scholarly journals Sociodemographic characteristics of missing data in digital phenotyping

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mathew V. Kiang ◽  
Jarvis T. Chen ◽  
Nancy Krieger ◽  
Caroline O. Buckee ◽  
Monica J. Alexander ◽  
...  
Keyword(s):  
Missing Data ◽  
Sex Education ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Sensor Data ◽  
Model Specification ◽  
Sensitivity Analyses ◽  
Natural Environments ◽  
Accelerometer Data ◽  
Digital Phenotyping

AbstractThe ubiquity of smartphones, with their increasingly sophisticated array of sensors, presents an unprecedented opportunity for researchers to collect longitudinal, diverse, temporally-dense data about human behavior while minimizing participant burden. Researchers increasingly make use of smartphones for “digital phenotyping,” the collection and analysis of raw phone sensor and log data to study the lived experiences of subjects in their natural environments using their own devices. While digital phenotyping has shown promise in fields such as psychiatry and neuroscience, there are fundamental gaps in our knowledge about data collection and non-collection (i.e., missing data) in smartphone-based digital phenotyping. In this meta-study using individual-level data from six different studies, we examined accelerometer and GPS sensor data of 211 participants, amounting to 29,500 person-days of observation, using Bayesian hierarchical negative binomial regression with study- and user-level random intercepts. Sensitivity analyses including alternative model specification and stratified models were conducted. We found that iOS users had lower GPS non-collection than Android users. For GPS data, rates of non-collection did not differ by race/ethnicity, education, age, or gender. For accelerometer data, Black participants had higher rates of non-collection, but rates did not differ by sex, education, or age. For both sensors, non-collection increased by 0.5% to 0.9% per week. These results demonstrate the feasibility of using smartphone-based digital phenotyping across diverse populations, for extended periods of time, and within diverse cohorts. As smartphones become increasingly embedded in everyday life, the insights of this study will help guide the design, planning, and analysis of digital phenotyping studies.

scholarly journals Sociodemographic Characteristics of Missing Data in Digital Phenotyping

2021 ◽  
Author(s):  
Mathew V Kiang ◽  
Jarvis T Chen ◽  
Nancy Krieger ◽  
Caroline O Buckee ◽  
Monica J Alexander ◽  
...  
Keyword(s):  
Missing Data ◽  
Data Collection ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Sensor Data ◽  
Sociodemographic Characteristics ◽  
Natural Environments ◽  
Accelerometer Data ◽  
Bayesian Hierarchical ◽  
Digital Phenotyping

AbstractThe ubiquity of smartphones, with their increasingly sophisticated array of sensors, presents an unprecedented opportunity for researchers to collect diverse, temporally-dense data about human behavior while minimizing participant burden. Researchers increasingly make use of smartphone applications for “digital phenotyping,” the collection of phone sensor and log data to study the lived experiences of subjects in their natural environments. While digital phenotyping has shown promise in fields such as psychiatry and neuroscience, there are fundamental gaps in our knowledge about data collection and non-collection (i.e., missing data) in smartphone-based digital phenotyping. Here, we show that digital phenotyping presents a viable method of data collection, over long time periods, across diverse study participants with a range of sociodemographic characteristics. We examined accelerometer and GPS sensor data of 211 participants, amounting to 29,500 person-days of observation, using Bayesian hierarchical negative binomial regression. We found that iOS users had higher rates of accelerometer non-collection but lower GPS non-collection than Android users. For GPS data, rates of non-collection did not differ by race/ethnicity, education, age, or gender. For accelerometer data, Black participants had higher rates of non-collection while Asian participants had slightly lower non-collection. For both sensors, non-collection increased by 0.5% to 0.9% per week. These results demonstrate the feasibility of using smartphone-based digital phenotyping across diverse populations, for extended periods of time, and within diverse cohorts. As smartphones become increasingly embedded in everyday life, the insights of this study will help guide the design, planning, and analysis of digital phenotyping studies.

scholarly journals 1113Depressive symptoms and objectively measured physical activity and sedentary behaviour in adolescents

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Aaron Kandola
Keyword(s):  
Physical Activity ◽  
Depressive Symptoms ◽  
Sedentary Behaviour ◽  
Adolescent Depression ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Population Based ◽  
Sensitivity Analyses ◽  
Accelerometer Data ◽  
Light Activity

