scholarly journals Epidemiology of dengue virus infections in Nepal, 2006–2019

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Komal Raj Rijal ◽  
Bipin Adhikari ◽  
Bindu Ghimire ◽  
Binod Dhungel ◽  
Uttam Raj Pyakurel ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
High Elevation ◽  
Emerging Diseases ◽  
Virus Infections ◽  
Environmental Predictors ◽  
Standard Deviation Increase ◽  
Binomial Regression ◽  
Control Division ◽  
Over Time

Abstract Background Dengue is one of the newest emerging diseases in Nepal with increasing burden and geographic spread over the years. The main objective of this study was to explore the epidemiological patterns of dengue since its first outbreak (2006) to 2019 in Nepal. Methods This study is a retrospective analysis that covers the last 14 years (2006–2019) of reported dengue cases from Epidemiology Diseases Control Division (EDCD), Ministry of Health and Population, Government of Nepal. Reported cases were plotted over time and maps of reported case incidence were generated (from 2016 through 2019). An ecological analysis of environmental predictors of case incidence was conducted using negative binomial regression. Results While endemic dengue has been reported in Nepal since 2006, the case load has increased over time and in 2019 a total of 17 992 dengue cases were reported from 68 districts (from all seven provinces). Compared to the case incidence in 2016, incidence was approximately five times higher in 2018 [incidence rate ratio (IRR): 4.8; 95% confidence interval (CI) 1.5–15.3] and over 140 times higher in 2019 (IRR: 141.6; 95% CI 45.8–438.4). A one standard deviation increase in elevation was associated with a 90% decrease in reported case incidence (IRR: 0.10; 95% CI 0.01–0.20). However, the association between elevation and reported cases varied across the years. In 2018 there was a cluster of cases reported from high elevation Kaski District of Gandaki Province. Our results suggest that dengue infections are increasing in magnitude and expanding out of the lowland areas to higher elevations over time. Conclusions There is a high risk of dengue outbreak in the lowland Terai region, with increasing spread towards the mid-mountains and beyond as seen over the last 14 years. Urgent measures are required to increase the availability of diagnostics and resources to mitigate future dengue epidemics.

Epidemiology of dengue virus infections in Nepal, 2006–2019

2021 ◽  
Author(s):  
Komal Raj Rijal ◽  
Bipin Adhikari ◽  
Bindu Ghimire ◽  
Binod Dhungel ◽  
Uttam Raj Pyakurel ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
High Elevation ◽  
Emerging Diseases ◽  
Virus Infections ◽  
Environmental Predictors ◽  
Standard Deviation Increase ◽  
Binomial Regression ◽  
Control Division ◽  
Over Time

Abstract Background: Dengue is one of the newest emerging diseases in Nepal with increasing burden and geographic spread over the years. The main objective of this study was to explore the epidemiological patterns of dengue since its first outbreak (2006) to 2019 in Nepal.Methods: This study is a retrospective analysis that covers the last 14 years (2006–2019) of reported dengue cases from Epidemiology Diseases Control Division (EDCD), Ministry of Health and Population, Government of Nepal. Reported cases were plotted over time and maps of reported case incidence were generated (from 2016 through 2019). An ecological analysis of environmental predictors of case incidence was conducted using negative binomial regression.Results: While endemic dengue has been reported in Nepal since 2006, the case load has increased over time and in 2019 a total of 17 992 dengue cases were reported from 68 districts (from all seven provinces). Compared to the case incidence in 2016, incidence was approximately five times higher in 2018 [incidence rate ratio (IRR): 4.8; 95% confidence interval (CI): 1.5 – 15.3] and over 140 times higher in 2019 (IRR: 141.6; 95% CI: 45.8 – 438.4). A one standard deviation increase in elevation was associated with a 90% decrease in reported case incidence (IRR: 0.10; 95% CI: 0.01 – 0.20). However, the association between elevation and reported cases varied across the years. In 2018 there was a cluster of cases reported from high elevation Kaski District of Gandaki Province. Our results suggest that dengue infections are increasing in magnitude and expanding out of the lowland areas to higher elevations over time.Conclusions: There is a high risk of dengue outbreak in the lowland Terai region, with increasing spread towards the mid-mountains and beyond as seen over the last 14 years. Urgent measures are required to increase the availability of diagnostics and resources to mitigate future dengue epidemics.

