Prescriptive variability of drugs by general practitioners

<div><p>Prescription drug spending is growing faster than any other sector of healthcare. However, very little is known about patterns of prescribing and cost of prescribing between general practices. In this study, we examined variation in prescription rates and prescription costs through time for 55 GP surgeries in Northern Ireland Western Health and Social Care Trust. Temporal changes in variability of prescribing rates and costs were assessed using the Mann–Kendall test. Outlier practices contributing to between practice variation in prescribing rates were identified with the interquartile range outlier detection method. The relationship between rates and cost of prescribing was explored with Spearman's statistics. The differences in variability and mean number of prescribing rates associated with the practice setting and socioeconomic deprivation were tested using t-test and <i>F</i>-test respectively. The largest between-practice difference in prescribing rates was observed for Apr-Jun 2015, with the number of prescriptions ranging from 3.34 to 8.36 per patient. We showed that practices with outlier prescribing rates greatly contributed to between-practice variability. The largest difference in prescribing costs was reported for Apr-Jun 2014, with the prescription cost per patient ranging from £26.4 to £64.5. In addition, the temporal changes in variability of prescribing rates and costs were shown to undergo an upward trend. We demonstrated that practice setting and socio-economic deprivation accounted for some of the between-practice variation in prescribing. Rural practices had higher between practice variability than urban practices at all time points. Practices situated in more deprived areas had higher prescribing rates but lower variability than those located in less deprived areas. Further analysis is recommended to assess if variation in prescribing can be explained by demographic characteristics of patient population and practice features. Identification of other factors contributing to prescribing variability can help us better address potential inappropriateness of prescribing.</p></div

Mother, Monster, Mrs, I:A critical evaluation of gendered naming strategies in English sentencing remarks of women who kill

In this article, we take a novel approach to analysing English sentencing remarks in cases of women who kill. We apply computational, quantitative, and qualitative methods from corpus linguistics to analyse recurrent patterns in a collection of English Crown Court sentencing remarks from 2012 to 2015, where a female defendant was convicted of a homicide offence. We detail the ways in which women who kill are referred to by judges in the sentencing remarks, providing frequency information on pronominal, nominative, and categorising naming strategies. In discussion of the various patterns of preference both across and within these categories (e.g. pronoun vs. nomination, title + surname vs. forename + surname), we remark upon the identities constructed through the references provided. In so doing, we: (1) quantify the extent to which members of the judiciary invoke patriarchal values and gender stereotypes within their sentencing remarks to construct female defendants, and (2) identify particular identities and narratives that emerge within sentencing remarks for women who kill. We find that judges refer to women who kill in a number of ways that systematically create dichotomous narratives of degraded victims or dehumanised monsters. We also identify marked absences in naming strategies, notably: physical identification normally associated with narrativization of women’s experiences; and the first person pronoun, reflecting omissions of women’s own voices and narratives of their lived experiences in the courtroom

Temporal variability in the actual cost of prescribed medications per patient.

<p>Each data point (dot): a single practice. Solid, horizontal line inside the box: median of data. Green diamond: mean. Lower and upper "hinges” of the boxplots: 1^st and 3^rd quartiles, respectively. Red, green, and blue lines: trend lines for maximum, average, and minimum values of prescription costs respectively.</p

Temporal variability in the standardized number of prescriptions.

<p>Each data point (dot): a single practice. Solid, horizontal line inside the box: median of data. Green diamond: mean. Lower and upper "hinges” of the boxplots: 1^st and 3^rd quartiles, respectively. Red, green, and blue lines: trend lines for maximum, average, and minimum values of prescription rates respectively. Lower and upper extremes of whiskers: interval boundaries of the non-outliers (black dots). Data outside interval (red dots): outliers.</p

Prescribing rates for practices located in areas of different levels of socio-economic deprivation.

<p>T-test <i>p</i>-value refers to the significance level of differences in mean number of prescribing rates between practices from less and more deprived areas. The <i>p</i>-value of F-test assesses the difference in variances in prescribing rates between practices from less and more deprived areas. Asterisk: Statistically significant difference (<i>p</i> < 0.05) in variability in prescribing rates.</p

Prescribing rates for rural and urban practices.

<p>T-test <i>p</i>-value refers to the significance level of differences in the mean number of prescribing rates between rural and urban practices for all considered time period. The <i>p</i>-value of F-test assesses the difference in variances in prescribing rates between rural and urban practices.</p