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. ^

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.

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.

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. ^

Health information exchange: Using metrics to address quality of care and return on investment

Health Information Exchange (HIE) will play a key part in our nation’s effort to improve healthcare. The evidence of HIEs transformational role in healthcare delivery systems is quite limited. The lack of such evidence led us to explore what exists in the healthcare industry that may provide evidence of effectiveness and efficiency of HIEs. The objective of the study was to find out how many fully functional HIEs are using any measurements or metrics to gauge impact of HIE on quality improvement (QI) and on return on investment (ROI).^ A web-based survey was used to determine the number of operational HIEs using metrics for QI and ROI. Our study highlights the fact that only 50 percent of the HIEs who responded use or plan to use metrics. However, 95 percent of the respondents believed HIEs improve quality of care while only 56 percent believed HIE showed positive ROI. Although operational HIEs present numerous opportunities to demonstrate the business model for improving health care quality, evidence to document the impact of HIEs is lacking. ^

Cancer perceptions: Implications from the 2007 Health Information National Trends Survey

Background. Research has demonstrated associations between sociodemographic characteristics and illness perceptions; however, the impact of cancer exposure through personal or family diagnoses is not well-studied. The purposes of this study were to examine the prevalence of different cancer beliefs and the disparity in cancer beliefs across groups of individuals with distinct cancer histories; and to identify whether, when adjusted for sociodemographic characteristics, cancer history predicts a set of cancer beliefs.^ Methods. Using Leventhal’s Common Sense Model and data from the 2007 Health Information National Trends Survey (N=7172), we constructed multivariable logistic regressions to evaluate the effect of different stimuli, including cancer experience, on cancer perceptions (e.g., risk, worry, causation, outcome).^ Results. Findings indicate significant associations between cancer history and cancer perceptions. Individuals with family and personal cancer histories were more likely than individuals without any cancer history to worry about getting cancer (OR=3.55, P<0.01), agree they will develop cancer in the future (OR=8.81, P<0.01), and disagree that cancer is most often caused by a person’s behavior or lifestyle (OR=1.24, P=0.03). Additionally, results support education’s role in forming cancer perceptions. Individuals with high levels of education were more likely to endorse cancer prevention (OR=1.68, P<0.01) and higher 5-year survival rates (OR=1.41, P<0.01). ^ Conclusions. Results indicate cancer history affects cancer perceptions throughout the cancer continuum. Additionally, cancer history may influence coping behaviors and outcomes related to cancer.^ Impact. Cancer education and survivorship programs should assess important variables (e.g., cancer history) to more effectively tailor services and monitor evolving needs throughout cancer care.^

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.)^

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.^

Consumer Health Information for Asians (CHIA): a collaborative project.

According to the 2000 United States Census, the Asian population in Houston, Texas, has increased more than 67% in the last ten years. To supplement an already active consumer health information program, the staff of the Houston Academy of Medicine-Texas Medical Center Library worked with community partners to bring health information to predominantly Asian neighborhoods. Brochures on health topics of concern to the Asian community were translated and placed in eight informational kiosks in Asian centers such as temples and an Asian grocery store. A press conference and a ribbon cutting ceremony were held to debut the kiosks and to introduce the Consumer Health Information for Asians (CHIA) program. Project goals for the future include digitizing the translated brochures, mounting them on the Houston HealthWays Website, and developing touch-screen kiosks. The CHIA group is investigating adding health resources in other Asian languages, as well as Spanish. Funding for this project has come from outside sources rather than from the regular library budget.

Camp For All Connection: a community health information outreach project

Purpose: The purpose of the Camp For All Connection project is to facilitate access to electronic health information resources at the Camp For All facility. Setting/Participants/Resources: Camp For All is a barrier-free camp working in partnership with organizations to enrich the lives of children and adults with chronic illnesses and disabilities and their families by providing camping and retreat experiences. The camp facility is located on 206 acres in Burton, Texas. The project partners are Texas Woman's University, Houston Academy of Medicine-Texas Medical Center Library, and Camp For All. Brief Description: The Camp For All Connection project placed Internet-connected workstations at the camp's health center in the main lodge and provided training in the use of electronic health information resources. A train-the-trainer approach was used to provide training to Camp For All staff. Results/Outcome: Project workstations are being used by health care providers and camp staff for communication purposes and to make better informed health care decisions for Camp For All campers. Evaluation Method: A post-training evaluation was administered at the end of the train-the-trainer session. In addition, a series of site visits and interviews was conducted with camp staff members involved in the project. The site visits and interviews allowed for ongoing dialog between project staff and project participants.

Trends in health information seeking among cancer and non-cancer adults between 2003 and 2005: A descriptive analysis of Health Information National Trends Survey (HINTS) data

Cancer is the second leading cause of death in the United States. With the advent of new technologies, changes in health care delivery, and multiplicity of provider types that patients must see, cancer care management has become increasingly complex. The availability of cancer health information has been shown to help cancer patients cope with the management and effects of their cancers. As a result, more cancer patients are using the internet to find resources that can aid in decision-making and recovery. ^ The Health Information National Trends Survey (HINTS) is a nationally representative survey designed to collect information about the experiences of cancer and non-cancer adults with health information sources. The HINTS survey focused on both conventional sources as well as newer technologies, particularly the internet. This study is a descriptive analysis of the HINTS 2003 and HINTS 2005 survey data. The purpose of the research is to explore the general trends in health information seeking and use by US adults, and especially by cancer patients. ^ From 2003 to 2005, internet use for various health-related activities appears to have increased among adults with and without cancer. Differences were found between the groups in the general trust in information media, particularly the internet. Non-cancer respondents tended to have greater trust in information media than cancer respondents. ^ The latter portion of this work examined characteristics of HINTS respondents that were thought to be relevant to how much trust individuals placed in the internet as a source of health information. Trust in health information from the internet was significantly greater among younger adults, higher-earning households, internet users, online seekers of health or cancer information, and those who found online cancer information useful. ^

The relationship between risk perception and ethnicity as an explanation for late stage skin cancer diagnosis in Black and Hispanic populations: An analysis using data from the 2005 Health Information and National Trends Survey

Objective. The purpose of this study was to determine the relationship between ethnicity and skin cancer risk perception while controlling for other risk factors: education, gender, age, access to healthcare, family history of skin cancer, fear, and worry. ^ Methods. This study utilized the Health Information National Trends Survey (HINTS) dataset, a nationally representative sample of 5,586 individuals 18 years of age or older. One third of the respondents were chosen at random and asked questions involving skin cancer. Analysis was based on questions that identified skin cancer risk perception, fear of finding skin cancer, and frequency of worry about skin cancer and a variety of sociodemographic factors. ^ Results. Ethnicity had a significant impact on risk perception scores while controlling for other risk factors. Other risk factors that also had a significant impact on risk perception scores included family history of skin cancer, age, and worry. ^