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Submission: On December 06 via api from US — Scanned from CH
Submission: On December 06 via api from US — Scanned from CH
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<option value="acoustics"> Acoustics </option>
<option value="amh"> Acta Microbiologica Hellenica (AMH) </option>
<option value="actuators"> Actuators </option>
<option value="admsci"> Administrative Sciences </option>
<option value="adolescents"> Adolescents </option>
<option value="arm"> Advances in Respiratory Medicine (ARM) </option>
<option value="aerobiology"> Aerobiology </option>
<option value="aerospace"> Aerospace </option>
<option value="agriculture"> Agriculture </option>
<option value="agriengineering"> AgriEngineering </option>
<option value="agrochemicals"> Agrochemicals </option>
<option value="agronomy"> Agronomy </option>
<option value="ai"> AI </option>
<option value="air"> Air </option>
<option value="algorithms"> Algorithms </option>
<option value="allergies"> Allergies </option>
<option value="alloys"> Alloys </option>
<option value="analytica"> Analytica </option>
<option value="analytics"> Analytics </option>
<option value="anatomia"> Anatomia </option>
<option value="anesthres"> Anesthesia Research </option>
<option value="animals"> Animals </option>
<option value="antibiotics"> Antibiotics </option>
<option value="antibodies"> Antibodies </option>
<option value="antioxidants"> Antioxidants </option>
<option value="applbiosci"> Applied Biosciences </option>
<option value="applmech"> Applied Mechanics </option>
<option value="applmicrobiol"> Applied Microbiology </option>
<option value="applnano"> Applied Nano </option>
<option value="applsci"> Applied Sciences </option>
<option value="asi"> Applied System Innovation (ASI) </option>
<option value="appliedchem"> AppliedChem </option>
<option value="appliedmath"> AppliedMath </option>
<option value="aquacj"> Aquaculture Journal </option>
<option value="architecture"> Architecture </option>
<option value="arthropoda"> Arthropoda </option>
<option value="arts"> Arts </option>
<option value="astronomy"> Astronomy </option>
<option value="atmosphere"> Atmosphere </option>
<option value="atoms"> Atoms </option>
<option value="audiolres"> Audiology Research </option>
<option value="automation"> Automation </option>
<option value="axioms"> Axioms </option>
<option value="bacteria"> Bacteria </option>
<option value="batteries"> Batteries </option>
<option value="behavsci"> Behavioral Sciences </option>
<option value="beverages"> Beverages </option>
<option value="BDCC"> Big Data and Cognitive Computing (BDCC) </option>
<option value="biochem"> BioChem </option>
<option value="bioengineering"> Bioengineering </option>
<option value="biologics"> Biologics </option>
<option value="biology"> Biology </option>
<option value="blsf"> Biology and Life Sciences Forum </option>
<option value="biomass"> Biomass </option>
<option value="biomechanics"> Biomechanics </option>
<option value="biomed"> BioMed </option>
<option value="biomedicines"> Biomedicines </option>
<option value="biomedinformatics"> BioMedInformatics </option>
<option value="biomimetics"> Biomimetics </option>
<option value="biomolecules"> Biomolecules </option>
<option value="biophysica"> Biophysica </option>
<option value="biosensors"> Biosensors </option>
<option value="biotech"> BioTech </option>
<option value="birds"> Birds </option>
<option value="blockchains"> Blockchains </option>
<option value="brainsci"> Brain Sciences </option>
<option value="buildings"> Buildings </option>
<option value="businesses"> Businesses </option>
<option value="carbon"> C </option>
<option value="cancers"> Cancers </option>
<option value="cardiogenetics"> Cardiogenetics </option>
<option value="catalysts"> Catalysts </option>
<option value="cells"> Cells </option>
<option value="ceramics"> Ceramics </option>
<option value="challenges"> Challenges </option>
<option value="ChemEngineering"> ChemEngineering </option>
<option value="chemistry"> Chemistry </option>
<option value="chemproc"> Chemistry Proceedings </option>
<option value="chemosensors"> Chemosensors </option>
<option value="children"> Children </option>
<option value="chips"> Chips </option>
<option value="civileng"> CivilEng </option>
<option value="cleantechnol"> Clean Technologies (Clean Technol.) </option>
<option value="climate"> Climate </option>
<option value="ctn"> Clinical and Translational Neuroscience (CTN) </option>
<option value="clinbioenerg"> Clinical Bioenergetics </option>
<option value="clinpract"> Clinics and Practice </option>
<option value="clockssleep"> Clocks & Sleep </option>
<option value="coasts"> Coasts </option>
<option value="coatings"> Coatings </option>
<option value="colloids"> Colloids and Interfaces </option>
<option value="colorants"> Colorants </option>
<option value="commodities"> Commodities </option>
<option value="complications"> Complications </option>
<option value="compounds"> Compounds </option>
<option value="computation"> Computation </option>
<option value="csmf"> Computer Sciences & Mathematics Forum </option>
<option value="computers"> Computers </option>
<option value="condensedmatter"> Condensed Matter </option>
<option value="conservation"> Conservation </option>
<option value="constrmater"> Construction Materials </option>
<option value="cmd"> Corrosion and Materials Degradation (CMD) </option>
<option value="cosmetics"> Cosmetics </option>
<option value="covid"> COVID </option>
<option value="crops"> Crops </option>
<option value="cryo"> Cryo </option>
<option value="cryptography"> Cryptography </option>
<option value="crystals"> Crystals </option>
<option value="cimb"> Current Issues in Molecular Biology (CIMB) </option>
<option value="curroncol"> Current Oncology </option>
<option value="dairy"> Dairy </option>
<option value="data"> Data </option>
<option value="dentistry"> Dentistry Journal </option>
<option value="dermato"> Dermato </option>
<option value="dermatopathology"> Dermatopathology </option>
<option value="designs"> Designs </option>
<option value="diabetology"> Diabetology </option>
<option value="diagnostics"> Diagnostics </option>
<option value="dietetics"> Dietetics </option>
<option value="digital"> Digital </option>
<option value="disabilities"> Disabilities </option>
<option value="diseases"> Diseases </option>
<option value="diversity"> Diversity </option>
<option value="dna"> DNA </option>
<option value="drones"> Drones </option>
<option value="ddc"> Drugs and Drug Candidates (DDC) </option>
<option value="dynamics"> Dynamics </option>
<option value="earth"> Earth </option>
<option value="ecologies"> Ecologies </option>
<option value="econometrics"> Econometrics </option>
<option value="economies"> Economies </option>
<option value="education"> Education Sciences </option>
<option value="electricity"> Electricity </option>
<option value="electrochem"> Electrochem </option>
<option value="electronicmat"> Electronic Materials </option>
<option value="electronics"> Electronics </option>
<option value="ecm"> Emergency Care and Medicine </option>
<option value="encyclopedia"> Encyclopedia </option>
<option value="endocrines"> Endocrines </option>
<option value="energies"> Energies </option>
<option value="esa"> Energy Storage and Applications (ESA) </option>
<option value="eng"> Eng </option>
<option value="engproc"> Engineering Proceedings </option>
<option value="entropy"> Entropy </option>
<option value="eesp"> Environmental and Earth Sciences Proceedings </option>
<option value="environments"> Environments </option>
<option value="epidemiologia"> Epidemiologia </option>
<option value="epigenomes"> Epigenomes </option>
<option value="ebj"> European Burn Journal (EBJ) </option>
<option value="ejihpe"> European Journal of Investigation in Health, Psychology and Education (EJIHPE) </option>
<option value="fermentation"> Fermentation </option>
<option value="fibers"> Fibers </option>
<option value="fintech"> FinTech </option>
<option value="fire"> Fire </option>
<option value="fishes"> Fishes </option>
<option value="fluids"> Fluids </option>
<option value="foods"> Foods </option>
<option value="forecasting"> Forecasting </option>
<option value="forensicsci"> Forensic Sciences </option>
<option value="forests"> Forests </option>
<option value="fossstud"> Fossil Studies </option>
<option value="foundations"> Foundations </option>
<option value="fractalfract"> Fractal and Fractional (Fractal Fract) </option>
<option value="fuels"> Fuels </option>
<option value="future"> Future </option>
<option value="futureinternet"> Future Internet </option>
<option value="futurepharmacol"> Future Pharmacology </option>
<option value="futuretransp"> Future Transportation </option>
<option value="galaxies"> Galaxies </option>
<option value="games"> Games </option>
<option value="gases"> Gases </option>
<option value="gastroent"> Gastroenterology Insights </option>
<option value="gastrointestdisord"> Gastrointestinal Disorders </option>
<option value="gastronomy"> Gastronomy </option>
<option value="gels"> Gels </option>
<option value="genealogy"> Genealogy </option>
<option value="genes"> Genes </option>
<option value="geographies"> Geographies </option>
<option value="geohazards"> GeoHazards </option>
<option value="geomatics"> Geomatics </option>
<option value="geometry"> Geometry </option>
<option value="geosciences"> Geosciences </option>
<option value="geotechnics"> Geotechnics </option>
<option value="geriatrics"> Geriatrics </option>
<option value="glacies"> Glacies </option>
<option value="gucdd"> Gout, Urate, and Crystal Deposition Disease (GUCDD) </option>
<option value="grasses"> Grasses </option>
<option value="hardware"> Hardware </option>
<option value="healthcare" selected="selected"> Healthcare </option>
<option value="hearts"> Hearts </option>
<option value="hemato"> Hemato </option>
<option value="hematolrep"> Hematology Reports </option>
<option value="heritage"> Heritage </option>
<option value="histories"> Histories </option>
<option value="horticulturae"> Horticulturae </option>
<option value="hospitals"> Hospitals </option>
<option value="humanities"> Humanities </option>
<option value="humans"> Humans </option>
<option value="hydrobiology"> Hydrobiology </option>
<option value="hydrogen"> Hydrogen </option>
<option value="hydrology"> Hydrology </option>
<option value="hygiene"> Hygiene </option>
<option value="immuno"> Immuno </option>
<option value="idr"> Infectious Disease Reports </option>
<option value="informatics"> Informatics </option>
<option value="information"> Information </option>
<option value="infrastructures"> Infrastructures </option>
<option value="inorganics"> Inorganics </option>
<option value="insects"> Insects </option>
<option value="instruments"> Instruments </option>
<option value="iic"> Intelligent Infrastructure and Construction </option>
<option value="ijerph"> International Journal of Environmental Research and Public Health (IJERPH) </option>
<option value="ijfs"> International Journal of Financial Studies (IJFS) </option>
<option value="ijms"> International Journal of Molecular Sciences (IJMS) </option>
<option value="IJNS"> International Journal of Neonatal Screening (IJNS) </option>
<option value="ijpb"> International Journal of Plant Biology (IJPB) </option>
<option value="ijt"> International Journal of Topology </option>
<option value="ijtm"> International Journal of Translational Medicine (IJTM) </option>
<option value="ijtpp"> International Journal of Turbomachinery, Propulsion and Power (IJTPP) </option>
<option value="ime"> International Medical Education (IME) </option>
<option value="inventions"> Inventions </option>
<option value="IoT"> IoT </option>
<option value="ijgi"> ISPRS International Journal of Geo-Information (IJGI) </option>
<option value="J"> J </option>
<option value="jal"> Journal of Ageing and Longevity (JAL) </option>
<option value="jcdd"> Journal of Cardiovascular Development and Disease (JCDD) </option>
<option value="jcto"> Journal of Clinical & Translational Ophthalmology (JCTO) </option>
<option value="jcm"> Journal of Clinical Medicine (JCM) </option>
<option value="jcs"> Journal of Composites Science (J. Compos. Sci.) </option>
<option value="jcp"> Journal of Cybersecurity and Privacy (JCP) </option>
<option value="jdad"> Journal of Dementia and Alzheimer's Disease (JDAD) </option>
<option value="jdb"> Journal of Developmental Biology (JDB) </option>
<option value="jeta"> Journal of Experimental and Theoretical Analyses (JETA) </option>
<option value="jfb"> Journal of Functional Biomaterials (JFB) </option>
<option value="jfmk"> Journal of Functional Morphology and Kinesiology (JFMK) </option>
<option value="jof"> Journal of Fungi (JoF) </option>
<option value="jimaging"> Journal of Imaging (J. Imaging) </option>
<option value="jintelligence"> Journal of Intelligence (J. Intell.) </option>
<option value="jlpea"> Journal of Low Power Electronics and Applications (JLPEA) </option>
<option value="jmmp"> Journal of Manufacturing and Materials Processing (JMMP) </option>
<option value="jmse"> Journal of Marine Science and Engineering (JMSE) </option>
<option value="jmahp"> Journal of Market Access & Health Policy (JMAHP) </option>
<option value="jmp"> Journal of Molecular Pathology (JMP) </option>
<option value="jnt"> Journal of Nanotheranostics (JNT) </option>
<option value="jne"> Journal of Nuclear Engineering (JNE) </option>
<option value="ohbm"> Journal of Otorhinolaryngology, Hearing and Balance Medicine (JOHBM) </option>
<option value="jop"> Journal of Parks </option>
<option value="jpm"> Journal of Personalized Medicine (JPM) </option>
<option value="jpbi"> Journal of Pharmaceutical and BioTech Industry (JPBI) </option>
<option value="jor"> Journal of Respiration (JoR) </option>
<option value="jrfm"> Journal of Risk and Financial Management (JRFM) </option>
<option value="jsan"> Journal of Sensor and Actuator Networks (JSAN) </option>
<option value="joma"> Journal of the Oman Medical Association (JOMA) </option>
<option value="jtaer"> Journal of Theoretical and Applied Electronic Commerce Research (JTAER) </option>
<option value="jvd"> Journal of Vascular Diseases (JVD) </option>
<option value="jox"> Journal of Xenobiotics (JoX) </option>
<option value="jzbg"> Journal of Zoological and Botanical Gardens (JZBG) </option>
<option value="journalmedia"> Journalism and Media </option>
<option value="kidneydial"> Kidney and Dialysis </option>
<option value="kinasesphosphatases"> Kinases and Phosphatases </option>
<option value="knowledge"> Knowledge </option>
<option value="labmed"> LabMed </option>
<option value="laboratories"> Laboratories </option>
<option value="land"> Land </option>
<option value="languages"> Languages </option>
<option value="laws"> Laws </option>
<option value="life"> Life </option>
<option value="limnolrev"> Limnological Review </option>
<option value="lipidology"> Lipidology </option>
<option value="liquids"> Liquids </option>
<option value="literature"> Literature </option>
<option value="livers"> Livers </option>
<option value="logics"> Logics </option>
<option value="logistics"> Logistics </option>
<option value="lubricants"> Lubricants </option>
<option value="lymphatics"> Lymphatics </option>
<option value="make"> Machine Learning and Knowledge Extraction (MAKE) </option>
