legacies of this landmark legislation.” Screening—especially risk screening near the time of diagnosis—is critical for any efforts at preventive intervention.
Criteria for Effective Screening
Morrison (1992, 1998) suggested a number of important criteria for screening.
These include:
1. Is the problem of sufficient concern and is prevalence high enough to warrant screening?
2. Can problems or risk for problems be detected at screening?
3. Are there reliable (sensitive and specific) methods for screening?
4. Is screening feasible?
5. Is screening acceptable to patients and their families?
6. Is there intervention for identified problems?
Each of these factors is addressed in turn below.
Is the problem of sufficient concern? As noted in Chap. 1, nonadherence can have a dramatic effect on the lives of individual patients, causing or contributing to serious complications of illness, both acute and long-term. Nonadherence also has a substan- tial impact on the health of populations throughout the world (Sabate 2003). Preva- lence rates suggest that some degree of nonadherence is more common than not.
Psychosocial sequelae of illness also have dramatic effects on the health and well- being of individuals and populations. Taking just depression as an example, current guidelines of the US Preventive Services Task Force (2009) note that depression in youth “is a disabling condition that is associated with serious long-term morbidities and risk of suicide. However, the majority of depressed youth are undiagnosed and untreated.” Depression is nearly twice as common in youth with diabetes, and it is associated with nonadherence and poor illness control (Hood et al. 2006).
Can problems or risk of problems be detected at diagnosis? Are there reliable screening tools? Problems with adapting to a chronic illness often occur at diag- nosis. Nearly a third of children develop an adjustment disorder at disease diagnosis (Cameron et al. 2007). While adjustment problems tend to resolve over the first year, children who experience them are at greater risk for subsequent problems with depression and anxiety (Grey et al. 1995). Acute stress symptoms at disease diagnosis are also elevated for a number of conditions (e.g., Cline et al. 2011) and predict later risk for PTSD.
A number of approaches have been developed for assessing risk at disease di- agnosis. Kazak et al. (2001) developed the Psychosocial Assessment Tool (PAT) for screening children diagnosed with cancer. The measure was able to reliably categorize risk according to a public health framework at three levels: Universal, Targeted, and Clinical (Fig. 12.1). Universal patients had adequate resources for coping, Targeted patients had identified risk factors, and Clinical patients had sig- nificant current psychosocial stressors. In follow-up work Kazak et al. 2003, 2011;
Pai et al, 2008), they demonstrated that the PAT predicted psychosocial resource utilization and its cutoff scores correlated with standard measures of parent acute stress, child behavior problems, and family conflict.
For children with type 1 diabetes, our group developed and validated a 9-item interview to assess risk for poor glycemic control in newly-diagnosed type 1 dia- betes patients, the Risk Index for Poor Glycemic Control (RI-PGC; Schwartz et al.
2013a). The measure was shown to have good sensitivity and specificity for poor glycemic control (A1c 9.5 %), and was also able to identify patients at risk for DKA. Most importantly, it was designed for easy use and scoring by physicians and other medical providers. The nine items are scored as a simple sum which translates directly into an estimation of the absolute increase in risk associated with that score (Table 12.1).
Table 12.1. Use of the Risk Index for Poor Glycemic Control (RI-PGC) to estimate absolute increase in risk for poor glycemic control (HbA1c 9.5 %), emergency room (ER) visits, and dia- betic ketoacidosis (DKA). Predicted values are approximations accurate to within 10 %. All values are rounded to the nearest multiple of five for ease of use. (From Schwartz et al. 2013a)
RI-PGC score Poor glycemic control (%) ER visits (%) DKA (%)
0 + 0 + 0 + 0
1 + 10 + 5 + 5
2 + 20 + 15 + 20
3 + 35 + 25 + 20
4 + 40 + 30 + 30
5 > + 40 > + 30 + 40
Fig. 12.1 The pediatric psychosocial preventative health model. (PPPHM; Kazak 2006)
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A more extensive interview that provides broader coverage of psychosocial risk factors has also been developed. The Psychosocial Risk Screening Measure (PRiSM) is a 36-item semi-structured interview assessing risk in five domains known to be important risk factors for poor diabetes control (Schwartz et al. 2010): sociodemo- graphic factors such as race/ethnicity and SES; child problems (e.g., behavior or mood problems); family conflict; caregiver problems (e.g., depression); and antici- pated diabetes-related problems (e.g., anticipated conflict over diabetes manage- ment). These different domains have been organized into a “simple model” of risk for nonadherence (Fig. 12.2) that was used to guide development of the PRiSM screening tools (for a detailed description of the initial development of the screening tool, see Schwartz, Axelrad et al. 2011). The model assumes that the critical “ac- tor” is the child/parent team, whose management abilities directly affect adherence, although they are also influenced by environmental factors and the healthcare team.
