Using demographic data to understand the distribution of H1N1 and COVID-19 pandemics cases among federal entities and municipalities of Mexico

Background The novel coronavirus disease (COVID-19) pandemic is the second global health emergency the world has faced in less than two decades, after the H1N1 Influenza pandemic in 2009–2010. Spread of pandemics is frequently associated with increased population size and population density. The geographical scales (national, regional or local scale) are key elements in determining the correlation between demographic factors and the spread of outbreaks. The aims of this study were: (a) to collect the Mexican data related to the two pandemics; (b) to create thematic maps using federal and municipal geographic scales; (c) to investigate the correlations between the pandemics indicators (numbers of contagious and deaths) and demographic patterns (population size and density). Methods The demographic patterns of all Mexican Federal Entities and all municipalities were taken from the database of “Instituto Nacional de Estadística y Geografía” (INEGI). The data of “Centro Nacional de Programas Preventivos y Control de Enfermedades” (CENAPRECE) and the geoportal of Mexico Government were also used in our analysis. The results are presented by means of tables, graphs and thematic maps. A Spearman correlation was used to assess the associations between the pandemics indicators and the demographic patterns. Correlations with a p value < 0.05 were considered significant. Results The confirmed cases (ccH1N1) and deaths (dH1N1) registered during the H1N1 Influenza pandemic were 72.4 thousand and 1.2 thousand respectively. Mexico City (CDMX) was the most affected area by the pandemic with 8,502 ccH1N1 and 152 dH1N1. The ccH1N1 and dH1N1 were positively correlated to demographic patterns; p-values higher than the level of marginal significance were found analyzing the % ccH1N1 and the % dH1N1 vs the population density. The COVID-19 pandemic data indicated 75.0 million confirmed cases (ccCOVID-19) and 1.6 million deaths (dCOVID-19) worldwide, as of date. The CDMX, where 264,330 infections were recorded, is the national epicenter of the pandemic. The federal scale did not allow to observe the correlation between demographic data and pandemic indicators; hence the next step was to choose a more detailed geographical scale (municipal basis). The ccCOVID-19 and dCOVID-19 (municipal basis) were highly correlated with demographic patterns; also the % ccCOVID-19 and % dCOVID-19 were moderately correlated with demographic patterns. Conclusion The magnitude of COVID-19 pandemic is much greater than the H1N1 Influenza pandemic. The CDMX was the national epicenter in both pandemics. The federal scale did not allow to evaluate the correlation between exanimated demographic variables and the spread of infections, but the municipal basis allowed the identification of local variations and “red zones” such as the delegation of Iztapalapa and Gustavo A. Madero in CDMX.

Pandemic Preparedness and Responses to the 2009 H1N1 Influenza: Crisis Management and Public Policy Insights