Abstract Background Physical activity and sedentary behaviour could be crucial risk factors for adolescent depression. This is the first study to use objective physical activity and sedentary behaviour measure to examine their association with depression in adolescents. Methods We analysed accelerometer data from population-based adolescents at ages 12, 14, and 16 and depressive symptoms at age 18. We used negative binomial regression and group-based trajectory models to analyse the data. Results We found that total physical activity decreased between 12 years and 16 years of age, driven by a decline in light activity and increase in sedentary behaviour. Each additional 60-minute increase in daily sedentary behaviour at ages 12, 14, and 16 was associated with an increased depression score at age 18 of 11·1% (95% CI, 5·1, 17·6), 8% (95% CI, 1·2, 15·2), and 10·5% (95% CI, 1·5, 20·8), respectively. Depression scores at age 18 were 9·6% (95% CI, 3·9, 15), 7.8% (95% CI, 0·8, 14.3), and 11·1% (95% CI, 2·6, 19.1) lower per additional 60-minutes of daily light activity time at ages 12, 14, and 16. These findings were robust to a series of sensitivity analyses. Conclusions Sedentary behaviour displaces light activity throughout adolescence and is associated with a higher risk of depressive symptoms at 18 years of age. Key messages Increasing light activity and decreasing sedentary behaviour during adolescence could be an important target for public health interventions aimed at reducing the prevalence of depression.

scholarly journals Ambient temperature and subsequent COVID-19 mortality in the OECD countries and individual United States

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Costas A. Christophi ◽  
Mercedes Sotos-Prieto ◽  
Fan-Yun Lan ◽  
Mario Delgado-Velandia ◽  
Vasilis Efthymiou ◽  
...  
Keyword(s):  
United States ◽  
Ambient Temperature ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Weather Conditions ◽  
Oecd Countries ◽  
Epidemiological Studies ◽  
Sociodemographic Factors ◽  
Sensitivity Analyses ◽  
Inverse Association

AbstractEpidemiological studies have yielded conflicting results regarding climate and incident SARS-CoV-2 infection, and seasonality of infection rates is debated. Moreover, few studies have focused on COVD-19 deaths. We studied the association of average ambient temperature with subsequent COVID-19 mortality in the OECD countries and the individual United States (US), while accounting for other important meteorological and non-meteorological co-variates. The exposure of interest was average temperature and other weather conditions, measured at 25 days prior and 25 days after the first reported COVID-19 death was collected in the OECD countries and US states. The outcome of interest was cumulative COVID-19 mortality, assessed for each region at 25, 30, 35, and 40 days after the first reported death. Analyses were performed with negative binomial regression and adjusted for other weather conditions, particulate matter, sociodemographic factors, smoking, obesity, ICU beds, and social distancing. A 1 °C increase in ambient temperature was associated with 6% lower COVID-19 mortality at 30 days following the first reported death (multivariate-adjusted mortality rate ratio: 0.94, 95% CI 0.90, 0.99, p = 0.016). The results were robust for COVID-19 mortality at 25, 35 and 40 days after the first death, as well as other sensitivity analyses. The results provide consistent evidence across various models of an inverse association between higher average temperatures and subsequent COVID-19 mortality rates after accounting for other meteorological variables and predictors of SARS-CoV-2 infection or death. This suggests potentially decreased viral transmission in warmer regions and during the summer season.

scholarly journals The impact of teaching on the duration of common urological operations

2016 ◽  
Vol 10 (5-6) ◽  
pp. 172 ◽  
Author(s):  
Blayne Welk ◽  
Jennifer Winick-Ng ◽  
Andrew McClure ◽  
Chris Vinden ◽  
Sumit Dave ◽  
...  
Keyword(s):  
Radical Prostatectomy ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Potential Effect ◽  
Sensitivity Analyses ◽  
Teaching Hospitals ◽  
Operative Duration ◽  
Open Radical Prostatectomy ◽  
Academic Hospitals ◽  
The Impact

