Endemic tuberculosis, the homeless, and public transportation: A merging of geographical information systems surveillance and the Houston Tuberculosis Initiative Surveillance project

To reach the goals established by the Institute of Medicine (IOM) and the Centers for Disease Control's (CDC) STOP TB USA, measures must be taken to curtail a future peak in Tuberculosis (TB) incidence and speed the currently stagnant rate of TB elimination. Both efforts will require, at minimum, the consideration and understanding of the third dimension of TB transmission: the location-based spread of an airborne pathogen among persons known and unknown to each other. This consideration will require an elucidation of the areas within the U.S. that have endemic TB. The Houston Tuberculosis Initiative (HTI) was a population-based active surveillance of confirmed Houston/Harris County TB cases from 1995–2004. Strengths in this dataset include the molecular characterization of laboratory confirmed cases, the collection of geographic locations (including home addresses) frequented by cases, and the HTI time period that parallels a decline in TB incidence in the United States (U.S.). The HTI dataset was used in this secondary data analysis to implement a GIS analysis of TB cases, the locations frequented by cases, and their association with risk factors associated with TB transmission. ^ This study reports, for the first time, the incidence of TB among the homeless in Houston, Texas. The homeless are an at-risk population for TB disease, yet they are also a population whose TB incidence has been unknown and unreported due to their non-enumeration. The first section of this dissertation identifies local areas in Houston with endemic TB disease. Many Houston TB cases who reported living in these endemic areas also share the TB risk factor of current or recent homelessness. Merging the 2004–2005 Houston enumeration of the homeless with historical HTI surveillance data of TB cases in Houston enabled this first-time report of TB risk among the homeless in Houston. The homeless were more likely to be US-born, belong to a genotypic cluster, and belong to a cluster of a larger size. The calculated average incidence among homeless persons was 411/100,000, compared to 9.5/100,000 among housed. These alarming rates are not driven by a co-infection but by social determinants. The unsheltered persons were hospitalized more days and required more follow-up time by staff than those who reported a steady housing situation. The homeless are a specific example of the increased targeting of prevention dollars that could occur if TB rates were reported for specific areas with known health disparities rather than as a generalized rate normalized over a diverse population. ^ It has been estimated that 27% of Houstonians use public transportation. The city layout allows bus routes to run like veins connecting even the most diverse of populations within the metropolitan area. Secondary data analysis of frequent bus use (defined as riding a route weekly) among TB cases was assessed for its relationship with known TB risk factors. The spatial distribution of genotypic clusters associated with bus use was assessed, along with the reported routes and epidemiologic-links among cases belonging to the identified clusters. ^ TB cases who reported frequent bus use were more likely to have demographic and social risk factors associated with poverty, immune suppression and health disparities. An equal proportion of bus riders and non-bus riders were cultured for Mycobacterium tuberculosis, yet 75% of bus riders were genotypically clustered, indicating recent transmission, compared to 56% of non-bus riders (OR=2.4, 95%CI(2.0, 2.8), p<0.001). Bus riders had a mean cluster size of 50.14 vs. 28.9 (p<0.001). Second order spatial analysis of clustered fingerprint 2 (n=122), a Beijing family cluster, revealed geographic clustering among cases based on their report of bus use. Univariate and multivariate analysis of routes reported by cases belonging to these clusters found that 10 of the 14 clusters were associated with use. Individual Metro routes, including one route servicing the local hospitals, were found to be risk factors for belonging to a cluster shown to be endemic in Houston. The routes themselves geographically connect the census tracts previously identified as having endemic TB. 78% (15/23) of Houston Metro routes investigated had one or more print groups reporting frequent use for every HTI study year. We present data on three specific but clonally related print groups and show that bus-use is clustered in time by route and is the only known link between cases in one of the three prints: print 22. (Abstract shortened by UMI.)^

Toxics Release Inventory facilities and childhood cancer: Geographic information systems based approach

