National Data Sources Research Articles

Assessing the Gun Violence Archive as an Epidemiologic Data Source for Community Firearm Violence in 4 US Cities

Firearm injury is a major public health burden in the US, and yet there is no single, validated national data source to study community firearm violence, including firearm homicide and nonfatal shootings that result from interpersonal violence. To assess the validity of the Gun Violence Archive as a source of data on events of community firearm violence and to examine the characteristics of individuals injured in shootings. This cross-sectional observational study compared data on community firearm violence from the Gun Violence Archive with publicly available police department data, which were assumed to be the reference standard, between January 1, 2015, and December 31, 2020. Cities included in the study (Philadelphia, Pennsylvania; New York, New York; Chicago, Illinois; and Cincinnati, Ohio) had a population of greater than 300 000 people according to the 2020 US Census and had publicly available shooting data from the city police department. A large city was defined as having a population greater than or equal to 500 000 (ie, Philadelphia, New York City, and Chicago). Data analysis was performed in December 2022. Events of community firearm violence from the Gun Violence Archive were matched to police department shootings by date and location. The sensitivity and positive predictive value of the data were calculated (0.9-1.0, excellent; 0.8-0.9, good; 0.7-0.8, fair; 0.6-0.7, poor; and <0.6, failed). A total of 26 679 and 32 588 shooting events were documented in the Gun Violence Archive and the police department databases, respectively, during the study period. The overall sensitivity of the Gun Violence Archive over the 6-year period was 81.1%, and the positive predictive value was 99.0%. The sensitivity steadily improved over time. Shootings involving multiple individuals and those involving women and children were less likely to be missing from the Gun Violence Archive, suggesting a systematic missingness. These findings support the use of the Gun Violence Archive in large cities for research requiring its unique advantages (ie, spatial resolution, timeliness, and geographic coverage), albeit with caution regarding a more granular examination of epidemiology given its apparent bias toward shootings involving multiple persons and those involving women and children.

Brazilian Dialysis Survey 2021.

The Brazilian Dialysis Survey (BDS) is an important source of national data about people on chronic dialysis that contributes to the formulation of health policies regarding kidney failure. To report the 2021 epidemiological data from the BDS of the Brazilian Society of Nephrology (BSN). A survey was carried out in Brazilian chronic dialysis centers using an online questionnaire covering clinical and epidemiological aspects of patients in chronic dialysis, data on dialysis therapy, characteristics of dialysis centers, and the impact of the COVID-19 pandemic of 2021. Thirty percent (n = 252) of the centers answered the questionnaire. In July 2021, the estimated total number of patients on dialysis was 148,363. The estimated prevalence and incidence rates of patients per million population (pmp) were 696 and 224, respectively. Of the prevalent patients, 94.2% were on hemodialysis (HD) (1.8% of these on hemodiafiltration), and 5.8% on peritoneal dialysis (PD); 21% were on the transplant waiting list. The incidence rate of confirmed COVID-19 between January and July 2021 was 1,236/10,000 dialysis patients, and the case-fatality rate reached 25.5%. Up to July 2021, 88.6% of patients had received at least one dose of the anti-SARS-CoV-2 vaccine. The estimated overall and COVID-19 crude annual mortality rates were 22.3% and 5.3%, respectively. The absolute number and the prevalence rate of patients on chronic dialysis continue to increase. Most dialysis patients were vaccinated against COVID-19 during the year. The COVID-19 pandemic was associated to the overall mortality rate.

Correlations and Timeliness of COVID-19 Surveillance Data Sources and Indicators - United States, October 1, 2020-March 22, 2023.

