Publications by authors named "Karla Diaz-Ordaz"

24 Publications

  • Page 1 of 1

Importance of patient bed pathways and length of stay differences in predicting COVID-19 hospital bed occupancy in England.

BMC Health Serv Res 2021 Jun 9;21(1):566. Epub 2021 Jun 9.

Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology & Population Health, London School of Hygiene & Tropical Medicine, London, UK.

Background: Predicting bed occupancy for hospitalised patients with COVID-19 requires understanding of length of stay (LoS) in particular bed types. LoS can vary depending on the patient's "bed pathway" - the sequence of transfers of individual patients between bed types during a hospital stay. In this study, we characterise these pathways, and their impact on predicted hospital bed occupancy.

Methods: We obtained data from University College Hospital (UCH) and the ISARIC4C COVID-19 Clinical Information Network (CO-CIN) on hospitalised patients with COVID-19 who required care in general ward or critical care (CC) beds to determine possible bed pathways and LoS. We developed a discrete-time model to examine the implications of using either bed pathways or only average LoS by bed type to forecast bed occupancy. We compared model-predicted bed occupancy to publicly available bed occupancy data on COVID-19 in England between March and August 2020.

Results: In both the UCH and CO-CIN datasets, 82% of hospitalised patients with COVID-19 only received care in general ward beds. We identified four other bed pathways, present in both datasets: "Ward, CC, Ward", "Ward, CC", "CC" and "CC, Ward". Mean LoS varied by bed type, pathway, and dataset, between 1.78 and 13.53 days. For UCH, we found that using bed pathways improved the accuracy of bed occupancy predictions, while only using an average LoS for each bed type underestimated true bed occupancy. However, using the CO-CIN LoS dataset we were not able to replicate past data on bed occupancy in England, suggesting regional LoS heterogeneities.

Conclusions: We identified five bed pathways, with substantial variation in LoS by bed type, pathway, and geography. This might be caused by local differences in patient characteristics, clinical care strategies, or resource availability, and suggests that national LoS averages may not be appropriate for local forecasts of bed occupancy for COVID-19.

Trial Registration: The ISARIC WHO CCP-UK study ISRCTN66726260 was retrospectively registered on 21/04/2020 and designated an Urgent Public Health Research Study by NIHR.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12913-021-06509-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8188158PMC
June 2021

Robust Inference for Mediated Effects in Partially Linear Models.

Psychometrika 2021 Jun 18;86(2):595-618. Epub 2021 May 18.

Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK.

We consider mediated effects of an exposure, X on an outcome, Y, via a mediator, M, under no unmeasured confounding assumptions in the setting where models for the conditional expectation of the mediator and outcome are partially linear. We propose G-estimators for the direct and indirect effects and demonstrate consistent asymptotic normality for indirect effects when models for the conditional means of M, or X and Y are correctly specified, and for direct effects, when models for the conditional means of Y, or X and M are correct. This marks an improvement, in this particular setting, over previous 'triple' robust methods, which do not assume partially linear mean models. Testing of the no-mediation hypothesis is inherently problematic due to the composite nature of the test (either X has no effect on M or M no effect on Y), leading to low power when both effect sizes are small. We use generalized methods of moments (GMM) results to construct a new score testing framework, which includes as special cases the no-mediation and the no-direct-effect hypotheses. The proposed tests rely on an orthogonal estimation strategy for estimating nuisance parameters. Simulations show that the GMM-based tests perform better in terms of power and small sample performance compared with traditional tests in the partially linear setting, with drastic improvement under model misspecification. New methods are illustrated in a mediation analysis of data from the COPERS trial, a randomized trial investigating the effect of a non-pharmacological intervention of patients suffering from chronic pain. An accompanying R package implementing these methods can be found at github.com/ohines/plmed.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/s11336-021-09768-zDOI Listing
June 2021

Changes in in-hospital mortality in the first wave of COVID-19: a multicentre prospective observational cohort study using the WHO Clinical Characterisation Protocol UK.

Lancet Respir Med 2021 07 14;9(7):773-785. Epub 2021 May 14.

Health Protection Research Unit in Emerging and Zoonotic Infections, Institute of Infection, Veterinary and Ecological Sciences, Faculty of Health and Life Sciences, University of Liverpool, Liverpool, UK; Department of Respiratory Medicine, Alder Hey Children's Hospital, Liverpool, UK.

Background: Mortality rates in hospitalised patients with COVID-19 in the UK appeared to decline during the first wave of the pandemic. We aimed to quantify potential drivers of this change and identify groups of patients who remain at high risk of dying in hospital.

Methods: In this multicentre prospective observational cohort study, the International Severe Acute Respiratory and Emerging Infections Consortium WHO Clinical Characterisation Protocol UK recruited a prospective cohort of patients with COVID-19 admitted to 247 acute hospitals in England, Scotland, and Wales during the first wave of the pandemic (between March 9 and Aug 2, 2020). We included all patients aged 18 years and older with clinical signs and symptoms of COVID-19 or confirmed COVID-19 (by RT-PCR test) from assumed community-acquired infection. We did a three-way decomposition mediation analysis using natural effects models to explore associations between week of admission and in-hospital mortality, adjusting for confounders (demographics, comorbidities, and severity of illness) and quantifying potential mediators (level of respiratory support and steroid treatment). The primary outcome was weekly in-hospital mortality at 28 days, defined as the proportion of patients who had died within 28 days of admission of all patients admitted in the observed week, and it was assessed in all patients with an outcome. This study is registered with the ISRCTN Registry, ISRCTN66726260.

