Visit Patterns Research Articles

Investigating neighborhood adaptability using mobility networks: a case study of the COVID-19 pandemic

What predicts a neighborhood’s adaptability to essential public health policies and shelter-in-place regulations that prevent the harmful spread of COVID-19? To answer this question, we present a novel application of human mobility patterns and human behavior in a network setting. We analyze 2 years of mobility data (January 2019 to December 2020) from New York City and construct weekly mobility networks between census block groups based on aggregated point-of-interest visit patterns. Our results indicate that neighborhoods’ socioeconomic and geographic characteristics play a significant role in predicting their adaptability to active shelter-in-place policies. Our simulation outcomes reveal that, alongside factors such as race, education, and income, the geographical attributes of neighborhoods, such as access to amenities that satisfy community needs are equally important factors in predicting neighborhood adaptability to public health policies. These findings offer valuable insights that can enhance urban planning strategies, thereby aiding pandemic mitigation efforts and fostering increased adaptability of urban areas in the face of exogenous shocks like the COVID-19 pandemic.

A Bayesian Bernoulli-Exponential joint model for binary longitudinal outcomes and informative time with applications to bladder cancer recurrence data

BackgroundA variety of methods exist for the analysis of longitudinal data, many of which are characterized with the assumption of fixed visit time points for study individuals. This, however is not always a tenable assumption. Phenomenon that alter subject visit patterns such as adverse events due to investigative treatment administered, travel or any other emergencies may result in unbalanced data and varying individual visit time points. Visit times can be considered informative, because subsequent or current subject outcomes can change or be adapted due to previous subject outcomes.MethodsIn this paper, a Bayesian Bernoulli-Exponential model for analyzing joint binary outcomes and exponentially distributed informative visit times is developed. Via statistical simulations, the influence of controlled variations in visit patterns, prior and sample size schemes on model performance is assessed. As an application example, the proposed model is applied to a Bladder Cancer Recurrence data.Results and conclusionsResults from the simulation analysis indicated that the Bayesian Bernoulli-Exponential joint model converged in stationarity, and performed relatively better for small to medium sample size scenarios with less varying time sequences regardless of the choice of prior. In larger samples, the model performed better for less varying time sequences. This model’s application to the bladder cancer data showed a statistically significant effect of prior tumor recurrence on the probability of subsequent recurrences.

The impact of TV advertising on website traffic

AbstractWe propose a modeling procedure for estimating immediate responses to TV ads and evaluating the factors influencing their size. First, we capture diurnal and seasonal patterns of website visits using the kernel smoothing method. Second, we estimate a gradual increase in website visits after an ad using the maximum likelihood method. Third, we analyze the nonlinear dependence of the estimated increase in website visits on characteristics of the ads using the random forest method. The proposed methodology is applied to a dataset containing minute‐by‐minute organic website visits and detailed characteristics of TV ads for an e‐commerce company in 2019. The results show that people are indeed willing to switch between screens and multitask. Moreover, the time of the day, the TV channel, and the advertising motive play a great role in the impact of the ads.

Disparities in Telehealth Use in HIV Care During the COVID-19 Pandemic: Study Findings from South Carolina.

Background: Telehealth was adopted to maintain HIV care continuity during the COVID-19 pandemic; however, its use was unequally distributed. This study examined variation in HIV care visit patterns and whether telehealth use was associated with viral suppression. Methods: Electronic health record (EHR) data from a large HIV clinic in South Carolina was analyzed using multivariable logistic regression to characterize variation in telehealth use, having a viral load (VL) test, and viral suppression in 2022. Results: EHR data from 2,375 people living with HIV (PWH) between March 2021 and March 2023 showed telehealth use among 4.8% of PWH. PWH who are 50+ years and non-Hispanic Black had lower odds of telehealth use (odds ratio [OR] 0.59, 95% confidence interval [CI 0.40-0.86]; OR 0.58, 95% CI [0.37-0.92] respectively). Telehealth use was not associated with viral suppression and VL testing. Conclusion: Telehealth disparities in HIV care affected older and non-Hispanic Black PWH, requiring tailored strategies to promote telehealth among them.

