Intracluster Correlation Research Articles

Performance of the Beta-Binomial Model for Clustered Binary Responses: Comparison with Generalized Estimating Equations

This study examined performance of the beta-binomial model in comparison with GEE using clustered binary responses resulting in non-normal outcomes. Monte Carlo simulations were performed under varying intracluster correlations and sample sizes. The results showed that the beta-binomial model performed better for small sample, while GEE performed well under large sample.

Longitudinal analyses suggest a higher burden of self-reported influenza-like illness among women and girls in a rural community in western Kenya

Longitudinal studies that quantify influenza-like illness (ILI) burden while determining associated risk factors and intra-cluster correlation coefficients (ICCs) are scarce in Africa. This study aimed to identify longitudinal patterns of ILI and the associated independent variables while quantifying the burden by age and sex. We enrolled and followed 390 subjects nested within 92 households for 12 weeks, collecting weekly data on prespecified self-reported symptoms in a rural community in western Kenya. Using a standard case definition, reporting at least any four prespecified symptoms attained an ILI syndrome. Analyses related relationship between attaining ILI syndrome and independent variables using multilevel logistic regression models adjusting for age and sex. ICCs quantified clustering of ILI syndrome computed at different levels of data aggregation. Of the 4261 reports received, 592 (14%) registered an illness, with 278 (48%) of these attaining ILI syndrome. The 278 reports belonged to 176 (41%) subjects dwelling in 76 (83%) households. On average, each study subject and household experienced 1.6 and 3.7 episodes of ILI syndrome, respectively. Making a visit outside the local area of residence (Odds ratio (OR) =2.7, 95% CI 1.8, 4.1) and living in a cement-floored house (OR = 1.9, 95% CI 1.1, 3.3) independently predicted attaining ILI syndrome. Correcting for the population structure revealed an excess risk of 16% in attaining the syndrome among the study women and girls. Clustering of ILI syndrome was lowest at the individual level (ICC = 0.04) and slightly higher within households (ICC = 0.14), suggesting moderate household contextual influences. Regardless of the cause, we provide evidence of a potentially higher ILI syndrome burden among women and girls in our study area. Studies are needed to establish granular exposures defining the increasing risk of ILI syndrome associated with gender, making a visit outside the local sub-county of residence and living in a cement-floored house.

Effect sizes and intra-cluster correlation coefficients measured from the Green Dot High School study for guiding sample size calculations when designing future violence prevention cluster randomized trials in school settings

PurposeCluster randomized controlled trials (cRCTs) are popular in school-based research designs where schools are randomized to different trial arms. To help guide future study planning, we provide information on anticipated effect sizes and intra-cluster correlation coefficients (ICCs), as well as school sizes, for dating violence (DV) and interpersonal violence outcomes based on data from a cRCT which evaluated the bystander-based violence intervention ‘Green Dot’. MethodsWe utilized data from 25 schools from the Green Dot High School study. Effect size and ICC values corresponding to dating and interpersonal violence outcomes are obtained from linear mixed effect models. We also calculated the required number of schools needed for future studies utilizing available methods that do and do not consider variation in school size. ResultsObserved effect sizes for DV outcomes range from 0.06 to 0.11. Observed ICC values for DV outcomes range from 0.0006 to 0.0032. The upper limit of 95% CIs for the true ICCs range from 0.0023 to 0.0070. ConclusionSchool-based evaluations with violence outcomes are expected to have small effect sizes. Observed ICCs are less than 0.005 and upper limit of of 95% CIs for the true ICCs are less than 0.01. Designing school-based cRCTs should account for the ICC, even if its value is assumed to be negligible. Furthermore, variation in school sizes should also be accounted for to avoid having too few schools to achieve the desired power.

Methods for dealing with unequal cluster sizes in cluster randomized trials: A scoping review.

