Improving the Quality and Use of Malaria Surveillance Data: Results from Evaluating an Integrated Malaria Information Storage System at the Health Facility Level in Selected Districts in Mozambique

Mozambique addressed critical malaria surveillance system challenges by rolling out an integrated malaria information storage system (iMISS) at the district level in February 2021. The iMISS integrates malaria data from existing systems across thematic program areas to improve data availability and use. In seven districts, the platform was extended to health facilities (HFs), allowing HFs to access iMISS and use tablets to submit monthly malaria reports to a central database, eliminating the need for paper-based reporting to districts. A structured evaluation of the iMISS rollout to HFs was carried out in February-July 2021. The four evaluation areas were data quality (reporting rate, timeliness, and fidelity) of monthly malaria reports electronically submitted to the iMISS, adoption of the iMISS for data-informed decision-making, system maintenance, and acceptability of the iMISS among target users. All 94 HFs in the seven targeted districts were assessed. Over the 6-month period, 86.1% of reported cases on the iMISS were consistent with cases recorded in paper-based reports, allowing for up to 10% discrepancy. In addition, 69.0% of expected monthly district meetings were held, and information from iMISS was discussed during 58.6% of these meetings. Maintenance issues, mostly related to tablet access and internet connectivity, were experienced by 74.5% of HFs; 33.7% of issues were resolved within 1 month. The iMISS and electronic submission of malaria reports were well accepted by HF- and district-level users. Continued political commitment and timely execution of issue management workflows are crucial to ensure trust in the new platform and facilitate higher levels of data use.

Utilização de banco de dados policiais como perspectiva para redução do sub-registro da violência contra mulheres / Using police databases as a prospect to reduce under-recording of violence against women / Utilizar las bases de datos policiales como perspectiva para reducir el subregistro de la violencia contra las mujeres

Objetivo: buscou-se avaliar a concordância entre o Sistema de Informação de Agravo de Notificação (SINAN) e dados policiais para casos de violência física e sexual contra mulheres, bem como o perfil das mulheres agredidas, das agressões e dos agressores. Método: foi realizado estudo transversal com casos ocorridos em uma cidade de Minas Gerais, notificados no SINAN e/ou identificados na base policial, entre os anos de 2015 e 2016. Criou-se uma base consolidada, composta pelos casos elegíveis das duas fontes, tendo sido realizadas análises descritivas. Em uma base pareada contendo casos comuns aos dois bancos, foi realizada análise de concordância pelo teste Fleiss'Kappa. Resultados: 1.185 casos compuseram a base consolidada e 56 constituíram a base pareada. Houve sub-registro de 83,54% nos dados do SINAN, além de incompletude importante de informações. A base policial apresentou cerca de oito vezes maior captação. A concordância de informações foi elevada/moderada para sete de 11 características avaliadas para os casos comuns. Na base consolidada, as vítimas foram predominantemente negras, solteiras ou viúvas, com idade entre 18 e 39 anos. Os homens, especialmente (ex)parceiros e familiares, foram os principais agressores. Conclusão: o sub-registro e a incompletude de informações sobre violência contra mulher no SINAN é uma realidade que precisa ser tratada. O cruzamento com fontes de dados policiais é uma alternativa para melhorar a qualidade das informações, reduzindo o sub-registro. Apesar dos dados subestimados, percebeu-se que a violência física e doméstica, cometida por (ex)parceiro contra mulheres jovens e negras continua sendo prevalente, atentando para o fato que se deve manter foco de políticas públicas.(AU)

Objective: this study sought to evaluate the agreement between the Information System for Notifiable Health Problems (Sistema de Informação de Agravo de Notificação, SINAN) and Police data for cases of physical and sexual violence against women, as well as the profile of the assaulted women, the aggressions and the aggressors. Method: a cross-sectional study was conducted with cases in a city from Minas Gerais, notified to the SINAN and/or identified in the Police database between 2015 and 2016. A consolidated database was created, comprised by the eligible cases from both sources, with performance of descriptive analyses. An agreement analysis by means of the Fleiss Kappa test was performed in a paired database containing cases common to both databases. Results: a total of 1,185 cases comprised the consolidated database, whereas 56 were included in the paired one. There was 83.54% under-recording in the SINAN data, in addition to important information incompleteness. The Police database presented nearly eight times more recording of cases. Agreement of all the information was high/moderate for seven out of 11 characteristics evaluated for the common cases. In the consolidated databases, the victims were predominantly black-skinned, single or widowed, and aged between 18 and 39 years old. The main aggressors were men, mainly (former) partners and family members. Conclusion: under-recording and incompleteness of diverse information about violence against women in the SINAN is a reality that needs to be dealt with. Cross-referencing with Police data sources represent an alternative to improve quality of the information, reducing under-recording. Despite the underestimated data, it was noticed that physical and domestic violence, perpetrated by (former) partners against young and black-skinned women, continues to be prevalent, pointing to the fact that it should remain as the focus of public policies.(AU)

