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author:("roots, Alan J")
1.  Improving mental health outcomes: achieving equity through quality improvement 
To investigate equity of patient outcomes in a psychological therapy service, following increased access achieved by a quality improvement (QI) initiative.
Retrospective service evaluation of health outcomes; data analysed by ANOVA, chi-squared and Statistical Process Control.
A psychological therapy service in Westminster, London, UK.
People living in the Borough of Westminster, London, attending the service (from either healthcare professional or self-referral) between February 2009 and May 2012.
Social marketing interventions were used to increase referrals, including the promotion of the service through local media and through existing social networks.
Main Outcome Measure(s)
(i) Severity of depression on entry using Patient Health Questionnaire-9 (PHQ9). (ii) Changes to severity of depression following treatment (ΔPHQ9). (iii) Changes in attainment of a meaningful improvement in condition assessed by a key performance indicator.
Patients from areas of high deprivation entered the service with more severe depression (M = 15.47, SD = 6.75), compared with patients from areas of low (M = 13.20, SD = 6.75) and medium (M = 14.44, SD = 6.64) deprivation. Patients in low, medium and high deprivation areas attained similar changes in depression score (ΔPHQ9: M = −6.60, SD = 6.41). Similar proportions of patients achieved the key performance indicator across initiative phase and deprivation categories.
QI methods improved access to mental health services; this paper finds no evidence for differences in clinical outcomes in patients, regardless of level of deprivation, interpreted as no evidence of inequity in the service with respect to this outcome.
PMCID: PMC3979278  PMID: 24521701
quality improvement; mental health; public health; inequalities; outcome assessment (health care)
2.  Statistical process control for data without inherent order 
The XmR chart is a powerful analytical tool in statistical process control (SPC) for detecting special causes of variation in a measure of quality. In this analysis a statistic called the average moving range is used as a measure of dispersion of the data. This approach is correct for data with natural underlying order, such as time series data. There is however conflict in the literature over the appropriateness of the XmR chart to analyse data without an inherent ordering.
We derive the maxima and minima for the average moving range in data without inherent ordering, and show how to calculate this for any data set. We permute a real world data set and calculate control limits based on these extrema.
In the real world data set, permuting the order of the data affected an absolute difference of 109 percent in the width of the control limits.
We prove quantitatively that XmR chart analysis is problematic for data without an inherent ordering, and using real-world data, demonstrate the problem this causes for calculating control limits. The resulting ambiguity in the analysis renders it unacceptable as an approach to making decisions based on data without inherent order.
The XmR chart should only be used for data endowed with an inherent ordering, such as a time series. To detect special causes of variation in data without an inherent ordering we suggest that one of the many well-established approaches to outlier analysis should be adopted. Furthermore we recommend that in all SPC analyses authors should consistently report the type of control chart used, including the measure of variation used in calculating control limits.
PMCID: PMC3464151  PMID: 22867269
Statistical process control (SPC); Individual and moving range (XmR); Ordering of data

Results 1-2 (2)