Literature DB >> 33740123

Telemedicine and urban diabetes during COVID-19 pandemic in Milano, Italy during lock-down: epidemiological and sociodemographic picture.

Livio Luzi1,2, Michele Carruba3,4, Roberta Crialesi5, Stefano Da Empoli6, Regina Dagani7,8, Elisabetta Lovati9,10, Antonio Nicolucci11, Cesare C Berra12, Elisa Cipponeri12, Ketty Vaccaro13, Andrea Lenzi14,15,16.   

Abstract

BACKGROUND: Since 2010, more than half of World population lives in Urban Environments. Urban Diabetes has arisen as a novel nosological entity in Medicine. Urbanization leads to the accrual of a number of factors increasing the vulnerability to diabetes mellitus and related diseases. Herein we report clinical-epidemiological data of the Milano Metropolitan Area in the contest of the Cities Changing Diabetes Program. Since the epidemiological picture was taken in January 2020, on the edge of COVID-19 outbreak in the Milano Metropolitan Area, a perspective addressing potential interactions between diabetes and obesity prevalence and COVID-19 outbreak, morbidity and mortality will be presented. To counteract lock-down isolation and, in general, social distancing a pilot study was conducted to assess the feasibility and efficacy of tele-monitoring via Flash Glucose control in a cohort of diabetic patients in ASST North Milano.
METHODS: Data presented derive from 1. ISTAT (National Institute of Statistics of Italy), 2. Milano ATS web site (Health Agency of Metropolitan Milano Area), which entails five ASST (Health Agencies in the Territories). A pilot study was conducted in 65 screened diabetic patients (only 40 were enrolled in the study of those 36 were affected by type 2 diabetes and 4 were affected by type 1 diabetes) of ASST North Milano utilizing Flash Glucose Monitoring for 3 months (mean age 65 years, HbA1c 7,9%. Patients were subdivided in 3 groups using glycemic Variability Coefficient (VC): a. High risk, VC > 36, n. 8 patients; Intermediate risk 20 < VC < 36, n. 26 patients; Low risk VC < 20, n. 4 patients. The control group was constituted by 26 diabetic patients non utilizing Flash Glucose monitoring.
RESULTS: In a total population of 3.227.264 (23% is over 65 y) there is an overall prevalence of 5.65% with a significant difference between Downtown ASST (5.31%) and peripheral ASST (ASST North Milano, 6.8%). Obesity and overweight account for a prevalence of 7.8% and 27.7%, respectively, in Milano Metropolitan Area. We found a linear relationship (R = 0.36) between prevalence of diabetes and aging index. Similarly, correlations between diabetes prevalence and both older people depending index and structural dependence index (R = 0.75 and R = 0.93, respectively), were found. A positive correlation (R = 0.46) with percent of unoccupied people and diabetes prevalence was also found. A reverse relationship between diabetes prevalence and University level instruction rate was finally identified (R = - 0.82). Our preliminary study demonstrated a reduction of Glycated Hemoglobin (p = 0.047) at 3 months follow-up during the lock-down period, indicating Flash Glucose Monitoring and remote control as a potential methodology for diabetes management during COVID-19 lock-down. HYPOTHESIS AND DISCUSSION: The increase in diabetes and obesity prevalence in Milano Metropolitan Area, which took place over 30 years, is related to several environmental factors. We hypothesize that some of those factors may have also determined the high incidence and virulence of COVID-19 in the Milano area. Health Agencies of Milano Metropolitan Area are presently taking care of diabetic patients facing the new challenge of maintaining sustainable diabetes care costs in light of an increase in urban population and of the new life-style. The COVID-19 pandemic will modify the management of diabetic and obese patients permanently, via the implementation of approaches that entail telemedicine technology. The pilot study conducted during the lock-down period indicates an improvement of glucose control utilizing a remote glucose control system in the Milano Metropolitan Area, suggesting a wider utilization of similar methodologies during the present "second wave" lock-down.

Entities:  

Keywords:  COVID-19; Diabetes and obesity prevalence; Telemonitoring of blood glucose; Urban diabetes

Year:  2021        PMID: 33740123      PMCID: PMC7977495          DOI: 10.1007/s00592-021-01700-2

Source DB:  PubMed          Journal:  Acta Diabetol        ISSN: 0940-5429            Impact factor:   4.280


  25 in total

1.  Prevalence of and secular trends in diagnosed diabetes in Italy: 1980-2013.

Authors:  R Gnavi; A Migliardi; M Maggini; G Costa
Journal:  Nutr Metab Cardiovasc Dis       Date:  2017-12-22       Impact factor: 4.222

2.  Similarity in Case Fatality Rates (CFR) of COVID-19/SARS-COV-2 in Italy and China.

Authors:  Rossella Porcheddu; Caterina Serra; David Kelvin; Nikki Kelvin; Salvatore Rubino
Journal:  J Infect Dev Ctries       Date:  2020-02-29       Impact factor: 0.968

3.  Routine resting energy expenditure measurement increases effectiveness of dietary intervention in obesity.

Authors:  Stefano Massarini; Anna Ferrulli; Federico Ambrogi; Concetta Macrì; Ileana Terruzzi; Stefano Benedini; Livio Luzi
Journal:  Acta Diabetol       Date:  2017-11-03       Impact factor: 4.280

4.  Global epidemiology of prediabetes - present and future perspectives.

Authors:  Ulrike Hostalek
Journal:  Clin Diabetes Endocrinol       Date:  2019-05-09

5.  Prevalence, awareness, treatment, control of type 2 diabetes mellitus and risk factors in Chinese rural population: the RuralDiab study.

