The local safety net, health insurance, preventable hospitalizations and costs: Harris County (Houston), Texas

The aim of this study was to examine the association between determinants of access to healthcare and preventable hospitalizations, based on Davidson et al.'s framework for evaluating the effects of individual and community determinants on access to healthcare. The study population consisted of the low income, non-elderly, hospitalized adults residing in Harris County, Texas in 2004. The objectives of this study were to examine the proportion of the variance in preventable hospitalizations at the ZIP-code level, to analyze the association between the proximity to the nearest safety net clinic and preventable hospitalizations, to examine how the safety net capacity relates to preventable hospitalizations, to compare the relative strength of the associations of health insurance and the proximity to the nearest safety net clinic with preventable hospitalizations, and to estimate and compare the costs of preventable hospitalizations in Harris County with the average cost in the literature. The data were collected from Texas Health Care Information Collection (2004), Census 2000, and Project Safety Net (2004). A total of 61,841 eligible individuals were included in the final data analysis. A random-intercept multi-level model was constructed with two different levels of data: the individual level and the ZIP-code level. The results of this study suggest that ZIP-code characteristics explain about two percent of the variance in preventable hospitalizations and safety net capacity was marginally significantly associated with preventable hospitalizations (p= 0.062). Proximity to the nearest safety net clinic was not related to preventable hospitalizations; however, health insurance was significantly associated with a decreased risk of preventable hospitalization. The average direct cost was $6,466 per preventable hospitalization, which is significantly different from reports in the literature. ^

Club drug use and unprotected anal intercourse among HIV-seronegative men who have sex with men seeking HIV/STD testing at a local STD clinic in Houston

Objective. To explore (1) the association between "club drug" use and unprotected anal intercourse (UAI) and (2) the association between binge drug use and UAI among HIV seronegative men who have sex with men (MSM) seeking HIV/STD testing at a local clinic in Houston. ^ Study design. A sub-sample of 297 HIV seronegative MSM from a cross-sectional study of drug and sexual behavior in Houston was conducted in 2006. Patients who were seeking HIV/STD testing at a local MSM-identified STD clinic were recruited for an anonymous computer-assisted interview. Analysis of identified secondary data consisted of self-reported information about demographic characteristics, use of drugs, and sexual behaviors. ^ Results. With new and casual sex partners, there was a strong and statistically significant association between use of "club drugs" and UAI. No association between binge drug use and UAI was evident. Men aware of HIV seropositivity or unaware of the HIV serostatus of their primary partner were less likely to report UAI. ^ Conclusion. These data suggest that in the Houston area, HIV-negative MSM club drug users, particularly multiple drug users, are at higher risk of UAI than comparable MSMs who do not use club drugs. Episode-level data regarding binge use of these and other drugs, and UAI should be collected in future studies to explore their relationship. The 'new partner' category should be added to sex partner types to measure sex and drug use behaviors in future studies.^ Keywords. HIV-negative MSM; club drugs; unprotected anal intercourse; binge drug use. ^

Smoke free restaurant ordinance, compliance with the regulation to reduce exposure to environmental tobacco smoke while dining at local restaurants in San Antonio, Texas

This study is a secondary analysis of a survey developed by Dr. Jimmy Perkins and administered by San Antonio/Bexar County Metropolitan Health District. The survey was developed subsequent to the implementation of the city smoking ordinance effective January 1, 2004. The survey had a multi-purpose plan to establish the number of restaurants having smoke free status prior to and following the ordinance, determine compliance as it relates to a necessary smoking section and proper signage, and expose the rationale for restaurants to become smoke free. The data resulting from the survey was presented to the San Antonio/Bexar County Metropolitan Health District. The summary presented the types of establishments surveyed, smoking status of the establishment, reasons for the establishment becoming smoke free, compliance with smoking sections, compliance with signage requirements, awareness of ordinance, and chain status of the establishment. ^ The results of this study display the relationships among the variables previously mentioned. The following relationships have been examined and the outcomes have determined whether each is significant. After careful analysis, knowledge translates into compliance with signage regulations, which then translate into ordinance compliance. Size does matter as it relates to an establishment's number of employees and seating capacity. The smaller the establishment the more likely the establishment is to have become smoke free before the ordinance went into effect. Restaurants, rather than fast food establishments most commonly cited their reason for becoming smoke free was to comply with the ordinance and only ten percent of restaurants gave policy as the main reason for becoming smoke free. ^ This study is important for public health because the negative health effects of environmental tobacco smoke (ETS) are still an overwhelming problem in the United States (3). ETS is a Known Human Group A Carcinogen (5). The Environmental Protection Agency (EPA) has estimated that around 3,000 non-smoking Americans die every year from lung cancer caused by ETS (6). This information illustrates the importance of providing smoke free establishments, especially to non-smoking patrons. ^

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. ^

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.)^

Translating knowledge into action: Information needs of decision-makers in healthcare

Purpose: The purpose of this study was to assess the healthcare information needs of decision-makers in a local US healthcare setting in efforts to promote the translation of knowledge into action. The focus was on the perceptions and preferences of decision-makers regarding usable information in making decisions as to identify strategies to maximize the contribution of healthcare findings to policy and practice. Methods: This study utilized a qualitative data collection and analysis strategy. Data was collected via open-ended key-informant interviews from a sample of 37 public and private-sector healthcare decision-makers in the Houston/Harris County safety net. The sample was comprised of high-level decision-makers, including legislators, executive managers, service providers, and healthcare funders. Decision-makers were asked to identify the types of information, the level of collaboration with outside agencies, useful attributes of information, and the sources, formats/styles, and modes of information preferred in making important decisions and the basis for their preferences. Results: Decision-makers report acquiring information, categorizing information as usable knowledge, and selecting information for use based on the application of four cross-cutting thought processes or cognitive frameworks. In order of apparent preference, these are time orientation, followed by information seeking directionality, selection of validation processes, and centrality of credibility/reliability. In applying the frameworks, decision-makers are influenced by numerous factors associated with their perceptions of the utility of information and the importance of collaboration with outside agencies in making decisions as well as professional and organizational characteristics. Conclusion: An approach based on the elucidated cognitive framework may be valuable in identifying the reported contextual determinants of information use by decision-makers in US healthcare settings. Such an approach can facilitate active producer/user collaborations and promote the production of mutually valued, comprehensible, and usable findings leading to sustainable knowledge translation efforts long-term.^