In a cluster-randomized trial (CRT), the number of participants enrolled often varies across clusters. This variation should be considered during both trial design and data analysis to ensure statistical performance goals are achieved. Most methodological literature on the CRT design has assumed equal cluster sizes. This scoping review focuses on methodology for unequal cluster size CRTs. EMBASE, Medline, Google Scholar, MathSciNet and Web of Science databases were searched to identify English-language articles reporting on methodology for unequal cluster size CRTs published until March 2021. We extracted data on the focus of the paper (power calculation, Type I error etc.), the type of CRT, the type and the range of parameter values investigated (number of clusters, mean cluster size, cluster size coefficient of variation, intra-cluster correlation coefficient, etc.), and the main conclusions. Seventy-nine of 5032 identified papers met the inclusion criteria. Papers primarily focused on the parallel-arm CRT (p-CRT, n = 60, 76%) and the stepped-wedge CRT (n = 14, 18%). Roughly 75% of the papers addressed trial design issues (sample size/power calculation) while 25% focused on analysis considerations (Type I error, bias, etc.). The ranges of parameter values explored varied substantially across different studies. Methods for accounting for unequal cluster sizes in the p-CRT have been investigated extensively for Gaussian and binary outcomes. Synthesizing the findings of these works is difficult as the magnitude of impact of the unequal cluster sizes varies substantially across the combinations and ranges of input parameters. Limited investigations have been done for other combinations of a CRT design by outcome type, particularly methodology involving binary outcomes-the most commonly used type of primary outcome in trials. The paucity of methodological papers outside of the p-CRT with Gaussian or binary outcomes highlights the need for further methodological development to fill the gaps.

Optimal design of cluster randomized trials allowing unequal allocation of clusters and unequal cluster size between arms.

There are sometimes cost, scientific, or logistical reasons to allocate individuals unequally in an individually randomized trial. In cluster randomized trials we can allocate clusters unequally and/or allow different cluster size between trial arms. We consider parallel group designs with a continuous outcome, and optimal designs that require the smallest number of individuals to be measured given the number of clusters. Previous authors have derived the optimal allocation ratio for clusters under different variance and/or intracluster correlations (ICCs) between arms, allowing different but prespecified cluster sizes by arm. We derive closed-form expressions to identify the optimal proportions of clusters and of individuals measured for each arm, thereby defining optimal cluster sizes, when cluster size can be chosen freely. When ICCs differ between arms but the variance is equal, the optimal design allocates more than half the clusters to the arm with the higher ICC, but (typically only slightly) less than half the individuals and hence a smaller cluster size. We also describe optimal design under constraints on the number of clusters or cluster size in one or both arms. This methodology allows trialists to consider a range for the number of clusters in the trial and for each to identify the optimal design. Except if there is clear prior evidence for the ICC and variance by arm, a range of values will need to be considered. Researchers should choose a design with adequate power across the range, while also keeping enough clusters in each arm to permit the intended analysis method.

Anopheles bionomics in a malaria endemic area of southern Thailand

BackgroundIvermectin mass drug administration (MDA) could accelerate malaria elimination in the Greater Mekong Subregion. This study was performed to characterize the bionomics of Anopheles in Surat Thani province, Thailand.MethodsMosquitoes were collected via human landing collections between February and October 2019. Anopheles mosquitoes were morphologically identified to species. Primary Anopheles malaria vectors were dissected to assess parity status, and a subset were evaluated for molecular identification and Plasmodium detection.ResultsA total of 17,348 mosquitoes were collected during the study period; of these, 5777 were Anopheles mosquitoes. Morphological studies identified 15 Anopheles species, of which the most abundant were Anopheles minimus (s.l.) (87.16%, n = 5035), An. dirus s.l. (7.05%, n = 407) and An. barbirostris s.l. (2.86%, n = 165). Molecular identification confirmed that of the An. minimus s.l. mosquitoes collected, 99.80% were An. minimus (s.s.) (n = 484) and 0.2% were An. aconitus (n = 1), of the An. dirus (s.l.) collected, 100% were An. baimaii (n = 348), and of the An. maculatus (s.l.) collected, 93.62% were An. maculatus (s.s.) (n = 44) and 6.38% were An. sawadwongporni (n = 3). No Anopheles mosquito tested was Plasmodium positive (0/879). An average of 11.46 Anopheles were captured per collector per night. There were differences between species in hour of collection (Kruskal–Wallis H-test: χ2 = 80.89, P < 0.0001, n = 5666), with more An. barbirostris (s.l.) and An. maculatus (s.l.) caught earlier compared to An. minimus (s.l.) (P = 0.0001 and P < 0.0001, respectively) and An. dirus (s.l.) (P = 0.0082 and P < 0.001, respectively). The proportion of parous An. minimus (s.l.) captured by hour increased throughout the night (Wald Chi-square: χ2 = 17.31, P = 0.000, odds ratio = 1.0535, 95% confidence interval 1.0279–1.0796, n = 3400). Overall, An. minimus (s.l.) parity was 67.68% (2375/3509) with an intra-cluster correlation of 0.0378. A power calculation determined that an An. minimus (s.l.) parity reduction treatment effect size = 34%, with four clusters per treatment arm and a minimum of 300 mosquitoes dissected per cluster, at an α = 0.05, will provide 82% power to detect a significant difference following ivermectin MDA.ConclusionsThe study area in Surat Thani province is an ideal location to evaluate the impact of ivermectin MDA on An. minimus parity.Graphical abstract