When the U.S. COVID-19 public health emergency declaration expires on May 11, 2023, national reporting of certain categories of COVID-19 public health surveillance data will be transitioned to other data sources or will be discontinued; COVID-19 hospitalization data will be the only data source available at the county level (1). In anticipation of the transition, national COVID-19 surveillance data sources and indicators were evaluated for purposes of ongoing monitoring. The timeliness and correlations among surveillance indicators were analyzed to assess the usefulness of COVID-19-associated hospital admission rates as a primary indicator for monitoring COVID-19 trends, as well as the suitability of other replacement data sources. During April 2022-March 2023, COVID-19 hospital admission rates from the National Healthcare Safety Network (NHSN)† lagged 1 day behind case rates and 4 days behind percentages of positive test results and COVID-19 emergency department (ED) visits from the National Syndromic Surveillance Program (NSSP). In the same analysis, National Vital Statistics System (NVSS) trends in the percentage of deaths that were COVID-19-associated, which is tracked by date of death rather than by report date, were observable 13 days earlier than those from aggregate death count data, which will be discontinued (1). During October 2020-March 2023, strong correlations were observed between NVSS and aggregate death data (0.78) and between the percentage of positive SARS-CoV-2 test results from the National Respiratory and Enteric Viruses Surveillance System (NREVSS) and COVID-19 electronic laboratory reporting (CELR) (0.79), which will also be discontinued (1). Weekly COVID-19 Community Levels (CCLs) will be replaced with levels of COVID-19 hospital admission rates (low, medium, or high) which demonstrated >99% concordance by county during February 2022-March 2023. COVID-19-associated hospital admission levels are a suitable primary metric for monitoring COVID-19 trends, the percentage of COVID-19 deaths is a timely disease severity indicator, and the percentages of positive SARS-CoV-2 test results from NREVSS and ED visits serve as early indicators for COVID-19 monitoring. Collectively, these surveillance data sources and indicators can support monitoring of the impact of COVID-19 and related prevention and control strategies as ongoing public health priorities.

Health-Related Data Sources Accessible to Health Researchers From the US Government: Mapping Review

Big data from large, government-sponsored surveys and data sets offers researchers opportunities to conduct population-based studies of important health issues in the United States, as well as develop preliminary data to support proposed future work. Yet, navigating these national data sources is challenging. Despite the widespread availability of national data, there is little guidance for researchers on how to access and evaluate the use of these resources. Our aim was to identify and summarize a comprehensive list of federally sponsored, health- and health care-related data sources that are accessible in the public domain in order to facilitate their use by researchers. We conducted a systematic mapping review of government sources of health-related data on US populations and with active or recent (previous 10 years) data collection. The key measures were government sponsor, overview and purpose of data, population of interest, sampling design, sample size, data collection methodology, type and description of data, and cost to obtain data. Convergent synthesis was used to aggregate findings. Among 106 unique data sources, 57 met the inclusion criteria. Data sources were classified as survey or assessment data (n=30, 53%), trends data (n=27, 47%), summative processed data (n=27, 47%), primary registry data (n=17, 30%), and evaluative data (n=11, 19%). Most (n=39, 68%) served more than 1 purpose. The population of interest included individuals/patients (n=40, 70%), providers (n=15, 26%), and health care sites and systems (n=14, 25%). The sources collected data on demographic (n=44, 77%) and clinical information (n=35, 61%), health behaviors (n=24, 42%), provider or practice characteristics (n=22, 39%), health care costs (n=17, 30%), and laboratory tests (n=8, 14%). Most (n=43, 75%) offered free data sets. A broad scope of national health data is accessible to researchers. These data provide insights into important health issues and the nation's health care system while eliminating the burden of primary data collection. Data standardization and uniformity were uncommon across government entities, highlighting a need to improve data consistency. Secondary analyses of national data are a feasible, cost-efficient means to address national health concerns.

