Home Location Research Articles

Clinical trials tranformation intiative-decentralized clinical trials: a review article

In clinical research, Decentralized clinical trials (DCTs) can provide opportunities to maximize efficiencies. Unlike the traditional clinical trial model, Decentralized Clinical Trials (DCTs) promote telemedicine, mobile/local healthcare providers (HCPs), mobile/web-based technology, and direct distribution of Investigational Medicinal Products (IMP) to patients, among other things. Hence, DCTs are in the spotlight as technology, infrastructure, and knowledge providing a backbone in clinical research.
 Background: Advances in electronic communication, data storage, emerging technologies, and biosensor development provides new opportunities to exchange information, such as the patient is tested from their home locations and are with locations distant from the investigator. Trials that take place at locations distant from the investigator (i.e., spectrum: CCTs → Hybrid Models → DCTs) in any or all study-related procedures and data is collected electronically fall in the category of the decentralized clinical trial. Trials should be designed to integrate the current healthcare system of the study subject, optimize convenience for study subjects and take advantage of existing programs and data sources including The study subjects themselves through the utilization of telecommunication, videoconferences, mobile or internet-based tools for patient reporting, mobile technology tools and biosensors. Local healthcare providers, home-based healthcare services, pharmacies, clinics, regional hospitals, their perceived obstacles have impeded the widespread use of mobile devices in clinical trials. To encourage solutions to these challenges, The Clinical Trial Transformation Initiative (CTTI) has released best practices and practical methods to advance solutions to these issues that clinical trial sponsors can now use.
 Conclusion: Decentralized clinical trials are not only operationally feasible and show high recruitment rates, have better compliance, lower dropout rates, and are conducted faster. DCT meets the goal of the industry in being low-risk, high returns trials and can offer a reliable, time-based, and cost-effective approach.

Exploring the relationship between food insecurity with hunger and academic progression at a large South African University

Food insecurity has been recognized as one of the key challenges currently affecting students in higher education. However, there has been limited research in South African higher education that has sought to understand the relationship between food insecurity and student success. The current study defined student success as a student meeting the requirements to progress from one year of study to another. The study aimed to understand the relationship between food insecurity and student academic progression. Further to this, it aimed to understand the prevalence of food insecurity, and the characteristics of students most likely to progress. This study was carried out at a large South African university and targeted at the entire 2019 first-time, first year undergraduate student cohort (n=5 356). All eligible students were invited via email to participate in a self-administered, online cross-sectional survey. The Household Food Insecurity Access Scale (HFIAS) was used to measure student food insecurity. Data were linked to the end of the year academic progress outcomes, which indicated whether or not students had met the requirements to progress to the following year of study. The survey was completed by 1 612 students, giving a 30 per cent response rate. Overall, nearly a quarter of the students (23%) were found to be experiencing food insecurity with hunger, with 5 per cent experiencing severe hunger. A retention rate of 94 per cent was recorded, and a little over 70 per cent of participants progressed to the second year of study. Student academic progression was found to be significantly associated with food security status (p<0.001), as well as first generation status (p=0.007) and home location (p<0.001) in the bivariate analysis. Further to this, a multivariate analysis revealed that students experiencing little to no hunger were almost twice as likely to progress to the next year of study when compared to those experiencing food insecurity with hunger (Odds Ratio [OR]=1.876; 95% Confidence Interval [CI]: 1.454-2.418; p<0.001). The results of this study demonstrated that food insecurity with hunger was negatively associated with student academic progression. This is one of the first, and largest, South African studies to demonstrate this relationship. This work advocates for students experiencing food insecurity with hunger to be prioritised in university student support programmes, such as food security interventions, as this may improve student success.