Characteristics and practices of school-based cluster randomised controlled trials for improving health outcomes in pupils in the United Kingdom: a methodological systematic review

BackgroundCluster randomised trials (CRTs) are increasingly used to evaluate non-pharmacological interventions for improving child health. Although methodological challenges of CRTs are well documented, the characteristics of school-based CRTs with pupil health outcomes have not been systematically described. Our objective was to describe methodological characteristics of these studies in the United Kingdom (UK).MethodsMEDLINE was systematically searched from inception to 30th June 2020. Included studies used the CRT design in schools and measured primary outcomes on pupils. Study characteristics were described using descriptive statistics.ResultsOf 3138 articles identified, 64 were included. CRTs with pupil health outcomes have been increasingly used in the UK school setting since the earliest included paper was published in 1993; 37 (58%) studies were published after 2010. Of the 44 studies that reported information, 93% included state-funded schools. Thirty six (56%) were exclusively in primary schools and 24 (38%) exclusively in secondary schools. Schools were randomised in 56 studies, classrooms in 6 studies, and year groups in 2 studies. Eighty percent of studies used restricted randomisation to balance cluster-level characteristics between trial arms, but few provided justification for their choice of balancing factors. Interventions covered 11 different health areas; 53 (83%) included components that were necessarily administered to entire clusters. The median (interquartile range) number of clusters and pupils recruited was 31.5 (21 to 50) and 1308 (604 to 3201), respectively. In half the studies, at least one cluster dropped out. Only 26 (41%) studies reported the intra-cluster correlation coefficient (ICC) of the primary outcome from the analysis; this was often markedly different to the assumed ICC in the sample size calculation. The median (range) ICC for school clusters was 0.028 (0.0005 to 0.21).ConclusionsThe increasing pool of school-based CRTs examining pupil health outcomes provides methodological knowledge and highlights design challenges. Data from these studies should be used to identify the best school-level characteristics for balancing the randomisation. Better information on the ICC of pupil health outcomes is required to aid the planning of future CRTs. Improved reporting of the recruitment process will help to identify barriers to obtaining representative samples of schools.

Evaluation of Psychometric Properties of the Persian Version of Brief Male Sexual Function Inventory: A Cross-Sectional Study.

IntroductionMale sexual dysfunction is a common problem, and there are many self‐report questionnaires for measuring sexual function among men; however, the Brief Male Sexual Function Inventory (BSFI) is a tool that has 5 subscales, which is more complete than others. a validated self‐report questionnaire, in the local language with modest expressions is required for men.AimTo determine the validity and reliability of the Persian version of the BSFI among men.MethodsThis cross-sectional study was conducted on 200 males. The sampling process was performed in several stages from health centers. After the accomplishment of the standard process of back-translating the questionnaire from English to Persian, its face, content, and construct validity were evaluated. The collected data were analyzed using confirmatory factor analysis, multivariate analysis of variance, and Pearson correlation coefficient. To determine the reliability of the instrument, the test-retest method was used with 2 weeks interval and the Cronbach's alpha coefficient method was applied to check the internal homogeneity.Main Outcome MeasuresReliability (internal consistency and test-retest) and validity were assessedResultsAccording to the research findings, confirmatory factor analysis had an acceptable fit. By modifying the measurement model and fitting the final model, the fitting indices were obtained as the following: Chi-square statistic = 21.63, NPAR = 36, P = .001 > 0.05; Tucker-Lewis index = 0.956; comparative fit indices = 0.976; Normed Fit Index = 0.952; and root mean square error of approximation = 0.068. These values indicated that the obtained model had a good fit for the data. Moreover, Cronbach's alpha and intra-cluster correlation coefficients of the whole questionnaire were calculated at 0.893 and 0.893, respectively (confidence interval between 0.811–0.950), showing the internal consistency of the items in the whole questionnaire and domain.ConclusionThe BSFI questionnaire showed a 5-factor structure similar to the original structure and the 11-item Persian version of the questionnaire of male sexual function can be considered a valid and reliable tool to assess the level of male sexual function. Rezaei N, Sharifi N, Fathnezhad-Kazemi A, et al. Evaluation of Psychometric Properties of the Persian Version of Brief Male Sexual Function Inventory: A Cross-Sectional Study. Sex Med 2021;9:100409.

Anemia design effects in cluster surveys of women and young children in refugee settings.