Findings: Between March 9, and Aug 2, 2020, we recruited 80 713 patients, of whom 63 972 were eligible and included in the study. Unadjusted weekly in-hospital mortality declined from 32·3% (95% CI 31·8-32·7) in March 9 to April 26, 2020, to 16·4% (15·0-17·8) in June 15 to Aug 2, 2020. Reductions in mortality were observed in all age groups, in all ethnic groups, for both sexes, and in patients with and without comorbidities. After adjustment, there was a 32% reduction in the risk of mortality per 7-week period (odds ratio [OR] 0·68 [95% CI 0·65-0·71]). The higher proportions of patients with severe disease and comorbidities earlier in the first wave (March and April) than in June and July accounted for 10·2% of this reduction. The use of respiratory support changed during the first wave, with gradually increased use of non-invasive ventilation over the first wave. Changes in respiratory support and use of steroids accounted for 22·2%, OR 0·95 (0·94-0·95) of the reduction in in-hospital mortality.

Interpretation: The reduction in in-hospital mortality in patients with COVID-19 during the first wave in the UK was partly accounted for by changes in the case-mix and illness severity. A significant reduction in in-hospital mortality was associated with differences in respiratory support and critical care use, which could partly reflect accrual of clinical knowledge. The remaining improvement in in-hospital mortality is not explained by these factors, and could be associated with changes in community behaviour, inoculum dose, and hospital capacity strain.

Funding: National Institute for Health Research and the Medical Research Council.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/S2213-2600(21)00175-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8121531PMC
July 2021

Increased mortality in community-tested cases of SARS-CoV-2 lineage B.1.1.7.

Nature 2021 05 15;593(7858):270-274. Epub 2021 Mar 15.

Department of Medical Statistics, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK.

SARS-CoV-2 lineage B.1.1.7, a variant that was first detected in the UK in September 2020, has spread to multiple countries worldwide. Several studies have established that B.1.1.7 is more transmissible than pre-existing variants, but have not identified whether it leads to any change in disease severity. Here we analyse a dataset that links 2,245,263 positive SARS-CoV-2 community tests and 17,452 deaths associated with COVID-19 in England from 1 November 2020 to 14 February 2021. For 1,146,534 (51%) of these tests, the presence or absence of B.1.1.7 can be identified because mutations in this lineage prevent PCR amplification of the spike (S) gene target (known as S gene target failure (SGTF)). On the basis of 4,945 deaths with known SGTF status, we estimate that the hazard of death associated with SGTF is 55% (95% confidence interval, 39-72%) higher than in cases without SGTF after adjustment for age, sex, ethnicity, deprivation, residence in a care home, the local authority of residence and test date. This corresponds to the absolute risk of death for a 55-69-year-old man increasing from 0.6% to 0.9% (95% confidence interval, 0.8-1.0%) within 28 days of a positive test in the community. Correcting for misclassification of SGTF and missingness in SGTF status, we estimate that the hazard of death associated with B.1.1.7 is 61% (42-82%) higher than with pre-existing variants. Our analysis suggests that B.1.1.7 is not only more transmissible than pre-existing SARS-CoV-2 variants, but may also cause more severe illness.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41586-021-03426-1DOI Listing
May 2021

Estimated transmissibility and impact of SARS-CoV-2 lineage B.1.1.7 in England.

Science 2021 04 3;372(6538). Epub 2021 Mar 3.

Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK.

A severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant, VOC 202012/01 (lineage B.1.1.7), emerged in southeast England in September 2020 and is rapidly spreading toward fixation. Using a variety of statistical and dynamic modeling approaches, we estimate that this variant has a 43 to 90% (range of 95% credible intervals, 38 to 130%) higher reproduction number than preexisting variants. A fitted two-strain dynamic transmission model shows that VOC 202012/01 will lead to large resurgences of COVID-19 cases. Without stringent control measures, including limited closure of educational institutions and a greatly accelerated vaccine rollout, COVID-19 hospitalizations and deaths across England in the first 6 months of 2021 were projected to exceed those in 2020. VOC 202012/01 has spread globally and exhibits a similar transmission increase (59 to 74%) in Denmark, Switzerland, and the United States.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1126/science.abg3055DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8128288PMC
April 2021

Invited Commentary: Treatment drop-in: making the case for causal prediction.

Am J Epidemiol 2021 Feb 17. Epub 2021 Feb 17.

Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, The Netherlands.