scholarly journals Prevalence of SARS-CoV-2 infection among people experiencing homelessness in Toronto during the first wave of the COVID-19 pandemic

2021 ◽  
Author(s):  
Linh Luong ◽  
Michaela Beder ◽  
Rosane Nisenbaum ◽  
Aaron Orkin ◽  
Jonathan Wong ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Infection Rates ◽  
Individual Site ◽  
Site Characteristics ◽  
Increased Risk ◽  
Binomial Regression ◽  
Great Heterogeneity ◽  
The Relationship ◽  
Over Time

Background: People experiencing homelessness are at increased risk of SARS-CoV-2 infection. This study reports the point prevalence of SARS-CoV-2 infection during testing conducted at sites serving people experiencing homelessness in Toronto during the first wave of the COVID-19 pandemic. We also explored the association between site characteristics and prevalence rates. Methods: The study included individuals who were staying at shelters, encampments, COVID-19 physical distancing sites, and drop-in and respite sites and completed outreach-based testing for SARS-CoV-2 during the period April 17 to July 31, 2020. We examined test positivity rates over time and compared them to rates in the general population of Toronto. Negative binomial regression was used to examine the relationship between each shelter-level characteristic and SARS-CoV-2 positivity rates. We also compared the rates across 3 time periods (T1: April 17-April 25; T2: April 26-May 23; T3: May 24-June 25). Results: The overall prevalence of SARS-CoV-2 infection was 8.5% (394/4657). Site-specific rates showed great heterogeneity with infection rates ranging from 0% to 70.6%. Compared to T1, positivity rates were 0.21 times lower (95% CI: 0.06, 0.75) during T2 and 0.14 times lower (95% CI: 0.043, 0.44) during T3. Most cases were detected during outbreak testing (384/394 [97.5%]) rather than active case finding. Interpretation: During the first wave of the pandemic, rates of SARS-CoV-2 infection at sites for people experiencing homelessness in Toronto varied significantly over time. The observation of lower rates at certain sites may be attributable to overall time trends, expansion of outreach-based testing to include sites without known outbreaks and/or individual site characteristics.

scholarly journals Measuring the effectiveness of COVID-19 containment policies in Italian regions: are we doing enough?

2021 ◽  
pp. 75-80
Author(s):  
Demetrio Panarello ◽  
Giorgio Tassinari
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Current Phase ◽  
Mobility Limitations ◽  
Italian Regions ◽  
Binomial Regression ◽  
Stringency Level ◽  
Restrictive Measures ◽  
Second Wave ◽  
Over Time

A successful fight against COVID-19 greatly depends on citizens’ adherence to the restrictive measures, which may not suffice alone. Making use of a containment index, data on sanctions, and Google’s movement trends across Italian regions, complemented by other sources, we investigate the extent to which compliance with the mobility limitations has affected the number of deaths over time in the period from the 24th of February 2020 to the 9th of November 2020, by using panel data for Italian regions, analysed through a negative binomial regression method. We also differentiated the study period, estimating two distinct models on two subsamples: until the 13th of September and since the 14th of September. In so doing, we show how the pandemic dynamics have changed between the first and the second wave of the emergency. Our results highlight that the importance of the restrictive measures and of citizens’ accord on their abidance has greatly increased since the end of the summer, also because the stringency level of the adopted measures has critically declined. Informing citizens about the effects and purposes of the restrictive measures is of paramount importance, especially in the current phase of the pandemic.

scholarly journals Prevalence of SARS-CoV-2 infection among people experiencing homelessness in Toronto during the first wave of the COVID-19 pandemic