BackgroundNutrition surveys in many refugee settings routinely estimate anemia prevalence in high-risk population groups. Given the lack of information on anemia design effects (DEFF) observed in surveys in these settings, the goal of this paper is to better understand the magnitude and distribution of DEFFs and intracluster correlation coefficients (ICCs) in order to inform future survey design.MethodsTwo-stage cluster surveys conducted during 2013–2016 were included if they measured hemoglobin in refugee children aged 6–59 months and/or non-pregnant women aged 15–49 years. Prevalence of anemia, anemia DEFFs and ICCs, mean cluster size, number of clusters, and total sample size were calculated per-survey for non-pregnant women and children. Non-parametric tests were used to assess differences and correlations of ICC and DEFF between women and children and inter-regional differences.ResultsEighty-seven unique cluster surveys from nine countries were included in this analysis. More than 90% of all surveys had ICC values for anemia below 0.10. Median ICC for children was 0.032 (IQR: 0.015–0.048), not significantly different from that observed for non-pregnant women for whom the median was 0.024 (IQR: -0.002–0.055). DEFFs were significantly higher for children [1.54 (IQR: 1.21–1.82)] versus women [1.20 (IQR: 0.99–1.46)]. Regional differences in DEFFs and ICCs were observed.ConclusionsBoth ICCs and DEFF were relatively small for both non-pregnant women and preschool children and fall in a narrow range. Differences in ICCs between women and children were non-significant, suggesting similar inter-cluster distributions of anemia; significant differences in DEFF were likely attributable to differing cluster sizes. Given regional differences in both ICCs and DEFFs, location-specific values are preferred. However, in the absence of other context-specific information, we suggest using DEFFs of 1.4–1.8 if mean cluster size is around 20, and DEFFs of 1.2–1.4 if mean cluster size is around 10.

GEECORR: A SAS macro for regression models of correlated binary responses and within-cluster correlation using generalized estimating equations

Background and objectives: Generalized estimating equations (GEE) provide population-averaged model inference for longitudinal and clustered outcomes via a generalized linear model for the effect of explanatory variables on the marginal mean, while intra-cluster correlations are ordinarily treated as nuisance parameters. Software to richly parameterize and conduct inference for complex correlation structures in the marginal modeling framework is scarce.Methods: A SAS macro, GEECORR, has been developed for the analysis of clustered binary data based on GEE to include additional estimating equations for modeling pairwise correlation between binary variates as a function of covariates.Results: We illustrate the macro in a surveillance study with repeated measures, a longitudinal study, and a study with biological clustering.Conclusions: This article provides an overview of the GEE method consisting of a pair of estimating equations, describes the features and capabilities of the GEECORR macro including regression diagnostics and finite-sample bias-corrected covariance estimators, and demonstrates the macro usage for three studies.

A Bayesian Estimator of the Intracluster Correlation Coefficient from Correlated Binary Responses

Clustered binary samples arise often in biomedical investigations. An important feature of such samples is that the binary responses within clusters tend to be correlated. The Beta-Binomial model is commonly applied to account for the intra-cluster correlation – the correlation between responses within the clusters – among dichotomous outcomes in cluster sampling. The intracluster correlation coefficient (ICC) quantifies this correlation or level of similarity. In this paper, we propose Bayesian point and interval estimators for the ICC under the Beta-Binomial model. Using Laplace’s method, the asymptotic posterior distribution of the ICC is approximated by a normal distribution. The posterior mean of this normal density is used as a central point estimator for the ICC, and 95% credible sets are calculated. A Monte Carlo simulation is used to evaluate the coverage probability and average length of the credible set of the proposed interval estimator. The simulations indicate that for the situation when the number of clusters is above 40, the underlying mean response probability falls in the range of [0.3;0.7], and the underlying ICC values are ≤ 0.4, the proposed interval estimator performs quite well and attains the correct coverage level. Even for number of clusters as small as 20, the proposed interval estimator may still be useful in the case of small ICC (≤ 0.2).

