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Background: Low-quality data presents a significant challenge for community health workers (CHWs) in low and middle-income countries (LMICs). Mobile health (mHealth) applications offer a solution by enabling CHWs to record and submit data electronically. However, the barriers and benefits of mHealth usage among CHWs in informal urban settlements remain poorly understood. This study sought to determine the barriers and benefits of mHealth among CHWs in Banda parish, Kampala. Methods: This qualitative study involved 12 key informant interviews (KIIs) among focal persons from Kampala City Council Authority (KCCA) and NGOs involved in data collected by CHWs, and officials from the Ministry of Health (MOH) and two mixed-sex Focused Group Discussions (FGDs) of CHWs from Banda parish, Kampala district. Data analysis utilised Atlas Ti Version 7.5.7. Thematic analysis was conducted, and themes were aligned with the social-ecological model. Results: Three themes of institutional and policy, community and interpersonal, and individual aligning to the Social ecological model highlighted the factors contributing to barriers and the benefits of mHealth among CHWs for iCCM. The key barriers to usability, acceptability and sustainability included high training costs, CHW demotivation, infrastructure limitations, data security concerns, community awareness deficits, and skill deficiencies. Conversely, mHealth offers benefits such as timely data submission, enhanced data quality, geo-mapping capabilities, improved CHW performance monitoring, community health surveillance, cost-effective reporting, and CHW empowering with technology. Conclusion: Despite limited mHealth experience, CHWs expressed enthusiasm for its potential. Implementation was viewed as a solution to multiple challenges, facilitating access to health information, efficient data reporting, and administrative processes, particularly in resource-constrained settings. Successful mHealth implementation requires addressing CHWs' demotivation, ensuring reliable power and network connectivity, and enhancing capacity for digital data ethics and management. By overcoming these barriers, mHealth can significantly enhance healthcare delivery at the community level, leveraging technology to optimize resource utilization and improve health outcomes. mHealth holds promise for transforming CHW practices, yet its effective integration necessitates targeted interventions to address systemic challenges and ensure sustainable implementation in LMIC contexts. [ABSTRACT FROM AUTHOR]

Background and purpose: The COVID-19 pandemic has presented unprecedented public health challenges worldwide. Understanding the factors contributing to COVID-19 mortality is critical for effective management and intervention strategies. This study aims to unlock the predictive power of data collected from personal, clinical, preclinical, and laboratory variables through machine learning (ML) analyses. Methods: A retrospective study was conducted in 2022 in a large hospital in Abadan, Iran. Data were collected and categorized into demographic, clinical, comorbid, treatment, initial vital signs, symptoms, and laboratory test groups. The collected data were subjected to ML analysis to identify predictive factors associated with COVID-19 mortality. Five algorithms were used to analyze the data set and derive the latent predictive power of the variables by the shapely additive explanation values. Results: Results highlight key factors associated with COVID-19 mortality, including age, comorbidities (hypertension, diabetes), specific treatments (antibiotics, remdesivir, favipiravir, vitamin zinc), and clinical indicators (heart rate, respiratory rate, temperature). Notably, specific symptoms (productive cough, dyspnea, delirium) and laboratory values (D-dimer, ESR) also play a critical role in predicting outcomes. This study highlights the importance of feature selection and the impact of data quantity and quality on model performance. Conclusion: This study highlights the potential of ML analysis to improve the accuracy of COVID-19 mortality prediction and emphasizes the need for a comprehensive approach that considers multiple feature categories. It highlights the critical role of data quality and quantity in improving model performance and contributes to our understanding of the multifaceted factors that influence COVID-19 outcomes. [ABSTRACT FROM AUTHOR]

Background: Research has highlighted a need to improve the quality of clinical documentation and data within aged care and disability services in Australia to support improved regulatory reporting and ensure quality and safety of services. However, the specific causes of data quality issues within aged care and disability services and solutions for optimisation are not well understood. Objectives: This study explored aged care and disability workforce (referred to as 'data-users') experiences and perceived root causes of clinical data quality issues at a large aged care and disability services provider in Western Australia, to inform optimisation solutions. Methods: A purposive sample of n = 135 aged care and disability staff (including community-based and residential-based) in clinical, care, administrative and/or management roles participated in semi-structured interviews and web-based surveys. Data were analysed using an inductive thematic analysis method, where themes and subthemes were derived. Results: Eight overarching causes of data and documentation quality issues were identified: (1) staff-related challenges, (2) education and training, (3) external barriers, (4) operational guidelines and procedures, (5) organisational practices and culture, (6) technological infrastructure, (7) systems design limitations, and (8) systems configuration-related challenges. Conclusion: The quality of clinical data and documentation within aged care and disability services is influenced by a complex interplay of internal and external factors. Coordinated and collaborative effort is required between service providers and the wider sector to identify behavioural and technical optimisation solutions to support safe and high-quality care and improved regulatory reporting. [ABSTRACT FROM AUTHOR]

Background: Patients may sign a consent form before the specific treatment is offered for a variety of reasons, including during an outpatient appointment. Healthcare professionals must obtain consent from patients or other legal persons before providing any treatment or performing any procedures. But, little attention has been given to the informed consent process in Ethiopia. Objective: To assess informed consent practice and associated factors among healthcare professionals in Wolaita Zone, Southern Ethiopia public hospitals from January, 2023. Methods: An institutional-based cross-sectional mixed-method study was conducted among 399 healthcare professionals. Simple random sampling and purposive sampling techniques were used to select healthcare professionals for quantitative and qualitative studies respectively. Data for both studies were collected using self-administered questionnaire and key informant interview respectively. EpiDataV4.6 and the Statistical Package for the Social Science was used for entry and analysis of quantitative data. OpenCode software was used for thematic analysis for qualitative data. Results: 339 respondents were included in the study, with a response rate of 94.3%. The good practice of informed consent among the healthcare professionals is 53.1%. There was a significant association between the good practice of informed consent and being male [AOR: 0.003 (95% CI: 0.000–0.017)], working in a comprehensive specialized hospital [AOR: 4.775 (95% CI: 1.45–15.74)] and in-service training [AOR: 0.038 (95% CI: 0.013–0.114)]. Conclusion and recommendations: More than half of healthcare professionals had good practices for informed consent. However, it is critical to plan and intervene various strategies with the goal of improving knowledge and attitude toward informed consent. [ABSTRACT FROM AUTHOR]

Background: Generating rigorous evidence to inform care for rare diseases requires reliable, sustainable, and longitudinal measurement of priority outcomes. Having developed a core outcome set for pediatric medium-chain acyl-CoA dehydrogenase (MCAD) deficiency, we aimed to assess the feasibility of prospective measurement of these core outcomes during routine metabolic clinic visits. Methods: We used existing cohort data abstracted from charts of 124 children diagnosed with MCAD deficiency who participated in a Canadian study which collected data from birth to a maximum of 11 years of age to investigate the frequency of clinic visits and quality of metabolic chart data for selected outcomes. We recorded all opportunities to collect outcomes from the medical chart as a function of visit rate to the metabolic clinic, by treatment centre and by child age. We applied a data quality framework to evaluate data based on completeness, conformance, and plausibility for four core MCAD outcomes: emergency department use, fasting time, metabolic decompensation, and death. Results: The frequency of metabolic clinic visits decreased with increasing age, from a rate of 2.8 visits per child per year (95% confidence interval, 2.3–3.3) among infants 2 to 6 months, to 1.0 visit per child per year (95% confidence interval, 0.9–1.2) among those ≥ 5 years of age. Rates of emergency department visits followed anticipated trends by child age. Supplemental findings suggested that some emergency visits occur outside of the metabolic care treatment centre but are not captured. Recommended fasting times were updated relatively infrequently in patients' metabolic charts. Episodes of metabolic decompensation were identifiable but required an operational definition based on acute manifestations most commonly recorded in the metabolic chart. Deaths occurred rarely in these patients and quality of mortality data was not evaluated. Conclusions: Opportunities to record core outcomes at the metabolic clinic occur at least annually for children with MCAD deficiency. Methods to comprehensively capture emergency care received at outside institutions are needed. To reduce substantial heterogeneous recording of core outcome across treatment centres, improved documentation standards are required for recording of recommended fasting times and a consensus definition for metabolic decompensations needs to be developed and implemented. [ABSTRACT FROM AUTHOR]

