing length of stay in care. For this reason, the expected effects of interventions can be expressed in terms of an anticipated reduction in care days. (i.e., “This population of children currently uses an average of 200 care days. If we implement such-and-such intervention with that population, we can expect to reduce their care day use to an average of 175 care days.”)
The care days your system currently uses has a monetary value (i.e., the “expenditures” value, above) and that value determines your state’s capped allocation. The intervention required to reduce those care days is also associated with a cost. The challenge of the IV-E waiver is to select interventions that have a favorable return on investment against the cost of the service alternatives in the context of a capped allocation.
2. Variation in length of stay helps you identify opportunities for saving care days.
The graph below shows how length of stay varies between two counties. (The graph could as easily be about two populations (e.g., younger vs. older children) in the same jurisdiction.) In the blue county, children leave care fairly quickly in the first 250 days, after which the rate of exit tapers off. In contrast, in the red county, children exit more slowly at first followed by a rapid increase in exits around the 200-day mark, but then the exit rate also flattens out after about 250 days. In both jurisdictions, after 250 days, children are leaving care at about the same rate; in fact, children in both counties seem to languish in care.
The pattern suggests two different opportunities for reducing length of stay: (1) We could make an effort to make the red county’s exit rate in the first 250 days look more like that in the blue county, and/or (2) we could make an effort in both counties to improve the exit rate for those who are in care past 250 days. The dashed line in the graph below shows the target length of stay.
The space between the baseline lengths of stay and the target length of stay represents potentially saveable care days. In the graph below, the space shaded in yellow represents the care days we could save if we were able to make the exit rate before 250 days in the red county comparable to the rate of exit in the blue county. The space shaded in green represents the care days we could save if we were able to improve the rate of exit for longer stayers in both counties.
3. Knowing where care days can be saved and for whom enables you to develop targeted waiver interventions.
The graph above shows two different opportunities for reducing care day use. Therefore, the type of effort required to save the yellow care days (i.e., the intervention) will likely be different from the interventions required to save the green care days.
To save the yellow care days, we would need to know the reason behind the slower rate of exit in the red county and select an intervention that addresses that reason. We would anticipate that, if applied in both jurisdictions, the intervention would probably have a different impact on the red county (the place where we want to see more change) than it would on the blue county (the place that is already performing “better”). To save the green care days, we would need to know the reason behind the stagnation of exits in both counties after 250 days; if the reasons were the same in both counties and we could justify the same theory of change in each, then we could design an intervention that could be applied to both jurisdictions.
4. Knowing how the purchasing power of your waiver allocation varies for different subpopulations enables you to estimate the return on investment in proposed interventions.
As your state sets out to determine whether to implement a particular waiver intervention, it must calculate the cost of the intervention relative to the expected savings and account for the fact that the value of the intervention (i.e., care days saved per dollar spent) will likely vary across subpopulations.
For example, above we noted that an intervention designed to speed permanency before 250 days (the yellow intervention) is likely to have a different effect on the red county than on the blue county; estimating the value of the effect in each jurisdiction provides the state with information it needs to allocate funds where they are likely to have the most benefit. A similar calculation would be called for if the state was trying to decide between the yellow intervention (promoting exits before 250 days) and green intervention (promoting exits after 250 days); if, say, these two interventions cost the same amount of money, but implementing the yellow intervention was expected to yield a greater reduction in care days, the state might think carefully about the ultimate value of the green intervention.
Again, though we have explored the value of a given intervention across counties in the example above, geographic subpopulations are not the only ones to consider. The effect of a particular intervention on infants is likely to be different than its effect on grade school children or teens; the effect of an intervention on children placed with kin is likely to be different than its effect on children placed with non-kinship foster parents; the effect of an intervention on children with mild to moderate mental health needs is likely to be different than its effect on children with more severe needs; and so on.
The Data Center’s suite of online analytic tools can help your state explore care day use as described in this post. Click here to learn more about how the FCDA web tool can help you generate graphs like the ones you see above. Click here to learn how to run the web tool’s Baseline Exits and Care Days report, which highlights the relationship between admissions, duration, and care day use. Click here to register for our February 21st webinar during which Fred Wulczyn will discuss the Data Center’s existing analytic software and products in development.