Understanding Barriers and Facilitators to Living Kidney Donation Within a Sociotechnical Systems Framework.

The objective of this study was to investigate factors influencing one's decision to become a live kidney donor under the framework of sociotechnical systems, by expanding the focus to include larger organizational influences and technological considerations. Semi-structured interviews were conducted with live kidney donors who donated through University of Louisville Health, Trager Transplant Center, a mid-scale transplant program, in the years 2017 through 2019. The interview transcripts were analyzed for barriers and facilitators to live kidney donation within a sociotechnical system. The most salient facilitators included: having an informative, caring, and available care team; the absence of any negative external pressure toward donating; donating to a family or friend; and the ability to take extra time off work for recovery. The most recurrent barriers included: short/medium-term (<1 year) negative health impacts because of donation; the need to make minor lifestyle changes (e.g., less alcohol consumption) after donation; and mental health deterioration stemming from the donation process. The sociotechnical systems framework promotes a balanced system comprised of social, technical, and environmental subsystems. Assessing the facilitators and barriers from the sociotechnical system perspective revealed the importance of and opportunities for developing strategies to promote integration of technical subsystem, such as social media apps and interactive AI platforms, with social and environmental subsystems to enable facilitators and reduce barriers effectively.

Classifying Comments on Social Media Related to Living Kidney Donation: Machine Learning Training and Validation Study.

BACKGROUND: Living kidney donation currently constitutes approximately a quarter of all kidney donations. There exist barriers that preclude prospective donors from donating, such as medical ineligibility and costs associated with donation. A better understanding of perceptions of and barriers to living donation could facilitate the development of effective policies, education opportunities, and outreach strategies and may lead to an increased number of living kidney donations. Prior research focused predominantly on perceptions and barriers among a small subset of individuals who had prior exposure to the donation process. The viewpoints of the general public have rarely been represented in prior research. OBJECTIVE: The current study designed a web-scraping method and machine learning algorithms for collecting and classifying comments from a variety of online sources. The resultant data set was made available in the public domain to facilitate further investigation of this topic. METHODS: We collected comments using Python-based web-scraping tools from the New York Times, YouTube, Twitter, and Reddit. We developed a set of guidelines for the creation of training data and manual classification of comments as either related to living organ donation or not. We then classified the remaining comments using deep learning. RESULTS: A total of 203,219 unique comments were collected from the above sources. The deep neural network model had 84% accuracy in testing data. Further validation of predictions found an actual accuracy of 63%. The final database contained 11,027 comments classified as being related to living kidney donation. CONCLUSIONS: The current study lays the groundwork for more comprehensive analyses of perceptions, myths, and feelings about living kidney donation. Web-scraping and machine learning classifiers are effective methods to collect and examine opinions held by the general public on living kidney donation.

Quantifying Disparities in Accessibility and Availability of Pediatric Primary Care across Multiple States with Implications for Targeted Interventions.

OBJECTIVE: To quantify disparities in accessibility and availability of pediatric primary care by modeling interventions across multiple states that compare publicly insured versus privately insured children, and urban versus rural communities. DATA SOURCES: Secondary data sources include 2013 National Plan and Provider Enumeration System, 2009 MAX Medicaid claims, 2012 American Community Survey. STUDY DESIGN: The study models accessibility and availability of care for all children in seven states. METHODS: Optimization modeling with access constraints is used to estimate access. Statistical hypothesis testing is used to quantify systematic disparities. PRINCIPAL FINDINGS: California has the best accessibility for privately insured children and Minnesota for publicly insured children. Mississippi has the lowest availability for both populations. Overall, the disparities in availability for pediatric primary care are not as significant as in accessibility. Both rural and urban communities are in need of improvement in accessibility to primary care for publicly insured children, although at varying levels across states. CONCLUSIONS: Disparities in availability are not as significant as disparities in accessibility. Opportunities to improve access to pediatric primary care vary by state. Generating specific recommendations for small areas is critical to enabling health policy decision makers to improvement access.

Disparities in Preventive Dental Care Among Children in Georgia.