Background: In the Diabetes domain, events such as meals and exercises play an important role in the disease management. For that, many studies focus on automatic meal detection, specially as part of the so-called artificial β -cell systems. Meals are associated to blood glucose (BG) variations, however such variations are not peculiar to meals, it mostly comes as a combination of external factors. Thus, general approaches such as the ones focused on glucose signal rate of change are not enough to detect personalized influence of such factors. By using a data-driven individualized approach for meal detection, our method is able to fit real data, detecting personalized meal responses even when such external factors are implicitly present. Methods: The method is split into model training and selection. In the training phase, we start observing meal responses for each individual, and identifying personalized patterns. Occurrences of such patterns are searched over the BG signal, evaluating the similarity of each pattern to each possible signal subsequence. The most similar occurrences are then selected as possible meal event candidates. For that, we include steps for excluding less relevant neighbors per pattern, and grouping close occurrences in time globally. Each candidate is represented by a set of time and response signal related qualitative variables. These variables are used as input features for different binary classifiers in order to learn to classify a candidate as Meal or Non-Meal. In the model selection phase, we compare all trained classifiers to select the one that performs better with the data of each individual. Results: The results show that the method is able to detect daily meals, providing a result with a balanced proportion between detected meals and false alarms. The analysis on multiple patients indicate that the approach achieves good outcomes when there is enough reliable training data, as this is reflected on the testing results. Conclusions: The approach aims at personalizing the meal detection task by relying solely on data. The premise is that a model trained with data that contains the implicit influence of external factors is able to recognize the nuances of the individual that generated the data. Besides, the approach can also be used to improve data quality by detecting meals, opening opportunities to possible applications such as detecting and reminding users of missing or wrongly informed meal events. [ABSTRACT FROM AUTHOR]

Background: Biclustering is increasingly used in biomedical data analysis, recommendation tasks, and text mining domains, with hundreds of biclustering algorithms proposed. When assessing the performance of these algorithms, more than real datasets are required as they do not offer a solid ground truth. Synthetic data surpass this limitation by producing reference solutions to be compared with the found patterns. However, generating synthetic datasets is challenging since the generated data must ensure reproducibility, pattern representativity, and real data resemblance. Results: We propose G-Bic, a dataset generator conceived to produce synthetic benchmarks for the normative assessment of biclustering algorithms. Beyond expanding on aspects of pattern coherence, data quality, and positioning properties, it further handles specificities related to mixed-type datasets and time-series data.G-Bic has the flexibility to replicate real data regularities from diverse domains. We provide the default configurations to generate reproducible benchmarks to evaluate and compare diverse aspects of biclustering algorithms. Additionally, we discuss empirical strategies to simulate the properties of real data. Conclusion: G-Bic is a parametrizable generator for biclustering analysis, offering a solid means to assess biclustering solutions according to internal and external metrics robustly. [ABSTRACT FROM AUTHOR]

Background: Research in paramedicine faces challenges in developing research capacity, including access to high-quality data. A variety of unique factors in the paramedic work environment influence data quality. In other fields of healthcare, data quality assessment (DQA) frameworks provide common methods of quality assessment as well as standards of transparent reporting. No similar DQA frameworks exist for paramedicine, and practices related to DQA are sporadically reported. This scoping review aims to describe the range, extent, and nature of DQA practices within research in paramedicine. Methods: This review followed a registered and published protocol. In consultation with a professional librarian, a search strategy was developed and applied to MEDLINE (National Library of Medicine), EMBASE (Elsevier), Scopus (Elsevier), and CINAHL (EBSCO) to identify studies published from 2011 through 2021 that assess paramedic data quality as a stated goal. Studies that reported quantitative results of DQA using data that relate primarily to the paramedic practice environment were included. Protocols, commentaries, and similar study types were excluded. Title/abstract screening was conducted by two reviewers; full-text screening was conducted by two, with a third participating to resolve disagreements. Data were extracted using a piloted data-charting form. Results: Searching yielded 10,105 unique articles. After title and abstract screening, 199 remained for full-text review; 97 were included in the analysis. Included studies varied widely in many characteristics. Majorities were conducted in the United States (51%), assessed data containing between 100 and 9,999 records (61%), or assessed one of three topic areas: data, trauma, or out-of-hospital cardiac arrest (61%). All data-quality domains assessed could be grouped under 5 summary domains: completeness, linkage, accuracy, reliability, and representativeness. Conclusions: There are few common standards in terms of variables, domains, methods, or quality thresholds for DQA in paramedic research. Terminology used to describe quality domains varied among included studies and frequently overlapped. The included studies showed no evidence of assessing some domains and emerging topics seen in other areas of healthcare. Research in paramedicine would benefit from a standardized framework for DQA that allows for local variation while establishing common methods, terminology, and reporting standards. [ABSTRACT FROM AUTHOR]

Background: Currently, there is a lack of clear guidance on hemoglobin (Hb) data quality parameters and plausible flagging ranges for population-representative surveys. There is a need to determine which properties of Hb data indicate lower data quality and increased measurement error and which represent intrinsic statistical properties of Hb distributions rather than quality problems. Methods: We explored statistical characteristics of Hb distributions and plausible exclusion ranges in population-representative surveys of non-pregnant women of reproductive age (WRA) (15–49 years, n = 401 surveys) and children (6–59 months, n = 461 surveys) conducted in refugee settings by the United Nations High Commissioner for Refugees (UNHCR). Hb distribution characteristics [standard deviation (SD), skewness and kurtosis] were compared to those from Demographic and Health Surveys (DHS). Results: Overall, 0.08% of child and 0.14% of WRA Hb values were outside of the previously proposed 4.0–18.0 g/dL plausible range. Surveys conducted in Uganda tended to have unusually high SD compared with surveys from other settings, possibly an indication of problematic measurement quality. We therefore used summary results on SD, skewness and kurtosis excluding surveys from Uganda when comparing with DHS results or proposing plausible ranges. Both WRA and child Hb distributions tended to be left-skewed and had excess positive kurtosis. Mean survey-level SD was greater, mean skewness more negative, and mean kurtosis more positive in WRA surveys compared to child surveys. All these findings were broadly similar to those from DHS surveys. Mean SD in DHS surveys was higher than that in our data for both children (1.48 vs. 1.34) and WRA (1.58 vs. 1.43). Conclusions: We observed several statistical characteristics of Hb distributions that may not necessarily be indicative of data quality problems and bear strong similarities with the characteristics found in DHS surveys. Hb distributions tended to be negatively skewed and positively kurtotic, and SD in many surveys exceeded 1.5 (previously proposed upper plausible range). Based on our empirical evidence, surveys with skewness above + 0.2 and kurtosis below -0.5 or Hb SD outside the range of 1.1–1.55 g/dL for children (6–59 mo) or 1.1–1.65 g/dL for non-pregnant WRA (15–49 y) may require further quality investigation. [ABSTRACT FROM AUTHOR]

Background: Ensuring the data quality of Individual Medical Records becomes a crucial strategy in mitigating maternal and newborn morbidity and mortality during and around childbirth. However, previous research in Ethiopia primarily focused on studying data quality of institutional birth at the facility level, overlooking the data quality within Individual Medical Records. This study examined the data completeness and consistency within Individual Medical Records of the institutional birth service and associated factors. Methods: An institution-based retrospective cross-sectional study was conducted in two districts of Northwest Ethiopia. Data were obtained by reviewing three sets of Individual Medical Records of 651 women: the delivery register, Integrated Individual Folder, and integrated card. The proportions of completeness and consistency were computed. A multilevel binary logistic regression was used to identify factors of completeness and consistency. An odds ratio with a 95% confidence interval was used to assess the level of significance. Results: Overall, 74.0% of women’s Individual Medical Records demonstrated good data completeness (> = 70%), 95%CI (70.5, 77.3), while 26% exhibited good consistency, 95%CI (22.9, 29.7). The presence of trained providers in data quality (AOR = 2.9, 95%CI: (1.5, 5.7)) and supportive supervision (AOR = 11.5, 95%CI: (4.8, 27.2)) were found to be associated with completeness. Health facilities’ practice of root cause analysis on data quality gaps (AOR = 8.7, 9%CI: (1.5, 50.9)) was statistically significantly associated with the consistency. Conclusions: Most medical records were found to have good completeness, but nearly only a quarter of them found to contain consistent data. Completeness and consistency varied on the type of medical record. Health facility’s root cause analysis of data quality gaps, the presence of trained providers in data quality, and supportive supervision from higher officials were identified as factors affecting data quality in institutional birth service. These results emphasize the importance of focused efforts to enhance data completeness and consistency within Individual Medical Records, particularly through consideration of Individual Medical Records in future provider training, supervision, and the implementation of root cause analysis practices. [ABSTRACT FROM AUTHOR]

