Category: Nevin Manimala

JMIR Med Inform. 2024 Mar 6;12:e51560. doi: 10.2196/51560.

ABSTRACT

BACKGROUND: Health care has not reached the full potential of the secondary use of health data because of-among other issues-concerns about the quality of the data being used. The shift toward digital health has led to an increase in the volume of health data. However, this increase in quantity has not been matched by a proportional improvement in the quality of health data.

OBJECTIVE: This review aims to offer a comprehensive overview of the existing frameworks for data quality dimensions and assessment methods for the secondary use of health data. In addition, it aims to consolidate the results into a unified framework.

METHODS: A review of reviews was conducted including reviews describing frameworks of data quality dimensions and their assessment methods, specifically from a secondary use perspective. Reviews were excluded if they were not related to the health care ecosystem, lacked relevant information related to our research objective, and were published in languages other than English.

RESULTS: A total of 22 reviews were included, comprising 22 frameworks, with 23 different terms for dimensions, and 62 definitions of dimensions. All dimensions were mapped toward the data quality framework of the European Institute for Innovation through Health Data. In total, 8 reviews mentioned 38 different assessment methods, pertaining to 31 definitions of the dimensions.

CONCLUSIONS: The findings in this review revealed a lack of consensus in the literature regarding the terminology, definitions, and assessment methods for data quality dimensions. This creates ambiguity and difficulties in developing specific assessment methods. This study goes a step further by assigning all observed definitions to a consolidated framework of 9 data quality dimensions.

PMID:38446534 | DOI:10.2196/51560

JMIR Med Inform. 2024 Mar 6;12:e47744. doi: 10.2196/47744.

ABSTRACT

BACKGROUND: The importance of real-world evidence is widely recognized in observational oncology studies. However, the lack of interoperable data quality standards in the fragmented health information technology landscape represents an important challenge. Therefore, adopting validated systematic methods for evaluating data quality is important for oncology outcomes research leveraging real-world data (RWD).

OBJECTIVE: This study aims to implement real-world time to treatment discontinuation (rwTTD) for a systemic anticancer therapy (SACT) as a new use case for the Use Case Specific Relevance and Quality Assessment, a framework linking data quality and relevance in fit-for-purpose RWD assessment.

METHODS: To define the rwTTD use case, we mapped the operational definition of rwTTD to RWD elements commonly available from oncology electronic health record-derived data sets. We identified 20 tasks to check the completeness and plausibility of data elements concerning SACT use, line of therapy (LOT), death date, and length of follow-up. Using descriptive statistics, we illustrated how to implement the Use Case Specific Relevance and Quality Assessment on 2 oncology databases (Data sets A and B) to estimate the rwTTD of an SACT drug (target SACT) for patients with advanced head and neck cancer diagnosed on or after January 1, 2015.

RESULTS: A total of 1200 (24.96%) of 4808 patients in Data set A and 237 (5.92%) of 4003 patients in Data set B received the target SACT, suggesting better relevance of the former in estimating the rwTTD of the target SACT. The 2 data sets differed with regard to the terminology used for SACT drugs, LOT format, and target SACT LOT distribution over time. Data set B appeared to have less complete SACT records, longer lags in incorporating the latest data, and incomplete mortality data, suggesting a lack of fitness for estimating rwTTD.

CONCLUSIONS: The fit-for-purpose data quality assessment demonstrated substantial variability in the quality of the 2 real-world data sets. The data quality specifications applied for rwTTD estimation can be expanded to support a broad spectrum of oncology use cases.

PMID:38446504 | DOI:10.2196/47744

Infect Dis (Lond). 2024 Mar 6:1-16. doi: 10.1080/23744235.2024.2324355. Online ahead of print.

ABSTRACT

BACKGROUND: Using SaTScan™ Geographical Information Systems (GIS), spatial cluster analysis was used to examine spatial trends and identify high-risk clusters of Coronavirus 2019 (COVID-19) incidence in response to changing levels of public health intervention phases including international and state border closures, statewide vaccination coverage, and masking requirements.

METHODS: Changes in COVID-19 incidence were mapped at the statistical area 2 (SA2) level using a GIS and spatial cluster analysis was performed using SaTScan™ to identify most-likely clusters (MLCs) during intervention phases.