Impact of Skill Training Programme (STP) Regarding Postnatal Care of Mothers Who Underwent Lower Segment Caesarean Section on Competencies among Students: A Quasi-Experimental Study

Background: In the twenty-first century, Caesarean section is the most frequent operation in obstetrics. Throughout the twentieth century, Caesarean sections have proven to be one of the most efficient abdominal procedures. Over the last three decades, the number of caesarean sections has risen considerably. Around the world, the number of caesarean sections performed is on the rise. From 2003 to 2018, it is more than doubled, reaching a peak of 21% and is growing at a rate of 4% every year. Today’s student nurse will become future nurse, so it’s vital create them competent while providing care to the mothers after lower section caesarean section.
 Objective: To assess current competencies among students on postnatal care of mothers who underwent lower section caesarean section in study group , to assess the impact of the skill training programme on the competencies of students in focused group & to find out the association between post-test competencies with the demographic variables of students of study group.
 Methods and Materials: The quasi-experimental with non-equivalent pre-test post-test design study was conducted on 30 students in each of experimental and control group by convenient sampling technique in two nursing institutions at Gurugram, Haryana.
 Results: Experimental group subjects enhanced the level of knowledge from 11.6 to 17.8 and their skill from 11.7 to 35.5 after implementing the skilled training programme, while the control group showed no improvement. There is a statistically significant transformation between the pre-test and post-test scores. Paired sample ‘t’ test was used to observe differences between pre and post-test mean scores. Students with prior knowledge were more competent, which is statistically noteworthy with a P value of < 0.001. Age, sex, religion, parents' education, and home location are not significant demographic characteristics. The Pearson chi square test was used to determine statistical significance.
 Conclusion: Skill training not only enhance knowledge in a specific field, but then also clarifies students how to network, accomplish to timeline, and connect efficiently with people. A competent student nurse can become a competent staff nurse by developing skill through effecting skill training program during study period.

A data-driven analysis of the potential of public transport for German commuters using accessibility indicators

BackgroundA significant mode shift will be required in order to meet the ambitious greenhouse gas emissions reduction targets in Germany and elsewhere. Such a mode shift can only be achieved by a combination of drastic push and pull measures. Getting commuters to switch modes might be particularly difficult and have a negative impact on their access to employment and welfare.MethodologyWe investigate the potential for a mode shift from car to public transport for German commuters using a data-driven approach based mainly on open data sources that avoids complex transport model runs. Different datasets on the home and workplace location of all employees in Germany are consolidated to create an origin-destination commuter matrix at traffic analysis zone level. The commuter matrix is merged with travel time data for car and public transport to calculate a spatially disaggregated and mode-specific measure of accessibility. The comparison of accessibility by car and public transport is used to derive the potential for a mode shift and identify potential challenges and barriers.ResultsPublic transport accessibility to workplaces is poorer across the country compared to access by car. On average, public transport travel times are almost three times higher than the corresponding car travel times. The differences in accessibility are largely independent of the region type. Results are validated by an independent dataset from a household travel survey. Based on these results, the potential for a mode shift appears to be very low.

A Simplified and High Accuracy Algorithm of RSSI-Based Localization Zoning for Children Tracking In-Out the School Buses Using Bluetooth Low Energy Beacon

To avoid problems related to a school bus service such as kidnapping, children being left in a bus for hours leading to fatality, etc., it is important to have a reliable transportation service to ensure students’ safety along journeys. This research presents a high accuracy child monitoring system for locating students if they are inside or outside a school bus using the Internet of Things (IoT) via Bluetooth Low Energy (BLE) which is suitable for a signal strength indication (RSSI) algorithm. The in/out-bus child tracking system alerts a driver to determine if there is a child left on the bus or not. Distance between devices is analyzed for decision making to affiliate the zone of the current children’s position. A simplified and high accuracy machine learning of least mean square (LMS) algorithm is used in this research with model-based RSSI localization techniques. The distance is calculated with the grid size of 0.5 m × 0.5 m similar in size to an actual seat of a school bus using two zones (inside or outside a school bus). The averaged signal strength is proposed for this research, rather than using the raw value of the signal strength in typical works, providing a robust position-tracking system with high accuracy while maintaining the simplicity of the classical trilateration method leading to precise classification of each student from each zone. The test was performed to validate the effectiveness of the proposed tracking strategy which precisely shows the positions of each student. The proposed method, therefore, can be applied for future autopilot school buses where students’ home locations can be securely stored in the system used for references to transport each student to their homes without a driver.

