Implications of Health Care Reform (Medical Ethics Series, 2011)

A video of a panel discussion on how Obama's Health Care Reform would affect Texas Medical Center institutions and health care in general.Speakers include Tom Cole (moderator), Roberta Schwartz (Methodist Hospital), Pauline Rosenau (UT-Houston School of Public Health), and Laurence McCullough (Baylor College of Medicine).

Factors affecting prenatal care use and pregnancy outcome in Texas: A comparative study among and within Black, Anglo, and Hispanic populations

This research project is a study in the field of public health to test the relationships of demographic, socioeconomic, behavioral, and biological factors with (1) prenatal care use and (2) pregnancy outcome, measured by birth weight. It has been postulated that demographic, socioeconomic, and behavioral factors are associated with differences in the use of prenatal care services. It has also been postulated that differences in demographic, socioeconomic, behavioral, and biological factors result in differences in birth weight. This research attempts to test these two basic conceptual frameworks. At the same time, an attempt is made to determine the population groups and subgroups that are at increased risk (1) of using fewer prenatal care visits, and (2) of displaying a higher incidence of low birth weight babies. An understanding of these relationships of the demographic, socioeconomic, behavioral, and biological factors in the use of prenatal care visits and pregnancy outcome, measured by birth weight, will potentially offer guidance in the planning and policy development of maternal and child health services. The research considers four major components of maternal characteristics: (1) Demographic factors. Ethnicity, household size, maternal parity, and maternal age; (2) Socioeconomic factors. Maternal education, family income, maternal employment, health insurance coverage, and household dwelling; (3) Behavioral factors. Maternal smoking, attendance at child development classes, mother's first prenatal care visit, total number of prenatal care visits, and adequacy of care; and, (4) Biological factors. Maternal weight gain during pregnancy.^ The research considers 16 independent variables and two dependent variables.^ It was concluded that: (1) Generally, differences in demographic, socioeconomic, and behavioral factors were associated with differences in the average number of prenatal care visits between and within population groups and subgroups. The Hispanic mothers were the lowest users of prenatal care services. (2) In some cases, differences in demographic, socioeconomic, behavioral, and biological factors demonstrated differences in the average birth weight of infants between and within population groups and subgroups. (3) Differences in demographic, socioeconomic, behavioral, and biological factors resulted in differences in the rates of low birth weight babies between and within population groups and subgroups. The Black mothers delivered the highest incidence of low birth weight infants.^ These findings could provide guidance in the formulation of public health policies such as MCH services, an increase in the use of prenatal care services by prospective mothers, resulting in reduction of the incidence of low birth weight babies, and consequently aid in reducing the rates of infant mortality. ^

PROJECTION OF HEALTH DEMAND IN TERMS OF HOSPITAL ADMISSIONS FOR SELECTED DIAGNOSES IN HEALTH SYSTEMS AGENCY SERVICE AREAS

The National Health Planning and Resources Development Act of 1974 (Public Law 93-641) requires that health systems agencies (HSAs) plan for their health service areas by the use of existing data to the maximum extent practicable. Health planning is based on the identificaton of health needs; however, HSAs are, at present, identifying health needs in their service areas in some approximate terms. This lack of specificity has greatly reduced the effectiveness of health planning. The intent of this study is, therefore, to explore the feasibility of predicting community levels of hospitalized morbidity by diagnosis by the use of existing data so as to allow health planners to plan for the services associated with specific diagnoses.^ The specific objectives of this study are (a) to obtain by means of multiple regression analysis a prediction equation for hospital admission by diagnosis, i.e., select the variables that are related to demand for hospital admissions; (b) to examine how pertinent the variables selected are; and (c) to see if each equation obtained predicts well for health service areas.^ The existing data on hospital admissions by diagnosis are those collected from the National Hospital Discharge Surveys, and are available in a form aggregated to the nine census divisions. When the equations established with such data are applied to local health service areas for prediction, the application is subject to the criticism of the theory of ecological fallacy. Since HSAs have to rely on the availability of existing data, it is imperative to examine whether or not the theory of ecological fallacy holds true in this case.^ The results of the study show that the equations established are highly significant and the independent variables in the equations explain the variation in the demand for hospital admission well. The predictability of these equations is good when they are applied to areas at the same ecological level but become poor, predominantly due to ecological fallacy, when they are applied to health service areas.^ It is concluded that HSAs can not predict hospital admissions by diagnosis without primary data collection as discouraged by Public Law 93-641. ^

