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A comparison of commercial and custom-made electronic tracking systems to measure patient flow through an ambulatory clinic

Background: Understanding how patients move through outpatient clinics is important for optimizing clinic processes. This study compares the costs, benefits, and challenges of two clinically important methods for measuring patient flow: (1) a commercial system using infrared (IR) technology that passively tracks patient movements and (2) a custom-built, low cost, networked radio frequency identification (RFID) system that requires active swiping by patients at proximity card readers. Methods: Readers for both the IR and RFID systems were installed in the General Eye Service of the Wilmer Eye Institute. Participants were given both IR and RFID tags to measure the time they spent in various clinic stations. Simultaneously, investigators recorded the times at which patients moved between rooms. These measurements were considered the standard against which the other methods were compared. Results: One hundred twelve patients generated a total of 252 events over the course of 6 days. The proportion of events successfully recorded by the RFID system (83.7 %) was significantly greater than that obtained with the IR system (75.4 %, p < 0.001). The cause of the missing events using the IR method was found to be a signal interruption between the patient tags and the check-in desk receiver. Excluding those data, the IR system successfully recorded 94.4 % of events (p = 0.002; OR = 3.83 compared to the RFID system). There was no statistical difference between the IR, RFID, and manual time measurements (p > 0.05 for all comparisons). Conclusions: Both RFID and IR methods are effective at providing patient flow information. The custom-made RFID system was as accurate as IR and was installed at about 10 % the cost. Given its significantly lower costs, the RFID option may be an appealing option for smaller clinics with more limited budgets.

Geographic disparities in late stage breast cancer incidence: results from eight states in the United States

Background: Late stage of cancer at diagnosis is an important predictor of cancer mortality. In many areas worldwide, cancer registry systems, available data and mapping technologies can provide information about late stage cancer by geographical regions, offering valuable opportunities to identify areas where further investigation and interventions are needed. The current study examined geographical variation in late stage breast cancer incidence across eight states in the United States with the objective to identify areas that might benefit from targeted interventions. Methods: Data from the Surveillance Epidemiology and End Results Program on late stage breast cancer incidence was used as dependent variable in regression analysis and certain factors known to contribute to high rates of late stage cancer (socioeconomic characteristics, health insurance characteristics, and the availability and utilization of cancer screening) as covariates. Geographic information systems were used to map and highlight areas that have any combination of high late stage breast cancer incidence and significantly associated risk factors. Results: The differences in mean rates of late stage breast cancer between eight states considered in this analysis are statistically significant. Factors that have statistically negative association with late stage breast cancer incidence across the eight states include: density of mammography facilities, percent population with Bachelor’s degree and English literacy while percent black population has statistically significant positive association with late stage breast cancer incidence. Conclusions: This study describes geographic disparities in late stage breast cancer incidence and identifies areas that might benefit from targeted interventions. The results suggest that in the eight US states examined, higher rates of late stage breast cancer are more common in areas with predominantly black population, where English literacy, percentage of population with college degree and screening availability are low. The approach described in this work may be utilized both within and outside US, wherever cancer registry systems and technologies offer the same opportunity to identify places where further investigation and interventions for reducing cancer burden are needed.

A nonparametric spatial scan statistic for continuous data

Background: Spatial scan statistics are widely used for spatial cluster detection, and several parametric models exist. For continuous data, a normal-based scan statistic can be used. However, the performance of the model has not been fully evaluated for non-normal data. Methods: We propose a nonparametric spatial scan statistic based on the Wilcoxon rank-sum test statistic and compared the performance of the method with parametric models via a simulation study under various scenarios. Results: The nonparametric method outperforms the normal-based scan statistic in terms of power and accuracy in almost all cases under consideration in the simulation study. Conclusion: The proposed nonparametric spatial scan statistic is therefore an excellent alternative to the normal model for continuous data and is especially useful for data following skewed or heavy-tailed distributions.