DCluster: Geospatial Analytics with PoI Identification

The generation of geospatial data is an inherent aspect for several applications that aim to track people, automobiles, or other mobile objects. Mining information from this type of data is a crucial factor for the development of Smart Cities. In many cases, it can help improve human mobility and the quality of citizens. In this sense, there is a growing demand for systems capable of extracting information from several data types, including the geospatial one. In this work, we present DCluster, a web system that aims to assist data analysts in exploring and visualizing the main types of data, including the geospatial one. Additionally, DCluster has the capability of discovering points of interest based on data of mobile users and classifying them as Home, Work, and Other locations. Data analysts can take advantage of DCluster to explore their data and extract knowledge from it.

Understanding Pediatric Surgery Cancellation: Geospatial Analysis.

BackgroundDay-of-surgery cancellation (DoSC) represents a substantial wastage of hospital resources and can cause significant inconvenience to patients and families. Cancellation is reported to impact between 2% and 20% of the 50 million procedures performed annually in American hospitals. Up to 85% of cancellations may be amenable to the modification of patients’ and families’ behaviors. However, the factors underlying DoSC and the barriers experienced by families are not well understood.ObjectiveThis study aims to conduct a geospatial analysis of patient-specific variables from electronic health records (EHRs) of Cincinnati Children’s Hospital Medical Center (CCHMC) and of Texas Children’s Hospital (TCH), as well as linked socioeconomic factors measured at the census tract level, to understand potential underlying contributors to disparities in DoSC rates across neighborhoods.MethodsThe study population included pediatric patients who underwent scheduled surgeries at CCHMC and TCH. A 5-year data set was extracted from the CCHMC EHR, and addresses were geocoded. An equivalent set of data >5.7 years was extracted from the TCH EHR. Case-based data related to patients’ health care use were aggregated at the census tract level. Community-level variables were extracted from the American Community Survey as surrogates for patients’ socioeconomic and minority status as well as markers of the surrounding context. Leveraging the selected variables, we built spatial models to understand the variation in DoSC rates across census tracts. The findings were compared to those of the nonspatial regression and deep learning models. Model performance was evaluated from the root mean squared error (RMSE) using nested 10-fold cross-validation. Feature importance was evaluated by computing the increment of the RMSE when a single variable was shuffled within the data set.ResultsData collection yielded sets of 463 census tracts at CCHMC (DoSC rates 1.2%-12.5%) and 1024 census tracts at TCH (DoSC rates 3%-12.2%). For CCHMC, an L2-normalized generalized linear regression model achieved the best performance in predicting all-cause DoSC rate (RMSE 1.299%, 95% CI 1.21%-1.387%); however, its improvement over others was marginal. For TCH, an L2-normalized generalized linear regression model also performed best (RMSE 1.305%, 95% CI 1.257%-1.352%). All-cause DoSC rate at CCHMC was predicted most strongly by previous no show. As for community-level data, the proportion of African American inhabitants per census tract was consistently an important predictor. In the Texas area, the proportion of overcrowded households was salient to DoSC rate.ConclusionsOur findings suggest that geospatial analysis offers potential for use in targeting interventions for census tracts at a higher risk of cancellation. Our study also demonstrates the importance of home location, socioeconomic disadvantage, and racial minority status on the DoSC of children’s surgery. The success of future efforts to reduce cancellation may benefit from taking social, economic, and cultural issues into account.

