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Nowadays, the Internet of Things (IoT) is used in every domain, especially in the healthcare system it plays a very important role in our life and made our life easier by providing various facilities in every area. On the Internet of Medical Things (IoMT), IoT devices collect real-time data from patients and send it to the cloud through the Internet. These gathered data are extorted to centralized processing and computational power. Centralization can lead to a single point of failure, data modification, snooping, security, and privacy evasion. Blockchain can resolve such issues by providing decentralized computational power and better storage capacity for IoT data. Therefore, integrating IoT and blockchain technologies can be an intelligent way to implement a decentralized IoT-based smart healthcare system. We find that integration of the blockchain and IoT is persuasive and can cause remarkable transformations across the healthcare system. We discussed blockchain and its applicability in the healthcare industry. The paper explores blockchain's dimensions, making the innovative healthcare system more secure and stable. We provide a comparative analysis of well-known recent research on IoT-based smart healthcare system security using blockchain based on different parameters such as architecture, data integrity, medical information sharing, distributed electronic health records, patient encryption key, access control, hardware implementation, etc. The paper focuses on the current security issues in IoT-based smart healthcare systems, architectures, taxonomy, and research gap and their possible solutions we point out various research challenges that should be considered before the deployment of a blockchain network in an IoT setting. This paper will be helpful to new researchers to get an overview of IoT-based healthcare system security. [ABSTRACT FROM AUTHOR]

CD4 cell count and viral load are the most widely used markers of Human Immunodeficiency Virus (HIV). Obtaining accurate measures of these laboratory markers can be challenging. The purpose of this study was to assess the reliability and validity of self-reported and abstracted paper medical record CD4 counts, viral load, and outpatient visits using electronic data as the gold standard. We sampled a subgroup of 114 HIV-infected individuals in Los Angeles from a larger multi-site study for whom we could confirm outpatient visits and CD4 count and viral load data through electronic data. Agreement between electronic and paper record abstractions was approximately 90% for CD4 count (kappa = 0.85; P < 0.001), 78% for viral load (kappa = 0.56; P < 0.001), and 62% for visits (kappa = 0.02; P = 0.36). Agreement between electronic and self-reported measures was 80% for CD4 count (kappa = 0.71; P < 0.001), 77% for viral load (kappa = 0.54; P < 0.001), and 62% for visits (kappa = 0.13; P < 0.05). Using electronic data as the gold standard, we found that the accuracy of self-report was as good as paper record abstraction regarding viral load levels and visits, and only slightly lower for CD4 count. Despite limited generalizability, these findings are important because they suggest that the accuracy of clinical information collected by self-report and paper medical records are similar and raise questions about whether conducting abstractions of paper medical records is worthwhile, especially if it increases the costs of data collection. [ABSTRACT FROM AUTHOR]

Healthcare quality measures are statistics that serve to evaluate healthcare providers and identify those that need to improve their care. Before using these measures in clinical practice, developers and reviewers assess measure reliability, which describes the degree to which differences in the measure values reflect actual variation in healthcare quality, as opposed to random noise. The Inter-Unit Reliability (IUR) is a popular statistic for assessing reliability, and it describes the proportion of total variation in a measure that is attributable to between-provider variation. However, Kalbfleisch et al. (Health Services and Outcomes Research Methodology, 18, 215–225, (2018)) have argued that the IUR has a severe limitation in that some of the between-provider variation may be unrelated to quality of care. In this paper, we illustrate the practical implications of this limitation through several concrete examples. We show that certain best-practices in measure development, such as careful risk adjustment and exclusion of unstable measure values, can decrease the sample IUR value. These findings uncover potential negative consequences of discarding measures with IUR values below some arbitrary threshold. [ABSTRACT FROM AUTHOR]

A method commonly used in health care research is the waiting room (WR) survey. While patients are waiting for their appointment, they are asked to complete a questionnaire measuring their attitudes, behaviors and other characteristics. In this paper we synthesize practical guidelines for WR surveys by comparing the method with two similar approaches: public intercept (PI) surveys and drop-off-pick-up (DOPU) surveys. In this comparison we use the Total Survey Error approach Groves (Survey Methodology, Wiley, New York, 2004); (Groves in Public Opinion Quarterly 74(5): 849-879, 2010) and apply it to three case examples in which one of the three surveys is used. We take into account measurement (validity, measurement- and processing error) and representation (coverage-, sampling- and nonresponse error). From our review, we conclude that waiting room surveys, though limited to patients and their caregivers, can provide useful information on patients' perspective on health care. Response rates in waiting rooms are usually high, but often not even reported. We recommend adjustment for sampling bias by taking into account the number of visits to the hospital per respondent and sample times proportionate to the number of sample members expected on a particular time. These surveys also allow for collection of para-data; i.e., relevant information in the circumstances of a request to participate in survey research, and behavior of surveyors can easily be controlled, or investigated in an experimental design. [ABSTRACT FROM AUTHOR]

This paper aims to apply a multi-criteria decision-making (MCDM) framework to evaluate the environmental performance of the medical service supply chain using a case study in India. An application of MCDM method, the fuzzy TOPSIS (Technique for order of preference by similarity to ideal solution) model, is used for Medical Support Service Provide firms (MSSPF) environmental performance evaluation. We require multiple and conflicting criteria to fix real-life decision-making challenges, which in turn led to the inception of (MCDM). However, MCDM is contracted with various contradictory requirements in which the knowledge of decision-makers is generally vague and thereby more challenging. In this situation, inclusions of fuzzy set theory in MCDM Methods would be good to solve the MCDM problem. In this study, the authors used a fuzzy TOPSIS MCDM model to solve the evaluation framework. Data were collected from the experts from the MSSPF about the more perfect criteria to assess environmental performance using a questionnaire. Case assesses the environmental performance, based on defined criteria, of three MSSPF by the experts. Fuzzy TOPSIS Method ranks three MSSPF according to environmental performance in the following order: Firm B, Firm A and Firm C. The result suggested that 'Firm B' performed superior environmental performance followed by 'Firm A' and 'Firm C'. This evaluation framework will help MSSPF to identify potential areas of further improvement in Environmental performance, and thereby creates strategies to overcome and formulate new innovations. Notably, this is one of the few studies to have used this method to gauge the environmental performance of the medical service supply chain in India. [ABSTRACT FROM AUTHOR]