A space&#8211;time analysis of the WikiLeaks Afghan War Diary: a resource for analyzing the conflict-health nexus

International Journal of Health Geographics - Pet., 10/16/2015 - 14:00
Background: Although it is widely acknowledged that areas of conflict are associated with a high health burden, from a geospatial perspective it is difficult to establish these patterns at fine scales because of a lack of data. The release of the “WikiLeaks” Afghan War Diary (AWD) provides an interesting opportunity to advance analysis and theory into this interrelationship. Methods: This paper will apply two different space time analyses to identify patterns of improvised explosive devices (IED) detonations for the period of 2004 to 2009 in Afghanistan. Results: There is considerable spatial and temporal heterogeneity in IED explosions, with concentrations often following transportation links. The results are framed in terms of a resource for subsequent analyses to other existing health research in Afghanistan. To facilitate this, in our discussion we present a Google Earth file of overlapping rates that can be distributed to any researcher interested in combining his/her fine scale health data with a similarly granular layer of violence. Conclusion: The release of the AWD presents a previously unavailable opportunity to consider how spatially detailed data about violence can be incorporated into understanding, and predicting, health related spillover effects. The AWD can enrich previous research conducted on Afghanistan, and provide a justification for future “official” data sharing at appropriately fine scales.

New indices for home nursing care resource disparities in rural and urban areas, based on geocoding and geographic distance barriers: a cross-sectional study

Background: Aging in place is the crucial object of long-term care policy worldwide. Approximately 15.6–19.4 % of people aged 15 or above live with a disability, and 15.3 % of them have moderate or severe disabilities. The allocation of home nursing care services is therefore an important issue. Service providers in Taiwan vary substantially across regions, and between rural and urban areas. There are no appropriate indices for describing the capacity of providers that it is due to the distances from care recipients. This study therefore aimed to describe and compare distance barriers for home nursing care providers using indices of the “profit willing distance” and the “tolerance limited distance”. Methods: This cross-sectional study was conducted during 2012 and 2013 using geocoding and a geographic information system to identify the distance from the providers’ locations to participants’ homes in urban (Taipei City) and rural (Hualien County) areas in Taiwan. Data were collected in-person by professionals in Taiwanese hospitals using the World Health Organization Disability Assessment Schedule 2.0. The indices were calculated using regression curves, and the first inflection points were determined as the points on the curves where the first and second derivatives equaled 0. Results: There were 5627 participants from urban areas and 956 from rural areas. In urban areas, the profit willing distance was 550–600 m, and we were unable to identify them in rural areas. This demonstrates that providers may need to supply services even when there is little profit. The tolerance limited distance were 1600–1650 m in urban areas and 1950–2000 m in rural areas. In rural areas, 33.3 % of those living inside the tolerance limited distance and there was no provider within this distance, but this figure fell to just 13.9 % in urban areas. There were strong disparities between urban and rural areas in home nursing care resource allocation. Conclusions: Our new “profit willing distance” and the “tolerance limited distance” are considered to be clearer and more equitable than other evaluation indices. They have practical application in considering resource distribution issues around the world, and in particular the rural–urban disparities for public resource.

Do the classification of areas and distance matter to the assessment results of achieving the treatment targets among type 2 diabetes patients?

Background: Type 2 diabetes is a major health concern all over the world. The prevention of diabetes is important but so is well-balanced diabetes care. Diabetes care can be influenced by individual and neighborhood socio-economic factors and geographical accessibility to health care services. The aim of the study is to find out whether two different area classifications of urban and rural areas give different area-level results of achieving the targets of control and treatment among type 2 diabetes patients exemplified by a Finnish region. The study exploits geo-referenced patient data from a regional primary health care patient database combined with postal code area-level socio-economic variables, digital road data and two grid based classifications of areas: an urban–rural dichotomy and a classification with seven area types. Methods: The achievement of control and treatment targets were assessed using the patient’s individual laboratory data among 9606 type 2 diabetes patients. It was assessed whether hemoglobin A1c (HbA1c) was controlled and whether the recommended level of HbA1c was achieved in patients by different area classes and as a function of distance. Chi square test and logistic regression analysis were used for testing. Results: The study reveals that area-level inequalities exist in the care of type 2 diabetes in a detailed 7-class area classification but if the simple dichotomy of urban and rural is applied differences vanish. The patient’s gender and age, area-level education and the area class they belonged to were associated with achievements of control and treatment targets. Longer distance to health care services was not a barrier to good achievements of control or treatment targets. Conclusions: A more detailed grid-based area classification is better for showing spatial differences in the care of type 2 diabetes patients. Inequalities exist but it would be misleading to state that the differences are simply due to urban or rural location or due to distance. From a planning point of view findings suggest that detailed geo-coded patient information could be utilized more in resourcing and targeting the health care services to find the area-level needs of care and to improve the cost-efficient allocation of resources.