Authors:  Xiaotian Liu; Yuqian Li; Linlin Li; Luning Zhang; Yongcheng Ren; Hao Zhou; Lingling Cui; Zhenxing Mao; Dongsheng Hu; Chongjian Wang
Journal:  Sci Rep       Date:  2016-08-11       Impact factor: 4.379

6.  Distribution of Patients at Risk for Complications Related to COVID-19 in the United States: Model Development Study.

Authors:  Renae Smith-Ray; Erin E Roberts; Devonee E Littleton; Tanya Singh; Thomas Sandberg; Michael Taitel
Journal:  JMIR Public Health Surveill       Date:  2020-06-18

7.  Influenza and obesity: its odd relationship and the lessons for COVID-19 pandemic.

Authors:  Livio Luzi; Maria Grazia Radaelli
Journal:  Acta Diabetol       Date:  2020-04-05       Impact factor: 4.280

8.  Prevalence and impact of diabetes among people infected with SARS-CoV-2.

Authors:  G P Fadini; M L Morieri; E Longato; A Avogaro
Journal:  J Endocrinol Invest       Date:  2020-03-28       Impact factor: 4.256

Review 9.  COVID-19: From bench to bed side.

Authors:  Akriti Singh; Altamash Shaikh; Ritu Singh; Awadhesh Kumar Singh
Journal:  Diabetes Metab Syndr       Date:  2020-04-09

Review 10.  Bat-borne virus diversity, spillover and emergence.

Authors:  Michael Letko; Stephanie N Seifert; Kevin J Olival; Raina K Plowright; Vincent J Munster
Journal:  Nat Rev Microbiol       Date:  2020-06-11       Impact factor: 78.297

View more
  8 in total

Review 1.  Telemedicine for the Clinical Management of Diabetes; Implications and Considerations After COVID-19 Experience.

Authors:  Saula Vigili de Kreutzenberg
Journal:  High Blood Press Cardiovasc Prev       Date:  2022-05-17

2.  Type 1 diabetes and the challenges of emotional support in crisis situations: results from a feasibility study of a multidisciplinary teleintervention.

Authors:  Janine Alessi; Alice Scalzilli Becker; Bibiana Amaral; Giovana Berger de Oliveira; Débora Wilke Franco; Carolina Padilla Knijnik; Gabriel Luiz Kobe; Ariane de Brito; Taíse Rosa de Carvalho; Guilherme Heiden Telo; Beatriz D Schaan; Gabriela Heiden Telo
Journal:  Sci Rep       Date:  2022-05-20       Impact factor: 4.996

3.  Assessing Urban Policies in a COVID-19 World.

Authors:  Przemysław Śleszyński; Paulina Legutko-Kobus; Mark Rosenberg; Viktoriya Pantyley; Maciej J Nowak
Journal:  Int J Environ Res Public Health       Date:  2022-04-27       Impact factor: 4.614

4.  The worldwide impact of telemedicine during COVID-19: current evidence and recommendations for the future.

Authors:  Stefano Omboni; Raj S Padwal; Tourkiah Alessa; Béla Benczúr; Beverly B Green; Ilona Hubbard; Kazuomi Kario; Nadia A Khan; Alexandra Konradi; Alexander G Logan; Yuan Lu; Maurice Mars; Richard J McManus; Sarah Melville; Claas L Neumann; Gianfranco Parati; Nicolas F Renna; Philippe Ryvlin; Hugo Saner; Aletta E Schutte; Jiguang Wang
Journal:  Connect Health       Date:  2022-01-04

5.  Telemedicine and diabetes during the COVID-19 era.

Authors:  Athanasia Papazafiropoulou
Journal:  Arch Med Sci Atheroscler Dis       Date:  2022-08-31

6.  Substitution of telemedicine for clinic visit during the COVID-19 pandemic of 2020: Comparison of telemedicine and clinic visit.

Authors:  Yukiko Onishi; Rieko Ichihashi; Yoko Yoshida; Tazu Tahara; Takako Kikuchi; Toshiko Kobori; Tetsuya Kubota; Masahiko Iwamoto; Shoko Hamano; Masato Kasuga
Journal:  J Diabetes Investig       Date:  2022-05-25       Impact factor: 3.681

7.  The impact of a telehealth intervention on the metabolic profile of diabetes mellitus patients during the COVID-19 pandemic - A randomized clinical trial.

Authors:  Debora Wilke Franco; Janine Alessi; Taíse Rosa de Carvalho; Gabriel Luiz Kobe; Giovana Berger Oliveira; Carolina Padilla Knijnik; Bibiana Amaral; Alice Scalzilli Becker; Beatriz D Schaan; Gabriela Heiden Telo
Journal:  Prim Care Diabetes       Date:  2022-09-30       Impact factor: 2.567

Review 8.  A Narrative Review of the Launch and the Deployment of Telemedicine in Italy during the COVID-19 Pandemic.

Authors:  Daniele Giansanti; Giovanni Morone; Alice Loreti; Marco Germanotta; Irene Aprile
Journal:  Healthcare (Basel)       Date:  2022-02-23
  8 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.