Data source profile reporting by studies that use routinely collected health data to explore the effects of drug treatment

BackgroundRoutinely collected health data (RCD) are important resource for exploring drug treatment effects. Adequate reporting of data source profiles may increase the credibility of evidence generated from these data. This study conducted a systematic literature review to evaluate the reporting characteristics of databases used by RCD studies to explore the effects of drug treatment.MethodsObservational studies published in 2018 that used RCD to explore the effects of drug treatment were identified by searching PubMed. We categorized eligible reports into two groups by journal impact factor (IF), including the top 5 general medical journals (NEJM, Lancet, JAMA, BMJ and JAMA Internal Medicine) and the other journals. The reporting characteristics of the databases used were described and compared between the two groups and between studies citing and not citing database references.ResultsA total of 222 studies were included, of which 53 (23.9%) reported that they applied data linkage, 202 (91.0%) reported the type of database, and 211 (95.0%) reported the coverage of the data source. Only 81 (36.5%) studies reported the timeframe of the database. Studies in high-impact journals were more likely to report that they applied data linkage (65.1% vs. 20.2%) and used electronic medical records (EMR) (73.7% vs. 30.0%) and national data sources (77.8% vs. 51.3%) than those published in other medical journals. There were 137/222 (61.7%) cited database references. Studies with database-specific citations had better reporting of the data sources and were more likely to publish in high-impact journals than those without (mean IF, 6.08 vs. 4.09).ConclusionsSome deficits were found in the reporting quality of databases in studies that used RCD to explore the effects of drug treatment. Studies citing database-specific references may provide detailed information regarding data source characteristics. The adoption of reporting guidelines and education on their use is urgently needed to promote transparency by research groups.

National Data and the Applicability to Understanding Rural and Remote Substance Use.

Responding to increases in overdose, addiction, and substance misuse, local public health experts need accurate data to plan and implement evidence-based prevention and treatment programs. In many countries, national data are the tool most readily available for these efforts. In the United States, the National Study on Drug Use and Health and the Treatment Episode Data Set are data sources used by states to determine the extent of addiction. This project sought to determine if these national data sources are applicable for local use in addiction prevention and program planning. NSDUH prevalence estimates from 2015 to 2019 were applied to the state population to determine the number of persons estimated to be substance users. The prevalence estimates were compared over time with the population data and substance use treatment admissions to assess the covariance and population change as an indicator of efficacy. The primary drivers of fatal overdose in Alaska are fentanyl, heroin, and methamphetamine. Fentanyl use was not assessed in either dataset. When applying the estimated use prevalence to the population, heroin users varied annually by 1777 persons and methamphetamine varied up to 2143 persons. These observed variances did not correspond with state population changes nor any trend in the persons seeking treatment for these substances. Our analyses do not support the use of NSDUH data for planning in rural and remote areas. The methods used in NSDUH data collection exclude ~ 20% of the state population, mostly Native persons, based on location and language. The annual prevalence estimates applied to the population did not correspond with changes in population nor changes in treatment. Fentanyl, which causes the most overdoses in Alaska and is of primary concern locally, was not assessed.

Multisystem Inflammatory Syndrome in New Zealand Children.

New Zealand (NZ) initially adopted an elimination approach to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Pre-Omicron variant, the NZ pediatric population was immunologically naïve to SARS-CoV-2. This study, utilizing national data sources, describes the NZ incidence of multisystem inflammatory syndrome in children (MIS-C) following infection with the Omicron variant. MIS-C incidence was 1.03 of 100,000 age-specific population and 0.04 of 1000 recorded SARS-CoV-2 infections.