2021 ◽  
Author(s):  
Linh Luong ◽  
Michaela Beder ◽  
Rosane Nisenbaum ◽  
Aaron Orkin ◽  
Jonathan Wong ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Infection Rates ◽  
Individual Site ◽  
Site Characteristics ◽  
Increased Risk ◽  
Binomial Regression ◽  
Great Heterogeneity ◽  
The Relationship ◽  
Over Time

Abstract Objectives People experiencing homelessness are at increased risk of SARS-CoV-2 infection. This study reports the point prevalence of SARS-CoV-2 infection during testing conducted at sites serving people experiencing homelessness in Toronto during the first wave of the COVID-19 pandemic. We also explored the association between site characteristics and prevalence rates. Methods The study included individuals who were staying at shelters, encampments, COVID-19 physical distancing sites, and drop-in and respite sites and completed outreach-based testing for SARS-CoV-2 during the period April 17 to July 31, 2020. We examined test positivity rates over time and compared them to rates in the general population of Toronto. Negative binomial regression was used to examine the relationship between each shelter-level characteristic and SARS-CoV-2 positivity rates. We also compared the rates across 3 time periods (T1: April 17–April 25; T2: April 26–May 23; T3: May 24–June 25). Results The overall prevalence of SARS-CoV-2 infection was 8.5% (394/4657). Site-specific rates showed great heterogeneity with infection rates ranging from 0% to 70.6%. Compared to T1, positivity rates were 0.21 times lower (95% CI: 0.06–0.75) during T2 and 0.14 times lower (95% CI: 0.04–0.44) during T3. Most cases were detected during outbreak testing (384/394 [97.5%]) rather than active case finding. Conclusion During the first wave of the pandemic, rates of SARS-CoV-2 infection at sites for people experiencing homelessness in Toronto varied significantly over time. The observation of lower rates at certain sites may be attributable to overall time trends, expansion of outreach-based testing to include sites without known outbreaks, and/or individual site characteristics.

scholarly journals GP consultation rates for sequelae after acute covid-19 in patients managed in the community or hospital in the UK: population based study

BMJ ◽  
2021 ◽  
pp. e065834
Author(s):  
Hannah R Whittaker ◽  
Claudia Gulea ◽  
Ardita Koteci ◽  
Constantinos Kallis ◽  
Ann D Morgan ◽  
...  
Keyword(s):  
Venous Thromboembolism ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Population Based ◽  
Negative Control ◽  
Healthcare Use ◽  
Population Based Study ◽  
Binomial Regression ◽  
Before And After ◽  
Over Time