<option value="machines"> Machines </option>
<option value="macromol"> Macromol </option>
<option value="magnetism"> Magnetism </option>
<option value="magnetochemistry"> Magnetochemistry </option>
<option value="marinedrugs"> Marine Drugs </option>
<option value="materials"> Materials </option>
<option value="materproc"> Materials Proceedings </option>
<option value="mca"> Mathematical and Computational Applications (MCA) </option>
<option value="mathematics"> Mathematics </option>
<option value="medsci"> Medical Sciences </option>
<option value="msf"> Medical Sciences Forum </option>
<option value="medicina"> Medicina </option>
<option value="medicines"> Medicines </option>
<option value="membranes"> Membranes </option>
<option value="merits"> Merits </option>
<option value="metabolites"> Metabolites </option>
<option value="metals"> Metals </option>
<option value="meteorology"> Meteorology </option>
<option value="methane"> Methane </option>
<option value="mps"> Methods and Protocols (MPs) </option>
<option value="metrics"> Metrics </option>
<option value="metrology"> Metrology </option>
<option value="micro"> Micro </option>
<option value="microbiolres"> Microbiology Research </option>
<option value="micromachines"> Micromachines </option>
<option value="microorganisms"> Microorganisms </option>
<option value="microplastics"> Microplastics </option>
<option value="minerals"> Minerals </option>
<option value="mining"> Mining </option>
<option value="modelling"> Modelling </option>
<option value="mmphys"> Modern Mathematical Physics </option>
<option value="molbank"> Molbank </option>
<option value="molecules"> Molecules </option>
<option value="mti"> Multimodal Technologies and Interaction (MTI) </option>
<option value="muscles"> Muscles </option>
<option value="nanoenergyadv"> Nanoenergy Advances </option>
<option value="nanomanufacturing"> Nanomanufacturing </option>
<option value="nanomaterials"> Nanomaterials </option>
<option value="ndt"> NDT </option>
<option value="network"> Network </option>
<option value="neuroglia"> Neuroglia </option>
<option value="neurolint"> Neurology International </option>
<option value="neurosci"> NeuroSci </option>
<option value="nitrogen"> Nitrogen </option>
<option value="ncrna"> Non-Coding RNA (ncRNA) </option>
<option value="nursrep"> Nursing Reports </option>
<option value="nutraceuticals"> Nutraceuticals </option>
<option value="nutrients"> Nutrients </option>
<option value="obesities"> Obesities </option>
<option value="oceans"> Oceans </option>
<option value="onco"> Onco </option>
<option value="optics"> Optics </option>
<option value="oral"> Oral </option>
<option value="organics"> Organics </option>
<option value="organoids"> Organoids </option>
<option value="osteology"> Osteology </option>
<option value="oxygen"> Oxygen </option>
<option value="parasitologia"> Parasitologia </option>
<option value="particles"> Particles </option>
<option value="pathogens"> Pathogens </option>
<option value="pathophysiology"> Pathophysiology </option>
<option value="pediatrrep"> Pediatric Reports </option>
<option value="pets"> Pets </option>
<option value="pharmaceuticals"> Pharmaceuticals </option>
<option value="pharmaceutics"> Pharmaceutics </option>
<option value="pharmacoepidemiology"> Pharmacoepidemiology </option>
<option value="pharmacy"> Pharmacy </option>
<option value="philosophies"> Philosophies </option>
<option value="photochem"> Photochem </option>
<option value="photonics"> Photonics </option>
<option value="phycology"> Phycology </option>
<option value="physchem"> Physchem </option>
<option value="psf"> Physical Sciences Forum </option>
<option value="physics"> Physics </option>
<option value="physiologia"> Physiologia </option>
<option value="plants"> Plants </option>
<option value="plasma"> Plasma </option>
<option value="platforms"> Platforms </option>
<option value="pollutants"> Pollutants </option>
<option value="polymers"> Polymers </option>
<option value="polysaccharides"> Polysaccharides </option>
<option value="populations"> Populations </option>
<option value="poultry"> Poultry </option>
<option value="powders"> Powders </option>
<option value="proceedings"> Proceedings </option>
<option value="processes"> Processes </option>
<option value="prosthesis"> Prosthesis </option>
<option value="proteomes"> Proteomes </option>
<option value="psychiatryint"> Psychiatry International </option>
<option value="psychoactives"> Psychoactives </option>
<option value="psycholint"> Psychology International </option>
<option value="publications"> Publications </option>
<option value="qubs"> Quantum Beam Science (QuBS) </option>
<option value="quantumrep"> Quantum Reports </option>
<option value="quaternary"> Quaternary </option>
<option value="radiation"> Radiation </option>
<option value="reactions"> Reactions </option>
<option value="realestate"> Real Estate </option>
<option value="receptors"> Receptors </option>
<option value="recycling"> Recycling </option>
<option value="rsee"> Regional Science and Environmental Economics (RSEE) </option>
<option value="religions"> Religions </option>
<option value="remotesensing"> Remote Sensing </option>
<option value="reports"> Reports </option>
<option value="reprodmed"> Reproductive Medicine (Reprod. Med.) </option>
<option value="resources"> Resources </option>
<option value="rheumato"> Rheumato </option>
<option value="risks"> Risks </option>
<option value="robotics"> Robotics </option>
<option value="ruminants"> Ruminants </option>
<option value="safety"> Safety </option>
<option value="sci"> Sci </option>
<option value="scipharm"> Scientia Pharmaceutica (Sci. Pharm.) </option>
<option value="sclerosis"> Sclerosis </option>
<option value="seeds"> Seeds </option>
<option value="sensors"> Sensors </option>
<option value="separations"> Separations </option>
<option value="sexes"> Sexes </option>
<option value="signals"> Signals </option>
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<option value="122795">2nd Edition of Advances in Otolaryngology from Diagnosis to Treatment</option>
<option value="119771">2nd Edition of Cancer in Human Health and Healthcare</option>
<option value="177998">2nd Edition of Health and Well-Being in Vulnerable Communities</option>
<option value="120231">2nd Edition of Neurorehabilitation: Looking Back and Moving Forward</option>
<option value="177278">2nd Edition of Perspective Expanded: Healthcare from the Perspectives of Patients, Actors, and System Users</option>
<option value="152619">2nd Edition: Pregnancy and Perinatal Health</option>
<option value="177608">Access to Healthcare for Vulnerable and Minority Populations: A Global Perspective</option>
<option value="150913">Active Brain and Body with Lifetime Physical Exercise for a Healthier Aging</option>
<option value="113252">Acute and Chronic Adaptations to Exercise and Complementary Interventions</option>
<option value="179838">Adolescent Mental Health: Current Issues and New Perspectives</option>
<option value="220642">Adolescent Substance Use: Prevention and Intervention Strategies</option>
<option value="116346">Adolescent-to-Parent Violence: Psychological and Contextual Influences</option>
<option value="187940">Adolescents’ Psychological Well-Being in Contemporary Contexts: The Role of the Parental Characteristics and Relationships</option>
<option value="120419">Advanced Treatment and Care Management in Dementia: From Bench to Bedside</option>
<option value="187735">Advancements and Challenges in Contemporary Endodontics: A Call for Evidence-Based Practice and Enhanced Education</option>
<option value="185301">Advancements in Virtual Reality Applications for Rehabilitation in Chronic Illness</option>
<option value="172191">Advances and Innovations in the Management of Spinal Conditions and Disorders</option>
<option value="221293">Advances in Ageing Care: Spotlight on the Role of Physical Activity</option>
<option value="124450">Advances in Cancer Rehabilitation</option>
<option value="224809">Advances in Health Technology Assessment in the Era of AI and Data Science</option>
<option value="121309">Advances in Neuroimmunology and Neuroinflammation</option>
<option value="165898">Advances in Oral Diseases with Orofacial Manifestations</option>
<option value="83582">Advances in Otolaryngology from Diagnosis to Treatment</option>
<option value="170479">Advances in Physical Therapy for the Lumbar–Pelvic–Femoral Complex</option>
<option value="147666">Advances in Sexual Health, Sexual Functioning, and Psychological Well-Being</option>
<option value="151166">Advances in Traditional and eHealth Research on Non-communicable Diseases</option>
<option value="31256">Advances, Caveats And Conundrums in Diabetes</option>
<option value="220527">Advancing Quality of Life Research in Cancer Survivors: Methodologies, Instruments, and Practical Applications</option>
<option value="219928">Adversarial Learning and Its Applications in Healthcare</option>
<option value="38240">Ageing Effects on Kinematics, Kinetics and Balance</option>
<option value="8181">Ageing with Chronic Disease and Disability</option>
<option value="20123">Aging, Chronic Disease, and the Impact of Long Term Care</option>
<option value="140815">Allergy and Immunology in Healthcare</option>
<option value="91455">Anesthetic Management of COVID-19, Post-COVID-19, and Long COVID-19 Patients</option>
<option value="208176">Applied Precision Medicine in Clinical Practice—an Approach Consisting of More Than Molecular Biology and Gene Therapy</option>
<option value="163489">Artificial Intelligence for Clinical Application</option>
<option value="20826">Asphyxia and Resuscitation in Neonates</option>
<option value="84156">Auditory Processing Disorder: A Forgotten Hearing Impairment</option>
<option value="116064">Behavioral Sleep Medicine: Current Research Developments</option>
<option value="157146">Beyond Healthcare Malaise: Risk and Protective Factors for Patients and Professionals Dealing with COVID-19, Acute and Chronic Diseases</option>
<option value="181772">Biological and Clinical Aspects of the Treatment of Schizophrenia and Related Disorders: Second Edition</option>
<option value="116150">Biological and Clinical Aspects on the Treatment of Schizophrenia and Related Disorders</option>
<option value="223178">Biological vs. Chronological Age: Clinical Implications in Multiple Sclerosis</option>
<option value="132732">Biomechanics and Motor Control of Gait and Postural Control</option>
<option value="145780">Biotechnology & Orthopaedics. Work in Progress—IORS 2023 Meeting Proceedings</option>
<option value="127181">Body Misperceptions: When the Brain and Body Don’t Match</option>
<option value="158531">Breast Cancer Diagnosis, Prevention and Control</option>
<option value="9681">Burden of Obesity on Health, Wellbeing and Healthcare</option>
<option value="66581">Burnout and Suicide in Healthcare and Allied Professionals</option>
<option value="200259">Burnout, Anxiety, and Depression in Healthcare Students</option>
<option value="82158">Cancer in Human Health and Healthcare</option>
<option value="134742">Cancer Patients: How to Improve Quality of Life?</option>
<option value="105306">Cardiovascular Diseases: Education, Prevention and Treatment</option>
<option value="156284">Cardiovascular Health and Stroke</option>
<option value="13713">Care of People Living with Dementia</option>
<option value="165926">Care Transition in Rare and Complex Diseases</option>
<option value="176790">Caring for Patients with Cirrhosis of the Liver</option>
<option value="15065">Chronic Obstructive Pulmonary Disease: Updates in Lung Health</option>
<option value="13094">Chronic Pain and the Aging Population</option>
<option value="154434">Clinical Advances in Chronic Pain Management</option>
<option value="101450">Clinical Epidemiology and Biostatistics for Health Sciences</option>
<option value="151450">Clinical Psychology: The State of the Art</option>
<option value="6001">Clinical Trials in Antenatal and Intrapartum Care</option>
<option value="36801">Cohort Study of Oral Health</option>
<option value="222607">Collaborative Approaches to Risk Management and Infection Control: A Patient Perspective</option>
<option value="210080">Common Sports Injuries and Rehabilitation</option>
<option value="159076">Comprehensive Alcohol-HIV/AIDS Research</option>
<option value="51446">Consequences of Stroke</option>
<option value="197577">Contemporary Oral and Dental Health Care: Issues and Challenges</option>
<option value="208197">Contemporary Treatment and Management of Periodontal and Peri-Implant Diseases and Conditions</option>
<option value="177207">COVID-19 Impact on Women and Gender Equality</option>
<option value="119465">COVID-19 in Healthcare and Educational Facilities: Epidemiology, Perceptions, Transmission, Prevention, and Potential Therapeutic Opportunities</option>
<option value="97987">COVID-19 in Medicine</option>
<option value="20010">Creating Age-friendly Communities: Housing and Technology</option>
<option value="167768">Current Advances in Cardiovascular Disease</option>
<option value="177132">Current and Future Directions in the Prevention and Treatment of HPV-Related Diseases</option>
<option value="115322">Current Perspectives on the Treatment of Binge Eating Disorder</option>
<option value="39763">Decision Modelling for Healthcare Evaluation</option>
<option value="164838">Dementia Caregivers’ Wellbeing—Challenges and Opportunities</option>
<option value="177976">Dementia Research: Epidemiology and Public Health</option>
<option value="12511">Dental Care</option>
<option value="224145">Depression, Anxiety and Emotional Problems Among Healthcare Workers</option>
<option value="211769">Depression: Recognizing and Addressing Mental Health Challenges</option>
<option value="111602">Detection and Prevention of Mild Cognitive Impairment and Dementia</option>
<option value="173997">Detection, Prevention and Management of Oral Health Issues</option>
<option value="163326">Developing People, Improving Processes, and including New Technologies for Better Public Health</option>
<option value="4136">Developmental Origins of Health and Disease (DOHaD): Implications for Health Policy and Health Promotion</option>
<option value="153254">Diagnosis and Management of Bone Diseases</option>
<option value="17766">Diagnosis and Treatment for Gynecologic Cancers</option>
<option value="177632">Diagnosis and Treatment of Dermatological Diseases</option>
<option value="4671">Diet Quality</option>
<option value="12512">Dietary Management of Obesity</option>
<option value="133713">Diving and Hyperbaric Physiology, Safety, and Medical Healthcare</option>
<option value="86715">Diving Physiology and Medical Healthcare</option>
<option value="36016">DNA Damage/Repair and Cancer</option>
<option value="141181">Early Life Determinants of Physical and Mental Health</option>
<option value="171137">Eating Disorders and Weight-Related Problems</option>
<option value="193769">Educational Practices for Health in Physical Education and Psychomotricity</option>
<option value="177665">Effect of Physical Activity, Sedentary, and Fitness on Cognitive Function and Well-Being</option>
<option value="208818">Effective Strategies for Promoting Mental Health: Global Perspectives and Innovations</option>
<option value="151277">Effects of Medicare and Medicaid</option>