A systems science approach to identifying data gaps in national data sources on adolescent suicidal ideation and suicide attempt in the United States

BackgroundSuicide is currently the second leading cause of death among adolescents ages 10–14, and third leading cause of death among adolescents ages 15–19 in the United States (U.S). Although we have numerous U.S. based surveillance systems and survey data sources, the coverage offered by these data with regard to the complexity of youth suicide had yet to be examined. The recent release of a comprehensive systems map for adolescent suicide provides an opportunity to contrast the content of surveillance systems and surveys with the mechanisms listed in the map.ObjectiveTo inform existing data collection efforts and advance future research on the risk and protective factors relevant to adolescent suicide.MethodsWe examined data from U.S. based surveillance systems and nationally-representative surveys that included (1) observations for an adolescent population and (2) questions or indicators in the data that identified suicidal ideation or suicide attempt. Using thematic analysis, we evaluated the codebooks and data dictionaries for each source to match questions or indicators to suicide-related risk and protective factors identified through a recently published suicide systems map. We used descriptive analysis to summarize where data were available or missing and categorized data gaps by social-ecological level.ResultsApproximately 1-of-5 of the suicide-related risk and protective factors identified in the systems map had no supporting data, in any of the considered data sources. All sources cover less than half the factors, except the Adolescent Brain Cognitive Development Study (ABCD), which covers nearly 70% of factors.ConclusionsExamining gaps in suicide research can help focus future data collection efforts in suicide prevention. Our analysis precisely identified where data is missing and also revealed that missing data affects some aspects of suicide research (e.g., distal factors at the community and societal level) more than others (e.g., proximal factors about individual characteristics). In sum, our analysis highlights limitations in current suicide-related data availability and provides new opportunities to identify and expand current data collection efforts.

Women's preconception health in England: a report card based on cross-sectional analysis of national maternity services data from 2018/2019.

To present the first national-level report card on the state of women's preconception health in England. Cross-sectional population-based study. Maternity services, England. All pregnant women in England with a first antenatal (booking) appointment recorded in the national Maternity Services Dataset (MSDS) from April 2018 to March 2019 (n= 652 880). We analysed the prevalence of 32 preconception indicator measures in the overall population and across socio-demographic subgroups. Ten of these indicators were prioritised for ongoing surveillance based on modifiability, prevalence, data quality and ranking by multidisciplinary UK experts. The three most prevalent indicators were the proportion of the 22.9% of women who smoked 1year before pregnancy who did not quit smoking before pregnancy (85.0%), those who had not taken folic acid supplementation before pregnancy (72.7%) and previous pregnancy loss (38.9%). Inequalities were observed by age, ethnicity and area-based deprivation level. The ten indicators prioritised were not taking folic acid supplementation before pregnancy, obesity, complex social factors, living in the most deprived areas, smoking around the time of conception, overweight, pre-existing mental health condition, pre-existing physical health condition, previous pregnancy loss and previous obstetric complication. Our findings suggest important opportunities to improve the state of preconception health and reduce socio-demographic inequalities for women in England. In addition to MSDS data, other national data sources that record further and possibly better quality indicators could be explored and linked to build a comprehensive surveillance infrastructure.

Nonexercise machine learning models for maximal oxygen uptake prediction in national population surveys.

Nonexercise algorithms are cost-effective methods to estimate cardiorespiratory fitness (CRF), but the existing models have limitations in generalizability and predictive power. This study aims to improve the nonexercise algorithms using machine learning (ML) methods and data from US national population surveys. We used the 1999-2004 data from the National Health and Nutrition Examination Survey (NHANES). Maximal oxygen uptake (VO2 max), measured through a submaximal exercise test, served as the gold standard measure for CRF in this study. We applied multiple ML algorithms to build 2 models: a parsimonious model using commonly available interview and examination data, and an extended model additionally incorporating variables from Dual-Energy X-ray Absorptiometry (DEXA) and standard laboratory tests in clinical practice. Key predictors were identified using Shapley additive explanation (SHAP). Among the 5668 NHANES participants in the study population, 49.9% were women and the mean (SD) age was 32.5years (10.0). The light gradient boosting machine (LightGBM) had the best performance across multiple types of supervised ML algorithms. Compared with the best existing nonexercise algorithms that could be applied to the NHANES, the parsimonious LightGBM model (RMSE: 8.51 ml/kg/min [95% CI: 7.73-9.33]) and the extended LightGBM model (RMSE: 8.26 ml/kg/min [95% CI: 7.44-9.09]) significantly reduced the error by 15% and 12% (P < .001 for both), respectively. The integration of ML and national data source presents a novel approach for estimating cardiovascular fitness. This method provides valuable insights for cardiovascular disease risk classification and clinical decision-making, ultimately leading to improved health outcomes. Our nonexercise models provide improved accuracy in estimating VO2 max within NHANES data as compared to existing nonexercise algorithms.

