A geographic analysis of population density thresholds in the influenza pandemic of 1918-19

Factors affecting the number of influenza patients before and during COVID-19 pandemic, Thailand

This study was aimed to explore the association between potential factors including public health and social measures and the number of influenza patients in Thailand between 2014-2021. Secondary data from relevant agencies were collected. Generalized Estimating Equation (GEE) and regression coefficient (β) were performed at a significance level of 0.05. We found factors associated with number of influenza patients during the time prior to COVID-19 pandemic were monthly income per household (Adjusted β = -0.02; 95% CI: -0.03, -0.01), population density (Adjusted β = 1.00; 95% CI: 0.82, 1.18), rainy season (Adjusted β = 137.15; 95% CI: 86.17, 188.13) and winter time (Adjusted β = 56.46; 95% CI: 3.21, 109.71). During the time of COVID-19 pandemic, population density (Adjusted β = 0.20; 95% CI: 0.15, 0.26), rainy season (Adjusted β = -164.23; 95% CI: -229.93, -98.52), winter time (Adjusted β = 61.06; 95% CI: 0.71, 121.41), public health control measures (prohibition of entering to into an area with high number of COVID-19 infections (Adjusted β = -169.34; 95% CI: -233.52, -105.16), and restriction of travelling also reduced the number of influenza patients (Adjusted β = -66.88; 95% CI: -125.15, -8.62) were associated with number of influenza patients. This study commends strategies in monitoring influenza patients to focus on the areas with low income, high population density, and in specific seasons. Public health and social measures which can be implemented are prohibition of entering to risk-areas (lock down), and restriction of travelling across provinces which their effectiveness in reducing influenza infections.

Is there an association between hospital staffing levels and inpatient-COVID-19 mortality rates?

Objective

This study aims to investigate the relationship between RNs and hospital-based medical specialties staffing levels with inpatient COVID-19 mortality rates.

Methods

We relied on data from AHA Annual Survey Database, Area Health Resource File, and UnitedHealth Group Clinical Discovery Database. In phase 1 of the analysis, we estimated the risk-standardized event rates (RSERs) based on 95,915 patients in the UnitedHealth Group Database 1,398 hospitals. We then used beta regression to analyze the association between hospital- and county- level factors with risk-standardized inpatient COVID-19 mortality rates from March 1, 2020, through December 31, 2020.

Results

Higher staffing levels of RNs and emergency medicine physicians were associated with lower COVID-19 mortality rates. Moreover, larger teaching hospitals located in urban settings had higher COVID-19 mortality rates. Finally, counties with greater social vulnerability, specifically in terms of housing type and transportation, and those with high infection rates had the worst patient mortality rates.

Conclusion

Higher staffing levels are associated with lower inpatient mortality rates for COVID-19 patients. More research is needed to determine appropriate staffing levels and how staffing levels interact with other factors such as teams, leadership, and culture to impact patient care during pandemics.

Stay-at-home and face mask policy intentions inconsistent with incidence and fatality during the US COVID-19 pandemic

During the COVID-19 pandemic, many states imposed stay-at-home (SAH) and mandatory face mask (MFM) orders to supplement the United States CDC recommendations. The purpose of this study was to characterize the relationship between SAH and MFM approaches with the incidence and fatality of COVID-19 during the pandemic period until 23 August 2020 (about 171 days), the period with no vaccines or specific drugs that had passed the phase III clinical trials yet. States with SAH orders showed a potential 50-60% decrease in infection and fatality during the SAH period (about 45 days). After normalization to population density, there was a 44% significant increase in the fatality rate in no-SAH + no-MFM states when compared to SAH + MFM. However, many results in this study were inconsistent with the intent of public health strategies of SAH and MFM. There were similar incidence rates (1.41, 1.81, and 1.36%) and significant differences in fatality rates (3.40, 2.12, and 1.25%; p < 0.05) and mortality rates (51.43, 34.50, and 17.42 per 100,000 residents; p < 0.05) among SAH + MFM, SAH + no-MFM, and no-SAH + no-MFM states, respectively. There were no significant differences in total positive cases, average daily new cases, and average daily fatality when normalized with population density among the three groups. This study suggested potential decreases in infection and fatality with short-term SAH order. However, SAH and MFM orders from some states' policies probably had limited effects in lowering transmission and fatality among the general population. At the policy-making level, if contagious patients would not likely be placed in strict isolation and massive contact tracing would not be effective to implement, we presume that following the CDC's recommendations with close monitoring of healthcare capacity could be appropriate in helping mitigate the COVID-19 disaster while limiting collateral socioeconomic damages.

