Analytics helps find better outcomesUsing SAS® for child safety to empower child advocates

As state and local government leaders and community advocates explore how they can use predictive analytics to analyze data to improve child well-being outcomes, many questions have surfaced.

For years, government youth services agencies across the United States have used actuarial risk and need assessment tools. These technologies use predictive analytics to identify static risk factors that indicate potential problems. Although these tools are a good starting point, there are many problems associated with their implementation, such as required fidelity, worker bias, amount of time to complete, etc.

A more operational analytics approach for risk assessment uses both static and dynamic risk factors to calculate the risk of maltreatment. More timely, dynamic data increases accuracy, improves scoring and limits worker bias.

The SAS approach for child safety allows government agencies to apply these operational analytics. Figure 1 demonstrates the differences between the previous, actuarial approach and an operational method for analytics.

Get a broader, more comprehensive

understanding of a child – and improve

child well-being outcomes – by

accessing and integrating and ana -

lyzing data from all available sources.

Addressing the perceived limits of analyticsAs government leaders and community advocates look to advanced analytic approaches to refine the delivery of human services, they often meet with barriers from those who are unclear on how analytics works – and what it can deliver. These misperceptions typically fall into one of four areas:

• False positives.

• Racial bias and disparity.

• Equation versus comprehensive process.

• Belief that current, non-analytics tools are sufficient.

Misperception: Analytics will yield a high number of false positives, giving workers added workChild safety models use a multitude of risk factors, which vary in complexity. These aren’t just yes/no responses. Risk factors are intuitive. By factoring in the conditions of

thousands of cases and their environment, analytics can determine a child’s situation when facing similar risk factors. For example, analytics can mine prior maltreatment alle-gations by the child’s primary caregivers.

To be in the highest-risk segments, a child needs to have extreme values in a multitude of risk factors. When identified, the risk of general harm to a child is also elevated, not just the risk of fatality. And the modeling for fatalities is traditionally focused on just fatal events. Analytics can help take into account other significant events (for example, a child ends up in ICU due to abuse) to help provide a more accurate picture of the situation.

With this information informing the analytics, the risk can increase and decrease given all of these factors. SAS Analytics helps identify cases that are at an increased risk, allowing for mitigation earlier than before. And as risk factors decrease (from a change in the child’s living arrangement or some other factor), the analytics can adjust recommen-dations accordingly.

Analytics helps child advocates more accu-rately find cases that require intervention.SAS for child safety typically identifies less than 5 percent of overall cases as being at the highest level of risk of maltreatment and/or fatality. As a comparison, current validated actuarial risk and needs assessment have routinely identified from 25 percent to 30 percent of cases as being high to very high risk of maltreatment – a difference in false positives of up to 500 percent.

Misperception: Predictive analytics can introduce racial bias and disparity within communitiesRacial disparity is not apparent in fatalities or maltreatment. SAS has been involved in multiple jurisdictions and has found no significant difference in maltreatment and/or fatality risk for black versus white households.

This race effect, while statistically significant, is eclipsed by other risk factors like prior maltreatment allegations. As a result, racial factors are of little value in risk models.

Misperception: Analytics is just a mathematical equation, not a comprehen-sive processAnalytics uses elements of statistics and mathematics, but the field of study is more than that. For child safety, analytics helps deliver a “golden record” of an individual, using accurate and complete data to find trends and patterns a human might miss. Once these cases are identified, it allows child advocates to be proactive. And analytics can encompass multiple scenarios (fatality, maltreatment, permanency, home-lessness, etc.) to build a more complete picture of each case.

Actuarial Tools Operational AnalyticsMinimum requirements for implementation

No minimum requirements

No minimum requirements

Format documentation By hand and/or entered into database

Automated – no worker involvement

Data used to develop model Not local Local data

Accuracy 30-60 percent 75-85 percent

Makes or informs decisions Informs Informs

Frequency of re-assessment Periodic at critical case junctures

Daily

Unbiased/untainted No Yes

Fidelity issues Yes No

Accurate picture of family history

No Yes

Time to assess risk 1-2 hours No staff time needed

Integration of other data sets No Yes

Social network analysis No Yes

Implementation timeline 6-9 months 3-6 months

Figure 1: The difference between traditional actuarial tools and analytics-driven operational methods of assessing child safety.

Collections of risk factors determine the level of risk, not individual facts or flags. This eliminates the one-size-fits-all approach to case response (e.g., children under age five are considered high-risk and require a full investigation). Instead, a risk model can apply a score based on a number of factors to provide a more precise evaluation of each case. A high risk score means there is multi-tude of simultaneous risk factors present. The score – simply put – brings this to the attention of the case worker to help inform decisions, coupled with professional judgment.

Misperception: Current, more static actuarial models are sufficientOne of the key benefits of analytics is that it can learn from the cases in the system and use a host of factors to determine the severity of each case. In fact, case workers

themselves are a form of analytic models (biological, not mathematical). Unfortunately, they can’t process more than a small set of data about the cases they are working. So using the judgment of case workers – consensus decision making – will underper-form more operational analytics due to:

• High case worker turnover rates.

• Limited information available to the cases due to incorrect data (data quality issues) or overwhelming amounts of data.

• High variations in case worker experi-ence and judgment.

• Case worker bias during the scoring process when using actuarial methods.

• Case workers potentially manipulating the scores to force certain actions like removals.

Operational analytical models work across the risk spectrum and can assist in screening and triaging cases based on available data. In addition, data can include information from a multitude of agencies to better inform the model and decisions, which allows for more accurate risk assessments in situations with limited report histories and offers a consistent summary of the typical risk observed for a child based on the data available to the agency.

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