Use of attribute association error probability estimates to evaluate quality of medical record geocodes

Background: The utility of patient attributes associated with the spatiotemporal analysis of medical records lies not just in their values but also the strength of association between them. Estimating the extent to which a hierarchy of conditional probability exists between patient attribute associations such as patient identifying fields, patient and date of diagnosis, and patient and address at diagnosis is fundamental to estimating the strength of association between patient and geocode, and patient and enumeration area. We propose a hierarchy for the attribute associations within medical records that enable spatiotemporal relationships. We also present a set of metrics that store attribute association error probability (AAEP), to estimate error probability for all attribute associations upon which certainty in a patient geocode depends. Methods: A series of experiments were undertaken to understand how error estimation could be operationalized within health data and what levels of AAEP in real data reveal themselves using these methods. Specifically, the goals of this evaluation were to (1) assess if the concept of our error assessment techniques could be implemented by a population-based cancer registry; (2) apply the techniques to real data from a large health data agency and characterize the observed levels of AAEP; and (3) demonstrate how detected AAEP might impact spatiotemporal health research. Results: We present an evaluation of AAEP metrics generated for cancer cases in a North Carolina county. We show examples of how we estimated AAEP for selected attribute associations and circumstances. We demonstrate the distribution of AAEP in our case sample across attribute associations, and demonstrate ways in which disease registry specific operations influence the prevalence of AAEP estimates for specific attribute associations. Conclusions: The effort to detect and store estimates of AAEP is worthwhile because of the increase in confidence fostered by the attribute association level approach to the assessment of uncertainty in patient geocodes, relative to existing geocoding related uncertainty metrics.

Diet-related chronic disease in the northeastern United States: a model-based clustering approach

International Journal of Health Geographics - Pet., 09/04/2015 - 14:00
Background: Obesity and diabetes are global public health concerns. Studies indicate a relationship between socioeconomic, demographic and environmental variables and the spatial patterns of diet-related chronic disease. In this paper, we propose a methodology using model-based clustering and variable selection to predict rates of obesity and diabetes. We test this method through an application in the northeastern United States. Methods: We use model-based clustering, an unsupervised learning approach, to find latent clusters of similar US counties based on a set of socioeconomic, demographic, and environmental variables chosen through the process of variable selection. We then use Analysis of Variance and Post-hoc Tukey comparisons to examine differences in rates of obesity and diabetes for the clusters from the resulting clustering solution. Results: We find access to supermarkets, median household income, population density and socioeconomic status to be important in clustering the counties of two northeastern states. The results of the cluster analysis can be used to identify two sets of counties with significantly lower rates of diet-related chronic disease than those observed in the other identified clusters. These relatively healthy clusters are distinguished by the large central and large fringe metropolitan areas contained in their component counties. However, the relationship of socio-demographic factors and diet-related chronic disease is more complicated than previous research would suggest. Additionally, we find evidence of low food access in two clusters of counties adjacent to large central and fringe metropolitan areas. While food access has previously been seen as a problem of inner-city or remote rural areas, this study offers preliminary evidence of declining food access in suburban areas. Conclusions: Model-based clustering with variable selection offers a new approach to the analysis of socioeconomic, demographic, and environmental data for diet-related chronic disease prediction. In a test application to two northeastern states, this method allows us to identify two sets of metropolitan counties with significantly lower diet-related chronic disease rates than those observed in most rural and suburban areas. Our method could be applied to larger geographic areas or other countries with comparable data sets, offering a promising method for researchers interested in the global increase in diet-related chronic disease.