Identification of Thresholds on Population Density for Understanding Transmission of COVID-19

Pathways of transmission of coronavirus (COVID-19) disease in the human population are still emerging. However, empirical observations suggest that dense human settlements are the most adversely impacted, corroborating a broad consensus that human-to-human transmission is a key mechanism for the rapid spread of this disease. Here, using logistic regression techniques, estimates of threshold levels of population density were computed corresponding to the incidence (case counts) in the human population. Regions with population densities greater than 3,000 person per square mile in the United States have about 95% likelihood to report 43,380 number of average cumulative cases of COVID-19. Since case numbers of COVID-19 dynamically changed each day until 30 November 2020, ca. 4% of US counties were at 50% or higher probability to 38,232 number of COVID-19 cases. While threshold on population density is not the sole indicator for predictability of coronavirus in human population, yet it is one of the key variables on understanding and rethinking human settlement in urban landscapes.

Influenza virus and its subtypes circulating during 2018-2019: A hospital-based study from Assam

PURPOSE

Influenza virus can cause serious respiratory illness sometimes resulting in epidemics and pandemics associated with significant morbidity and mortality across the globe. Hence, continuous surveillance of the activity of the influenza virus and its subtypes is necessary to help the policy makers to take effective and appropriate decisions regarding its control. The study aimed to determine distribution of influenza viruses in Assam of north-east India having subtropical climate that may lead to viral subtype divergence.

METHODS

Clinically suspected ninety cases with Influenza like illness (ILI) were included, irrespective of age and sex during the period 1st July 2018 to 30th June 2019. Aseptically collected Nasopharyngeal swabs in viral transport media (VTM) were tested by conventional Reverse Transcriptase Polymerase Chain Reaction (RT PCR) for detection of Influenza A and Influenza B viruses which were further processed for detection of subtypes such as H1N1 pdm09, H3N2 and Influenza B (Yamagata and Victoria lineage). Normally distributed continuous variables were summarised by mean and standard deviation. All categorical variables were summarised as percentages.

RESULTS

Influenza activity was seen in 42.2% of ILI cases with male predominance (57.9%). Influenza A was the predominant type (84.2%). Among the subtypes, A(H1N1) pdm09 was predominant (76.3%) followed by Influenza B (Victoria lineages) (15.8%) and AH3N2 (7.9%). Significant difference was observed between different subtypes with regard to age distribution only. Influenza activity in Assam showed two seasonal peaks; the primary one from May to July and the secondary from November to February.

CONCLUSION

The study described the distribution of different Influenza viruses and its subtypes in Assam along with their seasonal activities. These findings will help to formulate the policy for its prevention and control in Assam as well as to monitor the efficacy of the current influenza vaccine.

Respiratory pandemics, urban planning and design: A multidisciplinary rapid review of the literature

COVID-19 is the most recent respiratory pandemic to necessitate better knowledge about city planning and design. The complex connections between cities and pandemics, however challenge traditional approaches to reviewing literature. In this article we adopted a rapid review methodology. We review the historical literature on respiratory pandemics and their documented connections to urban planning and design (both broadly defined as being concerned with cities as complex systems). Our systematic search across multidisciplinary databases returned a total of 1323 sources, with 92 articles included in the final review. Findings showed that the literature represents the multi-scalar nature of cities and pandemics - pandemics are global phenomena spread through an interconnected world, but require regional, city, local and individual responses. We characterise the literature under ten themes: scale (global to local); built environment; governance; modelling; non-pharmaceutical interventions; socioeconomic factors; system preparedness; system responses; underserved and vulnerable populations; and future-proofing urban planning and design. We conclude that the historical literature captures how city planning and design intersects with a public health response to respiratory pandemics. Our thematic framework provides parameters for future research and policy responses to the varied connections between cities and respiratory pandemics.