Objetivo: se buscó evaluar la concordancia entre el Sistema de Informação de Agravo de Notificação (SINAN) y los datos policiales para los casos de violencia física y sexual contra las mujeres, así como el perfil de las mujeres maltratadas, de las agresiones y agresores. Método...(AU)

The limitations of hospital and law enforcement databases in characterizing the epidemiology of firearm injury.

BACKGROUND: Current data on the epidemiology of firearm injury in the United States are incomplete. Common sources include hospital, law enforcement, consumer, and public health databases, but each database has limitations that exclude injury subgroups. By integrating hospital (inpatient and outpatient) and law enforcement databases, we hypothesized that a more accurate depiction of the totality of firearm injury in our region could be achieved. METHODS: We constructed a collaborative firearm injury database consisting of all patients admitted as inpatients to the regional level 1 trauma hospital (inpatient registry), patients treated and released from the emergency department (ED), and subjects encountering local law enforcement as a result of firearm injury in Jefferson County, Kentucky. Injuries recorded from January 1, 2016, to December 31, 2020, were analyzed. Outcomes, demographics, and injury detection rates from individual databases were compared with those of the combined collaborative database and compared using χ2 testing across databases. RESULTS: The inpatient registry (n = 1,441) and ED database (n = 1,109) were combined, resulting in 2,550 incidents in the hospital database. The law enforcement database consisted of 2,665 patient incidents, with 2,008 incidents in common with the hospital database and 657 unique incidents. The merged collaborative database consisted of 3,207 incidents. In comparison with the collaborative database, the inpatient, total hospital (inpatient and ED), and law enforcement databases failed to include 55%, 20%, and 17% of all injuries, respectively. The hospital captured nearly 94% of survivors but less than 40% of nonsurvivors. Law enforcement captured 93% of nonsurvivors but missed 20% of survivors. Mortality (11-26%) and injury incidence were markedly different across the databases. DISCUSSION: The utilization of trauma registry or law enforcement databases alone do not accurately reflect the epidemiology of firearm injury and may misrepresent areas in need of greater injury prevention efforts. LEVEL OF EVIDENCE: Epidemiological, level IV.

Fatal overdoses after release from prison in British Columbia: a retrospective data linkage study.

BACKGROUND: People recently released from prison are at increased risk of preventable death; however, the impact of the current overdose epidemic on this population is unknown. We aimed to document the incidence and identify risk factors for fatal overdose after release from provincial prisons in British Columbia. METHODS: We conducted a retrospective, population-based, open cohort study of adults released from prisons in BC, using linked administrative data. Within a random 20% sample of the BC population, we linked provincial health and correctional records from 2010 to 2017 for people aged 23 years or older as of Jan. 1, 2015, who were released from provincial prisons at least once from 2015 to 2017. We identified exposures that occurred from 2010 to 2017 and deaths from 2015 to 2017. We calculated the piecewise incidence of overdose-related and all-cause deaths after release from prison. We used multivariable, mixed-effects Cox regression to identify predictors of all-cause death and death from overdose. RESULTS: Among 6106 adults released from prison from 2015 to 2017 and followed in the community for a median of 1.6 (interquartile range 0.9-2.3) years, 154 (2.5%) died, 108 (1.8%) from overdose. The incidence of all-cause death was 16.1 (95% confidence interval [CI] 13.7-18.8) per 1000 person-years. The incidence of overdose deaths was 11.2 (95% CI 9.2-13.5) per 1000 person-years, but 38.8 (95% CI 3.2-22.6) in the first 2 weeks after release from prison. After adjustment for covariates, the hazard of overdose death was 4 times higher among those who had been dispensed opioids for pain. INTERPRETATION: People released from prisons in BC are at markedly increased risk of overdose death. Overdose prevention must go beyond provision of opioid agonist treatment and naloxone on release to address systemic social and health inequities that increase the risk of premature death.

Toward a systematic conflict resolution framework for ontologies.

