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BACKGROUND: The two major class A scavenger receptors are scavenger receptor A (SRA), which is constitutively expressed on most macrophage populations, and macrophage receptor with collagenous structure (MARCO), which is constitutively expressed on a more restricted subset of macrophages, (e.g. alveolar macrophages) but whose expression increases on most macrophages during the course of infection. Although the primary role of SRA appears to be clearance of modified host proteins and lipids, mice defective in expression of either MARCO or SRA are immunocompromised in multiple models of infection and in vitro assays, the scavenger receptors have been demonstrated to bind bacteria and to enhance pro-inflammatory signalling to many bacterial lung pathogens; however their importance in Mycobacterium tuberculosis infection, is less clear. METHODS: To determine whether polymorphisms in either SRA or MARCO were associated with tuberculosis, a case-control study of was performed. DNA samples from newly-detected, smear-positive, pulmonary tuberculosis cases were collected from The Gambia. Controls for this study consisted of DNA from cord bloods obtained from routine births at local Gambian health clinics. Informed written consent was obtained from patients or their parents or guardians. Ethical approval was provided by the joint The Gambian Government/MRC Joint Ethics Committee. RESULTS: We studied the frequencies of 25 polymorphisms of MSR1 (SRA) and 22 in MARCO in individuals with tuberculosis (n=1284) and matched controls (n=1349). No SNPs within the gene encoding or within 1 kb of the promoter sequence of MSR1 were associated with either susceptibility or resistance to tuberculosis. Three SNPs in MARCO (rs4491733, Mantel-Haenszel 2x2 χ2 = 6.5, p = 0.001, rs12998782, Mantel-Haenszel 2x2 χ2 = 6.59, p = 0.001, rs13389814 Mantel-Haenszel 2x2 χ2 = 6.9, p = 0.0009) were associated with susceptibility to tuberculosis and one (rs7559955, Mantel-Haenszel 2x2 χ2 = 6.9, p = 0.0009) was associated with resistance to ...

In 2007 the German government passed smoke-free legislation, leaving the details of implementation to the individual federal states. In January 2008 Bavaria implemented one of the strictest laws in Germany. We investigated its impact on pregnancy outcomes and applied an interrupted time series (ITS) study design to assess any changes in preterm birth, small for gestational age (primary outcomes), and low birth weight, stillbirth and very preterm birth. We included 1,236,992 singleton births, comprising 83,691 preterm births and 112,143 small for gestational age newborns. For most outcomes we observed unclear effects. For very preterm births, we found an immediate drop of 10.4% (95%CI − 15.8, − 4.6%; p = 0.0006) and a gradual decrease of 0.5% (95%CI − 0.7, − 0.2%, p = 0.0010) after implementation of the legislation. The majority of subgroup and sensitivity analyses confirm these results. Although we found no statistically significant effect of the Bavarian smoke-free legislation on most pregnancy outcomes, a substantial decrease in very preterm births was observed. We cannot rule out that despite our rigorous methods and robustness checks, design-inherent limitations of the ITS study as well as country-specific factors, such as the ambivalent German policy context have influenced our estimation of the effects of the legislation.

In 2007 the German government passed smoke-free legislation, leaving the details of implementation to the individual federal states. In January 2008 Bavaria implemented one of the strictest laws in Germany. We investigated its impact on pregnancy outcomes and applied an interrupted time series (ITS) study design to assess any changes in preterm birth, small for gestational age (primary outcomes), and low birth weight, stillbirth and very preterm birth. We included 1,236,992 singleton births, comprising 83,691 preterm births and 112,143 small for gestational age newborns. For most outcomes we observed unclear effects. For very preterm births, we found an immediate drop of 10.4% (95%CI − 15.8, − 4.6%; p = 0.0006) and a gradual decrease of 0.5% (95%CI − 0.7, − 0.2%, p = 0.0010) after implementation of the legislation. The majority of subgroup and sensitivity analyses confirm these results. Although we found no statistically significant effect of the Bavarian smoke-free legislation on most pregnancy outcomes, a substantial decrease in very preterm births was observed. We cannot rule out that despite our rigorous methods and robustness checks, design-inherent limitations of the ITS study as well as country-specific factors, such as the ambivalent German policy context have influenced our estimation of the effects of the legislation.