Clinical prediction models (CPMs) are often used to guide treatment initiation, with individuals at high risk offered treatment. This implicitly assumes that the probability quoted from a CPM represents the risk to an individual of an adverse outcome in absence of treatment. However, for a CPM to correctly target this estimand requires careful causal thinking. One problem that needs to be overcome is treatment drop-in: where individuals in the development data commence treatment after the time of prediction but before the outcome occurs. The linked article by Xu et al (Am J Epidemiol. XXXX;XXX(XX):XXXX-XXXX) uses causal estimates from external data sources such as clinical trials, to adjust CPMs for treatment drop-in. This represents a pragmatic and promising approach to address this issue, and illustrates the value of utilising causal inference in prediction. Building causality into the prediction pipeline can also bring other benefits. These include the ability to make and compare hypothetical predictions under different interventions, to make CPMs more explainable and transparent, and to improve model generalisability. Enriching CPMs with causal inference therefore has the potential to add considerable value to the role of prediction in healthcare.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/aje/kwab030DOI Listing
February 2021

Increased hazard of death in community-tested cases of SARS-CoV-2 Variant of Concern 202012/01.

medRxiv 2021 Feb 3. Epub 2021 Feb 3.

Department of Medical Statistics, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK.

VOC 202012/01, a SARS-CoV-2 variant first detected in the United Kingdom in September 2020, has spread to multiple countries worldwide. Several studies have established that this novel variant is more transmissible than preexisting variants of SARS-CoV-2, but have not identified whether the new variant leads to any change in disease severity. We analyse a large database of SARS-CoV-2 community test results and COVID-19 deaths for England, representing approximately 47% of all SARS-CoV-2 community tests and 7% of COVID-19 deaths in England from 1 September 2020 to 22 January 2021. Fortuitously, these SARS-CoV-2 tests can identify VOC 202012/01 because mutations in this lineage prevent PCR amplification of the spike gene target (S gene target failure, SGTF). We estimate that the hazard of death among SGTF cases is 30% (95% CI 9-56%) higher than among non-SGTF cases after adjustment for age, sex, ethnicity, deprivation level, care home residence, local authority of residence and date of test. In absolute terms, this increased hazard of death corresponds to the risk of death for a male aged 55-69 increasing from 0.56% to 0.73% (95% CI 0.60-0.86%) over the 28 days following a positive SARS-CoV-2 test in the community. Correcting for misclassification of SGTF, we estimate a 35% (12-64%) higher hazard of death associated with VOC 202012/01. Our analysis suggests that VOC 202012/01 is not only more transmissible than preexisting SARS-CoV-2 variants but may also cause more severe illness.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1101/2021.02.01.21250959DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7872389PMC
February 2021

Informative presence and observation in routine health data: A review of methodology for clinical risk prediction.

J Am Med Inform Assoc 2021 01;28(1):155-166

Division of Informatics, Imaging and Data Sciences, School of Health Sciences, University of Manchester, Manchester, United Kingdom.

Objective: Informative presence (IP) is the phenomenon whereby the presence or absence of patient data is potentially informative with respect to their health condition, with informative observation (IO) being the longitudinal equivalent. These phenomena predominantly exist within routinely collected healthcare data, in which data collection is driven by the clinical requirements of patients and clinicians. The extent to which IP and IO are considered when using such data to develop clinical prediction models (CPMs) is unknown, as is the existing methodology aiming at handling these issues. This review aims to synthesize such existing methodology, thereby helping identify an agenda for future methodological work.

Materials And Methods: A systematic literature search was conducted by 2 independent reviewers using prespecified keywords.

Results: Thirty-six articles were included. We categorized the methods presented within as derived predictors (including some representation of the measurement process as a predictor in the model), modeling under IP, and latent structures. Including missing indicators or summary measures as predictors is the most commonly presented approach amongst the included studies (24 of 36 articles).

Discussion: This is the first review to collate the literature in this area under a prediction framework. A considerable body relevant of literature exists, and we present ways in which the described methods could be developed further. Guidance is required for specifying the conditions under which each method should be used to enable applied prediction modelers to use these methods.

Conclusions: A growing recognition of IP and IO exists within the literature, and methodology is increasingly becoming available to leverage these phenomena for prediction purposes. IP and IO should be approached differently in a prediction context than when the primary goal is explanation. The work included in this review has demonstrated theoretical and empirical benefits of incorporating IP and IO, and therefore we recommend that applied health researchers consider incorporating these methods in their work.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/jamia/ocaa242DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7810439PMC
January 2021

Living risk prediction algorithm (QCOVID) for risk of hospital admission and mortality from coronavirus 19 in adults: national derivation and validation cohort study.

BMJ 2020 10 20;371:m3731. Epub 2020 Oct 20.

Nuffield Department of Primary Care Health Sciences, Radcliffe Observatory Quarter, Oxford OX2 6GG, UK

Objective: To derive and validate a risk prediction algorithm to estimate hospital admission and mortality outcomes from coronavirus disease 2019 (covid-19) in adults.

Design: Population based cohort study.

Setting And Participants: QResearch database, comprising 1205 general practices in England with linkage to covid-19 test results, Hospital Episode Statistics, and death registry data. 6.08 million adults aged 19-100 years were included in the derivation dataset and 2.17 million in the validation dataset. The derivation and first validation cohort period was 24 January 2020 to 30 April 2020. The second temporal validation cohort covered the period 1 May 2020 to 30 June 2020.

