Sensing pedestrian flows for real-time assessment of non-pharmaceutical policy interventions during the COVID-19 pandemic

IntroductionTo combat and mitigate the transmission of the SARS-CoV-2 virus, reducing the number of social contacts within a population is highly effective. Non-pharmaceutical policy interventions, e.g. stay-at-home orders, closing schools, universities, and (non-essential) businesses, are expected to decrease pedestrian flows in public areas, leading to reduced social contacts. The extent to which such interventions show the targeted effect is often measured retrospectively by surveying behavioural changes. Approaches that use data generated through mobile phones are hindered by data confidentiality and privacy regulations and complicated by selection effects. Furthermore, access to such sensitive data is limited. However, a complex pandemic situation requires a fast evaluation of the effectiveness of the introduced interventions aiming to reduce social contacts. Location-based sensor systems installed in cities, providing objective measurements of spatial mobility in the form of pedestrian flows, are suited for such a purpose. These devices record changes in a population’s behaviour in real-time, do not have privacy problems as they do not identify persons, and have no selection problems due to ownership of a device. ObjectiveThis work aimed to analyse location-based sensor measurements of pedestrian flows in 49 metropolitan areas at 100 locations in Germany to study whether such technology is suitable for the real-time assessment of behavioural changes during a phase of several different pandemic-related policy interventions. MethodsSpatial mobility data of pedestrian flows was linked with policy interventions using the date as a unique linkage key. Data was visualised to observe potential changes in pedestrian flows before or after interventions. Furthermore, differences in time series of pedestrian counts between the pandemic and the pre-pandemic year were analysed. ResultsThe sensors detected changes in mobility patterns even before policy interventions were enacted. Compared to the pre-pandemic year, pedestrian counts were 85% lower. ConclusionsThe study illustrated the practical value of sensor-based real-time measurements when linked with non-pharmaceutical policy intervention data. This study’s core contribution is that the sensors detected behavioural changes before enacting or loosening non-pharmaceutical policy interventions. Therefore, such technologies should be considered in the future by policymakers for crisis management and policy evaluation.

Benefits of not smoking during pregnancy for non-Aboriginal women and their babies in New South Wales, Australia: a record linkage study

BackgroundSmoking rates among pregnant women in New South Wales (NSW) have plateaued at 8-9%. To inform relevant smoking reduction efforts, we aimed to quantify the benefits of not smoking during pregnancy for non-Aboriginal NSW mothers and their babies. The benefits of not smoking during pregnancy for NSW Aboriginal mothers have previously been described. These data are important inputs in modelling health and economic impacts of smoking cessation interventions. MethodsThis population-based cohort study used linked-data from routinely collected data sets. Not smoking during pregnancy was the exposure of interest among all NSW non-Aboriginal women who became mothers of singleton babies in 2012-2016. Unadjusted and adjusted relative risks (aRR) were used to examine associations between not smoking during pregnancy and adverse outcomes including severe morbidity, inter-hospital transfer, perinatal death, preterm birth and small-for-gestational age. Population attributable fractions (PAFs) were calculated to quantify adverse perinatal outcomes avoided in the population if all mothers were non-smokers. ResultsCompared with babies born to mothers who smoked during pregnancy, babies born to non-smoking mothers had a lower risk of all adverse perinatal outcomes including perinatal death (aRR}=0.68, 95%CI 0.61-0.76), preterm birth (aRR=0.58, 95%CI 0.56-0.61) and small-for-gestational age (aRR=0.48, 95%CI 0.47-0.50). PAFs(%) were 3.9% for perinatal death, 5.6% for preterm birth and 7.3% for small-for-gestational-age. Compared with women who smoked during pregnancy (n=36,518), those who did not smoke (n=413,072) had a lower risk of suffering severe maternal morbidity (aRR=0.87, 95%CI 0.81-0.93) and being transferred to another hospital (aRR=0.92, 95%CI 0.86-0.99). ConclusionsMothers who reported not smoking during pregnancy had a small reduction in their risk of morbidity and of being transferred to another hospital whilst their babies had substantially reduced risks of all adverse perinatal outcomes. Results have implications for clinician training, clinical care standards, and performance management.

