Identifying and quantifying variation between healthcare organisations and geographical regions: Using mixed-effects models

This is the final version. Available on open access from BMJ Publishing Group via the DOI in this recordWhen the degree of variation between healthcare organisations or geographical regions is quantified, there is often a failure to account for the role of chance, which can lead to an overestimation of the true variation. Mixed-effects models account for the role of chance and estimate the true/underlying variation between organisations or regions. In this paper, we explore how a random intercept model can be applied to rate or proportion indicators and how to interpret the estimated variance parameter.Public Health Englan

Do Differential Response Rates to Patient Surveys Between Organizations Lead to Unfair Performance Comparisons?: Evidence From the English Cancer Patient Experience Survey.

BACKGROUND: Patient surveys typically have variable response rates between organizations, leading to concerns that such differences may affect the validity of performance comparisons. OBJECTIVE: To explore the size and likely sources of associations between hospital-level survey response rates and patient experience. RESEARCH DESIGN, SUBJECTS, AND MEASURES: Cross-sectional mail survey including 60 patient experience items sent to 101,771 cancer survivors recently treated by 158 English NHS hospitals. Age, sex, race/ethnicity, socioeconomic status, clinical diagnosis, hospital type, and region were available for respondents and nonrespondents. RESULTS: The overall response rate was 67% (range, 39% to 77% between hospitals). Hospitals with higher response rates had higher scores for all items (Spearman correlation range, 0.03-0.44), particularly questions regarding hospital-level administrative processes, for example, procedure cancellations or medical note availability.From multivariable analysis, associations between individual patient experience and hospital-level response rates were statistically significant (P<0.05) for 53/59 analyzed questions, decreasing to 37/59 after adjusting for case-mix, and 25/59 after further adjusting for hospital-level characteristics.Predicting responses of nonrespondents, and re-estimating hypothetical hospital scores assuming a 100% response rate, we found that currently low performing hospitals would have attained even lower scores. Overall nationwide attainment would have decreased slightly to that currently observed. CONCLUSIONS: Higher response rate hospitals have more positive experience scores, and this is only partly explained by patient case-mix. High response rates may be a marker of efficient hospital administration, and higher quality that should not, therefore, be adjusted away in public reporting. Although nonresponse may result in slightly overestimating overall national levels of performance, it does not appear to meaningfully bias comparisons of case-mix-adjusted hospital results

Do Differential Response Rates to Patient Surveys between Organizations Lead to Unfair Performance Comparisons? Evidence from the English Cancer Patient Experience Survey

This is the final published version. Available from Lippincott Williams & Wilkins via the DOI in this record.Background: Patient surveys typically have variable response rates between organizations, leading to concerns that such differences may affect the validity of performance comparisons. Objective: To explore the size and likely sources of associations between hospital-level survey response rates and patient experience. Research Design, Subjects, and Measures: Cross-sectional mail survey including 60 patient experience items sent to 101,771 cancer survivors recently treated by 158 English NHS hospitals. Age, sex, race/ethnicity, socioeconomic status, clinical diagnosis, hospital type, and region were available for respondents and nonrespondents. Results: The overall response rate was 67% (range, 39% to 77% between hospitals). Hospitals with higher response rates had higher scores for all items (Spearman correlation range, 0.03-0.44), particularly questions regarding hospital-level administrative processes, for example, procedure cancellations or medical note availability. From multivariable analysis, associations between individual patient experience and hospital-level response rates were statistically significant (P < 0.05) for 53/59 analyzed questions, decreasing to 37/59 after adjusting for case-mix, and 25/59 after further adjusting for hospital-level characteristics. Predicting responses of nonrespondents, and re-estimating hypothetical hospital scores assuming a 100% response rate, we found that currently low performing hospitals would have attained even lower scores. Overall nationwide attainment would have decreased slightly to that currently observed. Conclusions: Higher response rate hospitals have more positive experience scores, and this is only partly explained by patient casemix. High response rates may be a marker of efficient hospital administration, and higher quality that should not, therefore, be adjusted away in public reporting. Although nonresponse may result in slightly overestimating overall national levels of performance, it does not appear to meaningfully bias comparisons of case-mixadjusted hospital results.Cancer Research U

Drivers of overall satisfaction with primary care: Evidence from the English General Practice Patient Survey

