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Relevant bibliographies by topics / Medical statistics Medical geography

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Academic literature on the topic 'Medical statistics Medical geography'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Medical statistics Medical geography"

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Gatrell, Tony. "Medical statistics on personal computers." Health & Place 1, no. 2 (June 1995): 130. http://dx.doi.org/10.1016/1353-8292(95)90011-x.

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Kennedy, Susan. "A geographic regression model for medical statistics." Social Science & Medicine 26, no. 1 (January 1988): 119–29. http://dx.doi.org/10.1016/0277-9536(88)90051-2.

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Kalantarzadeh, Mohammad Mehdi, and Mukaddes Fasli. "Branding Cities Through Medical Tourism." Open House International 43, no. 2 (June 1, 2018): 83–92. http://dx.doi.org/10.1108/ohi-02-2018-b0012.

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This study investigates the relationship between medical tourism and city branding as the main purpose. To this end, two measurement scales (questionnaires) were designed and validated. The data obtained through the questionnaires was analyzed using descriptive statistics, exploratory and confirmatory factor analyses as well as structural equation modeling via SPSS and LISREL. The study was done in the city of Shiraz which is a famous tourist destination worldwide and has high capacities in medical tourism as well. Participants included scholars and managerial staff in the fields of medical tourism and city branding. The results confirmed that the four underlying factors of medical tourism include medical facilities and services, doctors and staff, tourism factors and costs. Furthermore, city branding was confirmed to be comprised of three underlying factors including place and facilities, opportunities, and residents and culture. Finally, according to the findings from Structural Equation Modelling (SEM) method of assessment branding in shiraz it was found that medical tourism affects directly on city branding.

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Ruben, I., Cristina Preda, and Liliana Ana Tuta. "Spatial statistics detect clustering patterns of kidney diseases in south-eastern Romania." ARS Medica Tomitana 22, no. 1 (February 1, 2016): 10–14. http://dx.doi.org/10.1515/arsm-2016-0002.

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Abstract Medical geography was conceptualized almost ten years ago due to its obvious usefulness in epidemiological research. Still, numerous diseases in many regions were neglected in these aspects of research, and the prevalence of kidney diseases in Eastern Europe is such an example. We evaluated the spatial patterns of main kidney diseases in south-eastern Romania, and highlighted the importance of spatial modeling in medical management in Romania. We found two statistically significant hotspots of kidney diseases prevalence. We also found differences in the spatial patterns between categories of diseases. We propose to speed up the process of creating a national database of records on kidney diseases. Offering the researchers access to a national database will allow further epidemiology studies in Romania and finally lead to a better management of medical services.

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Rusanova, Nina E. "Assisted reproductive technologies in Russia: medical breakthroughs and social problems." Population and Economics 4, no. 4 (December 31, 2020): 5–18. http://dx.doi.org/10.3897/popecon.4.e58271.

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The paper considers the role of assisted reproductive technologies in changing the qualitative and quantitative characteristics of Russian fertility. The author analyzes the quantitative and qualitative characteristics of ART in Russia in 1986-2020, their public perception and current problems based on the data of national ART registers, reviews of international professional associations of reproductive specialists, secondary sociological information and hospital statistics. The general trend is the expansion of the geography of reproductive centers and the diversification of services, but the growing demand for ART as methods of alternative conception in the absence of reproductive disorders intensifies public debate around them, especially in the context of religion and children’s health. Nevertheless, in conditions of low fertility, the state considers ART as instruments of a pronatal demographic policy and finances them even against the backdrop of the coronavirus pandemic.

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Musa, George J., Po-Huang Chiang, Tyler Sylk, Rachel Bavley, William Keating, Bereketab Lakew, Hui-Chen Tsou, and Christina W. Hoven. "Use of GIS Mapping as a Public Health Tool–-From Cholera to Cancer." Health Services Insights 6 (January 2013): HSI.S10471. http://dx.doi.org/10.4137/hsi.s10471.