Introduction: The ability of academic (teaching) hospitals to offer the same level of efficiency as non-teaching hospitals in a publicly funded healthcare system is unknown. Our objective was to compare the operative duration of general urology procedures between teaching and non-teaching hospitals. Methods: We used administrative data from the province of Ontario to conduct a retrospective cohort study of all adults who underwent a specified elective urology procedure (2002–2013). Primary outcome was duration of surgical procedure. Primary exposure was hospital type (academic or non-teaching). Negative binomial regression was used to adjust relative time estimates for age, comorbidity, obesity, anesthetic, and surgeon and hospital case volume.Results: 114 225 procedures were included (circumcision n=12 280; hydrocelectomy n=7221; open radical prostatectomy n=22 951; transurethral prostatectomy n=56 066; or mid-urethral sling n=15 707). These procedures were performed in an academic hospital in 14.8%, 13.3%, 28.6%, 17.1%, and 21.3% of cases, respectively. The mean operative duration across all procedures was higher in academic centres; the additional operative time ranged from 8.3 minutes (circumcision) to 29.2 minutes (radical prostatectomy). In adjusted analysis, patients treated in academic hospitals were still found to have procedures that were significantly longer (by 10‒21%). These results were similar in sensitivity analyses that accounted for the potential effect of more complex patients being referred to tertiary academic centres.Conclusions: Five common general urology operations take significantly longer to perform in academic hospitals. The reason for this may be due to the combined effect of teaching students and residents or due to inherent systematic inefficiencies within large academic hospitals.

scholarly journals Noncompliance and Missing Data in Health Economic Evaluation

2019 ◽  
Author(s):  
Karla DiazOrdaz ◽  
Richard Grieve
Keyword(s):  
Health Economic ◽  
Economic Evaluation ◽  
Missing Data ◽  
Causal Effect ◽  
Health Economic Evaluation ◽  
Specific Treatment ◽  
Model Specification ◽  
Sensitivity Analyses ◽  
Economic Evaluations ◽  
Random Assignment

Health economic evaluations face the issues of noncompliance and missing data. Here, noncompliance is defined as non-adherence to a specific treatment, and occurs within randomized controlled trials (RCTs) when participants depart from their random assignment. Missing data arises if, for example, there is loss-to-follow-up, survey non-response, or the information available from routine data sources is incomplete. Appropriate statistical methods for handling noncompliance and missing data have been developed, but they have rarely been applied in health economics studies. Here, we illustrate the issues and outline some of the appropriate methods with which to handle these with application to health economic evaluation that uses data from an RCT. In an RCT the random assignment can be used as an instrument-for-treatment receipt, to obtain consistent estimates of the complier average causal effect, provided the underlying assumptions are met. Instrumental variable methods can accommodate essential features of the health economic context such as the correlation between individuals’ costs and outcomes in cost-effectiveness studies. Methodological guidance for handling missing data encourages approaches such as multiple imputation or inverse probability weighting, which assume the data are Missing At Random, but also sensitivity analyses that recognize the data may be missing according to the true, unobserved values, that is, Missing Not at Random. Future studies should subject the assumptions behind methods for handling noncompliance and missing data to thorough sensitivity analyses. Modern machine-learning methods can help reduce reliance on correct model specification. Further research is required to develop flexible methods for handling more complex forms of noncompliance and missing data.

Exploring Cognitive Decision-making Processes, Computer-focused Cyber Deviance Involvement and Victimization

2017 ◽  
Vol 54 (5) ◽  
pp. 639-679 ◽  
Author(s):  
Eric R. Louderback ◽  
Olena Antonaccio
Keyword(s):  
Decision Making ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Ordinary Least Squares ◽  
Sensitivity Analyses ◽  
Protective Effects ◽  
Individual Level ◽  
Older Employees ◽  
Decision Making Processes ◽  
Cognitive Decision

Objectives: Investigate the relationship between thoughtfully reflective decision-making (TRDM) and computer-focused cyber deviance involvement and computer-focused cybercrime victimization. Method: Survey data collected from samples of 1,039 employees and 418 students at a large private university were analyzed using ordinary least squares and negative binomial regression to test the effects of TRDM on computer-focused cyber deviance involvement and victimization. Results: TRDM reduces computer-focused cyber deviance involvement and computer-focused cybercrime victimization across measures and samples. The sensitivity analyses also indicated that TRDM is a more robust predictor of cyber deviance involvement than victimization. The results from moderation analyses showed that, whereas protective effects of TRDM are invariant across genders, they are less salient among older employees for the scenario-based measure of cybercrime victimization. Conclusions: Individual-level cognitive decision-making processes are important in predicting computer-focused cyber deviance involvement and victimization. These results can inform the development of targeted institutional and criminal justice policies aimed at reducing computer-focused cybercrime.

scholarly journals Statewide Interventions and Covid-19 Mortality in the United States: An Observational Study