RESULTS: Over the study period, significant high-risk clusters were identified in Brisbane city (relative risk = 30.83), the southeast region (RR = 1.71) and moving to Far North Queensland (FNQ) (RR = 2.64). For masking levels, cluster locations were similar, with MLC in phase 1 in the southeast region (RR = 2.56) spreading to FNQ in phase 2 (RR = 2.22) and phase 3 (RR = 2.64). All p values <.0001.

CONCLUSIONS: Movement restrictions in the form of state and international border closures were highly effective in delaying the introduction of COVID-19 into Queensland, with very low levels of transmission prior to border reopening while mandatory masking may have played a role in decreasing transmission through behavioural changes. Early clusters were in highly populated regions, as restrictions eased clusters were identified in regions more likely to be rural or remote, with higher numbers of Indigenous people, lower vaccination coverage or lower socioeconomic status.

PMID:38446488 | DOI:10.1080/23744235.2024.2324355

JAMA Netw Open. 2024 Mar 4;7(3):e241349. doi: 10.1001/jamanetworkopen.2024.1349.

ABSTRACT

IMPORTANCE: There are concerns about the safety of medications for treatment of attention-deficit/hyperactivity disorder (ADHD), with mixed evidence on possible cardiovascular risk.

OBJECTIVE: To assess whether short-term methylphenidate use is associated with risk of cardiovascular events.

DESIGN, SETTING, AND PARTICIPANTS: This retrospective, population-based cohort study was based on national Swedish registry data. Participants were individuals with ADHD aged 12 to 60 years with dispensed prescriptions of methylphenidate between January 1, 2007, and June 30, 2012. Each person receiving methylphenidate (n = 26 710) was matched on birth date, sex, and county to up to 10 nonusers without ADHD (n = 225 672). Statistical analyses were performed from September 13, 2022, to May 16, 2023.

MAIN OUTCOMES AND MEASURES: Rates of cardiovascular events, including ischemic heart disease, venous thromboembolism, heart failure, or tachyarrhythmias, 1 year before methylphenidate treatment and 6 months after treatment initiation were compared between individuals receiving methylphenidate and matched controls using a bayesian within-individual design. Analyses were stratified by history of cardiovascular events.

RESULTS: The cohort included 252 382 individuals (15 442 [57.8% men]; median age, 20 (IQR, 15-31) years). The overall incidence of cardiovascular events was 1.51 per 10 000 person-weeks (95% highest density interval [HDI], 1.35-1.69) for individuals receiving methylphenidate and 0.77 (95% HDI, 0.73-0.82) for the matched controls. Individuals treated with methylphenidate had an 87% posterior probability of having a higher rate of cardiovascular events after treatment initiation (incidence rate ratio [IRR], 1.41; 95% HDI, 1.09-1.88) compared with matched controls (IRR, 1.18; 95% HDI, 1.02-1.37). The posterior probabilities were 70% for at least a 10% increased risk of cardiovascular events in individuals receiving methylphenidate vs 49% in matched controls. No difference was found in this risk between individuals with and without a history of cardiovascular disease (IRR, 1.11; 95% HDI, 0.58-2.13).

CONCLUSIONS AND RELEVANCE: In this cohort study, individuals receiving methylphenidate had a small increased cardiovascular risk vs matched controls in the 6 months after treatment initiation. However, there was little evidence for an increased risk of 20% or higher and for differences in risk increase between people with and without a history of cardiovascular disease. Therefore, before treatment initiation, careful consideration of the risk-benefit trade-off of methylphenidate would be useful, regardless of cardiovascular history.

PMID:38446477 | DOI:10.1001/jamanetworkopen.2024.1349

JAMA Psychiatry. 2024 Mar 6. doi: 10.1001/jamapsychiatry.2024.0052. Online ahead of print.

ABSTRACT

IMPORTANCE: All-cause mortality and the risk for age-related medical disease is increased in individuals with psychiatric illness, but the underlying biological mechanisms are not known. Oxidative stress on nucleic acids (DNA and RNA; NA-OXS) is a molecular driver of aging and a potential pathophysiological mechanism in a range of age-related disorders.

OBJECTIVE: To study the levels of markers of NA-OXS in a large cohort of community-dwelling individuals with and without psychiatric illness and to evaluate their association with prospective all-cause mortality.