Disparities in Urban Park Visitation Patterns among Socioeconomically Vulnerable Communities during the COVID-19 Pandemic

Urban parks played an important role during the COVID-19 pandemic among urban dwellers. Numerous studies have shown that park visitations increased or decreased during the pandemic depending on the parks’ contexts, locations, and populations. However, a lack of research has been conducted regarding the impact of COVID-19 on vulnerable and non-vulnerable communities. Therefore, this study seeks to identify the differences between socioeconomic levels in responses to COVID-19′s impact on urban park visits. To observe park users’ movements in real-world scenarios, mobile signaling data were used to capture their movements. Then, using Repeated Measures ANOVA (RM ANOVA), the effectiveness of park visit patterns was statistically verified by considering two variables: “time” and “vulnerability”. The results showed that park visits increased during the early stages of the COVID-19 pandemic regardless of the vulnerability. As COVID-19 spread, underserved communities experienced decreased park visits, demonstrating park inequality after the pandemic. The comparisons in this study provide recommendations for park managers and policymakers in terms of reducing park inequality.

Trends in Health Service Use for Dry Eye Disease From 2017 to 2021: A Real-World Analysis of 369,755 Outpatient Visits.

We aimed to analyze the trends and patterns in outpatient health service treatment of dry eye disease (DED) using real-world data from Yinzhou District in China. The Yinzhou Health Information System is a comprehensive database including electronic medical records from 277 medical institutions representing over 1.64million residents. We extracted outpatient records from January 1, 2017, to December 31, 2021, that included the first diagnosis of DED according to the International Classification of Diseases, 10th Revision (H04.101, H04.103, H11.104, H16.202, or H18.803). We analyzed the trends and patterns of DED outpatient visits using the Mann-Kendall trend test and Cochran-Armitage trend test. We identified a total of 369,755 outpatient visits from 145,712 patients with DED of all ages (60.37% female; 54.10% 50years or older). Primary medical institutions had the largest number of DED outpatient visits (42%), followed by tertiary medical institutions (35%). Over the 5-year period, the number of DED outpatient visits increased from 59,260 to 90,807 (53.23%). We observed significant consecutive annual proportion increases in females (from 61.09% to 62.01%; P = 0.001), patients 50years or older (from 55.10% to 60.08%; P < 0.001), and outpatient visits in primary medical institutions (from 33.19% to 48.75%; P < 0.001). Our study found an increase in outpatient health service use for DED in Yinzhou from 2017 to 2021, with higher proportions and increases among females, patients 50years or older, and primary medical institutions. The rapid growth in the prevalence of DED indicates high eye healthcare needs in patients.

Practice Patterns and Sociodemographic Disparities in the Clinical Care of Anatomical Narrow Angles in the United States

PurposeTo assess treatment and visit patterns among patients with newly diagnosed anatomical narrow angle (ANA) and identify sociodemographic factors associated with disparities in care. DesignRetrospective practice pattern evaluation study. Methods263,422 patients diagnosed with ANA between 2007 to 2019 were identified in the Optum Clinformatics® Data Mart. Inclusion was limited to newly diagnosed ANA, defined as: 1) continuous enrollment during a 2-year lookback period and 1-year study period from first diagnosis, (2) diagnosis by an ophthalmologist or optometrist, (3) no history of pseudophakia, ANA treatments, or prior PACG diagnosis. Outcome measures were treatment with laser peripheral iridotomy (LPI), cataract surgery, or IOP-lowering medications and number of eyecare visits. Logistic and Poisson regression were performed to assess factors associated with treatment and eyecare visits, respectively. ResultsAmong 52,405 eligible cases, 27.7% received LPI, 13.9% received drops, and 15.1% received cataract surgery. Odds of LPI was higher in Asians and Hispanics (OR≥1.16, p<0.001). Non-whites had higher odds of drops (OR≥1.19, p<0.001), but Hispanics had lower odds of cataract surgery (OR=0.79, p<0.001). The mean number of eyecare visits was 2.6±2.1 visits including the day of diagnosis. Older age and treatment were associated with higher rates of eye care visits (RR>1.15, p<0.001). ConclusionMore than a quarter of patients with newly diagnosed ANA receive treatment with LPI. Racial minorities are more likely to receive ANA-specific treatments but less likely to receive cataract surgery. These differences may reflect racial differences in disease severity and the need for clearer practice guidelines in ANA care.

