bluuebunny/crossref_metadata_2025_split huggingface dataset:
Social Media Insights Into US Mental Health During the COVID-19 Pandemic: Longitudinal Analysis of Twitter Data
Authors: Valdez Danny , ten Thij Marijn , Bathina Krishna , Rutter Lauren A , Bollen Johan 
Published: December 2020

BackgroundThe COVID-19 pandemic led to unprecedented mitigation efforts that disrupted the daily lives of millions. Beyond the general health repercussions of the pandemic itself, these measures also present a challenge to the world’s mental health and health care systems. Considering that traditional survey methods are time-consuming and expensive, we need timely and proactive data sources to respond to the rapidly evolving effects of health policy on our population’s mental health. Many people in the United States now use social media platforms such as Twitter to express the most minute details of their daily lives and social relations. This behavior is expected to increase during the COVID-19 pandemic, rendering social media data a rich field to understand personal well-being.ObjectiveThis study aims to answer three research questions: (1) What themes emerge from a corpus of US tweets about COVID-19? (2) To what extent did social media use increase during the onset of the COVID-19 pandemic? and (3) Does sentiment change in response to the COVID-19 pandemic?MethodsWe analyzed 86,581,237 public domain English language US tweets collected from an open-access public repository in three steps. First, we characterized the evolution of hashtags over time using latent Dirichlet allocation (LDA) topic modeling. Second, we increased the granularity of this analysis by downloading Twitter timelines of a large cohort of individuals (n=354,738) in 20 major US cities to assess changes in social media use. Finally, using this timeline data, we examined collective shifts in public mood in relation to evolving pandemic news cycles by analyzing the average daily sentiment of all timeline tweets with the Valence Aware Dictionary and Sentiment Reasoner (VADER) tool.ResultsLDA topics generated in the early months of the data set corresponded to major COVID-19–specific events. However, as state and municipal governments began issuing stay-at-home orders, latent themes shifted toward US-related lifestyle changes rather than global pandemic-related events. Social media volume also increased significantly, peaking during stay-at-home mandates. Finally, VADER sentiment analysis scores of user timelines were initially high and stable but decreased significantly, and continuously, by late March.ConclusionsOur findings underscore the negative effects of the pandemic on overall population sentiment. Increased use rates suggest that, for some, social media may be a coping mechanism to combat feelings of isolation related to long-term social distancing. However, in light of the documented negative effect of heavy social media use on mental health, social media may further exacerbate negative feelings in the long-term for many individuals. Thus, considering the overburdened US mental health care structure, these findings have important implications for ongoing mitigation efforts.
Knowledge, Perceived Beliefs, and Preventive Behaviors Related to COVID-19 Among Chinese Older Adults: Cross-Sectional Web-Based Survey
Authors: Chen Ying , Zhou Rui , Chen Boyan , Chen Hao , Li Ying , Chen Zhi , Zhu Haihong , Wang Hongmei 
Published: December 2020

Background
The COVID-19 pandemic continues to pose an international public health threat. Prevention is of paramount importance to protect the high-risk group of older adults until specific treatments for COVID-19 become available; however, little work has been done to explore factors that promote preventive behaviors among this population.

Objective
This study aims to investigate the knowledge, perceived beliefs, and preventive behaviors towards COVID-19 of older adults in China and determine the factors that influence their practice of preventive behaviors.

Methods
From February 19 to March 19, 2020, a cross-sectional, web-based survey was administered to Chinese older adults in all 31 provinces of mainland China using a convenience sampling method to assess the respondents’ knowledge, perceived beliefs, and preventive behaviors towards COVID-19. Standard descriptive statistics and hierarchical linear regression analyses were conducted to analyze the data.