The PRiSM has been field-tested for feasibility and acceptability (Schwartz, Cline et al. 2011) and is currently being validated. A comprehensive training man- ual (and a supervisor’s guide) that provides detailed instructions for using the RI- PGC and the PRiSM has been peer-reviewed (Schwartz et al. 2014) and is available for free download from MedEd Portal at www.mededportal.org/publication/9643.
Moreover, we are also now developing modules for use with other pediatric popula- tions, beginning with children with cancer.
Can problems be reliably identified after diagnosis? In established patients, there are many evidence-based assessment tools for nonadherence that have been validated in pediatric patients (see Quittner et al. 2008 for review). Typically these are questionnaires that are completed by the patient (if old enough) and/or the parent.
Psychosocial screening tools can also be used to detect risk for nonadherence when there is a well documented relationship between the risk factor and the out- come. For example, Hilliard et al. (2011) used brief, validated measures of depres- sion (Children’s Depression Inventory) and anxiety (the state scale of the State-Trait Anxiety Inventory for Children) to predict adherence (blood glucose monitoring frequency) and glycemic control (HbA1c) in youth age 13–18 with type 1 diabetes
Fig. 12.2 The “Simple Model” of risk for nonadherence. (Schwartz et al. 2010)
1 year later. Symptoms of depression predicted reduced blood glucose monitoring, and anxiety predicted poorer glycemic control, possibly due to concurrent associa- tions with stress.
Healthcare providers can also assess for nonadherence more informally. While informal assessment is more prone to individual biases and lack of sensitivity to un- covering problems, it is often the only option available to clinicians in busy routine practice. Moreover, simply asking patients about current problems can be valuable in its own right, as it has been shown to relate to an improved therapeutic relationship.
Healthcare providers can start by acknowledging that most patients struggle with managing aspects of the medical regimen, and then ask what parts of the regimen are difficult for the patient. Assessing specific behaviors (e.g., frequency of blood glucose checks) is critical. Providers can also ask more general questions about the burden of illness management. Peyrot and Rubin (2007) suggest asking patients:
• Do you feel overwhelmed or burned out by the demands of illness management?
• Do you get the support you need from your family for illness management?
A downside of informal questioning is that it can be difficult to interpret findings and compare findings across patients, and important areas might be missed.
Is screening feasible and acceptable to patients and their families? Feasibility of screening is threatened by its potential burden on patients/families, healthcare providers, and the system of routine care. As noted earlier, screening services are often not reimbursable, and they can be quite resource-intensive in terms of admin- istration, scoring, and interpretation. To address these problems in screening newly diagnosed cancer patients, Kazak et al. (2011) arranged for nursing staff to admin- ister the measure to patients, who completed it as a questionnaire. They reported an 88 % completion and return rate, and 98 % of cases were scored, reviewed, and shared with the treatment team within a couple of days. They also reported a high degree of buy-in from nursing leadership and staff.
We took a different approach to screening newly diagnosed type 1 diabetes patients. For clinical reasons, we believed it was important to use a face-to-face interview approach rather than a questionnaire. Conducting interviews allowed us to provide appropriate support to families throughout the assessment process;
a secondary goal was to put a “face” on psychology to reduce potential stigma (Schwartz, Axelrad et al. 2011). To staff this service, we incorporated the screening into our training program for pediatric psychology pre-doctoral interns and fellows.
In an initial feasibility study (Schwartz, Cline et al. 2011), we were able to screen 75 % of patients, with an almost 97 % participation rate (121 out of 125 families approached). A subset of families ( n = 30) completed satisfaction ratings, with a satisfaction score of 90 %, reflecting an average rating of 4.5 out of 5 (Very Good to Excellent). No one rated the service “fair” or “poor.”
Is there effective intervention for identified problems? As discussed in Chap. 4, there are a multitude of effective, evidence-based approaches to treating nonadher- ence in pediatric patients that also have beneficial effects on children’s health. They do tend to be resource intensive, however, typically requiring implementation by a
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