Using funnel plots to explore variation in cancer mortality across primary care trusts in South-East England

Davies, Elizabeth; Mak, Vivian; Ferguson, Jamie; Conaty, Stephen; Møller, Henrik

doi:10.1093/pubmed/fdn044

Cited by 9 publications

(3 citation statements)

References 6 publications

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“…Funnel plots are an attractive way to present data to policy makers and a good tool to compare performance data, including population data 8. Funnel plots have been used in the developed world in a number of settings, including assessing institutional performance,9 comparing healthcare providers,10 and assessing variations in cardiac11 12 and cancer mortality 13 14. However, to the best of our knowledge, the application of this technique to child mortality rates in the context of low-income and middle-income countries has not been previously conducted.…”

Section: Introductionmentioning

confidence: 99%

Trends, patterns and predictive factors of infant and child mortality in well-performing and underperforming states of India: a secondary analysis using National Family Health Surveys

et al. 2019

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ObjectivesThis paper analyses the patterns and trends in the mortality rates of infants and children under the age of 5 in India (1992–2016) and quantifies the variation in performance between different geographical states through three rounds of nationally representative household surveys.DesignThree rounds of cross-sectional survey data.SettingThe study is conducted at the national level: India and its selected good-performing states, namely Haryana, Kerala, Maharashtra, Punjab and Tamil Nadu, and selected poor-performing states, namely Bihar, Chhattisgarh, Madhya Pradesh and Uttar Pradesh.ParticipantsAdopting a multistage, stratified random sampling, 601 509 households with 699 686 women aged 15–49 years in 2015–2016, 109 041 households with 124 385 women aged 15–49 years in 2005–2006, and 88 562 households with 89 777 ever married women in the age group 13–49 years in 1992–1993 were selected.ResultsThrough the use of maps, this paper clearly shows that the overall trend in infant and child mortality is on a decline in India. Computation of relative change shows that majority of the states have witnessed over 50% reduction in both infant and under-5 mortality rates from National Family Health Survey (NFHS)-I to NFHS-4. However, the improvements are not evenly distributed, and there is huge variation in performance between states over time. Funnel plots show that the most populous states like Uttar Pradesh Bihar and Madhya Pradesh have underperformed consistently across the survey period from 1992 to 2016. Regression analysis comparing high-performing and low-performing states revealed that female infants and women with shorter birth intervals had greater risk of infant deaths in poor-performing states.ConclusionAttempts to reduce infant and child mortality rates in India are heading in the right direction. Even so, there is huge variation in performance between states. This paper recommends a mix of strategies that reduce child and infant mortality among the high-impact states where the biggest improvements can be expected, including the need to address neonatal mortality.

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Section: Introductionmentioning

confidence: 99%

Trends, patterns and predictive factors of infant and child mortality in well-performing and underperforming states of India: a secondary analysis using National Family Health Surveys

et al. 2019

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“…In addition, control limit lines are drawn, creating the funnel. In recent years funnel plots have been proposed as one of the preferred tools for quality control and performance comparisons [8–15]. Their main advantages over other methods are that they do not promote ranking, they accommodate variable sample sizes, and the data are visually displayed for an intuitive interpretation.…”

Section: Discussionmentioning

confidence: 99%

Benchmarking inappropriate empirical antibiotic treatment

Kariv

Shani

Muchtar

et al. 2013

Clinical Microbiology and Infection

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Inappropriate empirical antibiotic treatment for severe infections is associated with increased mortality. Superfluous treatment is associated with resistance induction. We aimed to define acceptable rates of inappropriate empirical antibiotic treatment. We included all prospective cohort studies published between 1975 and 2009 reporting the proportion of appropriate and inappropriate empirical antibiotic treatment of microbiologically documented infections. Studies were identified in PubMed and in reference lists of included studies. Funnel plots were drawn using the proportion of inappropriate empirical treatment as the effect size. A pooled estimate of inappropriate empirical antibiotic treatment was calculated using a β-binomial model. Control limits were calculated with the overdispersion factor technique and 20% winsorized data. Heterogeneity was assessed through subgroup analysis for categorical moderators and meta-regression for continuous variables. Eighty-seven studies, comprising 92 study groups, with 27 628 patients met inclusion criteria. The pooled rate of inappropriate empirical antibiotic treatment was 28.6% (95% CI 25.4-31.8). Funnel plot analysis yielded a dispersed graph with only 37 (40%) studies falling within the control limits. Using the overdispersion factor technique with 20% winsorizing, 79 (86%) studies fell within the control limits. None of the clinical or methodological factors could explain the large heterogeneity observed. The funnel plot presented can be used to benchmark rates of inappropriate empirical antibiotic treatment. Based on the control limits found, at least 500 patients should be evaluated before establishing a local rate. Lower and higher than expected rates might indicate overly aggressive treatment or poor performance, respectively.

