Last data update: May 06, 2024. (Total: 46732 publications since 2009)
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Multistate outbreak of Salmonella Oranienburg infections linked to bulb onions imported from Mexico – United States, 2021
Mitchell MR , Kirchner M , Schneider B , McClure M , Neil KP , Madad A , Jemaneh T , Tijerina M , Nolte K , Wellman A , Neises D , Pightling A , Swinford A , Piontkowski A , Sexton R , McKenna C , Cornell J , Sandoval AL , Wang H , Bell RL , Stager C , Zamora Nava MC , Lara de la Cruz JL , Sánchez Córdova LI , Galván PR , Ortiz JA , Flowers S , Grisamore A , Gieraltowski L , Bazaco M , Viazis S . Food Control 2024 160 In 2021, the U.S. Food and Drug Administration (FDA), the Centers for Disease Control and Prevention (CDC), and state and local health and regulatory partners investigated an outbreak of Salmonella enterica serovar Oranienburg infections linked to bulb onions from Mexico, resulting in 1040 illnesses and 260 hospitalizations across 39 states, the District of Columbia, and Puerto Rico. The Kansas Department of Agriculture recovered the outbreak strain of Salmonella Oranienburg from a sample of condiment collected from an ill person's home. The condiment was made with cilantro, lime, and onions, but, at the time of collection, there were no onions remaining in it. FDA conducted traceback investigations for white, yellow, and red bulb onions, cilantro, limes, tomatoes, and jalapeño peppers. Growers in the state of Chihuahua, Mexico, were identified as supplying the implicated onions that could account for exposure to onions for all illnesses included in the traceback investigation, but investigators could not determine a single source or route of contamination. FDA collected product and environmental samples across the domestic supply chain but did not recover the outbreak strain of Salmonella. Binational collaboration and information sharing supported Mexican authorities in collecting environmental samples from two packing plants and onion, water, and environmental samples from 15 farms and firms in Chihuahua, Mexico identified through FDA's traceback investigation, but did not recover the outbreak strain. Distributors of the implicated onions issued voluntary recalls of red, yellow, and white whole, fresh onions imported from the state of Chihuahua, Mexico. This outbreak showcased how investigators overcame significant traceback and epidemiologic challenges, the need for strengthening the ongoing collaboration between U.S. and Mexican authorities and highlighted the need for identifying practices across the supply chain that can help improve the safety of onions. © 2024 |
Isolation of terbinafine-resistant trichophyton rubrum from onychomycosis patients who failed treatment at an academic center in New York, United States
Hwang JK , Bakotic WL , Gold JAW , Magro CM , Lipner SR . J Fungi (Basel) 2023 9 (7) Onychomycosis is a common nail infection. Terbinafine-resistant dermatophyte infections pose an emerging global public health concern, but few cases have been described in the United States. We retrospectively reviewed and characterized clinical, histopathological, and mycological features of patients with mycologically confirmed onychomycosis who failed oral terbinafine treatment for onychomycosis at a U.S. academic nail referral center and ascertained for terbinafine-resistant isolates. During 1 June 2022-31 January 2023 at Weill Cornell Medicine in New York City, USA, 96 patients with mycologically confirmed onychomycosis were treated with oral terbinafine. Among 64 patients with adequate follow-up, 36 had clinical or complete cure. Of 28 patients who failed treatment, 17 underwent terbinafine resistance testing. Trichophyton rubrum with terbinafine resistance-conferring mutations was isolated from two patients. Overall, terbinafine failures for onychomycosis were relatively common, with some cases associated with terbinafine-resistant T. rubrum infections. These findings underscore the need for a clinical awareness of this emerging problem and public health efforts to monitor and prevent spread. We highlight the importance of diagnostic testing and species identification for onychomycosis patients and the increasingly important role of fungal identification and susceptibility testing to guide therapy. |
The United States COVID-19 Forecast Hub dataset (preprint)
