Investigating trends in asthma and COPD through multiple data sources: A small area study

Boulieri, Areti; Hansell, Anna; Blangiardo, Marta

doi:10.1016/j.sste.2016.05.004

Cited by 17 publications

(12 citation statements)

References 39 publications

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“…In total, PR teams at 3 NHS sites in Northern England, United Kingdom, participated in the feasibility study. Northern England has one of the highest rates of lung disease in the United Kingdom, possibly due to greater socioeconomic deprivation and higher rates of smoking compared with the south of England [ 3 , 4 , 33 ]. Preliminary work was conducted to map current PR care pathways (eg, workshops with PR staff; observation of PR sessions, numbers, and demographics of referred patients; etc), and we worked together with the PR teams to determine how the intervention might best be used within the PR program.…”

Section: Methodsmentioning

confidence: 99%

The Use of a Smartphone App and an Activity Tracker to Promote Physical Activity in the Management of Chronic Obstructive Pulmonary Disease: Randomized Controlled Feasibility Study

Bentley¹,

Powell²,

Potter³

et al. 2020

JMIR Mhealth Uhealth

105

135

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Background Chronic obstructive pulmonary disease (COPD) is highly prevalent and significantly affects the daily functioning of patients. Self-management strategies, including increasing physical activity, can help people with COPD have better health and a better quality of life. Digital mobile health (mHealth) techniques have the potential to aid the delivery of self-management interventions for COPD. We developed an mHealth intervention (Self-Management supported by Assistive, Rehabilitative, and Telehealth technologies-COPD [SMART-COPD]), delivered via a smartphone app and an activity tracker, to help people with COPD maintain (or increase) physical activity after undertaking pulmonary rehabilitation (PR). Objective This study aimed to determine the feasibility and acceptability of using the SMART-COPD intervention for the self-management of physical activity and to explore the feasibility of conducting a future randomized controlled trial (RCT) to investigate its effectiveness. Methods We conducted a randomized feasibility study. A total of 30 participants with COPD were randomly allocated to receive the SMART-COPD intervention (n=19) or control (n=11). Participants used SMART-COPD throughout PR and for 8 weeks afterward (ie, maintenance) to set physical activity goals and monitor their progress. Questionnaire-based and physical activity–based outcome measures were taken at baseline, the end of PR, and the end of maintenance. Participants, and health care professionals involved in PR delivery, were interviewed about their experiences with the technology. Results Overall, 47% (14/30) of participants withdrew from the study. Difficulty in using the technology was a common reason for withdrawal. Participants who completed the study had better baseline health and more prior experience with digital technology, compared with participants who withdrew. Participants who completed the study were generally positive about the technology and found it easy to use. Some participants felt their health had benefitted from using the technology and that it assisted them in achieving physical activity goals. Activity tracking and self-reporting were both found to be problematic as outcome measures of physical activity for this study. There was dissatisfaction among some control group members regarding their allocation. Conclusions mHealth shows promise in helping people with COPD self-manage their physical activity levels. mHealth interventions for COPD self-management may be more acceptable to people with prior experience of using digital technology and may be more beneficial if used at an earlier stage of COPD. Simplicity and usability were more important for engagement with the SMART-COPD intervention than personalization; therefore, the intervention should be simplified for future use. Future evaluation will require consideration of individual factors and their effect on mHealth efficacy and use; within-subject comparison of step count values; and an opportunity for control group participants to use the intervention if an RCT were to be carried out. Sample size calculations for a future evaluation would need to consider the high dropout rates.

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Section: Methodsmentioning

confidence: 99%

The Use of a Smartphone App and an Activity Tracker to Promote Physical Activity in the Management of Chronic Obstructive Pulmonary Disease: Randomized Controlled Feasibility Study

Bentley¹,

Powell²,

Potter³

et al. 2020

JMIR Mhealth Uhealth

105

135

View full text Add to dashboard Cite

show abstract

“…Continued computational improvements have allowed researchers to build increasingly sophisticated spatial or spatio-temporal models [20][21][22][23]. Recent studies have employed Bayesian small-area statistical methods that can improve flexibility of models to incorporate structured and unstructured random effects in a hierarchical framework, such as a BHM [24][25][26]. Controlling for unmeasured confounding by assigning variation in the outcome to either spatial, temporal, and/or spatiotemporal effects, rather than to an error term, has two important benefits: (1) the overall model fit improves, and (2) covariate coefficient estimates in the model are more precise [27][28][29][30].…”