BACKGROUND: The ontology authoring step in ontology development involves having to make choices about what subject domain knowledge to include. This may concern sorting out ontological differences and making choices between conflicting axioms due to limitations in the logic or the subject domain semantics. Examples are dealing with different foundational ontologies in ontology alignment and OWL 2 DL's transitive object property versus a qualified cardinality constraint. Such conflicts have to be resolved somehow. However, only isolated and fragmented guidance for doing so is available, which therefore results in ad hoc decision-making that may not be the best choice or forgotten about later. RESULTS: This work aims to address this by taking steps towards a framework to deal with the various types of modeling conflicts through meaning negotiation and conflict resolution in a systematic way. It proposes an initial library of common conflicts, a conflict set, typical steps toward resolution, and the software availability and requirements needed for it. The approach was evaluated with an actual case of domain knowledge usage in the context of epizootic disease outbreak, being avian influenza, and running examples with COVID-19 ontologies. CONCLUSIONS: The evaluation demonstrated the potential and feasibility of a conflict resolution framework for ontologies.

GRADE Notes 2: Criteria for searching non-randomized or indirect evidence should be defined early in the guideline production process.

OBJECTIVE: To discuss two alternative approaches for complementing the body of direct evidence from Randomized Controlled Trials (RCTs) when it is judged insufficient from a guideline panel making recommendations. The approaches included expanding the evidence's body to non-randomises studies on the population of interest or to RCTs on indirect populations. STUDY DESIGN AND SETTING: In this report, we adopt the perspective of an evidence review team developing guidelines following the GRADE approach. Our experience is based on the development of two evidence-based guidelines promoted by The Italian National Institute of Health (ISS) and focusing on diagnosis and treatment of Autism Spectrum Disorders (ASD) in children/adolescents and adults. RESULTS: We left panel members deciding case by case whether the direct evidence from RCTs was sufficient or not and indicating which alternative to implement. This strategy presented unanticipated challenges both from an organizational and methodological standpoint. CONCLUSION: We suggest an early-stage production of a research protocol to define the criteria for expanding the body of evidence. These criteria should be informed by considerations around the certainty in the evidence, the clinical applicability of the results, feasibility and conflict of interest.

An adaptive spark-based framework for querying large-scale NoSQL and relational databases.

The growing popularity of big data analysis and cloud computing has created new big data management standards. Sometimes, programmers may interact with a number of heterogeneous data stores depending on the information they are responsible for: SQL and NoSQL data stores. Interacting with heterogeneous data models via numerous APIs and query languages imposes challenging tasks on multi-data processing developers. Indeed, complex queries concerning homogenous data structures cannot currently be performed in a declarative manner when found in single data storage applications and therefore require additional development efforts. Many models were presented in order to address complex queries Via multistore applications. Some of these models implemented a complex unified and fast model, while others' efficiency is not good enough to solve this type of complex database queries. This paper provides an automated, fast and easy unified architecture to solve simple and complex SQL and NoSQL queries over heterogeneous data stores (CQNS). This proposed framework can be used in cloud environments or for any big data application to automatically help developers to manage basic and complicated database queries. CQNS consists of three layers: matching selector layer, processing layer, and query execution layer. The matching selector layer is the heart of this architecture in which five of the user queries are examined if they are matched with another five queries stored in a single engine stored in the architecture library. This is achieved through a proposed algorithm that directs the query to the right SQL or NoSQL database engine. Furthermore, CQNS deal with many NoSQL Databases like MongoDB, Cassandra, Riak, CouchDB, and NOE4J databases. This paper presents a spark framework that can handle both SQL and NoSQL Databases. Four scenarios' benchmarks datasets are used to evaluate the proposed CQNS for querying different NoSQL Databases in terms of optimization process performance and query execution time. The results show that, the CQNS achieves best latency and throughput in less time among the compared systems.

Improving Collection of Real-World Data: The Experience of the Joven & Fuerte Prospective Cohort for Mexican Young Women With Breast Cancer.

The prospective collection of clinical data can generate detailed information on heterogeneous populations. This article reviews the strengths and limitations of the collection of real-world data and provides insight into the feasibility of routine collection of high-quality evidence even in a resource-constrained setting. The acquisition of high-quality data to assess the clinical and psychosocial needs of young Mexican patients with breast cancer has been enhanced through the use of preplanned, standardized data definitions and instrumentation to provide internally and externally comparable results, optimization of data collection with web-based surveys, engagement of participants to minimize missing data, and routine review for data consistency. A similar approach by other research groups could improve the quality of real-world data and accomplish enhanced inference of information.

Racial and ethnic disparities in benefits eligibility and spending among adults on the autism spectrum: A cohort study using the Medicare Medicaid Linked Enrollees Analytic Data Source.