Background: The Swedish Quality Register for Ear Surgery (SwedEar) is a national register monitoring surgical procedures and outcomes of ear surgery to facilitate quality improvement. The value of the register is dependent on the quality of its data. SwedEar has never been validated regarding data quality or missing entries. Therefor, the purpose of this study was to assess coverage, completeness and response rate in the register and validate the physicians' reported data accuracy. Methods: In this validation study, the completeness, response rate and missing registrations were analysed. Data in SwedEar were compared with the yearly collected statistics of otosurgical procedures in The Swedish Otosurgical Society and the comparison of rates between groups was calculated with Fisher's exact test. Validation of registered data accuracy was performed on every 20th registered case during a five-year period. Data were reabstracted from medical records and compared with the original registration. Interrater agreement, reliability measures, Cohen's kappa, Gwet's AC1 and positive predictive value were calculated. Results: SwedEar has a coverage of 100%. The completeness of registered cases was 84% and the response rate was 74%. The validation of data accuracy assessed 13 530 variables, including audiograms. Less than 3% of incorrect or missing variables were identified. For most of the pre- and postoperative variables the Kappa and Gwet´s AC1 results show an almost perfect agreement (> 0.80). For audiogram data the ICC shows an excellent reliability (> 0.9) for all but one value. Conclusion: This validation shows that SwedEar has excellent coverage, high completeness, and that the data in the register have almost perfect reliability. The data are suitable for both clinical and research purposes. Further efforts to improve completeness are warranted. [ABSTRACT FROM AUTHOR]

Background: In this evaluation, we aim to strengthen Routine Health Information Systems (RHIS) through the digitization of data quality assessment (DQA) processes. We leverage electronic data from the Kenya Health Information System (KHIS) which is based on the District Health Information System version 2 (DHIS2) to perform DQAs at scale. We provide a systematic guide to developing composite data quality scores and use these scores to assess data quality in Kenya. Methods: We evaluated 187 HIV care facilities with electronic medical records across Kenya. Using quarterly, longitudinal KHIS data from January 2011 to June 2018 (total N = 30 quarters), we extracted indicators encompassing general HIV services including services to prevent mother-to-child transmission (PMTCT). We assessed the accuracy (the extent to which data were correct and free of error) of these data using three data-driven composite scores: 1) completeness score; 2) consistency score; and 3) discrepancy score. Completeness refers to the presence of the appropriate amount of data. Consistency refers to uniformity of data across multiple indicators. Discrepancy (measured on a Z-scale) refers to the degree of alignment (or lack thereof) of data with rules that defined the possible valid values for the data. Results: A total of 5,610 unique facility-quarters were extracted from KHIS. The mean completeness score was 61.1% [standard deviation (SD) = 27%]. The mean consistency score was 80% (SD = 16.4%). The mean discrepancy score was 0.07 (SD = 0.22). A strong and positive correlation was identified between the consistency score and discrepancy score (correlation coefficient = 0.77), whereas the correlation of either score with the completeness score was low with a correlation coefficient of -0.12 (with consistency score) and -0.36 (with discrepancy score). General HIV indicators were more complete, but less consistent, and less plausible than PMTCT indicators. Conclusion: We observed a lack of correlation between the completeness score and the other two scores. As such, for a holistic DQA, completeness assessment should be paired with the measurement of either consistency or discrepancy to reflect distinct dimensions of data quality. Given the complexity of the discrepancy score, we recommend the simpler consistency score, since they were highly correlated. Routine use of composite scores on KHIS data could enhance efficiencies in DQA at scale as digitization of health information expands and could be applied to other health sectors beyondHIV clinics. [ABSTRACT FROM AUTHOR]

Background: Measuring the quality of provided healthcare presents many challenges, especially in the context of medical rehabilitation. Rehabilitation is based on a holistic biopsychosocial model of health that includes a person's long-term functioning; hence, outcome domains are very diverse. In Germany, rehabilitation outcomes are currently assessed via patient and physician surveys. Health insurance claims data has the potential to simplify current quality assurance procedures in Germany, since its comprehensive collection is federally mandated from every healthcare provider. By using a cross-sectoral approach, quality assessments in rehabilitation can be adjusted for the quality provided in previous sectors and individual patient risk factors. Methods: SEQUAR combines two studies: In a prospective longitudinal study, 600 orthopedic rehabilitation patients and their physicians are surveyed at 4 and 2 time points, respectively, throughout rehabilitation and a follow-up period of 6 months. The questionnaires include validated instruments used in the current best-practice quality assurance procedures. In a retrospective cohort study, a nationwide claims database with more than 312,000 orthopedic rehabilitation patients will be used to perform exploratory analysis for the identification of quality indicators. The identified SEQUAR claims data quality indicators will be calculated for our prospective study participants and tested for their ability to approximate or replace the currently used, best-practice quality indicators based on primary data. Discussion: The identified SEQUAR quality indicators will be used to draft a novel, state-of-the-art quality assurance procedure that reduces the administrative burden of current procedures. Further research into the applicability to other indications of rehabilitation is required. Trial registration: WHO UTN: U1111-1276-7141; DRKS-ID: DRKS00028747 (Date of Registration in DRKS: 2022/08/10). [ABSTRACT FROM AUTHOR]

Background: A national framework for population-based cancer registration was established in Russia in the late 1990s. Data comparability and validity analyses found substantial differences across ten population-based cancer registries (PBCRs)in Northwest Russia, and only four out of ten met international standards. This study aimed to assess the completeness of the PBCR data of those registries. Methods: Qualitative and quantitative methods recommended for completeness and timeliness assessment were applied to the data from ten Russian regional PBCRs in Northwest Russia, covering a population of 13 million. We used historic data methods (using several European PBCRs reference rates), mortality-to-incidence ratios (M:I) comparison, and death certificate methods to calculate the proportion of unregistered cases (Lincoln-Petersen estimator and Ajiki formula). Results: Incidence rate trends of different cancer types were stable over time (except one region - Leningrad oblast). A slight drop in incidence rates in older age groups for several sites in the Northwestern regions was observed compared to the reference from European countries. Comparing M:I ratios against five-year survival revealed systematic differences in Leningrad oblast and Vologda oblast. Assessment of completeness revealed low or unrealistic estimates in Leningrad oblast and completeness below 90% in St. Petersburg. In other regions, the completeness was above 90%. The national annual report between 2008-2017 did not include about 10% of the cases collected later in the registry database of St. Petersburg. This difference was below 3% for Arkhangelsk oblast, Murmansk oblast, Novgorod oblast, Vologda oblast and the Republic of Karelia. Conclusions: Eight out of ten regional PBCRs in Northwest Russia collected data with an acceptable degree of completeness. Mostly populated St. Petersburg and Leningrad oblast did not reach such completeness. PBCR data from several regions in Northwest Russia are suitable for epidemiological research and monitoring cancer control activities. [ABSTRACT FROM AUTHOR]

Background: One crucial obstacle to attaining universal immunization coverage in Sub-Saharan Africa is the paucity of timely and high-quality data. This challenge, in part, stems from the fact that many frontline immunization staff in this part of the world are commonly overburdened with multiple data-related responsibilities that often compete with their clinical tasks, which in turn could affect their data collection practices. This study assessed the data management practices of immunization staff and unveiled potential barriers impacting immunization data quality in Cameroon. Methods: A descriptive cross-sectional study was conducted, involving health districts and health facilities in all 10 regions in Cameroon selected by a multi-stage sampling scheme. Structured questionnaires and observation checklists were used to collect data from Expanded Program of Immunization (EPI) staff, and data were analyzed using STATA VERSION 13.0 (StataCorp LP. 2015. College Station, TX). Results: A total of 265 facilities in 68 health districts were assessed. There was limited availability of some data recording tools like vaccination cards (43%), maintenance registers (8%), and stock cards (57%) in most health facilities. Core data collection tools were incompletely filled in a significant proportion of facilities (37% for registers and 81% for tally sheets). Almost every health facility (89%) did not adhere to the recommendation of filling tally sheets during vaccination; the filling was instead done either before (51% of facilities) or after (25% of facilities) vaccinating several children. Moreso, about 8% of facilities did not collect data on vaccine administration. About a third of facilities did not collect data on stock levels (35%), vaccine storage temperatures (21%), and vaccine wastage (39%). Conclusion: Our findings unveil important gaps in data collection practices at the facility level that could adversely affect Cameroon's immunization data quality. It highlights the urgent need for systematic capacity building of frontline immunization staff on data management capacity, standardizing data management processes, and building systems that ensure constant availability of data recording tools at the facility level. [ABSTRACT FROM AUTHOR]