Assessment of study success using conditional power (CP), the predictive power of success (PPoS) and probability of success (PoS) is becoming increasingly common for resource optimization and adaption of trials in clinical investigation. Determination of these measures is often a non-trivial mathematical task. Further, the terminologies used across the literature are not consistent, and there is no consolidated presentation on this. We have made a structured presentation on these measures for both trial success and clinical success: first, we have summarized the expressions of CP, PPoS and PoS in a general setting and subsequently, expressions for these measures are obtained for continuous, binary, and time-to-event endpoints in single-arm and two-arm trial settings. Many of these expressions are previously published; however, some of the expressions are very new including the expressions for testing median of time-to-event endpoint in a single-arm trial. We have also shown that 1 / (no. of events) consistently underestimates the variance of log(median) and alternative expression for variance was derived. Examples are given along with the comparison of CP and PPoS. Expressions presented in this paper are implemented in LongCART package in R and in R shiny app https://ppos.shinyapps.io/public/. [ABSTRACT FROM AUTHOR]

From its infancy in the 1910s, healthcare group insurance continues to increase, creating a consistently rising burden on the government and taxpayers. The growing number of people enrolled in healthcare programs such as Medicare, along with the enormous volume of money in the healthcare industry, increases the appeal for and risk of fraudulent activities. One such fraud, known as upcoding, is a means by which a provider can obtain additional reimbursement by coding a certain provided service as a more expensive service than what was actually performed. With the proliferation of data mining techniques and the recent and continued availability of public healthcare data, the application of these techniques towards fraud detection, using this increasing cache of data, has the potential to greatly reduce healthcare costs through a more robust detection of upcoding fraud. Presently, there is a sizable body of healthcare fraud detection research available but upcoding fraud studies are limited. Audit data can be difficult to obtain, limiting the usefulness of supervised learning; therefore, other data mining techniques, such as unsupervised learning, must be explored using mostly unlabeled records in order to detect upcoding fraud. This paper is specific to reviewing upcoding fraud analysis and detection research providing an overview of healthcare, upcoding, and a review of the current data mining techniques used therein. [ABSTRACT FROM AUTHOR]

Purpose: This paper describes a step-by-step process on how to conduct a literature review using a qualitative analysis approach in conducting a literature review using NVivo to drive the analysis and explore the state of nursing innovation. Data synthesis: This manuscript makes a unique contribution highlighting the importance of a comprehensive literature review following conceptual and methodological guidance. Previous literature on the literature review process usually describes the process quickly and without a specific step by step process. This manuscript describes a six-step process to conduct a literature review using a qualitative analytical program. The researcher selects the scope and establishes a search strategy, determines and applies the criteria for the selection process, selects the qualitative software, imports the data, extracts and codes the data, and analyzes it. Conclusion: Qualitative research applications, such as NVivo, support nurses' literature review process by improving rigor and reproducibility. The tools within the applications help better organize the literature, enhance transparency to the analytical process, and provide tools to visualize the data to improve the review's overall quality. Having the steps documented and organized allows better collaboration between various researchers. [ABSTRACT FROM AUTHOR]

Growing healthcare costs have been accompanied by increased policymakers' interest in the efficiency of healthcare systems. Network formation by hospitals as a vehicle for consolidation and achieving economies of scale has emerged as an important topic of conversation among academics and practitioners. Within networks, consolidation of particular specialties or entire campuses is expected and encouraged to take place. This paper describes the main findings of an effort to build gravity-type models to describe patient choices in inpatient and daycare hospital facilities. It analyzes the distance decay effects as a function of car travel times and great-circle distance, and it offers a method for inclusion of university hospitals. Additionally, it reviews the impact of driving and transit accessibility on hospital attraction and reviews the differences in distance decay for patient age groups and hospitalization types. In the described application, the best models achieve a Mean Absolute Percentage Error of around 10% in non-metropolitan areas, and 14.5% across different region types. Results in metropolitan areas suggest that latent factors unrelated to proximity and size have a significant role in determining hospital choices. Furthermore, the effects of relative driving and transit accessibility are found to be small or non-existent. [ABSTRACT FROM AUTHOR]

Analysis of public policy affecting dual eligibles requires accurate identification of survey respondents eligible for both Medicare and Medicaid. Doing so for Medicaid is particularly challenging given the complex eligibility rules tied to income and assets. In this paper we provide guidance on how to best identify eligible respondents in household surveys that have limited income or asset information, such as the National Health Interview Survey, American Community Survey, Current Population Survey, and Medical Expenditure Panel Survey. We show how two types of errors—false negative and false positive errors—are impacted by incorporating limited income or asset information, relative to the commonly-used approach of solely comparing total income to the income threshold. With the 2018 Health and Retirement Study, which has detailed income and asset information, we mimic the income and asset information available in those other household surveys and quantify how errors change when imposing income or asset tests with limited information. We show that incorporating all available income and asset data results in the lowest number of errors and the lowest overall error rates. We recommend that researchers adjust income and impose the asset test to the fullest extent possible when imputing Medicaid eligibility for Medicare enrollees. [ABSTRACT FROM AUTHOR]

In a medical setting, observational studies commonly involve patients who initiate a particular treatment (e.g., medication therapy) and others who do not, and the goal is to draw causal inferences about the effect of treatment on a time-to-event outcome. A difficulty with such studies is that the notion of a treatment initiation time is not well-defined for the control group. In this paper, we propose a Bayesian approach to estimate treatment effects in longitudinal observational studies where treatment is given by indication and thereby the exact timing of treatment is only observed for treated units. We present a framework for conceptualizing an underlying randomized experiment in this setting based on separating the time of indication for treatment, which we model using a latent state-space process, from the mechanism that determines assignment to treatment versus control. Next, we develop a two-step inferential approach that uses Markov Chain Monte Carlo (MCMC) posterior sampling to (1) infer the unobserved indication times for units in the control group, and (2) estimate treatment effects based on inferential conclusions from Step 1. This approach allows us to incorporate uncertainty about the unobserved indication times which induces uncertainty in both the selection of the control group and the measurement of time-to-event outcomes for these controls. We demonstrate our approach to study the effects on mortality of inappropriately prescribing phosphodiesterase type 5 inhibitors (PDE5Is), a medication contraindicated for certain types of pulmonary hypertension, using data from the Veterans Affairs (VA) health care system. [ABSTRACT FROM AUTHOR]