<option value="176368">Effects of Physical Activity on Physical and Psychological Health</option>
<option value="127922">Effects of Prescribed Exercise and Physical Activity Interventions on Health Indices and Disease Improvement</option>
<option value="147529">Effects of Regular Swimming Exercise on Health Promotion</option>
<option value="203262">Effects of Self-Control and Motivation in Exercise and Physical Health</option>
<option value="161993">Effects of Strength Training on Rehabilitation</option>
<option value="188717">Emergency Medical Service: Challenges for Occupational Medicine and Public Health</option>
<option value="166357">Emerging Developments in Integrative and Complementary Medicine: Advancements in Research and Clinical Applications</option>
<option value="181752">Emerging Risk Factors Associated with Public Health</option>
<option value="174686">Enhancement of Public Health Professionals via Digital Transformation Competencies</option>
<option value="207237">Epidemiological Approaches to Infectious Diseases: From Endemics to Pandemics</option>
<option value="165565">Epidemiology, Conservative Management, and Outcome Measures in Sports Medicine Injuries</option>
<option value="146139">Epilepsy and Sleep Disorders</option>
<option value="19322">Epilepsy Surgery</option>
<option value="4385">Ethics, Health, and Natural Resources</option>
<option value="221680">Evaluation of Interventions for Health Systems and Services Strengthening</option>
<option value="138519">Excess Weight, Dietary Habits and Physical Activity in Children and Adolescents</option>
<option value="172119">Exercise and Its Role in Health and Disease</option>
<option value="157194">Exercise Biomechanics: Pathways to Improve Health</option>
<option value="190116">Exercise Interventions and Testing for Effective Health Promotion</option>
<option value="133248">Exercise Physiology and Its Performance in Health and Disease</option>
<option value="12640">Feature Papers in Healthcare in 2018</option>
<option value="23764">Feature Papers in Healthcare in 2019</option>
<option value="219071">Frailty and Resilience Across Aging: A Holistic Approach to Elderly Care</option>
<option value="155553">Frailty: Epidemiology, Risk Factors and Management</option>
<option value="96382">From Bench to Bed: Kidney Stones Still Challenge Healthcare in 2022</option>
<option value="167513">From Bench to Bed: The Challenge of Kidney Stones in Healthcare</option>
<option value="152628">From Heart to Brain: Innovations in Cardiovascular and Stroke Research</option>
<option value="5624">Frontiers in Medical Science Education</option>
<option value="129493">Functional Neurological Disorders: Pandemic and Beyond</option>
<option value="20345">Genetics of Metabolic Bone Diseases</option>
<option value="126734">Gynecological Cancer Diagnosis and Prevention</option>
<option value="194168">Health and Readiness of Tactical Populations</option>
<option value="147084">Health Economics, Social Policy and Healthcare Management</option>
<option value="8848">Health Effects of Race, Gender, Class, and Place: Complexities and Heterogeneities</option>
<option value="190867">Health Promotion Effectiveness in Prisons: Proposal of New Organizational Models, Clinical Practice and Forensic Considerations</option>
<option value="210183">Health Promotion, Disease Prevention, and Chronic Illness Experience: A Health Psychology Perspective</option>
<option value="4745">Health Psychology in Healthcare Settings</option>
<option value="189833">Health Service Interventions in Musculoskeletal Disorders</option>
<option value="137034">Health, Safety, and Readiness of Tactical Populations</option>
<option value="197396">Healthcare Advances in Trauma and Orthopaedic Surgery</option>
<option value="219831">Healthcare Disparities in Screening, Detection and Treatment of Cancer</option>
<option value="220822">Healthcare for Immigrants and Refugees</option>
<option value="106411">Healthcare in Digital Environments: An Interdisciplinary Perspective</option>
<option value="41435">Healthcare Resource Management in Large-Scale Epidemics</option>
<option value="29868">Heart Disease in Cancer Patients and Cancer in Patients with Heart Disease: How to Manage This Double Problem?</option>
<option value="218168">High-Risk Behavior: Impacts on the Health and Wellness of Young People</option>
<option value="175054">History, Philosophy and Ethical Perspectives on Healthcare</option>
<option value="3995">Holistic Needs of Those Living with and beyond Breast Cancer</option>
<option value="13825">Human Health and Bacteria</option>
<option value="8604">Humanities and Healthcare</option>
<option value="115551">Identifying Biomarkers of Mental Health Vulnerability</option>
<option value="47649">Imagination in Autism</option>
<option value="4151">Implementation of Public Health Genomics</option>
<option value="98699">Improving Athletes’ Performance and Avoiding Health Issues</option>
<option value="204531">Improving Primary Care through Healthcare Education</option>
<option value="127473">Inequality in Health Systems and Health Outcomes</option>
<option value="118821">Infectious Disease Prevention and Public Health Promotion</option>
<option value="116754">Information Technologies Applied on Healthcare</option>
<option value="170385">Innovation in Healthcare Education</option>
<option value="141307">Innovations and New Technologies in Obstructive Sleep Apnea and Snoring in the Era of Precision Medicine</option>
<option value="160927">Innovations in Doctors’ Healthcare in Acute Public Hospital Systems</option>
<option value="148840">Innovations in Forensic Medicine</option>
<option value="4015">Innovations in Measuring and Improving Patient Care Experiences</option>
<option value="175495">Innovations in Sport, Physical Activity and Exercise from Childhood to Older Adult</option>
<option value="213754">Innovations in Therapy for Exercise Rehabilitation</option>
<option value="132846">Innovative Approaches to HPV Vaccination Promotion</option>
<option value="172384">Innovative Treatments for Post-traumatic Stress Disorder (PTSD)</option>
<option value="30852">Insights into Stroke Care</option>
<option value="173992">Instruments and Measures for Health, Education, and Sport Research</option>
<option value="222100">Integrating Health Informatics and Artificial Intelligence for Advanced Medicine</option>
<option value="212063">Integrative Health: Advances in Pilates, Holistic Techniques, and Complementary Therapies in Special Populations</option>
<option value="179142">Intelligent Systems for One Digital Health</option>
<option value="177984">Intelligent Systems for Prevention and Diagnosis in the Digital Health Era</option>
<option value="143017">International Dermatology: Prevention and Treatment of Skin Diseases around the Globe</option>
<option value="199351">Interpersonal Violence among Adolescents</option>
<option value="160000">Intrinsic Capacity and Healthy Aging</option>
<option value="177793">Job Satisfaction and Stress among Healthcare Workers</option>
<option value="161591">Leisure Time, Physical Activity, and Physical Fitness: Perspectives on Healthy Aging</option>
<option value="184461">Leveraging ChatGPT and Large Language Models for Healthcare Advancements: Applications, Challenges, and Future Directions</option>
<option value="130507">LGBTQI+ Health</option>
<option value="190574">Looking Backwards: How Has COVID-19 Affected Oncological Patients?</option>
<option value="5102">Low Back Pain: Recent Advances And Perspectives</option>
<option value="90347">Lung Cancer Treatment & Advanced Therapies</option>
<option value="17477">Lyme Disease and Related Tickborne Infections</option>
<option value="12587">Lyme Disease: The Role of Big Data, Companion Diagnostics and Precision Medicine</option>
<option value="145118">Machine Learning Technology in Medical Image Processing</option>
<option value="18780">Management of Obstructive Sleep Apnoea</option>
<option value="221961">Measures to Improving Medication Adherence in Cardiovascular Medicine</option>
<option value="121084">Measuring and Improving Quality of Life in the Medical and Psychological Healthcare Context</option>
<option value="140112">Measuring Biomechanical Loads in Sports and Physical Activity</option>
<option value="80778">Medicaid and Public Health</option>
<option value="172664">Medical Management of Glaucoma</option>
<option value="2512">Melanoma and Neoplasms of Skin</option>
<option value="92957">Mental and Behavioral Healthcare</option>
<option value="159736">Mental Health and Stigma of Sexual Minorities</option>
<option value="146381">Mental Health and Well-Being among Adolescents: Current Evidence and Future Directions</option>
<option value="174946">Mental Health and Well-Being of Sexual and Gender Minority Groups: Risk and Protective Factors</option>
<option value="119250">Mental Health and Well-Being on School Campus in the Post-pandemic Era</option>
<option value="167474">Mental Health at a Personal, Communal, and Social Level</option>
<option value="159160">Mental Health in Healthcare Workers</option>
<option value="92822">Mental Health Matters: From Cross-Cultural Perspectives</option>
<option value="182241">Mental Health of Health Care Workers in the COVID-19 Era</option>
<option value="20446">Mental Health Stigma in Healthcare Settings</option>
<option value="83809">Metabolic Syndrome and Mental Illness</option>
<option value="17951">Military Mental Health</option>
<option value="214500">Mindfulness-Based Intervention for People with Psychiatric Disorders</option>
<option value="181173">Modern Dentistry: New Discoveries and Patient-Related Approaches</option>
<option value="153405">Musculoskeletal Disorders, Physical Therapy and Rehabilitation</option>
<option value="175091">Music: Social Impact and Health Benefits</option>
<option value="19352">N-of-1 Trials in Healthcare</option>
<option value="189668">Neurological and Developmental Disorders in Children: Rehabilitation and Care</option>
<option value="159357">Neuromuscular Fatigue and Recovery in Sports Health</option>
<option value="82108">Neuroprotective Strategies for Dry Eye Disease</option>
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<option value="156282">The Endodontic Space</option>
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<option value="207406">The Latest Advances in Visual Health</option>
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<option value="187349">Young Carers: Prevention, Intervention, Services and Outcomes—What Works?</option>
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<option value="encyclopedia">Encyclopedia</option>
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<option value="epigenomes">Epigenomes</option>
<option value="ebj">European Burn Journal</option>
<option value="ejihpe">European Journal of Investigation in Health, Psychology and Education</option>
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<option value="ijerph">International Journal of Environmental Research and Public Health</option>
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<option value="jcm">Journal of Clinical Medicine</option>
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<option value="jlpea">Journal of Low Power Electronics and Applications</option>
<option value="jmmp">Journal of Manufacturing and Materials Processing</option>
<option value="jmse">Journal of Marine Science and Engineering</option>
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<option value="jmp">Journal of Molecular Pathology</option>
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<option value="ohbm">Journal of Otorhinolaryngology, Hearing and Balance Medicine</option>
<option value="jop">Journal of Parks</option>
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<option value="proteomes">Proteomes</option>
<option value="psychiatryint">Psychiatry International</option>
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<option value="qubs">Quantum Beam Science</option>
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<option value="solids">Solids</option>
<option value="spectroscj">Spectroscopy Journal</option>
<option value="sports">Sports</option>
<option value="standards">Standards</option>
<option value="stats">Stats</option>
<option value="stresses">Stresses</option>
<option value="surfaces">Surfaces</option>
<option value="surgeries">Surgeries</option>
<option value="std">Surgical Techniques Development</option>
<option value="sustainability">Sustainability</option>
<option value="suschem">Sustainable Chemistry</option>
<option value="symmetry">Symmetry</option>
<option value="synbio">SynBio</option>
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<option value="targets">Targets</option>
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Open AccessArticle
GEOGRAPHIC MEDICAL OVERVIEW OF NONCOMMUNICABLE DISEASES (CARDIOVASCULAR DISEASES
AND DIABETES) IN THE TERRITORY OF THE AP VOJVODINA (NORTHERN SERBIA)
by
Emina Kričković
Emina Kričković
SciProfiles Scilit Preprints.org Google Scholar
1,*,
Tin Lukić
Tin Lukić
SciProfiles Scilit Preprints.org Google Scholar
2 and
Dejana Jovanović-Popović
Dejana Jovanović-Popović
SciProfiles Scilit Preprints.org Google Scholar
3
1
Faculty of Geography, University of Belgrade, Studentski Square 3/III, 11000
Belgrade, Serbia
2
Department of Geography, Tourism and Hotel Management, Faculty of Sciences,
University of Novi Sad, Dositeja Obradovića Square 3, 21000 Novi Sad, Serbia
3
The Faculty of Security Studies, University of Belgrade, 50 Gospodara Vučića
Street, 11118 Belgrade, Serbia
*
Author to whom correspondence should be addressed.
Healthcare 2023, 11(1), 48; https://doi.org/10.3390/healthcare11010048
Submission received: 13 October 2022 / Revised: 6 December 2022 / Accepted: 20
December 2022 / Published: 23 December 2022
(This article belongs to the Section Environmental Factors and Global Health)
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ABSTRACT
The objective of this study was a geographic medical analysis of noncommunicable
diseases (cardiovascular diseases from 2010 to 2020 and diabetes from 2010 to
2019) in the AP Vojvodina (northern Serbia) in order to identify the most and
least burdened counties as well as to present trends in the mentioned diseases.
The Mann-Kendall trend test, a cluster analysis, and Getis–Ord Gi* method for
hot spot analysis were applied in this analysis. Regarding acute coronary
syndrome and myocardial infarction, the North Backa County had a lower mortality
rate although the number of newly reported cases was above average. The largest
number of new cases of unstable angina pectoris was in the North Backa, North
Banat, and Middle Banat Counties, while the West Backa County was identified as
a county with a higher mortality rate. The cluster analysis showed that the
number of death cases from diabetes in the Srem County is significantly higher
than that in the other counties. Likewise, the West Backa County had a high
number of new diabetes patients, but also a much lower mortality rate. Chronic
noncommunicable diseases are predominant in newly diagnosed incidences and death
cases in the AP Vojvodina. Studies of this kind promote public health and
healthcare systems in the researched area and in the Republic of Serbia, as well
as in other countries.