Early childhood caries risk indicators among preschool children in rural Egypt: a case control study

BackgroundEarly childhood caries (ECC) is a public health problem, especially in developing countries like Egypt which has an ECC prevalence of 74%. This research aimed to assess the risk indicators associated with ECC in a rural, socially-disadvantaged population in Alexandria, Egypt.MethodsA case-control study was conducted in 8 nurseries of preschool children aged 3 to 5 years in rural, deprived areas in Alexandria, Egypt, from October 2019 till January 2020. Two groups, 93 with ECC and 93 without ECC. A validated questionnaire was used to collect sociodemographic data including age, sex, number of siblings, socioeconomic status, oral health practices including toothbrushing frequency, pattern of dental visits, daily frequency of sugary snacks. Also, salivary pH and buffering capacity were assessed. A trained and calibrated dentist assessed caries status clinically according to the World Health Organization (WHO) criteria using the dmft index and oral hygiene status using Silness and Loe Plaque Index. Chi-squared test, followed by multivariable logistic regression were performed to assess the relation between independent variables and ECC, P < 0.05 was considered to be statistically significant.ResultsBivariate analysis showed that age, mother’s education, dental visits, dietary habits, Plaque index, salivary pH and buffering capacity were significantly associated with ECC. The significant risk indicators for ECC in multivariable regression were age (AOR = 4.73, 95% CI: 2.76–7.83), mother’s education (illiterate vs. university educated, AOR = 28.36, 95% CI: 8.51-112.92), frequency of daily sugary snacks (twice vs. once, AOR = 2.00, 95% CI: 1.29–3.49, and three or more vs. once, AOR = 2.67, 95% CI: 1.72–3.27), night feeding (AOR = 1.89, 95% CI: 1.38–10.21), Plaque index (AOR = 21.34, 95% CI: 5.56–81.99), and salivary pH (AOR = 0.16, 95% CI: 0.05–0.58).ConclusionThis study suggests that sociodemographic indicators, dietary habits, plaque accumulation and salivary pH are risk indicators for ECC in the studied population.

Impact of COVID-19 Lockdown on Emergency Department Visits, Hospital Admissions, and Mortality in Northern Jordan: An Interrupted Time Series Analysis

Background: The Jordanian healthcare system was significantly affected by the COVID19 pandemic, as lockdowns and infections strained the healthcare system. The impact of the ensuing national lockdown on emergency department visits, hospital admissions, and mortality is unclear. Purpose: To assess the impact of COVID-19 lockdown on the pattern of emergency department visits, hospital admissions, and mortality at a major hospital in northern Jordan. Methods: An electronic database belonging to King Abdullah University Hospital was used for a retrospective analysis, and the data was accessed in 2022. All patients who visited the emergency department before, during, and after the lockdown in 2020 and 2019 were analyzed using an interrupted time-series analysis. Descriptive statistics were calculated for categorical variables, followed by the Wilcoxon Signed-Ranks Test to detect mean differences. Results: ED visits decreased by 53.5% during the lockdown period and by 22% after the lockdown period compared to the same periods a year before. During lockdown, hospital admissions rose significantly from 11.8% in 2019 to 17.1% in 2020, with 23.96% critical care unit admissions. Post-lockdown, ED admissions increased from 11.3% in 2019 to 16.3% in 2020, with critical care admissions rising to 30.10%, and the mortality rate increased by 50%. Conclusion: The study concluded a decrease in hospital admissions and emergency visits during lockdown, but an increase in mortality rates. Future planning should focus on optimizing care for lifethreatening emergencies, improving national emergency preparedness, and developing Hospital Readiness Checklists for effective response to emergencies like COVID-19. Implications for Nursing: Nursing has faced challenges and changes due to the COVID19 lockdown. Patient admissions decreased after the 2020 lockdown, impacting nursing practice, mental health, and patient care. Nurses should be adapted to telehealth services, maintain care remotely, and address nurses’ mental health issues. Virtual tools should be utilized to connect with patients. Keywords: Jordan, COVID-19, Lockdown, Emergency department, Hospital admissions, Interrupted time series analysis.

