The utility of rapid participatory appraisal for community assessment, diagnosis, and policy development

The selection of a model to guide the understanding and resolution of community problems is an important issue relating to the foundation of public health practice: assessment, policy development, and assurance. Many assessment models produce a diagnosis of community weaknesses, but fail to promote planning and interventions. Rapid Participatory Appraisal (RPA) is a participatory action research model which regards assessment as the first step in the problem solving process, and claims to achieve assessment and policy development within limited resources of time and money. Literature documenting the fulfillment of these claims, and thereby supporting the utility of the model, is relatively sparse and difficult to obtain. Very few articles discuss the changes resulting from RPA assessments in urban areas, and those that do describe studies conducted outside the U.S.A. ^ This study examines the utility of the RPA model and its underlying theories: systems theory, grounded theory, and principles of participatory change, as illustrated by the case study of a community assessment conducted for the Texas Diabetes Institute (TDI), San Antonio, Texas, and subsequent outcomes. Diabetes has a high prevalence and is a major issue in San Antonio. Faculty and students conducted the assessment by informal collaboration between two nursing and public health assessment courses, providing practical student experiences. The study area was large, and the flexibility of the model tested by its use in contiguous sub-regions, reanalyzing aggregated results for the study area. Official TDI reports, and a mail survey of agency employees, described policy development resulting from community diagnoses revealed by the assessment. ^ The RPA model met the criteria for utility from the perspectives of merit, worth, efficiency, and effectiveness. The RPA model best met the agencies' criteria (merit), met the data needs of TDI in this particular situation (worth), provided valid results within budget, time, and personnel constraints (efficiency), and stimulated policy development by TDI (effectiveness). ^ The RPA model appears to have utility for community assessment, diagnosis, and policy development in circumstances similar to the TDI diabetes study. ^

Development and Characterization of an in vitro Four-species Anaerobic Dental Biofilm Model

Dental caries is the most common chronic disease worldwide. It is characterized by the demineralization of tooth enamel caused by acid produced by cariogenic dental bacteria growing on tooth surfaces, termed bacterial biofilms. Cariogenesis is a complex biological process that is influence by multiple factors and is not attributed to a sole causative agent. Instead, caries is associated with multispecies microbial biofilm communities composed of some bacterial species that directly influence the development of a caries lesion and other species that are seemingly benign but must contribute to the community in an uncharacterized way. Clinical analysis of dental caries and its microbial populations is challenging due to many factors including low sensitivity of clinical measurement tools, variability in saliva chemistry, and variation in the microbiota. Our laboratory has developed an in vitro anaerobic biofilm model for dental carries to facilitate both clinical and basic research-based analyses of the multispecies dynamics and individual factors that contribute to cariogenicity. The rational for development of this system was to improve upon the current models that lack key elements. This model places an emphasis on physiological relevance and ease of maintenance and reproducibility. The uniqueness of the model is based on integrating four critical elements: 1) a biofilm community composed of four distinct and representative species typically associated with dental caries, 2) a semi-defined synthetic growth medium designed to mimic saliva, 3) physiologically relevant biofilm growth substrates, and 4) a novel biofilm reactor device designed to facilitate the maintenance and analysis. Specifically, human tooth sections or hydroxyapatite discs embedded into poly(methyl methacrylate) (PMMA) discs are incubated for an initial 24 hr in a static inverted removable substrate (SIRS) biofilm reactor at 37°C under anaerobic conditions in artificial saliva (CAMM) without sucrose in the presence of 1 X 106 cells/ml of each Actinomyces odontolyticus, Fusobacterium nucleatum, Streptococcus mutans, and Veillonella dispar. During days 2 and 3 the samples are maintained continually in CAMM with various exposures to 0.2% sucrose; all of the discs are transferred into fresh medium every 24 hr. To validate that this model is an appropriate in vitro representation of a caries-associated multispecies biofilm, research aims were designed to test the following overarching hypothesis: an in vitro anaerobic biofilm composed of four species (S. mutans, V. dispar, A. odontolyticus, and F. nucleatum) will form a stable biofilm with a community profile that changes in response to environmental conditions and exhibits a cariogenic potential. For these experiments the biofilms as described above were exposed on days 2 and 3 to either CAMM lacking sucrose (no sucrose), CAMM with 0.2% sucrose (constant sucrose), or were transferred twice a day for 1 hr each time into 0.2% sucrose (intermittent sucrose). Four types of analysis were performed: 1) fluorescence microscopy of biofilms stained with Syto 9 and hexidium idodine to determine the biofilm architecture, 2) quantitative PCR (qPCR) to determine the cell number of each species per cm2, 3) vertical scanning interferometry (VSI) to determine the cariogenic potential of the biofilms, and 4) tomographic pH imaging using radiometric fluorescence microscopy after exposure to pH sensitive nanoparticles to measure the micro-environmental pH. The qualitative and quantitative results reveal the expected dynamics of the community profile when exposed to different sucrose conditions and the cariogenic potential of this in vitro four-species anaerobic biofilm model, thus confirming its usefulness for future analysis of primary and secondary dental caries.

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. ^