Evaluation of the ASSIGN open-source deterministic address-matching algorithm for allocating Unique Property Reference Numbers to general practitioner-recorded patient addresses

IntroductionLinking places to people is a core element of the UK government's geospatial strategy. Matching patient addresses in electronic health records to their Unique Property Reference Numbers (UPRNs) enables spatial linkage for research, innovation and public benefit. Available algorithms are not transparent or evaluated for use with addresses recorded by health care providers. ObjectivesTo describe and quality assure the open-source deterministic ASSIGN address-matching algorithm applied to general practitioner-recorded patient addresses. MethodsBest practice standards were used to report the ASSIGN algorithm match rate, sensitivity and positive predictive value using gold-standard datasets from London and Wales. We applied the ASSIGN algorithm to the recorded addresses of a sample of 1,757,018 patients registered with all general practices in north east London. We examined bias in match results for the study population using multivariable analyses to estimate the likelihood of an address-matched UPRN by demographic, registration, and organisational variables. ResultsWe found a 99.5% and 99.6% match rate with high sensitivity (0.999,0.998) and positive predictive value (0.996,0.998) for the Welsh and London gold standard datasets respectively, and a 98.6% match rate for the study population. The 1.4% of the study population without a UPRN match were more likely to have changed registered address in the last 12 months (match rate: 95.4%), be from a Chinese ethnic background (95.5%), or registered with a general practice using the SystmOne clinical record system (94.4%). Conversely, people registered for more than 6.5 years with their general practitioner were more likely to have a match (99.4%) than those with shorter registration durations. ConclusionsASSIGN is a highly accurate open-source address-matching algorithm with a high match rate and minimal biases when evaluated against a large sample of general practice-recorded patient addresses. ASSIGN has potential to be used in other address-based datasets including those with information relevant to the wider determinants of health.

Characterizing social and policy determinants of hospital length of stay among paediatric inpatients with diabetes using linked population-based data

BackgroundEvidence is limited on the non-medical factors influencing hospital length of stay (LOS) among paediatric inpatients with diabetes, notably potential social and policy correlates. This study aimed to characterize the associations of socioeconomic status and health policy environment with diabetes-attributable LOS to help inform accountability monitoring of a provincial comprehensive diabetes strategy aiming to minimize time in hospital among this high-risk population. Data and methodsThis retrospective population-based study drew on multiple linked administrative and geospatial databases among all children aged 18 and under with a diabetes-related hospitalization in the province of New Brunswick, Canada, during the four-year period following implementation of an insulin pump funding program. Multiple linear regression was used to assess the role of access to the public insulin pump resourcing scheme and relative neighbourhood deprivation as predictors of days spent in acute care, controlling for age, sex, and place of residence. ResultsAmong the paediatric inpatient population (N=386), 21% had accessed social resources made available through the insulin pump funding policy and 42% resided in the most materially deprived neighbourhoods. Diabetes-related hospital stays averaged 3.87 days. Paediatric inpatients having accessed resources through the social insurance policy spent significantly fewer days in hospital (1.34 days less [95% CI: 0.63--2.05]) than those who had not, all else being equal. Observed differences in LOS by neighbourhood socioeconomic deprivation were not found to be statistically significant in the multivariate analysis. ConclusionFindings from this context of universal medical coverage suggested that public policy for supplemental financing of assistive technologies among children with diabetes may be associated with reduced burden to the hospital system. The causes of socioenvironmental disparities in LOS require further investigation to inform interventions to mitigate preventable patient-level variations in hospital-based health outcomes.

population level study of SARS-CoV-2 prevalence amongst people experiencing homelessness in Wales, UK