DESIGN, SETTING, AND PARTICIPANTS: This cohort study used a combined cohort of participants from 2 population-based health studies: the Danish General Suburban Population Study (January 2010 to October 2013) and nondiabetic control participants from the Vejle Diabetes Biobank study (March 2007 to May 2010). Individual history of psychiatric illness was characterized using register data on psychiatric diagnoses and use of psychotropic drugs before baseline examination. Urinary markers of systemic RNA (8-oxo-7,8-dihydroguanosine [8-oxoGuo]) and DNA (8-oxo-7,8-dihydro-2′-deoxyguanosine [8-oxodG]) damage from oxidation were measured by ultraperformance liquid chromatography-tandem mass spectrometry. Cox proportional hazard regression models were applied for survival analyses, using register-based all-cause mortality updated to May 2023. The follow-up time was up to 16.0 years.

EXPOSURES: History of psychiatric illness.

MAIN OUTCOMES AND MEASURES: Mortality risk according to psychiatric illness status and 8-oxoGuo or 8-oxodG excretion level.

RESULTS: A total of 7728 individuals were included (3983 [51.5%] female; mean [SD] age, 58.6 [11.9] years), 3095 of whom (40.0%) had a history of psychiatric illness. Mean (SD) baseline 8-oxoGuo was statistically significantly higher in individuals with psychiatric illness than in those without (2.4 [1.2] nmol/mmol vs 2.2 [0.9] nmol/mmol; P < .001), whereas 8-oxodG was not. All-cause mortality was higher in the psychiatric illness group vs the no psychiatric illness group (hazard ratio [HR], 1.44; 95% CI, 1.27-1.64; P < .001) and increased sequentially with each increasing tertile of 8-oxoGuo excretion in both groups to an almost doubled risk in the psychiatric illness/high 8-oxoGuo group compared to the no psychiatric illness/low 8-oxoGuo reference group (HR, 1.99; 95% CI, 1.58-2.52; P < .001). These results persisted after adjustment for a range of potential confounders and in a sensitivity analysis stratified for sex.

CONCLUSIONS AND RELEVANCE: This study establishes systemic oxidative stress-induced damage to RNA as a potential mechanism in the accelerated aging observed in psychiatric disorders and urinary 8-oxoGuo as a potentially useful marker of mortality risk in individuals with psychiatric illness.

PMID:38446448 | DOI:10.1001/jamapsychiatry.2024.0052

Biometrics. 2024 Jan 29;80(1):ujad038. doi: 10.1093/biomtc/ujad038.

ABSTRACT

Epidemiological studies based on 2-phase designs help ensure efficient use of limited resources in situations where certain covariates are prohibitively expensive to measure for a full cohort. Typically, these designs involve 2 steps: In phase I, data on an outcome and inexpensive covariates are acquired, and in phase II, a subsample is chosen in which the costly variable of interest is measured. For right-censored data, 2-phase designs have been primarily based on the Cox model. We develop efficient 2-phase design strategies for settings involving a fraction of long-term survivors due to nonsusceptibility. Using mixture models accommodating a nonsusceptible fraction, we consider 3 regression frameworks, including (a) a logistic “cure” model, (b) a proportional hazards model for those who are susceptible, and (c) regression models for susceptibility and failure time in those susceptible. Importantly, we introduce a novel class of bivariate residual-dependent designs to address the unique challenges presented in scenario (c), which involves 2 parameters of interest. Extensive simulation studies demonstrate the superiority of our approach over various phase II subsampling schemes. We illustrate the method through applications to the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial.

PMID:38446442 | DOI:10.1093/biomtc/ujad038

Biometrics. 2024 Jan 29;80(1):ujad034. doi: 10.1093/biomtc/ujad034.

ABSTRACT

Benkeser et al. demonstrate how adjustment for baseline covariates in randomized trials can meaningfully improve precision for a variety of outcome types. Their findings build on a long history, starting in 1932 with R.A. Fisher and including more recent endorsements by the U.S. Food and Drug Administration and the European Medicines Agency. Here, we address an important practical consideration: how to select the adjustment approach-which variables and in which form-to maximize precision, while maintaining Type-I error control. Balzer et al. previously proposed Adaptive Pre-specification within TMLE to flexibly and automatically select, from a prespecified set, the approach that maximizes empirical efficiency in small trials (N < 40). To avoid overfitting with few randomized units, selection was previously limited to working generalized linear models, adjusting for a single covariate. Now, we tailor Adaptive Pre-specification to trials with many randomized units. Using V-fold cross-validation and the estimated influence curve-squared as the loss function, we select from an expanded set of candidates, including modern machine learning methods adjusting for multiple covariates. As assessed in simulations exploring a variety of data-generating processes, our approach maintains Type-I error control (under the null) and offers substantial gains in precision-equivalent to 20%-43% reductions in sample size for the same statistical power. When applied to real data from ACTG Study 175, we also see meaningful efficiency improvements overall and within subgroups.