Mothers’ Knowledge toward Home Care for Children with Wilms Tumors at Pediatric Hospitals in Bagdad City

Objectives: To find out relationships between mothers’ knowledge and their demographic characteristics thatincludes (age, marital status, level of education, family income, home location, number of family members.Methods: A descriptive research was performed from October 1, 2020, to June 1, 2021, Non - probability(purposive) sample. In which the sample was objectively chosen from (100) mothers who participated in thestudy and were obtained from three Baghdad hospitals. It included 47 mothers at the Child welfare TeachingHospital and 28 at the Central Child Teaching Hospital and 25 in the city of Miamian al-Kadhimin (peacebe upon them both) in Baghdad.Results: Mothers’ knowledge towards homecare for “Children with Wilms Tumours” at pediatric hospital’sitems was not assigned at the established level in which that achieving the goal of this study.Conclusion: Mothers’ knowledge towards homecare for “Children with Wilms Tumours” at pediatrichospital’s items was not assigned at the established level in which that achieving the goal of this study.

The impact of COVID-19 on trips to urban amenities: Examining travel behavior changes in Somerville, MA.

While there has been much speculation on how the pandemic has affected work location patterns and home location choices, there is sparse evidence regarding the impacts that COVID-19 has had on amenity visits in American cities, which typically constitute over half of all urban trips. Using aggregate app-based GPS positioning data from smartphone users, this study traces the changes in amenity visits in Somerville, MA from January 2019 to December 2020, describing how visits to particular types of amenities have changed as a result of business closures during the public health emergency. Has the pandemic fundamentally shifted amenity-oriented travel behavior or is consumer behavior returning to pre-pandemic trends? To address this question, we calibrate discrete choice models that are suited to Census block-group level analysis for each of the 24 months in a two-year period, and use them to analyze how visitors' behavioral responses to various attributes of amenity clusters have shifted during different phases of the pandemic. Our findings suggest that in the first few months of the pandemic, amenity-visiting preferences significantly diverged from expected patterns. Even though overall trip volumes remained far below normal levels throughout the remainder of the year, preferences towards specific cluster attributes mostly returned to expected levels by September 2020. We also construct two scenarios to explore the implications of another shutdown and a full reopening, based on November 2020 consumer behavior. While government restrictions have played an important role in reducing visits to amenity clusters, our results imply that cautionary consumer behavior has played an important role as well, suggesting a likely long and slow path to economic recovery. By drawing on mobile phone location data and behavioral modeling, this paper offers timely insights to help decision-makers understand how this unprecedented health emergency is affecting amenity-related trips and where the greatest needs for intervention and support may exist.

Association of Race/Ethnicity and Social Determinants with Rehospitalization for Mental Health Conditions at Acute Care Children's Hospitals

To evaluate associations of race/ethnicity and social determinants with 90-day rehospitalization for mental health conditions to acute care nonpsychiatric children's hospitals. We conducted a retrospective cohort analysis of mental health hospitalizations for children aged 5-18years from 2016 to 2018 at 32 freestanding US children's hospitals using the Children's Hospital Association's Pediatric Health Information System database to assess the association of race/ethnicity and social determinants (insurance payer, neighborhood median household income, and rurality of patient home location) with 90-day rehospitalization. Risk factors for rehospitalization were modeled using mixed-effects multivariable logistic regression. Among 23 556 index hospitalizations, there were 1382 mental health rehospitalizations (5.9%) within 90days. Non-Hispanic Black children were 26% more likely to be rehospitalized than non-Hispanic White children (aOR 1.26, 95% CI 1.08-1.48). Those with government insurance were 18% more likely to be rehospitalized than those with private insurance (aOR 1.18, 95% CI 1.04-1.34). In contrast, those living in a suburban location were 22% less likely to be rehospitalized than those living in an urban location (suburban: aOR 0.78, 95% CI 0.63-0.97). Non-Hispanic Black children and those with public insurance were at greatest risk for 90-day rehospitalization, and risk was lower in those residing in suburban locations. Future work should focus on upstream interventions that will best attenuate social disparities to promote equity in pediatric mental healthcare.