An exploratory study to assess patterns of influenza- and pneumonia-related mortality among the Italian elderly

Older adults are at disproportionately high risk of severe influenza-related outcomes and represent the main target of the annual influenza vaccination. The protective effect of seasonal influenza vaccination on the observed mortality indicators is controversial. In this ecological study, spatiotemporal patterns of pneumonia- and influenza-related mortality registered in the Italian elderly over seven (2011–2017) consecutive seasons were explored and the epidemiological association between the observed local pneumonia- and influenza-related mortality and influenza vaccination campaign features were modeled by using both fixed- and random-effects panel regression models. The descriptive spatiotemporal analysis showed a clear North–South gradient, where northern regions tended to report more pneumonia- and influenza-related deaths. After adjustment for potential confounders, it was found that each 1% increase in influenza vaccination coverage rate would be associated (P < .001) with a 1.6–1.9% decrease in pneumonia- and influenza-related mortality. Moreover, each 1% increase in the use of MF59®-adjuvanted trivalent influenza vaccine would be associated (P < .05) with a further 0.4% decrease in pneumonia- and influenza-related mortality. This study supports the increase in annual influenza vaccination in Italy and suggests that a higher level of use of the adjuvanted influenza vaccine in the elderly may be beneficial.

Investigating Health Context: Using Geospatial Big Data Ecosystem (Preprint)

Background

Objective

Methods

Results

Conclusions

Regional variation in COVID-19 positive hospitalisation across Scotland during the first wave of the pandemic and its relation to population density: A cross-sectional observation study

Background

There have been large regional differences in COVID-19 virus activity across the UK with many commentators suggesting that these are related to age, ethnicity and social class. There has also been a focus on cases, hospitalisations and deaths rather than on hospitalisation rates expressed per 100,000 population. The purpose of our study was to examine regional variation in COVID-19 positive hospitalisation rates in Scotland during the first wave of the pandemic and the possibility that these might be related to population density.

Methods and findings

This was a repeated point prevalence study. The number of COVID-19 positive patients hospitalised in the eleven Scottish mainland health boards peaked at 1517 on 19th April, then fell to a low of 243 on 16th August before rising slightly to 262 on 15th September. In July, August and September only four boards had more than 5 hospitalised patients. There was a statistically significant relationship between hospitalisation rates and population density on 97.7% of individual days during the first wave of the pandemic (Pearson’s r 0.62–0.93, with 123 of a possible 174 days having p values <0.001). Multiple linear regression analyses performed on data from the 11 mainland boards across six time points suggest that population density accounted for 70.2% of the variation in hospitalisation rate in April, 72.3% in May, 81.2% in June, 91.0% in July, 91.0% in August, and 88.1% in September. Neither population median age nor median social deprivation score at health board level were statistically significant in the final model for hospitalisation.

Conclusion

There were large differences in crude COVID-19 hospitalisation rates across the 11 mainland Scottish health boards, that were significantly related to population density. Given that lockdown was originally introduced to prevent the NHS from being overwhelmed, we believe our results support a regional rather than a national approach to lifting or reimposing more restrictive measures, and that hospitalisation rates should be part of the decision making process.

Spatio-Temporal Analysis of Influenza-Like Illness and Prediction of Incidence in High-Risk Regions in the United States from 2011 to 2020

About 8% of the Americans contract influenza during an average season according to the Centers for Disease Control and Prevention in the United States. It is necessary to strengthen the early warning for influenza and the prediction of public health. In this study, Spatial autocorrelation analysis and spatial scanning analysis were used to identify the spatiotemporal patterns of influenza-like illness (ILI) prevalence in the United States, during the 2011-2020 transmission seasons. A seasonal autoregressive integrated moving average (SARIMA) model was constructed to predict the influenza incidence of high-risk states. We found the highest incidence of ILI was mainly concentrated in the states of Louisiana, District of Columbia and Virginia. Mississippi was a high-risk state with a higher influenza incidence, and exhibited a high-high cluster with neighboring states. A SARIMA (1, 0, 0) (1, 1, 0)₅₂ model was suitable for forecasting the ILI incidence of Mississippi. The relative errors between actual values and predicted values indicated that the predicted values matched the actual values well. Influenza is still an important health problem in the United States. The spread of ILI varies by season and geographical region. The peak season of influenza was the winter and spring, and the states with higher influenza rates are concentrated in the southeast. Increased surveillance in high-risk states could help control the spread of the influenza.