Epidemics of Chikungunya, Zika, and COVID-19 Reveal Bias in Case-Based Mapping

Background: In recent years, tropical countries have faced consecutive explosive epidemics of chikungunya, Zika, and now COVID-19, each of which have featured large proportions of subclinical infections. Spatial studies of epidemics typically use case-only datasets to estimate incidence rates (cases/total population), often misinterpreting them as infection risks (infections/total population) or disease risks (cases/infected population). Methods: We examined these three measures in a pediatric cohort (N≈3,000) over two chikungunya epidemics and one Zika epidemic and in a household cohort (N=1,793) over one COVID-19 epidemic in Nicaragua. We used generalized additive models, the intra-cluster correlation coefficient, cluster detection analyses, geostatistical models, and spatiotemporal models to characterize the spatial epidemiology of each epidemic and examine the limitations of the case-only incidence rate. Results: Across analyses and all epidemics, case-based incidence rates considerably underestimated both infection and disease risks, resulting in a high and spatially non-uniform degree of bias. The spatial infection risk differed from the spatial disease risk, showing that areas with many infections did not necessarily have many cases. Typical case-only approaches precluded a full understanding of the spatial contours of immunity. Standard incidence cluster analyses unpredictably resembled either clusters of infection or disease risk across all epidemics. Spatiotemporal modeling of disease incidence revealed less pronounced dynamics than spatiotemporal modeling of infection risk. Interpretation: During epidemics of pathogens that cause large numbers of subclinical infections, misinterpreting incidence rates, as is common, results in substantial bias, a general finding applicable to many pathogens of high human concern. Funding Information: This study was supported by grants R01 AI099631 (AB), P01 AI106695 (EH), R01 AI120997 (AG), and U19 AI118610 (EH) from the National Institute of Allergy and Infectious Diseases of the National Institutes of Health; the National Institutes of Health Centers of Excellence for Influenza Research and Surveillance [contract: HHS 272201400006C (AG)]; and the Open Philanthropy Project Fund for the production of recombinant SARS-CoV-2 spike protein, its receptor binding domain, and antibodies at the University of Michigan Center for Structural Biology. FBC was partially supported by a supplement to grant P01 AI106695. Declaration of Interests: Authors declare that they have no competing interests. Ethics Approval Statement: The Pediatric Dengue Cohort Study (PDCS) was approved by Institutional Review Boards of the University of California, Berkeley, the University of Michigan, Ann Arbor, and the Nicaraguan Ministry of Health. The Household Influenza Cohort Study (HICS) was approved by the University of Michigan, Ann Arbor and the Nicaraguan Ministry of Health. Participants’ parents or legal guardians provided written informed consent. Subjects six years and older provided verbal assent.

Association of intracluster correlation measures with outcome prevalence for binary outcomes in cluster randomised trials.

In cluster randomised trials, a measure of intracluster correlation such as the intraclass correlation coefficient (ICC) should be reported for each primary outcome. Providing intracluster correlation estimates may help in calculating sample size of future cluster randomised trials and also in interpreting the results of the trial from which they are derived. For a binary outcome, the ICC is known to be associated with its prevalence, which raises at least two issues. First, it questions the use of ICC estimates obtained on a binary outcome in a trial for sample size calculations in a subsequent trial in which the same binary outcome is expected to have a different prevalence. Second, it challenges the interpretation of ICC estimates because they do not solely depend on clustering level. Other intracluster correlation measures proposed for clustered binary data settings include the variance partition coefficient, the median odds ratio and the tetrachoric correlation coefficient. Under certain assumptions, the theoretical maximum possible value for an ICC associated with a binary outcome can be derived, and we proposed the relative deviation of an ICC estimate to this maximum value as another measure of the intracluster correlation. We conducted a simulation study to explore the dependence of these intracluster correlation measures on outcome prevalence and found that all are associated with prevalence. Even if all depend on prevalence, the tetrachoric correlation coefficient computed with Kirk's approach was less dependent on the outcome prevalence than the other measures when the intracluster correlation was about 0.05. We also observed that for lower values, such as 0.01, the analysis of variance estimator of the ICC is preferred.

Development and Psychometric Evaluation of the Gerontological Nursing Competence Questionnaire in Iranian Hospitals.

This study evaluated the Gerontological Nursing Competence Questionnaire-Farsi version (GNCQ-F) in Iranian hospitals. This exploratory sequential study used a mixed-methods design. The questionnaire items were developed based on a qualitative descriptive exploratory study and a review of the literature. The psychometric properties of the initial questionnaire including face validity, content validity, construct validity, internal consistency, and stability were evaluated. Exploratory factor analysis resulted in the extraction of 42 items in five factors including care competence and patient/older adult and family empowerment, ethical and compassionate care competence, patient safety competence, continuous professional development and self-care competence, and collaborative and team care competence. Cronbach's alpha coefficient confirmed the internal consistency of each subscale and the entire tool (0.942). The stability reliability was 0.973 and was determined by calculating the intra-cluster correlation coefficient. Nurses can use this self-evaluation instrument to assess their gerontological nursing competence. [J Contin Educ Nurs. 2021;52(7):335-343.].

