Primary Outcomes (end points)
Our primary outcomes are as follows. Outcomes will be measured as (1) difference between treatment and control, and (2) difference conditional on child’s underlying risk score. Analysis will be conducted at the referral, family, and child level.
Decision-Making:
- Number of total characters, discussion characters, and child welfare history characters typed during team decision process. These character counts will help to measure the effect of the tool on the attention given to different aspects of a referral and the referral as a whole, and potential effort spillovers to control cases.
- Time per team decision. Constructed using timestamps between cases, where the data are reliable.
- Rate of screen-in (investigation)
- Rate of screen-in by type: immediate, 3-day, 5-day, HRA, FAR (intensive margin of investigations)
- Rate of services provided/recommended
- Number of days until case closed
Investigations:
- Fraction of families found
- Fraction of investigations substantiated for abuse or neglect
- Fraction of investigations provided services within 1, 2, 3, 6, 12, and 24 months
- Fraction of investigations removed within 1, 2, 3, 6, 12, and 24 months
- Fraction of FAR changed to HRA (measure of how accurately case was originally classified)
- Rate of investigation in families where no services are open
False negatives:
- Fraction of screened-out cases re-referred within 1, 2, 3, 6, 12, and 24 months
- Fraction of screened out cases re-referred within 1, 2, 3, 6, 12, and 24 months with egregious injury (near fatal, death, either)
Overall:
-Fraction of total cases re-referred (or removed from home) within 1, 2, 3, 6, 12, and 24 months
-Fraction of total cases re-referred (or removed from home) within 1, 2, 3, 6, 12, and 24 months with egregious injury
- Fraction of investigations provided services within 1, 2, 3, 6, 12, and 24 months
-Fraction of cases receiving family visitor or CCR
- Fraction of total referrals with hospital or Medicaid claim for broken bone, ED visit, avoidable injury, unavoidable injury, any injury, well-child visit, vaccination, and asthma ED visit within 1, 2, 3, 6, 12, 24, and 36 months
- Child truancy and standardized test scores within 1, 2, 3, 6, 12, 24, and 36 months
*** 09/22/2022: We are preparing to receive linked hospital inpatient and ED records, as well as detailed text data from discussions, and wanted to pre-specify our main analyses for these records in advance as we know have just gotten access to the codebooks. All text below was added on 09/22/2022.
For hospital outcomes, to clarify what we had previously specified, we plan to examine both the extensive and intensive margins of child hospital visits (any instance and number of instances for each child and for all children in the household, as well as being first ICD code listed if applicable) over time of:
• ED visits
• Admissions of priority type: Emergency, Urgent, and Trauma
• Visits listing any injury, any intentional and any unintentional injuries
• Preventive medicine codes including well-child visit and vaccination (if not too few outpatient records)
• Preventable (vs unpreventable) child ED visits, using the Ambulatory care-sensitive conditions (ACSC) which are conditions that could be managed or addressed well in outpatient settings if attended to (such as asthma, dehydration, anaemia, etc)
• ICD-10 codes that are both suspected and suggestive of child maltreatment by the existing literature. ICD-10-CM codes are for suspected and confirmed maltreatment and so have been shown to underestimate instances of child maltreatment (Hughes et al. 2021), so we will look at a broader set of ICD codes suggestive of maltreatment. ICD-10 codes that are considered predictive of future mortality using the trauma mortality prediction model (TMPM). We plan to examine effects for all kids and kids 11-17years old as – to the best of our current knowledge – the TMPM has only been validated for kids 11-17years (Cassidy et al. 2014).
• ICD-10 codes for child cancer that are considered placebos (where we do not expect to find an effect).
• ICD-10 codes indicating exposure to substances (e.g., drugs)
• Cost of medical care provided paid for by public insurers, by non-insured clients and gone unpaid
We also plan to receive de-identified notes from the team discussions. We hope to use natural language text processing machine learning methods to explore rigorously what might be changing in the discussions. Of particular interest to us is to assess whether the tool changes workers’ attention and sensitivity to the severity of the current allegation (such as words suggesting or indicating a potential child injury).
Contrary to what we had originally hoped and listed in the original pre-register, accessing school records will not be possible.