Researchers are often faced with evaluating the effect of a policy or program that was simultaneously initiated across an entire population of units at a single point in time, and its effects over the targeted population can manifest at any time period afterwards. In the presence of data measured over time, Bayesian time series models have been used to impute what would have happened after the policy was initiated, had the policy not taken place, in order to estimate causal effects. However, the considerations regarding the definition of the target estimands, the underlying assumptions, the plausibility of such assumptions, and the choice of an appropriate model have not been thoroughly investigated. In this paper, we establish useful estimands for the evaluation of large-scale policies. We discuss that imputation of missing potential outcomes relies on an assumption which, even though untestable, can be partially evaluated using observed data. We illustrate an approach to evaluate this key causal assumption and facilitate model elicitation based on data from the time interval before policy initiation and using classic statistical techniques. As an illustration, we study the Hospital Readmissions Reduction Program (HRRP), a US federal intervention aiming to improve health outcomes for patients with pneumonia, acute myocardial infraction, or congestive failure admitted to a hospital. We evaluate the effect of the HRRP on population mortality among the elderly across the US and in four geographic subregions, and at different time windows. We find that the HRRP increased mortality from pneumonia and acute myocardial infraction across at least one geographical region and time horizon, and is likely to have had a detrimental effect on public health. [ABSTRACT FROM AUTHOR]

The supply chain is a complex network in healthcare that crosses organizational and geographical borders. The inherent complexity of such structures can introduce impurities inclusive of erroneous facts, lack of transparency, and restricted records provenance. In the healthcare business, counterfeit pills are one of the major reasons for the harmful impact on human health and also for financial losses. Thus, pharmaceutical supply chains and end-to-end tracking systems are the recent research in healthcare. In this paper, we propose blockchain based traceability of counterfeited drugs (BBTCD) that implements tracking of counterfeited drugs using smart contracts on the Ethereum blockchain. We propose a solution to fully decentralize the tracking in healthcare by storing BBTCD on IPFS (Inter Planetary File System) to provide transparency, cost-effectiveness. [ABSTRACT FROM AUTHOR]

In health intervention research, epidemiologists and economists are more and more interested in estimating causal effects based on observational data. However, collaboration and interaction between both disciplines are regularly clouded by differences in the terminology used. Amongst others, this is reflected in differences in labeling, handling, and interpreting the sources of bias in parameter estimates. For example, both epidemiologists and economists use the term selection bias. However, what economists label as selection bias is labeled as confounding by epidemiologists. This paper aims to shed light on this and other subtle differences between both fields and illustrate them with hypothetical examples. We expect that clarification of these differences will improve the multidisciplinary collaboration between epidemiologists and economists. Furthermore, we hope to empower researchers to select the most suitable analytical technique from either field for the research problem at hand. [ABSTRACT FROM AUTHOR]

Exploitation of the ever-increasing volume of observational data has become a major challenge for real-world evidence (RWE). Causal inference can be viewed as emulations of clinical trials (ECTs) from real-world data (RWD). This requires in-depth knowledge of data management and modern statistical models, which evolve considerably. We have developed Plug-Stat® to facilitate these analyses. Plug-Stat® is a user-friendly graphical interface of R software implemented with JavaScript and VB.net. The implementation of Plug-Stat® for a specific database consists of two main tasks. The first concerns the automatic importation of data to link the software with the data and to avoid the repetition of data management activities for each study. The second is to define the outcomes and the associated possible confounders. To perform an ECT, we proposed the use of inverse probability weighting, flexible parametric estimation of the propensity score model with B-splines, and robust estimation of the variance. The statistical modeling steps are automatized to direct the user toward the correct methodology without data management. We illustrated the usefulness of Plug-Stat® with several recently published ECTs. In this paper, we presented the outcome comparison of preemptive deceased-donor kidney transplantations versus transplantations after dialysis. The interactive interfaces assist the user in conducting causal research based on ECT, and tutorials are available, offering self-training opportunities. Plug-Stat® reduces the time and, consequently, the cost necessary for different studies from the same cohort. [ABSTRACT FROM AUTHOR]

Electronics-Health Care is one of the new ideas in the healthcare industry. For analyzing the patient's records, doctors need a centralized record of the patients, which gives useful and enormous information. There will be a threat of leaking of confidential e-health data, which will seriously endanger by modifying the health care data of medical and its individual entities. Protection of the confidential data of patients will be a primary constraint in the health care industry. There is a lot of progressiveness in the human services area as well as there is a lot of usage of the information gathered from various sensors and this information is stored in the cloud. As per the existing security algorithms, there is still some security breach and also it won't get satisfied with integrity and consistency while transferring the data from one vendor to others in the health care system. It also causes high spoofing of IP and side channel hackers. With the increase in the use of the internet, there is an increase in hacking also, especially in the health-care sector, e-commerce, military, and Education. Dealing with the huge volume of information in the electronics-healthcare area requires the processing of patient's information day by day and its memory allocation. Cloud is the best option to store these health records in a distributed manner rather than on a single server. So, whatever the information gathered from it will be encoded on block chain technology and an anonymous digital ring signature will be used to authorize the intended user and maintain the privacy of patient cloud-based data distributed Electronics health record system before sending the information to the cloud storage. In this paper, the protection of Personal data of the patient health data is enhanced with block chain and anonymous ring signatures in the e-healthcare cloud; thus making it suitable for real-time applications. [ABSTRACT FROM AUTHOR]

The Medicare Consumer Assessment of Healthcare Providers and Systems (CAHPS®) surveys collect standardized information about patient experiences of care from nationally representative samples of people with Medicare to support consumers' enrollment choices and enable the Centers for Medicare & Medicaid Services to monitor care quality and incentivize high quality patient-centered care. Since 2007, protocols for data collection, analysis, and reporting have evolved to address expanded Medicare coverage options and a shift from a single survey vendor to a model in which health plans hire approved vendors to administer the survey. During that time, response rates for all types of surveys have declined; increasing effort has gone toward increasing survey participation, especially among people whose preferred language is not English. In this paper, we describe the history, goals, and current use of the Medicare CAHPS surveys. We also summarize key methodological issues, such as sample design, field implementation and data cleaning, adjustment, scoring, and report production. Additionally, we discuss issues that may arise more generally in managing a large, annual national survey that has direct impact on policy, and consider how a long-running survey of this nature may need to evolve to reflect changes in health care delivery and promote standardization in survey administration while maintaining survey content. [ABSTRACT FROM AUTHOR]