Results
A total of 1501 participants responded to the survey, and 1263 valid responses (84.1%) were obtained for further analysis. The overall correct rate on the knowledge questionnaire was 87%, overall positive beliefs regarding COVID-19 were found, and the mean behavior score was 13.73/15 (SD 1.62, range 5-15). The hierarchical linear regression showed that respondents who were married or cohabitating and who lived in areas with community-level control measures were more likely to practice preventive behaviors (P<.01). Knowledge (β=0.198, P<.001), perceived susceptibility (β=0.263, P=.03), perceived benefits (β=0.643, P<.001), and self-efficacy in preventing COVID-19 (β=0.468, P<.001) were also found to be significantly associated with preventive behaviors.

Conclusions
Most older residents had adequate knowledge and positive beliefs regarding COVID-19 and engaged in proactive behaviors to prevent the disease. Knowledge and beliefs were confirmed to be significantly associated with behavior responses. Our findings have significant implications in enhancing the effectiveness of COVID-19 prevention programs targeting the older population; these programs must be continued and strengthened as the epidemic continues.
The Use of Density-Based Spatial Clustering of Application With Noise (DBSCAN) for Record Linkage in An Observational HIV Cohort
Authors: Olago Victor , Bartels Lina , Dhokotera Tafadzwa , Bartels Lina , Bohlius Julia , Egger Matthias , Singh Elvira , Sengayi Mazvita 
Published: December 2020

IntroductionThe South African HIV Cancer Match (SAM) study is a probabilistic record linkage study involving creation of an HIV cohort from laboratory records from the National Health Laboratory Service (NHLS). This cohort was linked to the pathology based South African National Cancer Registry to establish cancer incidences among HIV positive population in South Africa. As the number of HIV records increases, there is need for more efficient ways of de-duplicating this big-data. In this work, we used clustering to perform big-data deduplication.
Objectives and ApproachOur objective was to use DBSCAN as clustering algorithm together with bi-gram word analyser to perform big-data deduplication in resource-limited settings. We used HIV related laboratory records from entire South Africa collated in the NHLS Corporate Data Warehouse for period 2004-2014. This involved data pre-processing, deterministic deduplication, ngrams generation, features generation using Term Frequency Inverse Document Frequency vectorizer, clustering using DBSCAN and assigning cluster labels for records that potentially belonged to the same person. We used records with national identification numbers to assess quality of deduplication by calculating precision, recall and f-measure.
ResultsWe had 51,563,127 HIV related laboratory records. Deterministic deduplication resulted in 20,387,819 patient record deduplicates. With DBSCAN clustering we further reduced this to 14,849,524 patient record clusters. In this final dataset, 3,355,544 (22.60%) patients had negative HIV test, 11,316,937 (76.21%) had evidence for HIV infection, and for 177,043 (1.19%) the HIV status could not be determined. The precision, recall and f-measure based on 1,865,445 records with national identification numbers were 0.96, 0.94 and 0.95, respectively.
Conclusion / ImplicationsOur study demonstrated that DBSCAN clustering is an effective way of deduplicating big datasets in resource-limited settings. This enabled refining of an HIV observational database by accurately linking test records that potentially belonged to the same person. The methodology creates opportunities for easy data profiling to inform public health decision making.
2021 Census England And Wales: Developing Record Speed Linkage Methods to Produce Outputs in a Year
Authors: Shipsey Rachel , Tomlin Charlie , White Zoe , Plachta Josie , Gammon Shelley , Dygas Patricia 
Published: December 2020