A comprehensive methodology report for maternity and perinatal mortality research in the New Zealand Integrated Data Infrastructure.

The highest quality perinatal data in New Zealand is collected and collated by the Perinatal and Maternal Mortality Review Committee (PMMRC) and is made available to a limited number of researchers. Therefore, maternity, and perinatal mortality studies are generally performed on Government-held data. This report offers an alternative approach with in-depth justification for the methodology, while simultaneously improving the understanding of the data sources. A standardised method for creating a comprehensive maternity dataset within the Statistics New Zealand Integrated Data Infrastructure (IDI) was developed and a validation dataset was created to include all births between 2008 and 2017. A close approximation to the PMMRC annual report data was found, with 4.0% over-reporting of perinatal deaths and 0.05% over-reporting of live births in the IDI dataset. Several variables, including important pregnancy risk factors, were validated for use. Limitations to the datasets were explored and additional tables in the IDI were proposed, to include variables on pregnancy complications, ethnicity and country of birth, and socio-economic data. This methodological report describes an opportunity for standardised, high-quality maternity research in New Zealand using the IDI, including a variety of national data sources. Recommendations for further enhancement of these resources have been offered.

Determinants of Successful Human Immunodeficiency Virus Treatment Outcomes: A Linkage of National Data Sources in Malaysia.

Concerted efforts have been undertaken to reduce the human immunodeficiency virus (HIV) infection by the year 2030 in Malaysia. A situational analysis of the performance of successful HIV treatment and its determinants is vital; however, this information remains scarce. This study aimed to identify the determinants of undetectable viral load among people living with HIV (PLHIV). Newly diagnosed HIV cases (n = 493) registered under the Malaysia HIV/AIDS-related national databases from June 2018 to December 2019 were studied. The deterministic matching method was applied to link the records in two national databases (at Kuala Lumpur and Putrajaya Federal Territories Health Department, JKWPKLP HIV line-listing database and National AIDS Registry). Successful HIV treatment, an outcome variable, was measured by the undetectable viral load < 200 copies/mL after 1 year of antiretroviral therapy initiation. Logistic regression analysis was applied in the current study. Results showed that 454/493 (92.2%; 95% confidence interval [CI]: 89.8%, 94.6%) PLHIV had successful HIV treatment. Study participants had a mean (SD) age of 30 (8.10) years old, predominantly male (96.1%) and sexually transmission (99.9%). The multiple logistic regression analysis revealed two significant determinants including the timing of ART initiation (AOR = 3.94; 95% CI: 1.32, 11.70; P = 0.014) and establishment of Sexually Transmitted Infection Friendly Clinic (STIFC) (AOR = 3.40; 95% CI: 1.47, 7.85; P = 0.004). Non-significant variables included gender, education level, HIV risk exposure, and co-infections of tuberculosis and Hepatitis C. JKWPKLP is on the right track to achieving universal treatment as a prevention strategy. Reinforcement of early ART initiation and establishment of STIFC are recommended.

Influence of the Social Environment on Ideal Cardiovascular Health.