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“…In addition, serial funnel plots can be constructed to monitor changes in patient satisfaction scores over time. Funnel plots have proven useful to identify publication bias for meta‐analysis 3,18–20 and for assessing performance outcomes 21–24 …”

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confidence: 99%

Application of Statistical Process Control to Physician‐specific Emergency Department Patient Satisfaction Scores: A Novel Use of the Funnel Plot

Griffen

Callahan

Markwell

et al. 2012

Academic Emergency Medicine

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Objectives: Emergency department (ED) patient satisfaction remains a high priority for many hospitals. Patient surveys are a common tool for measuring patient satisfaction, and process improvement efforts are aimed at improving patient satisfaction scores. In some institutions, patient satisfaction scores can be calculated for each emergency physician (EP). ED leaders are faced with the task of interpreting individual as well as group physician scores to identify opportunities for improvement. Analysis of these data can be challenging because of the relatively small numbers of returned surveys assignable to a single physician, variable numbers of surveys returned for each physician and high standard deviations (SDs) for individual physician scores. The objective was to apply statistical process control methodology to analyze individual as well as group physician patient satisfaction scores. The novel use of funnel plots to interpret individual physician patient satisfaction scores, track individual physician scores over two successive 8-month periods, and monitor physician group performance is demonstrated.Methods: Patient satisfaction with physicians was measured using Press Ganey surveys for a 65,000-volume ED over two successive 8-month periods. Using funnel plots, individual physician patient satisfaction scores were plotted against the number of surveys completed for each physician for each 8-month period. Ninety-fifth and 99th percentile control limits were displayed on the funnel plots to illustrate individual physician patient satisfaction scores that are within, versus those that are outside of, expected random variation. Control limits were calculated using mean patient satisfaction scores and SDs for the entire group of physicians. Additional funnel plots were constructed to demonstrate changes in individual physicians' patient satisfaction scores as a function of increasing numbers of returned surveys and to illustrate changes in the group's patient satisfaction scores between the first and second 8-month intervals after the institution of process improvement efforts aimed at improving patient satisfaction.Results: For the first 8-month period, 34,632 patients were evaluated in and discharged from the ED, with 581 surveys returned for 21 physicians. The mean (±SD) overall group physician patient satisfaction score was 81.8 (±24.7). Returned surveys per physician ranged from 2 to 58. For the second period, 34,858 patients were evaluated and discharged from the ED, with 670 patient satisfaction surveys returned for 20 physicians. The mean (±SD) overall physician score for all surveys returned during the second period was 85.0 (±22.2). Returned surveys per physician ranged from 8 to 65. Conclusions:The application of statistical control methodology using funnel plots as a means of analyzing ED group and physician patient satisfaction scores was possible. The authors believe that using funnel plots to analyze scores graphically can rapidly help determine the significance of individual physician p...

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Using funnel plots to explore variation in cancer mortality across primary care trusts in South-East England

Cited by 9 publications

References 6 publications

Trends, patterns and predictive factors of infant and child mortality in well-performing and underperforming states of India: a secondary analysis using National Family Health Surveys

Trends, patterns and predictive factors of infant and child mortality in well-performing and underperforming states of India: a secondary analysis using National Family Health Surveys

Benchmarking inappropriate empirical antibiotic treatment

Application of Statistical Process Control to Physician‐specific Emergency Department Patient Satisfaction Scores: A Novel Use of the Funnel Plot

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