The impact of iterative removal of low-information cluster-period cells from a stepped wedge design.

BACKGROUND: Standard stepped wedge trials, where clusters switch from the control to the intervention condition in a staggered manner, can be costly and burdensome. Recent work has shown that the amount of information contributed by each cluster in each period differs, with some cluster-periods contributing a relatively small amount of information. We investigate the patterns of the information content of cluster-period cells upon iterative removal of low-information cells, assuming a model for continuous outcomes with constant cluster-period size, categorical time period effects, and exchangeable and discrete-time decay intracluster correlation structures. METHODS: We sequentially remove pairs of "centrosymmetric" cluster-period cells from an initially complete stepped wedge design which contribute the least amount of information to the estimation of the treatment effect. At each iteration, we update the information content of the remaining cells, determine the pair of cells with the lowest information content, and repeat this process until the treatment effect cannot be estimated. RESULTS: We demonstrate that as more cells are removed, more information is concentrated in the cells near the time of the treatment switch, and in "hot-spots" in the corners of the design. For the exchangeable correlation structure, removing the cells from these hot-spots leads to a marked reduction in study precision and power, however the impact of this is lessened for the discrete-time decay structure. CONCLUSIONS: Removing cluster-period cells distant from the time of the treatment switch may not lead to large reductions in precision or power, implying that certain incomplete designs may be almost as powerful as complete designs.

How representative is the Victorian Emergency Minimum Dataset (VEMD) for population-based injury surveillance in Victoria? A retrospective observational study of administrative healthcare data.

OBJECTIVE: The Victorian Emergency Minimum Dataset (VEMD) is a key data resource for injury surveillance. The VEMD collects emergency department data from 39 public hospitals across Victoria; however, rural emergency care services are not well captured. The aim of this study is to determine the representativeness of the VEMD for injury surveillance. DESIGN: A retrospective observational study of administrative healthcare data. SETTING AND PARTICIPANTS: Injury admissions in 2014/2015-2018/2019 were extracted from the Victorian Admitted Episodes Dataset (VAED) which captures all Victorian hospital admissions; only cases that arrived through a hospital's emergency department (ED) were included. Each admission was categorised as taking place in a VEMD-contributing versus a non-VEMD hospital. RESULTS: There were 535 477 incident injury admissions in the study period, of which 517 207 (96.6%) were admitted to a VEMD contributing hospital. Male gender (OR 1.13 (95% CI 1.10 to 1.17)) and young age (age 0-14 vs 45-54 years, OR 4.68 (95% CI 3.52 to 6.21)) were associated with VEMD participating (vs non-VEMD-participating) hospitals. Residing in regional/rural areas was negatively associated with VEMD participating (vs non-VEMD participating) hospitals (OR=0.11 (95% CI 0.10 to 0.11)). Intentional injury (assault and self-harm) was also associated with VEMD participation. CONCLUSIONS: VEMD representativeness is largely consistent across the whole of Victoria, but varies vastly by region, with substantial under-representation of some areas of Victoria. By comparison, for injury surveillance, regional rates are more reliable when based on the VAED. For local ED-presentation rates, the bias analysis results can be used to create weights, as a temporary solution until rural emergency services injury data is systematically collected and included in state-wide injury surveillance databases.

Evaluation of COVID-19 Treatments in Iran in Comparison with Local Therapeutic Recommendations: A Population-Level Study on Utilization and Costs of Prescription Drugs.

Objective: In this study, we assess population-level data of COVID-19 treatments in Iran compared to Ministry of Health (MOH)-published guidelines to gain a better insight into the quality of care for this disease. Methods: National sales data of each recommended and nonrecommended COVID-19 medicine were used to proxy utilization between March 21, 2020, and March 21, 2021, or Iranian year 1399. COVID-19-attributed sales volume and number of patients were estimated by adjusting sales data with pre-COVID-19 average growth rate, recommended dose, and duration of treatment. Next, they were compared with the MOH guidelines in outpatient and inpatient settings. Furthermore, the list of top 10 molecules of the market and top 10 COVID-19-indicated molecules in terms of values were extracted to assess the economic burden of COVID-19 prescription drugs and their share. Findings: The estimated number of patients receiving COVID-19 treatments in some outpatient medicines such as recommended hydroxychloroquine was over 2.2 million. Favipiravir and remdesivir were collectively about two inpatient medicines 260,000; however, neither of these two medicines was recommended in the MOH guidelines. In some fewer specific medicines such as dexamethasone, prednisolone, azithromycin, and naproxen, the estimated number of COVID-19-attributed patients were incomparable with the officially announced number of confirmed cases in the year of study, which could be related to nonconfirmed diagnosed cases, irrational use, or prescribing, or limitations of our data and study. The total COVID-19-attributed market of candidate medicines was over 15 trillion IR Rials (almost 4.3% of the total market). Remdesivir, with over 60% of the total COVID-19 attributed market, followed by favipiravir, was among the highest value medicines. Conclusion: Despite the release of the COVID-19 guideline by Iran MOH, misalignment in the enforcement of decisions was a serious weakness (cases of favipiravir and remdesivir). This weakness led to some economic burden on the health-care system and raised ethical concerns.