AbstractObjectivesTo describe the rates for consulting a general practitioner (GP) for sequelae after acute covid-19 in patients admitted to hospital with covid-19 and those managed in the community, and to determine how the rates change over time for patients in the community and after vaccination for covid-19.DesignPopulation based study.Setting1392 general practices in England contributing to the Clinical Practice Research Datalink Aurum database.Participants456 002 patients with a diagnosis of covid-19 between 1 August 2020 and 14 February 2021 (44.7% men; median age 61 years), admitted to hospital within two weeks of diagnosis or managed in the community, and followed-up for a maximum of 9.2 months. A negative control group included individuals without covid-19 (n=38 511) and patients with influenza before the pandemic (n=21 803).Main outcome measuresComparison of rates for consulting a GP for new symptoms, diseases, prescriptions, and healthcare use in individuals admitted to hospital and those managed in the community, separately, before and after covid-19 infection, using Cox regression and negative binomial regression for healthcare use. The analysis was repeated for the negative control and influenza cohorts. In individuals in the community, outcomes were also described over time after a diagnosis of covid-19, and compared before and after vaccination for individuals who were symptomatic after covid-19 infection, using negative binomial regression.ResultsRelative to the negative control and influenza cohorts, patients in the community (n=437 943) had significantly higher GP consultation rates for multiple sequelae, and the most common were loss of smell or taste, or both (adjusted hazard ratio 5.28, 95% confidence interval 3.89 to 7.17, P<0.001); venous thromboembolism (3.35, 2.87 to 3.91, P<0.001); lung fibrosis (2.41, 1.37 to 4.25, P=0.002), and muscle pain (1.89, 1.63 to 2.20, P<0.001); and also for healthcare use after a diagnosis of covid-19 compared with 12 months before infection. For absolute proportions, the most common outcomes ≥4 weeks after a covid-19 diagnosis in patients in the community were joint pain (2.5%), anxiety (1.2%), and prescriptions for non-steroidal anti-inflammatory drugs (1.2%). Patients admitted to hospital (n=18 059) also had significantly higher GP consultation rates for multiple sequelae, most commonly for venous thromboembolism (16.21, 11.28 to 23.31, P<0.001), nausea (4.64, 2.24 to 9.21, P<0.001), prescriptions for paracetamol (3.68, 2.86 to 4.74, P<0.001), renal failure (3.42, 2.67 to 4.38, P<0.001), and healthcare use after a covid-19 diagnosis compared with 12 months before infection. For absolute proportions, the most common outcomes ≥4 weeks after a covid-19 diagnosis in patients admitted to hospital were venous thromboembolism (3.5%), joint pain (2.7%), and breathlessness (2.8%). In patients in the community, anxiety and depression, abdominal pain, diarrhoea, general pain, nausea, chest tightness, and tinnitus persisted throughout follow-up. GP consultation rates were reduced for all symptoms, prescriptions, and healthcare use, except for neuropathic pain, cognitive impairment, strong opiates, and paracetamol use in patients in the community after the first vaccination dose for covid-19 relative to before vaccination. GP consultation rates were also reduced for ischaemic heart disease, asthma, and gastro-oesophageal disease.ConclusionsGP consultation rates for sequelae after acute covid-19 infection differed between patients with covid-19 who were admitted to hospital and those managed in the community. For individuals in the community, rates of some sequelae decreased over time but those for others, such as anxiety and depression, persisted. Rates of some outcomes decreased after vaccination in this group.

scholarly journals Is area deprivation associated with greater impacts of COVID-19 in care homes across England? A preliminary analysis of COVID-19 outbreaks and deaths

2021 ◽  
pp. jech-2020-215039 ◽  
Author(s):  
Anders Malthe Bach-Mortensen ◽  
Michelle Degli Esposti
Keyword(s):  
Negative Binomial ◽  
Care Home ◽  
Negative Binomial Regression ◽  
Population Characteristics ◽  
Care Homes ◽  
Future Research ◽  
Area Deprivation ◽  
Binomial Regression ◽  
Index Of Multiple Deprivation

IntroductionThe COVID-19 pandemic has disproportionately impacted care homes and vulnerable populations, exacerbating existing health inequalities. However, the role of area deprivation in shaping the impacts of COVID-19 in care homes is poorly understood. We examine whether area deprivation is linked to higher rates of COVID-19 outbreaks and deaths among care home residents across upper tier local authorities in England (n=149).MethodsWe constructed a novel dataset from publicly available data. Using negative binomial regression models, we analysed the associations between area deprivation (Income Deprivation Affecting Older People Index (IDAOPI) and Index of Multiple Deprivation (IMD) extent) as the exposure and COVID-19 outbreaks, COVID-19-related deaths and all-cause deaths among care home residents as three separate outcomes—adjusting for population characteristics (size, age composition, ethnicity).ResultsCOVID-19 outbreaks in care homes did not vary by area deprivation. However, COVID-19-related deaths were more common in the most deprived quartiles of IDAOPI (incidence rate ratio (IRR): 1.23, 95% CI 1.04 to 1.47) and IMD extent (IRR: 1.16, 95% CI 1.00 to 1.34), compared with the least deprived quartiles.DiscussionThese findings suggest that area deprivation is a key risk factor in COVID-19 deaths among care home residents. Future research should look to replicate these results when more complete data become available.

scholarly journals Association between fatality rate of COVID-19 and selenium deficiency in China