Objective: This study assessed the reproducibility, reliability, and validity of the DDQ-15, a 15-item quality of life questionnaire for patients with digestive disorders (Phases I and II). It also assessed the reliability of two modes of administration of the instrument: paper versus computer (Phase III). Data Sources/Study Setting: Digestive Disease Center (DDC) patients at the Medical University of South Carolina participated in the study. These patients were attending the DDC on an outpatient basis for a pancreatobiliary disorder, GI cancer, a luminal GI disorder, or liver disorder. Study Design: Over 200 patients participated in the validation study (Phase II) of the DDQ-15. They completed the instrument three times: within one week before a scheduled clinic visit, at the clinic visit, and approximately two months after the clinic visit. The patients also completed the SF-36 and Gastrointestinal Quality of Life Index during the clinic visit. For Phase III, 150 participants were randomized to take the DDQ-15 on computer first or on paper fist. All participants took both versions during the same clinic visit and prior to being seen by a physician. Data Collection Methods: Patients responded to the DDQ-15 items directly on the personal computer for the computer version. The paper version was self-administered by the patients, or if they could not read, the responses on the paper version required manual data entry by the study staff. Principal Findings: For the validation study (Phase II) Cronbach's alpha for the DDQ-15 was 0.92 indicating high internal consistency of the instrument. The intraclass correlation coefficient between pre-clinic and clinic visit scores was 0.97. Pearson's correlation coefficient between the clinic visit DDQ-15 and the Gastrointestinal Quality of Life Index was 0.84, and the coefficients between DDQ-15 and the subscales of SF-36 ranged from 0.55 to 0.79. For Phase III, high internal consistency was observed for both versions, and reproducibility analysis yielded excellent agreement between the two. Intraclass correlation scores indicated high concordance of DDQ-15 scores. Education level and the order in which the two versions were completed were significant factors affecting the concordance of responses. Conclusions: DDQ-15 was shown to be a reliable, reproducible, and valid instrument for assessing QOL in patients with digestive diseases. The simplicity of the instrument should facilitate its use in an ordinary clinical setting. The study also showed that agreement between computer and paper DDQ-15 versions was excellent, indicating that mode of administration minimally affected response to QOL items. This study showed the computer interface to be a reliable alternative to the conventional paper questionnaire. [ABSTRACT FROM AUTHOR]

Medical insurance claims are becoming increasingly common data sources to answer a variety of questions in biomedical research. Although comprehensive in terms of longitudinal characterization of disease development and progression for a potentially large number of patients, population-based inference using these datasets require thoughtful modifications to sample selection and analytic strategies relative to other types of studies. Along with complex selection bias and missing data issues, claims-based studies are purely observational, which limits effective understanding and characterization of the treatment differences between groups being compared. All these issues contribute to a crisis in reproducibility and replication of comparative findings using medical claims. This paper offers practical guidance to the analytical process, demonstrates methods for estimating causal treatment effects with propensity score methods for several types of outcomes common to such studies, such as binary, count, time to event and longitudinally varying measures, and also aims to increase transparency and reproducibility of reporting of results from these investigations. We provide an online version of the paper with readily implementable code for the entire analysis pipeline to serve as a guided tutorial for practitioners. The online version can be accessed at https://rydaro.github.io/. The analytic pipeline is illustrated using a sub-cohort of patients with advanced prostate cancer from the large Clinformatics TM Data Mart Database (OptumInsight, Eden Prairie, Minnesota), consisting of 73 million distinct private payer insures from 2001 to 2016. [ABSTRACT FROM AUTHOR]

At the 2018 International Conference on Health Policy Statistics (ICHPS) held in Charleston, South Carolina, Anirban Basu was awarded the Mid-Career Excellence Award from the American Statistical Association Section on Health Policy Statistics (HPSS). Anirban was exceptionally and uniquely qualified for this award. Highlights include his providing outstanding service to the HPSS, advancing statistical methodology, advancing methodology in other domains of health policy, and performing extensive and highly impactful applied work in medicine and health care. In this interview, we trace Anirban’s upbringing, schooling, early career, and mid-career phases to gain insights into his success. We also sought his opinions on salient topics or issues. [ABSTRACT FROM AUTHOR]

Opioid-related mortality increased by nearly 400% between 2000 and 2018. In response, federal, state, and local governments have enacted a heterogeneous collection of opioid-related policies in an effort to reverse the opioid crisis, producing a policy landscape that is both complex and dynamic. Correspondingly, there has been a rise in opioid-policy related evaluation studies, as policymakers and other stakeholders seek to understand which policies are most effective. In this paper, we provide an overview of methodological challenges facing opioid policy researchers when conducting opioid policy evaluation studies using observational data, as well as some potential solutions to those challenges. In particular, we discuss the following key challenges: (1) Obtaining high-quality opioid policy data; (2) Appropriately operationalizing and specifying opioid policies; (3) Obtaining high-quality opioid outcome data; (4) Addressing confounding due to systematic differences between policy and non-policy states; (5) Identifying heterogeneous policy effects across states, population subgroups, and time; (6) Disentangling effects of concurrent policies; and (7) Overcoming limited statistical power to detect policy effects afforded by commonly-used methods. We discuss each of these challenges and propose some ways forward to address them. Increasing the methodological rigor of opioid evaluation studies is imperative to identifying and implementing opioid policies that are most effective at reducing opioid-related harms. [ABSTRACT FROM AUTHOR]

As healthcare data is extremely sensitive, it poses a risk of invading individuals' privacy if stored or exported without proper security measures. De-identification entails pseudonymization or anonymization of data, which are methods for temporarily or permanently removing an individual's identity. These methods are most suitable to keep user healthcare data private. Inference attacks are a commonly overlooked weakness of de-identification techniques. In this paper, I discuss a method for de-identifying Electronic Healthcare Records (EHR) using chained hashing to generate short-lived pseudonyms to reduce the impact of inference attacks, as well as a mechanism for re-identification based on information self-determination. It also removes the weaknesses of existing de-identification algorithms and resolve them by using appropriate real-time de-identification algorithm, Ephemeral Pseudonym Generation Algorithm (EPGA). [ABSTRACT FROM AUTHOR]

Interrupted time series (ITS)—a quasi-experimental design—is often used to evaluate the effectiveness of a health policy intervention. When the outcome of interest is rare, for example, for certain hospital-acquired infections, a common practice is to focus on aggregated count outcomes. However, analyzing ITS with count outcomes is challenging due to the needs to consider possible overdispersion and to account for serial correlation. In this paper, we compare the performance of three estimation methods, the generalized estimating equation (GEE) method, the generalized linear model (GLM) method, and the composite likelihood (CL) method, to fit parameter-driven time series models with count outcomes, and develop a simulation-based approach to calculate the sample size and power for designing such studies. [ABSTRACT FROM AUTHOR]