Keywords:
noncommunicable diseases; AP Vojvodina; cardiovascular diseases; diabetes;
population health; geographic medical overview
1. INTRODUCTION
This work represents a part of the study “The effect of geographic medical
factors on the health of the population of the AP Vojvodina” which can be
regarded as a pioneer research work in the field of the medical geography of
Serbia. The Autonomous Province of Vojvodina (AP Vojvodina, northern Serbia) is
a geospatial entity with specific natural-geographic and social-economic
features and represents the researched area in this work. The aetiology of most
chronic noncommunicable diseases (NCDs) is quite complex and it is very
difficult to identify, with certainty, the factors which cause the
abovementioned diseases. The health of people depends significantly on the
conditions present in the environment and is related to its influences. A great
number of factors, the most common being smoking, inadequate diet habits,
insufficient physical activity, stress, and environmental pollution, influence
the occurrence of noncommunicable diseases. A look back at the health status of
the population in Vojvodina in the second half of the 20th century reveals that
the communicable diseases which used to pose leading health issues in the past
are now under control [1]. The abovementioned factors can be attributed to a
rapid socio-economic development, vaccination, conduction of programs for
combating, eliminating, and eradicating communicable diseases, environmental
recovery, and an increase in health culture. It is also necessary to acknowledge
the fact that certain demographic changes take place—first and foremost, the
ageing of the population, which leads to a higher number of people suffering
from mass noncommunicable diseases [1].
According to WHO’s NCD Portal, in 2019, noncommunicable diseases were
responsible for 74% of total deaths [2]. Furthermore, 72% of all male deaths and
75% of all female deaths were attributed to noncommunicable diseases [2]. In the
same year, the world’s mortality rate of NCDs was 479 per 100,000 population,
out of which the male mortality rate was higher than the female one, i.e., 576
to 397 per 100,000 population, respectively [2]. The mortality rate of NCDs in
Europe, in the same year, was 427.15 per 100,000 population. According to the
WHO report on noncommunicable diseases profiles across countries, the burden is
the greatest within low and middle-income countries, where 78% of all
noncommunicable disease deaths and 85% of premature deaths occurred [3]. The
probability of premature mortality from NCDs in the world in 2019 was 18% and in
Europe it was 16% [2]. The mortality rate overview of noncommunicable diseases
in Europe in 2019 is displayed in Figure 1, and Serbia is in 15th place among 50
countries.
Figure 1. Noncommunicable diseases age-standardized death rates (per 100,000
population) in Europe in 2019. [2].
Analyzing the data from the portal in 2019, one can see that Serbia is above the
world average with NCDs responsible for 95% of total deaths (94% of male and 96%
of female deaths) [2]. Following this, Serbia is also above the world and
European average NCDs mortality rate with over 600 per 100,000 population,
together with male and female NCDs mortality rates, i.e., 716 and 503 per
100,000 population, respectively. In addition, the probability of premature
mortality from NCDs in Serbia is higher than the world average of 22%. [2]
Furthermore, according to WHO’s NCD Data Portal, Serbia had a 600.81
noncommunicable diseases mortality rate in 2019, which is higher than the
European average of 427.15 [2].
Cardiovascular diseases pose a grave global problem nowadays. In 2019, there
were approximately 12.7 million new cases of cardiovascular diseases (CVDs) in
countries with data available in Europe [4]. According to Timmis, A. et al.
(2022), in the same year, Serbia had an incidence rate of 920 per 100,000
population and was in 18th place among 54 countries regarding the CVDs incidence
rate [4].
Maksimovic et al. (1999) researched the relationship between magnesium and
calcium in potable water and CVDs in 65 municipalities in Serbia while Velicki
(2017) researched the relationship between diet and acute coronary syndrome
among patients with this disease in the territory of the AP Vojvodina [5,6].
Poloniecki et al. (1997) and Jevtic et al. (2014) researched the relationship
between air pollution and the occurrence of CVDs in the researched area [7,8].
Diabetes is a disease which represents a major public health issue in today’s
world [9]. Over 90% of patients are diagnosed with type 2 diabetes. The highest
number of people with the abovementioned diabetes type are between 40 and 59
years of age although more and more younger people have recently been diagnosed
with it [9]. Samuelsson, U. and Löfman, O. (2004) within the study “Geographical
mapping of type 1 diabetes in children and adolescents in southeast Sweden”
established geographical variations of risks of type 2 diabetes incidences in
children and adolescents among the municipalities in the region mentioned [10].
Samuelsson, U. et al. (2020) studied geographical variations in the rate of type
1 diabetes incidence in Nordic countries [11]. In the study “Association between
Arsenic in Drinking Water and the Occurrence of Type 2 Diabetes”, Jovanovic D.
(2013) studied the relationship between arsenic concentrations in potable water
in the city of Zrenjanin (AP Vojvodina) and type 2 diabetes cases [12].
According to the International Diabetes Federation’s Diabetes Atlas (IDF
Diabetes Atlas), in 2021, Serbia was among the top five countries in Europe with
the highest age-adjusted prevalence of people with diabetes (20–79 years)—9.1%
[13]. In the same publication, it is stated that 1.1 million deaths in Europe
were caused by diabetes in 2021 [13].
In the AP Vojvodina, in 2019, the standardized incidence rate for type 1
diabetes from 0–29 years was 12.7 and for type 2 diabetes it was 157.6 [14]. In
the same year, the mortality rates of type 1 and type 2 diabetes in the
researched area were 6.0 and 5.9, respectively [14].
The health protection of the AP Vojvodina population is organized within 93
healthcare facilities through the primary, secondary, and tertiary levels [15].
The number of doctors per 1000 population in the province is below the national
and the European average—2.48 [16]. In Europe, there are 3.22 and in Serbia,
there are 2.86 doctors per 1000 population [16]. In the Republic of Serbia, the
registry for coronary heart disease and the registry for diabetes have been
legally established since 1980. The population registry for acute coronary
syndrome was established in 2006 and the reorganization of the registry for
diabetes in Serbia was initiated in the same year [14,17]. These registries are
conducted at the regional level while the database is established, maintained,
and updated at the national level [14,17].
This work will not examine the causes of noncommunicable diseases occurrence
(cardiovascular diseases and diabetes) but will provide a geographic medical
overview of the abovementioned diseases in the territory of the AP Vojvodina.
This overview will be supported by a hot spot analysis of the mentioned
diseases, by the incidence and mortality rates as well as by analyzing the
trends of the diseases using well-known methods such as cluster analysis, the
Mann–-Kendall test, and emerging hot spot analysis using the Getis–Ord Gi*
spatial clustering statistic method.
There are numerous studies describing the geospatial distribution of diseases
using emerging hot spot analysis. For instance, Bahri, M.A.S et al. (2014) in
the study “Comparison of Spatial Autocorrelation Analysis Methods for
Distribution Pattern of Diabetes Type 2 Patients in Iskandar Malaysia
Neighbourhoods” used Getis–Ord G* Statistics to compare diabetes type 2
patients’ data both in global and local scales [18]. Furthermore, Kuznetsov and
Sadovskaya (2021) and Shariati et al. (2020) used hot spot analysis to
understand the spatial distribution of COVID-19 at regional and global scales
[19,20]. McEntee and Ogneva Himmelberger (2008) carried out a hot spot analysis
for the exposure of diesel particulates using a t-test to determine significant
differences between the influence of lung cancer and asthma cases [21]. Razavi
Termeh, S.V. et al. (2021) in the study “Asthma-prone areas modelling using a
machine learning model” used the Getis–Ord Gi* index to map high-risk areas for
asthma [22] and Mahara, G. et al. (2018) identified the hot spots of
tuberculosis incidence in urban districts using space–time cluster analysis of
tuberculosis incidence in Beijing, China [23].
2. MATERIALS AND METHODS
The objective of this work is to research noncommunicable diseases
(cardiovascular diseases and diabetes) in the territory of the AP Vojvodina
(northern Serbia) in order to identify the most burdened counties and the
present trends in the diseases in question.
The data for certain noncommunicable diseases within the research area are not
publicly available for a continuous time period. Therefore, the time frame of
the research mostly depended on the very accessibility of the available data. In
addition, for certain diseases, the records are not published, i.e., the public
is not informed due to a legal regulation which enables relevant institutions to
decide on the scope and continuity of informing the public. The data on the
newly diagnosed and those who have died from cardiovascular diseases were taken
from the publication “Incidence and mortality from acute coronary syndrome in
Serbia” from 2010 to 2020, and this was issued by the registry of acute coronary
syndrome in Serbia and the Institute of Public Health of Serbia “Dr Milan
Jovanovic Batut” [17,24,25,26,27,28,29,30,31,32,33]. Likewise, we used “Health
statistical yearbooks of the Republic of Serbia” from 2010 to 2020 and these
were published by the Institute of Public Health of Serbia “Dr Milan Jovanovic
Batut” [16,34,35,36,37,38,39,40,41,42,43], as well as the publications “Health
condition of the population of the AP Vojvodina” released by the Institute of
Public Health of Vojvodina [15,44,45,46,47,48,49,50,51,52,53,54]. The data on
the number of new patients and the deceased as well as on the incidence and
mortality rates from diabetes were obtained from the publications “Diabetes
incidence and mortality” from 2010 to 2019 [14,55,56,57,58,59,60,61,62,63]
published by the Diabetes Registry of Serbia and the Institute of Public Health
of Serbia “Dr Milan Jovanovic Batut”.
The researched area represents a connection between central and western Europe
on one side and the Near East and the Balkans on the other. The Autonomous
Province of Vojvodina is located in the north of the Republic of Serbia and
boasts a favorable geographical position [64]. It is located between 44°38′ and
46°10′ north latitude and between 18°10′ and 21°15′ east longitude, in
south-eastern Europe, in the Balkans, and comprises the southern part of the
Pannonian Basin [64].
To the north, it is bordered by Hungary, to the east by Romania, to the west by
Croatia, whereas to the south-east it is bordered by Bosnia and Herzegovina, and
to the south by the Danube and Sava rivers [65]. The researched area covers the
surface of 21.506 km2. Bearing in mind the fact that as many as ten European
countries occupy a smaller territory (Slovenia, Montenegro, Cyprus, Luxembourg,
Andorra, Malta, Liechtenstein, San Marino, Monaco, and the Vatican) and
observing the area of Vojvodina, one can conclude that it is a relatively large
entity of regional autonomy [66].
The researched area accounts for 24.3% of the entire surface of the Republic of
Serbia and it has the population of 1.93 million, which constitutes 27.2% of the
entire population number if we do not take into account the territory of the AP
Kosovo and Metohija [66].
In this research the following methods were applied: cluster analysis, the
Mann–-Kendall test, and the cartographic method. For the sake of mapping, the
data on the diseases were used at the county level, whereas for the analysis via
the Mann–Kendall test, the data were used on the level of the province. The
incidence and mortality rates were used for analyzing a disease. The cluster
analysis was applied to identify the most burdened counties and also for the
areas in which the mentioned diseases are less present. The cluster analysis
represents a statistical method of processing the data by means of a number of
operations over the data in order to obtain, transform, or classify the
information within the data set being monitored. The cluster analysis functions
by the principle of organizing the data into groups or clusters in relation to
how these data are related, i.e., grouping the data or the objects in such a way
that the data or objects within the same group are more similar to each other
than to the ones in the other groups/clusters. The cluster analysis serves more
as a research tool than a means of prediction.
With the aim of statistical examination, we will use the k-center method which
is mostly used in the statistical cluster analysis. This algorithm attempts to
find groups by minimizing the distance between the data.
The calculation of the distance between the data is Euclidean, and it is
performed by means of the formula below:
𝑑(𝑥,𝑦)=∑𝑖=12(𝑥𝑖−𝑦𝑖)2−−−−−−−−−−−⎷d(x,y)=∑i=12(xi−yi)2
(1)
in which x = (x1,x2) = (newly diagnosed or dead) is a coordinate point from the
chart, whereas y is a point with different coordinates y = (y1,y2 ).
During the cluster analysis, the statistical program R Studio was used; it
contains the function k means for calculating the k-center and grouping the data
for a given number of clusters. It is quite significant to point out here that
before we apply the k-center (k means) algorithm, we need to assign a number of
clusters. In case we are not sure about the number of clusters, we can apply the
following formula to calculate it:
𝑁𝑜. 𝑜𝑓 𝑐𝑙𝑢𝑠𝑡𝑒𝑟𝑠=𝑛−−√2No. of clusters=n2
(2)
where n stands for the total number of the data obtained.
As a trend test, this study applies the widely used Mann–-Kendall test, a
non-parametric test assuming stable, independent, and random time series with
equal probability distributions [67]. In this research, it will be used in order
to examine the trends in the most frequent noncommunicable diseases in the
studied area where we will also examine which diseases tend to increase/decline.
By means of the abovementioned method, one can estimate how a variable can
change when influenced by another one. The MK test is simple and robust; it can
cope with missing values and values below the detection limit [68]. Starting
from the first test suggestions by Mann– [69] and Kendall [70], the test itself
was later extended for a wider application [71]. The Mann–-Kendall test can be
applied in cases when it can be assumed that for the value of xi time series,
the following model holds true [68]:
𝑆=∑𝑖=2𝑛∑𝑗=1𝑖−1𝑠𝑖𝑔𝑛(𝑥𝑖−𝑥𝑗)S=∑i=2n∑j=1i−1sign(xi−xj)
(3)
where sign 𝑥𝑖−𝑥𝑗xi−xj is:
𝑠𝑖𝑔𝑛(𝑥)=⎧⎩⎨1, 𝑓𝑜𝑟 𝑥𝑖−𝑥𝑗>00, 𝑓𝑜𝑟 𝑥𝑖−𝑥𝑗=0−1, 𝑓𝑜𝑟 𝑥𝑖−𝑥𝑗<0 sign(x)={1, for xi−xj>00, for xi−xj=0−1, for xi−xj<0
(4)
The statistic S tends to normality for a large n, with a mean and a variance
defined as follows [69],
𝐸(𝑆)=0E(S)=0
(5)
𝑉(𝑆)=1/18⎡⎣⎢⎢𝑛(𝑛−1)(2𝑛+5)−∑𝑃=1𝑞𝑡𝑃(𝑡𝑃−1)(2𝑡𝑃+5)⎤⎦⎥⎥V(S)=1/18[n(n−1)(2n+5)−∑P=1qtP(tP−1)(2tP+5)]
(6)
where n is the length of the times-series, 𝑡𝑃tP is the number of ties for the
𝑝𝑡ℎpth value, and q is the number of tied values (i.e., equals values). The
second term represents an adjustment for tied or censored data. The standardized
test statistic Z is given by [72,73]:
𝑍=⎧⎩⎨𝑆−1𝑉𝑎𝑟(𝑆)√0𝑆+1𝑉𝑎𝑟(𝑆)√𝑖𝑓 𝑆>0,𝑖𝑓 𝑆=0,𝑖𝑓 𝑆<0, Z={S−1Var(S)if S>0,0if S=0,S+1Var(S)if S<0,
(7)
The presence of a statistically significant trend is evaluated using the Z
value. This statistic is used to test the null hypothesis such that no trend
exists [72]. A positive Z indicates an increasing trend in the time-series,
while a negative Z indicates a decreasing trend [72]. To test either an
increasing or decreasing monotonic trend at the p significance level, the null
hypothesis is rejected if the absolute value of Z is greater than
𝑍1−𝑃/2Z1−P/2; where 𝑍1−𝑃/2Z1−P/2; is obtained from the standard normal
cumulative distribution tables [72]. According to the MK test, two hypotheses
were tested: the null hypothesis, H0, stating that there is no trend in the time
series; and the alternative hypothesis, Ha, stating that there is a significant
trend in the series for a given significance level limit [68]. The probability,
p, was calculated to determine the level of confidence in the hypothesis. If the
computed p-value is lower than the chosen significance level α (e.g., α = 5%),
the H0 (there is no trend) should be rejected and the Ha (there is a significant
trend) should be accepted; and if the p-value is greater than the significance
level α, then the H0 is accepted (or cannot be rejected) [74]. For calculating
the probability (p) and for hypothesis testing, XLSTAT statistical analysis
software was employed [74].