Main Outcome Measures: The primary outcome was time to death from covid-19, defined as death due to confirmed or suspected covid-19 as per the death certification or death occurring in a person with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the period 24 January to 30 April 2020. The secondary outcome was time to hospital admission with confirmed SARS-CoV-2 infection. Models were fitted in the derivation cohort to derive risk equations using a range of predictor variables. Performance, including measures of discrimination and calibration, was evaluated in each validation time period.

Results: 4384 deaths from covid-19 occurred in the derivation cohort during follow-up and 1722 in the first validation cohort period and 621 in the second validation cohort period. The final risk algorithms included age, ethnicity, deprivation, body mass index, and a range of comorbidities. The algorithm had good calibration in the first validation cohort. For deaths from covid-19 in men, it explained 73.1% (95% confidence interval 71.9% to 74.3%) of the variation in time to death (R); the D statistic was 3.37 (95% confidence interval 3.27 to 3.47), and Harrell's C was 0.928 (0.919 to 0.938). Similar results were obtained for women, for both outcomes, and in both time periods. In the top 5% of patients with the highest predicted risks of death, the sensitivity for identifying deaths within 97 days was 75.7%. People in the top 20% of predicted risk of death accounted for 94% of all deaths from covid-19.

Conclusion: The QCOVID population based risk algorithm performed well, showing very high levels of discrimination for deaths and hospital admissions due to covid-19. The absolute risks presented, however, will change over time in line with the prevailing SARS-C0V-2 infection rate and the extent of social distancing measures in place, so they should be interpreted with caution. The model can be recalibrated for different time periods, however, and has the potential to be dynamically updated as the pandemic evolves.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1136/bmj.m3731DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7574532PMC
October 2020

Causal graphs for the analysis of genetic cohort data.

Physiol Genomics 2020 09 20;52(9):369-378. Epub 2020 Jul 20.

Molecular and Clinical Sciences Institute, St. George's, University of London, London, United Kingdom.

The increasing availability of genetic cohort data has led to many genome-wide association studies (GWAS) successfully identifying genetic associations with an ever-expanding list of phenotypic traits. Association, however, does not imply causation, and therefore methods have been developed to study the issue of causality. Under additional assumptions, Mendelian randomization (MR) studies have proved popular in identifying causal effects between two phenotypes, often using GWAS summary statistics. Given the widespread use of these methods, it is more important than ever to understand, and communicate, the causal assumptions upon which they are based, so that methods are transparent, and findings are clinically relevant. Causal graphs can be used to represent causal assumptions graphically and provide insights into the limitations associated with different analysis methods. Here we review GWAS and MR from a causal perspective, to build up intuition for causal diagrams in genetic problems. We also examine issues of confounding by ancestry and comment on approaches for dealing with such confounding, as well as discussing approaches for dealing with selection biases arising from study design.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1152/physiolgenomics.00115.2019DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7509246PMC
September 2020

Domains of transmission and association of community, school, and household sanitation with soil-transmitted helminth infections among children in coastal Kenya.

PLoS Negl Trop Dis 2019 11 25;13(11):e0007488. Epub 2019 Nov 25.

Eastern and Southern Africa Centre of International Parasite Control, Kenya Medical Research Institute, Nairobi, Kenya.

Introduction: Few studies have simultaneously examined the role of sanitation conditions at the home, school, and community on soil-transmitted helminth (STH) infection. We examined the contribution of each domain that children inhabit (home, village, and school) to STH infection and estimated the association of STH infection with sanitation in each domain.

Methods: Using data from 4,104 children from Kwale County, Kenya, who reported attending school, we used logistic regression models with cross-classified random effects to calculate measures of general contextual effects and estimate associations of village sanitation coverage (percentage of households with reported access to sanitation), school sanitation coverage (number of usable toilets per enrolled pupil), and sanitation access at home with STH infection.

Findings: We found reported use of a sanitation facility by households was associated with reduced prevalence of hookworm infection but not with reduced prevalence of T. trichiura infection. School sanitation coverage > 3 toilets per 100 pupils was associated with lower prevalence of hookworm infection. School sanitation was not associated with T. trichiura infection. Village sanitation coverage > 81% was associated with reduced prevalence of T. trichiura infection, but no protective association was detected for hookworm infection. General contextual effects represented by residual heterogeneity between village and school domains had comparable impact upon likelihood of hookworm and T. trichiura infection as sanitation coverage in either of these domains.

Conclusion: Findings support the importance of providing good sanitation facilities to support mass drug administration in reducing the burden of STH infection in children.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1371/journal.pntd.0007488DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6901232PMC
November 2019

Using Animation to Self-Report Health: A Randomized Experiment with Children.

Patient 2020 04;13(2):175-188

London School of Hygiene and Tropical Medicine, London, UK.