This is the final version. Available from Wiley via the DOI in this record.Background/objectives: To determine which aspects of primary care matter most to patients, we aim to identify those aspects of patient experience that show the strongest relationship with overall satisfaction and examine the extent to which these relationships vary by socio-demographic and health characteristics. Design/setting: Data from the 2009/10 English General Practice Patient Survey including 2 169 718 respondents registered with 8362 primary care practices. Measures/analyses: Linear mixed-effects regression models (fixed effects adjusting for age, gender, ethnicity, deprivation, self-reported health, self-reported mental health condition and random practice effect) predicting overall satisfaction from six items covering four domains of care: access, helpfulness of receptionists, doctor communication and nurse communication. Additional models using interactions tested whether associations between patient experience and satisfaction varied by socio-demographic group. Results: Doctor communication showed the strongest relationship with overall satisfaction (standardized coefficient 0.48, 95% CI = 0.48, 0.48), followed by the helpfulness of reception staff (standardized coefficient 0.22, 95% CI = 0.22, 0.22). Among six measures of patient experience, obtaining appointments in advance showed the weakest relationship with overall satisfaction (standardized coefficient 0.06, 95% CI = 0.05, 0.06). Interactions showed statistically significant but small variation in the importance of drivers across different patient groups. Conclusions: For all patient groups, communication with the doctor is the most important driver of overall satisfaction with primary care in England, along with the helpfulness of receptionists. In contrast, and despite being a policy priority for government, measures of access, including the ability to obtain appointments, were poorly related to overall satisfaction.UK Department of HealthNational Institute for Health Research (NIHR

On modelling the constitutive and damage behaviour of highly non-linear bio-composites - Mesh sensitivity of the viscoplastic-damage law computations

The large strain fracture of non-linear complex solids concerns a wide range of applications, such as material forming, food oral processing, surgical instrumental penetration as well as more recently, the design of biodegradable composites for packaging and bio-medical use. Although simulations are a powerful tool towards understanding and designing such processes, modelling ductile fracture in materials such as soft natural composites imposes a new challenge, particularly when the fracture patterns cannot be pre-defined. Here we bring to light new information on these aspects of benefit to the multidisciplinary community, by characterising and modelling the deformation and fracture of short cellulose fibre starch extruded composites. Hyperviscoelastic-Mullins damage laws show merits in modelling such complex systems. Yet they are inferior to a viscoplastic-damage law able to capture exactly their highly non-linear, rate dependent and pressure dependent pseudo-plastic stress-strain response. The viscoplastic-damage law also predicts fracture based on experimental toughness values without pre-specifying the crack path in a Finite Element (FE) model, displaying superiority over the conventional cohesive zone approach. Yet, despite using a toughness parameter to drive crack propagation, spurious mesh dependency is still observed while other previously unreported sources of error imposed by the finite element aspect ratio are also highlighted. The latter is rectified by developing a novel numerical strategy for calculating the characteristic element length used in the damage computations. Inherent mesh dependency suggests that non-local damage models may be essential to model this newly investigated class of natural composites

Development and validation of the Cambridge Multimorbidity Score

BACKGROUND: Health services have failed to respond to the pressures of multimorbidity. Improved measures of multimorbidity are needed for conducting research, planning services and allocating resources. METHODS: We modelled the association between 37 morbidities and 3 key outcomes (primary care consultations, unplanned hospital admission, death) at 1 and 5 years. We extracted development (n = 300 000) and validation (n = 150 000) samples from the UK Clinical Practice Research Datalink. We constructed a general-outcome multimorbidity score by averaging the standardized weights of the separate outcome scores. We compared performance with the Charlson Comorbidity Index. RESULTS: Models that included all 37 conditions were acceptable predictors of general practitioner consultations (C-index 0.732, 95% confidence interval [CI] 0.731-0.734), unplanned hospital admission (C-index 0.742, 95% CI 0.737-0.747) and death at 1 year (C-index 0.912, 95% CI 0.905-0.918). Models reduced to the 20 conditions with the greatest combined prevalence/weight showed similar predictive ability (C-indices 0.727, 95% CI 0.725-0.728; 0.738, 95% CI 0.732-0.743; and 0.910, 95% CI 0.904-0.917, respectively). They also predicted 5-year outcomes similarly for consultations and death (C-indices 0.735, 95% CI 0.734-0.736, and 0.889, 95% CI 0.885-0.892, respectively) but performed less well for admissions (C-index 0.708, 95% CI 0.705-0.712). The performance of the general-outcome score was similar to that of the outcome-specific models. These models performed significantly better than those based on the Charlson Comorbidity Index for consultations (C-index 0.691, 95% CI 0.690-0.693) and admissions (C-index 0.703, 95% CI 0.697-0.709) and similarly for mortality (C-index 0.907, 95% CI 0.900-0.914). INTERPRETATION: The Cambridge Multimorbidity Score is robust and can be either tailored or not tailored to specific health outcomes. It will be valuable to those planning clinical services, policymakers allocating resources and researchers seeking to account for the effect of multimorbidity