The cartographic method was applied to examine the geographical distribution of
cardiovascular diseases and diabetes within the researched area. ArcGIS Pro
software (a product of the American ESRI company) was used.
We used the Getis–Ord 𝐺∗𝑖Gi* statistic within the emerging hot spot analysis
(Space Time Pattern Mining) tool to determine the areas characterized as “hot
spots and cold spots” with regard to noncommunicable diseases (cardiovascular
diseases and diabetes). This classifies the spatial patterns into clusters and
outliers where the former can either be positive (hot spots) or negative (cold
spots), while the latter are spatial objects whose attribute values are
distinctly different from those of their spatial neighbors [75]. The space–time
analysis we used in ArcGIS Pro uses a space–time cube (netCDF cube) as an input
and identifies trends in data, such as new, intensifying, diminishing, and
sporadic hot and cold spots. The space–time cube in ArcGIS Pro was made of
defined locations (counties) and it structured time-stamped features into a
netCDF data cube by generating space–time bins with defined features (in this
case counties) with the associated spatiotemporal attributes. By creating this
cube, the statistic values were calculated and the trend for bin values across
time in each county was measured using the Mann–-Kendall statistical test.
This hot spot analysis utilizes the 𝐺∗𝑖Gi* statistic that can be calculated as
follows:
𝐺∗𝑖=∑𝑛𝑗=1𝜔𝑖,𝑗𝑥𝑗−𝑋 ∑𝑛𝑗=1𝜔𝑖,𝑗𝑆𝑛∑𝑛𝑗=1𝜔2𝑖,𝑗−(∑𝑛𝑗=1𝜔𝑖,𝑗)2𝑛−1−−−−−−−−−−−−−−−−√Gi*=∑j=1nωi,jxj−X¯ ∑j=1nωi,jSn∑j=1nωi,j2−(∑j=1nωi,j)2n−1
(8)
where 𝐺∗𝑖Gi* is the spatial autocorrelation (spatial dependency) statistics of
an event i over the n events, the term 𝑥𝑗xj defines the magnitude of the
variable x at the events j over all n, and the term 𝜔𝑖,𝑗ωi,j defines the
weight value between the events i and j that represents their spatial
interrelationship [76,77,78] and:
𝑋=∑𝑛𝑗=1𝑥𝑗𝑛X¯=∑j=1nxjn
(9)
𝑆=∑𝑛𝑗=1𝑥2𝑗𝑛−−−−−−−√−(𝑋)2S=∑j=1nxj2n−(X¯)2
(10)
The 𝐺∗𝑖Gi* statistics consider the magnitude of each feature in the data set
in the context of its neighbours’ values [76,77,78]. The local sum of a feature
and its neighbors has been compared to the sum of all features. If there is a
significant difference between the local sum and the expected local sum, where
the difference is too large due to randomness, a statistically significant
z-score is the result [76,77,78]. With the resultant trend z-score and the
p-value for each location with data, and with the hot spot z-score and the
p-value for each bin, the emerging hot spot analysis tool classification follows
the official ESRI’s ArcGIS Pro 3.0 categorization scheme (pattern name and
definition) for each study area location.
The utilization of geographic information systems (GIS) for spatial
representation of diseases and the conditions in the health sector (government,
provinces, and municipalities) of the given population can contribute to its
intensive development and applicative importance according to Kričković et al.
(2022) [79]. For this purpose, ArcMap software was applied in this study.
Figure 2 presents the integration of the abovementioned methods with the aim of
analyzing noncommunicable diseases (cardiovascular diseases and diabetes) within
the researched area.
Figure 2. The process from the data collection to the analysis and the research
results.
3. RESULTS
According to the publications by the Institute of Public Health of Vojvodina
[15,44,45,46,47,48,49,50,51,52,53,54], among the noncommunicable diseases in the
researched area, the most significant public health problems are blood
circulatory system diseases and diabetes. As it has been defined by the
methodology of the research, in this part of the work we will present the
results of the cluster analysis and the cartographic overviews made in the
already mentioned ArcMap software, along with the trends in the most common
noncommunicable diseases in the researched area.
3.1. THE RESULTS OF THE CARDIOVASCULAR DISEASES RESEARCH
In 2019, blood circulatory system diseases accounted for 50.5% of all the death
causes in the population of the researched area while in 2005 the percentage was
56.8% [15,44]. Table A1 presents the data on the number of new patients and the
ones who died from acute coronary syndrome, as well as the incidence and
mortality rates from 2010 to 2020.
In order to compare the data on the number of the newly diagnosed, the dead, and
the incidence and mortality rates in the course of different years within the
analyzed period, we will provide an overview for the following three years
(2020, 2015, and 2010). During 2020 the total number of new patients with acute
coronary syndrome was 4421, whereas the incidence rate at the level of the
entire AP Vojvodina territory was 240 [17]. The highest incidence rate was
registered in the North Backa County, whereas the lowest rate was in the South
Backa County. The mortality rate in the aforementioned year equaled 52.9 for the
entire researched area. The highest mortality rate in 2020 was recorded in the
North Banat County, and the lowest rate was recorded in the North Backa County
[43]. In 2015, 5502 new patients with the disease in question were registered;
more men than women (3448 men and 2054 women) were registered [51]. The
incidence rate of 290.8 per population of 100,000 was higher in men than in
women. There were 1206 death events (738 men and 468 women died) [51]. During
2010, the number of new cases of acute coronary syndrome was registered as 3399
men and 2195 women [24]. The highest incidence rate was recorded in the area of
the North Banat County, whereas the lowest rate was in the North Backa County.
The mortality rate in 2010 reached the highest value in the Middle Banat County,
and the lowest rate was in the Srem County [46]. Figure 3 presents the ratio of
the number of deceased (mortality rate) and that of new patients (incidence
rate) with acute coronary syndrome during the period from 2010 to 2020. Each dot
in this chart represents the ratio between the incidence and mortality rate for
each year in the observed period. A chart was used to determine if there was a
simple linear regression in the data over time. As it was determined there is no
linear regression and the cluster analysis, the Mann–-Kendall test, as well as
the hot spot analysis were applied to determine the disease trends and hot
spots.
Figure 3. Acute coronary syndrome incidence and mortality rate ratio from 2010
to 2020.
By means of the cluster analysis and by applying the k-means function in the R
programming environment, we have noticed that one cluster particularly stands
out—the North Backa County (Figure 4). Unlike the situation in other counties,
this county has a much lower mortality rate from acute coronary syndrome
although it has an average or even higher than average number of new patients.
Figure 4. Identification of clusters during the analysis of acute coronary
syndrome in the researched area.
However, if we opt to observe two instead of three clusters, we will come up
with Figure 5. In that Figure, there are two clusters presented, each of them
includes a few different counties. The Middle Banat and North Banat counties
diverge from the others (red color in cluster 1) and if we look at the table
data overview, we will notice that these two counties have above average numbers
of new patients and deceased ones from acute coronary syndrome per population of
100,000.
Figure 5. Isolation of two clusters during the analysis of acute coronary
syndrome in the researched area.
The identified patterns of acute coronary syndrome in the hot spot analysis of
the researched area are presented in Figure 6. Regarding the acute coronary
syndrome incidence rate per 100,000 population, only in the North Backa County
has no trend been identified, while in all of the six other counties, a rising
trend has been identified, e.g., “oscillating hot spot”. As for the mortality
rate, a rising trend has been identified only in the South Backa and Middle
Banat Counties while no trend has been identified in all of the five other
counties.
Figure 6. Identified patterns in the emerging hot spot analysis for the acute
coronary syndrome incidence/mortality rates per 100,000 population in the
province: (a) incidence rates, and (b) mortality rates.
Figure 14a shows the geographical distribution of the acute coronary syndrome
incidence, i.e., the average values during the period analyzed. The North Banat
County stands out in particular as it has the highest incidence rate, with the
Middle Banat and North Backa Counties follow. The lowest incidence rates have
been recorded in the South Backa and Srem Counties. Figure 15a presents the mean
values of the mortality rate of acute coronary syndrome from 2010 to 2020. The
highest average mortality rate was registered in the North Banat and Middle
Banat Counties, followed by the West Backa County, whereas the counties with the
lowest average mortality rate are North Backa and South Banat.
Apart from acute coronary syndrome, out of the other cardiovascular diseases, we
also analyzed myocardial infarction and unstable angina pectoris. During 2020,
there were 3848 newly diagnosed patients with myocardial infarction and the
incidence rate equaled 209 [17]. The highest incidence rate of the disease in
question was recorded in the North Backa County, and the lowest rate was
recorded in the Middle Banat County. In the North Banat County, during the same
year, the highest mortality rate was registered, whereas the lowest rate was in
the West Backa County.
The number of newly diagnosed patients with myocardial infarction during 2015
was 4669 and the incidence rate was 246.8 [51]. In the Middle Banat County,
there was the highest registered rate of newly diagnosed cases in the same year,
equaling 305.4. The mortality rate from myocardial infarction in 2015 was 63.1;
a total of 1186 patients died [51].
In 2010, myocardial infarction was registered in 2944 men and 1829 women [46].
The incidence rate in the territory of the AP Vojvodina was 243.8, whereas the
highest incidence rate during 2010 was registered in the Middle Banat County
(338.7) [24]. The mortality rate from myocardial infarction in the researched
area in 2010 equaled 86.5, whereas the highest mortality rate was recorded in
the Middle Banat County (134.5) [24]. Table A2 presents the number of new
patients and deceased ones, as well as the incidence and mortality rates of
myocardial infarction and unstable angina pectoris during the period from 2010
to 2020.
Figure 7 presents the ratio between the incidence and mortality rates from
myocardial infarction during the analyzed time period. As in the previous acute
coronary syndrome ratio chart, dots in this chart also represent the ratio
between the incidence and mortality rates for each county. It can be noticed
that there is certain linear regression, but it is also visible that the data
are dispersed. It could be concluded that the greater the number of newly
diagnosed, the t deceased from myocardial infarction. As in the previous case,
the same methods were used in order to determine disease trends and hot spots.
Figure 7. Myocardial infarction incidence and mortality rate ratio from 2010 to
2020.
Another cluster analysis was applied to the data on the number of newly
diagnosed patients and those deceased from myocardial infarction in the AP
Vojvodina in the period from 2010 to 2020, i.e., the incidence and mortality
rates. The issue with this analysis is that we need to estimate beforehand how
many clusters we might have judging by the previous chart. The k-center
algorithm is not suitable for variables which come in the form of text as it is
necessary to calculate the distance between the data. If we happen to not be
sure about the number of clusters, according to the mentioned formula (2) in
this set of data n = 77, the number of clusters that we want to examine equals
√77/2 = 4.39 ≈ 4.
Figure 8 presents the identified clusters for myocardial infarction during the
analyzed time period. One cluster stands out among the others, and that is the
North Backa County where we may clearly see that, although there are between 150
and 250 new patients per 100,000 population, the number of deceased is much
lower in relation to other districts in the AP Vojvodina.
Figure 8. Identification of clusters in the course of analyzing myocardial
infarction in the researched area.
The identified patterns in the hot spot analysis of myocardial infarction are
given in Figure 9. In this figure, regarding the myocardial infarction incidence
rate, only the Srem County has not shown any trend. In four counties—West Backa,
North, Middle, and South Banat Counties—“new cold spots” were identified
(decreasing trend), and in the North and South Backa Counties, “oscillating cold
spots” were identified (decreasing trend). In the same analysis, only the North
and South Backa Counties saw a rising trend in the myocardial infarction
mortality rate identified as “oscillating hot spots”, while in the other five
counties, there was no trend detected.
Figure 9. Identified patterns in the emerging hot spot analysis for the
myocardial infarction incidence/mortality rates per 100,000 population in the
province: (a) incidence rates, and (b) mortality rates.
Figure 14b presents the incidence rates of myocardial infarction in the period
from 2010 to 2020. The South Banat County had the highest incidence rate, and
the North Banat and Middle Banat Counties followed. The counties with the lowest
incidence rate of myocardial infarction are North Backa and Srem. The mortality
rates of myocardial infarction in the 2010–2020 period are shown in Figure 15b.
The highest mortality rate during the analyzed period was recorded in the North
Banat County, and the Middle Banat County comes next. The counties with the
lowest mortality rates during the analyzed period were the North Backa County
and the South Banat County.