Section: Spatio-temporal Bayesian Frameworkmentioning

confidence: 99%

A geographic identifier assignment algorithm with Bayesian variable selection to identify neighborhood factors associated with emergency department visit disparities for asthma

et al. 2020

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Background: Ecologic health studies often rely on outcomes from health service utilization data that are limited by relatively coarse spatial resolutions and missing geographic information, particularly neighborhood level identifiers. When fine-scale geographic data are missing, the ramifications and strategies for addressing them are not well researched or developed. This study illustrates a novel spatio-temporal framework that combines a geographic identifier assignment (i.e., geographic imputation) algorithm with predictive Bayesian variable selection to identify neighborhood factors associated with disparities in emergency department (ED) visits for asthma. Methods: ED visit records with missing fine-scale spatial identifiers (~ 20%) were geocoded using information from known, coarser, misaligned spatial units using an innovative geographic identifier assignment algorithm. We then employed systematic variable selection in a spatio-temporal Bayesian hierarchical model (BHM) predictive framework within the NIMBLE package in R. Our novel methodology is illustrated in an ecologic case study aimed at identifying neighborhood-level predictors of asthma ED visits in South Carolina, United States, from 1999 to 2015. The health outcome was annual ED visit counts in small areas (i.e., census tracts) with primary diagnoses of asthma (ICD9 codes 493.XX) among children ages 5 to 19 years. Results: We maintained 96% of ED visit records for this analysis. When the algorithm used areal proportions as probabilities for assignment, which addressed differential missingness of census tract identifiers in rural areas, variable selection consistently identified significant neighborhood-level predictors of asthma ED visit risk including pharmacy proximity, average household size, and carbon monoxide interactions. Contrasted with common solutions of removing geographically incomplete records or scaling up analyses, our methodology identified critical differences in parameters estimated, predictors selected, and inferences. We posit that the differences were attributable to improved data resolution, resulting in greater power and less bias. Importantly, without this methodology, we would have inaccurately identified predictors of risk for asthma ED visits, particularly in rural areas.

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“…Such a certainty map has been shown to have high specificity, although the sensitivity of identifying a truely elevated/lowered) risk areas might be moderate [ 18 ]. Recently, Boulieri et al [ 19 ] have proposed another method based on multiple data sources for detecting areas where time-trends have been unusual which may add an alternative picture of the childhood caries in future studies.…”

Section: Discussionmentioning

confidence: 99%

Effect of risk-based payment model on caries inequalities in preschool children assessed by geo-mapping

et al. 2018

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BackgroundTo describe, with aid of geo-mapping, the effects of a risk-based capitation model linked to caries-preventive guidelines on the polarization of caries in preschool children living in the Halland region of Sweden.MethodsThe new capitation model was implemented in 2013 in which more money was allocated to Public Dental Clinics surrounded by administrative parishes inhabited by children with increased caries risk, while a reduced capitation was allocated to those clinics with a low burden of high risk children. Regional geo-maps of caries risk based on caries prevalence, level of education and the families purchasing power were produced for 3-6-year-old children in 2010 (n = 10,583) and 2016 (n = 7574). Newly migrated children to the region (n = 344 in 2010 and n = 522 in 2016) were analyzed separately. A regional caries polarization index was calculated as the ratio between the maximum and minimum estimates of caries frequency on parish-level, based on a Bayesian hierarchical mapping model.ResultsOverall, the total caries prevalence (dmfs > 0) remained unchanged from 2010 (10.6%) to 2016 (10.5%). However, the polarization index decreased from 7.0 in 2010 to 5.6 in 2016. Newly arrived children born outside Sweden had around four times higher caries prevalence than their Swedish-born peers.ConclusionsA risk-based capitation model could reduce the socio-economic inequalities in dental caries among preschool children living in Sweden. Although updated evidence-based caries-preventive guidelines were released, the total prevalence of caries on dentin surface level was unaffected 4 years after the implementation.Electronic supplementary materialThe online version of this article (10.1186/s12903-017-0470-6) contains supplementary material, which is available to authorized users.

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Investigating trends in asthma and COPD through multiple data sources: A small area study

Abstract: HighlightsBayesian modelling of asthma and COPD.Use of multiple data sources to assess disease prevalence, morbidity and mortality.Spatial and temporal patterns across England over the period August 2010 to March 2011.Detection of areas with unusual temporal patterns.

Cited by 17 publications

References 39 publications

The Use of a Smartphone App and an Activity Tracker to Promote Physical Activity in the Management of Chronic Obstructive Pulmonary Disease: Randomized Controlled Feasibility Study

The Use of a Smartphone App and an Activity Tracker to Promote Physical Activity in the Management of Chronic Obstructive Pulmonary Disease: Randomized Controlled Feasibility Study

A geographic identifier assignment algorithm with Bayesian variable selection to identify neighborhood factors associated with emergency department visit disparities for asthma

Effect of risk-based payment model on caries inequalities in preschool children assessed by geo-mapping

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