Level and trend of total plasma cholesterol in national and subnational of Iran: a systematic review and age-spatio-temporal analysis from 1990 to 2016.

Purpose: We aimed to estimate the level and trend of plasma cholesterol and raised total cholesterol (TC > 200 mg/dl) prevalence at national and subnational level of Iran. Methods: Nine national surveys and 27 studies, encompassing 3,505 unique points on over 500,000 adults, aged > 25 years with a report of laboratory measurement of TC were found. Age-spatio-temporal model and Gaussian Process Regression were used to estimate mean TC for each sex, 5-year age groups, and 31 provinces from 1990 to 2016. Results: At national level, age-standardized prevalence of TC > 200 mg/dL has decreased from 57·2%(53·3-61·1) to 22·4%(20·5-24·3) in women and 53·2%(49·1-57·3) to 18·0%(16·4-19·6) in men. TC distribution presented a condensation between 170-200 mg/dL. At subnational level, decreasing and converging patterns of raised TC prevalence were detected. Conclusion: The decrease in raised TC is likely the result of statin widespread use, food industry improvements, and the expanded primary health care. Supplementary Information: The online version contains supplementary material available at 10.1007/s40200-022-01052-w.

Use of information criteria for selecting a correlation structure for longitudinal cluster randomised trials.

BACKGROUND: When designing and analysing longitudinal cluster randomised trials, such as the stepped wedge, the similarity of outcomes from the same cluster must be accounted for through the choice of a form for the within-cluster correlation structure. Several choices for this structure are commonly considered for application within the linear mixed model paradigm. The first assumes a constant intra-cluster correlation for all pairs of outcomes from the same cluster (the exchangeable/Hussey and Hughes model); the second assumes that correlations of outcomes measured in the same period are higher than outcomes measured in different periods (the block exchangeable model) and the third is the discrete-time decay model, which allows the correlation between pairs of outcomes to decay over time. Currently, there is limited guidance on how to select the most appropriate within-cluster correlation structure. METHODS: We simulated continuous outcomes under each of the three considered within-cluster correlation structures for a range of design and parameter choices, and, using the ASReml-R package, fit each linear mixed model to each simulated dataset. We evaluated the performance of the Akaike and Bayesian information criteria for selecting the correct within-cluster correlation structure for each dataset. RESULTS: For smaller total sample sizes, neither criteria performs particularly well in selecting the correct within-cluster correlation structure, with the simpler exchangeable model being favoured. Furthermore, in general, the Bayesian information criterion favours the exchangeable model. When the cluster auto-correlation (which defines the degree of dependence between observations in adjacent time periods) is large and number of periods is small, neither criteria is able to distinguish between the block exchangeable and discrete time decay models. However, for increasing numbers of clusters, periods, and subjects per cluster period, both the Akaike and Bayesian information criteria perform increasingly well in the detection of the correct within-cluster correlation structure. CONCLUSIONS: With increasing amounts of data, be they number of clusters, periods or subjects per cluster period, both the Akaike and Bayesian information criteria are increasingly likely to select the correct correlation structure. We recommend that if there are sufficient data available when planning a trial, that the Akaike or Bayesian information criterion is used to guide the choice of within-cluster correlation structure in the absence of other compelling justifications for a specific correlation structure. We also suggest that researchers conduct supplementary analyses under alternate correlation structures to gauge sensitivity to the initial choice.

Evaluating equality in prescribing Novel Oral Anticoagulants (NOACs) in England: The protocol of a Bayesian small area analysis.