Purpose. To examine the association between living in proximity to Toxics Release Inventory (TRI) facilities and the incidence of childhood cancer in the State of Texas. ^ Design. This is a secondary data analysis utilizing the publicly available Toxics release inventory (TRI), maintained by the U.S. Environmental protection agency that lists the facilities that release any of the 650 TRI chemicals. Total childhood cancer cases and childhood cancer rate (age 0-14 years) by county, for the years 1995-2003 were used from the Texas cancer registry, available at the Texas department of State Health Services website. Setting: This study was limited to the children population of the State of Texas. ^ Method. Analysis was done using Stata version 9 and SPSS version 15.0. Satscan was used for geographical spatial clustering of childhood cancer cases based on county centroids using the Poisson clustering algorithm which adjusts for population density. Pictorial maps were created using MapInfo professional version 8.0. ^ Results. One hundred and twenty five counties had no TRI facilities in their region, while 129 facilities had at least one TRI facility. An increasing trend for number of facilities and total disposal was observed except for the highest category based on cancer rate quartiles. Linear regression analysis using log transformation for number of facilities and total disposal in predicting cancer rates was computed, however both these variables were not found to be significant predictors. Seven significant geographical spatial clusters of counties for high childhood cancer rates (p<0.05) were indicated. Binomial logistic regression by categorizing the cancer rate in to two groups (<=150 and >150) indicated an odds ratio of 1.58 (CI 1.127, 2.222) for the natural log of number of facilities. ^ Conclusion. We have used a unique methodology by combining GIS and spatial clustering techniques with existing statistical approaches in examining the association between living in proximity to TRI facilities and the incidence of childhood cancer in the State of Texas. Although a concrete association was not indicated, further studies are required examining specific TRI chemicals. Use of this information can enable the researchers and public to identify potential concerns, gain a better understanding of potential risks, and work with industry and government to reduce toxic chemical use, disposal or other releases and the risks associated with them. TRI data, in conjunction with other information, can be used as a starting point in evaluating exposures and risks. ^

Correlation of tuberculosis and human immunodeficiency virus in Harris County, Texas from 2009 through 2010 using Geographic Information Systems

A population based ecological study was conducted to identify areas with a high number of TB and HIV new diagnoses in Harris County, Texas from 2009 through 2010 by applying Geographic Information Systems to determine whether distinguished spatial patterns exist at the census tract level through the use of exploratory mapping. As of 2010, Texas has the fourth highest occurrence of new diagnoses of HIV/AIDS and TB.[31] The Texas Department of State Health Services (DSHS) has identified HIV infected persons as a high risk population for TB in Harris County.[29] In order to explore this relationship further, GIS was utilized to identify spatial trends. ^ The specific aims were to map TB and HIV new diagnoses rates and spatially identify hotspots and high value clusters at the census tract level. The potential association between HIV and TB was analyzed using spatial autocorrelation and linear regression analysis. The spatial statistics used were ArcGIS 9.3 Hotspot Analysis and Cluster and Outlier Analysis. Spatial autocorrelation was determined through Global Moran's I and linear regression analysis. ^ Hotspots and clusters of TB and HIV are located within the same spatial areas of Harris County. The areas with high value clusters and hotspots for each infection are located within the central downtown area of the city of Houston. There is an additional hotspot area of TB located directly north of I-10 and a hotspot area of HIV northeast of Interstate 610. ^ The Moran's I Index of 0.17 (Z score = 3.6 standard deviations, p-value = 0.01) suggests that TB is statistically clustered with a less than 1% chance that this pattern is due to random chance. However, there were a high number of features with no neighbors which may invalidate the statistical properties of the test. Linear regression analysis indicated that HIV new diagnoses rates (β=−0.006, SE=0.147, p=0.970) and census tracts (β=0.000, SE=0.000, p=0.866) were not significant predictors of TB new diagnoses rates. ^ Mapping products indicate that census tracts with overlapping hotspots and high value clusters of TB and HIV should be a targeted focus for prevention efforts, most particularly within central Harris County. While the statistical association was not confirmed, evidence suggests that there is a relationship between HIV and TB within this two year period.^

DEVELOPING A METHOD FOR IDENTIFYING AND REDUCING FUNCTIONAL DISCREPANCIES OF INFORMATION SYSTEMS

Currently more than half of Electronic Health Record (EHR) projects fail. Most of these failures are not due to flawed technology, but rather due to the lack of systematic considerations of human issues. Among the barriers for EHR adoption, function mismatching among users, activities, and systems is a major area that has not been systematically addressed from a human-centered perspective. A theoretical framework called Functional Framework was developed for identifying and reducing functional discrepancies among users, activities, and systems. The Functional Framework is composed of three models – the User Model, the Designer Model, and the Activity Model. The User Model was developed by conducting a survey (N = 32) that identified the functions needed and desired from the user’s perspective. The Designer Model was developed by conducting a systemic review of an Electronic Dental Record (EDR) and its functions. The Activity Model was developed using an ethnographic method called shadowing where EDR users (5 dentists, 5 dental assistants, 5 administrative personnel) were followed quietly and observed for their activities. These three models were combined to form a unified model. From the unified model the work domain ontology was developed by asking users to rate the functions (a total of 190 functions) in the unified model along the dimensions of frequency and criticality in a survey. The functional discrepancies, as indicated by the regions of the Venn diagrams formed by the three models, were consistent with the survey results, especially with user satisfaction. The survey for the Functional Framework indicated the preference of one system over the other (R=0.895). The results of this project showed that the Functional Framework provides a systematic method for identifying, evaluating, and reducing functional discrepancies among users, systems, and activities. Limitations and generalizability of the Functional Framework were discussed.