Death by political party: The relationship between COVID-19 deaths and political party affiliation in the United States

This study explored social factors that are associated with the US deaths caused by COVID-19 after the declaration of economic reopening on May 1, 2020 by President Donald Trump. We seek to understand how county-level support for Trump interacted with social distancing policies to impact COVID-19 death rates. Overall, controlling for several potential confounders, counties with higher levels of Trump support do not necessarily experience greater mortality rates due to COVID-19. The predicted weekly death counts per county tended to increase over time with the implementation of several key health policies. However, the difference in COVID-19 outcomes between counties with low and high levels of Trump support grew after several weeks of the policy implementation as counties with higher levels of Trump support suffered relatively higher death rates. Counties with higher levels of Trump support exhibited lower percentages of mobile staying at home and higher percentages of people working part time or full time than otherwise comparable counties with lower levels of Trump support. The relative negative performance of Trump-supporting counties is robust after controlling for these measures of policy compliance. Counties with high percentages of older (aged 65 and above) persons tended to have greater death rates, as did more populous counties in general. This study indicates that policymakers should consider the risks inherent in controlling public health crises due to divisions in political ideology and confirms that vulnerable communities are at particularly high risk in public health crises.

Demographic and territorial characteristics of COVID-19 cases and excess mortality in the European Union during the first wave

This article explores for a large number of countries in the European Union (plus the United Kingdom) the main demographic differentials in positive tested COVID-19 cases and excess mortality during the first wave in 2020, accounting for differences at territorial level, where population density and size play a main role in the diffusion and effects of the disease in terms of morbidity and mortality. This knowledge complements and refines the epidemiological information about the spread and impact of the virus. For this analysis, we rely on the descriptive exploration of (1) data from The European Surveillance System (TESSy) database developed at the European Centre for Disease Prevention and Control (ECDC) on the number of cases and fatality rates and (2) of weekly mortality data collected by Eurostat. The analysis at territorial level studies the changes in R0-the basic reproduction number-and median excess mortality, across territories with different levels of urbanization. The unique findings of this study encompassing most European Union Member States confirm and define the demographic and territorial differential impacts in terms of infections and fatalities during the first wave of the pandemic in 2020. The information is important for stakeholders at European Union, national and sub-national levels in charge of designing containment measures for COVID-19 and adaptation policies for the future by anticipating the rebound for certain segments of the population with differential medical and economic needs.

Nexus between population density and novel coronavirus (COVID-19) pandemic in the south Indian states: A geo-statistical approach

The unprecedented growth of the novel coronavirus (SARS-CoV-2) as a severe acute respiratory syndrome escalated to the coronavirus disease 2019 (COVID-19) pandemic. It has created an unanticipated global public health crisis that is spreading rapidly in India as well, posing a serious threat to 1350 million persons. Among the factors, population density is foremost in posing a challenge in controlling the COVID-19 contagion. In such extraordinary times, evidence-based knowledge is the prime requisite for pacifying the effect. In this piece, we have studied the district wise transmissions of the novel coronavirus in five south Indian states until 20th July 2020 and its relationship with their respective population density. The five states are purposefully selected for their records in better healthcare infrastructure vis-à-vis other states in India. The study uses Pearson's correlation coefficient to account for the direct impact of population density on COVID-19 transmission rate. Response surface methodology approach is used to validate the correlation between density and transmission rate and spatiotemporal dynamics is highlighted using Thiessen polygon method. The analysis has found that COVID-19 transmission in four states (Kerala, Tamil Nadu, Karnataka and Telangana) strongly hinges upon the spatial distribution of population density. In addition, the results indicate that the long-term impacts of the COVID-19 crisis are likely to differ with demographic density. In conclusion, those at the helm of affairs must take cognizance of the vulnerability clusters together across districts.

Impact of the Healthcare System, Macro Indicator, General Mandatory Quarantine, and Mask Obligation on COVID-19 Cases and Death in Six Latin American Countries: An Interrupted Time Series Study

Background: Different coping strategies have been implemented by various governments worldwide to address the emerging health crisis of COVID-19. While most developed countries count on supporting healthcare and social systems, developing countries face additional challenges due to low macro indicators. The implementation of measurements such as quarantine are shown to be successful to flatten the curve of infection and death. In this context, it is important to test whether those measurements have an impact on the distribution of cases of COVID-19 in developing countries that face additional challenges such as lack of social security due to informal employment. A country comparison for Colombia, Costa Rica, Peru, Ecuador, Mexico, and Chile has therefore been conducted.