Background: The Child Health Utility-9D (CHU-9D) is the only generic preference-based measure specifically developed to elicit health-related quality of life directly from children aged 7-11 years. The aim of this study was to investigate whether the use of animation on a touch screen device (tablet) is a better way of collecting health status information from children aged 4-14 years compared to a traditional paper questionnaire. The specific research questions were firstly, do young children (4-7 years) find an animated questionnaire easier to understand; secondly, independent of age, is completion of an animated questionnaire easier for sick children in hospital settings; and thirdly, do children's preferences for the different formats of the questionnaire vary by the age of the child.

Methods: Using a balanced cross-over trial, we administered different formats of the CHU-9D to 221 healthy children in a school setting and 217 children with health problems in a hospital setting. The study tested five versions of the CHU-9D questionnaire: paper text, tablet text, tablet still image, paper image and tablet animation.

Results: Our results indicated that the majority of the children aged 4-7 years found the CHU-9D questions easy to answer independent of the format of the questionnaire administered. Amongst children aged 7-14 with health problems, the format of questionnaire influenced understanding. Children aged 7-11 years found the tablet image and animation formats easier compared to text questionnaires, while the oldest children in hospital found text-based questionnaires easier compared to image and animation.

Conclusion: Children in all three age groups preferred animation on a tablet to other methods of assessment. Our results highlight the potential for using an animated preference-based measure to assess the health of children as young as 4 years.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/s40271-019-00392-9DOI Listing
April 2020

Links between causal effects and causal association for surrogacy evaluation in a gaussian setting.

Stat Med 2017 Nov 8;36(27):4243-4265. Epub 2017 Aug 8.

Department of Biostatistics, University of Michigan, Ann Arbor, MI, U.S.A.

Two paradigms for the evaluation of surrogate markers in randomized clinical trials have been proposed: the causal effects paradigm and the causal association paradigm. Each of these paradigms rely on assumptions that must be made to proceed with estimation and to validate a candidate surrogate marker (S) for the true outcome of interest (T). We consider the setting in which S and T are Gaussian and are generated from structural models that include an unobserved confounder. Under the assumed structural models, we relate the quantities used to evaluate surrogacy within both the causal effects and causal association frameworks. We review some of the common assumptions made to aid in estimating these quantities and show that assumptions made within one framework can imply strong assumptions within the alternative framework. We demonstrate that there is a similarity, but not exact correspondence between the quantities used to evaluate surrogacy within each framework, and show that the conditions for identifiability of the surrogacy parameters are different from the conditions, which lead to a correspondence of these quantities.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/sim.7430DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5675829PMC
November 2017

Effects of the financial crisis and Troika austerity measures on health and health care access in Portugal.

Health Policy 2016 Jul 27;120(7):833-9. Epub 2016 Apr 27.

London School of Hygiene and Tropical Medicine, London, United Kingdom.

Although Portugal has been deeply affected by the global financial crisis, the impact of the recession and subsequent austerity on health and to health care has attracted relatively little attention. We used several sources of data including the European Union Statistics for Income and Living Conditions (EU-SILC) which tracks unmet medical need during the recession and before and after the Troika's austerity package. Our results show that the odds of respondents reporting having an unmet medical need more than doubled between 2010 and 2012 (OR=2.41, 95% CI 2.01-2.89), with the greatest impact on those in employment, followed by the unemployed, retired, and other economically inactive groups. The reasons for not seeking care involved a combination of factors, with a 68% higher odds of citing financial barriers (OR=1.68, 95% CI 1.32-2.12), more than twice the odds of citing waiting times and inability to take time off work or family responsibilities (OR 2.18, 95% CI 1.20-3.98), and a large increase of reporting delaying care in the hope that the problem would resolve on its own (OR=13.98, 95% CI 6.51-30.02). Individual-level studies from Portugal also suggest that co-payments at primary and hospital level are having a negative effect on the most vulnerable living in disadvantaged areas, and that health care professionals have concerns about the impact of recession and subsequent austerity measures on the quality of care provided. The Portuguese government no longer needs external assistance, but these findings suggest that measures are now needed to mitigate the damage incurred by the crisis and austerity.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.healthpol.2016.04.009DOI Listing
July 2016

Missing continuous outcomes under covariate dependent missingness in cluster randomised trials.

Stat Methods Med Res 2017 Jun 13;26(3):1543-1562. Epub 2016 May 13.

2 Statistical Innovation Group, AstraZeneca, Cambridge, UK.

Attrition is a common occurrence in cluster randomised trials which leads to missing outcome data. Two approaches for analysing such trials are cluster-level analysis and individual-level analysis. This paper compares the performance of unadjusted cluster-level analysis, baseline covariate adjusted cluster-level analysis and linear mixed model analysis, under baseline covariate dependent missingness in continuous outcomes, in terms of bias, average estimated standard error and coverage probability. The methods of complete records analysis and multiple imputation are used to handle the missing outcome data. We considered four scenarios, with the missingness mechanism and baseline covariate effect on outcome either the same or different between intervention groups. We show that both unadjusted cluster-level analysis and baseline covariate adjusted cluster-level analysis give unbiased estimates of the intervention effect only if both intervention groups have the same missingness mechanisms and there is no interaction between baseline covariate and intervention group. Linear mixed model and multiple imputation give unbiased estimates under all four considered scenarios, provided that an interaction of intervention and baseline covariate is included in the model when appropriate. Cluster mean imputation has been proposed as a valid approach for handling missing outcomes in cluster randomised trials. We show that cluster mean imputation only gives unbiased estimates when missingness mechanism is the same between the intervention groups and there is no interaction between baseline covariate and intervention group. Multiple imputation shows overcoverage for small number of clusters in each intervention group.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1177/0962280216648357DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5467798PMC
June 2017

Are missing data adequately handled in cluster randomised trials? A systematic review and guidelines.