Sexual Minorities in England Have Poorer Health and Worse Health Care Experiences: A National Survey

This is the final published version. Available from Springer via the DOI in this record.BACKGROUND: The health and healthcare of sexual minorities have recently been identified as priorities for health research and policy. OBJECTIVE: To compare the health and healthcare experiences of sexual minorities with heterosexual people of the same gender, adjusting for age, race/ethnicity, and socioeconomic status. DESIGN: Multivariate analyses of observational data from the 2009/2010 English General Practice Patient Survey. PARTICIPANTS: The survey was mailed to 5.56 million randomly sampled adults registered with a National Health Service general practice (representing 99 % of England’s adult population). In all, 2,169,718 people responded (39 % response rate), including 27,497 people who described themselves as gay, lesbian, or bisexual. MAIN MEASURES: Two measures of health status (fair/poor overall self-rated health and self-reported presence of a longstanding psychological condition) and four measures of poor patient experiences (no trust or confidence in the doctor, poor/very poor doctor communication, poor/very poor nurse communication, fairly/very dissatisfied with care overall). KEY RESULTS: Sexual minorities were two to three times more likely to report having a longstanding psychological or emotional problem than heterosexual counterparts (age-adjusted for 5.2 % heterosexual, 10.9 % gay, 15.0 % bisexual for men; 6.0 % heterosexual, 12.3 % lesbian and 18.8 % bisexual for women; p < 0.001 for each). Sexual minorities were also more likely to report fair/poor health (adjusted 19.6 % heterosexual, 21.8 % gay, 26.4 % bisexual for men; 20.5 % heterosexual, 24.9 % lesbian and 31.6 % bisexual for women; p < 0.001 for each). Adjusted for sociodemographic characteristics and health status, sexual minorities were about one and one-half times more likely than heterosexual people to report unfavorable experiences with each of four aspects of primary care. Little of the overall disparity reflected concentration of sexual minorities in low-performing practices. CONCLUSIONS: Sexual minorities suffer both poorer health and worse healthcare experiences. Efforts should be made to recognize the needs and improve the experiences of sexual minorities. Examining patient experience disparities by sexual orientation can inform such efforts.The Department of Health (England

Web-based textual analysis of free-text patient experience comments from a survey in primary care.

BACKGROUND: Open-ended questions eliciting free-text comments have been widely adopted in surveys of patient experience. Analysis of free text comments can provide deeper or new insight, identify areas for action, and initiate further investigation. Also, they may be a promising way to progress from documentation of patient experience to achieving quality improvement. The usual methods of analyzing free-text comments are known to be time and resource intensive. To efficiently deal with a large amount of free-text, new methods of rapidly summarizing and characterizing the text are being explored. OBJECTIVE: The aim of this study was to investigate the feasibility of using freely available Web-based text processing tools (text clouds, distinctive word extraction, key words in context) for extracting useful information from large amounts of free-text commentary about patient experience, as an alternative to more resource intensive analytic methods. METHODS: We collected free-text responses to a broad, open-ended question on patients' experience of primary care in a cross-sectional postal survey of patients recently consulting doctors in 25 English general practices. We encoded the responses to text files which were then uploaded to three Web-based textual processing tools. The tools we used were two text cloud creators: TagCrowd for unigrams, and Many Eyes for bigrams; and Voyant Tools, a Web-based reading tool that can extract distinctive words and perform Keyword in Context (KWIC) analysis. The association of patients' experience scores with the occurrence of certain words was tested with logistic regression analysis. KWIC analysis was also performed to gain insight into the use of a significant word. RESULTS: In total, 3426 free-text responses were received from 7721 patients (comment rate: 44.4%). The five most frequent words in the patients' comments were "doctor", "appointment", "surgery", "practice", and "time". The three most frequent two-word combinations were "reception staff", "excellent service", and "two weeks". The regression analysis showed that the occurrence of the word "excellent" in the comments was significantly associated with a better patient experience (OR=1.96, 95%CI=1.63-2.34), while "rude" was significantly associated with a worse experience (OR=0.53, 95%CI=0.46-0.60). The KWIC results revealed that 49 of the 78 (63%) occurrences of the word "rude" in the comments were related to receptionists and 17(22%) were related to doctors. CONCLUSIONS: Web-based text processing tools can extract useful information from free-text comments and the output may serve as a springboard for further investigation. Text clouds, distinctive words extraction and KWIC analysis show promise in quick evaluation of unstructured patient feedback. The results are easily understandable, but may require further probing such as KWIC analysis to establish the context. Future research should explore whether more sophisticated methods of textual analysis (eg, sentiment analysis, natural language processing) could add additional levels of understanding