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hai-Yang Zhang ◽  
An-Ran Zhang ◽  
Qing-Bin Lu ◽  
Xiao-Ai Zhang ◽  
Zhi-Jie Zhang ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Viral Infections ◽  
Case Fatality ◽  
Selenium Deficiency ◽  
Negative Binomial Regression ◽  
Negative Binomial Regression Model ◽  
Fatality Rate ◽  
Selenium Content ◽  
Individual Level ◽  
Binomial Regression

Abstract Background COVID-19 has impacted populations around the world, with the fatality rate varying dramatically across countries. Selenium, as one of the important micronutrients implicated in viral infections, was suggested to play roles. Methods An ecological study was performed to assess the association between the COVID-19 related fatality and the selenium content both from crops and topsoil, in China. Results Totally, 14,045 COVID-19 cases were reported from 147 cities during 8 December 2019–13 December 2020 were included. Based on selenium content in crops, the case fatality rates (CFRs) gradually increased from 1.17% in non-selenium-deficient areas, to 1.28% in moderate-selenium-deficient areas, and further to 3.16% in severe-selenium-deficient areas (P = 0.002). Based on selenium content in topsoil, the CFRs gradually increased from 0.76% in non-selenium-deficient areas, to 1.70% in moderate-selenium-deficient areas, and further to 1.85% in severe-selenium-deficient areas (P < 0.001). The zero-inflated negative binomial regression model showed a significantly higher fatality risk in cities with severe-selenium-deficient selenium content in crops than non-selenium-deficient cities, with incidence rate ratio (IRR) of 3.88 (95% CIs: 1.21–12.52), which was further confirmed by regression fitting the association between CFR of COVID-19 and selenium content in topsoil, with the IRR of 2.38 (95% CIs: 1.14–4.98) for moderate-selenium-deficient cities and 3.06 (1.49–6.27) for severe-selenium-deficient cities. Conclusions Regional selenium deficiency might be related to an increased CFR of COVID-19. Future studies are needed to explore the associations between selenium status and disease outcome at individual-level.

scholarly journals Statistical models for analyzing count data: predictors of length of stay among HIV patients in Portugal using a multilevel model

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ahmed Nabil Shaaban ◽  
Bárbara Peleteiro ◽  
Maria Rosario O. Martins
Keyword(s):  
Length Of Stay ◽  
Regression Model ◽  
Random Effects ◽  
Count Data ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Comprehensive Approach ◽  
Negative Binomial Regression Model ◽  
Hiv Patients ◽  
Binomial Regression

Abstract Background This study offers a comprehensive approach to precisely analyze the complexly distributed length of stay among HIV admissions in Portugal. Objective To provide an illustration of statistical techniques for analysing count data using longitudinal predictors of length of stay among HIV hospitalizations in Portugal. Method Registered discharges in the Portuguese National Health Service (NHS) facilities Between January 2009 and December 2017, a total of 26,505 classified under Major Diagnostic Category (MDC) created for patients with HIV infection, with HIV/AIDS as a main or secondary cause of admission, were used to predict length of stay among HIV hospitalizations in Portugal. Several strategies were applied to select the best count fit model that includes the Poisson regression model, zero-inflated Poisson, the negative binomial regression model, and zero-inflated negative binomial regression model. A random hospital effects term has been incorporated into the negative binomial model to examine the dependence between observations within the same hospital. A multivariable analysis has been performed to assess the effect of covariates on length of stay. Results The median length of stay in our study was 11 days (interquartile range: 6–22). Statistical comparisons among the count models revealed that the random-effects negative binomial models provided the best fit with observed data. Admissions among males or admissions associated with TB infection, pneumocystis, cytomegalovirus, candidiasis, toxoplasmosis, or mycobacterium disease exhibit a highly significant increase in length of stay. Perfect trends were observed in which a higher number of diagnoses or procedures lead to significantly higher length of stay. The random-effects term included in our model and refers to unexplained factors specific to each hospital revealed obvious differences in quality among the hospitals included in our study. Conclusions This study provides a comprehensive approach to address unique problems associated with the prediction of length of stay among HIV patients in Portugal.

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