CON: COVID-19 will not result in increased antimicrobial resistance prevalence.

Antimicrobial resistance (AMR) is affected by many factors, but too much of our focus has been on antimicrobial usage. The major factor that drives resistance rates globally is spread. The COVID-19 pandemic should lead to improved infection prevention and control practices, both in healthcare facilities and the community. COVID-19 will also have ongoing and profound effects on local, national and international travel. All these factors should lead to a decrease in the spread of resistant bacteria. So overall, COVID-19 should lead to a fall in resistance rates seen in many countries. For this debate we show why, overall, COVID-19 will not result in increased AMR prevalence. But globally, changes in AMR rates will not be uniform. In wealthier and developed countries, resistance rates will likely decrease, but in many other countries there are already too many factors associated with poor controls on the spread of bacteria and viruses (e.g. poor water and sanitation, poor public health, corrupt government, inadequate housing, etc.). In these countries, if economies and governance deteriorate further, we might see even more transmission of resistant bacteria.

Socioeconomic Enablers for Contagion: Factors Impelling the Antimicrobial Resistance Epidemic.

Antimicrobial resistance is a growing global problem that causes increased deaths as well as increased suffering for people. Overall, there are two main factors that drive antimicrobial resistance: the volumes of antimicrobials used and the spread of resistant micro-organisms along with the genes encoding for resistance. Importantly, a growing body of evidence points to contagion (i.e., spread) being the major, but frequently under-appreciated and neglected, factor driving the increased prevalence of antimicrobial resistance. When we aggregate countries into regional groupings, it shows a pattern where there is an inverse aggregate relationship between AMR and usage. Poor infrastructure and corruption levels, however, are highly and positively correlated with antimicrobial resistance levels. Contagion, antibiotic volumes, governance, and the way antibiotics are used are profoundly affected by a host of social and economic factors. Only after we identify and adequately address these factors can antimicrobial resistance be better controlled.

Anthropological and socioeconomic factors contributing to global antimicrobial resistance: a univariate and multivariable analysis.

BACKGROUND: Understanding of the factors driving global antimicrobial resistance is limited. We analysed antimicrobial resistance and antibiotic consumption worldwide versus many potential contributing factors. METHODS: Using three sources of data (ResistanceMap, the WHO 2014 report on antimicrobial resistance, and contemporary publications), we created two global indices of antimicrobial resistance for 103 countries using data from 2008 to 2014: Escherichia coli resistance-the global average prevalence of E coli bacteria that were resistant to third-generation cephalosporins and fluoroquinolones, and aggregate resistance-the combined average prevalence of E coli and Klebsiella spp resistant to third-generation cephalosporins, fluoroquinolones, and carbapenems, and meticillin-resistant Staphylococcus aureus. Antibiotic consumption data were obtained from the IQVIA MIDAS database. The World Bank DataBank was used to obtain data for governance, education, gross domestic product (GDP) per capita, health-care spending, and community infrastructure (eg, sanitation). A corruption index was derived using data from Transparency International. We examined associations between antimicrobial resistance and potential contributing factors using simple correlation for a univariate analysis and a logistic regression model for a multivariable analysis. FINDINGS: In the univariate analysis, GDP per capita, education, infrastructure, public health-care spending, and antibiotic consumption were all inversely correlated with the two antimicrobial resistance indices, whereas higher temperatures, poorer governance, and the ratio of private to public health expenditure were positively correlated. In the multivariable regression analysis (confined to the 73 countries for which antibiotic consumption data were available) considering the effect of changes in indices on E coli resistance (R2 0·54) and aggregate resistance (R2 0·75), better infrastructure (p=0·014 and p=0·0052) and better governance (p=0·025 and p<0·0001) were associated with lower antimicrobial resistance indices. Antibiotic consumption was not significantly associated with either antimicrobial resistance index in the multivariable analysis (p=0·64 and p=0·070). INTERPRETATION: Reduction of antibiotic consumption will not be sufficient to control antimicrobial resistance because contagion-the spread of resistant strains and resistance genes-seems to be the dominant contributing factor. Improving sanitation, increasing access to clean water, and ensuring good governance, as well as increasing public health-care expenditure and better regulating the private health sector are all necessary to reduce global antimicrobial resistance. FUNDING: None.

Neighborhood disadvantage, network social capital, and depressive symptoms.

Research on why neighborhood disadvantage matters for health focuses on the capacity of neighborhoods to regulate residents' behavior through informal social control. The authors extend this research by conducting a multilevel analysis of data from a 1995 telephone survey of 497 residents of 32 neighborhoods in a U.S. city. The authors find that network social capital mediates the contextual effect of neighborhood disadvantage on depressive symptoms and that health effects of network social capital persist when perceived neighborhood disorder, a standard indicator of low informal social control, is controlled for. The findings demonstrate the value of a conceptualization and measurement of network social capital that (1) considers ties that transcend neighborhood boundaries, (2) investigates health benefits of network social capital in the forms of closure and embedded support resources and range and embedded instrumental resources, and (3) uses network data on specific network members with strong and weak ties to respondents.

The disaster framing of the stress process : A test of an expanded model

This document develops and expanded model of the disaster framing of the disaster framing of the stress process and test its main effects and buffer specifications whit data on the preparation on short-term recovery pahses of Hurricane Andrew. Instrumental forms of social support ameliorated psychological distress, but there was only weak support for the buffer model. The results suggest that expanding the range of environmental changes that is included in conceptualizations of stress and exploring contextual effects at the personal network and local community levels would improve the understanding of the stress inside and outside the disaster context. Also the importance of paying close attention to the types and timing of support transactions following life-threatening events is highlighted (AU)