Multivariate binary responses from the same subject are usually correlated. For example, malnutrition of children are usually measured using ‘stunting’ (low height-for-age) and ‘wasting’ (low weight-for-age) calculated from their height, weight and age, and hence the status of being stunted may depend on the status of being wasted and vice-versa. For analyzing such malnutrition data, one needs special statistical models allowing for dependence between the responses to avoid misleading inference. The problem of dependence in multivariate binary responses is generally addressed by using marginal models with generalized estimating equation. However, using the marginal models alone, it is difficult to specify the measures of dependence between the responses precisely. Islam et al. (J Appl Stat 40(5):1064-1075, 2013) proposed a joint modeling approach for bivariate binary responses using both the conditional and marginal models where the dependence between the responses can be measured and tested using a link function of the models. However, the author didn’t examine the properties of the regression coefficient except for the dependence parameter. This paper has given further insight into the joint model and investigated the properties of regression coefficients using an extensive simulation study. The simulation results showed that the maximum likelihood estimators (MLEs) of the regression coefficients of the joint model showed well performance in terms of bias, mean squared error and coverage probability particularly when sample size large. Generally speaking, the MLEs of the parameters associated with joint models possessed the same asymptotic properties as the MLEs of those associated with standard generalized linear models, except for the interpretations. Further the paper provided an application of joint model for analyzing malnutrition data from Bangladesh demographic and health survey 2011. The results revealed that the estimates of the both marginal and condition regression coefficients of the joint model have meaningful interpretation and explanation, which will in turn help the policy makers for designing appropriate policies for improving nutrition status. [ABSTRACT FROM AUTHOR]

Policymakers seeking to target health policies efficiently towards specific population groups need to know which individuals stand to benefit the most from each of these policies. While traditional approaches for subgroup analyses are constrained to only consider a small number of pre-defined subgroups, recently proposed causal machine learning (CML) approaches help explore treatment-effect heterogeneity in a more flexible yet principled way. Causal forests use a generalisation of the random forest algorithm to estimate heterogenous treatment effects both at the individual and the subgroup level. Our paper aims to explore this approach in the setting of health policy evaluation with strong observed confounding, applied specifically to the context of mothers' health insurance enrolment in Indonesia. Comparing two health insurance schemes (subsidised and contributory) against no insurance, we find beneficial average impacts of enrolment in contributory health insurance on maternal health care utilisation and infant mortality, but no impact of subsidised health insurance. The causal forest algorithm identified significant heterogeneity in the impacts of contributory insurance, not just along socioeconomic variables that we pre-specified (indicating higher benefits for poorer, less educated, and rural women), but also according to some other characteristics not foreseen prior to the analysis, suggesting in particular important geographical impact heterogeneity. Our study demonstrates the power of CML approaches to uncover unexpected heterogeneity in policy impacts. The findings from our evaluation of past health insurance expansions can potentially guide the re-design of the eligibility criteria for subsidised health insurance in Indonesia. [ABSTRACT FROM AUTHOR]

Despite considerable advances in developing new and more sophisticated impact evaluation methodologies and toolkits, policy research continues to suffer from persistent challenges in achieving the evaluation trifecta: identifying effects, isolating mechanisms, and influencing policy. For example, evaluation studies are routinely hampered by problems of establishing valid counterfactuals due to endogeneity and selection effects with respect to policy reform. Additionally, robust evaluation studies often must contend with heterogeneity in treatment, staggered timing, and variation in uptake. And finally, on practical grounds, researchers frequently struggle to involve policymakers and practitioners throughout the research process in order to engender the type of trust needed for policy influence. While it can be difficult to generalize about appropriate evaluation methodologies across contexts, prominent policy interventions like governance reforms for improving health services delivery nonetheless demand rigorous and comprehensive evaluation strategies that can produce valid results and engage policymakers. Drawing on illustrations from our research on health sector decentralization in Honduras, in this paper we present a quasi-experimental, multi-method, and participatory approach that addresses these persistent challenges to policy evaluation. [ABSTRACT FROM AUTHOR]

Emergency general surgery (EGS) is a critical component of emergency care in the United States. Due to the time sensitiveness of EGS conditions, ensuring adequate spatial access to EGS services is paramount for reducing patient morbidity and mortality. Past studies have used travel time to measure spatial access to EGS services, which has its limitations. The major purpose of this paper is to evaluate the utility of a gravity-based spatial access model in measuring spatial access to EGS services in California. Our data sources include the American Hospital Association 2015 Annual Survey, the American Community Survey 2013–2017 five-year average dataset, and background geospatial datasets. We implemented both the gravity-based model and the shortest travel time method and compared them in measuring spatial access to EGS-capable hospitals in California at the census block group level. We analyzed each metric's ability to identify disparities in spatial access for the population as a whole, and subsequently to identify socio-demographic disparities. Overall, we found that both methods identified similar geographic and socio-demographic patterns of the spatial access. Native Americans and rural residents experienced the greatest disadvantage in spatial access to both general EGS services and advanced EGS services. However, the gravity-based model revealed more disparities in spatial access to EGS services than the travel time model, suggesting that using travel cost alone to measure spatial access to EGS services may underestimate the magnitude of disparities. These findings call for the use of gravity-based models that incorporate measures of population demand and hospital capacity when assessing spatial access to surgical services, and have implications for reallocating surgery resources to address disparities in spatial access. [ABSTRACT FROM AUTHOR]

The U.S. federal government is spending billions of dollars to test a multitude of new approaches to pay for healthcare. Unintended consequences are a major consideration in the testing of these value-based payment (VBP) models. Since participation is generally voluntary, any unintended consequences may be magnified as VBP models move beyond the early testing phase. In this paper, we propose a straightforward unsupervised outlier detection approach based on ranked percentage changes to identify participants (e.g., healthcare providers) whose behavior may represent an unintended consequence of a VBP model. The only data requirements are repeated measurements of at least one relevant variable over time. The approach is generalizable to all types of VBP models and participants and can be used to address undesired behavior early in the model and ultimately help avoid undesired behavior in scaled-up programs. We describe our approach, demonstrate how it can be applied with hypothetical data, and simulate how efficiently it detects participants who are truly bad actors. In our hypothetical case study, the approach correctly identifies a bad actor in the first period in 86% of simulations and by the second period in 96% of simulations. The trade-off is that 9% of honest participants are mistakenly identified as bad actors by the second period. We suggest several ways for researchers to mitigate the rate or consequences of these false positives. Researchers and policymakers can customize and use our approach to appropriately guard VBP models against undesired behavior, even if only by one participant. [ABSTRACT FROM AUTHOR]

Intraclass correlation coefficients (ICCs) are frequently employed in health science research, often to assess intrarater and interrater reliability. In many cases, insufficient details are provided about these ICCs and there seem to be misunderstandings about their selection and how they should be interpreted. This paper is intended primarily to provide a clear, accessible description of ICCs, including how they should be selected, interpreted, and reported. Emphasis is given to areas where researchers seem to encounter the greatest conceptual difficulties and to exhibit the greatest misconceptions. Two extended examples are used to support the points being made. Major additional aims of this paper are to raise the awareness of authors, reviewers, and editors concerning the importance of using appropriate ICCs, and to encourage them to ensure that complete and accurate information about ICCs is reported in journal articles. Failure to do so perpetuates a risk that incorrect decisions might be made about matters that are of crucial importance for people's health. [ABSTRACT FROM AUTHOR]