Introduction2021 will herald the next census in England and Wales. The Office for National Statistics (ONS) have a goal of publishing outputs within one year, 4 months earlier than in 2011. Since we produce estimates rather than counts, the linkage of the 2021 Census to the Census Coverage Survey which comprises ~710,000 person and ~370,000 household records, has to be carried out in record time (eight weeks) whilst maintaining incredibly high accuracy (less than 0.1% false positives and 0.25% false negatives).
Objectives and ApproachOur approach is to utilise the ONS Distributed Access Platform to write automated matching algorithms that are both efficient and accurate. These methods use parallelisation to speed things up, active machine learning to iteratively improve our parameters, and associative matching to squeeze every last match out automatically without impairing the accuracy.
As in 2011, we will be using clerical matchers to resolve cases that cannot be matched automatically. Speeding up the clerical matching process is imperative. We have therefore developed a pre-search algorithm that takes the hard work out of clerical matching by replacing clerical searching (here’s a record can you find a match?) with clerical resolution (here are two or more records, do they match?).
ResultsAs a result of our improvements we estimate that we have increased our automatic matching rates from 70% to 91% for person matching, and from 60% to 95% for household matching, without loss of accuracy. However, the biggest gains in terms of speed are delivered by our pre-search algorithm which, at the current iteration, is limiting false negatives to ~0.13% according to the 2011 gold standard.
ConclusionWe estimate that overall our improvements will mean that in 2021 we will need less than half the clerical resource that was required in 2011 and will meet our eight-week deadline.
Using Additive and Relative Hazards to Quantify Colorectal Survival Inequalities for Patients with A Severe Psychiatric Illness
Authors: Mahar Alyson L , Davis Laura E , Kurdyak Paul , Hanna Timothy P , Coburn Natalie G , Groome Patti A 
Published: December 2020

IntroductionDespite recommendations, most studies examining health inequalities fail to report both absolute and relative summary measures. We examine colorectal cancer (CRC) survival for patients with and without severe psychiatric illness (SPI) to demonstrate the use and importance of relative and absolute effects.
Objectives and ApproachWe conducted a retrospective cohort study of CRC patients diagnosed between 01/04/2007 and 31/12/2012, using linked administrative databases. SPI was defined as diagnoses of major depression, bipolar disorder, schizophrenia, and other psychotic illnesses six months to five years preceding cancer diagnosis and categorized as inpatient, outpatient or none. Associations between SPI history and risk of death were examined using Cox Proportional Hazards regression to obtain hazard ratios and Aalen’s semi-parametric additive hazards regression to obtain absolute differences. Both models controlled for age, sex, primary tumour location, and rurality.
ResultsThe final cohort included 24,507 CRC patients, 482 patients had an outpatient SPI history and 258 patients had an inpatient SPI history. 58.1% of patients with inpatient SPI history died, and 47.1% of patients with outpatient SPI history died. Patients with an outpatient SPI history had a 40% (HR 1.40, 95% CI: 1.22-1.59) increased risk of death and patients with an inpatient SPI history had a 91% increased risk of death (HR 1.91, 95% CI: 1.63-2.25), relative to no history of a mental illness. An outpatient SPI history was associated with an additional 33 deaths per 1000 person years, and an inpatient SPI was associated with an additional 82 deaths per 1000 person years after controlling for confounders.
Conclusion / ImplicationsWe demonstrated that reporting of both relative and absolute effects is possible and calculating risk difference is relatively simple using Aalen models. We encourage future studies examining inequalities with time-to-event data to use this method and report both relative and absolute effect measures.
Data Linkage of Hashed Data: Derive and Conquer
Authors: Plachta Josie , Tomlin Charlie , Shipsey Rachel 
Published: December 2020