BACKGROUND: Atrial fibrillation (AF) is the most common cardiac arrhythmia, affecting about 1.6% of the population in England. Novel oral anticoagulants (NOACs) are approved AF treatments that reduce stroke risk. In this study, we estimate the equality in individual NOAC prescriptions with high spatial resolution in Clinical Commissioning Groups (CCGs) across England from 2014 to 2019. METHODS: A Bayesian spatio-temporal model will be used to estimate and predict the individual NOAC prescription trend on 'prescription data' as an indicator of health services utilisation, using a small area analysis methodology. The main dataset in this study is the "Practice Level Prescribing in England," which contains four individual NOACs prescribed by all registered GP practices in England. We will use the defined daily dose (DDD) equivalent methodology, as recommended by the World Health Organization (WHO), to compare across space and time. Four licensed NOACs datasets will be summed per 1,000 patients at the CCG-level over time. We will also adjust for CCG-level covariates, such as demographic data, Multiple Deprivation Index, and rural-urban classification. We aim to employ the extended BYM2 model (space-time model) using the RStan package. DISCUSSION: This study suggests a new statistical modelling approach to link prescription and socioeconomic data to model pharmacoepidemiologic data. Quantifying space and time differences will allow for the evaluation of inequalities in the prescription of NOACs. The methodology will help develop geographically targeted public health interventions, campaigns, audits, or guidelines to improve areas of low prescription. This approach can be used for other medications, especially those used for chronic diseases that must be monitored over time.

A Double-Blind, Randomized Comparison Study between Zytux™ vs MabThera® in Treatment of CLL with FCR Regimen: Non-Inferiority Clinical Trial.

BACKGROUND: Chronic lymphocytic leukemia (CLL) is characterized by accumulation of B cells in blood, lymphoid tissues and bone marrow. Addition of rituximab to CLL chemotherapy regimens has been associated with improved survival. The aim of this study was to establish efficacy and safety of Zytux™ in comparison to MabThera® in treatment of CLL. MATERIALS AND METHODS: Seventy CLL patients who met the criteria for entering the study were randomized into two groups (35 patients in each group). Both groups received Fludarabine and Cyclophosphamide plus Rituximab as part of the FCR regimen. Group A was treated with Zytux™, and group B was treated with MabThera®. A non-inferiority margin of 20% for the primary outcome was defined to examine the similarity between Zytux™ and MabThera®. RESULTS: Baseline demographic characteristics showed no statistically significant difference between the two groups. The two treatment groups were comparable in terms of laboratory and clinical findings, cellular index changes and CD (5, 19, 20 and 23) counts during therapy cycles and at the end of the treatment period. Regarding safety results, Zytux™ demonstrated a similar profile of adverse reactions in comparison to MabThera®. Moreover, the overall response rate was 88% and 89% for Zytux™ and MabThera®, respectively (CI -0.17, 0.18). CONCLUSION: Results showed non-inferiority of Zytux™ in terms of efficacy and adverse events as a biosimilar version of MabThera®.

Prevalence of cardio-metabolic risk factors in a nationally representative sample of Iranian adolescents: The CASPIAN-III Study.

Introduction: The aim of the present study is to explore the prevalence and mean of cardiometabolic risk factors and liver enzymes of Iranian adolescents living in regions with different socioeconomic status (SES). To the best of our knowledge this is the first study reporting these data at sub-national level in Iran. Methods: This multi-centric study was performed in 2009-2010 on a stratified multi-stage probability sample of 5940 students aged 10-18 years, living in urban and rural areas of 27 provinces of Iran. Trained healthcare professionals measured anthropometric indices, systolic and diastolic blood pressures (SBP, DBP) according to standard protocols. Fasting venous blood was examined for fasting blood sugar (FBS), lipid profile and liver enzymes including alanine aminotransferase (ALT) and aspartate aminotransferase (AST). We classified the country into four sub-national regions based on criteria of the combination of geography and SES. Mean and frequency of risk factors were compared across these regions. Results: The mean of body mass index had linear rise with increase in the regions' SES (P for trend <0.001). The mean levels of DBP, total cholesterol (TC), high-density lipoproteincholesterol (HDL-C), triglycerides (TG), FBS, ALT, and AST had linear association with regions' SES in the whole population and in both genders (P for trend < 0.05), whereas the corresponding figure was statistically significant for the mean SBP only in girls (P for trend: 0.03) and for the mean of LDL-C in the whole population and in boys (P for trend <0.001). In total and in both genders, there was an escalating trend in the prevalence of elevated FBS, TC and liver enzymes, low HDL-C, and metabolic syndrome by increase in the SES of the region(P for trend <0.01). Conclusion: This study proposes that in addition to national health policies on preventing cardiometabolic risk factors, specific interventions should be considered according to the regional SES level.