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The field of medical geographic information systems (Medical GIS) has become extremely useful in understanding the bigger picture of public health. The discipline holds a substantial capacity to understand not only differences, but also similarities in population health all over the world. The main goal of marrying the disciplines of medical geography, public health and informatics is to understand how countless health issues impact populations, and the trends by which these populations are affected. From the 1990s to today, this practical approach has become a valued and progressive system in analyzing medical and epidemiological phenomena ranging from cholera to cancer. The instruments supporting this field include geographic information systems (GIS), disease surveillance, big data, and analytical approaches like the Geographical Analysis Machine (GAM), Dynamic Continuous Area Space Time Analysis (DYCAST), cellular automata, agent-based modeling, spatial statistics and self-organizing maps. The positive effects on disease mapping have proven to be tremendous as these instruments continue to have a great impact on the mission to improve worldwide health care. While traditional uses of GIS in public health are static and lacking real-time components, implementing a space-time animation in these instruments will be monumental as technology and data continue to grow.

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Nordt, Sean Patrick, Lisa E. Vivero, and Daniel Joseph. "Differences in Medical Marijuana Card Registration by Geographic Regions in California." Californian Journal of Health Promotion 14, no. 1 (May 1, 2016): 69–73. http://dx.doi.org/10.32398/cjhp.v14i1.1866.

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California was the first state to legalize marijuana for medical purposes. Individuals are issued identification card for medical marijuana use for patients with chronic medical conditions if directed by a physician. Surveys have suggested there are regional differences between northern California residents, particularly the Bay Area, and southern California residents with respect to medical marijuana use and legalization. The purpose of this paper is to discuss the California Department of Public Health statistics regarding medical marijuana card registrations over various geographic areas of California. The California Department of Public Health Medical Marijuana Card Registration database reflects this with more individuals in the Bay Area registered compared to those in the ten southernmost counties of California. The reasons for this are unclear but could fall along political party lines as more registered Democrats support medical marijuana legalization compared to registered Republicans. The benefits of medical marijuana and potential adverse medical and socioeconomic effects have not been fully elucidated. Conclusion: There is a need for further study of the potential benefits for medical marijuana use and the reasons for apparent regional different beliefs of medical marijuana use and medical marijuana card registration.

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ALONSO, NIVALDO, BENJAMIN B. MASSENBURG, RAFAEL GALLI, LUCAS SOBRADO, and DARIO BIROLINI. "Surgery in Brazilian Health Care: funding and physician distribution." Revista do Colégio Brasileiro de Cirurgiões 44, no. 2 (April 2017): 202–7. http://dx.doi.org/10.1590/0100-69912017002016.

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ABSTRACT Objective: to analyze demographic Brazilian medical data from the national public healthcare system (SUS), which provides free universal health coverage for the entire population, and discuss the problems revealed, with particular focus on surgical care. Methods: data was obtained from public healthcare databases including the Medical Demography, the Brazilian Federal Council of Medicine, the Brazilian Institute of Geography and Statistics, and the National Database of Healthcare Establishments. Density and distribution of the medical workforce and healthcare facilities were calculated, and the geographic regions were analyzed using the public private inequality index. Results: Brazil has an average of two physicians for every 1,000 inhabitants, who are unequally distributed throughout the country. There are 22,276 board certified general surgeons in Brazil (11.49 for every 100,000 people). The country currently has 257 medical schools, with 25,159 vacancies for medical students each year, with only around 13,500 vacancies for residency. The public private inequality index is 3.90 for the country, and ranges from 1.63 in the Rio de Janeiro up to 12.06 in Bahia. Conclusions: A significant part of the local population still faces many difficulties in accessing surgical care, particularly in the north and northeast of the country, where there are fewer hospitals and surgeons. Physicians and surgeons are particularly scarce in the public health system nationwide, and better incentives are needed to ensure an equal public and private workforce.

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Silva, Ana Carolina Vieira da, Dannielle Fernandes Godoi, and Fabricio Souza Neves. "Medical schools in Brasil: population, economic and historical analysis." Revista da Associação Médica Brasileira 66, no. 2 (February 2020): 194–200. http://dx.doi.org/10.1590/1806-9282.66.2.194.