Clin Trials 2014 Oct 5;11(5):590-600. Epub 2014 Jun 5.

Centre for Primary Care and Public Health, Queen Mary University of London, London, UK.

Background: Missing data are a potential source of bias, and their handling in the statistical analysis can have an important impact on both the likelihood and degree of such bias. Inadequate handling of the missing data may also result in invalid variance estimation. The handling of missing values is more complex in cluster randomised trials, but there are no reviews of practice in this field.

Objectives: A systematic review of published trials was conducted to examine how missing data are reported and handled in cluster randomised trials.

Methods: We systematically identified cluster randomised trials, published in English in 2011, using the National Library of Medicine (MEDLINE) via PubMed. Non-randomised and pilot/feasibility trials were excluded, as were reports of secondary analyses, interim analyses, and economic evaluations and those where no data were at the individual level. We extracted information on missing data and the statistical methods used to deal with them from a random sample of the identified studies.

Results: We included 132 trials. There was evidence of missing data in 95 (72%). Only 32 trials reported handling missing data, 22 of them using a variety of single imputation techniques, 8 using multiple imputation without accommodating the clustering and 2 stating that their likelihood-based complete case analysis accounted for missing values because the data were assumed Missing-at-Random.

Limitations: The results presented in this study are based on a large random sample of cluster randomised trials published in 2011, identified in electronic searches and therefore possibly missing some trials, most likely of poorer quality. Also, our results are based on information in the main publication for each trial. These reports may omit some important information on the presence of, and reasons for, missing data and on the statistical methods used to handle them. Our extraction methods, based on published reports, could not distinguish between missing data in outcomes and missing data in covariates. This distinction may be important in determining the assumptions about the missing data mechanism necessary for complete case analyses to be valid.

Conclusions: Missing data are present in the majority of cluster randomised trials. However, they are poorly reported, and most authors give little consideration to the assumptions under which their analysis will be valid. The majority of the methods currently used are valid under very strong assumptions about the missing data, whose plausibility is rarely discussed in the corresponding reports. This may have important consequences for the validity of inferences in some trials. Methods which result in valid inferences under general Missing-at-Random assumptions are available and should be made more accessible.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1177/1740774514537136DOI Listing
October 2014

The agreement between proxy and self-completed EQ-5D for care home residents was better for index scores than individual domains.

J Clin Epidemiol 2014 Sep 15;67(9):1035-43. Epub 2014 May 15.

Warwick Medical School, Coventry, United Kingdom, CV4 7AL.

Objective: Proxy measures are an alternative source of data for care home residents who are unable to complete the health utility measure, but the agreement levels between residents and care home staff for the EQ-5D have not been investigated previously. The objective of the present study was to examine the inter-rater agreement levels for the reporting of EQ-5D by care home residents and staff, adjusting for the impact of clustering.

Study Design And Setting: The data consist of EQ-5D scores for 565 pairs of care home residents and proxies and quality-adjusted life-years (QALYs) for 248 pairs. Cluster-adjusted agreement was compared for the domains, index scores, and QALYs from the EQ-5D. Factors influencing index score agreement are also described.

Results: The results show poor to fair agreement at the domain level (cluster-adjusted Kappa -0.03 to 0.26) and moderate agreement at the score level (cluster-adjusted intra-class correlation coefficient [ICC] 0.44-0.50) and for QALYs (cluster-adjusted ICC 0.59). A higher likelihood of depression and lower cognitive impairment were both associated with smaller discrepancy between proxy and self-completed scores.

Conclusion: Proxies appear to be an acceptable source of data for index scores and QALYs but may be less reliable if individual domains are considered.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jclinepi.2014.04.005DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4126106PMC
September 2014

Coping with persistent pain, effectiveness research into self-management (COPERS): statistical analysis plan for a randomised controlled trial.

Trials 2014 Feb 15;15:59. Epub 2014 Feb 15.

Pragmatic Clinical Trials Unit, Queen Mary University of London, 58 Turner St, London E1 2AB, UK.

Background: The Coping with Persistent Pain, Effectiveness Research into Self-management (COPERS) trial assessed whether a group-based self-management course is effective in reducing pain-related disability in participants with chronic musculoskeletal pain. This article describes the statistical analysis plan for the COPERS trial.