During 2020, the highest incidence rate of unstable angina pectoris was
registered in the West Backa County, whereas the lowest rate was in the South
Backa County. Overall, 775 newly diagnosed patients were registered, and the
incidence rate was 42.1 [17]. The total of 23 patients died in the researched
area and the mortality rate in the mentioned year equaled 1.2 [17]. The highest
mortality rate was registered in the West Backa County and the lowest rate was
registered in the Middle Banat County. In 2015, unstable angina pectoris was
confirmed in 833 people and the incidence rate was 44. The total number of
deceased patients from the disease in question in 2015 was 20 and the mortality
rate equaled 1.1 [29]. The highest incidence rate was 126.6 in the North Backa
County and the lowest was registered in Srem County (20.7) [21]. During 2010,
unstable angina pectoris was registered in 455 male patients and 366 females
[46]. The incidence rate was 41.9, whereas the mortality rate was 2.6. The total
of 50 patients died from this disease in the course of 2010. In the South Banat
County, there was the lowest incidence rate of unstable angina pectoris in 2010
(21.1), while the highest rate was in the North Banat County (131.6) [46]. The
West Backa County had the highest mortality rate during the analyzed year and it
equaled 7.8. [46] Figure 10 presents the ratio of the number of deceased and
newly diagnosed patients with unstable angina pectoris during the analyzed time
period. As in the previous cases, the ratio chart between the incidence and
mortality rates was made in order to determine if there is linear regression in
the data. Each dot in this chart represents this ratio in each county over the
period. In this case, it is evident that there is no linear regression, and the
same methods were used as before in order to determine disease trends and hot
spots.
Figure 10. Unstable angina pectoris incidence and mortality rate ratio from 2010
to 2020.
The application of the k-means function for the cluster analysis shows that
there are three clusters (Figure 11).
Figure 11. Identification of clusters during the analysis of newly diagnosed
patients with unstable angina pectoris in the researched area.
The first cluster includes the North Backa, North Banat, and Middle Banat
Counties with the highest number of new patients, but also the average number of
patients deceased from unstable angina pectoris. The third cluster includes the
West Backa County only with a noticeably higher number of patients deceased from
unstable angina pectoris. If we also study the next figure (Figure 12) where the
clusters are divided according to the number of the deceased, we obtain the
result in which the West Backa County clearly diverges from the other counties,
provided that we take into account only the number of those deceased.
Figure 12. Identification of clusters during the analysis of patients deceased
from unstable angina pectoris in the researched area.
The identified patterns in the hot spot analysis of unstable angina pectoris for
the incidence/mortality rates per population of 100,000 are presented in Figure
13. In this figure, the Srem County has not displayed a trend in the incidence
rate, while the Middle Banat County was identified as a “new cold spot”
(decreasing trend) and all of the other five counties—West, North, and South
Backa, and North and South Banat were identified as “oscillating cold spots”
(decreasing trend). North and South Backa and Middle and South Banat have not
shown a trend in the mortality rate, while the West Backa and North Banat
Counties were identified as “oscillating cold spots” (decreasing trend) and the
Srem County was identified as an “oscillating hot spot” (increasing trend).
Figure 13. Identified patterns in the emerging hot spot analysis for the
unstable angina pectoris incidence/mortality rates per 100,000 population in the
province: (a) incidence rates, and (b) mortality rates.
Figure 14c presents the incidence rates of unstable angina pectoris for the time
period from 2010 to 2020. The highest incidence rates during the analyzed period
were recorded in the North Backa and North Banat Counties, whereas the lowest
ones were in the South Backa, Srem, and South Banat Counties. During the
examined time period, the highest unstable angina pectoris mortality rate was
registered in the West Backa County and the lowest rate was recorded in the
Middle Banat and South Banat Counties (Figure 15c).
Figure 14. Incidence rates per 100,000 population for cardiovascular diseases in
the researched area from 2010 to 2020: (a) acute coronary syndrome incidence
rates, (b) myocardial infarction incidence rates, and (c) unstable angina
pectoris incidence rates.
Figure 15. Cardiovascular diseases mortality rates per 100,000 population in the
researched area from 2010 to 2020: (a) acute coronary syndrome, (b) myocardial
infarction, and (c) unstable angina pectoris.
3.2. THE RESULTS OF THE DIABETES RESEARCH IN THE STUDIED AREA
Table A3 presents the data on the number of the newly diagnosed individuals with
type 1 and type 2 diabetes, the number of the deceased, as well as the incidence
and mortality rates from 2010 to 2019. Based on the analysis of these data, in
2019, the number of new, type 1 and 2, diabetes patients was 70 (aged 0 to 29),
and the incidence rate was 12.2 [14]. There were 5363 new type 2 diabetes
patients registered and the incidence rate was 289.6 [14]. According to “The
health statistical yearbook of the Republic of Serbia in 2019”, in the
researched area, 681 people died and the mortality rate was 32.8 [42].
According to the report “Health of the population of the AP Vojvodina, 2015”, in
2015, 69 new patients with type 1 diabetes were registered and the incidence
rate equaled 11.4 (13.3 at women, and 9.6 at men) [51]. In the same period,
there were 5563 newly diagnosed patients with type 2 diabetes. The incidence
rate of type 2 diabetes was 283.5 (284.5 for men and 282.6 for women) during the
same year [51]. According to the data by the Statistical Office of the Republic
of Serbia, in 2015 there were 935 deceased from diabetes (339 people from type
1, 416 people from type 2, and 180 people from unspecified type) [51]. In 2015,
the mortality rate of type 1 diabetes was 17.9 (16.8 for men and 19.0 for
women), whereas the rate of type 2 was 22.0 (19.3 for men and 24.5 for women)
[51].
In 2011, the number of new patients registered with type 1 diabetes was 92, out
of which 48 were women and 44 were men [56]. In the same period, there was a
significantly higher number of newly registered patients with type 2 diabetes.
The incidence rate of type 1 diabetes was higher in female patients (14.7) than
in male patients (12.7). The incidence rate of type 2 diabetes was 246.9 for men
and 247.2 for women. During 2011, 339 people died from type 1 diabetes in the AP
Vojvodina as well as 361 people dying from type 2 [47]. The mortality rate of
type 1 diabetes was 14.8 for men and 19.9 for women, whereas the mortality rate
of type 2 diabetes was 15.6 for men and 21.3 for women [47].
Figure 16 represents the ratio between the diabetes incidence and mortality
rates during the analysed period. As in the previous cases, dots represent the
ratio between the incidence and mortality rates in each county for each year in
the period. It is clearly visible that there is no linear regression and,
therefore, the same methods were used in order to determine the disease trends
and hot spots as before. For the analysis of these data, the k-center algorithm
was also applied, but number three was taken as the number of clusters. The
following chart was obtained (Figure 17).
Figure 16. Diabetes incidence and mortality rate ratio per 100,000 population
from 2010 to 2019.
Figure 17. Identification of clusters during the diabetes analysis.
The first cluster clearly includes the values for the Srem County, and it
obviously deviates from all the remaining counties. If we take a look back at
the table with the data, we can notice that the number of the deceased from
diabetes in the Srem County is significantly higher than that in any other
counties, although the number of new patients in that county is not
significantly higher than the number of new patients in other counties. The
second cluster comprises a few counties: the West Backa and Middle Banat
Counties. The West Backa County can particularly be set apart as, unlike the
Srem County, it may have a high number of newly diagnosed individuals with
diabetes (types 1 and 2), but it also has significantly fewer deceased patients.
Figure 18 represents identified patterns in the emerging hot spot analysis for
the debates incidence and mortality per 100,000 population in the researched
area. In this analysis, the West Backa and South Banat Counties have not
demonstrated any trend in the diabetes type 1 incidence rate, while the North
Backa County was identified as a “new cold spot” (decreasing trend) and the
North Banat, South Backa, and Srem Counties were identified as “oscillating cold
spots” (decreasing trend). Apart from this, the Middle Banat County was
identified as an “oscillating hot spot” (increasing trend). Following this, the
North Banat and Srem Counties have not shown any trend in the diabetes type 2
incidence rate, while all of the five other counties—West, North, and South
Backa and Middle and South Banat—were identified as “oscillating hot spots”
(increasing trend). The North Backa and Banat counties have not demonstrated any
trend in the diabetes mortality rate, while all of the five other counties—West
and South Backa, Middle and South Banat, and Srem were identified as
“oscillating hot spots” (increasing trend).
Figure 18. Identified patterns in the emerging hot spot analysis for the
diabetes incidence/mortality rates per 100,000 population in the province: (a)
incidence rates for type 1 diabetes (b) incidence rates for type 2 diabetes (c)
mortality rates for both diabetes types.
From 2010 to 2019, the highest values of the incidence rates of both diabetes
types were recorded in the Middle Banat County, followed by the West Backa,
North Banat, and South Banat Counties (Figure 19a). The lowest value of the
incidence rate in the course of the analyzed period was registered in the Srem
County.
Figure 19. Geographical distribution of the diabetes incidence/mortality rates
per 100,000 population in the researched area from 2010 to 2019: (a) incidence
rates for both diabetes types, (b) incidence rates for type 1 diabetes, (c)
incidence rates for type 2 diabetes, and (d) mortality rates for both diabetes
types.
Figure 19b clearly makes it visible that the highest value of the incidence rate
of type 1 diabetes was recorded in the North Backa County, followed by North
Banat, while the county with the lowest incidence rate of type 1 diabetes was
South Banat.
The incidence rate of type 2 diabetes during the studied period, from 2010 to
2020, is also presented in Figure 19c. The highest incidence rate of type 2
diabetes was recorded in Middle Banat, preceding the West Backa, North Banat,
and South Banat Counties, while Srem had the lowest value. The highest mortality
rate of both diabetes types during the analyzed period was registered in Srem,
and it was followed by the North Banat and South Banat Counties. The lowest
mortality rate was recorded in the West Backa County, as can be clearly seen in
Figure 19d.
3.3. THE RESULTS OF THE TRENDS IN CARDIOVASCULAR DISEASES AND DIABETES IN THE
RESEARCHED AREA
By means of the Mann–-Kendall test, we calculated the trends in newly diagnosed
patients and those who are deceased from cardiovascular diseases and diabetes.
Table 1 presents the parameters of the Mann–-Kendall test for the abovementioned
diseases where one can clearly see the statistical reliability of each test.
Table 1. Parameters of the Mann–-Kendall test for particular diseases in the
researched area (α—significance level, +α = 0.1, * α = 0.05, *** α = 0.001, /—no
significance; b—so called Sen’s slope).
From Table 1 and Figure 20a, it can be concluded that, during the analyzed
period, the number of new patients with acute coronary syndrome declined, with
the probability of the model being 95%. Unlike the number of new patients, with
the probability of 99.9% one can claim that the number of the deceased from the
disease in question is on the decrease, as shown in Figure 20b. Figure 20c shows
the number of the newly diagnosed individuals with myocardial infarction with
the overview of the number trend of new patients. The number of the newly
diagnosed individuals with the disease in question is on the decrease, but with
the test probability of 90%. Analyzing the aforementioned table along with
Figure 20d, one can claim with a high probability (99%) that the number of
patients deceased from myocardial infarction in the territory of the AP
Vojvodina declined during the assigned period. The number of new patients with
unstable angina pectoris along with the trend in the number of new patients
during the period from 2010 to 2020 is shown in Figure 20e. It can be noted that
the number of new patients with the disease in question is on the decrease, but
not with such a high reliability and probability, as can be observed in Table 1.
Figure 20f presents the trend in numbers of the deceased from unstable angina
pectoris in the period from 2010 to 2020. It can be noted that the number of
people who died from the abovementioned disease has a decreasing trend, but
without any statistical significance, as seen from Table 1.
Figure 20. Results of the trends in the number of new patients and patients who
died from cardiovascular diseases during the period 2010 to 2020: (a) overview
of the number of new patients with acute coronary syndrome along with the trend
line of the disease, (b) overview of the number of patients who died from acute
coronary syndrome with the mortality trend, (c) overview of the number of new
patients with myocardial infarction along with the trend in the number of new
patients, (d) overview of the number of patients who died from myocardial
infarction and the mortality trend, (e) overview of the number of new patients
with unstable angina pectoris with the trend line of new patients, and (f)
overview of the number of patients who died from unstable angina pectoris with
the mortality trend line.
Analyzing the number of new patients and the number of the deceased from
diabetes in the researched area in the period from 2010 to 2019, one can say
that the results seem interesting, but without a high reliability of the test
applied. Namely, if we observe the number of new type 1 diabetes patients in the
AP Vojvodina, then according to Table 1 and Figure 21c, we can claim with a
probability of 95% that the number of new patients is decreasing. According to
the same table, one can also claim the number of new type 2 diabetes patients is
on the rise. The trend growth of the number of new patients with both diabetes
types in the AP Vojvodina equals 73.5, whereas its value is 77.3 for the number
of new patients with type 2 diabetes. This case with different diabetes types
can be explained in the following way: the number of new type 1 diabetes
patients does not account for a big percentage in the total number of new
patients with both diabetes types, i.e., the number of new type 1 diabetes
patients is lower than the number of new type 2 diabetes patients. The number of
the deceased is taken as an overall sum for the entire researched area for the
abovementioned reason and, in accordance with Figure 21b, we can notice that
this number is declining. However, according with Table 1, the previously
mentioned test did not prove to be statistically significant.
Figure 21. Results of the trends showing the number of new patients and those
deceased from diabetes in the researched area: (a) overview of the number of new
patients with both diabetes types from 2010 to 2019, (b) overview of the number
of people who died from both diabetes types from 2010 to 2019, (c) overview of
the number of new patients with type 1 diabetes from 2010 to 2019, (d) and
overview of the number of new patients with type 2 diabetes from 2010 to 2019.
4. DISCUSSION
This study has not dealt with the causes of the occurrence of noncommunicable
diseases (cardiovascular diseases and diabetes) but rather with identifying the
most and least burdened areas. Despite the fact that cluster analyses rarely
prove fruitful in identifying causation, they may—like single case reports—have
the potential to generate new knowledge [80]. Growing public awareness of
environmental hazards has led to an increased demand for public health
authorities to investigate geographical clustering of diseases [80]. Although
such a cluster analysis is nearly always ineffective in identifying the causes
of a disease, it often has to be used to address public concern about
environmental hazards [80]. With research studies of this kind, it is possible
to research the areas where certain diseases occur more intensively and thus
identify their hot spots. By doing so, we can facilitate carrying out additional
spatial analyses in order to examine the causation of certain diseases in
different geographical environments.
In this research, hot spot analysis was used to identify more reliable trends.
There were certain discrepancies between the results of the classic cluster
analysis and the hot spot analysis, which was expected due to a well-known fact
that a hot spot analysis provides better results than a classic cluster
analysis. The hot spot analysis is much more detailed, and the results differ
from this. It is important to note that certain limitations of this procedure
can be identified. Due to data scarcity (the data sets for this research were
only available for seven counties the AP Vojvodina ), the results obtained by
the emerging hot spot analysis (spatial pattern mining) tool are somewhat less
significant due to the available data resolution [81]. Due to this, it is not
possible to identify hot spots with great statistical significance in contrast
to the performed cluster analysis.
