Catálogo de publicaciones - revistas

<jats:p>IntroductionHealth and Demographic Surveillance Systems (HDSS) are important sources of population health data in sub-Saharan Africa, but the recording of pregnancies, pregnancy outcomes, and early mortality is often incomplete. ObjectiveThis study assessed HDSS pregnancy reporting completeness and identified predictors of unreported pregnancies that likely ended in adverse outcomes. MethodsThe analysis utilized individually-linked HDSS and antenatal care (ANC) data from Siaya, Kenya for pregnancies in 2018-2020. We cross-checked ANC records with HDSS pregnancy registrations and outcomes. Pregnancies observed in the ANC that were missing reports in the HDSS despite a data collection round following the expected delivery date were identified as likely adverse outcomes, and we investigated the characteristics of such individuals. Clinical data were used to investigate the timing of HDSS pregnancy registration relative to care seeking and gestational age, and examine misclassification of miscarriages and stillbirths. ResultsFrom an analytical sample of 2,475 pregnancies observed in the ANC registers, 46% had pregnancy registrations in the HDSS, and 89% had retrospectively reported pregnancy outcomes. 1% of registered pregnancies were missing outcomes, compared to 10% of those lacking registration. Registered pregnancies had higher rates of stillbirth and perinatal mortality than those lacking registration. In 77% of cases, women accessed ANC prior to registering the pregnancy in the HDSS. Half of reported miscarriages were misclassified stillbirths. We identified 141 unreported pregnancies that likely ended in adverse outcomes. Such cases were more common among those who visited ANC clinics during the first trimester, made fewer overall visits, were HIV-positive, and outside of formal union. ConclusionsRecord linkage with ANC clinics revealed pregnancy underreporting in HDSS, resulting in biased measurement of perinatal mortality. Integrating records of ANC usage into routine data collection can augment HDSS pregnancy surveillance and improve monitoring of adverse pregnancy outcomes and early mortality.</jats:p>

<jats:p>Introduction Unstructured text data (UTD) are increasingly found in many databases that were never intended to be used for research, including electronic medical record (EMR) databases. Data quality can impact the usefulness of UTD for research. UTD are typically prepared for analysis (i.e., preprocessed) and analyzed using natural language processing (NLP) techniques. Different NLP methods are used to preprocess UTD and may affect data quality.   Objective Our objective was to systematically document current research and practices about NLP preprocessing methods to describe or improve the quality of UTD, including UTD found in EMR databases.   Methods A scoping review was undertaken of peer-reviewed studies published between December 2002 and January 2021. Scopus, Web of Science, ProQuest, and EBSCOhost were searched for literature relevant to the study objective. Information extracted from the studies included article characteristics (i.e., year of publication, journal discipline), data characteristics, types of preprocessing methods, and data quality topics. Study data were presented using a narrative synthesis.   Results A total of 41 articles were included in the scoping review; over 50% were published between 2016 and 2021. Almost 20% of the articles were published in health science journals. Common preprocessing methods included removal of extraneous text elements such as stop words, punctuation, and numbers, word tokenization, and parts of speech tagging. Data quality topics for articles about EMR data included misspelled words, security (i.e., de-identification), word variability, sources of noise, quality of annotations, and ambiguity of abbreviations.   Conclusions Multiple NLP techniques have been proposed to preprocess UTD, with some differences in techniques applied to EMR data. There are similarities in the data quality dimensions used to characterize structured data and UTD. While a few general-purpose measures of data quality that do not require external data; most of these focus on the measurement of noise.</jats:p>

<jats:p>Though societal acceptance of same-sex unions has grown, resulting in more inclusive government programs and policies and expanded legal protections, analysts remain uncertain about how to identify and enumerate same-sex households. Presently, the counts available of same-sex households in the United States oftentimes disagree. We show that the origins of these conflicting counts can be traced back to definitional and measurement issues in household surveys. In this study, we demonstrate how counts of same-sex households conflict, mislead, and undermine the goal of accurately representing the population of households with same-sex couples. By providing alternative approaches to counting household with same-sex couples we highlight the challenges in enumerating these households. We draw upon three federal household surveys to demonstrate the inconsistencies in the counts of same-sex households and to illustrate our methods. We argue that our proposed methods lead to more comprehensive and credible counts of households containing same-sex couples.</jats:p>