2020 ◽  
Author(s):  
Nadir Yehya ◽  
Atheendar Venkataramani ◽  
Michael O Harhay
Keyword(s):  
United States ◽  
Negative Binomial ◽  
State Level ◽  
Negative Binomial Regression ◽  
The United States ◽  
School Closure ◽  
Sensitivity Analyses ◽  
Test Model ◽  
School Closures ◽  
Social Distancing

ABSTRACT Background Social distancing is encouraged to mitigate viral spreading during outbreaks. However, the association between distancing and patient-centered outcomes in Covid-19 has not been demonstrated. In the United States social distancing orders are implemented at the state level with variable timing of onset. Emergency declarations and school closures were two early statewide interventions. Methods To determine whether later distancing interventions were associated with higher mortality, we performed a state-level analysis in 55,146 Covid-19 non-survivors. We tested the association between timing of emergency declarations and school closures with 28-day mortality using multivariable negative binomial regression. Day 1 for each state was set to when they recorded ≥ 10 deaths. We performed sensitivity analyses to test model assumptions. Results At time of analysis, 37 of 50 states had ≥ 10 deaths and 28 follow-up days. Both later emergency declaration (adjusted mortality rate ratio [aMRR] 1.05 per day delay, 95% CI 1.00 to 1.09, p=0.040) and later school closure (aMRR 1.05, 95% CI 1.01 to 1.09, p=0.008) were associated with more deaths. When assessing all 50 states and setting day 1 to the day a state recorded its first death, delays in declaring an emergency (aMRR 1.05, 95% CI 1.01 to 1.09, p=0.020) or closing schools (aMRR 1.06, 95% CI 1.03 to 1.09, p<0.001) were associated with more deaths. Results were unchanged when excluding New York and New Jersey. Conclusions Later statewide emergency declarations and school closure were associated with higher Covid-19 mortality. Each day of delay increased mortality risk 5 to 6%.

Burden and trajectory of multimorbidity in rheumatoid arthritis: a matched cohort study from 2006 to 2015

2020 ◽  
pp. annrheumdis-2020-218282 ◽  
Author(s):  
Bryant R England ◽  
Punyasha Roul ◽  
Yangyuna Yang ◽  
Harlan Sayles ◽  
Fang Yu ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Cohort Study ◽  
Chronic Conditions ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Sensitivity Analyses ◽  
Cross Sectional ◽  
Diagnostic Codes ◽  
Binomial Regression

ObjectivesTo compare the onset and trajectory of multimorbidity between individuals with and without rheumatoid arthritis (RA).MethodsA matched, retrospective cohort study was completed in a large, US commercial insurance database (MarketScan) from 2006 to 2015. Using validated algorithms, patients with RA (overall and incident) were age-matched and sex-matched to patients without RA. Diagnostic codes for 44 preidentified chronic conditions were selected to determine the presence (≥2 conditions) and burden (count) of multimorbidity. Cross-sectional comparisons were completed using the overall RA cohort and conditional logistic and negative binomial regression models. Trajectories of multimorbidity were assessed within the incident RA subcohort using generalised estimating equations.ResultsThe overall cohort (n=277 782) and incident subcohort (n=61 124) were female predominant (76.5%, 74.1%) with a mean age of 55.6 years and 54.5 years, respectively. The cross-sectional prevalence (OR 2.29, 95% CI 2.25 to 2.34) and burden (ratio of conditions 1.68, 95% CI 1.66 to 1.70) of multimorbidity were significantly higher in RA than non-RA in the overall cohort. Within the incident RA cohort, patients with RA had more chronic conditions than non-RA (β 1.13, 95% CI 1.10 to 1.17), and the rate of accruing chronic conditions was significantly higher in RA compared with non-RA (RA × follow-up year, β 0.21, 95% CI 0.20 to 0.21, p<0.001). Results were similar when including the pre-RA period and in several sensitivity analyses.ConclusionsMultimorbidity is highly prevalent in RA and progresses more rapidly in patients with RA than in patients without RA during and immediately following RA onset. Therefore, multimorbidity should be aggressively identified and targeted early in the RA disease course.

scholarly journals Association between homicide rates and suicide rates: a countrywide longitudinal analysis of 5507 Brazilian municipalities