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SUMMARY BACKGROUND To describe the current distribution and historical evolution of undergraduate courses in medicine in Brasil. METHODS Analytical cross-sectional study of secondary data. Through the Ministry of Education, the data of the medical courses were obtained, and through the Brazilian Institute of Geography and Statistics, the population and economic data of the Brazilian states were obtained. RESULTS In Brasil, there were 298 medical courses (1,42 courses / million inhabitants) in January 2018, totaling 31,126 vacancies per year, with 9,217 gratuitous vacancies (29.6%) and 17,963 vacancies in the hinterland (57, 7%). In Brazilian states, there are positive and statistically significant (p <0.001) correlations of the variables: “vacancies” and “population” (R 0.92); “vacancies” and “gross domestic product” (“GDP”) (R 0.83); “percentage of vacancies in the hinterland” and “population in the hinterland” (R 0.71) and “percentage of vacancies in the hinterland” and “GDP” (R 0.64). There was a negative and statistically significant correlation between “gratuitous vacancy percentage” and “GDP” (R -0.54, p = 0.003). More paid courses than gratuitous courses and more courses in the hinterland than in the capitals have been created since 1964, in proportions that have remained similar since then, but in higher numbers since 2002. CONCLUSIONS The distribution of medical courses in Brasil correlates with the population and economical production of each state. The expansion of Brazilian medical education, which has been accelerated since 2002, is based mainly on paid courses in the hinterland, in the same pattern since 1964.

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Kuznetsov, I., E. Panidi, A. Kolesnikov, P. Kikin, V. Korovka, and V. Galkin. "GIS-BASED INFECTIOUS DISEASE DATA MANAGEMENT ON A CITY SCALE, CASE STUDY OF ST. PETERSBURG, RUSSIA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 22, 2020): 1463–67. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-1463-2020.

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Abstract. Medical geography and medical cartography can be denoted as classical application domains for Geographical Information Systems (GISs). GISs can be applied to retrospective analysis (e.g., human population health analysis, medical infrastructure development and availability assessment, etc.), and to operative disaster detection and management (e.g., monitoring of epidemics development and infectious diseases spread). Nevertheless, GISs still not a daily-used instrument of medical administrations, especially on the city and municipality scales. In different regions of the world situation varies, however in general case GIS-based medical data accounting and management is the object of interest for researchers and national administrations operated on global and national scales. Our study is focused onto the investigation and design of the methodology and software prototype for GIS-based support of medical administration and planning on a city scale when accounting and controlling infectious diseases. The study area is the administrative territory of the St. Petersburg (Russia). The study is based upon the medical statistics data and data collection system of the St. Petersburg city. All the medical data used in the study are impersonalized accordingly to the Russian laws.

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Dissertations / Theses on the topic "Medical statistics Medical geography"

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Kettermann, Anna. "Estimation of Standardized Mortality Ratio in Geographic Epidemiology." Fogler Library, University of Maine, 2004. http://www.library.umaine.edu/theses/pdf/KettermanA2004.pdf.

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Hernandez, Andres M. "Spatial Modeling of the Social Health Determinants Impact on the Epidemiology of Diseases in Low-, Middle-, and High-income Settings." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613686108382205.

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Rytkönen, M. (Mika). "Geographical study on childhood type 1 diabetes mellitus (T1DM) in Finland." Doctoral thesis, University of Oulu, 2004. http://urn.fi/urn:isbn:9514272862.

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Abstract Type 1 diabetes mellitus (T1DM) among children is of a particular importance in Finland, where its incidence is the highest in the world and still increasing. However, the aetiology of T1DM is not fully known. According to current knowledge, both genetic and environmental factors operate together, leading to an attack by the immune system on the insulin-producing beta cells. The purpose of this study was to investigate the geographical variation in the incidence of T1DM among children aged up to 14 years in Finland. Geographical Information Systems (GIS) and Bayesian spatial statistics were applied in a search for unusual spatial patterns and risk factor associations. The incidence of T1DM among children aged up to 14 years showed clear geographical variations in Finland. Living in a rural environment increased the risk for T1DM, and the risk was particularly high among children living in rural heartland areas. There was no association between the variation in T1DM incidence and the zinc and nitrate concentrations of drinking water. A male excess in the incidence of T1DM was seen in the low-incidence areas. The geographical variation in the risk of T1DM was marked only among children aged up to 9 years. Because genetics is a necessary but not a sufficient cause of T1DM, it could be hypothesized that there are some thus far unknown environmental risk factors affecting particularly younger children in Finland. Some of those factors may be related to a rural environment. The geographical variation in the M/F ratio of T1DM was a challenging observation and warrants more analytical study.

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Kim, Hoon. "Bayesian hierarchical spatio-temporal analysis of mortality rates with disease mapping /." free to MU campus, to others for purchase, 1999. http://wwwlib.umi.com/cr/mo/fullcit?p9953872.

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Liu, Lixun. "Exploring ethnic inequalities in cardiovascular disease using Hospital Episode Statistics." Thesis, St Andrews, 2009. http://hdl.handle.net/10023/819.