<jats:p>The ability of hospitals and health systems to learn from those who use its services (i.e., patients and families) is crucial for quality improvement and the delivery of high-quality patient-centered care. To this end, many hospitals and health systems regularly collect survey data from patients and their families, and are engaged in activities to publicly report the results. Despite this, there has been limited research into the experiences of patients and families, and how to improve them. Since 2015, our research team has conducted a variety of studies which have explored patient experience survey data, in isolation, and in linkages with routinely-captured administrative data sets across Alberta; a Canadian province of 4.4 million residents. Via secondary analyses, these studies have shed light upon the drivers of inpatient experience, the specific aspects of care which are most correlated with one's overall experiences, and the association of elements of the patient experience with other measures, such as patient safety indicators and unplanned hospital readmissions. The aim of this paper is to provide an overview of the methods we have used, including further details about the data sets and linkage protocol. The main findings from these papers have been presented for readers and those who wish to conduct their own work in this area.</jats:p>

<jats:p>IntroductionThe patient journey for residents of New South Wales (NSW) Australia with ST-elevation myocardial infarction (STEMI) often involves transfer between hospitals and these can include stays in hospitals in other jurisdictions. ObjectiveTo estimate the change in enumeration of STEMI hospitalisations and time to subsequent cardiac procedures for NSW residents using cross-jurisdictional linkage of administrative health data. MethodsRecords for NSW residents aged 20 years and over admitted to hospitals in NSW and four adjacent jurisdictions (Australian Capital Territory, Queensland, South Australia, and Victoria) between 1 July 2013 and 30 June 2018 with a principal diagnosis of STEMI were linked with records of the Australian Government Medicare Benefits Schedule (MBS). The number of STEMI hospitalisations, and rates of angiography, percutaneous coronary intervention and coronary artery bypass graft were compared for residents of different local health districts within NSW with and without inclusion of cross-jurisdictional data. ResultsInclusion of cross-jurisdictional hospital and MBS data increased the enumeration of STEMI hospitalisations for NSW residents by 8% (from 15,420 to 16,659) and procedure rates from 85.6% to 88.2%. For NSW residents who lived adjacent to a jurisdictional border, hospitalisation counts increased by up to 210% and procedure rates by up to 70 percentage points. ConclusionsCross-jurisdictional linked hospital data is essential to understand patient journeys of NSW residents who live in border areas and to evaluate adherence to treatment guidelines for STEMI. MBS data are useful where hospital data are not available and for procedures that may be conducted in out-patient settings.</jats:p>

<jats:p>Data collection, analysis, and data driven action cycles have been viewed as vital components of healthcare for decades. Throughout the COVID-19 pandemic, case incidence and mortality data have consistently been used by various levels of governments and health institutions to inform pandemic strategies and service distribution. However, these responses are often inequitable, underscoring pre-existing healthcare disparities faced by marginalized populations. This has prompted governments to finally face these disparities and find ways to quickly deliver more equitable pandemic support. These rapid data informed supports proved that learning health systems (LHS) could be quickly mobilized and effectively used to develop healthcare actions that delivered healthcare interventions that matched diverse populations' needs in equitable and affordable ways. Within LHS, data are viewed as a starting point researchers can use to inform practice and subsequent research. Despite this innovative approach, the quality and depth of data collection and robust analyses varies throughout healthcare, with data lacking across the quadruple aims. Often, large data gaps pertaining to community socio-demographics, patient perceptions of healthcare quality and the social determinants of health exist. This prevents a robust understanding of the healthcare landscape, leaving marginalized populations uncounted and at the sidelines of improvement efforts. These gaps are often viewed by researchers as indication that more data is needed rather than an opportunity to critically analyze and iteratively learn from multiple sources of pre-existing data. This continued cycle of data collection and analysis leaves one to wonder if healthcare has a data problem or a learning problem. In this commentary, we discuss ways healthcare data are often used and how LHS disrupts this cycle, turning data into learning opportunities that inform healthcare practice and future research in real time. We conclude by proposing several ways to make learning from data just as important as the data itself.</jats:p>

<jats:p>IntroductionHealth inequalities in Brazil have deepened on Covid-19 pandemic, and the most vulnerable people were the more affected. A multidisciplinary team from Cidacs/Fiocruz Bahia developed a Social Disparities Index for Covid-19 (IDS-COVID-19) to support the evaluation of effects of health inequalities on the pandemic in Brazil. Public Involvement and Engagement were the pillars of this research because they allowed us to access first hand experiences about the social context in our country. ObjectivesThis paper aims to describe our Public Involvement and Engagement experience by analysing our challenges, strategies, activities, results, and lessons learned during the construction of IDS-COVID-19. MethodsThe basis of the IDS-Covid-19 public engagement model was the participation of different social groups through methods and techniques that allow dialogue. Several activities and communication products supported the continuous interactions. Another guideline was the inclusion and the welcoming of participants from the beginning of the project to ensure that the participant's contributions could drive decision-making about the research. ResultsParticipants made several contributions to the research as a new layer of information to the Index, and improvements were made to the interactive panel. They also compromised to support the dissemination and use of the product. Eight representatives of community groups and 29 policymakers participated in our engagement activities during the project. More than 500 people were in our open webinars. In addition, more than 140 news items about IDS-Covid-19 were published in national and international media. ConclusionsWe highlight as lessons learned the adaptation of some dissemination formats to the public, and the necessity of being flexible and accessible to participants. We strengthened the relationship with relevant stakeholders by exploring individual conversations by phone, WhatsApp, email, and interviews to produce a documentary that registered this whole experience. Cidacs/Fiocruz Bahia has also embedded public engagement and involvement in the study agenda.</jats:p>