Method: The healthcare systems and macro indicator as well as the distribution of death due to COVID-19 per thousand inhabitants are compared descriptively. Using Multiple Interrupted Time Series Analysis with synthetic control units the impact of the General Mandatory Quarantine in Colombia, Peru, and Ecuador as well as the impact of Mask Obligation in public in Colombia and Chile have been tested.

Results: No clear impact of the poverty headcount ratio at the national poverty line and urban population on the percentage of death within the confirmed cases has been found. The out-of-pocked spending within health expenditure as a barrier in access to healthcare can be considered as a determinant of death within the confirmed cases of COVID-19. The implementation of a general mandatory quarantine did not show a curve-flattening effect in Ecuador and Peru but did so in Colombia. The implementation of Mask obligation in public spaced showed positive impact on the distribution of confirmed case in both countries tested.

Conclusion: The implementation of a general mandatory quarantine does not guarantee the curve-flattening effect. Various macro indicators should therefore always be considered while analyzing the effect of policies.

A spatial analysis of the COVID-19 period prevalence in U.S. counties through June 28, 2020: where geography matters?

PURPOSE

This study aims to understand how spatial structures, the interconnections between counties, matter in understanding the coronavirus disease 2019 (COVID-19) period prevalence across the United States.

METHODS

We assemble a county-level data set that contains COVID-19-confirmed cases through June 28, 2020, and various sociodemographic measures from multiple sources. In addition to an aspatial regression model, we conduct spatial lag, spatial error, and spatial autoregressive combined models to systematically examine the role of spatial structure in shaping geographical disparities in the COVID-19 period prevalence.

RESULTS

The aspatial ordinary least squares regression model tends to overestimate the COVID-19 period prevalence among counties with low observed rates, but this issue can be effectively addressed by spatial modeling. Spatial models can better estimate the period prevalence for counties, especially along the Atlantic coasts and through the Black Belt. Overall, the model fit among counties along both coasts is generally good with little variability evident, but in the Plain states, the model fit is conspicuous in its heterogeneity across counties.

CONCLUSIONS

Spatial models can help partially explain the geographic disparities in the COVID-19 period prevalence. These models reveal spatial variability in the model fit including identifying regions of the country where the fit is heterogeneous and worth closer attention in the immediate short term.

Lifestyle Acquired Immunity, Decentralized Intelligent Infrastructures, and Revised Healthcare Expenditures May Limit Pandemic Catastrophe: A Lesson From COVID-19

Throughout history, the human race has often faced pandemics with substantial numbers of fatalities. As the COVID-19 pandemic has now affected the whole planet, even countries with moderate to strong healthcare support and expenditure have struggled to contain disease transmission and casualties. Countries affected by COVID-19 have different demographics, socioeconomic, and lifestyle health indicators. In this context, it is important to find out to what extent these parametric variations are modulating disease outcomes. To answer this, this study selected demographic, socioeconomic, and health indicators e.g., population density, percentage of the urban population, median age, health expenditure per capita, obesity, diabetes prevalence, alcohol intake, tobacco use, case fatality of non-communicable diseases (NCDs) as independent variables. Countries were grouped according to these variables and influence on dependent variables e.g., COVID-19 positive tests, case fatality, and case recovery rates were statistically analyzed. The results suggested that countries with variable median age had a significantly different outcome on positive test rate (P < 0.01). Both the median age (P = 0.0397) and health expenditure per capita (P = 0.0041) showed a positive relation with case recovery. An increasing number of tests per 100 K of the population showed a positive and negative relationship with the number of positives per 100 K population (P = 0.0001) and the percentage of positive tests (P < 0.0001), respectively. Alcohol intake per capita in liter (P = 0.0046), diabetes prevalence (P = 0.0389), and NCDs mortalities (P = 0.0477) also showed a statistical relation to the case fatality rate. Further analysis revealed that countries with high healthcare expenditure along with high median age and increased urban population showed more case fatality but also had a better recovery rate. Investment in the health sector alone is insufficient in controlling the severity of the pandemic. Intelligent and sustainable healthcare both in urban and rural settings and healthy lifestyle acquired immunity may reduce disease transmission and comorbidity induced fatalities, respectively.