THE ALCOHOL TREATMENT PROFILE SYSTEM: ITS DEVELOPMENT AND AN EVALUATION OF ITS CAPABILITIES

The development of the Alcohol Treatment Profile System (ATPS) was described and an evaluation of its perceived value by various States was undertaken, The ATPS is a treatment needs assessment tool based on the unification of several large national epidemiologic and treatment data sets. It was developed by the National Institute on Alcohol Abuse and Alcoholism (NIAAA) and responsibility for its creation was given to the NIAAA's Alcohol Epidemiologic Data System (AEDS). The ATPS merges county-level measures of alcohol problem prevalence (the specially constructed AEDS Alcohol Problem Indicators), indicating "need" for treatment, and treatment utilization measures (the National Drug and Alcohol Treatment Utilization Survey), indicating treatment "demand." The capabilities of the ATPS in the unique planning and policy-making settings of several States were evaluated.^

Planning, implementation, and evaluation of an HIV -screening program for pregnant women

The purpose of this study was to evaluate the effectiveness of an HIV-screening program at a private health-care institution where the providers were trained to counsel pregnant women about the HIV-antibody test according to the latest recommendations made by the U.S. Public Health Service (PHS) and the Texas legislature. A before-and-after study design was selected for the study. The participants were OB/GYN nurses who attended an educational program and the patients they counseled about the HIV test. Training improved the nurses' overall knowledge about the content of the program and nurses were more likely to offer the HIV test to all pregnant women regardless of their risk of infection. Still, contrary to what was predicted, the nurses did not give more information to increase the knowledge pregnant women had about HIV infection, transmission, and available treatments. Consequently, many women were not given the chance to correctly assess their risk during the counseling session and there was no evidence that knowledge would reduce the propensity of many women to deny being at risk for HIV. On the other hand, pregnant women who received prenatal care after the implementation of the HIV-screening program were more likely to be tested than women who received prenatal care before its implementation (96% vs. 48%); in turn, the likelihood that more high-risk women would be tested for HIV also increased (94% vs. 60%). There was no evidence that mandatory testing with right of refusal would deter women from being tested for HIV. When the moment comes for a woman to make her decision, other concerns are more important to her than whether the option to be tested is mandatory or not. The majority of pregnant women indicated that their main reasons for being tested were: (a) the recommendation of their health-care provider; and (b) concern about the risks to their babies. Recommending that all pregnant women be tested regardless of their risk of infection, together with making the HIV test readily available to all women, are probably the two best ways of increasing the patients' participation in an HIV-screening program for pregnant women. ^

THE MULTIPLE LOCATION TIME WEIGHTED INDEX: USING PATIENT ACTIVITY SPACES TO CALCULATE PRIMARY CARE SERVICE AREAS

Geographic health planning analyses, such as service area calculations, are hampered by a lack of patient-specific geographic data. Using the limited patient address information in patient management systems, planners analyze patient origin based on home address. But activity space research done sparingly in public health and extensively in non-health related arenas uses multiple addresses per person when analyzing accessibility. Also, health care access research has shown that there are many non-geographic factors that influence choice of provider. Most planning methods, however, overlook non-geographic factors influencing choice of provider, and the limited data mean the analyses can only be related to home address. This research attempted to determine to what extent geography plays a part in patient choice of provider and to determine if activity space data can be used to calculate service areas for primary care providers. During Spring 2008, a convenience sample of 384 patients of a locally-funded Community Health Center in Houston, Texas, completed a survey that asked about what factors are important when he or she selects a health care provider. A subset of this group (336) also completed an activity space log that captured location and time data on the places where the patient regularly goes. Survey results indicate that for this patient population, geography plays a role in their choice of health care provider, but it is not the most important reason for choosing a provider. Other factors for choosing a health care provider such as the provider offering “free or low cost visits”, meeting “all of the patient’s health care needs”, and seeing “the patient quickly” were all ranked higher than geographic reasons. Analysis of the patient activity locations shows that activity spaces can be used to create service areas for a single primary care provider. Weighted activity-space-based service areas have the potential to include more patients in the service area since more than one location per patient is used. Further analysis of the logs shows that a reduced set of locations by time and type could be used for this methodology, facilitating ongoing data collection for activity-space-based planning efforts.