A Conceptual Research of Satellite Tourism Destinations (Stod)

“Satellite Tourism Destination” or SToD is a concept proposed in this study and aimed at supporting the development of the tourism industry for the sustainability of the region and local communities. Increasingly intense global competition, imbalanced development, a lack of change strategy to a new paradigm and a pattern of visits focused on popularity have caused the movement of tourists leading to overcrowding, density issues, and less exploration of the countryside than the cities. Therefore, the concept of Satellite Tourism Destination is proposed to focus on sub-destination or destination support. The development of this SToD concept entails adapting the satellite concept, putting pressure on the movement of tourists, and empower tourism destinations through new products to attract visitors. The results of previous articles show the importance of creating a destination with the satellite tourism concept. Even though the term “Satellite Tourism” is normally used by industry players, such as the Malaysian media, which reports on the growth of satellite tourism in Malaysia, unfortunately there is no in-depth discussion in terms of academics or published papers. There is no concrete definition in academic studies. The impact of this study is critical in ensuring that future discussions about Satellite Tourism Destinations include stronger scientific and theoretical references.

Socioeconomic Disparity in the Effect of SARS-CoV-2 on Outpatient Visits among People with Type 2 Diabetes in Taiwan.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak posed impact on healthcare. This study evaluated the effect of SARS-CoV-2 outbreak on the outpatient visits of patients with type 2 diabetes and determined the most affected groups. We analyzed Taiwan's National Health Insurance data, including 1,922,702 patients diagnosed with type 2 diabetes from 2018 to 2021. Group-based trajectory modelling identified four distinct outpatient visit patterns, namely, consistently high (Group 1, 74.2%), low-to-high (Group 2, 8.1%), high-to-low (Group 3, 6.0%) and consistently low (Group 4, 11.7%) utilization. Logistic regression was used to analyze correlations between trajectory types and patients' demographics and health statuses. Group 3 members had higher odds of being male [adjusted odds ratio (aOR) = 1.04, 95% confidence interval (CI) 1.03-1.05] and earning below 20,000 New Taiwan Dollar monthly (aOR = 1.29, 95% CI 1.26-1.31) than those in Group 1. However, they were less likely to be under 80 years old (aOR = 0.70-0.97), from lower median family income regions (aOR = 0.81-0.89) or possess a Charlson Comorbidity Index score > 2 (aOR = 0.67, 95% CI 0.66-0.68). Patients with lower income in affluent areas displayed the highest likelihood of falling into Group 3. Patients with type 2 diabetes and low income from wealthy areas were vulnerable during the pandemic. This result emphasizes the need to target resources and support for this subgroup during such crises.

Beyond residential terms: Estimating racial segregation in daily environments in the United States

Much research on racial segregation focuses on segregation in a purely residential sense, measuring the spatial separation of individuals' actual residences. Subsequently, it is commonly surmised that the true extent to which racial groups are spatially separated in everyday life is well represented by these residential separation metrics. Less research has articulated the degree to which different racial groups' everyday lives are spatially separated. Utilizing anonymized cell phone data on 8 billion visits to 4 million unique points of interest in the United States throughout 2018, I analyze how Black-White racial segregation operationalizes in non-residential contexts. Distinct from previous approaches that tend to underestimate racial segregation, I utilize a novel method to approximate the extent to which within-neighborhood visit patterns vary racially, resulting in a more flexible and realistic simulation of the extent to which visitors are racially segregated across points of interest in the 50 largest metropolitan areas in the United States. I find that everyday racial segregation varies substantially depending on the type of context. Additionally, habits seem to structurally discourage interracial ties, as Black Americans are less likely to make visits to more integrated spaces and, when they do, are less likely to repeat them. Further, the integrated spaces Black Americans visit generally lack the right characteristics to be conducive to interpersonal and cross-racial contact. Ultimately, I argue that residential segregation metrics provide an incomplete picture of how actual segregation in everyday contexts manifests itself.