Using small area estimation and geographic information systems technology to target health services for the uninsured

The three articles that comprise this dissertation describe how small area estimation and geographic information systems (GIS) technologies can be integrated to provide useful information about the number of uninsured and where they are located. Comprehensive data about the numbers and characteristics of the uninsured are typically only available from surveys. Utilization and administrative data are poor proxies from which to develop this information. Those who cannot access services are unlikely to be fully captured, either by health care provider utilization data or by state and local administrative data. In the absence of direct measures, a well-developed estimation of the local uninsured count or rate can prove valuable when assessing the unmet health service needs of this population. However, the fact that these are “estimates” increases the chances that results will be rejected or, at best, treated with suspicion. The visual impact and spatial analysis capabilities afforded by geographic information systems (GIS) technology can strengthen the likelihood of acceptance of area estimates by those most likely to benefit from the information, including health planners and policy makers. ^ The first article describes how uninsured estimates are currently being performed in the Houston metropolitan region. It details the synthetic model used to calculate numbers and percentages of uninsured, and how the resulting estimates are integrated into a GIS. The second article compares the estimation method of the first article with one currently used by the Texas State Data Center to estimate numbers of uninsured for all Texas counties. Estimates are developed for census tracts in Harris County, using both models with the same data sets. The results are statistically compared. The third article describes a new, revised synthetic method that is being tested to provide uninsured estimates at sub-county levels for eight counties in the Houston metropolitan area. It is being designed to replicate the same categorical results provided by a current U.S. Census Bureau estimation method. The estimates calculated by this revised model are compared to the most recent U.S. Census Bureau estimates, using the same areas and population categories. ^

Clinical decision support capabilities of commercially-available clinical information systems.

BACKGROUND: The most effective decision support systems are integrated with clinical information systems, such as inpatient and outpatient electronic health records (EHRs) and computerized provider order entry (CPOE) systems. Purpose The goal of this project was to describe and quantify the results of a study of decision support capabilities in Certification Commission for Health Information Technology (CCHIT) certified electronic health record systems. METHODS: The authors conducted a series of interviews with representatives of nine commercially available clinical information systems, evaluating their capabilities against 42 different clinical decision support features. RESULTS: Six of the nine reviewed systems offered all the applicable event-driven, action-oriented, real-time clinical decision support triggers required for initiating clinical decision support interventions. Five of the nine systems could access all the patient-specific data items identified as necessary. Six of the nine systems supported all the intervention types identified as necessary to allow clinical information systems to tailor their interventions based on the severity of the clinical situation and the user's workflow. Only one system supported all the offered choices identified as key to allowing physicians to take action directly from within the alert. Discussion The principal finding relates to system-by-system variability. The best system in our analysis had only a single missing feature (from 42 total) while the worst had eighteen.This dramatic variability in CDS capability among commercially available systems was unexpected and is a cause for concern. CONCLUSIONS: These findings have implications for four distinct constituencies: purchasers of clinical information systems, developers of clinical decision support, vendors of clinical information systems and certification bodies.

A project to improve the information seeking skills and increase the use of evidence-based research in public health practice

The ability of public health practitioners (PHPs) to work efficiently and effectively is negatively impacted by their lack of knowledge of the broad range of evidence-based practice information resources and tools that can be utilized to guide them in their development of health policies and programs. This project, a three-hour continuing education hands-on workshop with supporting resources, was designed to increase knowledge and skills of these resources. The workshop was presented as a pre-conference continuing education program for the Texas Public Health Association (TPHA) 2008 Annual Conference. Topics included: identification of evidence-based practice resources to aid in the development of policies and programs; identification of sources of publicly available data; utilization of data for community assessments; and accessing and searching the literature through a collection of databases available to all citizens of Texas. Supplemental resources included a blog that served as a gateway to the resources explored during the presentation, a community assessment workbook that incorporates both Healthy People 2010 objectives and links to reliable sources of data, and handouts providing additional instruction on the use of the resources covered during the workshop.^ Before- and after-workshop surveys based on Kirkpatrick's 4-level model of evaluation and the Theory of Planned Behavior were administered. Of the questions related to the trainer, the workshop, and the usefulness of the workshop, participants gave "Good" to "Excellent" responses to all one question. Confidence levels overall increased a statistically significant amount; measurements of attitude, social norms, and control showed no significant differences before and after the workshop. Lastly, participants indicated they were likely to use resources shown during the workshop within a one to three month time period on average. ^ The workshop and creation of supplemental resources served as a pilot for a funded project that will be continued with the development and delivery of four 4-week long webinar-based training sessions to be completed by December 2008. ^

Objective assessment of the built environment and its relationship to physical activity and obesity