Detecting spatiotemporal clusters of accidental poisoning mortality among Texas counties, U.S., 1980 – 2001

Background Accidental poisoning is one of the leading causes of injury in the United States, second only to motor vehicle accidents. According to the Centers for Disease Control and Prevention, the rates of accidental poisoning mortality have been increasing in the past fourteen years nationally. In Texas, mortality rates from accidental poisoning have mirrored national trends, increasing linearly from 1981 to 2001. The purpose of this study was to determine if there are spatiotemporal clusters of accidental poisoning mortality among Texas counties, and if so, whether there are variations in clustering and risk according to gender and race/ethnicity. The Spatial Scan Statistic in combination with GIS software was used to identify potential clusters between 1980 and 2001 among Texas counties, and Poisson regression was used to evaluate risk differences. Results Several significant (p < 0.05) accidental poisoning mortality clusters were identified in different regions of Texas. The geographic and temporal persistence of clusters was found to vary by racial group, gender, and race/gender combinations, and most of the clusters persisted into the present decade. Poisson regression revealed significant differences in risk according to race and gender. The Black population was found to be at greatest risk of accidental poisoning mortality relative to other race/ethnic groups (Relative Risk (RR) = 1.25, 95% Confidence Interval (CI) = 1.24 – 1.27), and the male population was found to be at elevated risk (RR = 2.47, 95% CI = 2.45 – 2.50) when the female population was used as a reference. Conclusion The findings of the present study provide evidence for the existence of accidental poisoning mortality clusters in Texas, demonstrate the persistence of these clusters into the present decade, and show the spatiotemporal variations in risk and clustering of accidental poisoning deaths by gender and race/ethnicity. By quantifying disparities in accidental poisoning mortality by place, time and person, this study demonstrates the utility of the spatial scan statistic combined with GIS and regression methods in identifying priority areas for public health planning and resource allocation.

Alcohol consumption and breast cancer risk among Hispanic and non-Hispanic women in New Mexico

Breast cancer incidence and mortality rates for Hispanic women are lower than for non-Hispanic white (NHW) women, but recently rates have increased more rapidly among Hispanic women. Many studies have shown a consistent increased breast cancer risk associated with modest or high alcohol intake, but few included Hispanic women. Alcohol consumption and risk of breast cancer was investigated in a New Mexico statewide population-based case-control study. The New Mexico Tumor Registry ascertained women, newly diagnosed with breast cancer (1992–1994) aged 30–74 years. Controls were identified by random digit dialing and were frequency-matched for ethnicity, age-group, and health planning district. In-person interviews of 712 cases and 844 controls were conducted. Data were collected for breast cancer risk factors, including alcohol intake. Recent alcohol intake data was collected for a four-week period, six months prior to interview. Past alcohol intake included information on alcohol consumption at ages 25, 35, and 50. History of alcohol consumption was reported by 81% of cases and 85% of controls. Of these women, 42% of cases and 48% of controls reported recent alcohol intake. Results for past alcohol intake did not show any trend with breast cancer risk, and were nonsignificant. Multivariate-adjusted odds ratios for recent alcohol intake and breast cancer suggested an increased risk at the highest level for both ethnic groups, but estimates were unstable and statistically nonsignificant. Low level of recent alcohol intake (<148 grams/week) was associated with a reduced risk for NHW women (Odds Ratio (OR) = 0.49 95% Confidence Interval (CI) 0.35–0.69). This pattern was independent of hormone-receptor status. The reduced breast cancer risk for low alcohol intake was present for premenopausal (OR = 0.29, 95% CI 0.15–0.56) and postmenopausal NHW women (OR = 0.56, 95% CI 0.35–0.90). The possibility of an increased risk associated with high alcohol intake could not be adequately addressed, because there were few drinkers with more than light to moderate intake, especially among Hispanic women. An alcohol-estrogen link is hypothesized to be the mechanism responsible for increased breast cancer risk, but has not been consistently substantiated. More studies are needed of the underlying mechanism for an association between alcohol intake and breast cancer. ^