Multiserver time window allowance schedules for virtual visits with uncertain time-dependent no-shows and service times

Virtual care serves as a new mode that can divert non-urgent visits from traditional office visits. Whether virtual service can improve the access to medical treatment and reduce the burden of traditional office services, the key issue is to generate efficient appointment schedules with the lowest operation cost. In this paper, considering the uncertainty of time-dependent no-shows and service times, we investigate a multiserver time window allowance appointment scheduling problem, where time window constraints that restricts virtual visits to be served during the particular period are explicitly modeled. We formulate the problem as a stochastic mixed-integer program to optimize decisions of physician allocation and appointment time simultaneously. Based on the sample average approximation, a stabilized Benders decomposition algorithm is developed by incorporating acceleration techniques, such as cut aggregation and feasibility cuts. Numerical results based on real data indicate the effectiveness of the proposed multiserver time window allowance schedules (MTWAS) and algorithm. Comparing with the off-the-shelf solver Gurobi, the developed algorithm demonstrates high performance in terms of computation speed and solution quality. Under different time-dependent no-show patterns of virtual and office visits, the obtained MTWAS perform better than previous solutions in almost all test cases. In addition, we offer useful managerial insights to aid the virtual service provider in making better scheduling decisions.

The dynamic impact of COVID-19 pandemic on park visits: A longitudinal study in the United States

During the COVID-19 pandemic, people’s park visit pattern has received great attention from both scholars and policy-makers, attributed to parks’ established health benefits and thus serving as an effective strategy to mitigate people’s stress and improve people’s physical and mental health. While burgeoning empirical evidence has been generated, both negative and positive impacts of the pandemic on park visit behaviors have been reported. However, the dynamics of park visits along with the pandemic progress remains underinvestigated. Using locational-based mobile data, this study investigates the longitudinal dynamics of park visits in terms of visiting frequency, travel distance, and time spent within parks across the contiguous United States from January to December 2020. The year-over-year (2019–2020) variations of park visitation patterns are associated with pandemic-relevant variables (i.e., the number of infection cases, and policy stringency index), and the locational characteristics of parks (i.e., “local parks” and “non-local parks” as classified according to ESRI’s US parks dataset). The analytical results reveal that (1) on average, park visit frequency, travel distance, and length of stay reduced since the outbreak of the pandemic; (2) the number of infection cases exerted a negative impact on visit frequency, a positive impact on visitors’ dwelling time, but the inconsistent impact on travel distance in different pandemic periods; (3) the stringency of containment policies negatively affected visit frequency and travel distance, but its impact on park visitors’ dwelling time was inconsistent; and (4) local parks received much fewer visits, even though visitors traveled a longer distance to access some local parks located in peri-urban areas. This study depicts a comprehensive picture of the dynamics of park visitation along with the pandemic progress and sheds light on the ways that parks can aid in recovering from a public health crisis.

Using Big Data to Assess Park System Performance during the COVID-19 Pandemic

Parks provide essential services to urban dwellers, but the global COVID-19 pandemic significantly disrupted park usage. Despite this, little is known about the adaptation of visiting behaviors by the public and how visitation patterns vary across different types of parks. In this study, we utilized SafeGraph cellular human movement data to compare park visits in Tucson, Arizona (USA) before and during the pandemic (2019 vs. 2020). We reviewed park management measures in response to the pandemic alongside park visit data. Furthermore, we conducted a GIS analysis to compare the changes in park visits across different park types throughout various days and months. Results indicate that (1) fluctuations in park visits are strongly correlated with COVID-19-related measures; (2) different types of parks experience vastly different processes of visit decline and recovery; (3) river and linear parks maintain their appeal, likely due to the perception of reduced virus transmission risk associated with their primary activities, such as walking and bicycling; and (4) the contrast between weekend and weekday visit patterns reflects the extent of the pandemic impact. These findings offer valuable guidance for park management and park usage, attendance prediction, and design adaptations for future pandemics. We conclude that SafeGraph big data are effective for evaluating park system performance on a broader scale.

Fairness of Machine Learning Algorithms for Predicting Foregone Preventive Dental Care for Adults