Spatial variation in socio-ecological vulnerability to Covid-19 in the contiguous United States

The health and economic impacts of the Covid-19 pandemic vary across space because social, economic, health and ecological factors are also spatially variable. Social vulnerability indices are attempts to create a relative ranking of vulnerability to a natural or anthropogenic hazard across space and have been widely used to quantify community vulnerability to natural disasters. Here, we develop a hierarchical socio-ecological vulnerability index that compares counties in the contiguous United States based on 18 variables grouped into four dimensions (ecological, social, health, and economic) in order to capture a range of factors that might contribute to community vulnerability to Covid-19. Variables were chosen based on a review of the emerging literature about the factors associated with poor health outcomes from Covid-19, information about the economic sectors most at risk from the pandemic and pandemic response, and existing social vulnerability indices. We find that socio-ecological vulnerability to Covid-19 and its related economic effects varies across the contiguous U.S., with especially high vulnerability in the Southeast U.S. and especially low vulnerability in the Upper Midwest, Great Plains, and Mountain West.

Assessment of Epidemiological Determinants of COVID-19 Pandemic Related to Social and Economic Factors Globally

The COVID-19 pandemic has manifested more than a health crisis and has severely impacted on social, economic, and development crises in the world. The relationship of COVID-19 with countries’ economic and other demographic statuses is an important criterion with which to assess the impact of this current outbreak. Based on available data from the online platform, we tested the hypotheses of a country’s economic status, population density, the median age of the population, and urbanization pattern influence on the test, attack, case fatality, and recovery rates of COVID-19. We performed correlation and multivariate multinomial regression analysis with relative risk ratio (RRR) to test the hypotheses. The correlation analysis showed that population density and test rate had a significantly negative association (r = −0.2384, p = 0.00). In contrast, the median age had a significant positive correlation with recovery rate (r = 0.4654, p = 0.00) and case fatality rate (r = 0.2847, p = 0.00). The urban population rate had a positive significant correlation with recovery rate (r = 0.1610, p = 0.04). Lower-middle-income countries had a negative significant correlation with case fatality rate (r= −0.3310, p = 0.04). The multivariate multinomial logistic regression analysis revealed that low-income countries are more likely to have an increased risk of case fatality rate (RRR = 0.986, 95% Confidence Interval; CI = 0.97−1.00, p < 0.05) and recovery rate (RRR = 0.967, 95% CI = 0.95–0.98, p = 0.00). The lower-income countries are more likely to have a higher risk in case of attack rate (RRR = 0.981, 95% CI = 0.97–0.99, p = 0.00) and recovery rate (RRR = 0.971, 95% CI = 0.96–0.98, p = 0.00). Similarly, upper middle-income countries are more likely to have higher risk in case of attack rate (RRR = 0.988, 95% CI = 0.98–1.0, p = 0.01) and recovery rate (RRR = 0.978, 95% CI = 0.97–0.99, p = 0.00). The low- and lower-middle-income countries should invest more in health care services and implement adequate COVID-19 preventive measures to reduce the risk burden. We recommend a participatory, whole-of-government and whole-of-society approach for responding to the socio-economic challenges of COVID-19 and ensuring more resilient and robust health systems to safeguard against preventable deaths and poverty by improving public health outcomes.

Longitudinal analyses of the relationship between development density and the COVID-19 morbidity and mortality rates: Early evidence from 1,165 metropolitan counties in the United States

This longitudinal study aims to investigative the impacts of development density on the spread and mortality rates of COVID-19 in metropolitan counties in the United States. Multilevel Linear Modeling (MLM) is employed to model the infection rate and the mortality rate of COVID-19, accounting for the hierarchical (two-level) and longitudinal structure of the data. This study finds that large metropolitan size (measured in terms of population) leads to significantly higher COVID-19 infection rates and higher mortality rates. After controlling for metropolitan size and other confounding variables, county density leads to significantly lower infection rates and lower death rates. These findings recommend that urban planners and health professionals continue to advocate for compact development and continue to oppose urban sprawl for this and many other reasons documented in the literature, including the positive relationship between compact development and fitness and general health.