PMID:38446441 | DOI:10.1093/biomtc/ujad034

J Sports Sci. 2024 Mar 6:1-14. doi: 10.1080/02640414.2024.2326275. Online ahead of print.

ABSTRACT

This paper introduces the Composite Rating Method (CRM), a novel approach for the integrated evaluation of basketball player and team performances across multiple leagues. Utilizing data from Euroleague, EuroCup, and Basketball Champions League, the presented method provides comprehensive and accurate rankings, including accounting for actions not included in personal statistics. Drawing inspiration from established methodologies such as ELO, PER, Offensive and Defensive ratings, CRM offers a balanced assessment of player and team capabilities. The paper delineates the data collection and preprocessing procedures, details the algorithmic framework of CRM, and showcases its predictive capacity. By presenting a well-rounded approach to ranking, this paper aims to contribute to the advancement of performance evaluation methods in basketball and sports in general.

PMID:38446425 | DOI:10.1080/02640414.2024.2326275

Rheumatol Ther. 2024 Mar 6. doi: 10.1007/s40744-024-00655-4. Online ahead of print.

ABSTRACT

INTRODUCTION: An overarching principle for the management of psoriatic arthritis (PsA) is a shared decision-making process between physicians and patients. The aim of this study is to assess the patient-physician relationship in a group of patients with PsA, by using the Perceived Efficacy in Patient-Physician Interactions (PEPPI) and CollaboRATE instruments.

METHODS: This is a cross-sectional multicenter study where consecutive patients with PsA were enrolled. For each patient, the main demographic, comorbid conditions, and clinical data were collected, including the assessment of disease activity, function, quality of life, and impact of disease. PEPPI and CollaboRATE questionnaires were used, respectively, to evaluate the patient’s perception of the patient-physician relationship and the shared decision-making process.

RESULTS: A total of 81 patients with PsA were enrolled at four centers in Italy. Overall, our patients showed a high level of confidence in obtaining needed health care, with relatively high median (IQR) values of PEPPI (20; 16-23), and a good shared decision-making process, with high median (IQR) values of CollaboRATE questionnaire (7; 6-9). PEPPI and CollaboRATE scores showed a statistically significant inverse correlation with different clinical variables such as disease duration, Leeds Enthesitis Index, PsA impact of Disease, Health Assessment Questionnaire, pain, patient’s global assessment of disease activity and clinical disease activity for PsA. The presence of comorbidities did not appear to be associated with lower values of PEPPI and CollaboRATE.

CONCLUSIONS: In this study, few patients with PsA were at risk of suboptimal communication with their physician. This phenomenon appeared to be primarily related to higher disease activity and burden.

PMID:38446398 | DOI:10.1007/s40744-024-00655-4

Psychometrika. 2024 Mar 6. doi: 10.1007/s11336-024-09952-x. Online ahead of print.

ABSTRACT

Temporal network data is often encoded as time-stamped interaction events between senders and receivers, such as co-authoring scientific articles or communication via email. A number of relational event frameworks have been proposed to address specific issues raised by complex temporal dependencies. These models attempt to quantify how individual behaviour, endogenous and exogenous factors, as well as interactions with other individuals modify the network dynamics over time. It is often of interest to determine whether changes in the network can be attributed to endogenous mechanisms reflecting natural relational tendencies, such as reciprocity or triadic effects. The propensity to form or receive ties can also, at least partially, be related to actor attributes. Nodal heterogeneity in the network is often modelled by including actor-specific or dyadic covariates. However, comprehensively capturing all personality traits is difficult in practice, if not impossible. A failure to account for heterogeneity may confound the substantive effect of key variables of interest. This work shows that failing to account for node level sender and receiver effects can induce ghost triadic effects. We propose a random-effect extension of the relational event model to deal with these problems. We show that it is often effective over more traditional approaches, such as in-degree and out-degree statistics. These results that the violation of the hierarchy principle due to insufficient information about nodal heterogeneity can be resolved by including random effects in the relational event model as a standard.

PMID:38446394 | DOI:10.1007/s11336-024-09952-x