Background The environment plays a large role in the health of individuals; however, more research is needed to better understand aspects of the environment that most influence health. Specifically, our study examines how the social environment influences cardiovascular health (CVH). Methods and Results The social environment was characterized using measures of belonging and life and work stress in individuals, as well as nationally derived measures of marginalization, deprivation, economic status, and community well-being in neighborhoods. CVH was defined by the American Heart Association's Cardiovascular Health Index-a summed score of 7 clinical and behavioral components known to have the greatest impact on CVH. Data were obtained from the Canadian Community Health Survey 2015 to 2016 and multiple national data sources. Multilevel regression models were used to analyze the associations between CVH and the social environment. Overall, 27% of Canadians reported ideal CVH (6-7 score points), 68% reported intermediate CVH (3-5 score points), and 5% reported poor CVH (0-2 score points). The neighborhood environment contributed up to 7% of the differences in CVH between individuals. Findings indicated that residing in a neighborhood with greater community well-being (odds ratio [OR], 1.33 [95% CI, 1.26-1.41]) was associated with achieving higher odds of ideal CVH, while weaker community belonging (OR, 0.67 [95% CI, 0.62-0.72]) and residing in a neighborhood with greater marginalization (OR, 0.87 [95% CI, 0.82-0.91]) and deprivation (OR, 0.67 [95% CI, 0.64-0.69]) were associated with achieving lower odds of ideal CVH. Conclusions Aspects of individual-level social environment and residing in a neighborhood with a more favorable social environment were both independently and significantly associated with achieving ideal CVH.

The use of medications approved for alcohol use disorders in Italy.

Italy has the highest per capita alcohol consumption among European countries. Several pharmacological treatments for alcohol use disorders (AUDs) are currently available in Italy, but no consumption data are available. A first analysis of national drug consumption, comprising the whole Italian population over a long-term period covering the COVID-19 pandemic, was performed. To analyze the consumption of medications indicated for therapy of alcohol dependence, different national data sources were used. Consumption was measured as a defined daily dose (DDD) per 1,000,000 inhabitants per day. In 2020, the total consumption of medicines used in the treatment of AUDs amounted to 310.3 DDD per 1 million inhabitants per day (0.018% of the overall drug consumption in Italy) with a decreasing gradient from the north (373.9 DDD) to the south (250.7 DDD). 53.2% of the overall doses were dispensed by public healthcare facilities and 23.5% by community pharmacies, while the remaining 23.3% were purchased privately. The temporal trend of consumption seemed to be stable across the last few years, although an impact of the COVID-19 pandemic was observed. Disulfiram was the most consumed medicine over years. All Italian regions offer pharmacological treatments to patients with AUDs, but differences in the number of dispensed doses suggest a different local organization of patient care, which can be partly explained by the different severity of the clinical condition of residing patients. Pharmacotherapy of alcoholism should be deeply investigated to describe the clinical characteristics of treated patients (i.e., comorbidities) and evaluate the appropriateness of prescribed medications.

ASSESSMENT OF THE RELATIONSHIPS BETWEEN ECONOMIC GROWTH AND SAVINGS

Purpose. The purpose of this study is to develop the evaluation characteristics of the relationship between household savings decisions and macroeconomic growth in order to form a base of identification parameters of effective household financial behavior. Design/methodology/approach. In the article, the authors used the literature review method to justify the importance of implementing the problem of the relationship between savings and economic growth. Based on the trends analysis the authors identified the main features of households’ saving changes during last 17 years, their relationships with changes in GDP, national income and total income of households. The use of correlation-regression analysis made it possible to assess the sensitivity of the impact on the savings of their main elements. Empirical results have been obtained based on official sources of national statistical data and Wold Bank data. Findings. Households play a major role in the economic growth of a nation. Their financial behavior and savings habits can determine the overall economic performance of a country. This paper discusses the elements of household savings, how it affects economic growth, and why it has been on a downtrend lately. It also explores the potential solutions to address this issue and how households can be encouraged to save more for their future. The financial behavior of households in Ukraine has seen significant changes over the past 17 years, and the period of change in priorities between current and deferred consumption is the financial crisis of 2008. Since 2009, seriousness towards savings has been significantly lost. The saving behavior of households in Ukraine is in dissonance with the saving behavior of enterprises and state structures. Households’ savings decisions depend to a small extent on both the absolute size of their income and GDP, as well as their rates of change. Practical implications. The materials presented in the article may be useful for researchers interested in economic growth modeling. Also, the results of this research are of interest to policymakers for creating effective measures of macroeconomic stimulation. Originality/value. In the article, the authors have identified the main regularities of the behavior of households regarding the formation of savings. The changes in the GDP and household income do have not a significant impact on the saving rates both in the short-term and long-term. The changes in the volume of household savings are mainly determined by the dynamics of the volume of net household loans. Households increase indebtedness when the business cycle intensifies (GDP and national income growth). Households in Ukraine are characterized by a high priority of current consumption over deferred (savings).