While record linkage can expand analyses performable from survey microdata, it also incurs greater risk of privacy-encroaching disclosure. One way to mitigate this risk is to replace some of the information added through linkage with synthetic data elements. This paper describes a case study using the National Hospital Care Survey (NHCS), which collects patient records under a pledge of protecting patient privacy from a sample of U.S. hospitals for statistical analysis purposes. The NHCS data were linked to the National Death Index (NDI) to enhance the survey with mortality information. The added information from NDI linkage enables survival analyses related to hospitalization, but as the death information includes dates of death and detailed causes of death, having it joined with the patient records increases the risk of patient re-identification (albeit only for deceased persons). For this reason, an approach was tested to develop synthetic data that uses models from survival analysis to replace vital status and actual dates-of-death with synthetic values and uses classification tree analysis to replace actual causes of death with synthesized causes of death. The degree to which analyses performed on the synthetic data replicate results from analysis on the actual data is measured by comparing survival analysis parameter estimates from both data files. Because synthetic data only have value to the degree that they can be used to produce statistical estimates that are like those based on the actual data, this evaluation is an essential first step in assessing the potential utility of synthetic mortality data. [ABSTRACT FROM AUTHOR]

Digitization of health records in public health facility and its instant availability in the form of electronic records anywhere any time health service is yet to be implemented in developing nations like India and other countries. In India, patient care is mainly delivered through 3 levels namely Primary/Community Healthcare Centre (PHC/CHC), Secondary healthcare centre (District Hospital), and Tertiary Healthcare Centre (National level). The healthcare facilities face many challenges in collecting, processing, and storing these data and managing it without compromising security and privacy. Presently, some of the secondary and tertiary care facilities have started implementing healthcare IT application in terms of Hospital Information System, Hospital Management Information System, Electronic Medical Records (EMR) etc. to manage the patient data in electronic format. However, these systems are developed by different vendors by using different programming languages and databases. This approach makes the system unique but the patient details remains in the same hospital and cannot be shared with other hospitals when patient moves from one hospital to other for advanced or specialized treatment. This is because the data is not interoperable and semantic. In the proposed work, a standard secure Electronic Health Record(EHR) framework is developed using standard medical terminology and coding standards. Implementation of EHR framework for Indian health system will improve the work-flow of health services to the population. EHR at all levels of healthcare system enable efficient and continuous care to the patient. [ABSTRACT FROM AUTHOR]

Data derived from electronic health records (EHR) are heterogeneous with availability of specific measures dependent on the type and timing of patients' healthcare interactions. This creates a challenge for research using EHR-derived exposures because gold-standard exposure data, determined by a definitive assessment, may only be available for a subset of the population. Alternative approaches to exposure ascertainment in this case include restricting the analytic sample to only those patients with gold-standard exposure data available (exclusion); using gold-standard data, when available, and using a proxy exposure measure when the gold standard is unavailable (best available); or using a proxy exposure measure for everyone (common data). Exclusion may induce selection bias in outcome/exposure association estimates, while incorporating information from a proxy exposure via either the best available or common data approaches may result in information bias due to measurement error. The objective of this paper was to explore the bias and efficiency of these three analytic approaches across a broad range of scenarios motivated by a study of the association between chronic hyperglycemia and 5-year mortality in an EHR-derived cohort of colon cancer survivors. We found that the best available approach tended to mitigate inefficiency and selection bias resulting from exclusion while suffering from less information bias than the common data approach. However, bias in all three approaches can be severe, particularly when both selection bias and information bias are present. When risk of either of these biases is judged to be more than moderate, EHR-based analyses may lead to erroneous conclusions. [ABSTRACT FROM AUTHOR]

The preponderance of large-scale healthcare databases provide abundant opportunities for comparative effectiveness research. Evidence necessary to making informed treatment decisions often relies on comparing effectiveness of multiple treatment options on outcomes of interest observed in a small number of individuals. Causal inference with multiple treatments and rare outcomes is a subject that has been treated sparingly in the literature. This paper designs three sets of simulations, representative of the structure of our healthcare database study, and propose causal analysis strategies for such settings. We investigate and compare the operating characteristics of three types of methods and their variants: Bayesian Additive Regression Trees (BART), regression adjustment on multivariate spline of generalized propensity scores (RAMS) and inverse probability of treatment weighting (IPTW) with multinomial logistic regression or generalized boosted models. Our results suggest that BART and RAMS provide lower bias and mean squared error, and the widely used IPTW methods deliver unfavorable operating characteristics. We illustrate the methods using a case study evaluating the comparative effectiveness of robotic-assisted surgery, video-assisted thoracoscopic surgery and open thoracotomy for treating non-small cell lung cancer. [ABSTRACT FROM AUTHOR]

The nominal group technique (NGT) is a method to elicit healthcare priorities. Yet, there is variability on how to conduct the NGT, and limited guidance on how to analyse a diverse sample of multiple groups. This paper addresses some of this ambiguity, and explores whether different approaches to analysis provide the same outcome/s. Conceptual papers and empirical studies were identified via PubMed and informed an adapted version of the NGT. Twenty-six nominal groups were conducted, which provided in-depth knowledge on how to best conduct this method. Pilot group data were used to compare different analysis methods and to explore how this impacted on reported outcomes. Data analyses for large data-sets are complex; thematic analysis is needed to be able to conduct across group comparisons of participant priorities. Consideration should be given not just to the strength, i.e. sum of votes, or relative importance of the priority, but to the voting frequency, i.e. the popularity of the idea amongst participants; our case study demonstrated that this can affect priority rankings for those ideas with the same score. As a case study, this paper provides practical information on analysis for complex data sets. Researchers need to consider more than one analysis process to ensure that the results truly reflect participant priorities. A priority that has a high score may not necessarily reflect its popularity within the group; the voting frequency may also need to be considered. [ABSTRACT FROM AUTHOR]

Policy makers increasingly rely on hospital competition to incentivize patients to choose high-value care. Amongst all possible drivers, the travel distance without any doubt is one of the most important. In this paper we propose the use of a spatial Bayesian hierarchical model to assess the impact of distance on the number of patient admissions in hospitals, and thereby, compare hospital attractiveness. To this aim a MCMC sampler has been designed. We apply our methodology to patient admissions for asthma in four hospitals located in the Hérault department of France. Results indicate that the most attractive hospital is the CHU Montpellier. [ABSTRACT FROM AUTHOR]