Methods And Design: COPERS was a pragmatic, multicentre, unmasked, parallel group, randomised controlled trial. This article describes (a) the overall analysis principles (including which participants will be included in each analysis, how results will be presented, which covariates will be adjusted for, and how we will account for clustering in the intervention group); (b) the primary and secondary outcomes, and how each outcome will be analysed; (c) sensitivity analyses; (d) subgroup analyses; and (e) adherence-adjusted analyses.

Trial Registration: ISRCTN24426731.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/1745-6215-15-59DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3930300PMC
February 2014

Multilevel models for cost-effectiveness analyses that use cluster randomised trial data: An approach to model choice.

Stat Methods Med Res 2016 10 16;25(5):2036-2052. Epub 2013 Dec 16.

Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK.

Multilevel models provide a flexible modelling framework for cost-effectiveness analyses that use cluster randomised trial data. However, there is a lack of guidance on how to choose the most appropriate multilevel models. This paper illustrates an approach for deciding what level of model complexity is warranted; in particular how best to accommodate complex variance-covariance structures, right-skewed costs and missing data. Our proposed models differ according to whether or not they allow individual-level variances and correlations to differ across treatment arms or clusters and by the assumed cost distribution (Normal, Gamma, Inverse Gaussian). The models are fitted by Markov chain Monte Carlo methods. Our approach to model choice is based on four main criteria: the characteristics of the data, model pre-specification informed by the previous literature, diagnostic plots and assessment of model appropriateness. This is illustrated by re-analysing a previous cost-effectiveness analysis that uses data from a cluster randomised trial. We find that the most useful criterion for model choice was the deviance information criterion, which distinguishes amongst models with alternative variance-covariance structures, as well as between those with different cost distributions. This strategy for model choice can help cost-effectiveness analyses provide reliable inferences for policy-making when using cluster trials, including those with missing data.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1177/0962280213511719DOI Listing
October 2016

A systematic review of cluster randomised trials in residential facilities for older people suggests how to improve quality.

BMC Med Res Methodol 2013 Oct 22;13:127. Epub 2013 Oct 22.

Centre for Primary Care and Public Health, Queen Mary University of London, London, E1 2AB, UK.

Background: Previous reviews of cluster randomised trials have been critical of the quality of the trials reviewed, but none has explored determinants of the quality of these trials in a specific field over an extended period of time. Recent work suggests that correct conduct and reporting of these trials may require more than published guidelines. In this review, our aim was to assess the quality of cluster randomised trials conducted in residential facilities for older people, and to determine whether (1) statistician involvement in the trial and (2) strength of journal endorsement of the Consolidated Standards of Reporting Trials (CONSORT) statement influence quality.

Methods: We systematically identified trials randomising residential facilities for older people, or parts thereof, without language restrictions, up to the end of 2010, using National Library of Medicine (Medline) via PubMed and hand-searching. We based quality assessment criteria largely on the extended CONSORT statement for cluster randomised trials. We assessed statistician involvement based on statistician co-authorship, and strength of journal endorsement of the CONSORT statement from journal websites.

Results: 73 trials met our inclusion criteria. Of these, 20 (27%) reported accounting for clustering in sample size calculations and 54 (74%) in the analyses. In 29 trials (40%), methods used to identify/recruit participants were judged by us to have potentially caused bias or reporting was unclear to reach a conclusion. Some elements of quality improved over time but this appeared not to be related to the publication of the extended CONSORT statement for these trials. Trials with statistician/epidemiologist co-authors were more likely to account for clustering in sample size calculations (unadjusted odds ratio 5.4, 95% confidence interval 1.1 to 26.0) and analyses (unadjusted OR 3.2, 1.2 to 8.5). Journal endorsement of the CONSORT statement was not associated with trial quality.

Conclusions: Despite international attempts to improve methods in cluster randomised trials, important quality limitations remain amongst these trials in residential facilities. Statistician involvement on trial teams may be more effective in promoting quality than further journal endorsement of the CONSORT statement. Funding bodies and journals should promote statistician involvement and co-authorship in addition to adherence to CONSORT guidelines.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/1471-2288-13-127DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4015673PMC
October 2013

Multiple imputation methods for handling missing data in cost-effectiveness analyses that use data from hierarchical studies: an application to cluster randomized trials.

Med Decis Making 2013 11 1;33(8):1051-63. Epub 2013 Aug 1.

Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK (MGK)

Purpose: Multiple imputation (MI) has been proposed for handling missing data in cost-effectiveness analyses (CEAs). In CEAs that use cluster randomized trials (CRTs), the imputation model, like the analysis model, should recognize the hierarchical structure of the data. This paper contrasts a multilevel MI approach that recognizes clustering, with single-level MI and complete case analysis (CCA) in CEAs that use CRTs.

Methods: We consider a multilevel MI approach compatible with multilevel analytical models for CEAs that use CRTs. We took fully observed data from a CEA that evaluated an intervention to improve diagnosis of active labor in primiparous women using a CRT (2078 patients, 14 clusters). We generated scenarios with missing costs and outcomes that differed, for example, according to the proportion with missing data (10%-50%), the covariates that predicted missing data (individual, cluster-level), and the missingness mechanism: missing completely at random (MCAR), missing at random (MAR), or missing not at random (MNAR). We estimated incremental net benefits (INBs) for each approach and compared them with the estimates from the fully observed data, the "true" INBs.