1223 IDENTIFYING SCOTLAND'S CARE HOME POPULATION LIVING WITH DEMENTIA - IS DATA LINKAGE USEFUL?

Abstract Background People living with dementia are more likely to move into care homes. The true prevalence of dementia among care home residents in Scotland is not known. People living with dementia often interact with multiple social and healthcare services, thus routine data may offer a way to enhance understanding. Aim To compare national health and social care data sources recording dementia status for Scottish care home residents. Methods A retrospective cohort study of adult (≥ 18 years) care home residents in Scotland during financial years 2012/13 and 2013/14. An indexing process linked data from the Scottish Care Home Census (SCHC) to Community Health Index numbers to allow linkage to healthcare datasets. Anonymised individual data was accessed in a secure environment, within the National Safe Haven. A linked dataset with acute/general and psychiatric hospitalisations (SMR01, SMR04), prescriptions (Prescribing Information System), Scottish Patients at Risk of Admission and Readmission (SPARRA) data, and National Records of Scotland (NRS) mortality records was analysed. Dementia recording was studied across these datasets. Results In 2012/13 and 2013/14, 31,589 and 31,504 care home residents were included for analysis. In 2012/13, 17,548 (55.5%) had dementia according to SCHC. PIS and SMR01 confirm 4,701 (26.8%) and 4,254 (24.3%) SCHC dementia records, respectively. SMR04 and SPARRA confirm 1,830 (10.4%) and 964 (5.5%). Among 2012/13 residents, 19,593 (62.0%) have at least one dementia record across datasets. Of these, 10,445 (53.3%) have one record – 83.9% SCHC records, 7.3% SMR01 records, and 5.0% PIS records. Of 15,781 residents who die within 5 years from 2012/13, 6,984 (44.3%) have death records confirming dementia. Results for 2013/14 are similar. Conclusion Routine data enhances dementia ascertainment amongst care home residents, with most confirmation from general hospitalisations and prescriptions. Primary care data and analysis of more financial years would enable further exploration of dementia recording patterns.

TECNOLOGIAS EDUCACIONAIS PARA CUIDADORES NO CONTEXTO DE UNIDADES HOSPITALARES DE ONCOLOGIA PEDIÁTRICA: REVISÃO DE ESCOPO

ABSTRACT Objective: mapping the scientific evidence about the use of educational technologies for caregivers in the context of Pediatric Oncology hospital units. Method: this is a scoping review based on the PRISMA-ScR recommendations and on the Joanna Briggs Institute methodology. The search was performed by two independent reviewers in 12 national and international data sources. Publications available in full and free of charge in electronic means were included, with no language or time restrictions. Abstracts were excluded, as well as letters to the editor, opinion articles, books, monographies, dissertations, theses, blog postings, and theoretical and reflection articles. Data analysis was descriptive, with elaboration of charts and absolute and relative frequency statistics. Results: the final sample was comprised by 15 studies published between 2010 and 2020 and mainly from developed countries. Apps and videos were the predominant educational technologies, followed by printed materials, contributing to increasing the caregivers' knowledge about the disease and cancer treatment, symptom management and side effects of the chemotherapy drugs. In addition, when compared to printed materials, the videos showed a reduction in the caregivers' anxiety levels. The professionals most involved with the technologies were nurses and physicians. Conclusion: it was possible to map that apps and videos are the main educational technologies that are being developed to instruct caregivers, addressing diagnosis and treatment of child-youth cancer, symptom management and self-care promotion.