Background: The Norwegian Trauma Registry (NTR) is designed to monitor and improve the quality and outcome of trauma care delivered by Norwegian trauma hospitals. Patient care is evaluated through specific quality indicators, which are constructed of variables reported to the registry by certified registrars. Having high-quality data recorded in the registry is essential for the validity, trust and use of data. This study aims to perform a data quality check of a subset of core data elements in the registry by assessing agreement between data in the NTR and corresponding data in electronic patient records (EPRs). Methods: We validated 49 of the 118 variables registered in the NTR by comparing those with the corresponding ones in electronic patient records for 180 patients with a trauma diagnosis admitted in 2019 at eight public hospitals. Agreement was quantified by calculating observed agreement, Cohen's Kappa and Gwet's first agreement coefficient (AC1) with 95% confidence intervals (CIs) for 27 nominal variables, quadratic weighted Cohen's Kappa and Gwet's second agreement coefficient (AC2) for five ordinal variables. For nine continuous, one date and seven time variables, we calculated intraclass correlation coefficient (ICC). Results: Almost perfect agreement (AC1 /AC2/ ICC > 0.80) was observed for all examined variables. Nominal and ordinal variables showed Gwet's agreement coefficients ranging from 0.85 (95% CI: 0.79–0.91) to 1.00 (95% CI: 1.00–1.00). For continuous and time variables there were detected high values of intraclass correlation coefficients (ICC) between 0.88 (95% CI: 0.83–0.91) and 1.00 (CI 95%: 1.00–1.00). While missing values in both the NTR and EPRs were in general negligeable, we found a substantial amount of missing registrations for a continuous "Base excess" in the NTR. For some of the time variables missing values both in the NTR and EPRs were high. Conclusion: All tested variables in the Norwegian Trauma Registry displayed excellent agreement with the corresponding variables in electronic patient records. Variables in the registry that showed missing data need further examination. [ABSTRACT FROM AUTHOR]

Systematic reviews (SRs) provide a solution to handle information overload for busy clinicians by summarising and synthesizing studies on a specific issue. However, because SRs are complicated and often boring to read, the busy or reluctant reader may make do with the abstract. When, as it has been shown, many authors overstate efficacy or understate harm in their abstracts, not consulting the underlying article could be misleading. This means that the prudent reader must have the ability to identify the 'tender points' of SRs to avoid falling for 'spin'. To this end we briefly review the method of SRs and ways to relatively quickly determine trustworthiness. [ABSTRACT FROM AUTHOR]

Background: The aim of this study was to assess the content, readability, and quality of online resources on septic arthritis, a crucial orthopedic condition necessitating immediate diagnosis and treatment to avert serious complications, with a particular focus on the relevance to individuals from the general public. Methods: Two search terms ("septic arthritis" and "joint infection") were input into three different search engines on the Internet (Google, Yahoo, and Bing) and 60 websites were evaluated, with the top 20 results in each search engine. The websites underwent categorization based on their type, and their content and quality were assessed utilizing the DISCERN score, the Journal of the American Medical Association (JAMA) benchmark, the Global Quality Score (GQS), and the Information Value Score (IVS). The readability of the text was assessed through the utilization of the Flesch Kincaid Grade Level (FKGL) and the Flesch Reading Ease Score (FKRS). The presence or absence of the Health on Net (HON) code was evaluated on each website. Results: The DISCERN, JAMA, GQS, FKGL, and IVS scores of the academic category were found to be substantially greater when compared with the physician, medical, and commercial categories. But at the same time, academic sites had high readability scores. Websites with HON code had significantly higher average FKGL, FCRS, DISCERN, JAMA, GQS, and IVS scores than those without. Conclusion: The quality of websites giving information on septic arthritis was variable and not optimal. Although the content of the academic group was of higher quality, it could be difficult to understand. One of the key responsibilities of healthcare professionals should be to provide high quality and comprehensible information concerning joint infections on reputable academic platforms, thereby facilitating patients in attaining a fundamental level of health literacy. [ABSTRACT FROM AUTHOR]

Background: Despite great promise, trials that ascertain patient clinical data from electronic health records (EHR), referred to here as "EHR-sourced" trials, are limited by uncertainty about how existing trial sites and infrastructure can be best used to operationalize study goals. Evidence is needed to support the practical use of EHRs in contemporary clinical trial settings. Main text: We describe a demonstration project that used EHR data to complement data collected for a contemporary multi-center pharmaceutical industry outcomes trial, and how a central coordinating center supported participating sites through the technical, governance, and operational aspects of this type of activity. We discuss operational considerations related to site selection, data extraction, site performance, and data transfer and quality review, and we outline challenges and lessons learned. We surveyed potential sites and used their responses to assess feasibility, determine the potential capabilities of sites and choose an appropriate data extraction strategy. We designed a flexible, multimodal approach for data extraction, enabling each site to either leverage an existing data source, create a new research datamart, or send all data to the central coordinating center to produce the requisite data elements. We evaluated site performance, as reflected by the speed of contracting and IRB approval, total patients enrolled, enrollment yield, data quality, and compared performance by data collection strategy. Conclusion: While broadening the type of sites able to participate in EHR-sourced trials may lead to greater generalizability and improved enrollment, sites with fewer technical resources may require additional support to participate. Central coordinating center support is essential to facilitate the execution of operational processes. Future work should focus on sharing lessons learned and creating reusable tools to facilitate participation of heterogeneous trial sites. [ABSTRACT FROM AUTHOR]

Background: This paper proposes Cyrus, a new transparency evaluation framework, for Open Knowledge Extraction (OKE) systems. Cyrus is based on the state-of-the-art transparency models and linked data quality assessment dimensions. It brings together a comprehensive view of transparency dimensions for OKE systems. The Cyrus framework is used to evaluate the transparency of three linked datasets, which are built from the same corpus by three state-of-the-art OKE systems. The evaluation is automatically performed using a combination of three state-of-the-art FAIRness (Findability, Accessibility, Interoperability, Reusability) assessment tools and a linked data quality evaluation framework, called Luzzu. This evaluation includes six Cyrus data transparency dimensions for which existing assessment tools could be identified. OKE systems extract structured knowledge from unstructured or semi-structured text in the form of linked data. These systems are fundamental components of advanced knowledge services. However, due to the lack of a transparency framework for OKE, most OKE systems are not transparent. This means that their processes and outcomes are not understandable and interpretable. A comprehensive framework sheds light on different aspects of transparency, allows comparison between the transparency of different systems by supporting the development of transparency scores, gives insight into the transparency weaknesses of the system, and ways to improve them. Automatic transparency evaluation helps with scalability and facilitates transparency assessment. The transparency problem has been identified as critical by the European Union Trustworthy Artificial Intelligence (AI) guidelines. In this paper, Cyrus provides the first comprehensive view of transparency dimensions for OKE systems by merging the perspectives of the FAccT (Fairness, Accountability, and Transparency), FAIR, and linked data quality research communities. Results: In Cyrus, data transparency includes ten dimensions which are grouped in two categories. In this paper, six of these dimensions, i.e., provenance, interpretability, understandability, licensing, availability, interlinking have been evaluated automatically for three state-of-the-art OKE systems, using the state-of-the-art metrics and tools. Covid-on-the-Web is identified to have the highest mean transparency. Conclusions: This is the first research to study the transparency of OKE systems that provides a comprehensive set of transparency dimensions spanning ethics, trustworthy AI, and data quality approaches to transparency. It also demonstrates how to perform automated transparency evaluation that combines existing FAIRness and linked data quality assessment tools for the first time. We show that state-of-the-art OKE systems vary in the transparency of the linked data generated and that these differences can be automatically quantified leading to potential applications in trustworthy AI, compliance, data protection, data governance, and future OKE system design and testing. [ABSTRACT FROM AUTHOR]

Background: Collection of accurate patient race, ethnicity, preferred language (REaL) and gender identity in the electronic health record (EHR) is essential for equitable and inclusive care. Misidentification of these factors limits quality measurement of health outcomes in at-risk populations. Therefore, the aim of our study was to assess the accuracy of REaL and gender identity data at our institution. Methods: A survey was administered to 117 random patients, selected from prior day admissions at a large academic medical center in urban central New York. Patients (or guardians) self-reported REaL and gender identity data, selecting from current EHR options. Variables were coded for the presence or absence of a difference from data recorded in the EHR. Results: Race was misreported in the EHR for 13% of patients and ethnicity for 6%. For most White and Black patients, race was concordant. However, self-identified data for all multiracial patients were discordant with the EHR. Most Non-Hispanic patients had ethnicity correctly documented. Some Hispanic patients were misidentified. There was a significant association between reporting both a race and an ethnicity which differed from the EHR on chi square analysis (P < 0.001). Of those who reported an alternative ethnicity, 71.4% also reported an alternative race. Gender identity was missing for most patients and 11% of the gender-identity entries present in the EHR were discordant with the patient's self-identity. Preferred language was 100% concordant with the EHR. Conclusions: At an academic medical center, multiracial and Hispanic patients were more likely to have their demographics misreported in the EHR, and gender identity data were largely missing. Healthcare systems need strategies that support accurate collection of patients' self-reported ReAL and gender identity data to improve the future ability to identify and address healthcare disparities. [ABSTRACT FROM AUTHOR]