<jats:p>IntroductionData linkage for health research purposes enables the answering of countless new research questions, is said to be cost effective and less intrusive than other means of data collection. Nevertheless, health researchers are currently dealing with a complicated, fragmented, and inconsistent regulatory landscape with regard to the processing of data, and progress in health research is hindered. AimWe designed a qualitative study to assess what different stakeholders perceive as ethical and legal obstacles to data linkage for health research purposes, and how these obstacles could be overcome. MethodsTwo focus groups and eighteen semi-structured in-depth interviews were held to collect opinions and insights of various stakeholders. An inductive thematic analysis approach was used to identify overarching themes. ResultsThis study showed that the ambiguity regarding the `correct' interpretation of the law, the fragmentation of policies governing the processing of personal health data, and the demandingness of legal requirements are experienced as causes for the impediment of data linkage for research purposes by the participating stakeholders. To remove or reduce these obstacles authoritative interpretations of the laws and regulations governing data linkage should be issued. The participants furthermore encouraged the harmonisation of data linkage policies, as well as promoting trust and transparency and the enhancement of technical and organisational measures. Lastly, there is a demand for legislative and regulatory modifications amongst the participants. ConclusionsTo overcome the obstacles in data linkage for scientific research purposes, perhaps we should shift the focus from adapting the current laws and regulations governing data linkage, or even designing completely new laws, towards creating a more thorough understanding of the law and making better use of the flexibilities within the existing legislation. Important steps in achieving this shift could be clarification of the legal provisions governing data linkage by issuing authoritative interpretations, as well as the strengthening of ethical-legal oversight bodies.</jats:p>

<jats:p>IntroductionWe set out to assess the impact of Choosing Wisely Canada recommendations (2014) on reducing unnecessary health investigations and interventions in primary care across Southwestern Ontario. MethodsWe used the Deliver Primary Healthcare Information (DELPHI) database, which stores deidentified electronic medical records (EMR) of nearly 65,000 primary care patients across Southwestern Ontario. When conducting research using EMR data, data provenance (i.e., how the data came to be) should first be established. We first considered DELPHI data provenance in relation to longitudinal analyses, flagging a change in EMR software that occurred during 2012 and 2013. We attempted to link records between EMR databases produced by different software using probabilistic linkage and inspected 10 years of data in the DELPHI database (2009 to 2019) for data quality issues, including comparability over time. ResultsWe encountered several issues resulting from this change in EMR software. These included limited linkage of records between software without a common identifier; data migration issues that distorted procedure dates; and unusual changes in laboratory test and medication prescription volumes. ConclusionThis study reinforces the necessity of assessing data provenance and quality for new research projects. By understanding data provenance, we can anticipate related data quality issues such as changes in EMR data over time-which represent a growing concern as longitudinal data analyses increase in feasibility and popularity.</jats:p>

<jats:p>IntroductionRecent years have seen an increase in linkages between survey and administrative data. It is important to evaluate the quality of such data linkages to discern the likely reliability of ensuing research. Evaluation of linkage quality and bias can be conducted using different approaches, but many of these are not possible when there is a separation of processes for linkage and analysis to help preserve privacy, as is typically the case in the UK (and elsewhere). ObjectivesWe aimed to describe a suite of generalisable methods to evaluate linkage quality and population representativeness of linked survey and administrative data which remain tractable when users of the linked data are not party to the linkage process itself. We emphasise issues particular to longitudinal survey data throughout. MethodsOur proposed approaches cover several areas: i) Linkage rates, ii) Selection into response, linkage consent and successful linkage, iii) Linkage quality, and iv) Linked data population representativeness. We illustrate these methods using a recent linkage between the 1958 National Child Development Study (NCDS; a cohort following an initial 17,415 people born in Great Britain in a single week of 1958) and Hospital Episode Statistics (HES) databases (containing important information regarding admissions, accident and emergency attendances and outpatient appointments at NHS hospitals in England). ResultsOur illustrative analyses suggest that the linkage quality of the NCDS-HES data is high and that the linked sample maintains an excellent level of population representativeness with respect to the single dimension we assessed. ConclusionsThrough this work we hope to encourage providers and users of linked data resources to undertake and publish thorough evaluations. We further hope that providing illustrative analyses using linked NCDS-HES data will improve the quality and transparency of research using this particular linked data resource.</jats:p>