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Hua, Hairui. "Survival modelling in mathematical and medical statistics." Thesis, University of Birmingham, 2015. http://etheses.bham.ac.uk//id/eprint/5808/.

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An essential aspect of survival analysis is the estimation and prediction of survival probabilities for individuals. For this purpose, mathematical modelling of the hazard rate function is a fundamental issue. This thesis focuses on the novel estimation and application of hazard rate functions in mathematical and medical research. In mathematical research we focus on the development of a semiparametric kernel-based estimate of hazard rate function and a L\(_1\) error optimal kernel hazard rate estimate. In medical research we concentrate on the development and validation of survival models using individual participant data from multiple studies. We also consider how to fit survival models that predict individual response to treatment effectiveness, given IPD from multiple trials.

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Coupal, Louis. "The EM algorithm : an overview with applications to medical data." Thesis, McGill University, 1992. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=56644.

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Owing to their complex design and use of live subjects as experimental units, missing or incomplete data is common place in medical experiments. The great increase in difficulty of maximum likelihood based analysis of incomplete data experiments compared to a similar complete data analysis encourages many medical researchers to ignore cases with missing data in favour of performing a "complete" cases analysis.
The expectation maximization algorithm (EM for short) is often an easily implemented algorithm that provides estimates of parameters in models with missing data. The EM algorithm unifies the theory of maximum likelihood estimation in the context of "missing" data. The general problem of missing data also includes structurally unobservable quantities such as parameters, hyperparameters and latent variables. The nature of its defining steps, the expectation or E-step and the maximization or M-step, gives the user intuitive understanding of the maximization process.
In this Thesis, the EM algorithm is first illustrated through an example borrowed from the field of genetics. The theory of the EM algorithm is formally developed and the special case of exponential families is considered. Issues concerning convergence and inference are discussed. Many examples taken from the medical literature serve to highlight the method's broad spectrum of application in both missing data and unobservable parameter problems.

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Wong, Sik-kwan Francis. "Outcome of a web-based statistic laboratory for teaching and learning of medical statistics." Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B43251687.

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Walker, Stephen Graham. "Bayesian parametric and nonparametric methods with applications in medical statistics." Thesis, Imperial College London, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.307519.

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Tom, Brian Dermot Ming. "Modelling event-history data in the context of medical statistics." Thesis, University of Cambridge, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.624771.

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Books on the topic "Medical statistics Medical geography"

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Bhasin, Veena. People, health, and disease: The Indian scenario. Delhi, India: Kamla-Raj Enterprises, 1994.

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Boussouf, Rabah. Géographie et santé en Algérie. Alger: Office des publications universitaires, 1992.

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Boussouf, Rabah. Géographie et santé en Algérie. Alger: Office des publications universitaires, 1992.

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Zur Geographie von Krebserkrankungen. Regensburg: F. Pustet, 1986.

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al-Wafayāt fī al-Kuwayt wa-ikhtilāf asbābihā, 1970-1975: Dirāsah fī al-jughrāfīyah al-ṭibbīyah. [Kuwait]: Jāmiʻat al-Kuwayt, 1988.

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ʻAzīz, Makkī Muḥammad. al- Wafayāt fī al-Kuwayt wa-ikhtilāf asbābihā, 1970-1975: Dirāsah fī al-jughrāfiyah al-ṭibbīyah. [Kuwait]: Jāmiʻat al-Kuwayt, 1988.

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Eriksen, Michael P. The tobacco atlas. 4th ed. Atlanta, GA: American Cancer Society, 2012.

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Peter, Haggett, ed. Atlas of disease distributions: Analytic approaches to epidemiological data. Oxford, UK: Blackwell Reference, 1992.

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Cliff, A. D. Atlas of disease distributions: Analytic approaches to epidemiological data. Oxford, UK: Basil Blackwell, 1988.

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The diffusion of influenza: Patterns and paradigms. Totowa, N.J: Rowman & Littlefield, 1986.

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Book chapters on the topic "Medical statistics Medical geography"

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Shekhar, Shashi, and Hui Xiong. "Medical Geography." In Encyclopedia of GIS, 652. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_775.

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Riley, James C. "Medical Geography and Medical Climatology." In The Eighteenth-Century Campaign to Avoid Disease, 31–53. London: Palgrave Macmillan UK, 1987. http://dx.doi.org/10.1007/978-1-349-18616-7_2.