Data is an essential tool for valid and reliable healthcare management. Access to high-quality data is critical to ensuring the early identification of problems, the design of appropriate interventions, and the effective implementation and evaluation of health intervention outcomes. During the COVID-19 pandemic, the need for strong information systems and the value of producing high-quality data for timely response and tracking resources and progress have been very evident across countries. The availability of and access to high-quality data at all levels of the health systems of low and middle-income countries is a challenge, which is exacerbated by multiple parallels and poorly integrated data sources, a lack of data-sharing standards and policy frameworks, their weak enforcement, and inadequate skills among those handling data. Completeness, accuracy, integrity, validity, and timeliness are challenges to data availability and use. "Big Data" is a necessity and a challenge in the current complexities of health systems. In transitioning to digital systems with proper data standards and policy frameworks for privacy protection, data literacy, ownership, and data use at all levels of the health system, skill enhancement of the staff is critical. Adequate funding for strengthening routine information systems and periodic surveys and research, and reciprocal partnerships between high-income countries and low- and middle-income countries in data generation and use, should be prioritized by the low- and middle-income countries to foster evidence-based healthcare practices. [ABSTRACT FROM AUTHOR]

Background: Audit and feedback (A&F) is an implementation strategy that can facilitate implementation tailoring by identifying gaps between desired and actual clinical care. While there are several theory-based propositions on which A&F components lead to better implementation outcomes, many have not been empirically investigated, and there is limited guidance for stakeholders when applying A&F in practice. The current study aims to illustrate A&F procedures in six community mental health clinics, with an emphasis on reporting A&F components that are relevant to theories of how feedback elicits behavior change. Methods: Six clinics from a larger trial using a tailored approach to implement measurement-based care (MBC) were analyzed for feedback content, delivery mechanisms, barriers to feedback, and outcomes of feedback using archival data. Pattern analysis was conducted to examine relations between A&F components and changes in MBC use. Results: Several sites utilized both aggregate and individualized data summaries, and data accuracy concerns were common. Feedback cycles featuring individual-level clinician data, data relevant to MBC barriers, and information requested by data recipients were related to patterns of increased MBC use. Conclusions: These findings support extant theory, such as Feedback Intervention Theory. Mental health professionals wishing to apply A&F should consider establishing reciprocal feedback mechanisms on the quality and amount of data being received and adopting specific roles communicating and addressing data quality concerns. Trial registration: ClinicalTrials.gov Identifier: NCT02266134. [ABSTRACT FROM AUTHOR]

Background: Epidemiologic and medical studies often rely on evaluators to obtain measurements of exposures or outcomes for study participants, and valid estimates of associations depends on the quality of data. Even though statistical methods have been proposed to adjust for measurement errors, they often rely on unverifiable assumptions and could lead to biased estimates if those assumptions are violated. Therefore, methods for detecting potential 'outlier' evaluators are needed to improve data quality during data collection stage. Methods: In this paper, we propose a two-stage algorithm to detect 'outlier' evaluators whose evaluation results tend to be higher or lower than their counterparts. In the first stage, evaluators' effects are obtained by fitting a regression model. In the second stage, hypothesis tests are performed to detect 'outlier' evaluators, where we consider both the power of each hypothesis test and the false discovery rate (FDR) among all tests. We conduct an extensive simulation study to evaluate the proposed method, and illustrate the method by detecting potential 'outlier' audiologists in the data collection stage for the Audiology Assessment Arm of the Conservation of Hearing Study, an epidemiologic study for examining risk factors of hearing loss in the Nurses' Health Study II. Results: Our simulation study shows that our method not only can detect true 'outlier' evaluators, but also is less likely to falsely reject true 'normal' evaluators. Conclusions: Our two-stage 'outlier' detection algorithm is a flexible approach that can effectively detect 'outlier' evaluators, and thus data quality can be improved during data collection stage. [ABSTRACT FROM AUTHOR]

Introduction: Infant and neonatal mortality estimates are typically derived from retrospective birth histories collected through surveys in countries with unreliable civil registration and vital statistics systems. Yet such data are subject to biases, including under-reporting of deaths and age misreporting, which impact mortality estimates. Prospective population-based cohort studies are an underutilized data source for mortality estimation that may offer strengths that avoid biases. Methods: We conducted a secondary analysis of data from the Child Health Epidemiology Reference Group, including 11 population-based pregnancy or birth cohort studies, to evaluate the appropriateness of vital event data for mortality estimation. Analyses were descriptive, summarizing study designs, populations, protocols, and internal checks to assess their impact on data quality. We calculated infant and neonatal morality rates and compared patterns with Demographic and Health Survey (DHS) data. Results: Studies yielded 71,760 pregnant women and 85,095 live births. Specific field protocols, especially pregnancy enrollment, limited exclusion criteria, and frequent follow-up visits after delivery, led to higher birth outcome ascertainment and fewer missing deaths. Most studies had low follow-up loss in pregnancy and the first month with little evidence of date heaping. Among studies in Asia and Latin America, neonatal mortality rates (NMR) were similar to DHS, while several studies in Sub-Saharan Africa had lower NMRs than DHS. Infant mortality varied by study and region between sources. Conclusions: Prospective, population-based cohort studies following rigorous protocols can yield high-quality vital event data to improve characterization of detailed mortality patterns of infants in low- and middle-income countries, especially in the early neonatal period where mortality risk is highest and changes rapidly. [ABSTRACT FROM AUTHOR]

Primary care physicians (PCPs) play an indispensable role in providing comprehensive care and referring patients for specialty care and other medical services. As the COVID-19 outbreak disrupts patient access to care, understanding the quality of primary care is critical at this unprecedented moment to support patients with complex medical needs in the primary care setting and inform policymakers to redesign our primary care system. The traditional way of collecting information from patient surveys is time-consuming and costly, and novel data collection and analysis methods are needed. In this review paper, we describe the existing algorithms and metrics that use the real-world data to qualify and quantify primary care, including the identification of an individual's likely PCP (identification of plurality provider and major provider), assessment of process quality (for example, appropriate-care-model composite measures), and continuity and regularity of care index (including the interval index, variance index and relative variance index), and highlight the strength and limitation of real world data from electronic health records (EHRs) and claims data in determining the quality of PCP care. The EHR audits facilitate assessing the quality of the workflow process and clinical appropriateness of primary care practices. With extensive and diverse records, administrative claims data can provide reliable information as it assesses primary care quality through coded information from different providers or networks. The use of EHRs and administrative claims data may be a cost-effective analytic strategy for evaluating the quality of primary care. [ABSTRACT FROM AUTHOR]

Background: To produce quality data that informs valid clinical trial results and withstands regulatory inspection, trial sites should adhere to many complex and dynamic requirements. Understanding non-conformance to requirements informs the emerging field of improvement science. We describe protocol deviations in South Africa's largest HIV vaccine efficacy trial. Methods: We analysed data from the HVTN 702 trial using mixed methods. We obtained descriptive statistics, from protocol deviation case report forms collected from 2016–2022, of deviation by participant, trial site, and time to site awareness. We thematically analysed text narratives of deviation descriptions, corrective and preventive actions, generating categories, codes and themes which emerged from the data. Results: For 5407 enrollments, 4074 protocol deviations were reported (75 [95% CI: 73.0–77.6] deviations per 100 enrolments). There was a median of 1 protocol deviation per participant (IQR 1–2). Median time from deviation to site awareness was 31 days (IQR 0–146). The most common category of deviation type was omitted data and/or procedures (69%), and 54% of these omissions were stated to have arisen because of the national lockdown at the beginning of the COVID-19 pandemic. The ratio of protocol deviations to cumulative enrolments was highest in the year 2020 (0.34). Major themes of deviations were: COVID-19 and climate disasters giving rise to deviation trends, subroutines introducing an opportunity for deviation, and document fragmentation (such as requirements dispersed across multiple guidance documents) as an obstacle. Preventive action categories were: no preventive measures; discipline, training and/or awareness; quality review, checking and verifying and changing the process and/or implementation tools. Major themes of preventive actions were that systems-based actions are unusual, with people-based actions dominating, and that root cause analysis was rarely mentioned. Conclusions: In the age of infectious and climate disaster risks, trials may benefit from simple study designs and trial-related documents. To optimise protocol adherence, sponsors and sites should consider ongoing training, and routinely review deviation reports with a view to adjusting processes. These data quality lessons may inform future trial design, training and implementation. Trial registration: HVTN 702 was registered with the South African National Clinical Trials Register (DOH-27–0916-5327) and ClinicalTrials.gov (NCT02968849). [ABSTRACT FROM AUTHOR]