Ivermectin treatment in humans for reducing malaria transmission.

BackgroundMalaria is transmitted through the bite of Plasmodium‐infected adult female Anopheles mosquitoes. Ivermectin, an anti‐parasitic drug, acts by killing mosquitoes that are exposed to the drug while feeding on the blood of people (known as blood feeds) who have ingested the drug. This effect on mosquitoes has been demonstrated by individual randomized trials. This effect has generated interest in using ivermectin as a tool for malaria control.ObjectivesTo assess the effect of community administration of ivermectin on malaria transmission.Search methodsWe searched the Cochrane Infectious Diseases Group (CIDG) Specialized Register, CENTRAL, MEDLINE, Embase, LILACS, Science Citation index ‐ expanded, the World Health Organization (WHO) International Clinical Trials Registry Platform, ClinicalTrials.gov, and the National Institutes of Health (NIH) RePORTER database to 14 January 2021.We checked the reference lists of included studies for other potentially relevant studies, and contacted researchers working in the field for unpublished and ongoing trials.Selection criteriaWe included cluster‐randomized controlled trials (cRCTs) that compared ivermectin, as single or multiple doses, with a control treatment or placebo given to populations living in malaria‐endemic areas, in the context of mass drug administration. Primary outcomes were prevalence of malaria parasite infection and incidence of clinical malaria in the community.Data collection and analysisTwo review authors independently extracted data on the number of events and the number of participants in each trial arm at the time of assessment. For rate data, we noted the total time at risk in each trial arm. To assess risk of bias, we used Cochrane's RoB 2 tool for cRCTs. We documented the method of data analysis, any adjustments for clustering or other covariates, and recorded the estimate of the intra‐cluster correlation (ICC) coefficient.We re‐analysed the trial data provided by the trial authors to adjust for cluster effects. We used a Poisson mixed‐effect model with small sample size correction, and a cluster‐level analysis using the linear weighted model to adequately adjust for clustering. Main resultsWe included one cRCT and identified six ongoing trials. The included cRCT examined the incidence of malaria in eight villages in Burkina Faso, randomized to two arms. Both trial arms received a single dose of ivermectin 150 µg/kg to 200 µg/kg, together with a dose of albendazole. The villages in the intervention arm received an additional five doses of ivermectin, once every three weeks. Children were enrolled into an active cohort, in which they were repeatedly screened for malaria infection. The primary outcome was the cumulative incidence of uncomplicated malaria in a cohort of children aged five years and younger, over the 18‐week study. We judged the study to be at high risk of bias, as the analysis did not account for clustering or correlation between participants in the same village.The study did not demonstrate an effect of Ivermectin on the cumulative incidence of uncomplicated malaria in the cohort of children over the 18‐week study (risk ratio 0.86, 95% confidence interval (CI) 0.62 to 1.17; P = 0.2607; very low‐certainty evidence).Authors' conclusionsWe are uncertain whether community administration of ivermectin has an effect on malaria transmission, based on one trial published to date.

Imputing intracluster correlation coefficients from a posterior predictive distribution is a feasible method of dealing with unit of analysis errors in a meta-analysis of cluster RCTs

BackgroundIncorporating cluster randomized trials (CRTs) into meta-analyses is challenging because appropriate standard errors of study estimates accounting for clustering are not always reported. Systematic reviews of CRTs often use a single constant external estimate of the intraclass correlation coefficient (ICC) to adjust study estimate standard errors and facilitate meta-analyses; an approach that fails to account for possible variation of ICCs among studies and the imprecision with which they are estimated. Using a large systematic review of the effects of diabetes quality improvement interventions, we investigated whether we could better account for ICC variation and uncertainty in meta-analyzed effect estimates by imputing missing ICCs from a posterior predictive distribution constructed from a database of relevant ICCs. MethodsWe constructed a dataset of ICC estimates from applicable studies. For outcomes with two or more available ICC estimates, we constructed posterior predictive ICC distributions in a Bayesian framework. For a selected continuous outcome, glycosylated hemoglobin (HbA1c), we compared the impact of incorporating a single constant ICC versus imputing ICCs drawn from the posterior predictive distribution when estimating the effect of intervention components on post treatment mean in a case study of diabetes quality improvement trials. ResultsUsing internal and external ICC estimates, we were able to construct a database of 59 ICCs for 12 of the 13 review outcomes (range 1–10 per outcome) and estimate the posterior predictive ICC distribution for 11 review outcomes. Synthesized results were not markedly changed by our approach for HbA1c. ConclusionBuilding posterior predictive distributions to impute missing ICCs is a feasible approach to facilitate principled meta-analyses of cluster randomized trials using prior data. Further work is needed to establish whether the application of these methods leads to improved review inferences for different reviews based on different factors (e.g., proportion of CRTs and CRTs with missing ICCs, different outcomes, variation and precision of ICCs).