Hidden Markov models (HMM) presents an attractive analytical framework for capturing the state-switching process for auto-correlated data. These models have been extended to longitudinal data setting where simultaneous multiple processes are observed by including subject specific random effects. However, application of HMMs for intensive longitudinal data, where each subject gets measured intensively over relatively short period of time, has not been widely studied. In this paper, we extend the mixed hidden Markov model and allow subject heterogeneity with respect to the mean and within subject variance by including subject random effects in both perspectives. We focus on the application of this model to intensive longitudinal studies in psychological and behavioral research where individual's latent states and state-switching process are of interest. Models are estimated using forward–backward algorithm via Bayesian sampling approach. Advantages over regular HMM and mixed HMM that only accounts for the subjects' mean heterogeneity are illustrated through a series of simulation studies. Finally, models are applied to an adolescent mood study data set and results show that the proposed mixed location scale HMM achieves better model fit and more interpretative mood state identification in terms of state specific covariate effects compared to regular HMM and mixed HMM. [ABSTRACT FROM AUTHOR]

At the 2020 International Conference on Health Policy Statistics held in San Diego, Thomas (Tom) R. Belin was awarded the Long-Term Excellence Award from the Health Policy Statistics Section of the American Statistical Association. Dr. Belin was exceptionally and uniquely qualified for this award. Highlights include his innovative statistical applications for health care research and his substantial contributions to the statistics and health policy communities through mentoring and service. In this interview, we asked Tom to share stories about his upbringing, schooling, and career phases to gain insights into his numerous achievements. [ABSTRACT FROM AUTHOR]

Elizabeth A. Stuart is a Professor in the Departments of Mental Health, Biostatistics, and Health Policy and Management at the Johns Hopkins Bloomberg School of Public Health, and Associate Dean for Education at the school. She is a renowned expert in the area of causal inference, including propensity score methods for observational data and the generalizability of randomized trial results, and is also a Fellow of the American Statistical Association. Prior to her appointment to the faculty at Johns Hopkins, Professor Stuart received her Ph.D. in statistics from Harvard University and a bachelor's degree in mathematics from Smith College. In 2015, Professor Stuart was recognized at the International Conference on Health Policy Statistics with the Mid-Career Award from the Health Policy Statistics Section of the American Statistical Association. [ABSTRACT FROM AUTHOR]

The 11th International Conference on Health Policy Statistics (ICHPS) conference was successfully held from October 7 to 9, 2015, in Providence, Rhode Island, espousing the theme 'Statistical Science at the Forefront of Health Policy Research'. Authors of research presented at ICHPS 2015, in both posters and talks, were invited to submit their completed papers in two special issues of Health Services and Outcomes Research Methodology. In addition, the issues feature interviews with the winners of the American Statistical Association's Health Policy Statistics Section of the Long-Term Excellence and Mid-career Awards. [ABSTRACT FROM AUTHOR]

Patient-reported outcome and experience measures are increasingly important in health care and health research. The use of these measures is growing in the US and overseas, and performance measures that incorporate patient-reported outcomes are being considered, particularly in cancer. A major challenge for the use of these measures is patient non-response, especially for diseases such as cancer and dementia. A commonly used approach is to ask a proxy such as the patient's spouse or child to complete the measure on their behalf. Proxy reporting is used in major surveys, including those used in pay-for-performance approaches. No standards exist regarding how to adjust for the use of proxy-reported measures in analyses. As patients requiring proxies likely differ in important ways from those who can self-report, adjusting for these differences is important. In this paper, we evaluate the use of propensity score models when adjusting for proxy-reported data, including weighting, matching with replacement, and non-parametric multiple imputation. Additionally, because previous analyses using propensity scores for proxy reports have employed stepwise or p value based algorithms, we evaluated the sensitivity of our results to the inclusion of respondent-sensitive variables such as proxy reports of patient health status, as well as auxiliary covariates. Under all propensity score methods, estimates obtained from propensity scores using respondent-insensitive variables were different from those obtained when respondent-sensitive variables were incorporated in the propensity score. Propensity score methods have limitations in these contexts and their assumptions should be carefully examined. [ABSTRACT FROM AUTHOR]

This paper proposes the use of signal-to-noise ratio (SNR) as another index of a measurement’s reproducibility. We derive its maximum likelihood estimation and discuss confidence interval construction within the framework of the one-way random effect model. We investigate the validity of the approximate normal confidence interval by Monte-Carlo simulations. The paper also derives the optimal allocation for the number of subject and the number of repeated measurements needed to minimize the variance of the maximum likelihood estimator of the SNR. We discuss efficiency in estimation and cost considerations for the optimal allocation of the sample resources. The approach is illustrated on two examples: one from MRI data and the other on the WHO immunization coverage data. [ABSTRACT FROM AUTHOR]

The English healthcare sector underwent extensive system reform over the period from 2010 to 2015, aimed principally at improving technical efficiency. This paper examines the effect of the reforms on foundation trusts in England with particular emphasis on technical efficiency. By employing data envelopment analysis (DEA) and a second-stage regression, we found evidence of an overall improvement in efficiency, notwithstanding some fluctuations. Specifically, we found that bed utilization had positive and statistically significant association with the efficiency of acute foundation trusts; suggesting that better management of patient flows and bed utilization might be expected to improve hospital efficiency. We also found evidence to suggest that efficiency might also be improved through better management of staff numbers, optimizing liquidity, and better utilization of assets such as buildings and information technology. [ABSTRACT FROM AUTHOR]

The choice of dialysis treatment, as well as of the type of centre in which to receive it, can be the result of a selection process where perceived quality, and the reputation of medical centres and physicians, as well as other observable and unobservable factors play a key role. ESRD patients can choose among alternative treatments, namely HD, HDF and PD. While HD and HDF can be received at public hospitals and private centres, with no difference between the two options in terms of assistance provided, PD is mainly carried out at home. This paper considers the possibility of adopting different econometric techniques to model such simultaneous patients' choice by the patients. After having examined conditional logit models and a multinomial logit framework that might suit our purposes, we selected a recursive bivariate probit model. The analysis tools presented in this paper proved to be appropriate in this context and succeeded in identifying a link between the selection of HD as a dialysis modality with patients' characteristics, and type of dialysis centre. [ABSTRACT FROM AUTHOR]