Background: Access to essential medicines is a vital component of universal health coverage. The low availability of essential medicines for children (EMC) has led the World Health Organization (WHO) to issue a number of resolutions calling on member states on its improvement. But its global progress has been unclear. We aimed to systematically evaluate the progress of availability of EMC over the past decade across economic regions and countries. Methods: We searched eight databases from inception to December 2021 and reference lists to identify included studies. Two reviewers independently conducted literature screening, data extraction and quality evaluation. This study was registered with PROSPERO, CRD42022314003. Results: Overall, 22 cross-sectional studies covering 17 countries, 4 income groups were included. Globally, the average availability rates of EMC were 39.0% (95%CI: 35.5-42.5%) in 2009–2015 and 43.1% (95%CI: 40.1-46.2%) in 2016–2020. Based on the World Bank classification of economic regions, income was not proportional to availability. Nationally, the availability rate of EMC was reasonable and high (> 50%) in only 4 countries, and low or very low for the rest 13 countries. The availability rates of EMC in primary healthcare centers had increased, while that for other levels of hospitals slightly declined. The availability of original medicines decreased while that of generic medicines was stable. All drug categories had not achieved the high availability rate. Conclusion: The availability rate of EMC was low globally, with slight increase in the last decade. Continuous monitoring and timely reporting of the availability of EMC are also needed to facilitate targets setting and inform relevant policy making. [ABSTRACT FROM AUTHOR]

This document is a letter published in BMC Medicine titled "Radiomics in precision oncology: hype or ludum mutante." The letter discusses the use of radiomics in precision oncology, which involves using imaging data to extract quantitative features that can help predict outcomes and guide treatment decisions for cancer patients. The authors review a specific study that developed a radiomics signature to predict post-radiation nasopharyngeal necrosis in patients with locoregionally-recurrent nasopharyngeal carcinoma. They highlight the strengths and limitations of the study and propose steps to bridge the gap between research and clinical implementation of radiomics tools. The authors emphasize the need for standardization, automation of ROI segmentation, data quality assurance, transparency of study results, and the inclusion of clinical and biological associations in radiomics models. They conclude by calling for prospective radiomics-directed clinical trials to validate these models and fulfill the promise of radiomics in precision oncology. [Extracted from the article]

Background: Sham acupuncture is usually used to assess the specific effects of acupuncture. However, the reporting quality of sham acupuncture remains unclear despite its critical importance in understanding and analyzing the effects of acupuncture. This paper presents a literature review aimed at assessing the quality of reporting of sham acupuncture in randomized controlled trials (RCTs) based on STRICTA 2010 and TIDieR-Placebo. Methods: Three electronic English-language databases (PubMed, MEDLINE and Embase) were searched from inception to March 7, 2022, and RCTs of sham acupuncture were identified. The reporting quality of sham acupuncture was assessed in accordance with the items recommended in STRICTA 2010 and TIDieR-Placebo. The reporting quality of other items related to sham acupuncture apart from items from these two checklists was also captured to further assess the reporting quality of sham acupuncture. Results: A total of 609 eligible studies were included. For all of the items recommended in STRICTA 2010 and TIDieR-Placebo, 100% of the studies reported a brief name that described the sham acupuncture, 93.9% studies reported the needle type, and 90.0% reported the names of the points used. Other items for which the reporting rates were above 50% included the number, frequency and duration of treatment sessions; needle retention time; and number of needle insertions per subject per session. Overall, 49.4% of the studies revealed the rationale why sham acupuncture was chosen, 39.7% of the studies involving insertion processes reported the depth of insertion, and 37.9% of the studies reported the needle manufacturer. Other items for which the reporting rates were below 30% included practitioner-related information, response sought, evaluation of blinding, intervention mode and environment, assisting tools, and the extent to which the treatment was varied. The items "Modifications", "How well (planned)" and "How well (actual)" were not reported in any of the analyzed studies. Conclusions: The overall reporting quality of sham acupuncture in RCTs was suboptimal. Although STRICTA 2010 and TIDieR-Placebo could be beneficial for describing sham acupuncture, neither can offer recommendations specifically for sham acupuncture. There is thus an urgent need to develop specialized guidelines for reporting sham acupuncture in clinical trials. [ABSTRACT FROM AUTHOR]

Background: Cancer registries collect patient-specific information about cancer diseases. The collected information is verified and made available to clinical researchers, physicians, and patients. When processing information, cancer registries verify that the patient-specific records they collect are plausible. This means that the collected information about a particular patient makes medical sense. Methods: Unsupervised machine learning approaches can detect implausible electronic health records without human guidance. Therefore, this article investigates two unsupervised anomaly detection approaches, a pattern-based approach (FindFPOF) and a compression-based approach (autoencoder), to identify implausible electronic health records in cancer registries. Unlike most existing work that analyzes synthetic anomalies, we compare the performance of both approaches and a baseline (random selection of records) on a real-world dataset. The dataset contains 21,104 electronic health records of patients with breast, colorectal, and prostate tumors. Each record consists of 16 categorical variables describing the disease, the patient, and the diagnostic procedure. The samples identified by FindFPOF, the autoencoder, and a random selection—a total of 785 different records—are evaluated in a real-world scenario by medical domain experts. Results: Both anomaly detection methods are good at detecting implausible electronic health records. First, domain experts identified 8 % of 300 randomly selected records as implausible. With FindFPOF and the autoencoder, 28 % of the proposed 300 records in each sample were implausible. This corresponds to a precision of 28 % for FindFPOF and the autoencoder. Second, for 300 randomly selected records that were labeled by domain experts, the sensitivity of the autoencoder was 22 % and the sensitivity of FindFPOF was 26 % . Both anomaly detection methods had a specificity of 94 % . Third, FindFPOF and the autoencoder suggested samples with a different distribution of values than the overall dataset. For example, both anomaly detection methods suggested a higher proportion of colorectal records, the tumor localization with the highest percentage of implausible records in a randomly selected sample. Conclusions: Unsupervised anomaly detection can significantly reduce the manual effort of domain experts to find implausible electronic health records in cancer registries. In our experiments, the manual effort was reduced by a factor of approximately 3.5 compared to evaluating a random sample. [ABSTRACT FROM AUTHOR]

Background: During the COVID-19 pandemic, governments and researchers have used routine health data to estimate potential declines in the delivery and uptake of essential health services. This research relies on the data being high quality and, crucially, on the data quality not changing because of the pandemic. In this paper, we investigated those assumptions and assessed data quality before and during COVID-19. Methods: We obtained routine health data from the DHIS2 platforms in Ethiopia, Haiti, Lao People's Democratic Republic, Nepal, and South Africa (KwaZulu-Natal province) for a range of 40 indicators on essential health services and institutional deaths. We extracted data over 24 months (January 2019–December 2020) including pre-pandemic data and the first 9 months of the pandemic. We assessed four dimensions of data quality: reporting completeness, presence of outliers, internal consistency, and external consistency. Results: We found high reporting completeness across countries and services and few declines in reporting at the onset of the pandemic. Positive outliers represented fewer than 1% of facility-month observations across services. Assessment of internal consistency across vaccine indicators found similar reporting of vaccines in all countries. Comparing cesarean section rates in the HMIS to those from population-representative surveys, we found high external consistency in all countries analyzed. Conclusions: While efforts remain to improve the quality of these data, our results show that several indicators in the HMIS can be reliably used to monitor service provision over time in these five countries. [ABSTRACT FROM AUTHOR]

Introduction: The Semantic Web community provides a common Resource Description Framework (RDF) that allows representation of resources such that they can be linked. To maximize the potential of linked data - machine-actionable interlinked resources on the Web - a certain level of quality of RDF resources should be established, particularly in the biomedical domain in which concepts are complex and high-quality biomedical ontologies are in high demand. However, it is unclear which quality metrics for RDF resources exist that can be automated, which is required given the multitude of RDF resources. Therefore, we aim to determine these metrics and demonstrate an automated approach to assess such metrics of RDF resources. Methods: An initial set of metrics are identified through literature, standards, and existing tooling. Of these, metrics are selected that fulfil these criteria: (1) objective; (2) automatable; and (3) foundational. Selected metrics are represented in RDF and semantically aligned to existing standards. These metrics are then implemented in an open-source tool. To demonstrate the tool, eight commonly used RDF resources were assessed, including data models in the healthcare domain (HL7 RIM, HL7 FHIR, CDISC CDASH), ontologies (DCT, SIO, FOAF, ORDO), and a metadata profile (GRDDL). Results: Six objective metrics are identified in 3 categories: Resolvability (1), Parsability (1), and Consistency (4), and represented in RDF. The tool demonstrates that these metrics can be automated, and application in the healthcare domain shows non-resolvable URIs (ranging from 0.3% to 97%) among all eight resources and undefined URIs in HL7 RIM, and FHIR. In the tested resources no errors were found for parsability and the other three consistency metrics for correct usage of classes and properties. Conclusion: We extracted six objective and automatable metrics from literature, as the foundational quality requirements of RDF resources to maximize the potential of linked data. Automated tooling to assess resources has shown to be effective to identify quality issues that must be avoided. This approach can be expanded to incorporate more automatable metrics so as to reflect additional quality dimensions with the assessment tool implementing more metrics. [ABSTRACT FROM AUTHOR]