Mortality review as a tool to assess the contribution of healthcare-associated infections to death: results of a multicentre validity and reproducibility study, 11 European Union countries, 2017 to 2018.

IntroductionThe contribution of healthcare-associated infections (HAI) to mortality can be estimated using statistical methods, but mortality review (MR) is better suited for routine use in clinical settings. The European Centre for Disease Prevention and Control recently introduced MR into its HAI surveillance.AimWe evaluate validity and reproducibility of three MR measures.MethodsThe on-site investigator, usually an infection prevention and control doctor, and the clinician in charge of the patient independently reviewed records of deceased patients with bloodstream infection (BSI), pneumonia, Clostridioides difficile infection (CDI) or surgical site infection (SSI), and assessed the contribution to death using 3CAT: definitely/possibly/no contribution to death; WHOCAT: sole cause/part of causal sequence but not sufficient on its own/contributory cause but unrelated to condition causing death/no contribution, based on the World Health Organization’s death certificate; QUANT: Likert scale: 0 (no contribution) to 10 (definitely cause of death). Inter-rater reliability was assessed with weighted kappa (wk) and intra-cluster correlation coefficient (ICC). Reviewers rated the fit of the measures.ResultsFrom 2017 to 2018, 24 hospitals (11 countries) recorded 291 cases: 87 BSI, 113 pneumonia , 71 CDI and 20 SSI. The inter-rater reliability was: 3CAT wk 0.68 (95% confidence interval (CI): 0.61–0.75); WHOCAT wk 0.65 (95% CI: 0.58–0.73); QUANT ICC 0.76 (95% CI: 0.71–0.81). Inter-rater reliability ranged from 0.72 for pneumonia to 0.52 for CDI. All three measures fitted ‘reasonably’ or ‘well’ in > 88%.ConclusionFeasibility, validity and reproducibility of these MR measures was acceptable for use in HAI surveillance.

Intra-cluster correlations from the CLustered OUtcome Dataset bank to inform the design of longitudinal cluster trials.

Sample size calculations for longitudinal cluster randomised trials, such as crossover and stepped-wedge trials, require estimates of the assumed correlation structure. This includes both within-period intra-cluster correlations, which importantly differ from conventional intra-cluster correlations by their dependence on period, and also cluster autocorrelation coefficients to model correlation decay. There are limited resources to inform these estimates. In this article, we provide a repository of correlation estimates from a bank of real-world clustered datasets. These are provided under several assumed correlation structures, namely exchangeable, block-exchangeable and discrete-time decay correlation structures. Longitudinal studies with clustered outcomes were collected to form the CLustered OUtcome Dataset bank. Forty-four available continuous outcomes from 29 datasets were obtained and analysed using each correlation structure. Patterns of within-period intra-cluster correlation coefficient and cluster autocorrelation coefficients were explored by study characteristics. The median within-period intra-cluster correlation coefficient for the discrete-time decay model was 0.05 (interquartile range: 0.02-0.09) with a median cluster autocorrelation of 0.73 (interquartile range: 0.19-0.91). The within-period intra-cluster correlation coefficients were similar for the exchangeable, block-exchangeable and discrete-time decay correlation structures. Within-period intra-cluster correlation coefficients and cluster autocorrelations were found to vary with the number of participants per cluster-period, the period-length, type of cluster (primary care, secondary care, community or school) and country income status (high-income country or low- and middle-income country). The within-period intra-cluster correlation coefficients tended to decrease with increasing period-length and slightly decrease with increasing cluster-period sizes, while the cluster autocorrelations tended to move closer to 1 with increasing cluster-period size. Using the CLustered OUtcome Dataset bank, an RShiny app has been developed for determining plausible values of correlation coefficients for use in sample size calculations. This study provides a repository of intra-cluster correlations and cluster autocorrelations for longitudinal cluster trials. This can help inform sample size calculations for future longitudinal cluster randomised trials.