Resilient urban form to pandemics: Lessons from COVID-19

Background: The worldwide emergence of future pandemics emphasizes the need to assess the pandemic resilient urban form to prevent infectious disease transmission during this epidemic. According to the lessons of the COVID-19 outbreak, this study aimed to review the current strategies of responding to pandemics through disaster risk management (DRM) to develop a pandemic-resilient urban form in phases of response, mitigation, and preparedness. Methods: The research method is developed through desk study was used to explore the current literature of urban form responded to COVID-19 pandemic and for the text analysis; qualitative content analysis was applied developing a conceptual framework. Results: To create pandemic resilient urban form, this study proposes principles to enhance the urban form resiliency in 3 scales of housing, neighborhoods/public spaces, and cities. These principles focus on the concept of resilient urban form from new perspectives focusing on the physical and nonphysical aspects of resilient urban form, which develops a new understanding of pandemics as a disaster and health-related emergency risks. The physical aspect of resiliency to epidemic outbreaks includes urban form, access, infrastructure, land use, and natural environment factors. Moreover, the nonphysical aspect can be defined by the sociocultural, economic, and political (including good governance) factors. By providing and enhancing the physical and nonphysical prerequisites, several benefits can be gained and the effectiveness of all response, mitigation, and preparedness activities can be supported. Conclusion: As the pandemic's disruptions influence the citizens' lifestyle dramatically, the prominent role of place characteristics in the outbreak of pandemics, policymakers, urban planners, and urban designers should be pulled together to make urban areas more resilient places for epidemics and pandemics.

Longitudinal analyses of the relationship between development density and the COVID-19 morbidity and mortality rates: Early evidence from 1,165 metropolitan counties in the United States

This longitudinal study aims to investigative the impacts of development density on the spread and mortality rates of COVID-19 in metropolitan counties in the United States. Multilevel Linear Modeling (MLM) is employed to model the infection rate and the mortality rate of COVID-19, accounting for the hierarchical (two-level) and longitudinal structure of the data. This study finds that large metropolitan size (measured in terms of population) leads to significantly higher COVID-19 infection rates and higher mortality rates. After controlling for metropolitan size and other confounding variables, county density leads to significantly lower infection rates and lower death rates. These findings recommend that urban planners and health professionals continue to advocate for compact development and continue to oppose urban sprawl for this and many other reasons documented in the literature, including the positive relationship between compact development and fitness and general health.

Distribution of influenza virus types by age using case-based global surveillance data from twenty-nine countries, 1999-2014

Background

Influenza disease burden varies by age and this has important public health implications. We compared the proportional distribution of different influenza virus types within age strata using surveillance data from twenty-nine countries during 1999-2014 (N=358,796 influenza cases).

Methods

For each virus, we calculated a Relative Illness Ratio (defined as the ratio of the percentage of cases in an age group to the percentage of the country population in the same age group) for young children (0-4 years), older children (5-17 years), young adults (18-39 years), older adults (40-64 years), and the elderly (65+ years). We used random-effects meta-analysis models to obtain summary relative illness ratios (sRIRs), and conducted meta-regression and sub-group analyses to explore causes of between-estimates heterogeneity.

Results

The influenza virus with highest sRIR was A(H1N1) for young children, B for older children, A(H1N1)pdm2009 for adults, and (A(H3N2) for the elderly. As expected, considering the diverse nature of the national surveillance datasets included in our analysis, between-estimates heterogeneity was high (I²>90%) for most sRIRs. The variations of countries’ geographic, demographic and economic characteristics and the proportion of outpatients among reported influenza cases explained only part of the heterogeneity, suggesting that multiple factors were at play.

Conclusions

These results highlight the importance of presenting burden of disease estimates by age group and virus (sub)type.

Electronic supplementary material

The online version of this article (10.1186/s12879-018-3181-y) contains supplementary material, which is available to authorized users.

Influence of Media on Seasonal Influenza Epidemic Curves

BACK GROUND

Theoretical investigations predicting the epidemic curves of seasonal influenza have been demonstrated so far; however, there is little empirical research using ever accumulated epidemic curves. The effects of vaccine coverage and information distribution on influenza epidemics were evaluated.

MATERIALS AND METHODS

Four indices for epidemics (i.e., onset-peak duration, onset-end duration, ratio of the onset-peak duration to onset-end duration and steepness of epidemic curves) were defined, and the correlations between these indices and anti-flu drug prescription dose, vaccine coverage, the volume of media and search trend on influenza through internet were analyzed. Epidemiological data on seasonal influenza epidemics from 2002/2003 to 2013/2014 excluding 2009/2010 season were collected from National Institute of Infectious Diseases of Japan.

RESULTS

The onset-peak duration and its ratio to onset-end duration correlated inversely with the volume of anti-flu drug prescription. Onset-peak duration correlated positively with media information volume on influenza. The steepness of the epidemic curve, and anti-flu drug prescription dose inversely correlated with the volume of media information. Pre-epidemic search trend and media volume on influenza correlated with the vaccine coverage in the season. Vaccine coverage had no strong effect on epidemic curve.