The South Asian genome

Genetics of disease Microarrays Variant genotypes Population genetics Sequence alignment AllelesThe genetic sequence variation of people from the Indian subcontinent who comprise one-quarter of the world's population, is not well described. We carried out whole genome sequencing of 168 South Asians, along with whole-exome sequencing of 147 South Asians to provide deeper characterisation of coding regions. We identify 12,962,155 autosomal sequence variants, including 2,946,861 new SNPs and 312,738 novel indels. This catalogue of SNPs and indels amongst South Asians provides the first comprehensive map of genetic variation in this major human population, and reveals evidence for selective pressures on genes involved in skin biology, metabolism, infection and immunity. Our results will accelerate the search for the genetic variants underlying susceptibility to disorders such as type-2 diabetes and cardiovascular disease which are highly prevalent amongst South Asians.Whole genome sequencing to discover genetic variants underlying type-2 diabetes, coronary heart disease and related phenotypes amongst Indian Asians. Imperial College Healthcare NHS Trust cBRC 2011-13 (JS Kooner [PI], JC Chambers)

Wind Energy Resource Assessment of the Caribbean and Central America

A wind energy resource assessment of the Caribbean and Central America has identified many areas with good to outstanding wind resource potential for wind turbine applications. Annual average wind resource maps and summary tables have been developed for 35 island/country areas throughout the Caribbean and Central America region. The wind resource maps highlight the locations of major resource areas and provide estimates of the wind energy resource potential for typical well-exposed sites in these areas. The average energy in the wind flowing in the layer near the ground is expressed as a wind power class: the greater the average wind energy, the higher the wind power class. The summary tables that are included with each of the 35 island/country wind energy maps provide information on the frequency distribution of the wind speeds (expressed as estimates of the Weibull shape factor, k) and seasonal variations in the wind resource for the major wind resource areas identified on the maps. A new wind power class legend has been developed for relating the wind power classes to values of mean wind power density, mean wind speed, and Weibull k. Guidelines are presented on how to adjust these values to various heights above ground for different roughness and terrain characteristics. Information evaluated in preparing the assessment included existing meteorological data from airports and other weather stations, and from ships and buoys in offshore and coastal areas. In addition, new data from recent measurement sites established for wind energy siting studies were obtained for a few areas of the Caribbean. Other types of information evaluated in the assessment were climatological data and maps on winds aloft, surface pressure, air flow, and topography. The various data were screened and evaluated for their usefulness in preparing the wind resource assessment. Much of the surface data from airports and other land-based weather stations were determined to be from sheltered sites and were thus not very useful in assessing the wind resource at locations that are well exposed to the winds. Ship data were determined to be the most useful for estimating the large-scale wind flow and assessing the spatial distribution of the wind resource throughout the region. Techniques were developed for analyzing and correcting ship wind data and extrapolating these data to coastal and inland areas by considering terrain influences on the large-scale wind flow. In areas where extrapolation of ship wind data was not entirely feasible, such as interior areas of Central America, other techniques were developed for estimating the wind flow and distribution of the wind resource. Through the application of the various innovative techniques developed for assessing the wind resource throughout the Caribbean and Central America region, many areas with potentially good to outstanding wind resource were identified that had not been previously recognized. In areas where existing site data were available from exposed locations, the measured wind resource was compared with the estimated wind resource that was derived using the assessment techniques. In most cases, there was good agreement between the measured wind resource and the estimated wind resource. This assessment project supported activities being pursued by the U.S. Committee for Renewable Energy Commerce and Trade (CORECT), the U.S. government's interagency program to assist in overseas marketing and promote renewable energy exports. An overall goal of the program is to improve U.S. competitiveness in the world renewable energy market. The Caribbean and Central America assessment, which is the first of several possible follow-on international wind energy resource assessments, provides valuable information needed by the U.S. wind energy industry to identify suitable wind resource areas and concentrate their efforts on these areas