INTRODUCTION: We compared access to preventive dental care among low-income children eligible for public dental insurance to access among children with private dental insurance and/or high family income (>400% of the federal poverty level) in Georgia, and the effect of policies toward increasing access to dental care for low-income children. METHODS: We used multiple sources of data (eg, US Census, Georgia Board of Dentistry) to estimate, by census tract, measures of preventive dental care access in 2015 for children aged 0 to 18 years. Measures were percentage of met need, 1-way travel distance to a dentist, and scarcity of dentists. We used an optimization model to estimate access, quantify disparities, and evaluate policies. RESULTS: About 1.5 million children were eligible for public insurance; 600,000 had private insurance and/or high family income. Across census tracts, average met need was 59% for low-income children and 96% for high-income children; for rural census tracts, these values were 33% and 84%, respectively. The average 1-way travel distance for all census tracts was 3.7 miles for high-income and/or privately insured children and 17.2 miles for low-income children; for rural census tracts, these values were 11.6 and 32.9 miles, respectively. Increasing dentists' acceptance of public insurance-eligible children increased met need more in rural areas than in urban areas. To achieve 100% met need in rural tracts, however, an 80% participation rate among dentists would be required. CONCLUSION: Across census tracts, high-income children had better access to preventive dental care than low-income children had. Identifying tracts with disparities in access could result in more efficient allocation of public health dental resources.

Estimating Demand for and Supply of Pediatric Preventive Dental Care for Children and Identifying Dental Care Shortage Areas, Georgia, 2015.

OBJECTIVES: Demand for dental care is expected to outpace supply through 2025. The objectives of this study were to determine the extent of pediatric dental care shortages in Georgia and to develop a general method for estimation that can be applied to other states. METHODS: We estimated supply and demand for pediatric preventive dental care for the 159 counties in Georgia in 2015. We compared pediatric preventive dental care shortage areas (where demand exceeded twice the supply) designated by our methods with dental health professional shortage areas designated by the Health Resources & Services Administration. We estimated caries risk from a multivariate analysis of National Health and Nutrition Examination Survey data and national census data. We estimated county-level demand based on the time needed to perform preventive dental care services and the proportion of time that dentists spend on pediatric preventive dental care services from the Medical Expenditure Panel Survey. RESULTS: Pediatric preventive dental care supply exceeded demand in Georgia in 75 counties: the average annual county-level pediatric preventive dental care demand was 16 866 hours, and the supply was 32 969 hours. We identified 41 counties as pediatric dental care shortage areas, 14 of which had not been designated by the Health Resources & Services Administration. CONCLUSIONS: Age- and service-specific information on dental care shortage areas could result in more efficient provider staffing and geographic targeting.

Projecting the Impact of the Affordable Care Act Provisions on Accessibility and Availability of Primary Care Providers for the Adult Population in Georgia.

OBJECTIVES: To evaluate how met need for accessibility and availability of primary care among nonelderly individuals in Georgia will be affected by the Patient Protection and Affordable Care Act (ACA) over the next 10 years. METHODS: We used a stock-and-flow model to predict the number of available visits from 2013 to 2025, regression models to project needed visits, and an optimization model to estimate met need. The outputs of these models were used to estimate unmet need and the availability and accessibility of primary care. RESULTS: Our findings showed that the number of primary care providers will increase by 9.2% to 11.7% by 2025 and that the number of needed visits will increase by 20%. Under Medicaid expansion, the percentage of met need will increase from 67% to 80%. Accessibility will improve by 20% under expansion, and availability will decrease by 13% to 19% under expansion. CONCLUSIONS: The ACAs' provisions will reduce unmet need and positively affect accessibility while reducing availability in some communities. Increased need because of a larger Medicaid population under Medicaid expansion will not be a significant burden on the privately insured population.

Small-Area Estimation of Spatial Access to Care and Its Implications for Policy.

Local or small-area estimates to capture emerging trends across large geographic regions are critical in identifying and addressing community-level health interventions. However, they are often unavailable due to lack of analytic capabilities in compiling and integrating extensive datasets and complementing them with the knowledge about variations in state-level health policies. This study introduces a modeling approach for small-area estimation of spatial access to pediatric primary care that is data "rich" and mathematically rigorous, integrating data and health policy in a systematic way. We illustrate the sensitivity of the model to policy decision making across large geographic regions by performing a systematic comparison of the estimates at the census tract and county levels for Georgia and California. Our results show the proposed approach is able to overcome limitations of other existing models by capturing patient and provider preferences and by incorporating possible changes in health policies. The primary finding is systematic underestimation of spatial access, and inaccurate estimates of disparities across population and across geography at the county level with respect to those at the census tract level with implications on where to focus and which type of interventions to consider.

Redesigning Organ Allocation Boundaries for Liver Transplantation in the United States.

Geographic disparities in access to and outcomes in transplantation have been a persistent problem widely discussed by transplant researchers and the transplant community. One of the alleged causes of disparities in the United States is administratively determined organ allocation boundaries that limit organ sharing across regions. This paper applies mathematical programming to construct alternative liver allocation boundaries that achieve more geographic equity in access to transplants than the current system. The performance of the optimal boundaries were evaluated and compared to that of current allocation system using discrete event simulation.