IntroductionData Linkage of hashed datasets is much more difficult than linking in-the-clear data. Hashing prevents the use of matching tools that overcome messy data such as ‘contained-within’ functions and edit distance metrics. Hashing sensitive data received from third parties is becoming more common due to increased Data Security concerns. Institutions need to be ready to link hashed data with high accuracy, otherwise the quality of outputs from these linked datasets will suffer.
Objectives and ApproachWe designed an innovative matching method, Derive and Conquer (D&C). We derived variables containing substrings or patterns of the full variable (e.g. Soundex or first 4 characters of a string) to match on instead. However, using lots of combinations of these derived variables would require thousands of traditional match keys to be programmed, run, and reviewed. Instead, D&C runs matchkeys on a derived agreement variable which amalgamates information stored in multiple derived variables into one value, reducing the number of matchkeys to a manageable amount. D&C runs on distributing computing systems using PySpark to link datasets containing millions of records in a timely manner.
ResultsD&C was developed using in-the-clear UK Census and health records with results comparable to the in-the-clear gold standard. It is currently being tested on hashed data to link UK tax and benefits data to UK health records. 66.4 million records were declared matched - a realistic match rate for the UK population. Research into the linkage quality is ongoing to produce estimates on the amount of bias in the linkage and the precision and recall. We will be excited to present these results at the Conference in October. These results will be used to improve D&C.
Conclusion / ImplicationsUsing these derived variables, we have been able to overcome the challenge of matching massive hashed datasets with a realistic match rate and in a realistic time frame.
Identifying Prenatal Opioid Exposure in Health Administrative Data for Public Health Surveillance and Epidemiologic Research
Authors: Camden Andi , To Teresa , Ray Joel G , Gomes Tara , Bai Li , Fung Kinwah , Guttmann Astrid 
Published: December 2020

IntroductionAccurate estimation of prenatal opioid exposure (POE) is needed for population-based surveillance & research but can be challenging with health administrative data due to varying definitions & methods. Prior research has relied primarily on infant records with a diagnosis of neonatal abstinence syndrome (NAS).
Objectives and Approach1) Evaluate the impact of using different definitions of maternal opioid use in the estimation of POE; 2) Investigate whether maternal characteristics vary by the type of definition used. Population-based cross-sectional study of all hospital births (N= 454,746) from 2014-2017 in Ontario, Canada. Multiple linked population-based health administrative databases were used to identify opioid-related pre- & perinatal Emergency Department visits & hospitalizations & opioid prescriptions. We examined how pre-conception & in-pregnancy maternal characteristics varied by using different approaches to ascertain POE.
ResultsThere were 9624 live/still births with POE. Ascertainment of POE was highest using maternal prescription drug data (79%) & infant hospital records with NAS (45%). Maternal characteristics varied by data source used for POE ascertainment. Opioid-related health care during pregnancy identified a high-risk phenotype, contrasted with those ascertained through prescription data, with respective rates of 64% vs. 54% for social assistance, 37% vs. 12% for polydrug use, 23% vs. 6% for alcohol use, 26% vs. 19% for 3+ live births, 13% vs. 5% for victim of violence, 12% vs. 6% for involvement in criminal justice system & 64% vs. 17% for mental health & addictions hospital care.
Conclusion / ImplicationsPOE ascertainment differs by health administrative data source & ability to link both across maternal records and with infant. Prescription drug data identified the highest number of opioid-exposed births and, with linked healthcare records, is useful to identify illicit opioid use & additional risk factors. Clinically meaningful differences in maternal characteristics of opioid users exist by POE ascertainment method.
Stats NZ Integrated Data Infrastructure (IDI) – Back to the Future: How a prototype design developed into a world leading tool for critical decision making
Authors: Strijbis Grace 
Published: December 2020

IntroductionStats NZ maintains and provides access to the Integrated Data Infrastructure (IDI). This is a critical tool in the government's efforts to reduce poverty, improve health, and support a growing, inclusive economy. Insights are used at all levels of government decision making.
Objectives and ApproachSince the IDI prototype was created in 2011, it has grown rapidly. The use cases for the IDI continue to develop, with more complex projects that extend the boundaries of what might be considered traditional research. The Ministry of Education is now running a key school funding model, the Equity Index, out of the IDI. We are seeing an increased interest for the IDI to support fast responses to policy questions. Statistical production areas in and outside Stats NZ increasingly need administrative data for production, and the IDI is the best source of relevant linked administrative data.
ResultsStats NZ has received one-off funding to continue sustainable expansion of the IDI and improve the data and services it provides. This ‘IDI 2’ project will enhance the IDI, making it easier, quicker and more stable for our customers to engage with data. We have focussed on strengthening, and futureproofing, our core offering. The IDI 2 project is currently looking to replace the systems that are used to build our databases and streamline production to provide the technical capability to cope with a much higher volume of data.
Conclusion / ImplicationsWhile the IDI 2 project looks to shore up the infrastructure and lay a platform for future development, Stats NZ seeks to undertake customer focussed improvements to be responsive to other customer needs. There are opportunities to grow the integrated data community and provide more ways for researchers to connect with each other.
Do Home Adaptations Prevent Falls for Older People?
Authors: Hollinghurst Joe , Fry Richard , Akbari Ashley , Williams Neil , Hillcoat-Nalletamby Sarah , Lyons Ronan A , Watkins Alan , Clegg Andrew , Rodgers Sarah E 
Published: December 2020

