scholarly journals Health inequities in influenza transmission and surveillance

2021 ◽  
Vol 17 (3) ◽  
pp. e1008642
Author(s):  
Casey M. Zipfel ◽  
Vittoria Colizza ◽  
Shweta Bansal

The lower an individual’s socioeconomic position, the higher their risk of poor health in low-, middle-, and high-income settings alike. As health inequities grow, it is imperative that we develop an empirically-driven mechanistic understanding of the determinants of health disparities, and capture disease burden in at-risk populations to prevent exacerbation of disparities. Past work has been limited in data or scope and has thus fallen short of generalizable insights. Here, we integrate empirical data from observational studies and large-scale healthcare data with models to characterize the dynamics and spatial heterogeneity of health disparities in an infectious disease case study: influenza. We find that variation in social and healthcare-based determinants exacerbates influenza epidemics, and that low socioeconomic status (SES) individuals disproportionately bear the burden of infection. We also identify geographical hotspots of influenza burden in low SES populations, much of which is overlooked in traditional influenza surveillance, and find that these differences are most predicted by variation in susceptibility and access to sickness absenteeism. Our results highlight that the effect of overlapping factors is synergistic and that reducing this intersectionality can significantly reduce inequities. Additionally, health disparities are expressed geographically, and targeting public health efforts spatially may be an efficient use of resources to abate inequities. The association between health and socioeconomic prosperity has a long history in the epidemiological literature; addressing health inequities in respiratory-transmitted infectious disease burden is an important step towards social justice in public health, and ignoring them promises to pose a serious threat.

Author(s):  
Casey M. Zipfel ◽  
Shweta Bansal

AbstractMotivationThe lower an individual’s socio-economic position, the higher their risk of poor health in low-, middle-, and high-income settings alike. As health inequities grow, it is imperative that we develop an empirically-driven mechanistic understanding of the determinants of health disparities, and capture disease burden in at-risk populations to prevent exacerbation of disparities. Past work has been limited in data or scope and has thus fallen short of generating generalizable insights.Approach & ResultsHere, we integrate empirical data from observational studies and large-scale healthcare data with models to characterize the dynamics and spatial heterogeneity of health disparities in an infectious disease case study: influenza. We find that variation in social, behavioral, and physiological determinants exacerbates influenza epidemics, and that low SES individuals disproportionately bear the burden of infection. We also identify geographical hotspots of disproportionate influenza burden in low SES populations, and find that these differences are most predicted by variation in healthcare utilization and susceptibility.ConclusionThe negative association between health and socio-economic prosperity has a long history in the epidemiological literature. Addressing health inequities in respiratory infectious disease burden is an important step towards social justice in public health, and ignoring them promises to pose a serious threat to the entire population. Our results highlight that the effect of overlapping behavioral social, and physiological factors is synergistic and that reducing this intersectionality can significantly reduce inequities. Additionally, health disparities are expressed geographically, as targeting public health efforts spatially may be an efficient use of resources to abate inequities.


Author(s):  
Catherine Bliss

This chapter discusses a paradigm shift in the genomic sciences wherein scientists have gone from ignoring race to studying it. It argues that the field has adopted a sociogenomic approach to race, in which scientists understand race as a muddled mix of genetic and social factors. Scientists responsible for seminal genome projects, who have faced pressure from the US public health establishment and an array of experts on race, now prioritize race-targeted research, minority recruitment, and analysis of genomic health disparities. As a result large-scale sequencing projects, pharmaceuticals, and postgenomic research have become ever more racialized, while race has taken on an irrevocably genomic imprimatur. This paradigm shift has occurred because of changes across a number of powerful social domains of expertise within science, medicine, and policy. This chapter thus draws upon events taking place in a variety of institutional, regulatory, and normative contexts.


2012 ◽  
Vol 9 (73) ◽  
pp. 1983-1997 ◽  
Author(s):  
Daniel P. Word ◽  
Derek A. T. Cummings ◽  
Donald S. Burke ◽  
Sopon Iamsirithaworn ◽  
Carl D. Laird

Mathematical models can enhance our understanding of childhood infectious disease dynamics, but these models depend on appropriate parameter values that are often unknown and must be estimated from disease case data. In this paper, we develop a framework for efficient estimation of childhood infectious disease models with seasonal transmission parameters using continuous differential equations containing model and measurement noise. The problem is formulated using the simultaneous approach where all state variables are discretized, and the discretized differential equations are included as constraints, giving a large-scale algebraic nonlinear programming problem that is solved using a nonlinear primal–dual interior-point solver. The technique is demonstrated using measles case data from three different locations having different school holiday schedules, and our estimates of the seasonality of the transmission parameter show strong correlation to school term holidays. Our approach gives dramatic efficiency gains, showing a 40–400-fold reduction in solution time over other published methods. While our approach has an increased susceptibility to bias over techniques that integrate over the entire unknown state-space, a detailed simulation study shows no evidence of bias. Furthermore, the computational efficiency of our approach allows for investigation of a large model space compared with more computationally intensive approaches.