The multiple location time weighted index: Using patient activity spaces to calculate primary care service areas

Geographic health planning analyses, such as service area calculations, are hampered by a lack of patient-specific geographic data. Using the limited patient address information in patient management systems, planners analyze patient origin based on home address. But activity space research done sparingly in public health and extensively in non-health related arenas uses multiple addresses per person when analyzing accessibility. Also, health care access research has shown that there are many non-geographic factors that influence choice of provider. Most planning methods, however, overlook non-geographic factors influencing choice of provider, and the limited data mean the analyses can only be related to home address. This research attempted to determine to what extent geography plays a part in patient choice of provider and to determine if activity space data can be used to calculate service areas for primary care providers. ^ During Spring 2008, a convenience sample of 384 patients of a locally-funded Community Health Center in Houston, Texas, completed a survey that asked about what factors are important when he or she selects a health care provider. A subset of this group (336) also completed an activity space log that captured location and time data on the places where the patient regularly goes. ^ Survey results indicate that for this patient population, geography plays a role in their choice of health care provider, but it is not the most important reason for choosing a provider. Other factors for choosing a health care provider such as the provider offering "free or low cost visits", meeting "all of the patient's health care needs", and seeing "the patient quickly" were all ranked higher than geographic reasons. ^ Analysis of the patient activity locations shows that activity spaces can be used to create service areas for a single primary care provider. Weighted activity-space-based service areas have the potential to include more patients in the service area since more than one location per patient is used. Further analysis of the logs shows that a reduced set of locations by time and type could be used for this methodology, facilitating ongoing data collection for activity-space-based planning efforts. ^

A categorical data analysis approach to estimation of demographic characteristics for Texas county populations

The need for timely population data for health planning and Indicators of need has Increased the demand for population estimates. The data required to produce estimates is difficult to obtain and the process is time consuming. Estimation methods that require less effort and fewer data are needed. The structure preserving estimator (SPREE) is a promising technique not previously used to estimate county population characteristics. This study first uses traditional regression estimation techniques to produce estimates of county population totals. Then the structure preserving estimator, using the results produced in the first phase as constraints, is evaluated.^ Regression methods are among the most frequently used demographic methods for estimating populations. These methods use symptomatic indicators to predict population change. This research evaluates three regression methods to determine which will produce the best estimates based on the 1970 to 1980 indicators of population change. Strategies for stratifying data to improve the ability of the methods to predict change were tested. Difference-correlation using PMSA strata produced the equation which fit the data the best. Regression diagnostics were used to evaluate the residuals.^ The second phase of this study is to evaluate use of the structure preserving estimator in making estimates of population characteristics. The SPREE estimation approach uses existing data (the association structure) to establish the relationship between the variable of interest and the associated variable(s) at the county level. Marginals at the state level (the allocation structure) supply the current relationship between the variables. The full allocation structure model uses current estimates of county population totals to limit the magnitude of county estimates. The limited full allocation structure model has no constraints on county size. The 1970 county census age - gender population provides the association structure, the allocation structure is the 1980 state age - gender distribution.^ The full allocation model produces good estimates of the 1980 county age - gender populations. An unanticipated finding of this research is that the limited full allocation model produces estimates of county population totals that are superior to those produced by the regression methods. The full allocation model is used to produce estimates of 1986 county population characteristics. ^