BACKGROUND: Research on children and youth on the autism spectrum reveal racial and ethnic disparities in access to healthcare and utilization, but there is less research to understand how disparities persist as autistic adults age. We need to understand racial-ethnic inequities in obtaining eligibility for Medicare and/or Medicaid coverage, as well as inequities in spending for autistic enrollees under these public programs. METHODS: We conducted a cross-sectional cohort study of U.S. publicly-insured adults on the autism spectrum using 2012 Medicare-Medicaid Linked Enrollee Analytic Data Source (n = 172,071). We evaluated differences in race-ethnicity by eligibility (Medicare-only, Medicaid-only, Dual-Eligible) and spending. FINDINGS: The majority of white adults (49.87%) were full-dual eligible for both Medicare and Medicaid. In contrast, only 37.53% of Black, 34.65% Asian/Pacific Islander, and 35.94% of Hispanic beneficiaries were full-dual eligible for Medicare and Medicare, with most only eligible for state-funded Medicaid. Adjusted logistic models controlling for gender, intellectual disability status, costly chronic condition, rural status, county median income, and geographic region of residence revealed that Black beneficiaries were significantly less likely than white beneficiaries to be dual-eligible across all ages. Across these three beneficiary types, total spending exceeded $10 billion. Annual total expenditures median expenditures for full-dual and Medicaid-only eligible beneficiaries were higher among white beneficiaries as compared with Black beneficiaries. CONCLUSIONS: Public health insurance in the U.S. including Medicare and Medicaid aim to reduce inequities in access to healthcare that might exist due to disability, income, or old age. In contrast to these ideals, our study reveals that racial-ethnic minority autistic adults who were eligible for public insurance across all U.S. states in 2012 experience disparities in eligibility for specific programs and spending. We call for further evaluation of system supports that promote clear pathways to disability and public health insurance among those with lifelong developmental disabilities.

A Canadian Prospective Study of Linkage of Randomized Clinical Trial to Cancer and Mortality Registry Data.

In a prospective study, we sought to determine acceptability of linkage of administrative and clinical trial data among Canadian patients and Research Ethics Boards (REBs). The goal is to develop a more harmonized approach to data, with potential to improve clinical trial conduct through enhanced data quality collected at reduced cost and inconvenience for patients. On completion of the original LY.12 randomized clinical trial in lymphoma (NCT00078949), participants were invited to enrol in the Long-term Innovative Follow-up Extension (LIFE) component. Those consenting to do so provided comprehensive identifying information to facilitate linkage with their administrative data. We prospectively designed a global assessment of this innovative approach to clinical trial follow-up including rates of REB approval and patient consent. The pre-specified benchmark for patient acceptability was 80%. Of 16 REBs who reviewed the research protocol, 14 (89%) provided approval; two in Quebec declined due to small patient numbers. Of 140 patients invited to participate, 115 (82%, 95% CI 76 to 88%) from across 9 Canadian provinces provided consent and their full name, date of birth, health insurance number and postal code to facilitate linkage with their administrative data for long-term follow-up. Linkage of clinical trial and administrative data is feasible and acceptable. Further collaborative work including many stakeholders is required to develop an optimized secure approach to research. A more coordinated national approach to health data could facilitate more rapid testing and identification of new effective treatments across multiple jurisdictions and diseases from diabetes to COVID-19.

Integrating contextual sentiment analysis in collaborative recommender systems.

Recently. recommender systems have become a very crucial application in the online market and e-commerce as users are often astounded by choices and preferences and they need help finding what the best they are looking for. Recommender systems have proven to overcome information overload issues in the retrieval of information, but still suffer from persistent problems related to cold-start and data sparsity. On the flip side, sentiment analysis technique has been known in translating text and expressing user preferences. It is often used to help online businesses to observe customers' feedbacks on their products as well as try to understand customer needs and preferences. However, the current solution for embedding traditional sentiment analysis in recommender solutions seems to have limitations when involving multiple domains. Therefore, an issue called domain sensitivity should be addressed. In this paper, a sentiment-based model with contextual information for recommender system was proposed. A novel solution for domain sensitivity was proposed by applying a contextual information sentiment-based model for recommender systems. In evaluating the contributions of contextual information in sentiment-based recommendations, experiments were divided into standard rating model, standard sentiment model and contextual information model. Results showed that the proposed contextual information sentiment-based model illustrates better performance as compared to the traditional collaborative filtering approach.

'Evidence' within local authority decision making.