Results: When costs and outcomes were assumed to be MCAR, the INBs for each approach were similar to the true estimates. When data were MAR, the point estimates from the CCA differed from the true estimates. Multilevel MI provided point estimates and standard errors closer to the true values than did single-level MI across all settings, including those in which a high proportion of observations had cost and outcome data MAR and when data were MNAR.

Conclusions: Multilevel MI accommodates the multilevel structure of the data in CEAs that use cluster trials and provides accurate cost-effectiveness estimates across the range of circumstances considered.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1177/0272989X13492203DOI Listing
November 2013

Exercise for depression in elderly residents of care homes: a cluster-randomised controlled trial.

Lancet 2013 Jul 2;382(9886):41-9. Epub 2013 May 2.

Warwick Clinical Trials Unit, Warwick Medical School, The University of Warwick, Coventry, UK.

Background: Depression is common and is associated with poor outcomes among elderly care-home residents. Exercise is a promising low-risk intervention for depression in this population. We tested the hypothesis that a moderate intensity exercise programme would reduce the burden of depressive symptoms in residents of care homes.

Methods: We did a cluster-randomised controlled trial in care homes in two regions in England; northeast London, and Coventry and Warwickshire. Residents aged 65 years or older were eligible for inclusion. A statistician independent of the study randomised each home (1 to 1·5 ratio, stratified by location, minimised by type of home provider [local authority, voluntary, private and care home, private and nursing home] and size of home [<32 or ≥32 residents]) into intervention and control groups. The intervention package included depression awareness training for care-home staff, 45 min physiotherapist-led group exercise sessions for residents (delivered twice weekly), and a whole home component designed to encourage more physical activity in daily life. The control consisted of only the depression awareness training. Researchers collecting follow-up data from individual participants and the participants themselves were inevitably aware of home randomisation because of the physiotherapists' activities within the home. A researcher masked to study allocation coded NHS routine data. The primary outcome was number of depressive symptoms on the geriatric depression scale-15 (GDS-15). Follow-up was for 12 months. This trial is registered with ISRCTN Register, number ISRCTN43769277.

Findings: Care homes were randomised between Dec 15, 2008, and April 9, 2010. At randomisation, 891 individuals in 78 care homes (35 intervention, 43 control) had provided baseline data. We delivered 3191 group exercise sessions attended on average by five study participants and five non-study residents. Of residents with a GDS-15 score, 374 of 765 (49%) were depressed at baseline; 484 of 765 (63%) provided 12 month follow-up scores. Overall the GDS-15 score was 0·13 (95% CI -0·33 to 0·60) points higher (worse) at 12 months for the intervention group compared with the control group. Among residents depressed at baseline, GDS-15 score was 0·22 (95% CI -0·52 to 0·95) points higher at 6 months in the intervention group than in the control group. In an end of study cross-sectional analysis, including 132 additional residents joining after randomisation, the odds of being depressed were 0·76 (95% CI 0·53 to 1·09) for the intervention group compared with the control group.

Interpretation: This moderately intense exercise programme did not reduce depressive symptoms in residents of care homes. In this frail population, alternative strategies to manage psychological symptoms are required.

Funding: National Institute for Health Research Health Technology Assessment.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/S0140-6736(13)60649-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3919159PMC
July 2013

Quality of cluster randomized controlled trials in oral health: a systematic review of reports published between 2005 and 2009.

Community Dent Oral Epidemiol 2012 Feb;40 Suppl 1:3-14

Centre for Health Sciences, Queen Mary University of London, Whitechapel, London, UK.

Objectives: To assess the quality of methods and reporting of recently published cluster randomized trials (CRTs) in oral health.

Methods: We searched PubMed for CRTs that included at least one oral health-related outcome and were published from 2005 to 2009 inclusive. We developed a list of criteria for assessing trial quality and reporting. This was influenced largely by the extended CONSORT statement for CRTs but also included criteria suggested by other authors. We examined the extent to which trials were consistent with these criteria.

Results: Twenty-three trials were included in the review. In 15 (65%) trials, clustering had been accounted for in sample size calculations, and in 18 (78%) authors had accounted for clustering in analysis. Intraclass correlation coefficients (ICCs) were reported for eight (35%) trials; the outcome assessor was reported as having been blinded to allocation in 12 (52%) trials; 17 (74%) described eligibility criteria at individual level, but only nine (39%) described such criteria at cluster level. Sixteen of 20 trials (80%), in which individuals were recruited, reported that individual informed consent was obtained.

Conclusions: These results suggest that the quality of recent CRTs in oral health is relatively high and appears to compare favourably with other fields. However, there remains room for improvement. Authors of future trials should endeavour to ensure sample size calculations and analyses properly account for clustering (and are reported as such), consider the potential for recruitment/identification bias at the design stage, describe the steps taken to avoid this in the final report and report observed ICCs and cluster-level eligibility criteria.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1111/j.1600-0528.2011.00660.xDOI Listing
February 2012
-->