The increasing availability of Geographical Information Systems (GIS) in health organisations, together with the proliferation of spatially disaggregate data, has led to a number of studies that have been concerned with developing measures of access to health care services. The main aim of this paper is to review the use of GIS-based measures in exploring the relationship between geographic access, utilisation, quality and health outcomes. The varieties of approaches taken by researchers concerned with teasing out the relative importance of geographical factors that may influence access are examined. To date, in the absence of detailed data on health utilisation patterns, much of this research has focused on developing measures of potential accessibility. This paper then critically evaluates the situation with regard to the use of such measures in a broad range of accessibility studies. In particular, there has been less research to date that examines the relationship between such measures and health outcomes. In the final sections of the paper, I draw on the review to outline areas where a broader research agenda is needed, particularly in relation to more recent innovations in health care delivery. [ABSTRACT FROM AUTHOR]

This paper presents a methodology to impute a binary variable to a primary dataset from a secondary dataset, which shares many potential explanatory variables with the primary dataset, when the primary dataset contains only restricted information about that variable. While the recipient dataset holds information on the variable of interest for a sub- population, a potentially donor dataset contains the variable of interest on everyone, but it cannot identify the sub-population. The methodology is simple and exploits that restricted information. This useful and easy to implement technique of imputation uses logistic regression instead of a cell-based approach or other imputation techniques since the variable to be imputed is binary and a structural or behavioural model exists for that variable. The methodology works well in an example involving prescription drug coverage in Canada. [ABSTRACT FROM AUTHOR]

Randomized controlled trials (RCTs) are universally recognized as the preferred way to infer treatment effects because RCTs typically minimize validity challenges while maximizing estimation efficiency. In practice, simple RCTs-where individual study subjects are assigned to treatment or control status at random-are infeasible when an intervention must be implemented at the cluster level, for example, for all patients within a hospital, or for all residents within a nursing home. An alternative in these situations is a cluster randomization (CR) design, where each cluster is randomly assigned to treatment or control status. While standard principles of randomization still apply, the CR design is less efficient than the simple RCT. In some situations, efficiency of a CR design can be improved by matching clusters, and, within each pair, assigning one cluster to treatment and the other to control status, otherwise known as a pairwise cluster randomization (PCR) design. The choice between CR and PCR is not clear, since the optimal approach depends on information about the intraclass and interclass correlation coefficients, which in practice are rarely known to the evaluator. Theory alone does not provide a concrete answer; however, a Monte Carlo simulation can provide useful evidence at the design stage. This paper demonstrates the utility of the Monte Carlo approach in the context of a planned evaluation of an intervention to reduce falls among nursing home residents and provides recommendations for researchers on key design questions, including the choice between CR and PCR, and selection of parametric or nonparametric estimators. [ABSTRACT FROM AUTHOR]

The purpose of this study is to illustrate how matching approaches based on different balancing scores lead to variations in the treatment effect estimators. We introduced a case study evaluating the effect of anti-thrombotic agents on the severe progression of patients with intracerebral hemorrhage. We extracted subpopulations based on propensity and prognostic scores and then estimated the relative risk between the treatment groups. Furthermore, to illustrate the situation where the treatment effect estimates varied depending on employed balancing scores, we conducted a simulation experiment. In the case study, the matching using different balancing scores extracted subpopulations with different characteristics. Then, the estimated relative risk (95% confidence interval) was 1.27 (0.98–1.94) among the propensity score matched cohort, whereas it was 0.91 (0.76–1.08) among the prognostic score matched cohort. In the simulation experiments, the results indicated that the matching schemes based on different balancing scores created distinct matched cohorts, leading to varying estimates under treatment effect heterogeneity. Moreover, the variability of the estimated effect becomes substantial when there are covariates strongly related to the dependent variable of the nuisance functions. The difference in the selected subpopulation via matching based on different balancing scores is a thoughtful factor that can result in different estimates when there is effect heterogeneity. In practice, we recommend assessing the characteristics of the matched subpopulation and employing the balancing score that can estimate the treatment effect among the target population of interest in each study. Plain Language Summary: This study aimed to understand why different methods of matching patients in a study can lead to different estimates of how well a treatment works. We used a real example of a study where the treatment's effectiveness varied for different patients and compared how different methods of matching patients affected the results. We also did a series of experiments to see how the different methods affected estimates of treatment effect when the effectiveness varied among patients. Our study found that the different matching methods extracted the different patients. The difference in the matched patients led to very different results, especially when the treatment's effectiveness was not the same for all patients. Therefore, it is important to check the characteristics of patients after matching and consider whether the treatment's effectiveness is not the same for all patients. [ABSTRACT FROM AUTHOR]

Social processes and social context are increasingly recognized as key factors shaping health-related behaviors and outcomes. One social process that may be acting within social networks is social influence, in which an individual's characteristic (e.g., specific health behavior) is potentially impacted by the corresponding characteristic of connected individuals in the network. In the health services context, healthcare providers who work together and share patients may influence each other through the knowledge transmission or development of clinical practice norms. Although many statistical techniques assume independence of data points, when analyzing data that may reflect social processes acting across a social network, it is imperative to account for the interdependencies (i.e., non-independence) across individuals. In practice, studies account for nonindependence in the context of estimating bivariate relations (correlations or linear regression) using a variety of analytic methods (some of which have previously been shown to yield biased results). To date, it is unclear which methods yield acceptable false positive rates, unbiased coefficient estimates, and acceptable statistical power, because there have been no systematic simulation studies comparing methods for addressing network nonindependence arising from social influence. To address this gap, we compared eight commonly used methods that purport to account for nonindependence using simulated network data. While results indicated that none of the techniques reduced false positive rates to the predicted (nominal) 0.05 level, random sampling of network nodes was the method that yielded the smallest false positive rates, yet came at a price of reduced statistical power. Further methodological development is needed. [ABSTRACT FROM AUTHOR]

Difference-in-differences (DID) is a popular approach in observational and quasi-experimental studies to estimate the effects of a treatment with discrete statuses. In many studies, however, the treatment can have a range of dosages or exposure levels. In our paper, "medical homeness" is a semi-continuous score ranging from 0 to 100 to indicate the extent to which a patient-centered medical home model is achieved. We developed a causal DID approach to estimating the effects of a treatment with semi-continuous dosages. The proposed approach allows for mixed-type designs as well as different propensity models. We applied the proposed approach to evaluate the dosage effect of medical homeness scores on the utilization and quality of children's health care. We found that there was a roughly linear effect of medical homeness scores on the annual number of visits to doctor offices when medical homeness scores were below 60 points. The number of office visits did not further increase when medical homeness scores were above 60. A similar relationship was found between medical homeness scores and ratings for health care quality. [ABSTRACT FROM AUTHOR]