Michael Cook looks at the role of an embedded Public Health Information Specialist highlighting the ways the core evidence, information and knowledge skills are used to progress Public Health activity in local government settings. Acknowledging the current pandemic, he explores how COVID-19 has dominated all aspects of health and social care, and outlines how evidence services have work within these complex Public Health systems to lead the local response and recovery efforts.

A prospective comparison of evidence synthesis search strategies developed with and without text-mining tools.

OBJECTIVE: We compared the process of developing searches with and without using text-mining tools (TMTs) for evidence synthesis products. STUDY DESIGN: This descriptive comparative analysis included seven systematic reviews, classified as simple or complex. Two librarians created MEDLINE strategies for each review, using either usual practice (UP) or TMTs. For each search we calculated sensitivity, number-needed-to-read (NNR) and time spent developing the search strategy. RESULTS: We found UP searches were more sensitive (UP 92% (95% CI, 85-99); TMT 84.9% (95% CI, 74.4-95.4)), with lower NNR (UP 83 (SD 34); TMT 90 (SD 68)). UP librarians spent an average of 12 h (SD 8) developing search strategies, compared to TMT librarians' 5 hours (SD 2). CONCLUSION: Across all reviews, TMT searches were less sensitive than UP searches, but confidence intervals overlapped. For simple SR topics, TMT searches were faster and slightly less sensitive than UP. For complex SR topics, TMT searches were faster and less sensitive than UP searches but identified unique eligible citations not found by the UP searches.

El bibliotecario como coautor de revisiones y síntesis de evidencia / Librarian as co-author of reviews and evidence synthesis

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Confronting the opioid crisis with consumer health information: a look at East Tennessee.

BACKGROUND: Starting in the 1990s, health care providers began prescribing opioids to patients as pain relievers, believing they were safe. However, many patients became addicted to these pills. In 2017, the US Department of Health and Human Services declared a public health emergency to fight the opioid epidemic. This crisis was prevalent in East Tennessee, where many residents were prescribed opioids. CASE PRESENTATION: Librarians at an academic medical center library in East Tennessee analyzed the health information requests related to pain, mental health, and addiction over the last fifteen years. We reviewed the pattern of requests related to these topics, the counties requesting this information, and the impact that these hospital policies had on these requests. CONCLUSIONS: From 2005 to 2014, there were few requests about mental health, pain, and substance abuse. However, once the library moved into the hospital and there was an increase in awareness of opioid addiction, requests on those topics increased. Most of the requests were about pain, with the height occurring in 2017, during which year the public health emergency to fight the epidemic was declared. Additionally, 2017 was the year the hospital implemented visitor limitations for patients with infections associated with intravenous drug use, which might explain the drastic drop in substance abuse information requests in 2018. Future outreach will target counties that have a high opioid prescription rate.

Natural language processing was effective in assisting rapid title and abstract screening when updating systematic reviews.

BACKGROUND AND OBJECTIVE: To examine whether the use of natural language processing (NLP) technology is effective in assisting rapid title and abstract screening when updating a systematic review. STUDY DESIGN: Using the searched literature from a published systematic review, we trained and tested an NLP model that enables rapid title and abstract screening when updating a systematic review. The model was a light gradient boosting machine (LightGBM), an ensemble learning classifier which integrates four pretrained Bidirectional Encoder Representations from Transformers (BERT) models. We divided the searched citations into two sets (ie, training and test sets). The model was trained using the training set and assessed for screening performance using the test set. The searched citations, whose eligibility was determined by two independent reviewers, were treated as the reference standard. RESULTS: The test set included 947 citations; our model included 340 citations, excluded 607 citations, and achieved 96% sensitivity, and 78% specificity. If the classifier assessment in the case study was accepted, reviewers would lose 8 of 180 eligible citations (4%), none of which were ultimately included in the systematic review after full-text consideration, while decreasing the workload by 64.1%. CONCLUSION: NLP technology using the ensemble learning method may effectively assist in rapid literature screening when updating systematic reviews.

Medical and health informatics services during and after the COVID-19 pandemic should be virtual, tailored, responsive and interactive: a case study in Belgium.

This is part of a new series in this regular feature regarding trends in the provision of information by health science libraries. By sharing expertise and drawing together relevant trends the series intends to serve as a road map for both health science librarians and health informatics professionals. This article shows how a medical and biomedical research library changed practices, and reassessed user needs for the COVID-19 emergency. Discusses changes to online education (and collaborative working) to provide user-friendly services, researcher support tailored to need and re-visioning library space. J.M.