Entangled footprints: Understanding urban neighbourhoods by measuring distance, diversity, and direction of flows in Singapore

Traditional approaches to human mobility analysis in Geography often rely on census or survey data that is resource-intensive to collect and often has a limited spatio-temporal scope. The advent of new technologies (e.g. geosocial media platforms) provides opportunities to overcome these limitations and, if properly leveraged, can yield more granular insights about human mobility. In this paper, we use an anonymized Twitter dataset collected in Singapore from 2012 to 2016 to investigate this potential to help understand the footprints of urban neighbourhoods from both a spatial and a relational perspective.We construct home-to-destination networks of individual users based on their inferred home locations. In aggregated form, these networks allow us to analyze three specific mobility indicators at the neighbourhood level, namely the distance, diversity, and direction of urban interactions. By mapping these three indicators of the spatial footprint of each neighbourhood, we can capture the nuances in the position of individual neighbourhoods within the larger urban network. An exploratory spatial regression reveals that socio-economic characteristics (e.g. share of rental housing) and the built environment (i.e. land use) only partially explain these three indicators and a residual analysis points to the need to explicitly include each neighbourhood's position within the transportation network in future work.

COVID-19: Access to Information, Health Service, Daily Life Facility and Risk Perception of Foreigners during Coronavirus pandemic in South Korea

IntroductionCoronavirus disease outbreak has become a top global challenge. Critical issues have emerged regarding access to information, health services, and daily necessities. Effective access to such components is expected to promote public safety and survival as well as to help combat social fear and risk perception. This study aims to fill this gap by exploring public perceptions of access, risk, and government response during the COVID-19 outbreak.Material and methodsA quantitative field survey using a structured mailed questionnaire was conducted to collect primary data from 100 foreigners living in various cities in South Korea. Access to information, health and daily necessities, and overall risk perception were examined based on individual reports regarding sources, availability, credibility, and overall satisfaction.ResultsOur finding suggest that foreigners are receiving reliable information from public and private sources. Although access to health services, daily necessities, and protection measures were perceived as limited due to government protection measures, survey respondents tended to perceive an overall satisfactory government response in terms of providing resources and reducing risks related to COVID-19.ConclusionsModel analysis indicates that gender, home location, and duration of stay are significantly related to lower levels of risk perception. This study provides a new lens for policymakers, administrators, and academicians by which they can ensure smooth public access to information, health, and daily necessities regarding the protection and containment of coronavirus.

Effective approach to manage COVID-19 challenges in Hamad General Hospital dialysis facilities

Background: Hamad Medical Corporation (HMC) is providing dialysis treatment to approximately 1050 patients. COVID-19 started from China in December 2019, and the first case in Qatar was confirmed on 27th February 2020. There were challenges to provide dialysis treatment for COVID-19 positive and negative patients during the pandemic due to severe staff shortage, staff fear and psychological distress, workload, lack of dialysis slots, prolonged working hours and staff fatigue. Some staff were even deployed to COVID-19 facilities (modular dialysis services, hotel and quarantine facilities) to provide treatment. Methods: 1) A COVID-19 management committee was established 2) An on-call team was assigned to manage new cases and review dialysis slots availability. 3) Staff performance and adherence to safety measures was monitored. 4) A hierarchy model was implemented for COVID-19. A) Elimination: – Confirmed COVID-19 patients were not to receive dialysis at Ambulatory Dialysis centres. – Unit meetings were only held online. B) Substitution: – Dialysis services were to be provided in HMC dialysis facilities, COVID-19 hospitals, and isolation/quarantine facilities (home/hotels). – Administrators with chronic disease worked from home. C) Engineering: – Reduce number of chairs in tearoom and waiting area – Rearrange offices, working spaces, and conference room to keep everyone 2 meters apart. D) Administrative: – Staff, patient and family education – Screening by using visual triaging scale – Deployment of staff – Managing staff mental health and psychosocial well-being Results: 76 dialysis patients and 30 dialysis staff were infected. 900 hemodialysis sessions were provided inside quarantine and isolation facilities (home, hotel, Bu-Sidra, and other locations) and Hazm Mebaireek General Hospital (HMGH) Modular Dialysis Unit from March to October 2020 . The number of COVID-19 positive patients reduced from 39 in May 2020 to 12 in July 2020 (p = 0002). Conclusion: Thanks to our approach, we were able to provide a high quality and safe dialysis service for in-centre dialysis and in COVID-19 facilities and quarantine centres (home/hotels).

