Spatial epidemiology: An empirical framework for syndemics research

Shrestha, Shikhar; Bauer, Cici; Hendricks, Brian; Stopka, Thomas J.

doi:10.1016/j.socscimed.2020.113352

Cited by 24 publications

(22 citation statements)

References 57 publications

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“…The SARS-CoV-2 epidemics overlaps with endemic diseases [NCD, malaria, schistosomiasis, tuberculosis, hepatitis C, HIV, dengue and other neglected tropical diseases (NTD)] and with seasonal diseases (such as influenza and other respiratory diseases), a host of cultural and social determinants (fear, stigmatization, racism, gender, economic inequalities, mis-information, risk behaviors, food and nutrition insecurity, occupation, climate, exposure to different types of pollution, supply of health and social care services, health care seeking behavior and violence, supply of drugs and addictive behaviors) and climate and environment [ 31 , [42] , [43] , [44] , [45] , [46] , [47] , [48] , [49] , [50] , [51] , [52] , [53] , [54] , [55] , [56] , [57] ] ( Fig. 1 ).…”

Section: Evidence Of the Syndemic Nature Of The Sars-cov-2 Pandemicmentioning

confidence: 99%

The SARS-CoV-2 pandemic: A syndemic perspective

et al. 2021

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The SARS-CoV-2 pandemic has affected communities, populations, and countries throughout the world. As the SARS-CoV-2 pandemic developed, the extent to which the disease interacted with already existing endemic, non-communicable and infectious diseases became evident, hence deeply influencing health outcomes. Additionally, a synergistic effect has been demonstrated also with socio-economic, cultural, and contextual determinants of health which seem to contribute to poorer health and accumulating social disadvantages. In this essay, using as a starting point the syndemic theory that translates the cumulative and intertwined factors between different epidemics, we argue that the SARS-CoV-2 is a one health issue of a syndemic nature and that the failure to acknowledge this contributes to weakened policy-making processes and public health responses and ineffective health policies and programs.

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Section: Evidence Of the Syndemic Nature Of The Sars-cov-2 Pandemicmentioning

confidence: 99%

The SARS-CoV-2 pandemic: A syndemic perspective

et al. 2021

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“…A pesar de sus límites, este marco conceptual tiene el potencial de contribuir a la comprensión de la transformación de la pandemia del nuevo coronavirus (10) . Para ello, en los planos teórico y metodológico, será necesario complementar algunos de sus vacíos y reemplazar elementos conceptuales esenciales, especialmente en la interfaz político-cultural, dado el desborde teórico de los fenómenos sindémicos asociados al COVID-19 (32) .…”

Section: Teoría De La Sindemiaunclassified

Sindemia, infodemia, pandemia de COVID-19: Hacia una pandemiología de enfermedades emergentes

Almeida-Filho

2021

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Como base para una teoría de la coocurrencia de procesos epidémicos y pandémicos, en primer lugar, se introduce el concepto de sindemia, creado en la epidemia de VIH/sida para comprender componentes sociales, conductuales y culturales de enfermedades emergentes, tal como la actual pandemia de COVID-19. En segundo lugar, se destaca la noción de infodemia, que tiene gran potencial para comprender los impactos de la pandemia desde planos que generalmente se descuidan en los enfoques epidemiológicos convencionales. En tercer lugar, como complemento e ilustración, se presenta un estudio de caso “microarqueológico” de la infodemia resultante de la pandemia de COVID-19, centrado en la situación concreta de Brasil. Luego, se analizan las correlaciones entre la evidencia científica, los modelos de intervención y las medidas para controlar la pandemia en varios países y su adopción o rechazo en la realidad brasileña, estructurada sobre profundas desigualdades económicas, inequidades sociales e inequidades en salud.

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Section: Introductionmentioning

confidence: 99%

“…For epidemiology in HIV, data are often characterised by a spatial or a spatio-temporal structure [1], however most studies in the field of HIV often ignore these spatial characteristics during data analysis. Examining the data provided by spatial analysis allows to identify men who have sex with men (MSM) with HIV (MSMHIV) clusters and to explore how these clusters originate [1, 2]. Therefore, spatial information and influence are likely to produce better surveillance models [1, 3, 4] in the context of a declining HIV epidemic in certain Global North regions, and reaching the 90-90-90 goals set by UNAIDS [5, 6].…”

Section: Introductionmentioning

confidence: 99%

MSM with HIV: Improving prevalence and risk estimates by a Bayesian small area estimation modelling approach for public health service areas in the Netherlands

Wang

Daas

Coul

et al. 2022

Preprint

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In many countries, HIV infections among MSM (MSMHIV) are closely monitored, and updated epidemiological reports are made available annually, yet the true prevalence of MSMHIV can be masked for areas with small population density or lack of data. Therefore, this study aimed to investigate the feasibility of small area estimation with a Bayesian approach to improve HIV surveillance. Data from the European MSM Internet Survey 2017 (EMIS-2017, Dutch subsample, n=3,459) and the Dutch survey Men & Sexuality-2018 (SMS-2018, n=5,653) were utilized in this study. We first applied a frequentist calculation to compare the observed relative risk of MSMHIV per Public Health Services (GGD) region in the Netherlands. We then applied a Bayesian spatial analysis and ecological regression to account for variance due to space and determinants associated with HIV among MSM to obtain more robust estimates. Results of the prevalence and risk estimations from EMIS-2017 and SMS-2018 converged with minor differences. Both estimations confirmed that the risk of MSMHIV is heterogenous across the Netherlands with some GGD regions, such as GGD Amsterdam [RR=1.21 (95% credible interval 1.05-1.38) by EMIS-2017; RR=1.39 (1.14-1.68) by SMS-2018], having a higher-than-average risk. Results from our ecological regression modelling revealed significant regional determinants which can impact on the risk for MSMHIV. In sum, our Bayesian approach to assess the risk of HIV among MSM was able to close data gaps and provide more robust prevalence and risk estimations. It is feasible and directly applicable for future HIV surveillance as a statistical adjustment tool.

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Spatial epidemiology: An empirical framework for syndemics research

Cited by 24 publications

References 57 publications

The SARS-CoV-2 pandemic: A syndemic perspective

The SARS-CoV-2 pandemic: A syndemic perspective

Sindemia, infodemia, pandemia de COVID-19: Hacia una pandemiología de enfermedades emergentes

MSM with HIV: Improving prevalence and risk estimates by a Bayesian small area estimation modelling approach for public health service areas in the Netherlands

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