Background: Low birth weight (LBW) is a significant public health concern given its association with early-life mortality and other adverse health consequences that can impact the entire life cycle. In many countries, accurate estimates of LBW prevalence are lacking due to inaccuracies in collection and gaps in available data. Our study aimed to determine LBW prevalence among facility-born infants in selected areas of Kenya and Tanzania and to assess whether the introduction of an intervention to improve the accuracy of birth weight measurement would result in a meaningfully different estimate of LBW prevalence than current practice. Methods: We carried out a historically controlled intervention study in 22 health facilities in Kenya and three health facilities in Tanzania. The intervention included: provision of high-quality digital scales, training of nursing staff on accurate birth weight measurement, recording and scale calibration practices, and quality maintenance support that consisted of enhanced supervision and feedback (prospective arm). The historically controlled data were birth weights from the same facilities recorded in maternity registers for the same calendar months from the previous year measured using routine practices and manual scales. We calculated mean birth weight (95% confidence interval CI), mean difference in LBW prevalence, and respective risk ratio (95% CI) between study arms. Results: Between October 2019 and February 2020, we prospectively collected birth weights from 8441 newborns in Kenya and 4294 in Tanzania. Historical data were available from 9318 newborns in Kenya and 12,007 in Tanzania. In the prospective sample, the prevalence of LBW was 12.6% (95% confidence intervals [CI]: 10.9%–14.4%) in Kenya and 18.2% (12.2%–24.2%) in Tanzania. In the historical sample, the corresponding prevalence estimates were 7.8% (6.5%–9.2%) and 10.0% (8.6%–11.4%). Compared to the retrospective sample, the LBW prevalence in the prospective sample was 4.8% points (3.2%–6.4%) higher in Kenya and 8.2% points (2.3%–14.0%) higher in Tanzania, corresponding to a risk ratio of 1.61 (1.38–1.88) in Kenya and 1.81 (1.30–2.52) in Tanzania. Conclusion: Routine birth weight records underestimate the risk of LBW among facility-born infants in Kenya and Tanzania. The quality of birth weight data can be improved by a simple intervention consisting of provision of digital scales and supportive training. [ABSTRACT FROM AUTHOR]

Patient registries serve to overcome the research limitations inherent in the study of rare diseases, where patient numbers are typically small. Despite the value of real-world data collected through registries, adequate design and maintenance are integral to data quality. We aimed to describe an overview of the challenges in design, quality management, and maintenance of rare disease registries. A systematic search of English articles was conducted in PubMed, Ovid Medline/Embase, and Cochrane Library. Search terms included "rare diseases, patient registries, common data elements, quality, hospital information systems, and datasets". Inclusion criteria were any manuscript type focused upon rare disease patient registries describing design, quality monitoring or maintenance. Biobanks and drug surveillances were excluded. A total of 37 articles, published between 2001 and 2021, met the inclusion criteria. Patient registries covered a wide range of disease areas and covered multiple geographical locations, with a predisposition for Europe. Most articles were methodological reports and described the design and setup of a registry. Most registries recruited clinical patients (92%) with informed consent (81%) and protected the collected data (76%). Whilst the majority (57%) collected patient-reported outcome measures, only few (38%) consulted PAGs during the registry design process. Few reports described details regarding quality management (51%) and maintenance (46%). Rare disease patient registries are valuable for research and evaluation of clinical care, and an increasing number have emerged. However, registries need to be continuously evaluated for data quality and long-term sustainability to remain relevant for future use. [ABSTRACT FROM AUTHOR]

Background: Linked electronic medical records and administrative data have the potential to support a learning health system and data-driven quality improvement. However, data completeness and accuracy must first be assessed before their application. We evaluated the processes, feasibility, and limitations of linking electronic medical records and administrative data for the purpose of quality improvement within five specialist diabetes clinics in Edmonton, Alberta, a province known for its robust health data infrastructure. Methods: We conducted a retrospective cross-sectional analysis using electronic medical record and administrative data for individuals ≥ 18 years attending the clinics between March 2017 and December 2018. Descriptive statistics were produced for demographics, service use, diabetes type, and standard diabetes benchmarks. The systematic and iterative process of obtaining results is described. Results: The process of integrating electronic medical record with administrative data for quality improvement was found to be non-linear and iterative and involved four phases: project planning, information generating, limitations analysis, and action. After limitations analysis, questions were grouped into those that were answerable with confidence, answerable with limitations, and not answerable with available data. Factors contributing to data limitations included inaccurate data entry, coding, collation, migration and synthesis, changes in laboratory reporting, and information not captured in existing databases. Conclusion: Electronic medical records and administrative databases can be powerful tools to establish clinical practice patterns, inform data-driven quality improvement at a regional level, and support a learning health system. However, there are substantial data limitations that must be addressed before these sources can be reliably leveraged. [ABSTRACT FROM AUTHOR]

Low and middle-income countries continue to use Verbal autopsies (VAs) as a World Health Organisation-recommended method to ascertain causes of death in settings where coverage of vital registration systems is not yet comprehensive. Whilst the adoption of VA has resulted in major improvements in estimating cause-specific mortality in many settings, well documented limitations have been identified relating to the standardisation of the processes involved. The WHO has invested significant resources into addressing concerns in some of these areas; there however remains enduring challenges particularly in operationalising VA surveys for deaths amongst women and children, challenges which have measurable impacts on the quality of data collected and on the accuracy of determining the final cause of death. In this paper we describe some of our key experiences and recommendations in conducting VAs from over two decades of evaluating seminal trials of maternal and child health interventions in rural Ghana. We focus on challenges along the entire VA pathway that can impact on the success rates of ascertaining the final cause of death, and lessons we have learned to optimise the procedures. We highlight our experiences of the value of the open history narratives in VAs and the training and skills required to optimise the quality of the information collected. We describe key issues in methods for ascertaining cause of death and argue that both automated and physician-based methods can be valid depending on the setting. We further summarise how increasingly popular information technology methods may be used to facilitate the processes described. Verbal autopsy is a vital means of increasing the coverage of accurate mortality statistics in low- and middle-income settings, however operationalisation remains problematic. The lessons we share here in conducting VAs within a long-term surveillance system in Ghana will be applicable to researchers and policymakers in many similar settings. [ABSTRACT FROM AUTHOR]

Background: Patient-reported outcomes such as health-related quality of life (HRQoL) are increasingly used as endpoints in randomized cancer clinical trials. However, the patients often drop out so that observation of the HRQoL longitudinal outcome ends prematurely, leading to monotone missing data. The patients may drop out for various reasons including occurrence of toxicities, disease progression, or may die. In case of informative dropout, the usual linear mixed model analysis will produce biased estimates. Unbiased estimates cannot be obtained unless the dropout is jointly modeled with the longitudinal outcome, for instance by using a joint model composed of a linear mixed (sub)model linked to a survival (sub)model. Our objective was to investigate in a clinical trial context the consequences of using the most frequently used linear mixed model, the random intercept and slope model, rather than its corresponding joint model. Methods: We first illustrate and compare the models on data of patients with metastatic pancreatic cancer. We then perform a more formal comparison through a simulation study. Results: From the application, we derived hypotheses on the situations in which biases arise and on their nature. Through the simulation study, we confirmed and complemented these hypotheses and provided general explanations of the bias mechanisms. Conclusions: In particular, this article reveals how the linear mixed model fails in the typical situation where poor HRQoL is associated with an increased risk of dropout and the experimental treatment improves survival. Unlike the joint model, in this situation the linear mixed model will overestimate the HRQoL in both arms, but not equally, misestimating the difference between the HRQoL trajectories of the two arms to the disadvantage of the experimental arm. [ABSTRACT FROM AUTHOR]

Implementation studies evaluate strategies to move evidence-based practices into routine clinical practice. Often, implementation scientists use healthcare quality measures to evaluate the integration of an evidence-based clinical practice into real-world healthcare settings. Healthcare quality measures have standardized definitions and are a method to operationalize and monitor guideline-congruent care. Implementation scientists can access existing data on healthcare quality measures through various sources (e.g. operations-calculated), or they can calculate the measures directly from healthcare claims and administrative data (i.e. researcher-calculated). Implementation scientists need a better understanding of the advantages and disadvantages of these methods of obtaining healthcare quality data for designing, planning and executing an implementation study. The purpose of this paper is to describe the advantages, risks and lessons learned when using operations- versus researcher-calculated healthcare quality measures in site selection, implementation monitoring and implementation outcome evaluation. A key lesson learned was that relying solely on operations-calculated healthcare quality measures during an implementation study poses risks to site selection, accurate feedback on implementation progress to stakeholders, and the integrity of study results. A possible solution is using operations-calculated quality measures for monitoring of evidence-based practice uptake and researcher-calculated measures for site section and outcomes evaluation. This approach provides researchers greater control over the data and consistency of the measurement from site selection to outcomes evaluation while still retaining measures that are familiar and understood by key stakeholders whom implementation scientists need to engage in practice change efforts. [ABSTRACT FROM AUTHOR]