From the 1990s and onward, governments and global health actors have dedicated resources and policy attention to threats from emerging infectious diseases, particularly those with pandemic (i.e., global epidemic) potential. Between April 2009 and August 2010, the world experienced the first pandemic in this new era of global preparedness, the 2009 H1N1 influenza pandemic. In line with expectations generated during preparedness efforts in the preceding years, the 2009 H1N1 outbreak consisted of the rapid spread of a novel influenza virus. At the urging of the World Health Organization (WHO) in the years prior to 2009, governments had written pandemic plans for what to do if a pandemic influenza occurred. Some had also taken costly steps to improve response capacity by stockpiling antiviral drugs developed against influenza viruses, pre-purchasing vaccines (which, in turn, led pharmaceutical companies to develop pandemic influenza vaccine models and production capacity), asking domestic healthcare institutions and other organizations to write their own specific pandemic plans, and running live exercises based on constructed scenarios. Aside from departments and agencies of national governments, these preparations involved international organizations, private companies, local governments, hospitals, and healthcare professionals. How can social science scholarship make use of policies and actions related to pandemic preparedness and response, and 2009 H1N1 responses in particular, to generate new insights? The existing literatures on pandemic preparedness and responses to the 2009 H1N1 pandemic illustrate that sites of similarity and difference in pandemic preparedness and response offer opportunities for practical guidance and theory development about crisis management and public policy, as well as policy learning between jurisdictions. Because many jurisdictions and governmental actors were involved, pandemic preparations during the early 2000s and responses to the 2009 H1N1 influenza pandemic offer rich grounds for comparative social science as well as transboundary crisis management research. This includes opportunities to identify whether and how crises involve unique or relatively ordinary political dynamics. It also involves unusual opportunities for learning between jurisdictions that dealt with related issues. Government preparations and responses were often informed by biomedical experts and officials who were networked with each other, as well as by international public health organizations, such as WHO. Yet the loci of preparedness and response were national governments, and implementation relied on local hospitals and healthcare professionals. Hence, the intense period of pandemic preparedness and response between about 2000 and 2010 pitted the isomorphic forces of uniform biology and international collaboration against the differentiating forces of human societies. Social scientific accounts of biosecuritization have charted the emerging awareness of new and untreatable infectious diseases and the pandemic preparedness efforts that followed. First, since about 1990, public health scholars and agencies have been increasingly concerned with general biosecurity linked to numerous disease threats, both natural and man-made. This informed a turn from public health science and policy practice that relied on actuarial statistics about existing diseases to use of scenarios and simulations with projected (or imagined) threats. Second, new disease-fighting prospects presented opportunities for entrepreneurial political and public administrative bodies to “securitize” infectious disease threats in the late 1990s and early 2000s, implying greater empowerment of some agencies and groups within policy systems. Finally, influenza gained a particularly prominent role as a “natural” biosecurity threat as major powers dedicated significant resources to managing the risks of bioterror after September 2001. In subsequent pandemic preparedness efforts, potentially very deadly and contagious influenza became the world community’s primary focus. In turn, the 2009 H1N1 influenza pandemic occurred in the wake of this historic surge in global and national pandemic and, more broadly, biosecurity preparedness efforts. The pandemic led to responses from almost every government in the world throughout 2009 and into 2010, as well as international organizations for public health and medicines. In the wake of the pandemic, formal and scholarly reviews of “lessons learned” sought to inform and influence next steps in pandemic preparedness using the rich panoply of 2009 H1N1 response successes and failures. These generally show that many of the problems often identified in crisis response were repeated in pandemic response. But they also suggest that the rich and varied pandemic experiences offer potential to spread good crisis management practice between jurisdictions, rather than just between events within one jurisdiction. Finally, the 2009 H1N1 pandemic experience allowed careful and in-depth studies of policymaking dynamics relevant to political science, public policy, and public administration theory. Interest-based politics (“politics as usual”) offers partial explanations of the 2009 H1N1 responses, as it does for many public policies. However, the studies of 2009 H1N1 response-making reveal that science and scientific advice (“unusual” politics because scientists are often sidelined in day-to-day policymaking) strongly shaped 2009 H1N1 responses in some contexts. Hence, some of the pandemic response experiences offer insights that are otherwise hard to empirically verify into how sciences (or scientific advisors and networks) become powerful and use power when they have it. As mentioned, the numerous national pandemic response processes during 2009 generated sharply differing pandemic responses. Notably, this was true even among relatively similar countries (e.g., EU member states) and, indeed, subnational regions (e.g., U.S. states). It was also true even when policymaking was dominated by epidemiological and medical experts (e.g., countries in Northwestern Europe). The studies show that global and national scientific leaders, and the pandemic response guidance or policies they made, relied mostly on pre-pandemic established ideas and practices (national ideational trajectories, or paradigms) in their pandemic response decisions. While data about 2009 H1N1 were generated and shared internationally, and government agencies and experts in numerous settings engaged in intense deliberation and sensemaking about 2009 H1N1, such emerging information and knowledge only affected global and national responses slowly (if ever), and, at most, as course alterations.