IntroductionPrior research into the prevalence of SARS-CoV-2 infection amongst people experiencing homelessness (PEH) largely relates to people in communal forms of temporary accommodation in contexts where this type of accommodation remained a major part of the response to homelessness during the COVID-19 pandemic. Little is known about the prevalence of SARS-CoV-2 amongst PEH more broadly, and in a policy and practice context that favoured self-contained accommodation, such as Wales, UK. ObjectiveDescribe the prevalence of SARS-CoV-2 amongst PEH in Wales, UK, using routinely collected administrative data from the Secure Anonymised Information Linkage Databank. MethodsRoutinely collected data were used to identify PEH in Wales between 1st March 2020 and 1st March 2021. Using SARS-CoV-2 pathology testing data, prevalence rates were generated for PEH and three comparator groups: (1) the not-homeless population; (2) a cohort `exact matched' for age, sex, local authority and area deprivation; and (3) a matched comparison group created using these same variables and Propensity Score Matching (PSM). Three logistic regressions were run on samples containing each of the comparator groups to explore the effect of experiencing homelessness on testing positive for SARS-CoV-2. ResultsThe prevalence of SARS-CoV-2 infection amongst PEH was 5.0%, compared to the not-homeless population at 5.6%. For the exact matched and PSM match comparator groups, prevalence was 6.9% and 6.7%, respectively. Logistic regression found that SARS-CoV-2 infection was 0.9 times less likely amongst PEH compared to people not experiencing homelessness from the general population. The odds of SARS-CoV-2 infection for PEH was 0.75 and 0.73 where the `not-homeless' comparators were from the exact match and PSM samples, respectively. ConclusionOur analysis revealed that a year into the COVID-19 pandemic, the prevalence of SARS-CoV-2 amongst PEH in Wales was lower than the general population. A policy response to homelessness that moved away from communal accommodation may be partly responsible for the reduced SAR-CoV-2 infection amongst PEH.

Data Harmonization and Data Pooling from Cohort Studies: A Practical Approach for Data Management

Data pooling from pre-existing multiple datasets can be useful to increase study sample size and statistical power to answer a research question. However, individual datasets may contain variables that measure the same construct differently, posing challenges for data pooling. Variable harmonization, an approach that can generate comparable datasets from heterogeneous sources, can address this issue in some circumstances. As an illustrative example, this paper describes the data harmonization strategies that helped generate comparable datasets across two Canadian pregnancy cohort studies– the All Our Families and the Alberta Pregnancy Outcomes and Nutrition. Variables were harmonized considering multiple features across the datasets: the construct measured; question asked/response options; the measurement scale used; the frequency of measurement; timing of measurement, and the data structure. Completely matching, partially matching, and completely un-matching variables across the datasets were determined based on these features. Variables that were an exact match were pooled as is. Partially matching variables were synchronized across the datasets considering the frequency of measurement, the timing of measurement, and response options. Variables that were completely unmatching could not be harmonized into a single variable. The variable harmonization strategies that were used to generate comparable cohort datasets for data pooling are applicable to other data sources. Future studies may employ or evaluate these strategies. Variable harmonization and pooling provide an opportunity to increase study power and the utility of existing data, permitting researchers to answer novel research questions in a statistically efficient, timely, and cost-efficient manner that could not be achieved using a single data source.

Estimation of age of transition from children's to adult healthcare for young people with long term conditions using linked routinely collected healthcare data

IntroductionHealthcare transitions, including from paediatric to adult services, can be disruptive and cause a lack of continuity in care. Existing research on the paediatric-adult healthcare transition often uses a simple age cut-off to assign transition status. This risks misclassification bias, reducing observed changes at transition (adults are included in the paediatric group and vice versa) possibly to differing extents between groups that transition at different ages. ObjectiveTo develop and assess methods for estimating the transition point from paediatric to adult healthcare from routine healthcare records. MethodsA retrospective cohort of young people (12 to 23 years) with long term conditions was constructed from linked primary and secondary care data in England. Inpatient and outpatient records were classified as paediatric or adult based on treatment and clinician specialities. Transition point was estimated using three methods based on record classification (First Adult: the date of first adult record; Last Paediatric: date of last paediatric record; Fitted: a date determined by statistical fitting). Estimated transition age was compared between methods. A simulation explored impacts of estimation approaches compared to a simple age cut-off when assessing associations between transition status and healthcare events. ResultsSimulations showed using an age-based cut-off at 16 or 18 years as transition point, common in research on transition, may underestimate transition-associated changes. Many health records for those aged 14 years were classified as adult, limiting utility of the First Adult approach. The Last Paediatric approach is least sensitive to this possible misclassification and may best reflect experience of the transition. ConclusionsEstimating transition point from routine healthcare data is possible and offers advantages over a simple age cut-off. These methods, adapted as necessary for data from other countries, should be used to reduce risk of misclassification bias in studies of transition in nationally representative data.

