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 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.

Considering the Consequences of Child Welfare Service Decisions

The following commentary serves as a response to the article, “That Wall is Around My Heart,” underscoring the importance of decision making in child welfare services. The commentary supports the need for child welfare systems to carefully consider the long-term consequences of various service intervention strategies. Child welfare systems must attend to both the internal external elements of safety, giving special attention to the emotional trauma of child maltreatment and the trauma resulting from removal and placement in alternative care. The commentary supports the need for child welfare systems to provide effective interventions that prevent and respond to child abuse and neglect, as well as break the cyclical nature of child maltreatment, helping ensure the safety of children and families for future generations.

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. ^

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. ^

Distribution of quality adjusted life expectancy: A systems approach

Statistical methods are developed which assess survival data for two attributes; (1) prolongation of life, (2) quality of life. Health state transition probabilities correspond to prolongation of life and are modeled as a discrete-time semi-Markov process. Imbedded within the sojourn time of a particular health state are the quality of life transitions. They reflect events which differentiate perceptions of pain and suffering over a fixed time period. Quality of life transition probabilities are derived from the assumptions of a simple Markov process. These probabilities depend on the health state currently occupied and the next health state to which a transition is made. Utilizing the two forms of attributes the model has the capability to estimate the distribution of expected quality adjusted life years (in addition to the distribution of expected survival times). The expected quality of life can also be estimated within the health state sojourn time making more flexible the assessment of utility preferences. The methods are demonstrated on a subset of follow-up data from the Beta Blocker Heart Attack Trial (BHAT). This model contains the structure necessary to make inferences when assessing a general survival problem with a two dimensional outcome. ^

Corporate decision-making: A case study of decision support systems and financial performance in health care organizations

The purpose of this study was twofold: (1) To describe the relation of the intensity of DSS implementation to financial performance as an empirical exploration of improved performance at the organizational level. (2) To describe the relation of the intensity of DSS implementation to the type of organizational decision culture. A multiple case study design was utilized to compare three groups of paired cases. A pattern matching strategy was applied in this study. Four predictions were specified and compared to the empirical data. A progressively upward trend in the scores was predicted for the following theoretical relationships. (1) The greater the number of DSSs, the higher the sophistication index. (2) The greater the number of DSSs, the higher the financial ratios. (3) The greater the number of DSSs, the higher the culture score. (4) The higher the culture score, the higher the financial ratios. The data did not support any of the predicted trends except the relation between the number of DSSs and the financial ratios. The Income/Revenue ratio indicates the efficiency of a company's operations. One would expect that this ratio would be most affected by the operational and financial decision support systems. The majority of the systems measured in the study supported decisions tangential to the patient service areas. The evidence suggested that the type and number of decision support systems affects the bottom line. ^