2012 ◽  
Vol 7 (6) ◽  
pp. 741-745 ◽  
Author(s):  
Satoshi Mimura ◽  
◽  
Taro Kamigaki ◽  
Hitoshi Oshitani

Infectious disease outbreaks in postdisaster settings provide significant social impact although those outbreaks do not always occur. It is important to assess the potential risks of infectious disease in each setting. The Great East Japan Earthquake, which occurred March 11, 2011, imposed a huge impact on public health services. After the earthquake and following tsunami, many evacuation centers were sites of crowding as well as poor sanitation conditions because of the large- scale of destruction. Some shelters became sites of infectious disease outbreaks such as influenza and norovirus enteritis, although the size of these outbreaks was quite localized. Improvements in the response to infectious diseases through lessons learned from the Great East Japan Earthquake are expected to be the triggers for improving preparedness for public health emergencies.


2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Tara Kirk Sell ◽  
Kelsey Lane Warmbrod ◽  
Crystal Watson ◽  
Marc Trotochaud ◽  
Elena Martin ◽  
...  

Abstract Background The global spread of COVID-19 has shown that reliable forecasting of public health related outcomes is important but lacking. Methods We report the results of the first large-scale, long-term experiment in crowd-forecasting of infectious-disease outbreaks, where a total of 562 volunteer participants competed over 15 months to make forecasts on 61 questions with a total of 217 possible answers regarding 19 diseases. Results Consistent with the “wisdom of crowds” phenomenon, we found that crowd forecasts aggregated using best-practice adaptive algorithms are well-calibrated, accurate, timely, and outperform all individual forecasters. Conclusions Crowd forecasting efforts in public health may be a useful addition to traditional disease surveillance, modeling, and other approaches to evidence-based decision making for infectious disease outbreaks.


Author(s):  
Rebekah McWhirter

The European Convention on Human Rights has given rise to the most extensive and influential case law of any human rights jurisdiction, and the inclusion of an express infectious diseases exception to the right to liberty suggests that its jurisprudence is likely to provide the best available guidance to states on the circumstances in which such measures may be justifiable and lawful. However, this article argues that the principles developed to date are limited in their applicability to the current crisis, and are insufficient for determining the appropriate balance between public health and the right to liberty when seeking to control the spread of a large-scale, highly infectious disease.


Author(s):  
Wenting Yang ◽  
Jiantong Zhang ◽  
Ruolin Ma

Objective: The outbreak of infectious diseases has a negative influence on public health and the economy. The prediction of infectious diseases can effectively control large-scale outbreaks and reduce transmission of epidemics in rapid response to serious public health events. Therefore, experts and scholars are increasingly concerned with the prediction of infectious diseases. However, a knowledge mapping analysis of literature regarding the prediction of infectious diseases using rigorous bibliometric tools, which are supposed to offer further knowledge structure and distribution, has been conducted infrequently. Therefore, we implement a bibliometric analysis about the prediction of infectious diseases to objectively analyze the current status and research hotspots, in order to provide a reference for related researchers. Methods: We viewed “infectious disease*” and “prediction” or “forecasting” as search theme in the core collection of Web of Science from inception to 1 May 2020. We used two effective bibliometric tools, i.e., CiteSpace (Drexel University, Philadelphia, PA, USA) and VOSviewer (Leiden University, Leiden, The Netherlands) to objectively analyze the data of the prediction of infectious disease domain based on related publications, which can be downloaded from the core collection of Web of Science. Then, the leading publications of the prediction of infectious diseases were identified to detect the historical progress based on collaboration analysis, co-citation analysis, and co-occurrence analysis. Results: 1880 documents that met the inclusion criteria were extracted from Web of Science in this study. The number of documents exhibited a growing trend, which can be expressed an increasing number of experts and scholars paying attention to the field year by year. These publications were published in 427 different journals with 11 different document types, and the most frequently studied types were articles 1618 (83%). In addition, as the most productive country, the United States has provided a lot of scientific research achievements in the field of infectious diseases. Conclusion: Our study provides a systematic and objective view of the field, which can be useful for readers to evaluate the characteristics of publications involving the prediction of infectious diseases and for policymakers to take timely scientific responses.


2003 ◽  
Vol 1 (1) ◽  
pp. 49-59
Author(s):  
Mark Tomita

The Global Health Disparities CD-ROM Project reaffirmed the value of professional associations partnering with academic institutions to build capacity of the USA public health education workforce to meet the challenges of primary prevention services. The Society for Public Health Education (SOPHE) partnered with the California State University, Chico to produce a CD-ROM that would advocate for global populations that are affected by health disparities while providing primary resources for public health educators to use in programming and professional development. The CD-ROM development process is discussed


2003 ◽  
Vol 1 (1) ◽  
pp. 49-59
Author(s):  
Mark Tomita

The Global Health Disparities CD-ROM Project reaffirmed the value of professional associations partnering with academic institutions to build capacity of the USA public health education workforce to meet the challenges of primary prevention services. The Society for Public Health Education (SOPHE) partnered with the California State University, Chico to produce a CD-ROM that would advocate for global populations that are affected by health disparities while providing primary resources for public health educators to use in programming and professional development. The CD-ROM development process is discussed.


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