Background: Corrosive ingestion is still a major health problem, and its outcomes are often unpredicted. The implementation of a registry system for poisoning with corrosive substances may improve the quality of patient care and might be useful to manage this type of poisoning and its complications. Therefore, our study aimed to establish a minimum data set (MDS) for corrosive ingestion. Methods: This was an applied study performed in 2022. First, a literature review was conducted to identify the potential data items to be included in the corrosive ingestion MDS. Then, a two-round Delphi survey was performed to attain an agreement among experts regarding the MDS content, and an additional Delphi step was used for confirming the final MDS by calculating the individual item content validity index (CVI) and content validity ratio (CVR) and by using other statistical tests. Results: After the literature review, 285 data items were collected and sent to a two-round Delphi survey in the form of a questionnaire. In total, 75 experts participated in the Delphi stage, CVI, kappa, and CVR calculation. Finally, the MDS of the corrosive ingestion registry system was identified in two administrative and clinical sections with 21 and 152 data items, respectively. Conclusions: The development of an MDS, as the first and most important step towards developing the corrosive ingestion registry, can become a standard basis for data collection, reporting, and analysis of corrosive ingestion. We hope this MDS will facilitate epidemiological surveys and assist policymakers by providing higher quality data capture to guide clinical practice and improve patient-centered outcomes. [ABSTRACT FROM AUTHOR]

Background: Empirical evidence suggests data and insights from the clinical practice guidelines and clinical quality registries are not being fully utilised, leaving health service managers, clinicians and providers without clear guidance on how best to improve healthcare delivery. This lack of uptake of existing research knowledge represents low value to the healthcare system and needs to change. Methods: Five electronic databases (MEDLINE, Embase, CINAHL, Cochrane Central and Cochrane Database of Systematic Reviews) were systematically searched. Included studies were published between 2000 and 2020 reporting on the attributes, evidence usage and impact of clinical practice guidelines and clinical quality registries on health service delivery. Results: Twenty-six articles including one randomised controlled trial, eight before-and-after studies, eight case studies/reviews, five surveys and four interview studies, covering a wide range of medical conditions and conducted in the USA, Australia and Europe, were identified. Five complementary strategies were derived to maximise the likelihood of best practice health service delivery: (1) feedback and transparency, (2) intervention sustainability, (3) clinical practice guideline adherence, (4) productive partnerships and (5) whole-of-team approach. Conclusion: These five strategies, used in context-relevant combinations, are most likely to support the application of existing high-quality data, adding value to health service delivery. The review highlighted the limitations of study design in opportunistic registry studies that do not produce clear, usable evidence to guide changes to health service implementation practices. Recommendations include exploration of innovative methodologies, improved coordination of national registries and the use of incentives to encourage guideline adherence and wider dissemination of strategies used by successful registries. [ABSTRACT FROM AUTHOR]

Background: Amplicon sequencing is an established and cost-efficient method for profiling microbiomes. However, many available tools to process this data require both bioinformatics skills and high computational power to process big datasets. Furthermore, there are only few tools that allow for long read amplicon data analysis. To bridge this gap, we developed the LotuS2 (less OTU scripts 2) pipeline, enabling user-friendly, resource friendly, and versatile analysis of raw amplicon sequences. Results: In LotuS2, six different sequence clustering algorithms as well as extensive pre- and post-processing options allow for flexible data analysis by both experts, where parameters can be fully adjusted, and novices, where defaults are provided for different scenarios. We benchmarked three independent gut and soil datasets, where LotuS2 was on average 29 times faster compared to other pipelines, yet could better reproduce the alpha- and beta-diversity of technical replicate samples. Further benchmarking a mock community with known taxon composition showed that, compared to the other pipelines, LotuS2 recovered a higher fraction of correctly identified taxa and a higher fraction of reads assigned to true taxa (48% and 57% at species; 83% and 98% at genus level, respectively). At ASV/OTU level, precision and F-score were highest for LotuS2, as was the fraction of correctly reported 16S sequences. Conclusion: LotuS2 is a lightweight and user-friendly pipeline that is fast, precise, and streamlined, using extensive pre- and post-ASV/OTU clustering steps to further increase data quality. High data usage rates and reliability enable high-throughput microbiome analysis in minutes. Availability: LotuS2 is available from GitHub, conda, or via a Galaxy web interface, documented at http://lotus2.earlham.ac.uk/. 6hTcPpJ8wvT9bQx45qQZLR Video Abstract [ABSTRACT FROM AUTHOR]

Vector-borne diseases are among the most burdensome infectious diseases worldwide with high burden to health systems in developing regions in the tropics. For many of these diseases, vector control to reduce human biting rates or arthropod populations remains the primary strategy for prevention. New vector control interventions intended to be marketed through public health channels must be assessed by the World Health Organization for public health value using data generated from large-scale trials integrating epidemiological endpoints of human health impact. Such phase III trials typically follow large numbers of study subjects to meet necessary power requirements for detecting significant differences between treatment arms, thereby generating substantive and complex datasets. Data is often gathered directly in the field, in resource-poor settings, leading to challenges in efficient data reporting and/or quality assurance. With advancing technology, mobile data collection (MDC) systems have been implemented in many studies to overcome these challenges. Here we describe the development and implementation of a MDC system during a randomized-cluster, placebo-controlled clinical trial evaluating the protective efficacy of a spatial repellent intervention in reducing human infection with Aedes-borne viruses (ABV) in the urban setting of Iquitos, Peru, as well as the data management system that supported it. We discuss the benefits, remaining capacity gaps and the key lessons learned from using a MDC system in this context in detail. [ABSTRACT FROM AUTHOR]

Background: The demand for quality maternal and child health (MCH) data is critical for tracking progress towards attainment of the Sustainable Development Goal 3. However, MCH cannot be adequately monitored where health data are inaccurate, incomplete, untimely, or inconsistent. Thus, this study assessed the level of MCH data quality.Method: A facility-based cross-sectional study design was adopted, including a review of MCH service records. It was a stand-alone study involving 13 healthcare facilities of different levels that provided MCH services in the Cape Coast Metropolis. Data quality was assessed using the dimensions of accuracy, timeliness, completeness, and consistency. Health facilities registers were counted, collated, and compared with data on aggregate monthly forms, and a web-based data collation and reporting system, District Health Information System (DHIS2). The aggregate monthly forms were also compared with data in the DHIS2. Eight MCH variables were selected to assess data accuracy and consistency and two monthly reports were used to assess completeness and timeliness. Percentages and verification factor were estimated in the SPSS version 22 package.Results: Data accuracy were recorded between the data sources: Registers and Forms, 102.1% (95% CI = 97.5%-106.7%); Registers and DHIS2, 102.4% (95% CI = 94.4%-110.4%); and Forms and DHIS2, 100.1% (95% CI = 96.4%-103.9%). Across the eight MCH variables, data were 93.2% (95% CI = 82.9%-103.5%) complete in Registers, 91.0% (95% CI = 79.5%-102.5%) in the Forms, and 94.9% (95% CI = 89.9%-99.9%) in DHIS2 database. On the average, 87.2% (95% CI = 80.5%-93.9%) of the facilities submitted their Monthly Midwife's Returns reports on time, and Monthly Vaccination Report was 94% (95% CI = 89.3%-97.3%). The overall average data consistency was 93% (95% CI = 84%-102%).Conclusion: Given the WHO standard for data quality, the level of MCH data quality in the health care facilities at the Cape Coast Metropolis, available through the DHIS2 is complete, reported on timely manner, consistent, and reflect accurately what exist in facility's source document. Although there is evidence that data quality is good, there is still room for improvement in the quality of the data. [ABSTRACT FROM AUTHOR]

Background: Studies have shown that data collection by medical record abstraction (MRA) is a significant source of error in clinical research studies relying on secondary use data. Yet, the quality of data collected using MRA is seldom assessed. We employed a novel, theory-based framework for data quality assurance and quality control of MRA. The objective of this work is to determine the potential impact of formalized MRA training and continuous quality control (QC) processes on data quality over time.Methods: We conducted a retrospective analysis of QC data collected during a cross-sectional medical record review of mother-infant dyads with Neonatal Opioid Withdrawal Syndrome. A confidence interval approach was used to calculate crude (Wald's method) and adjusted (generalized estimating equation) error rates over time. We calculated error rates using the number of errors divided by total fields ("all-field" error rate) and populated fields ("populated-field" error rate) as the denominators, to provide both an optimistic and a conservative measurement, respectively.Results: On average, the ACT NOW CE Study maintained an error rate between 1% (optimistic) and 3% (conservative). Additionally, we observed a decrease of 0.51 percentage points with each additional QC Event conducted.Conclusions: Formalized MRA training and continuous QC resulted in lower error rates than have been found in previous literature and a decrease in error rates over time. This study newly demonstrates the importance of continuous process controls for MRA within the context of a multi-site clinical research study. [ABSTRACT FROM AUTHOR]