Threshold detection and analysis: MARS methodology in Macroeconomics and Public Health

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At present, antibiotic stewardship is informed by a ‘use it and lose it’ principle, in which antibiotic use by the population is linearly related to resistance rates. However, theoretical and mathematical models suggest that use–resistance relationships are nonlinear. One explanation for this is that resistance genes are commonly associated with ‘fitness costs’ that impair the replication or transmissibility of the pathogen. Therefore, resistant genes and pathogens may only gain a survival advantage where antibiotic selection pressures exceed critical thresholds. These thresholds may provide quantitative targets for stewardship—optimizing the control of resistance while avoiding over-restriction of antibiotics. Here, we evaluated the generalizability of a nonlinear time-series analysis approach for identifying thresholds using historical prescribing and microbiological data from five populations in Europe. We identified minimum thresholds in temporal relationships between the use of selected antibiotics and incidence rates of carbapenem-resistant Acinetobacter baumannii (Hungary), extended-spectrum β-lactamase-producing Escherichia coli (Spain), cefepime-resistant E. coli (Spain), gentamicin-resistant Pseudomonas aeruginosa (France) and methicillin-resistant Staphylococcus aureus (Northern Ireland) in different epidemiological phases. Using routinely generated data, our approach can identify context-specific quantitative targets for rationalizing population antibiotic use and controlling resistance. Prospective intervention studies that restrict antibiotic consumption are needed to validate these thresholds. [post_title] => A nonlinear time-series analysis approach to identify thresholds in associations between population antibiotic use and rates of resistance [post_excerpt] => [post_status] => publish [comment_status] => open [ping_status] => open [post_password] => [post_name] => a-nonlinear-time-series-analysis-approach-to-identify-thresholds-in-associations-between-population-antibiotic-use-and-rates-of-resistance [to_ping] => [pinged] => [post_modified] => 2021-07-28 10:53:18 [post_modified_gmt] => 2021-07-28 08:53:18 [post_content_filtered] => [post_parent] => 0 [guid] => http://doctoradodecide.com/?post_type=publicaciones&p=2654 [menu_order] => 0 [post_type] => publicaciones [post_mime_type] => [comment_count] => 0 [filter] => raw ) [1] => WP_Post Object ( [ID] => 2584 [post_author] => 19 [post_date] => 2019-05-03 12:11:34 [post_date_gmt] => 2019-05-03 10:11:34 [post_content] => We apply a non-parametric procedure, Multiple Adaptive Regression Splines (MARS), to endogenously select the best multiple threshold model for the European Central Bank (ECB) Taylor Rule (TR). MARS offers the advantage of not excluding simpler models if they better fit the data. On monthly data from 1/2000 to 9/2016, the TR exhibits thresholds for both the output gap and inflation and uncover interesting information about the different sensitivities of the ECB to each variable. We conclude that the ECB cares more about the economic activity than officially declared and reacts to inflation deviations only if they sufficiently exceed the official 2% target. We also detect an overall shift in the monetary policy during the 2008 financial crisis that pushed the system towards the Zero Lower Bound (ZLB). We reach a more complete description of the ECB TR than published so far. [post_title] => New insights into the nonlinearity of the ECB Taylor Rule [post_excerpt] => [post_status] => publish [comment_status] => open [ping_status] => open [post_password] => [post_name] => new-insights-into-the-nonlinearity-of-the-ecb-taylor-rule [to_ping] => [pinged] => [post_modified] => 2021-07-28 10:53:18 [post_modified_gmt] => 2021-07-28 08:53:18 [post_content_filtered] => [post_parent] => 0 [guid] => http://doctoradodecide.com/?post_type=publicaciones&p=2584 [menu_order] => 0 [post_type] => publicaciones [post_mime_type] => [comment_count] => 0 [filter] => raw ) ) [post_count] => 2 [current_post] => -1 [in_the_loop] => [post] => WP_Post Object ( [ID] => 2654 [post_author] => 19 [post_date] => 2019-06-03 12:14:08 [post_date_gmt] => 2019-06-03 10:14:08 [post_content] => Balancing access to antibiotics with the control of antibiotic resistance is a global public health priority. At present, antibiotic stewardship is informed by a ‘use it and lose it’ principle, in which antibiotic use by the population is linearly related to resistance rates. However, theoretical and mathematical models suggest that use–resistance relationships are nonlinear. One explanation for this is that resistance genes are commonly associated with ‘fitness costs’ that impair the replication or transmissibility of the pathogen. Therefore, resistant genes and pathogens may only gain a survival advantage where antibiotic selection pressures exceed critical thresholds. These thresholds may provide quantitative targets for stewardship—optimizing the control of resistance while avoiding over-restriction of antibiotics. Here, we evaluated the generalizability of a nonlinear time-series analysis approach for identifying thresholds using historical prescribing and microbiological data from five populations in Europe. We identified minimum thresholds in temporal relationships between the use of selected antibiotics and incidence rates of carbapenem-resistant Acinetobacter baumannii (Hungary), extended-spectrum β-lactamase-producing Escherichia coli (Spain), cefepime-resistant E. coli (Spain), gentamicin-resistant Pseudomonas aeruginosa (France) and methicillin-resistant Staphylococcus aureus (Northern Ireland) in different epidemiological phases. Using routinely generated data, our approach can identify context-specific quantitative targets for rationalizing population antibiotic use and controlling resistance. Prospective intervention studies that restrict antibiotic consumption are needed to validate these thresholds. 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