The North Backa County is the one with the lowest cardiovascular diseases
mortality rate. This represents a positive instance of the approach to health,
where we may see that although there is the highest number of new patients with
cardiovascular diseases, the number of deceased is the lowest one in the
province. Similarly, the fact that the West Backa County was identified as the
one with a higher number of people who died from unstable angina pectoris and
with a higher number of new patients with diabetes speaks volumes on how serious
the issue of the patients’ approach to health is in this county. It is possible
to notice that, in this case, the classic cluster analysis and the hot spot
analysis showed the same results, with the only difference being within the
North Backa County, where the classic cluster analysis showed a decreased
mortality rate, and the hot spot analysis did not identify any trends. The Srem
County has a significantly higher number of individuals deceased from diabetes
than any other county although its number of new patients is not significantly
higher than in other counties. Some of possible reasons could be the habit of
the Srem County population in not testing their sugar blood level and neither
taking precautions nor following therapeutic procedures to avoid certain medical
complications and mortality from diabetes.
According to the Demographic Statistics 2018 publication, issued by the
Statistical Office of the Republic of Serbia, the age structure of the
researched area has changed significantly since 1950 [82]. The median age in
1950 was 30.96 years and in 2018 it was 42.8 [82]. According to the Statistical
Office of the Republic of Serbia online database in 2021, the median age in the
researched area was 43.1 years and in the entire Republic of Serbia it was 43.5
[83], which are both below the European average of 44.1 years, according to the
Eurostat website [84]. Only two counties were below the national average—the
South Backa County (41.5 years) and the North Backa County (43.3). In one
county, the average was the same as the national median age—in the South Banat
County (43.5 years). All of the four other counties had a median age above the
national average—the West Backa County (44.9), the North Banat County (44.1),
the Middle Banat County (43.8), and the Srem County (43.7) [83].
In 2019, life expectancy in Vojvodina for women was 77.9 years and for men it
was 72.0 years. Every fifth person in Vojvodina is aged 65 or above (19.7%)
[15]. The researched area population belongs to the regressive type of the
population, which is defined by a high proportion of the older population and
the small participation of young people, considering that 41% of the population
is aged 50 and only 14.4% of the population is below 15 [15].
In 2020, the highest number of new patients diagnosed with cardiovascular
diseases was recorded in the population aged 75 and above—1033 patients with
myocardial infarction with a 749.0 incidence rate per 100,000 population, 188
patients with unstable angina pectoris with a 136.3 incidence rate, and 1169
patients with acute coronary syndrome with an 847.6 incidence rate [17]. The
highest number of deceased patients with cardiovascular diseases was also
recorded in the population category aged 75 and over in the same year, with 387
deceased from myocardial infarction and a 280.6 mortality rate per 100,000
population, with 10 deceased from unstable angina pectoris and a 7.3 incidence
rate, and with 397 deceased from the acute coronary syndrome and a 287.9
incidence rate [17].
In 2019, the highest number of patients newly diagnosed with type 2 diabetes was
recorded in the population aged from 65 to 69 (995 patients) and in the same
category, the incidence rate was 762.8 [14]. In the same year, the diabetes
mortality rate was 138.4, when 382 patient deaths were recorded in patients aged
75 and above [14].
The study carried out by Maksimovic et al. (1999) showed that in the territory
of the AP Vojvodina, potable waters have a relatively low magnesium content and
a medium calcium content [5]. However, the sodium content in these waters is
rather high, so further examination of this subject matter is needed [5,85]. The
research studies carried out by Poloniecki et al. (1997) and Jevtic et al.
(2014) reached the following conclusions: the average daily concentration of NO2
is connected with the hospital admission of patients with cardiovascular
diseases in the territory of Novi Sad [7,8]. The study carried out by the
Institute of Public Health of Vojvodina, which analyzed in 2017 the amount of
salt in meals prepared for preschool children, indicated that the amount of salt
in all of the three meals provided daily mostly exceeded the recommended daily
intake [53]. Velicki (2017) carried out a study on a sample of patients from the
researched area who were diagnosed with acute coronary syndrome and the results
showed that consumption of certain food types (fruit, vegetables, poultry meat,
and olive oil) can contribute to reducing the risk of the mentioned disease [6].
Likewise, consumption of red meat and processed meat food increases the risk of
acute coronary syndrome occurrence, which was also confirmed by the
aforementioned study [6]. Vorgucin et al. (2011) examined metabolic syndrome in
the AP Vojvodina and they came to the conclusion that obesity in the researched
area is correlated with metabolic syndrome occurrence [86], which poses a huge
risk of diabetes and cardiovascular diseases. Similarly, Radic (2016) in her
research drew the conclusion that every fourth adult in Vojvodina is obese and
every third person is pre-obese, which also presents a high risk factor for the
occurrence of the aforementioned diseases [87]. The study by the title of
“Research of the health of the population in Serbia in 2019” indicated that, in
the researched area, there was a significantly higher percentage of obese people
(25.4%) than in the overall territory of the Republic of Serbia, as people there
use animal fat and salty food more often and consume tobacco more [88].
Therefore, it is clear that the physical-geographical environment as well as the
lifestyle and approach to health can have an immense effect on the occurrence of
the mentioned diseases. It is thus necessary to conduct more thorough research
in the burdened counties in order to identify the risk factors from the
environment and to determine the reasons for the origins of the diseases in
question.
This study should represent the basis for future research on the territory of
the AP Vojvodina in order to assess the findings from the aforementioned studies
on the causative agents of noncommunicable diseases in the affected areas. By
identifying the hot spots and causations, we would reduce the number of new
patients and death from noncommunicable diseases. As a result, the costs
involved in treating these patients could be reduced. A large number of patients
allocate significant financial resources for diagnostics, treatments, and
therapeutic procedures so finances are quite often a limiting factor. Patients,
in practice, quite frequently neither go to regular checks nor do they go for
diagnostic tests due to a lack of money; therefore, these diseases are diagnosed
too late when a lot of side complications have also appeared. It is well known
that in practice, diabetes often occurs in patients suffering from
cardiovascular diseases and the other way round, which indicates the importance
of prevention. Mortality from cardiovascular diseases and diabetes can be
considerably slowed down and reduced thanks to prevention measures, therapy
administration, and carrying out regular medical checks. Consequently, we should
have a bigger influence on taking prevention measures and protection from
noncommunicable diseases which would particularly be directed towards burdened
categories.
As the results of the trend analyses have shown, the number of new patients and
the number of deceased from cardiovascular diseases is on the decrease, but when
it comes to the number of people who died from unstable angina pectoris, the
test did not exhibit statistical significance. In addition, the number of people
who died from diabetes is declining, but the test showed no statistical
significance. It is notable that the numbers of new patients and those who died
from cardiovascular diseases have been significantly lower since the outbreak of
the COVID-19 pandemic. This can be explained by an assumption that a certain
number of the deceased from noncommunicable diseases were categorized as those
who died from COVID-19, since these diseases also contributed to their death.
For all of the listed reasons, one may wonder if the number of the deceased from
the diseases in question has truly dropped or the reduction in numbers is linked
to some records and the categorization of mortality into other disease
categories.
Due to lack of publicly available statistics on the number of new patients and
those deceased from other noncommunicable diseases for a rather long time as
well as due to data inconsistency, this research has been carried out with a
focus on cardiovascular diseases and diabetes. What can be emphasized as a
burdening factor is the impossibility of accessing certain data owned by some
institutions. In addition, life and work conditions during the COVID-19 pandemic
influenced the abilities of some institutions to deliver the requested data.
A suggestion of the study is to make statistical data on noncommunicable
diseases available at the level of settlements and municipalities, so that one
can carry out a more-detailed geographical analysis of a disease. It is
necessary for experts in various fields to take part in analyses of
noncommunicable diseases, where they would examine disease causations together
rather than separately. As a result, the health conditions of the population of
the AP Vojvodina would be significantly improved.
The health care institutions in the researched area should put more effort in
improving the measures of psychological support offered to patients affected by
noncommunicable diseases. Both medical and non-medical staff should be actively
involved in providing measures of psychological support. Open communication
raises hope and helps patients find strength when facing a tough situation and
it simultaneously confirms that the disease has not disabled them nor excluded
them from everyday life [89]. By the same token, implementing the measures of
psychological support should also be directed towards patients’ families who,
along with medical and non-medical staff, should take an active part in taking
these measures.
By adopting a healthy lifestyle and a right approach to people’s health, it is
possible to improve general health conditions and reduce the occurrence of
noncommunicable diseases [90].
5. CONCLUSIONS
This study represents a pioneer study in Serbia which used the Getis–Ord Gi*
statistic within the emerging hot spot analysis (space time pattern mining) tool
and cluster analysis for the purpose of identifying “hot spots” and “colds
pots”of noncommunicable diseases (cardiovascular diseases and diabetes). As
presented in this research, noncommunicable diseases (cardiovascular diseases
and diabetes) prevail in the pathology of diseases of the population of the AP
Vojvodina. This study has cast light on the geographic medical overview of the
diseases in question and identified both the most and the least burdened
counties within the researched area.
By means of the obtained results of the calculated trends, it is possible to
track and follow a disease trend within a specific population and to predict the
further course of the disease. Consequently, the population’s health conditions
can be maintained and improved. Similarly, there can be an effect on
implementing the health policy measures, and it can be evaluated whether the
health policy in a particular area is adequate or whether it is necessary to
carry out certain modifications to the policy in accordance with the needs of
the population in a certain region. By identifying the disease hot spots/cold
spots and by estimating their tendencies, one can also take part in developing
suitable strategies for protecting both health and the environment. Not only can
research in this field contribute to the improvement of health management in a
specific area but it can also influence the improvement of the overall health
care system not only in the AP Vojvodina, but also in the Republic of Serbia and
some other neighboring countries.
AUTHOR CONTRIBUTIONS
Conceptualization, E.K. and T.L.; methodology, E.K.; software, E.K. and T.L.;
validation, T.L., D.J.-P. and E.K.; formal analysis, D.J.-P.; investigation,
E.K.; resources, E.K.; data curation, E.K.; writing—original draft preparation,
E.K.; writing—review and editing, T.L. and D.J.-P.; visualization, E.K.;
supervision, T.L.; project administration, E.K.; funding acquisition, E.K. All
authors have read and agreed to the published version of the manuscript.
FUNDING
The study was supported by the Ministry of Education, Science and Technological
Development of the Republic of Serbia (Contract numbers 451-03-68/2022-14/200091
and 451-03-68/2022-14/200125).
INSTITUTIONAL REVIEW BOARD STATEMENT
Not applicable.
INFORMED CONSENT STATEMENT
Not applicable.
DATA AVAILABILITY STATEMENT
To obtain the data for this study, please contact the authors via email.
CONFLICTS OF INTEREST
The authors declare no conflict of interest.
APPENDIX A
Table A1. Number of new patients and patients who died from acute coronary
syndrome in the researched area.
Table A1. Number of new patients and patients who died from acute coronary
syndrome in the researched area.
YearProvince/CountyAcute Coronary SyndromeYearProvince/CountyAcute Coronary
SyndromeNew PatientsMortalityNew PatientsMortalityNumberPer 100,000Number of
DeceasedPer 100,000NumberPer 100,000Number of
DeceasedPer
100,0002010Vojvodina5594285.8174489.12016Vojvodina4974264.4108858.2South
Backa1708280.654689.7Soth Backa142623129447.6South Banat777260.423478.4South
Banat778274.615755.4North Backa429224.715882.8North Backa646356.28345.8North
Banat621408.6175115.1North Banat548391.612690Middle Banat757396.3258135.1Middle
Banat531296.713877.1Srem752232.722669.9Srem638210.614447.5West
Backa550284.514776West
Backa407229.314682.32011Vojvodina5839300.1154879.62017Vojvodina4099219103855.5South
Backa1595261.749781.6South Backa1426230.828946.8South Banat97032818763.2South
Banat743264.214350.9North Backa496261.612163.8North Backa646358.25429.9North
Banat560372.7163108.5North Bnaat54839611986Middle Banat70537318798.9Middle
Banat531299.510056.4Srem851265.320965.2Srem63821219364.1West
Backa662346.518496.3West
Backa407232.114079.82012Vojvodina5313276.4145775.82018Vojvodina4781256.896952South
Backa1454236.246174.9South Backa1406227.428445.9South Banat780267.417760.7South
Banat84930412243.7North Backa542291.78947.9North Backa493274.87039North
Banat521356.213491.6North Banat391285.69770.8Middle Banat688369.916990.9Middle
Banat486276.710760.9Srem753242.628391.2Srem775259.116655.5West
Backa575308.814477.3West
Backa381220123712013Vojvodina4736249,6124164.92019Vojvodina5039272.1103455.8South
Backa144323738762.8South Backa1311211.928746.4South Banat850295.314048.3South
Banat103237213749.4North Backa561306.6510959North Backa449251.86838.1North
Banat411285.711881.6North Banat48936112995.2Middle Banat52728715081.4Middle
Banat449258.211968.4Srem522169.9520165.2Srem776261.120569West
Backa422230.913673.9West
Backa533311.689522014Vojvodina5368282.2121362.22020Vojvodina4421240.297352.9South
Backa147924034455.8South Backa1149185.728846.6South Banat833289.517460.5South
Banat732265.913649.4North Backa514279.910959.4North Backa6643758145.8North
Banat537375.111983.1North Banat400295.512492.6Middle Banat771422.317998.1Middle
Banat361209.910158.7Srem711231.817456.7Srem695235.515452.2West
Backa523287.711462.7West Backa420248.88952.72015Vojvodina5502290.8120664.,15
South Backa1510244.836459 South Banat880308.116858.8 North Backa674369.410959.7
North Banat527372.112185.4 Middle Banat628347.412166.9 Srem799262.220466.9 West
Backa484296.411966.2
Source: Analysis by the authors on the basis of the publications
[17,24,25,26,27,28,29,30,31,32,33].
Table A2. Number of newly diagnosed and deceased patients, as well as the
incidence and mortality rates from myocardial infarction and unstable angina
pectoris.
Table A2. Number of newly diagnosed and deceased patients, as well as the
incidence and mortality rates from myocardial infarction and unstable angina
pectoris.
