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About the Authors
Nikita S. Politsinsky
7/9, Universitetskaya emb., St. Petersburg, Russia, 199034,
E-mail: nik.polit@mail.ru, st086803@student.spbu.ru
Ilia S. Kuznetsov
7/9, Universitetskaya emb., St. Petersburg, Russia, 199034,
St. Petersburg Research Institute of Phthisiopulmonology, Ministry of Health of the Russian Federation,
2/4, Ligovskij blvd., St. Petersburg, 191036, Russia,
E-mail: ilya.kuznetsov.ilya@gmail.com, st062514@student.spbu.ru
Evgeny A. Panidi
7/9, Universitetskaya emb., St. Petersburg, Russia, 199034,
E-mail: panidi@ya.ru, e.panidi@spbu.ru
Abstract
The article describes an approach to the desktop geographic information system application to optimization and (partial) automation of the spatial and non-spatial medical data processing and analysis when estimating the grid of medical lots in an area of medical service, and when forming (considering current morbidity and demographic indicators) updated boundaries of medical lots. Software tools of the QGIS platform were used to process spatial data (QGIS itself and its extension modules, including the GeoMedic module designed with the participation of the authors for medical statistics data geocoding). All medical statistics data used in the study were previously depersonalized in accordance with the requirements of Russian laws. A methodology prototype is elaborated for preparation, analysis, and processing of the data on patients (potential patients) number in the medical service area, together with spatial data (geographical map) of this area. The data of the St. Petersburg city tuberculosis service were used for experimental purposes in the study. Specialists of the St. Petersburg Research Institute of Phthisiopulmonology are involved in the work. Developed methodological toolset prototype ensures partially automated processing of initial data with the formation of boundaries of the medical lots in the analyzed area of medical service. Formation of the boundaries is carried out taking into account the normative number of patients, which must be ensured for the formed lots. The study was performed on the city scale, on the example of St. Petersburg (Russia). Data analysis was performed for the Moskovsky, Petrogradsky, Primorsky and Nevsky Administrative Districts of St. Petersburg. A grid of medical lot boundaries was formed and mapped in the results of the performed analysis. The methodology prototype proposed in the result of the conducted study can be applied in the analysis of existing schemas of medical lots and their updating.
Keywords
References
- Chistobayev A.I., Semenova Z.A. Medico-geographical mapping in the former USSR and modern Russia. Vestnik of Saint-Petersburg University. Series 7. Geology. Geography, 2013. V. 4. P. 109–112 (in Russian).
- Franch-Pardo I., Napoletano B.M., Rosete-Verges F., Billa L. Spatial analysis and GIS in the study of COVID-19. A review. Science of the Total Environment, 2020. V. 739. Art. 140033. DOI: 10.1016/j.scitotenv.2020.140033.
- Golovanova M.N. Improvement of anti-tuberculosis actions using a computer program for monitoring tuberculosis foci. Dissertation for the degree of PhD of medical sciences. Yaroslavl: Yaroslavl State Medical University, 2020. 137 p. (in Russian).
- Gordon A., Womersley J. The use of mapping in public health and planning health services. Journal of Public Health, 1997. V. 19 (2). P. 139–147. DOI: 10.1093/oxfordjournals.pubmed.a024601.
- Korovka V.G., Galkin V.B., Panidi E.A., Kuznetsov I.S., Bel’tyukov M.V., Sokolovich E.G., Panteleeva O.V., Voronov D.V., Kozlov V.V., Fedorov S.V., Yablonskij P.K. Potential of geoinformation technologies to improve the monitoring of socially significant infections outbreaks. Preventive Medicine, 2021. V. 24 (10). P. 7–13 (in Russian). DOI: 10.17116/profmed2021241017.
- Kuznetsov I., Panidi E., Kolesnikov A., Kikin P., Korovka V., Galkin V. GIS-based infectious disease data management on a city scale, case study of St. Petersburg, Russia. International Archives of the Photogrammetry. Remote Sensing and Spatial Information Sciences, 2020. V. XLIII-B3-2020. P. 1463–1467. DOI: 10.5194/isprs-archives-XLIII-B3-2020-1463-2020.
- Lesnykh S.I., Mel’nikova O.V. Generation of databases and visualization of current epidemiological information for purposes of a medical-ecological monitoring of a region. Geography and Natural Resources, 2019. V. 40 (2). P. 115–121 (in Russian). DOI: 10.1134/S1875372819020033.
- Plieva S.L. Prediction of respiratory tuberculosis relapses in modern conditions. Dissertation for the degree of PhD of Medical Sciences. Moscow: Russian Medical Academy of Continuing Postgraduate Education, 2020. 137 p. (in Russian).
- Schweikart J., Kistemann T. Mapping health and health care [Kartographie der Gesundheit]. Kartographische Nachrichten, 2013. V. 63 (1). P. 3–11 (in German).
- Shulmin A.V. Evaluation of main factors of the medical sector system functioning, according to healthcare managers and therapists in the districts. Siberian Medical Review, 2013. V. 1. P. 78–81 (in Russian).
For citation: Politsinsky N.S., Kuznetsov I.S., Panidi E.A. Automation applied to medical lot delineation, case study of St. Petersburg city. InterCarto. InterGIS. GI support of sustainable development of territories: Proceedings of the International conference. Moscow: MSU, Faculty of Geography, 2023. V. 29. Part 2. P. 29–43. DOI: 10.35595/2414-9179-2023-2-29-29-43 (in Russian)