Precise Localization of Homes and Activities: Detecting Drinking-While-Tweeting Patterns in Communities

There has been an explosion of research on analyzing social media to map human behavior relevant to public health, such as drinking or drug use. However, it is important not only to detect the local regions where these activities occur, but also to analyze the degree of participation in them by local residents. We develop powerful new methods for fine-grained localization of activities and home locations using Twitter data. We apply these methods to discover and compare alcohol use patterns in a large city, New York City, and a more suburban and rural area, Monroe County.

Our House, in the Middle of Our Tweets

Twitter, Flickr, Instagram, and other public social media sites have inspired lots of analysis of public geotagged posts. In order to understand these posts, it is important to know where their authors live. Based on a study of 195 prolific Twitter users in the Pittsburgh area, and their ground truth home locations, we show that simple algorithms can find about 80% of people’s home addresses within 1 kilometer. We show why this is near the upper bound of feasibility, show that studying as few as 10 tweets can achieve almost the same results, and discuss implications for future social media analyses.

Towards Lifestyle Understanding: Predicting Home and Vacation Locations from User's Online Photo Collections

Semantic place labeling has been actively studied in the past few years due to its importance in understanding human mobility and lifestyle patterns. In the last decade, the rapid growth of geotagged multimedia data from online social networks provides a valuable opportunity to predict people's POI locations from temporal, spatial and visual cues. Among the massive amount of social media data, one important type of data is the geotagged web images from image-sharing websites. In this paper, we develop a reliable photo classifier based on the Convolutional Neutral Networks to classify the photo-taking scene of real-life photos. We then present a novel approach to home location and vacation locations prediction by fusing together the visual content of photos and the spatiotemporal features of people's mobility patterns. Using a well-trained classifier, we showed that the robust fusion of visual and spatiotemporal features achieves significant accuracy improvement over each of the features alone for both home and vacation detection.

Where Is This Tweet From? Inferring Home Locations of Twitter Users

We present a new algorithm for inferring the home locations of Twitter users at different granularities, such as city, state, or time zone, using the content of their tweets and their tweeting behavior. Unlike existing approaches, our algorithm uses an ensemble of statistical and heuristic classifiers to predict locations. We find that a hierarchical classification approach can improve prediction accuracy. Experimental evidence suggests that our algorithm works well in practice and outperforms the best existing algorithms for predicting the location of Twitter users.

Modeling regional disparity and the reverse commute

• A dimension reduction approach is employed to model the reverse commute. • Greater distances between origin and destination predict the reverse commute. • Higher percent of population African American at origin predict reverse commute. • Lower incomes at origin and higher incomes at destination predict reverse commute. • Effect of metropolitan fragmentation on reverse commute is unclear. This paper measures a) whether the reverse commute – a commute where a worker travels from a home location in the city to a workplace location in the suburbs – is characterized by a uniquely disparate socioeconomic and demographic space between home and workplace, and b) what role greater fragmentation between municipalities in a region might play in this theoretically inequitable circumstance. We employ administrative origin–destination data from the metropolitan Philadelphia, PA area and contemporary dimension reduction methodologies to model the likelihood of an origin destination pair being classified as a reverse commute. Particular attention is given to interactions between socioeconomic and metropolitan fragmentation terms and non-linear specifications of predictor variables. Greater socioeconomic and demographic disparity between home and work geographies is found to uniquely predict the reverse commute. We also find that fragmentation at the metropolitan scale has a multidimensional impact in relation to this socio-economic difference. Together, our findings appear to signal that, while greater regional socioeconomic disparity does indeed correlate with the reverse commute, greater metropolitan fragmentation may not. This portrait highlights new areas for transport research around political fragmentation and makes the case for renewed, equity-oriented policy investment in the reverse commuting problem.