Wealth-related Inequality in Utilization of Antihypertensive Medicines in Iran: an Ecological Study on Population Level Data.

BACKGROUND: We aim to evaluate the trend of national and sub-national (provincial) utilization pattern of antihypertensive medicines in the Iranian population in the past decade and evaluate whether there is any wealth-related inequality in utilization of these medicines among different provinces. METHOD: Either fixed effect or random effect linear panel data model was used to check the effect of wealth index on utilization of all antihypertensive medicines and each class, adjusting for other covariates including years of schooling, urbanization, mean age, and food type of provinces. The principal component analysis was applied to make summery measures for covariates using available national datasets. RESULTS: The effect of wealth category on the utilization of all antihypertensive medicines among Iranian provinces was positive and significant (0.84; 95% CI: 0.09, 1.59). Accordingly as subgroup analysis, in BBs and CCBs classes, the effects of wealth category on utilization of medicines were positive and significant (0.36; 95% CI: 0.12, 0.60 and 0.27; 95% CI: 0.07, 0.40, respectively). However in ACEIs and Diuretics classes, the effects of wealth category were positive but not significant. In ARBs class, the effect of wealth on utilization was negative and not significant (-0.04; 95% CI: -0.27, 0.18). CONCLUSION: According to this study, an inequality could be observed in Iran related to wealth category in utilization of total antihypertensive medicines between provinces.

Detecting Diseases in Medical Prescriptions Using Data Mining Tools and Combining Techniques.

Data about the prevalence of communicable and non-communicable diseases, as one of the most important categories of epidemiological data, is used for interpreting health status of communities. This study aims to calculate the prevalence of outpatient diseases through the characterization of outpatient prescriptions. The data used in this study is collected from 1412 prescriptions for various types of diseases from which we have focused on the identification of ten diseases. In this study, data mining tools are used to identify diseases for which prescriptions are written. In order to evaluate the performances of these methods, we compare the results with Naïve method. Then, combining methods are used to improve the results. Results showed that Support Vector Machine, with an accuracy of 95.32%, shows better performance than the other methods. The result of Naive method, with an accuracy of 67.71%, is 20% worse than Nearest Neighbor method which has the lowest level of accuracy among the other classification algorithms. The results indicate that the implementation of data mining algorithms resulted in a good performance in characterization of outpatient diseases. These results can help to choose appropriate methods for the classification of prescriptions in larger scales.

National and sub-national prevalence, trend, and burden of asthma in Iran from 1990 to 2013; the study protocol.

BACKGROUND: Asthma is a chronic inflammatory airway disease caused or worsened by environmental factors in genetically vulnerable people. The study of national and sub-national burden of asthma aims to provide a quantitative method and valid estimates for the prevalence, incidence, and economic burden of asthma disease in Iran from 1990 to 2013 and this papers explains measures, data sources, methods, and challenges that we will use in the study. METHODS: In order to conduct this study, we will use all available unpublished data sources, including claim databases and data collected by the food and drug organization (FDO). Moreover, we will devise and run a systematic review of all studies and literature published about asthma epidemiology in Iran, which includes all cross-sectional, cohort and case-control studies with asthma epidemiology focus that are population based. In this study, we will use two statistical models, including spatio-temporal and multilevel autoregressive models to estimate mean and uncertainty intervals for the parameters under study by gender, age, year, and province. All programs will be written in R statistical packages (version 3.0.1). CONCLUSION: This study helps to obtain information concerning the variation among regions and provinces, and in general among sub-national divisions. Our study can be contribute to better allocation of resources, since it helps policymakers to recognize inequalities between regions and provinces and consequently help them to allocate resources more efficiently.

Comparison of two data mining techniques in labeling diagnosis to Iranian pharmacy claim dataset: artificial neural network (ANN) versus decision tree model.