BMJ Open ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. e040069
Author(s):  
Daiane Borges Machado ◽  
Keltie McDonald ◽  
Luis F S Castro-de-Araujo ◽  
Delan Devakumar ◽  
Flávia Jôse Oliveira Alves ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Population Based ◽  
Sensitivity Analyses ◽  
Secondary Outcome ◽  
Mortality Data ◽  
Homicide Rates ◽  
Suicide Rates ◽  
Socioeconomic And Demographic Factors ◽  
The Relationship

ObjectiveTo estimate the association between homicide and suicide rates in Brazilian municipalities over a period of 7 years.DesignWe conducted a longitudinal ecological study using annual mortality data from 5507 Brazilian municipalities between 2008 and 2014. Multivariable negative binomial regression models were used to examine the relationship between homicide and suicide rates. Robustness of results was explored using sensitivity analyses to examine the influence of data quality, population size, age and sex on the relationship between homicide and suicide rates.SettingA nationwide study of municipality-level data.ParticipantsMortality data and corresponding population estimates for municipal populations aged 10 years and older.Primary and secondary outcome measuresAge-standardised suicide rates per 100 000.ResultsMunicipal suicide rates were positively associated with municipal homicide rates; after adjusting for socioeconomic and demographic factors, a doubling of the homicide rate was associated with 22% increase in suicide rate (rate ratio=1.22, 95% CI: 1.13 to 1.33). A dose–response effect was observed with 4% increase in suicide rates at the third quintile, 9% at the fourth quintile and 12% at the highest quintile of homicide rates compared with the lowest quintile. The observed effect estimates were robust to sensitivity analyses.ConclusionsMunicipalities with higher homicide rates have higher suicide rates and the relationship between homicide and suicide rates in Brazil exists independently of many sociodemographic and socioeconomic factors. Our results are in line with the hypothesis that changes in homicide rates lead to changes in suicide rates, although a causal association cannot be established from this study. Suicide and homicide rates have increased in Brazil despite increased community mental health support and incarceration, respectively; therefore, new avenues for intervention are needed. The identification of a positive relationship between homicide and suicide rates suggests that population-based interventions to reduce homicide rates may also reduce suicide rates in Brazil.

A dose response relationship between accelerometer assessed daily steps and depressive symptoms in older adults: a two-year cohort study

2020 ◽  
Author(s):  
Ming-Chun Hsueh ◽  
Brendon Stubbs ◽  
Yun-Ju Lai ◽  
Chi-Kuang Sun ◽  
Li-Jung Chen ◽  
...  
Keyword(s):  
Older Adults ◽  
Depressive Symptoms ◽  
Negative Binomial ◽  
Geriatric Depression Scale ◽  
Depression Scale ◽  
Negative Binomial Regression ◽  
Sensitivity Analyses ◽  
Daily Step ◽  
Daily Step Count ◽  
The Stability

Abstract Objectives this study investigated the prospective associations of accelerometer assessed daily steps with subsequent depressive symptoms in older adults. Methods a 2-year prospective study was performed in the community. A total of 285 older adults ≥65 years (mean age = 74.5) attended the baseline assessment in 2012. The second wave of assessment was carried out in 2014 including 274 (96.1%) participants. Daily step counts were measured with a triaxial accelerometer (ActiGraph GT3X+), and participants were divided into three categories (&lt;3,500, 3,500–6,999 and ≥ 7,000 steps/day). The 15-item Geriatric Depression Scale was used to measure depressive symptoms. Negative binomial regression models with multivariable adjustment for covariates (baseline depressive symptoms, accelerometer wear time, age, gender, education, chronic disease, activities of daily living) were conducted to examine the association between daily steps and subsequent depressive symptoms. Results each 1,000-step increase in daily walking was linearly associated with a reduced rate of subsequent depressive symptoms (rate ratio [RR] = 0.95, 95% confidence interval [CI] = 0.92–0.98). Participants with daily step count in 3,500–6,999 (RR = 0.84, 95% CI = 0.70–0.99) and ≥7,000 steps (RR = 0.71, 95% CI = 0.55–0.92) per day had fewer depressive symptoms at follow-up. Sensitivity analyses assessing confounding and reverse causation provided further support for the stability of our findings. Conclusion older adults engaging in more daily steps had fewer depressive symptoms after 2 years. Even as few as 3,500–6,999 steps a day was associated with a protecting effect. Accumulating ≥7,000 steps a day could provide the greatest protection against depressive symptoms.

Sign in / Sign up

Export Citation Format

Share Document