Prevalence of stillbirth and its associated factors in East Africa: generalized linear mixed modeling

BackgroundStillbirth is the most frequently reported adverse pregnancy outcome worldwide, which imposes significant psychological and economic consequences to mothers and affected families. East African countries account for one-third of the 2.6 million stillbirths globally. Though stillbirth is a common public health problem in East African countries, there is limited evidence on the pooled prevalence and associated factors of stillbirth in East Africa. Therefore, this study aimed to investigate the prevalence of stillbirth and its associated factors in East Africa.MethodsThis study was based on the most recent Demographic and Health Surveys (DHSs) of 12 East African countries. A total weighted sample of 138,800 reproductive-age women who gave birth during the study period were included in this study. The prevalence of stillbirth with the 95% Confidence Interval (CI) was reported using a forest plot. A mixed-effect binary logistic regression analysis was done to identify significantly associated factors of stillbirth. Since the DHS data has hierarchical nature, the presence of clustering effect was assessed using the Likelihood Ratio (LR) test, and Intra-cluster Correlation Coefficient (ICC), and deviance were used for model comparison. Variables with a p-value of less than 0.2 in the bi-variable analysis were considered for the multivariable analysis. In the multivariable mixed-effect binary logistic regression analysis, the Adjusted Odds Ratio (AOR) with 95% CI were reported to declare the strength and significance of the association.ResultsThe prevalence of stillbirth in East Africa was 0.86% (95% CI: 0.82, 0.91) ranged from 0.39% in Kenya to 2.28% in Burundi. In the mixed-effect analysis; country, women aged 25–34 years (AOR = 1.27, 95% CI: 1.11, 1.45), women aged ≥ 35 years (AOR = 1.19, 95% CI: 1.01, 1.44), poor household wealth (AOR = 1.07, 95% CI: 1.02, 1.23), women who didn’t have media exposure (AOR = 1.11, 95% CI: 1.01, 1.25), divorced/widowed/separated marital status (AOR = 2.99, 95% CI: 2.04, 4.39), caesarean delivery (AOR = 1.81, 95% CI: 1.52, 2.15), preceding birth interval < 24 months (AOR = 1.15, 95% CI: 1.06, 1.24), women attained secondary education or above (AOR = 0.68, 95% CI: 0.56, 0.81) and preceding birth interval ≥ 49 months (AOR = 1.45, 95% CI: 1.28, 1.65) were significantly associated with stillbirth.ConclusionsStillbirth remains a major public health problem in East Africa, which varied significantly across countries. These findings highlight the weak health care system of East African countries. Preceding birth interval, county, maternal education media exposure, household wealth status, marital status, and mode of delivery were significantly associated with stillbirth. Therefore, public health programs enhancing maternal education, media access, and optimizing birth spacing should be designed to reduce the incidence of stillbirth.

Antibiotic Prescription in Veterinary Consultations in Bhutan: A Retrospective Cross-Sectional Study.

The veterinary prescription of antibiotics in low- and middle-income countries (LMIC) remains largely undocumented. In Bhutan, however, the national veterinary service keeps records of their activities and prescriptions, which offer an opportunity to establish a benchmark to assess the use of these agents in this and other LMIC. A cross-sectional retrospective study was designed and 2,266 handwritten veterinary records from 2017 were sampled from 23 animal health premises (AHPs) to estimate individual and an overall proportion of consultations that resulted in an antibiotic prescription. The frequency of antibiotic prescription per species, type of AHP, and according to WHO's AWaRe index and OIE list of priority antimicrobials were also explored. It was estimated that 31% (95% confidence interval: 29–33%; intracluster correlation: 0.03) of the veterinary consultations resulted in an antibiotic prescription. The incidence of antibiotic prescription was highest in consultations of poultry across AHP. Across species, diarrhea and wounds were frequently treated with broad-spectrum antibiotics including sulfonamides, tetracyclines, trimethoprim + sulfa, and penicillin. Between 45% and 70% antibiotics prescribed correspond to AWaRe's access group and up to 25% to AWaRe's watch group. Over 70% of antibiotics dispensed in veterinary consultations for any species correspond to the OIE's veterinary critically important antimicrobial agents. Overall, the study demonstrated positive features of veterinary antimicrobial stewardship in Bhutan, given the conservative proportion of consultation that results in this type of prescription and the type of antibiotic prescribed. Although the veterinary service closely follows the Bhutanese Standard Treatment Guidelines, the prescription of antibiotics to key species should be closely monitored. Our study suggests that further improvements of antibiotic stewardship can be achieved through standardisation of antibiotic prescription to some species, a revision of the guidelines toward reducing the prescription of antibiotics of high relevance for human medicine, and by including details of clinical investigation, use of tests, and treatment outcomes in veterinary consultation records.