IntroductionAround a third of people aged 65+, and around half of people aged 80+ fall at least once a year in the United Kingdom. Homes may be adapted to try to prevent falls, but evidence on the effectiveness of home adaptations is limited.
Objectives and ApproachOur objective was to determine if proactive (Care&Repair service) and reactive (rapid response) home adaptations provided by Care&Repair Cymru resulted in a reduced risk of a fall for older people aged 60+ in Wales.
We constructed a longitudinal dataset from the Secure Anonymised Information Linkage Databank. We created quarterly intervals for 5-years pre and post the date a home adaptation was received, or a randomly assigned date for the comparator. Per quarter, we created a binary indicator of whether someone had a fall at home that was recorded in either the emergency department or hospital admission data sources for Wales. We included key demographic variables as covariates. We analysed the data using logistic regression and a difference-in-difference approach.
ResultsWe analysed 634,046 individuals, of whom 60,794 received the proactive Care&Repair service, and 47,244 received the reactive rapid response service. People receiving proactive or reactive home adaptations from Care & Repair were at around a two-fold increased risk of falling, compared to those who did not receive home adaptation with Odds Ratios (ORs) of 2.13 [95%CI: 2.07, 2.20] and 2.73 [2.64,2.81] respectively. Falls risk increased per quarter for all individuals, but after intervention delivery, the rate of increase fell in the intervention groups.
ConclusionPeople receiving home adaptations from Care&Repair had a higher chance of a fall, indicating the service was successfully identifying those in need. Falls risk increased for everyone over time, but this was counteracted for people receiving an intervention.
The New Brunswick Act Respecting Research Three Years Later: A Data Trickle Turns into a Flood
Authors: McDonald Ted 
Published: December 2020

IntroductionIn New Brunswick Canada (NB) in 2017, the Provincial Government passed a bill called the Act Respecting Research. This Act took the form of an omnibus bill modifying 20 pieces of legislation to define a legal authority for the New Brunswick Institute for Research, Data and Training (NB-IRDT) to receive prepared, linkable microdata from multiple public bodies in NB. This was followed in 2019 by a second Act Respecting Research that broadened the scope of the first. Together this legislation has helped to expand NB-IRDT’s data holdings from its first dataset – hospital records received in 2015 – to over 40 different linkable datasets as of March 2020. These datasets are underpinning ambitious research partnerships between NB-IRDT and the Provincial Government.
Objectives and ApproachThis presentation will detail the rapid progress made since the first Act was passed in 2017 and presented at IPDLN-2018. It will outline enabling factors, including secured funding, the central role of the Department of Health, engagement with senior decisionmakers, and interaction with other provincial data centres and national networks including HDRN Canada. Ongoing and new challenges arising from the rapid increase in the scale of data collection and their resolution will be discussed.
ResultsIn addition to a wide range of provincial health administrative and clinical datasets, recent datasets include school records (report cards, standardized testing, attendance), income support data, workers compensation claims data, higher education program and graduation data, court appearances, adult training/retraining programs and immigration landing records. All files are linkable at the individual level. Multi-year research projects to support program evaluation are underway, with departments now able to access other agency data through NB-IRDT.
Conclusion / ImplicationsNB-IRDT and the experience in NB offer important lessons for other jurisdictions aiming to expand access to linkable multi-agency data for research and evaluation.