The role of physical activity in the promotion of individual and population health has been well documented in research and policy publications. Significant research activities have produced compelling evidence for the support of the positive association between physical activity and improved health. Despite the knowledge about these public health benefits of physical activity, over half of US adults do not engage in physical activity at levels consistent with public health recommendations. Just as physical inactivity is of significant public health concern in the US, the prevalence of obesity (and its attendant co-morbidities) is also increasing among US adults.^ Research suggests racial and ethnic disparities relevant to physical inactivity and obesity in the US. Various studies have shown more favorable outcomes among non-Hispanic whites when compared to other minority groups as far as physical activity and obesity are concerned. The health disparity issue is especially important because Mexican-Americans who are the fastest growing segment of the US population are disproportionately affected by physical inactivity and obesity by a significant margin (when compared to non-Hispanic whites), so addressing the physical inactivity and obesity issues in this group is of significant public health concern. ^ Although the evidence for health benefits of physical activity is substantial, various research questions remain on the potential motivators for engaging in physical activity. One area of emerging interest is the potential role that the built environment may play in facilitating or inhibiting physical activity.^ In this study, based on an ongoing research project of the Department of Epidemiology at the University of Texas M. D. Anderson Cancer Center, we examined the built environment, measured objectively through the use of geographical information systems (GIS), and its association with physical activity and obesity among a cohort of Mexican- Americans living in Harris County, Texas. The overall study hypothesis was that residing in dense and highly connected neighborhoods with mixed land-use is associated with residents’ increased participation in physical activity and lowered prevalence of obesity. We completed the following specific aims: (1) to generate a land-use profile of the study area and create a “walkability index” measure for each block group within the study area; (2) to compare the level of engagement in physical activity between study participants that reside in high walkability index block groups and those from low walkability block groups; (3) to compare the prevalence of obesity between study participants that reside in high walkability index block groups and those from low walkability block groups. ^ We successfully created the walkability index as a form of objective measure of the built environment for portions of Harris County, Texas. We used a variety of spatial and non-spatial dataset to generate the so called walkability index. We are not aware of previous scholastic work of this kind (construction of walkability index) in the Houston area. Our findings from the assessment of relationships among walkability index, physical activity and obesity suggest the following, that: (1) that attempts to convert people to being walkers through health promotion activities may be much easier in high-walkability neighborhoods, and very hard in low-walkability neighborhoods. Therefore, health promotion activities to get people to be active may require supportive environment, walkable in this case, and may not succeed otherwise; and (2) Overall, among individuals with less education, those in the high walkability index areas may be less obese (extreme) than those in the low walkability area. To the extent that this association can be substantiated, we – public health practitioners, urban designers, and policy experts – we may need to start thinking about ways to “retrofit” existing urban forms to conform to more walkable neighborhoods. Also, in this population especially, there may be the need to focus special attention on those with lower educational attainment.^

Formalizing a Conceptual Framework of Work Domain Knowledge

Background: The failure rate of health information systems is high, partially due to fragmented, incomplete, or incorrect identification and description of specific and critical domain requirements. In order to systematically transform the requirements of work into real information system, an explicit conceptual framework is essential to summarize the work requirements and guide system design. Recently, Butler, Zhang, and colleagues proposed a conceptual framework called Work Domain Ontology (WDO) to formally represent users’ work. This WDO approach has been successfully demonstrated in a real world design project on aircraft scheduling. However, as a top level conceptual framework, this WDO has not defined an explicit and well specified schema (WDOS) , and it does not have a generalizable and operationalized procedure that can be easily applied to develop WDO. Moreover, WDO has not been developed for any concrete healthcare domain. These limitations hinder the utility of WDO in real world information system in general and in health information system in particular. Objective: The objective of this research is to formalize the WDOS, operationalize a procedure to develop WDO, and evaluate WDO approach using Self-Nutrition Management (SNM) work domain. Method: Concept analysis was implemented to formalize WDOS. Focus group interview was conducted to capture concepts in SNM work domain. Ontology engineering methods were adopted to model SNM WDO. Part of the concepts under the primary goal “staying healthy” for SNM were selected and transformed into a semi-structured survey to evaluate the acceptance, explicitness, completeness, consistency, experience dependency of SNM WDO. Result: Four concepts, “goal, operation, object and constraint”, were identified and formally modeled in WDOS with definitions and attributes. 72 SNM WDO concepts under primary goal were selected and transformed into semi-structured survey questions. The evaluation indicated that the major concepts of SNM WDO were accepted by 41 overweight subjects. SNM WDO is generally independent of user domain experience but partially dependent on SNM application experience. 23 of 41 paired concepts had significant correlations. Two concepts were identified as ambiguous concepts. 8 extra concepts were recommended towards the completeness of SNM WDO. Conclusion: The preliminary WDOS is ready with an operationalized procedure. SNM WDO has been developed to guide future SNM application design. This research is an essential step towards Work-Centered Design (WCD).