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Farewell, Vern T., and Daniel M. Farewell. "Medical Statistics." In International Encyclopedia of Statistical Science, 809–11. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-04898-2_362.

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Grieve, Andrew P. "Medical Statistics." In The Textbook of Pharmaceutical Medicine, 189–218. Oxford, UK: Blackwell Publishing Ltd., 2013. http://dx.doi.org/10.1002/9781118532331.ch9.

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HK, Ramakrishna. "Introduction." In Medical Statistics, 1–2. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1923-4_1.

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HK, Ramakrishna. "Model Example." In Medical Statistics, 167–77. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1923-4_10.

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HK, Ramakrishna. "My Journey from a Rural Surgeon to an Author." In Medical Statistics, 3–8. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1923-4_2.

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HK, Ramakrishna. "Understanding Biostatistics, Probability, and Tests of Significance." In Medical Statistics, 9–19. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1923-4_3.

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HK, Ramakrishna. "Understanding Basic Statistical Terms." In Medical Statistics, 21–34. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1923-4_4.

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HK, Ramakrishna. "Tests of Significance." In Medical Statistics, 35–66. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1923-4_5.

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Conference papers on the topic "Medical statistics Medical geography"

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Lei, Tianhu. "Statistics of MR signals: revisited." In Medical Imaging, edited by Jiang Hsieh and Michael J. Flynn. SPIE, 2007. http://dx.doi.org/10.1117/12.709388.

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Whiting, Bruce R., and Edward Muka. "Image quantization: statistics and modeling." In Medical Imaging '98, edited by James T. Dobbins III and John M. Boone. SPIE, 1998. http://dx.doi.org/10.1117/12.317025.

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Wu, Kenong, Steven Schreiner, Brent Mittelstadt, and Leland Witherspoon. "Image segmentation by gradient statistics." In Medical Imaging '98, edited by Kenneth M. Hanson. SPIE, 1998. http://dx.doi.org/10.1117/12.310885.

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Whiting, Bruce R. "Fundamental statistics of the imaging process." In Medical Imaging 1995, edited by Richard L. Van Metter and Jacob Beutel. SPIE, 1995. http://dx.doi.org/10.1117/12.208373.

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Xu, Shun, Martin Styner, John Gilmore, and Guido Gerig. "Multivariate longitudinal statistics for neonatal-pediatric brain tissue development." In Medical Imaging, edited by Joseph M. Reinhardt and Josien P. W. Pluim. SPIE, 2008. http://dx.doi.org/10.1117/12.773966.

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Li, Dunling, and Murray Loew. "Closed-form compression noise in images with known statistics." In Medical Imaging, edited by Miguel P. Eckstein and Yulei Jiang. SPIE, 2005. http://dx.doi.org/10.1117/12.596017.

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Abbey, Craig K., Jascha N. Sohl-Dickstein, Bruno A. Olshausen, Miguel P. Eckstein, and John M. Boone. "Higher-order scene statistics of breast images." In SPIE Medical Imaging, edited by Berkman Sahiner and David J. Manning. SPIE, 2009. http://dx.doi.org/10.1117/12.813797.

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Lee, Sangyeol, Michael D. Abramoff, and Joseph M. Reinhardt. "Retinal atlas statistics from color fundus images." In SPIE Medical Imaging, edited by Benoit M. Dawant and David R. Haynor. SPIE, 2010. http://dx.doi.org/10.1117/12.843714.

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Whiting, Bruce R. "Signal statistics in x-ray computed tomography." In Medical Imaging 2002, edited by Larry E. Antonuk and Martin J. Yaffe. SPIE, 2002. http://dx.doi.org/10.1117/12.465601.

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Nillesen, Maartje M., Richard G. P. Lopata, Inge H. Gerrits, Livia Kapusta, Henkjan H. Huisman, Johan M. Thijssen, and Chris L. de Korte. "Three-dimensional segmentation of the heart muscle using image statistics." In Medical Imaging, edited by Stanislav Emelianov and William F. Walker. SPIE, 2006. http://dx.doi.org/10.1117/12.653259.

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Reports on the topic "Medical statistics Medical geography"

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Altonen, Brian. Asiatic cholera and dysentery on the Oregon Trail : a historical medical geography study. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.6189.

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Gindi, Renee. Health, United States, 2019. Centers for Disease Control and Prevention (U.S.), 2021. http://dx.doi.org/10.15620/cdc:100685.