Model-Based Recursive Partitioning of Patients’ Return Visits to Multispecialty Clinic During the 2009 H1N1 Pandemic Influenza (pH1N1)

Background During the 2009 H1N1 influenza pandemic (pH1N1), the proportion of outpatient visits to emergency departments, clinics and hospitals became elevated especially during the early months of the pandemic due to surges in sick, ‘worried well’ or returning patients seeking care. We determined the prevalence of return visits to a multispecialty clinic during the 2009 H1N1 influenza pandemic and identify subgroups at risk for return visits using model-based recursive partitioning. Methods This was a retrospective analysis of ILI-related medical care visits to multispecialty clinic in Houston, Texas obtained as part of the Houston Health Department Influenza Sentinel Surveillance Project (ISSP) during the 2009 H1N1 pandemic influenza (April 2009 – April 2010). The data comprised of 2680 individuals who made a total of 2960 clinic visits. Return visit was defined as any visit following the index visit after the wash-out phase prior to the study period. We applied nominal logistic regression and recursive partition models to determine the independent predictors and the response probabilities of return visits. The sensitivity and specificity of the outcomes probabilities was determined using receiver operating characteristic (ROC) curve. Results Overall, 4.56% (Prob. 0.0%-17.5%) of the cohort had return visits with significant variations observed attributed to age group (76.0%) and type of vaccine received by patients (18.4%) and Influenza A (pH1N1) test result (5.6%). Patients in age group 0-4 years were 9 times (aOR: 8.77, 95%CI: 3.39-29.95, p<0.0001) more likely than those who were 50+ years to have return visits. Similarly, patients who received either seasonal flu (aOR: 1.59, 95% CI 1.01-2.50, p=0.047) or pH1N1 (aOR: 1.74, 95%CI: 1.09-2.75, p=0.022) vaccines were about twice more likely to have return visits compared to those with no vaccination history. Model-based recursive partitioning yielded 19 splits with patients in subgroup I (patients of age group 0-4 years, who tested positive for pH1N1, and received both seasonal flu and pH1N1 vaccines) having the highest risk of return visits (Prob.=17.5%). The area under the curve (AUC) for both return and non-return visits was 72.9%, indicating a fairly accurate classification of the two groups. Conclusions Return visits in our cohort was more prevalent among children and young adults and those that received either seasonal flu or pH1N1 or both vaccines. Understanding the dynamics in care-seeking behavior during pandemic would assist policymakers with appropriate resource allocation, and in the design of initiatives aimed at mitigating surges and recurrent utilization of the healthcare system. Keywords: Model-based recursive partitioning, subgroup analysis, Influenza-like-illness, H1N1, influenza pandemic, care-seeking behavior, return visit

The comparative analysis of global pandemics of two respective genre

The ongoing global pandemic that has been demolishing every aspect of humankind is truly unprecedented. The mankind experienced the variety of catastrophe since last few centuries, however, this deadly epidemic is extremely unique. This is not restricted to a particular geographical periphery, more importantly, it is not ethnicity dependent. If we could revert back, the last global epidemic of such proportion that is Spanish Flu (1918 H1N1 influenza pandemic) had plenty of similarities with the ongoing disaster in terms of the prevalence across the globe, epidemiology and associated attributes. Scientific fraternity across the world is trying the heart out to depict the origin of this deadly disaster but to say the least, there has been more argument than settlement. However, the most crucial part that coincidentally blends both the epidemics in a perfect order is the infodemic that without a shadow of doubt is the most staggering obstacle to deal with. In this paper a comprehensive effort has been put forward to illustrate the comparative analysis between the global pandemics of two respective genre. At the same time, the best possible lay outs have been also discussed to overhaul the ongoing crisis.

COVID-19 pandemic spread across geographical and social borders: Can we face it?

The global health threat of the novel coronavirus virus SARS-CoV-2 has been the most severe virus since the (A) H1N1 influenza pandemic of 1918–1920. The aim of this paper is to document the spread of the COVID-19 epidemic, on the basis of daily WHO and Chinese CDC data, from the time of the first recorded outbreak of the epidemic. Furthermore, the aim of the paper, based on knowledge of the epidemic cycle in the province of Hubei, is to attempt to simulate the future development of the epidemic in the Czech population. According to the optimistic prediction model, it is expected that the epidemic peak could occur in Czechia in mid-April with a daily number of 700–750 new cases. The total number of people with confirmed disease could reach roughly 20,000 (20% of people may experience serious health complications). The conclusion of the article points to the need for Czechia to build its own infrastructure to cover the needs of the state – especially in the areas of preparedness of medical facilities, medical staff, and the availability of protective equipment and medicines.