BACKGROUND: This study aimed to evaluate and compare the prediction accuracy of two data mining techniques, including decision tree and neural network models in labeling diagnosis to gastrointestinal prescriptions in Iran. METHODS: This study was conducted in three phases: data preparation, training phase, and testing phase. A sample from a database consisting of 23 million pharmacy insurance claim records, from 2004 to 2011 was used, in which a total of 330 prescriptions were assessed and used to train and test the models simultaneously. In the training phase, the selected prescriptions were assessed by both a physician and a pharmacist separately and assigned a diagnosis. To test the performance of each model, a k-fold stratified cross validation was conducted in addition to measuring their sensitivity and specificity. RESULT: Generally, two methods had very similar accuracies. Considering the weighted average of true positive rate (sensitivity) and true negative rate (specificity), the decision tree had slightly higher accuracy in its ability for correct classification (83.3% and 96% versus 80.3% and 95.1%, respectively). However, when the weighted average of ROC area (AUC between each class and all other classes) was measured, the ANN displayed higher accuracies in predicting the diagnosis (93.8% compared with 90.6%). CONCLUSION: According to the result of this study, artificial neural network and decision tree model represent similar accuracy in labeling diagnosis to GI prescription.

Application of Gaussian Process Regression (GPR) in estimating under-five mortality levels and trends in Iran 1990 - 2013, study protocol.

BACKGROUND: Searching for the latest methods of estimating mortality rates is a major concern for researchers who are working in burden of diseases. Child mortality is an important indicator for assessing population health care services in a country. The National and Sub-national Burden of Diseases, Injuries, and Risk Factors (NASBOD) is conducted in Iran with comparative methods and definitions of Global Burden of Disease (GBD) 2010 to estimate major population health measures including child mortality rate. The need to have accurate and valid estimation of under-5 mortality rate led to apply more powerful and reliable methods. METHOD: The available datasets consist of under-five mortality rates from different sources including death registration systems and summary birth history (SBH) questions from censuses and Demographic Health Survey. These datasets are gathered at national and sub-national levels. We have five time series of under-five mortality rates from SBH method that each one contains 25-year time period. We also calculated Child mortality rates from death registration for 5 years. The main challenge is how to combine and integrate these different time series and how to produce unified estimates of child mortality rates during the course of study. By synthesizing the result of other models, Gaussian Process Regression (GPR) is used as the final stage for generating yearly child mortality rates in this study. GPR is a Bayesian technique that uses data information and defines several hierarchical prior parameters for model. In corporation of GPR and MCMC methods, predicted rates are updated using data and defined parameters in model. This method, also captures both sampling and non-sampling errors and provides uncertainty intervals. The existence of uncertainty for predicting mortality rate is one of the considerable advantages of GPR that distinguish it from other alternative methods. DISCUSSION: Estimating accurate and reliable child mortality rates at national and sub-national levels is one of the important parts of NASBOD project in Iran. Gaussian Process Regression with its special features improves achievement of this goal. GPR is a serious competitor for other supervised mortality predictive methods. This article aims to explain the application and preferences of GPR method in estimating child mortality rate.

The trend of national and sub-national burden of gastrointestinal and liver diseases in Iran 1990 to 2013; study protocol.

BACKGROUND: It is expected that gastrointestinal (GI) and liver diseases inflict considerable burden on health systems in Iran; therefore, highlighting the significance of GI disorders across the other most burdensome diseases requires comprehensive assessment and regular updates of the statistics of such diseases in Iran. OBJECTIVE: To assess in-depth sub-national estimates and trends for the incidence and prevalence of selected GI and liver diseases by age, gender and province over the period 1990 - 2013 in Iran. METHODS: This is a national and sub-national burden of disease study on 21 GI diseases using all available data sources, including cancer registry, death registration system data, hospital data, and all available published data. Analyses will be performed separately by gender, age groups, year, and province. We will conduct 21 separated systematic reviews of the literature for 21 diseases categories through searching online international electronic databases (i.e. the Medline database of the National Library of Medicine, Web of Science, and Scopus), Iranian search engines (i.e., IranMedex, Scientific Information Database (SID), and IRANDOC), and gray literature. We will search the medical literature published between January 1985 and December 2013. We generated two models, Spatio-temporal and Multilevel Autoregressive models, to estimate mean and uncertainty interval for the parameters of interest by gender, age, year, and province. The models will be informed by data of gender, age, year, and province. Markov Chain Monte Carlo (MCMC) methods will be used to perform Bayesian inference in both modeling framework. All programs will be written in R statistical packages (version 3.0.1). RESULTS: We will calculate and present 1990 to 2013 trends in terms of prevalence, years of life lost due to premature mortality (YLLs), years lived with disability (YLDs), and disability-adjusted life years DALYs for the 21 selected GI diseases by gender, and province. We will also quantify the uncertainty interval for the estimates of interest. CONCLUSION: Results of the present study will have implications for policy making; as they allow for understanding geographic distributions of the selected GI diseases, and identifying health disparities across provinces.