62 Network Analysis To Understand Regional Patient Flow

Consolidation of health care systems in the United States has created integrated enterprises with large geographical reach and complex interacting components. Specialty services vary among sites, and patients often need to travel between facilities for different aspects of care. Network science is an established method employed to investigate these complex systems, and can be used to identify bottlenecks, opportunities to increase value and patient-centeredness in health care. The COVID-19 pandemic changed care demand patterns unexpectedly. We wanted to investigate if network analysis would allow us to better understand these changes and challenges - where our patients receive hospital-based care, what services they used and how far they travelled. We focused this analysis on a multispecialty, tertiary care academic center emergency department (“AC_ED”), with 70,000 patient visits annually. We extracted patient location information from electronic health records, including originating location and level of care, to create a network representation of all care pathways that passed through our ED. The volume of transfers between nodes and the distance travelled were encoded as weighted/colored edge attributes, with edges between nodes in closer proximity being darker. Nodes include: communities within our service area, EDs, and academic center units, and an outcome of mortality. They are sized/colored by betweenness centrality, reflecting the importance of the node in the integrity of the network. The figure shows the overall network structure was similar for pre- and post-pandemic onset with some changes in details. AC_ED receives patients from many home locations and referring hospitals. A large proportion of visits come from the local area reflected by M16 and M12. There are many patients who travel far to access emergency care at AC_ED, bypassing local EDs though the average distance travelled to access care reduced from 114 to 85 miles. During the pandemic there was more traffic to the AC_ED from the local area (M16), fewer connections to surrounding hospitals and disproportionately reduced visits from distant areas (O and OT). Low ED volumes and restricted outpatient clinic availability during the pandemic time frame likely affected this. Inter-hospital transfer volumes declined overall, several sites transferred very few patients to AC_ED post-pandemic start, and other sites increased their transfer rate (eg, CAH13). Looking at hospital systems through the lens of network science can reveal changes in patterns of referrals, allows for identification of unexpected results by presenting data visually and can assist identifying crucial components of a health care system. Application of this methodology to other variables has the potential to identify new areas of improvement to increase value, outcomes and services to improve patient-oriented care.

The effect of distance on the adherence to postpartum follow-up in women with gestational diabetes

Despite the increasing prevalence of gestational diabetes mellitus and well-known long-term metabolic consequences, a low rate of postpartum screening in this population is reported. Few studies focused on environmental factors of attending and performing blood glucose screening tests in women with gestational diabetes. This work aimed to assess the proportion of uptake of postpartum follow-up after the first recall and to study the adherence-related factors in women with gestational diabetes. All women with gestational diabetes were recalled for postpartum screening in a tertiary care center as the center of the cohort study in 2016. The postal addresses were geocoded, and precise spatial (x, y) was provided for each mother's home location. SPSS and GIS were used for data analysis. The incidence rate of gestational diabetes was 8.5% (826/9630). Of women with gestational diabetes, 21.3% accepted to return and completed postpartum screening tests in the first recall. The distance from the cohort center, history of diabetes in the family, and a number of pregnancies were significant predictors for return to follow-up using binary logistic regression (P < 0.01). The first 25% of patients had a distance of 2346 m from the cohort center, and all of the mothers referred to the hospital were 0 to 5 km away, i.e., those who did not return were more than 5 km away (95% confidence interval). Overall screening uptake rate was low. Distance from the center of the screening was an essential factor in deciding to return and adhere to postpartum care in women with gestational diabetes. Geographic inequalities must be considered as a risk factor of visiting the healthcare center in addition to individual contributors. A more accessible center may improve the postpartum follow-up rate in women with a history of gestational diabetes.