Objective Robson's Ten Group Classification System (TGCS) creates clinically relevant sub‐groups for monitoring caesarean birth rates. This study assesses whether this classification can be derived from routine data in Europe and uses it to analyse national caesarean rates. Design Observational study using routine data. Setting Twenty‐seven EU member states plus Iceland, Norway, Switzerland and the UK. Population All births at ≥22 weeks of gestational age in 2015. Methods National statistical offices and medical birth registers derived numbers of caesarean births in TGCS groups. Main outcome measures Overall caesarean rate, prevalence and caesarean rates in each of the TGCS groups. Results Of 31 countries, 18 were able to provide data on the TGCS groups, with UK data available only from Northern Ireland. Caesarean birth rates ranged from 16.1 to 56.9%. Countries providing TGCS data had lower caesarean rates than countries without data (25.8% versus 32.9%, P = 0.04). Countries with higher caesarean rates tended to have higher rates in all TGCS groups. Substantial heterogeneity was observed, however, especially for groups 5 (previous caesarean section), 6, 7 (nulliparous/multiparous breech) and 10 (singleton cephalic preterm). The differences in percentages of abnormal lies, group 9, illustrate potential misclassification arising from unstandardised definitions. Conclusions Although further validation of data quality is needed, using TGCS in Europe provides valuable comparator and baseline data for benchmarking and surveillance. Higher caesarean rates in countries unable to construct the TGCS suggest that effective routine information systems may be an indicator of a country's investment in implementing evidence‐based caesarean policies. Tweetable abstract Many European countries can provide Robson's Ten‐Group Classification to improve caesarean rate comparisons.

OBJECTIVE: To generate a global reference for caesarean section (CS) rates at health facilities. DESIGN: Cross-sectional study. SETTING: Health facilities from 43 countries. POPULATION/SAMPLE: Thirty eight thousand three hundred and twenty-four women giving birth from 22 countries for model building and 10,045,875 women giving birth from 43 countries for model testing. METHODS: We hypothesised that mathematical models could determine the relationship between clinical-obstetric characteristics and CS. These models generated probabilities of CS that could be compared with the observed CS rates. We devised a three-step approach to generate the global benchmark of CS rates at health facilities: creation of a multi-country reference population, building mathematical models, and testing these models. MAIN OUTCOME MEASURES: Area under the ROC curves, diagnostic odds ratio, expected CS rate, observed CS rate. RESULTS: According to the different versions of the model, areas under the ROC curves suggested a good discriminatory capacity of C-Model, with summary estimates ranging from 0.832 to 0.844. The C-Model was able to generate expected CS rates adjusted for the case-mix of the obstetric population. We have also prepared an e-calculator to facilitate use of C-Model (www.who.int/reproductivehealth/publications/maternal_perinatal_health/c-model/en/). CONCLUSIONS: This article describes the development of a global reference for CS rates. Based on maternal characteristics, this tool was able to generate an individualised expected CS rate for health facilities or groups of health facilities. With C-Model, obstetric teams, health system managers, health facilities, health insurance companies, and governments can produce a customised reference CS rate for assessing use (and overuse) of CS. TWEETABLE ABSTRACT: The C-Model provides a customized benchmark for caesarean section rates in health facilities and systems.