Cramer EY , Huang Y , Wang Y , Ray EL , Cornell M , Bracher J , Brennen A , Rivadeneira AJC , Gerding A , House K , Jayawardena D , Kanji AH , Khandelwal A , Le K , Mody V , Mody V , Niemi J , Stark A , Shah A , Wattanchit N , Zorn MW , Reich NG , US COVID-19 Forecast Hub Consortium , Lopez VK , Walker JW , Slayton RB , Johansson MA , Biggerstaff M . medRxiv 2021 2021.11.04.21265886 Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident hospitalizations, incident cases, incident deaths, and cumulative deaths due to COVID-19 at national, state, and county levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages.Competing Interest StatementAV, MC, and APP report grants from Metabiota Inc outside the submitted work. Funding StatementFor teams that reported receiving funding for their work, we report the sources and disclosures below: AIpert-pwllnod: Natural Sciences and Engineering Research Council of Canada; Caltech-CS156: Gary Clinard Innovation Fund; CEID-Walk: University of Georgia; CMU-TimeSeries: CDC Center of Excellence, gifts from Google and Facebook; COVIDhub: This work has been supported by the US Centers for Disease Control and Prevention (1U01IP001122) and the National Institutes of General Medical Sciences (R35GM119582). The content is solely the responsibility of the authors and does not necessarily represent the official views of CDC, NIGMS or the National Institutes of Health; Johannes Bracher was supported by the Helmholtz Foundation via the SIMCARD Information & Data Science Pilot Project; Tilmann Gneiting gratefully acknowledges support by the Klaus Tschira Foundation; CU-select: NSF DMS-2027369 and a gift from the Morris-Singer Foundation; DDS-NBDS: NSF III-1812699; epiforecasts-ensemble1: Wellcome Trust (210758/Z/18/Z) FDANIHASU: supported by the Intramural Research Program of the NIH/NIDDK; GT_CHHS-COVID19: William W. George Endowment, Virginia C. and Joseph C. Mello Endowment, NSF DGE-1650044, NSF MRI 1828187, research cyberinfrastructure resources and services provided by the Partnership for an Advanced Computing Environment (PACE) at Georgia Tech, and the following benefactors at Georgia Tech: Andrea Laliberte, Joseph C. Mello, Richard Rick E. & Charlene Zalesky, and Claudia & Paul Raines, CDC MInD-Healthcare U01CK000531-Supplement; IHME: This work was supported by the Bill & Melinda Gates Foundation, as well as funding from the state of Washington and the National Science Foundation (award no. FAIN: 2031096); Imperial-ensemble1: SB acknowledges funding from the Wellcome Trust (219415); Institute of Business Forecasting: IBF; IowaStateLW-STEM: NSF DMS-1916204, Iowa State University Plant Sciences Institute Scholars Program, NSF DMS-1934884, Laurence H. Baker Center for Bioinformatics and Biological Statistics; IUPUI CIS: NSF; JHU_CSSE-DECOM: JHU CSSE: National Science Foundation (NSF) RAPID Real-time Forecasting of COVID-19 risk in the USA. 2021-2022. Award ID: 2108526. National Science Foundation (NSF) RAPID Development of an interactive web-based dashboard to track COVID-19 in real-time. 2020. Award ID: 2028604; JHU_IDD-CovidSP: State of California, US Dept of Health and Human Services, US Dept of Homeland Security, Johns Hopkins Health System, Office of the Dean at Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University Modeling and Policy Hub, Centers for Disease Control and Prevention (5U01CK000538-03), University of Utah Immunology, Inflammation, & Infectious Disease Initiative (26798 Seed Grant); JHU_UNC_GAS-StatMechP ol: NIH NIGMS: R01GM140564; JHUAPL-Bucky: US Dept of Health and Human Services; KITmetricslab-select_ensemble: Daniel Wolffram gratefully acknowledges support by the Klaus Tschira Foundation; LANL-GrowthRate: LANL LDRD 20200700ER; MIT-Cassandra: MIT Quest for Intelligence; MOBS-GLEAM_COVID: COVID Supplement CDC-HHS-6U01IP001137-01; CA NU38OT000297 from the Council of State and Territorial Epidemiologists (CSTE); NotreDame-FRED: NSF RAPID DEB 2027718; NotreDame-mobility: NSF RAPID DEB 2027718; PSI-DRAFT: NSF RAPID Grant # 2031536; QJHong-Encounter: NSF DMR-2001411 and DMR-1835939; SDSC_ISG-TrendModel: The development of the dashboard was partly funded by the Fondation Privee des Hopitaux Universitaires de Geneve; UA-EpiCovDA: NSF RAPID Grant # 2028401; UChicagoCHATTOPADHYAY-UnIT: Defense Advanced Research Projects Agency (DARPA) #HR00111890043/P00004 (I. Chattopadhyay, University of Chicago); UCSB-ACTS: NSF RAPID IIS 2029626; UCSD_NEU-DeepGLEAM: Google Faculty Award, W31P4Q-21-C-0014; UMass-MechBayes: NIGMS #R35GM119582, NSF #1749854, NIGMS #R35GM119582; UMich-RidgeTfReg: This project is funded by the University of Michigan Physics Department and the University of Michigan Office of Research; UVA-Ensemble: National Institutes of Health (NIH) Grant 1R01GM109718, NSF BIG DATA Grant IIS-1633028, NSF Grant No.: OAC-1916805, NSF Expeditions in Computing Grant CCF-1918656, CCF-1917819, NSF RAPID CNS-2028004, NSF RAPID OAC-2027541, US Centers for Disease Control and Prevention 75D30119C05935, a grant from Google, University of Virginia Strategic Investment Fund award number SIF160, Defense Threat Reduction Agency (DTRA) under Contract No. HDTRA1-19-D-0007, and Virginia Dept of Health Grant VDH-21-501-0141; Wadnwani_AI-BayesOpt: This study is made possible by the generous support of the American People through the United States Agency for International Development (USAID). The work described in this article was implemented under the TRACETB Project, managed by WIAI under the terms of Cooperative Agreement Number 72038620CA00006. The contents of this manuscript are the sole responsibility of the authors and do not necessarily reflect the views of USAID or the United States Government; WalmartLabsML-LogForecasting: Team acknowledges Walmart to support this study Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesI confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesAll data produced are available online at https://github.com/reichlab/covid19-forecast-hub https://github.com/reichlab/covid19-forecast-hub |
The United States COVID-19 Forecast Hub dataset.
Cramer EY , Huang Y , Wang Y , Ray EL , Cornell M , Bracher J , Brennen A , Rivadeneira AJC , Gerding A , House K , Jayawardena D , Kanji AH , Khandelwal A , Le K , Mody V , Mody V , Niemi J , Stark A , Shah A , Wattanchit N , Zorn MW , Reich NG , US COVID-19 Forecast Hub Consortium , Lopez VK , Walker JW , Slayton RB , Johansson MA , Biggerstaff M . Sci Data 2022 9 (1) 462 Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages. |
An Outbreak Investigation of Vibrio parahaemolyticus Infections in the United States Linked to Crabmeat Imported from Venezuela: 2018.
Seelman SL , Whitney BM , Stokes EK , Elliot EL , Griswold T , Patel K , Bloodgood S , Jones JL , Cripe J , Cornell J , Luo Y , Williams DL , Boyle MM , Cahoon J , Brennan C , Wildey LM , Grover VM , Simonson S , Crosby AJ , Bazaco MC , Viazis S . Foodborne Pathog Dis 2023 20 (4) 123-131 Vibrio parahaemolyticus is the leading cause of seafood-related foodborne illness globally. In 2018, the U.S. federal, state, and local public health and regulatory partners investigated a multistate outbreak of V. parahaemolyticus infections linked to crabmeat that resulted in 26 ill people and nine hospitalizations. State and U.S. Food and Drug Administration (FDA) laboratories recovered V. parahaemolyticus, Salmonella spp., and Listeria monocytogenes isolates from crabmeat samples collected from various points of distribution and conducted phylogenetic analyses of whole-genome sequencing data. Federal, state, and local partners conducted traceback investigations to determine the source of crabmeat. Multiple Venezuelan processors that supplied various brands of crabmeat were identified, but a sole firm was not confirmed as the source of the outbreak. Travel restrictions between the United States and Venezuela prevented FDA officials from conducting on-site inspections of cooked crabmeat processors. Based on investigation findings, partners developed public communications advising consumers not to eat crabmeat imported from Venezuela and placed potentially implicated firms on import alerts. While some challenges limited the scope of the investigation, epidemiologic, traceback, and laboratory evidence identified the contaminated food and country of origin, and contributed to public health and regulatory actions, preventing additional illnesses. This multistate outbreak illustrates the importance of adhering to appropriate food safety practices and regulations for imported seafood. |
Seroprevalence of SARS-CoV-2 following the largest initial epidemic wave in the United States: Findings from New York City, May 13-July 21, 2020.