Geographic analysis of the variation in the incidence of ADHD in a country with free access to healthcare: a Danish cohort study

Background: The prevalence of citizens diagnosed with Attention Deficit Hyperactivity Disorder (ADHD) has risen dramatically over the past decades in many countries, however, with large variations. Countries such as Denmark with centrally organized well fare systems, free access to health services and individual tracking based on unique personal identification may in particular contribute to our understanding of the reasons for this increase. Based on Danish registers we aimed to examine the geographical patterns of the distribution of ADHD diagnosis and medication use and explore the association with access to diagnostic services, diagnostic culture, neighbourhood socioeconomic status and municipal spending on health care for children. Methods: We combined information on registered diagnosis of ICD-10 Hyperkinetic Disorder and ADHD medication use in a Danish register-based cohort of children born between 1990 and 2000. We mapped incidence proportions of diagnoses and medication use within the 98 Danish Municipalities. Global and local clustering of ADHD was identified using spatial analysis. Information on contextual factors in the municipalities was obtained from national registers. The associations between the incidence of ADHD and contextual factors were analysed using Bayesian spatial regression models. Results: We found a considerable variation in the incidence of ADHD across the municipalities. Significant clustering of both high and low incidence of ADHD was identified and mapped using the local Moran’s I. Clustering of low incidence of diagnosis and medication use was observed in less populated areas with limited diagnostic resources and in contrast clustering of high incidence in densely populated areas and greater diagnostic resources. When considering the spatial autocorrelation between neighbouring municipalities, no significant associations were found between ADHD and access to diagnostic services, different diagnostic culture, socioeconomic status at municipality level or the municipal spending on health care for children. Conclusions: A large geographical variation of ADHD in the municipalities was observed despite tax-financed and free access to healthcare. Although not statistically significant, results indicate that accessibility to diagnostic resources might explain some of the variation in ADHD incidence. In contrast to US studies the observed variation was not statistically associated to contextual factors in terms of SES, municipal spending on health care for children or differences in diagnostic practices.

Spatial video geonarratives and health: case studies in post-disaster recovery, crime, mosquito control and tuberculosis in the homeless

Background: A call has recently been made by the public health and medical communities to understand the neighborhood context of a patient’s life in order to improve education and treatment. To do this, methods are required that can collect “contextual” characteristics while complementing the spatial analysis of more traditional data. This also needs to happen within a standardized, transferable, easy-to-implement framework. Methods: The Spatial Video Geonarrative (SVG) is an environmentally-cued narrative where place is used to stimulate discussion about fine-scale geographic characteristics of an area and the context of their occurrence. It is a simple yet powerful approach to enable collection and spatial analysis of expert and resident health-related perceptions and experiences of places. Participants comment about where they live or work while guiding a driver through the area. Four GPS-enabled cameras are attached to the vehicle to capture the places that are observed and discussed by the participant. Audio recording of this narrative is linked to the video via time stamp. A program (G-Code) is then used to geotag each word as a point in a geographic information system (GIS). Querying and density analysis can then be performed on the narrative text to identify spatial patterns within one narrative or across multiple narratives. This approach is illustrated using case studies on post-disaster psychopathology, crime, mosquito control, and TB in homeless populations. Results: SVG can be used to map individual, group, or contested group context for an environment. The method can also gather data for cohorts where traditional spatial data are absent. In addition, SVG provides a means to spatially capture, map and archive institutional knowledge. Conclusions: SVG GIS output can be used to advance theory by being used as input into qualitative and/or spatial analyses. SVG can also be used to gain near-real time insight therefore supporting applied interventions. Advances over existing geonarrative approaches include the simultaneous collection of video data to visually support any commentary, and the ease-of-application making it a transferable method across different environments and skillsets.