Cohort profile of the Sloane Project: methodology for a prospective UK cohort study of >15 000 women with screen-detected non-invasive breast neoplasia

PurposeThe introduction of breast screening in the UK led to an increase in the detection of non-invasive breast neoplasia, predominantly ductal carcinoma in situ (DCIS), a non-obligatory precursor of invasive...

PD21 Data Sources And Real-World Data On Medical Devices In The Brazilian Scenario

IntroductionThe Brazilian government has made efforts in systems to generate data from medical devices (MD). This work explores the main systems and data sources in the perspective of contributing as a source to generate real world data (RDW).MethodsDocument review of relevant national data sources for MD. In addition, a structured search was carried out in EMBASE using key descriptors for RWD applied to the regulatory context and to the management of health technologies, without date or language restrictions.ResultsEighteen primary federal government data sources for MD were identified. Not all sources are publicly accessible. Of the articles, the search returned 1,185 results, of which 29 titles were selected and 8 met the protocol’s objective. Included articles were from Europe, the United States and Canada. As in other countries, Brazil initially systematized DM administrative data to meet commercial and financial demands. With the evolution of health technology assessment methods, the use of RDW has become imperative to assess the value of MD to society. Common examples from these countries are implantable MD databases. Current challenges focus on data linkage and quality, in addition to standardized naming. The adoption of the Unique Device Identification (UDI) is one of the promising initiatives to facilitate traceability throughout the lifecycle proposed in the International Medical Device Regulators Forum (IMDRF) of which Brazil is a member. Among the systems, the following stand out: i) ConectSUS, which intends to provide access to health information centered on the patient, anywhere and at any time; ii) National implant registry that generates data on implanted prostheses and stents, surgical techniques used, the profile of patients and the health services involved.ConclusionsThis work showed the similarities between Brazil and other countries in the management of MD data throughout its life cycle, as well as mapped the national primary data sources for MD.

COVID-19 and drivers of excess death rate in Peru: A longitudinal ecological study

BackgroundPeru has experienced unprecedented mortality and economic toll due to the COVID-19 (Coronavirus disease 2019) pandemic in 2020. We aimed to assess the association between socioeconomic factors and excess death rate, and to explore the relative contribution of these factors to the differences in excess death rate during January–December 2020. MethodsDifferent national secondary data sources were used to describe excess death rates and different determinants, from distal to proximal. A confounding-adjusted multilevel mixed-effects linear regression was used to assess the association between these variables and excess death rates. Their relative contributions to the differences in excess death rate between the periods with the highest and lowest excess death rates were analyzed through regression-based Oaxaca-Blinder decomposition methods. FindingsThe excess death rate showed an increasing trend in all regions, with different slopes. The confounding-adjusted multilevel analysis showed that higher healthcare access was associated with lower excess death rates (difference (95%CI) -0.004 (-0.005, -0.002)), whereas COVID-19 incidence was associated with higher excess death rates (difference (95%CI) 0.052 (0.042, 0.063)). The decomposition analysis showed COVID-19 incidence (41.9%), per capita income (19.4%) and unemployment rate (14.6%) as the main risk factors, while the main protective factors included per capita health expenditure (44.7%), healthcare access (33.2%) and health insurance (12.1%). InterpretationOur study suggests that the excess death rate during the COVID-19 pandemic in Peru may have been influenced by other factors besides COVID-19 incidence, from distal to proximal drivers, including socioeconomic determinants, factors outside and within the health sector, and susceptibility factors. Further studies at individual level are needed to corroborate our findings.