ObjectiveTo generate a global reference for caesarean section (CS) rates at health facilities. DesignCross-sectional study. SettingHealth facilities from 43 countries. Population/SampleThirty eight thousand three hundred and twenty-four women giving birth from 22 countries for model building and 10045875 women giving birth from 43 countries for model testing. MethodsWe hypothesised that mathematical models could determine the relationship between clinical-obstetric characteristics and CS. These models generated probabilities of CS that could be compared with the observed CS rates. We devised a three-step approach to generate the global benchmark of CS rates at health facilities: creation of a multi-country reference population, building mathematical models, and testing these models. Main outcome measuresArea under the ROC curves, diagnostic odds ratio, expected CS rate, observed CS rate. ResultsAccording to the different versions of the model, areas under the ROC curves suggested a good discriminatory capacity of C-Model, with summary estimates ranging from 0.832 to 0.844. The C-Model was able to generate expected CS rates adjusted for the case-mix of the obstetric population. We have also prepared an e-calculator to facilitate use of C-Model (). ConclusionsThis article describes the development of a global reference for CS rates. Based on maternal characteristics, this tool was able to generate an individualised expected CS rate for health facilities or groups of health facilities. With C-Model, obstetric teams, health system managers, health facilities, health insurance companies, and governments can produce a customised reference CS rate for assessing use (and overuse) of CS. Tweetable abstractThe C-Model provides a customized benchmark for caesarean section rates in health facilities and systems. Tweetable abstract The C-Model provides a customized benchmark for caesarean section rates in health facilities and systems. ; NICHD NIH HHS ; World Health Organization ; Univ Sao Paulo, Ribeirao Preto Med Sch, Dept Social Med, Av Bandeirantes, BR-3900 Ribeirao Preto, Brazil ; WHO, World Bank Special Programme Res Dev & Res Traini, UNDP UNFPA UNICEF WHO, Dept Reprod Hlth & Res, CH-1211 Geneva, Switzerland ; Univ Paris 05, Sorbonne Paris Cite, UMR 216, Inst Dev Res, Paris, France ; WHO Reg Off Amer, Women & Reprod Hlth CLAP WR, Latin Amer Ctr Perinatol, Montevideo, Uruguay ; Emory Univ, Rollins Sch Publ Hlth, Dept Epidemiol, Atlanta, GA 30322 USA ; Paris Descartes Univ, Ctr Epidemiol & Biostat, Obstetr Perinatal & Pediat Epidemiol Res Team, Inserm U1153, Paris, France ; Natl Inst Publ Hlth, Ctr Populat Hlth Res, Cuernavaca, Morelos, Mexico ; Univ Technol, Fac Hlth, Sydney, NSW, Australia ; Natl Ctr Child Hlth & Dev, Dept Hlth Policy, Tokyo, Japan ; Ctr Rosarino Estudios Perinat, Rosario, Argentina ; Lindsay Stewart R&D Ctr, Off Res & Clin Audit, Royal Coll Obstetricians & Gynaecologists, London, England ; London Sch Hyg & Trop Med, Dept Hlth Serv Res & Policy, London WC1, England ; Shanghai Jiao Tong Univ, Sch Med, Xinhua Hosp, Shanghai Key Lab Childrens Environ Hlth,Minist Ed, Shanghai 200030, Peoples R China ; Univ Estadual Campinas, Sch Med Sci, Dept Obstet & Gynaecol, Campinas, SP, Brazil ; Family Hlth Bur, Minist Hlth, Colombo, Sri Lanka ; Fiocruz MS, ENSP, BR-21045900 Rio De Janeiro, Brazil ; Natl Inst Hlth & Welf, Helsinki, Finland ; Univ Tokyo, Grad Sch Med, Dept Paediat, Tokyo, Japan ; Bayer Krankenhausgesellschaft, Bayer Arbeitsgemeinschaft Qualitatssicherun Stati, Munich, Germany ; Khon Kaen Univ, Fac Med, Dept Obstet & Gynecol, Khon, Kaen, Thailand ; Univ Sao Paulo, Ribeirao Preto Med Sch, Dept Obstet & Gynaecol, BR-14049 Ribeirao Preto, Brazil ; Minist Sante, Direct Sante Famille, Ouagadougou, Burkina Faso ; Univ Washington, Inst Hlth Metr & Evaluat, Seattle, WA 98195 USA ; Univ Mongolia, Hlth Sci, Sch Publ Hlth, Ulaanbaatar, Mongol Peo Rep ; GLIDE Tech Cooperat & Res, Ribeirao Preto, SP, Brazil ; Univ Sao Paulo, Ribeirao Preto Med Sch, Dept Paediat, BR-14049 Ribeirao Preto, SP, Brazil ; Univ Calif San Francisco, Dept Obstet & Gynaecol & Global Hlth Sci, San Francisco, CA 94143 USA ; Khon Kaen Univ, Fac Publ Hlth, Dept Biostat & Demog, Khon Kaen, Thailand ; Univ Fed Sao Paulo, Sch Med Sao Paulo, Dept Obstet, Sao Paulo, Brazil ; Inter Amer Dev Bank, Social Protect & Hlth Div, Mexico City, DF, Mexico ; Fortis Mem Res Inst, Gurgaon, Haryana, India ; Hosp Nacl Itaugua, Itaugua, Paraguay ; Univ Fed Sao Paulo, Sch Med Sao Paulo, Dept Obstet, Sao Paulo, Brazil ; NICHD NIH HHS: T32 HD052460 ; World Health Organization: 001 ; Web of Science