CONCLUSION

Education through media has an effect on the epidemic curve of seasonal influenza.

The evolution of pandemic influenza: evidence from India, 1918-19

Background

The 1918–19 ‘Spanish’ Influenza was the most devastating pandemic in recent history, with estimates of global mortality ranging from 20 to 50 million. The focal point of the pandemic was India, with an estimated death toll of between 10 and 20 million. We will characterize the pattern of spread, mortality, and evolution of the 1918 influenza across India using spatial or temporal data.

Methods

This study estimates weekly deaths in 213 districts from nine provinces in India. We compute statistical measures of the severity, speed, and duration of the virulent autumn wave of the disease as it evolved and diffused throughout India. These estimates create a clear picture of the spread of the pandemic across India.

Results

Analysis of the timing and mortality patterns of the disease reveals a striking pattern of speed deceleration, reduction in peak-week mortality, a prolonging of the epidemic wave, and a decrease in overall virulence of the pandemic over time.

Conclusions

The findings are consistent with a variety of possible causes, including the changing nature of the dominant viral strain and the timing and severity of the monsoon. The results significantly advance our knowledge of this devastating pandemic at its global focal point.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2334-14-510) contains supplementary material, which is available to authorized users.

The influence of race/ethnicity and place of service on breast reconstruction for Medicare beneficiaries with mastectomy

Racial disparities in breast reconstruction for breast cancer are documented. Place of service has contributed to disparities in cancer care; but the interaction of race/ethnicity and place of service has not been explicitly examined. We examined whether place of service modified the effect of race/ethnicity on receipt of reconstruction.

We included women with a mastectomy for incident breast cancer in SEER-Medicare from 2005–2009. Using Medicare claims, we determined breast reconstruction within 6 months. Facility characteristics included: rural/urban location, teaching status, NCI Cancer Center designation, cooperative oncology group membership, Disproportionate Share Hospital (DSH) status, and breast surgery volume. Using multivariable logistic regression, we analyzed reconstruction in relation to minority status and facility characteristics.

Of the 17,958 women, 14.2% were racial/ethnic women of color and a total of 9.3% had reconstruction. Caucasians disproportionately received care at non-teaching hospitals (53% v. 42%) and did not at Disproportionate Share Hospitals (77% v. 86%). Women of color had 55% lower odds of reconstruction than Caucasians (OR = 0.45; 95% CI 0.37-0.55). Those in lower median income areas had lower odds of receiving reconstruction, regardless of race/ethnicity. Odds of reconstruction reduced at rural, non-teaching and cooperative oncology group hospitals, and lower surgery volume facilities. Facility effects on odds of reconstruction were similar in analyses stratified by race/ethnicity status.

Race/ethnicity and facility characteristics have independent effects on utilization of breast reconstruction, with no significant interaction. This suggests that, regardless of a woman’s race/ethnicity, the place of service influences the likelihood of reconstruction.

The influenza pandemic of 1918-1919 in Sri Lanka: its demographic cost, timing, and propagation

Background

As an island and a former British colony, Sri Lanka is a case of special interest for the study of 1918–1919 influenza pandemic because of its potential for isolation from as well as integration into the world epidemiologic system.

Objectives

To estimate population loss attributable to the influenza pandemic and weekly district-level excess mortality from the pandemic to analyze its spread across the island.

Methods

To measure population loss, we estimated a population growth model using a panel of 100 district-level observations on population for five consecutive censuses from 1891 to 1931, allowing for a one-time drop in population in 1918–1919. To estimate weekly excess mortality from the pandemic, we estimated a seasonally adjusted weekly time series of district-specific mortality estimates from vital registration records, ranked them, and plotted the ranks on weekly maps to create a picture of the geographic pattern of propagation across Sri Lanka.

Results

Total loss of population from the influenza pandemic was 307 000 or approximately 6·7% of the population. The pandemic peaked in two discrete (northern and southern) regions in early October of 1918 and in a third (central) region in early March 1919.

Conclusions

The population loss estimate is significantly higher than earlier estimates of mortality from the pandemic in Sri Lanka, suggesting underreporting of influenza-attributable deaths and a role for influenza-related fertility declines. The spatial pattern of peak mortality indicates the presence of two distinct entry points and three distinct epidemiologic regions, defined by population density and ethnicity, in colonial Sri Lanka.