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Table 1 Statistical terminology for validated instruments and interpretation of child mental health and psychosocial support (MHPS) research in Low and Middle Income Countries (LAMIC)
| Concept | Calculation | Application to child MHPS research in LAMIC | |
|---|---|---|---|
| Area under the curve (AUC) | The probability that the instrument will yield a higher score for a randomly chosen individual with the target condition than for a randomly chosen individual without the condition | Area under the graph with sensitivity on the Y axis by one minus specificity on the X axis | The ideal instrument for screening and/or evaluation of an intervention for children in LAMIC will have a high AUC (close to 1.0). The closer to 0.5 the AUC, the less utility of the screening instrument and the less cost-effectiveness of screening |
| Cutoff score | The score on the instrument chosen to differentiate cases from non-cases; may be chosen to maximize specificity, sensitivity, or both | Chosen by researcher based on ROC curve | Based on the type of intervention program, a higher or lower cutoff score could be chosen to prioritize sensitivity or specificity |
| Sensitivity | The ability of an instrument, at a selected cutoff score, to identify persons with a target condition. At a sensitivity of 1.0, all persons with the condition are identified, and there are no false negatives |
| Instruments with high sensitivity are ideal to screen children when trying to identify the majority of children in distress needing intervention. At high sensitivity, few children with a condition will be mistakenly deprived of the intervention |
| Specificity | The ability of an instrument to include persons who do not have the target condition below the cutoff score. At a specificity of 1.0, no persons without a target condition score above the cutoff |
| Instruments with high specificity minimize the number of children who are incorrectly identified with a high score, but who do not have the target condition. Specificity is a concern when there are negative consequences to being inappropriately included in an intervention, such as stigma or high expense |
| Positive predictive value (PPV) | The proportion of persons with scores above cutoff who are correctly classified as having the target condition compared to all persons who score above the cutoff |
| PPV produces more accurate cost estimates of improperly including participants than specificity alone because of accounting for prevalence of a condition in the target population |
| Negative predictive value (NPV) | The proportion of persons who score below the selected cutoff who do not have the target condition compared to all persons below the cutoff |
| NPV is used to determine the proportion improperly excluded from an intervention, taking prevalence into account. NPV helps to estimate the cost of not including a proportion of children in an intervention |
| Reliability (Cronbach's alpha) | A measures of internal consistency based on the degree of inter-correlation among all items on a scale |
| Reliability is important for newly developed measures or adapted measures in LAMIC to help identify items that may not be culturally or contextually relevant, such as stomachaches in Nepal |
- Abbreviations: Receiver operating characteristic (ROC) curve is the graphical plot of sensitivity and 1-specificity. True Positives (TP) are persons who score above the selected cutoff and have the target condition; True Negatives (TN) are persons who score below the selected cutoff and do not have the target condition; False Positives (FP) are persons who score above the selected condition but do not have the target condition; False Negatives (FN) are persons who score below the selected cutoff but do have the target condition. For the Cronbach's alpha calculation, K is the number of instrument items,
is the average of all covariances between the components, and 
is the average variance.
is the average of all covariances between the components, and 
is the average variance.