Supporting policy and practice in Ontario through ICES’ Applied Health Research Question (AHRQ) Program

ICES upholds a strong reputation for generating high-quality evidence to inform policy and practice through its collaborations with a broad range of health system stakeholders including government policymakers and healthcare providers including clinicians. Supported by the Ontario Ministry of Health and Ministry of Long-Term Care, the ICES Applied Health Research Question (AHRQ) Program leverages the data holdings and, scientific and clinical expertise to generate evidence tailored to the information needs of requestors. This paper outlines the approach, process, strengths, challenges and the resulting influence and impact to the healthcare landscape in Ontario.

Chronic Disease Surveillance in Alberta’s Tomorrow Project using Administrative Health Data

IntroductionAlberta's Tomorrow Project (ATP) is the largest population-based prospective cohort study of cancer and chronic diseases in Alberta, Canada. The ATP cohort data were primarily self-reported by participants on lifestyle behaviors and disease risk factors at the enrollment, which lacks sufficient and accurate data on chronic disease diagnosis for longer-term follow-up. ObjectivesTo characterize the occurrence rate and trend of chronic diseases in the ATP cohort by linking with administrative healthcare data. MethodsA set of validated algorithms using ICD codes were applied to Alberta Health (AH) administrative data (October 2000-March 2018) linked to the ATP cohort to determine the prevalence and incidence of common chronic diseases. ResultsThere were 52,770 ATP participants (51.2± 9.4 years old at enrollment and 63.7% females) linked to the AH data with average follow-up of 10.1± 4.4 years. In the ATP cohort, hypertension (18.5%), depression (18.1%), chronic pain (12.8%), osteoarthritis (10.1%) and cardiovascular diseases (8.7%) were the most prevalent chronic conditions. The incidence rates varied across diseases, with the highest rates for hypertension (22.1 per 1000 person-year), osteoarthritis (16.2 per 1000 person-year) and ischemic heart diseases (13.0 per 1000 person-year). All chronic conditions had increased prevalence over time (p <0.001 for trend tests), while incidence rates were relatively stable. The proportion of participants with two or more of these conditions (multi-morbidity) increased from 3.9% in 2001 to 40.3% in 2017. ConclusionsThis study shows an increasing trend of chronic diseases in the ATP cohort, particularly related to cardiovascular diseases and multi-morbidity. Using administrative health data to monitor chronic diseases for large population-based prospective cohort studies is feasible in Alberta, and our approach could be further applied in a broader research area, including health services research, to enhance research capacity of these population-based studies in Canada.

Describing agreement in the Main Condition coding field using Canadian ICD-11 inpatient data

IntroductionCountries use varying coding standards, which impact international coded data comparability. The `main condition' (MC) field is coded within the Discharge Abstract Database as "reason for admission" or "largest resource use". ObjectiveWe offer a preliminary analysis on the frequency of and contributing factors to MC definition agreements within an inpatient Canadian dataset. MethodsSix professional coders performed a chart review between August 2016 and June 2017 on 3,000 randomly selected inpatient charts from three acute care hospitals in Calgary, Alberta. Coders classified the MC as "reason for admission", "largest resource use" or "both". Patients were admitted between 1st January and 30th June 2015 and met the inclusion criteria if they were >18 years, had an Alberta personal health care number, and had an inpatient visit for any service outside of obstetrics. Agreement between the two MC definitions was stratified by length of stay (LOS), emergency department admission, hospital of origin, discharge location, age, sex, procedures, and comorbidities. Chi-square analysis and frequency of inconsistencies were reported. ResultsOnly 34 (1.51%) of the 2,250 patient charts had disagreeing MC definitions. Age, emergency visit on admit, LOS, hospital, and discharge location were associated with MC agreement. Chronic conditions were seen more often in MC definition agreements, and acute conditions seen within those disagreeing. ConclusionThere was a small proportion of cases in which the condition bringing the patient to hospital was not also the condition occupying the largest resources. Within disagreements, further research using a larger sample size is needed to explore the presence of MC in a secondary/tertiary condition, the association between patient complexity and disagreeing MC definitions, and the nature of the conditions seen in the inconsistent MC definitions.