ObjectiveTo generate a global reference for caesarean section (CS) rates at health facilities. DesignCross-sectional study. SettingHealth facilities from 43 countries. Population/SampleThirty eight thousand three hundred and twenty-four women giving birth from 22 countries for model building and 10045875 women giving birth from 43 countries for model testing. MethodsWe hypothesised that mathematical models could determine the relationship between clinical-obstetric characteristics and CS. These models generated probabilities of CS that could be compared with the observed CS rates. We devised a three-step approach to generate the global benchmark of CS rates at health facilities: creation of a multi-country reference population, building mathematical models, and testing these models. Main outcome measuresArea under the ROC curves, diagnostic odds ratio, expected CS rate, observed CS rate. ResultsAccording to the different versions of the model, areas under the ROC curves suggested a good discriminatory capacity of C-Model, with summary estimates ranging from 0.832 to 0.844. The C-Model was able to generate expected CS rates adjusted for the case-mix of the obstetric population. We have also prepared an e-calculator to facilitate use of C-Model (). ConclusionsThis article describes the development of a global reference for CS rates. Based on maternal characteristics, this tool was able to generate an individualised expected CS rate for health facilities or groups of health facilities. With C-Model, obstetric teams, health system managers, health facilities, health insurance companies, and governments can produce a customised reference CS rate for assessing use (and overuse) of CS. Tweetable abstractThe C-Model provides a customized benchmark for caesarean section rates in health facilities and systems. Tweetable abstract The C-Model provides a customized benchmark for caesarean section rates in health facilities and systems. ; NICHD NIH HHS ; World Health Organization ; Univ Sao Paulo, Ribeirao Preto Med Sch, Dept Social Med, Av Bandeirantes, BR-3900 Ribeirao Preto, Brazil ; WHO, World Bank Special Programme Res Dev & Res Traini, UNDP UNFPA UNICEF WHO, Dept Reprod Hlth & Res, CH-1211 Geneva, Switzerland ; Univ Paris 05, Sorbonne Paris Cite, UMR 216, Inst Dev Res, Paris, France ; WHO Reg Off Amer, Women & Reprod Hlth CLAP WR, Latin Amer Ctr Perinatol, Montevideo, Uruguay ; Emory Univ, Rollins Sch Publ Hlth, Dept Epidemiol, Atlanta, GA 30322 USA ; Paris Descartes Univ, Ctr Epidemiol & Biostat, Obstetr Perinatal & Pediat Epidemiol Res Team, Inserm U1153, Paris, France ; Natl Inst Publ Hlth, Ctr Populat Hlth Res, Cuernavaca, Morelos, Mexico ; Univ Technol, Fac Hlth, Sydney, NSW, Australia ; Natl Ctr Child Hlth & Dev, Dept Hlth Policy, Tokyo, Japan ; Ctr Rosarino Estudios Perinat, Rosario, Argentina ; Lindsay Stewart R&D Ctr, Off Res & Clin Audit, Royal Coll Obstetricians & Gynaecologists, London, England ; London Sch Hyg & Trop Med, Dept Hlth Serv Res & Policy, London WC1, England ; Shanghai Jiao Tong Univ, Sch Med, Xinhua Hosp, Shanghai Key Lab Childrens Environ Hlth,Minist Ed, Shanghai 200030, Peoples R China ; Univ Estadual Campinas, Sch Med Sci, Dept Obstet & Gynaecol, Campinas, SP, Brazil ; Family Hlth Bur, Minist Hlth, Colombo, Sri Lanka ; Fiocruz MS, ENSP, BR-21045900 Rio De Janeiro, Brazil ; Natl Inst Hlth & Welf, Helsinki, Finland ; Univ Tokyo, Grad Sch Med, Dept Paediat, Tokyo, Japan ; Bayer Krankenhausgesellschaft, Bayer Arbeitsgemeinschaft Qualitatssicherun Stati, Munich, Germany ; Khon Kaen Univ, Fac Med, Dept Obstet & Gynecol, Khon, Kaen, Thailand ; Univ Sao Paulo, Ribeirao Preto Med Sch, Dept Obstet & Gynaecol, BR-14049 Ribeirao Preto, Brazil ; Minist Sante, Direct Sante Famille, Ouagadougou, Burkina Faso ; Univ Washington, Inst Hlth Metr & Evaluat, Seattle, WA 98195 USA ; Univ Mongolia, Hlth Sci, Sch Publ Hlth, Ulaanbaatar, Mongol Peo Rep ; GLIDE Tech Cooperat & Res, Ribeirao Preto, SP, Brazil ; Univ Sao Paulo, Ribeirao Preto Med Sch, Dept Paediat, BR-14049 Ribeirao Preto, SP, Brazil ; Univ Calif San Francisco, Dept Obstet & Gynaecol & Global Hlth Sci, San Francisco, CA 94143 USA ; Khon Kaen Univ, Fac Publ Hlth, Dept Biostat & Demog, Khon Kaen, Thailand ; Univ Fed Sao Paulo, Sch Med Sao Paulo, Dept Obstet, Sao Paulo, Brazil ; Inter Amer Dev Bank, Social Protect & Hlth Div, Mexico City, DF, Mexico ; Fortis Mem Res Inst, Gurgaon, Haryana, India ; Hosp Nacl Itaugua, Itaugua, Paraguay ; Univ Fed Sao Paulo, Sch Med Sao Paulo, Dept Obstet, Sao Paulo, Brazil ; NICHD NIH HHS: T32 HD052460 ; World Health Organization: 001 ; Web of Science