Access to routine dental care prevents advanced dental disease and improves oral and overall health. Identifying individuals at risk of foregoing preventive dental care can direct prevention efforts toward high-risk populations. To predict foregone preventive dental care among adults overall and in sociodemographic subgroups and to assess the algorithmic fairness. This prognostic study was a secondary analyses of longitudinal data from the US Medical Expenditure Panel Survey (MEPS) from 2016 to 2019, each with 2 years of follow-up. Participants included adults aged 18 years and older. Data analysis was performed from December 2022 to June 2023. A total of 50 predictors, including demographic and socioeconomic characteristics, health conditions, behaviors, and health services use, were assessed. The outcome of interest was foregoing preventive dental care, defined as either cleaning, general examination, or an appointment with the dental hygienist, in the past year. Among 32 234 participants, the mean (SD) age was 48.5 (18.2) years and 17 386 participants (53.9%) were female; 1935 participants (6.0%) were Asian, 5138 participants (15.9%) were Black, 7681 participants (23.8%) were Hispanic, 16 503 participants (51.2%) were White, and 977 participants (3.0%) identified as other (eg, American Indian and Alaska Native) or multiple racial or ethnic groups. There were 21 083 (65.4%) individuals who missed preventive dental care in the past year. The algorithms demonstrated high performance, achieving an area under the receiver operating characteristic curve (AUC) of 0.84 (95% CI, 0.84-0.85) in the overall population. While the full sample model performed similarly when applied to White individuals and older adults (AUC, 0.88; 95% CI, 0.87-0.90), there was a loss of performance for other subgroups. Removing the subgroup-sensitive predictors (ie, race and ethnicity, age, and income) did not impact model performance. Models stratified by race and ethnicity performed similarly or worse than the full model for all groups, with the lowest performance for individuals who identified as other or multiple racial groups (AUC, 0.76; 95% CI, 0.70-0.81). Previous pattern of dental visits, health care utilization, dental benefits, and sociodemographic characteristics were the highest contributing predictors to the models' performance. Findings of this prognostic study using cohort data suggest that tree-based ensemble machine learning models could accurately predict adults at risk of foregoing preventive dental care and demonstrated bias against underrepresented sociodemographic groups. These results highlight the importance of evaluating model fairness during development and testing to avoid exacerbating existing biases.

National Patterns of Outpatient Follow-Up Visits After Emergency Care for Acute Bronchiolitis

This cohort study examines the frequency of postdischarge follow-up visits among US emergency department encounters for bronchiolitis and assesses whether follow-up was associated with decreased hospital reutilization or increased treatment with nonrecommended medications.

Patterns in School-Based Mental Health Visits and Psychotropic Medication Prescribing Before and During the COVID-19 Pandemic

Patterns in School-Based Mental Health Visits and Psychotropic Medication Prescribing Before and During the COVID-19 Pandemic

COVID-19 Pandemisinin Kuzey Kıbrıs'ta Madde Bağımlılığı ve Aşırı Dozda Uyuşturucu Nedeniyle Acil Servis Başvurularına Etkisi

Introduction: Following the COVID-19 outbreak in China in December 2019, viral spread started to be seen in many countries. In March 2020, simultaneous with the pandemic declaration, the first case was observed in Northern Cyprus. During this time, the emergency department visit patterns showed differences worldwide. &#x0D; Aim: Thus, this study aims to compare and contrast the differences between pre-pandemic and pandemic period emergency department visits resulting from any kind of drug and/or substance intoxication. To do so, a retrospective analysis was held using two private university hospitals’ electronic recording systems for the periods of 1st December 2017-9th March 2020 (before the pandemic) and 10th March 2020-30th June 2022 (during the COVID-19 pandemic). &#x0D; Results: The demographic data were collected, and the study-specific data were grouped into 5 different intoxication categories. Data collected were compared in terms of pre- and post-pandemic timescales. The results showed that there is no significant difference between the demographic data of patients however, intoxication-related emergency department visits were significantly lower during the pandemic compared to the pre-pandemic period.&#x0D; Conclusion: This study will be a pioneer in contributing to many social problems, especially substance abuse, which was previously lacking in scientific data in Northern Cyprus.

Multi-method investigations of the impact of lockdown relaxation on tourists’ and residents’ movements

ABSTRACT This research examines the impact of lockdown relaxation on tourists’ and residents’ movements in the context of restaurant patronage in Jeju Island, South Korea. Drawing on spatial interaction and multi-attribute attitude theories, we hypothesized the difference between tourists and residents in terms of restaurant visit patterns and choices during lockdown relaxation. A multi-method approach was adopted: spatial, data-driven analysis, and experiments. Lockdown relaxation is found to affect how tourists and residents choose and visit restaurants in different ways. This research provides explanations about tourists’ and residents’ mobility and guidelines that practitioners can use to prepare for “living with COVID-19.”