YearProvince/CountyMyocardial InfarctionUnstable Angina PectorisNew
PatientsMortalityNew PatientsMortalityNumberPer 100,000Number of DeceasedPer
100,000NumberPer 100,000Number of DeceasedPer
100,0002010Vojvodina4773243.8169486.582141.9502.6South
Backa1518249.453688.119031.2101.6South Banat714239.323378.16321.110.3North
Backa35918815581.27036.731.6North Bnaat421277172113.2200131.632Middle
Banat647338.7257134.511057.610.5Srem665205.820964.78726.9175.3West
Backa449232.213268.310152.2157.82011Vojvodina4986256.2150277.285343.8462.4South
Backa1415232.248579.618029.5122South Banat881297.918662.98930.110.3North
Backa375197.711661.212163.852.6North Banat413274.8163108.514797.800Middle
Banat589311.618195.811661.463.2Srem766238.820563.98526.541.2West
Backa547286.316686.911560.2189.42012Vojvodina4354226.5142974.395949.9281.5South
Backa1301211.345473.715324.971.1South Banat713244.417459.7672331North
Backa251135.18947.9291156.600North Banat387264.613088.913491.642.7Middle
Banat546293.516890.314276.310.5Srem675217.428190.57825.120.6West
Backa481258.313371.49450.5115.92013Vojvodina3981208.2121963.875539.5221.2South
Backa1296210.438362.214723.940.6South Banat785270.9139486522.410.3North
Backa396214.31095916589.300North Banat284196.311277.412787.810.5Middle
Banat397215.414980.813070.510.5Srem461149.419362.66119.882.6West
Backa362196.813472.86032.621.12014Vojvodina4507237119062.686145.3231.2South
Backa1258204.133754.722135.971.1South Banat77126817159.46221.531North
Backa419228.210557.29551.742.2North Banat386269.711781.7151105.521.4Middle
Banat599328.117897.517294.210.5Srem632206.117155.877925.831West
Backa442243.211161.18144.631.72015Vojvodina4699246.8118663.183344201.1South
Backa1329215.536058.918129.340.6South Banat836292.716457.54415.441.4North
Backa443242.810759.3231126.621.1North Banat380268.311984.6147103.821.4Middle
Banat552305.412167.4764200Srem736241.419965.56320.751.6West
Backa393218.811664.99150.731.72016Vojvodina4248225.8105756.272638.6311.6South
Backa1325214.628946.810116.450.8South Banat749264.315755.42910.200North
Backa397218.97943.6249137.342.2North Banat372265.812488.6176125.821.4Middle
Banat471263.113776.56033.510.6Srem596196.714146.54213.931West
Backa338190.413073.26938.91692017Vojvodina3446184.1100453.665334.9341.8South
Backa1281207.3284468513.850.8South Banat609216.614049.813447.731.1North
Backa257142.55329.421111710.6North Banat360260.211885.38662.210.7Middle
Banat189106.69855.35430.521.1Srem561186.419163.56220.620.7West
Backa189107.812068.421122011.42018Vojvodina4269229.391953.651227.5502.7South
Backa1294209.39685.311218.1223.6South Banat786281.4262466322.610.4North
Backa431240.36829.46234.621.1North Banat302220.69685.3896510.7Middle
Banat459261.410655.32715.410.6Srem677226.415663.59832.8103.3West
Backa320184.711049.86135.2137.52019Vojvodina43332341.,01154.670638.1231.2South
Backa121319628245.69815.850.8South Banat902325.213749.413046.900North
Backa344192.9663710558.921.1North Banat402296.8126938764.232.2Middle
Banat344197.811968.410560.400Srem669225.120067.31073651.7West
Backa459268.38147.47443.384.72020Vojvodina384820995051.677542.1231.2South
Backa1090176.228646.27712.420.3South Banat695230.713448.75921.420.7North
Backa472266.68045.2192108.410.6North Banat318234.912290.17152.521.5Middle
Banat276160.510158.73822.100Srem627212.415251.5682320.7West
Backa430254.77544.4270159.9148.3
Source: Analysis by the authors based on the publications
[17,24,25,26,27,28,29,30,31,32,33].
Table A3. Number of new patients suffering from type 1 and 2 diabetes, incidence
rates, number of deceased, and mortality rates.
Table A3. Number of new patients suffering from type 1 and 2 diabetes, incidence
rates, number of deceased, and mortality rates.
YearCountyDiabetes Type 1Diabetes Type 2Mortality (Both Types)YearCountyDiabetes
Type 1Diabetes Type 2Mortality (Both Types)New PatientsNew PatientsNew
PatientsNew PatientsNumberPer 100,000NumberPer 100,000Number of
DeceasedPer 100,000NumberPer 100,000NumberPer 100,000Number of
DeceasedPer 100,0002010South Backa198.71196196.59415.42015South
Backa3215.41567254.122336.2South Banat1211.4689230.99130.5South
Banat55.5963337.214350.1North Backa1726.2540282.83719.4North
Backa712.2459251.58446North Banat611.7401263.87348North
Banat613.7495349.510070.6Middle Banat812.45983132412.6Middle
Banat23.5579320.38345.9Srem1311.3833257.79429.1Srem99.4735241.127188.9West
Backa69.5410212.163,1West Backa814.9565314.53117.32011South
Backa2913.41299213.279132016South Backa2612.61661269.119631.7South
Banat1716.4617208.65117.2South Banat1213.5956337.413146.2North
Backa1523.5561295.84825.3North Backa1119.4450248.26133.6North
Banat816457304.15637.3North Banat818.6454324.49970.7Middle
Banat69.5542286.82010.6Middle
Banat610.7557311.29754.2Srem108.8896279.49930.9Srem77.4628207.325584.2West
Backa711.3435227.784.2West Backa1120.9609343.13519.72012South
Backa219.81455236.38714.52017South Backa138.81565253.320232.7South
Banat1212.6927317.85920.2South Banat711.4977347.414852.6North
Backa914.9525282.53921North Backa619.6493273.45832.2North
Banat510.7384262.54933.5North Banat516.6433312.910173Middle
Banat1423.3592318.31910.2Middle
Banat310.9644363.27944.6Srem109.9662213.35317.1Srem611.8563187.126788.7West
Backa1017.4503270.263.2West Backa23.9623355.34123.42013South
Backa2612.31631264.774122018South Backa2411.91624262.617628.5South
Banat77.5926319.68027.6South Banat78.1989354.112645.1North
Backa813.55082753619.5North Backa916.3555309.47541.8North
Banat715.3413285.55638.7North Banat614.5447326.58360.6Middle
Banat1322598324.52312.5Middle
Banat814.8591336.58850.1Srem1212.1741240.25116.5Srem88.767322519866.2West
Backa814.2467253.8105.4West Backa1019.9687396.63821.92014South
Backa2411.51734281.37311.82019South Backa2311.41538248.516626.8South
Banat1010.9934324.65418.8South Banat1011.7920331.78028.8North
Backa58.6517281.64222.9North Backa59.1402225.56637North
Banat511.2421294.14833.5North Banat614.7465343.37756.8Middle
Banat813.8535293.184.4Middle
Banat1018.8633364.17643.7Srem1717.5836272.66320.5Srem1415.5713239.916354.8West
Backa916.4539296.5179,4West Backa24.1692404.65331
Source: Analysis by the authors on the basis of publications
[14,55,56,57,58,59,60,61,62,63].
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Figure 1. Noncommunicable diseases age-standardized death rates (per 100,000
population) in Europe in 2019. [2].
Figure 2. The process from the data collection to the analysis and the research
results.
Figure 3. Acute coronary syndrome incidence and mortality rate ratio from 2010
to 2020.
Figure 4. Identification of clusters during the analysis of acute coronary
syndrome in the researched area.
Figure 5. Isolation of two clusters during the analysis of acute coronary
syndrome in the researched area.
Figure 6. Identified patterns in the emerging hot spot analysis for the acute
coronary syndrome incidence/mortality rates per 100,000 population in the
province: (a) incidence rates, and (b) mortality rates.
Figure 7. Myocardial infarction incidence and mortality rate ratio from 2010 to
2020.
Figure 8. Identification of clusters in the course of analyzing myocardial
infarction in the researched area.
Figure 9. Identified patterns in the emerging hot spot analysis for the
myocardial infarction incidence/mortality rates per 100,000 population in the
province: (a) incidence rates, and (b) mortality rates.
Figure 10. Unstable angina pectoris incidence and mortality rate ratio from 2010
to 2020.
Figure 11. Identification of clusters during the analysis of newly diagnosed
patients with unstable angina pectoris in the researched area.
Figure 12. Identification of clusters during the analysis of patients deceased
from unstable angina pectoris in the researched area.
Figure 13. Identified patterns in the emerging hot spot analysis for the
unstable angina pectoris incidence/mortality rates per 100,000 population in the
province: (a) incidence rates, and (b) mortality rates.
Figure 14. Incidence rates per 100,000 population for cardiovascular diseases in
the researched area from 2010 to 2020: (a) acute coronary syndrome incidence
rates, (b) myocardial infarction incidence rates, and (c) unstable angina
pectoris incidence rates.
Figure 15. Cardiovascular diseases mortality rates per 100,000 population in the
researched area from 2010 to 2020: (a) acute coronary syndrome, (b) myocardial
infarction, and (c) unstable angina pectoris.
Figure 16. Diabetes incidence and mortality rate ratio per 100,000 population
from 2010 to 2019.
Figure 17. Identification of clusters during the diabetes analysis.
Figure 18. Identified patterns in the emerging hot spot analysis for the
diabetes incidence/mortality rates per 100,000 population in the province: (a)
incidence rates for type 1 diabetes (b) incidence rates for type 2 diabetes (c)
mortality rates for both diabetes types.
Figure 19. Geographical distribution of the diabetes incidence/mortality rates
per 100,000 population in the researched area from 2010 to 2019: (a) incidence
rates for both diabetes types, (b) incidence rates for type 1 diabetes, (c)
incidence rates for type 2 diabetes, and (d) mortality rates for both diabetes
types.
Figure 20. Results of the trends in the number of new patients and patients who
died from cardiovascular diseases during the period 2010 to 2020: (a) overview
of the number of new patients with acute coronary syndrome along with the trend
line of the disease, (b) overview of the number of patients who died from acute
coronary syndrome with the mortality trend, (c) overview of the number of new
patients with myocardial infarction along with the trend in the number of new
patients, (d) overview of the number of patients who died from myocardial
infarction and the mortality trend, (e) overview of the number of new patients
with unstable angina pectoris with the trend line of new patients, and (f)
overview of the number of patients who died from unstable angina pectoris with
the mortality trend line.
Figure 21. Results of the trends showing the number of new patients and those
deceased from diabetes in the researched area: (a) overview of the number of new
patients with both diabetes types from 2010 to 2019, (b) overview of the number
of people who died from both diabetes types from 2010 to 2019, (c) overview of
the number of new patients with type 1 diabetes from 2010 to 2019, (d) and
overview of the number of new patients with type 2 diabetes from 2010 to 2019.
Table 1. Parameters of the Mann–-Kendall test for particular diseases in the
researched area (α—significance level, +α = 0.1, * α = 0.05, *** α = 0.001, /—no
significance; b—so called Sen’s slope).
Type of DiseaseNumber of New PatientsNumber of the DeceasedZαbZAbAcute coronary
syndrome−2.02*−0.0112−3.89***−0.643Myocardial
infarction−1.87+−0.816−3.74***−0.690Unstable angina
pectoris−1.87+−0.230−0.63/−1.000Type 1 diabetes−2.23*−1.833−0.72/−0.110Type 2
diabetes2.42*0.773
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Kričković, E.; Lukić, T.; Jovanović-Popović, D. Geographic Medical Overview of
Noncommunicable Diseases (Cardiovascular Diseases and Diabetes) in the Territory
of the AP Vojvodina (Northern Serbia). Healthcare 2023, 11, 48.
https://doi.org/10.3390/healthcare11010048
AMA Style
Kričković E, Lukić T, Jovanović-Popović D. Geographic Medical Overview of
Noncommunicable Diseases (Cardiovascular Diseases and Diabetes) in the Territory
of the AP Vojvodina (Northern Serbia). Healthcare. 2023; 11(1):48.
https://doi.org/10.3390/healthcare11010048
Chicago/Turabian Style
Kričković, Emina, Tin Lukić, and Dejana Jovanović-Popović. 2023. "Geographic
Medical Overview of Noncommunicable Diseases (Cardiovascular Diseases and
Diabetes) in the Territory of the AP Vojvodina (Northern Serbia)" Healthcare 11,
no. 1: 48. https://doi.org/10.3390/healthcare11010048
APA Style
Kričković, E., Lukić, T., & Jovanović-Popović, D. (2023). Geographic Medical
Overview of Noncommunicable Diseases (Cardiovascular Diseases and Diabetes) in
the Territory of the AP Vojvodina (Northern Serbia). Healthcare, 11(1), 48.
https://doi.org/10.3390/healthcare11010048
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MDPI and ACS Style
Kričković, E.; Lukić, T.; Jovanović-Popović, D. Geographic Medical Overview of
Noncommunicable Diseases (Cardiovascular Diseases and Diabetes) in the Territory
of the AP Vojvodina (Northern Serbia). Healthcare 2023, 11, 48.
https://doi.org/10.3390/healthcare11010048
AMA Style
Kričković E, Lukić T, Jovanović-Popović D. Geographic Medical Overview of
Noncommunicable Diseases (Cardiovascular Diseases and Diabetes) in the Territory
of the AP Vojvodina (Northern Serbia). Healthcare. 2023; 11(1):48.
https://doi.org/10.3390/healthcare11010048
Chicago/Turabian Style
Kričković, Emina, Tin Lukić, and Dejana Jovanović-Popović. 2023. "Geographic
Medical Overview of Noncommunicable Diseases (Cardiovascular Diseases and
Diabetes) in the Territory of the AP Vojvodina (Northern Serbia)" Healthcare 11,
no. 1: 48. https://doi.org/10.3390/healthcare11010048
APA Style
Kričković, E., Lukić, T., & Jovanović-Popović, D. (2023). Geographic Medical
Overview of Noncommunicable Diseases (Cardiovascular Diseases and Diabetes) in
the Territory of the AP Vojvodina (Northern Serbia). Healthcare, 11(1), 48.
https://doi.org/10.3390/healthcare11010048
Note that from the first issue of 2016, this journal uses article numbers
instead of page numbers. See further details here.
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