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Health, United States, 2019 is the 43rd report on the health status of the nation and is submitted by the Secretary of the Department of Health and Human Services to the President and the Congress of the United States in compliance with Section 308 of the Public Health Service Act. This report was compiled by the National Center for Health Statistics (NCHS) of the Centers for Disease Control and Prevention (CDC). The Health, United States series presents an annual overview of national trends in key health indicators. The 2019 report presents trends and current information on selected measures of morbidity, mortality, health care utilization and access, health risk factors, prevention, health insurance, and personal health care expenditures in a 20-figure chartbook. The Health, United States, 2019 Chartbook is supplemented by several other products including Trend Tables, an At-a-Glance table, and Appendixes available for download on the Health, United States website at: https://www.cdc.gov/nchs/hus/ index.htm. The Health, United States, 2019 Chartbook contains 20 figures and 20 tables on health and health care in the United States. Examining trends in health informs the development, implementation, and evaluation of health policies and programs. The first section (Figures 1–13) focuses on health status and determinants: life expectancy, infant mortality, selected causes of death, overdose deaths, suicide, maternal mortality, teen births, preterm births, use of tobacco products, asthma, hypertension, heart disease and cancer, and functional limitations. The second section (Figures 14–15) presents trends in health care utilization: use of mammography and colorectal tests and unmet medical needs. The third section (Figures 16–17) focuses on health care resources: availability of physicians and dentists. The fourth section (Figures 18–20) describes trends in personal health care expenditures, health insurance coverage, and supplemental insurance coverage among Medicare beneficiaries. The Highlights section summarizes major findings from the Chartbook. Suggested citation: National Center for Health Statistics. Health, United States, 2019. Hyattsville, MD. 2021.

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Brown, Yolanda, Twonia Goyer, and Maragaret Harvey. Heart Failure 30-Day Readmission Frequency, Rates, and HF Classification. University of Tennessee Health Science Center, December 2020. http://dx.doi.org/10.21007/con.dnp.2020.0002.

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30 Day Hospital Readmission Rates, Frequencies, and Heart Failure Classification for Patients with Heart Failure Background Congestive heart failure (CHF) is the leading cause of mortality, morbidity, and disability worldwide among patients. Both the incidence and the prevalence of heart failure are age dependent and are relatively common in individuals 40 years of age and older. CHF is one of the leading causes of inpatient hospitalization readmission in the United States, with readmission rates remaining above the 20% goal within 30 days. The Center for Medicare and Medicaid Services imposes a 3% reimbursement penalty for excessive readmissions including those who are readmitted within 30 days from prior hospitalization for heart failure. Hospitals risk losing millions of dollars due to poor performance. A reduction in CHF readmission rates not only improves healthcare system expenditures, but also patients’ mortality, morbidity, and quality of life. Purpose The purpose of this DNP project is to determine the 30-day hospital readmission rates, frequencies, and heart failure classification for patients with heart failure. Specific aims include comparing computed annual re-admission rates with national average, determine the number of multiple 30-day re-admissions, provide descriptive data for demographic variables, and correlate age and heart failure classification with the number of multiple re-admissions. Methods A retrospective chart review was used to collect hospital admission and study data. The setting occurred in an urban hospital in Memphis, TN. The study was reviewed by the UTHSC Internal Review Board and deemed exempt. The electronic medical records were queried from July 1, 2019 through December 31, 2019 for heart failure ICD-10 codes beginning with the prefix 150 and a report was generated. Data was cleaned such that each patient admitted had only one heart failure ICD-10 code. The total number of heart failure admissions was computed and compared to national average. Using age ranges 40-80, the number of patients re-admitted withing 30 days was computed and descriptive and inferential statistics were computed using Microsoft Excel and R. Results A total of 3524 patients were admitted for heart failure within the six-month time frame. Of those, 297 were re-admitted within 30 days for heart failure exacerbation (8.39%). An annual estimate was computed (16.86%), well below the national average (21%). Of those re-admitted within 30 days, 50 were re-admitted on multiple occasions sequentially, ranging from 2-8 re-admissions. The median age was 60 and 60% male. Due to the skewed distribution (most re-admitted twice), nonparametric statistics were used for correlation. While graphic display of charts suggested a trend for most multiple re-admissions due to diastolic dysfunction and least number due to systolic heart failure, there was no statistically significant correlation between age and number or multiple re-admissions (Spearman rank, p = 0.6208) or number of multiple re-admissions and heart failure classification (Kruskal Wallis, p =0.2553).

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