Pathela P , Crawley A , Weiss D , Maldin B , Cornell J , Purdin J , Schumacher PK , Marovich S , Li J , Daskalakis D . J Infect Dis 2021 224 (2) 196-206 BACKGROUND: New York City (NYC) was the U.S. epicenter of the Spring 2020 COVID-19 pandemic. We present seroprevalence of SARS-CoV-2 infection and correlates of seropositivity immediately after the first wave. METHODS: From a serosurvey of adult NYC residents (May 13-July 21, 2020), we calculated the prevalence of SARS-CoV-2 antibodies stratified by participant demographics, symptom history, health status, and employment industry. We used multivariable regression models to assess associations between participant characteristics and seropositivity. RESULTS: Seroprevalence among 45,367 participants was 23.6% (95% CI, 23.2%-24.0%). High seroprevalence (>30%) was observed among Black and Hispanic individuals, people from high poverty neighborhoods, and people in health care or essential worker industry sectors. COVID-19 symptom history was associated with seropositivity (adjusted relative risk=2.76; 95% CI, 2.65-2.88). Other risk factors included sex, age, race/ethnicity, residential area, employment sector, working outside the home, contact with a COVID-19 case, obesity, and increasing numbers of household members. CONCLUSIONS: Based on a large serosurvey in a single U.S. jurisdiction, we estimate that just under one-quarter of NYC adults were infected in the first few months of the COVID-19 epidemic. Given disparities in infection risk, effective interventions for at-risk groups are needed during ongoing transmission. |
Periconceptional surveillance for prevention of anaemia and birth defects in Southern India: protocol for a biomarker survey in women of reproductive age
Finkelstein JL , Fothergill A , Johnson CB , Guetterman HM , Bose B , Jabbar S , Zhang M , Pfeiffer CM , Qi YP , Rose CE , Krisher JT , Ruth CJ , Mehta R , Williams JL , Bonam W , Crider KS . BMJ Open 2020 10 (10) e038305 INTRODUCTION: Women of reproductive age (WRA) are a high-risk population for anaemia and micronutrient deficiencies. Evidence supports the role of periconceptional nutrition in the development of adverse pregnancy complications. However, in India, there are limited population-based data to guide evidence-based recommendations and priority setting. The objective of this study is to conduct a population-based biomarker survey of anaemia and vitamin B(12) and folate status in WRA as part of a periconceptional surveillance programme in Southern India. METHODS: WRA (15-40 years) who are not pregnant or lactating and reside within 50 km(2) of our community research site in Southern India will be screened and invited to participate in the biomarker survey at our research facility at Arogyavaram Medical Centre. After informed consent/assent, structured interviews will be conducted by trained nurse enumerators to collect sociodemographic, dietary, anthropometry, health and reproductive history data. Venous blood samples will be collected at enrolment; whole blood will be analysed for haemoglobin. Plasma, serum and red blood cells (RBCs) will be processed and stored <-80°C until batch analysis. Vitamin B(12) concentrations will be measured via chemiluminescence, and RBC and serum folate concentrations will be evaluated using the World Health Organisation (WHO)-recommended microbiological assay at our laboratory in Bangalore. A WHO surveillance system will also be established to determine the baseline prevalence of birth defects in this setting. ETHICS AND DISSEMINATION: This study has obtained clearance from the Health Ministry Screening Committee of the Indian Council of Medical Research. The study protocol was reviewed and approved by the Institutional Review Board at Cornell University and the Institutional Ethics Committees at Arogyavaram Medical Centre and St. John's Research Institute. Findings from this biomarker survey will establish the burden of anaemia and micronutrient deficiencies in WRA and directly inform a randomised trial for anaemia and birth defects prevention in Southern India. The results of this study will be disseminated at international research conferences and as published articles in peer-reviewed journals. TRIAL REGISTRATION NUMBERS: Clinical trials registration number NCT04048330, NCT03853304 and Clinical Trials Registry of India (CTRI) registration number REF/2019/03/024479. |
Seroprevalence of Brucella canis antibodies in dogs entering a Minnesota humane society, Minnesota, 20162017
Whitten TV , Brayshaw G , Patnayak D , Alvarez J , Larson CM , Root Kustritz M , Holzbauer SM , Torrison J , Scheftel JM . Prev Vet Med 2019 168 90-94 Background: Canine brucellosis, caused by the bacterium Brucella canis, is a zoonotic and largely reproductive disease of dogs. The disease is a recognized problem in canine breeding populations, and the risk to individuals assisting with birthing is well described. Prior to 2015, all cases of canine brucellosis reported to the Minnesota Board of Animal Health were in dogs used for breeding. In 2015, canine brucellosis was identified in eight Minnesota rescue dogs, all originating from specific geographic areas in South Dakota. Our objective was to measure the seroprevalence of B. canis in stray and previously owned dogs entering a large Minnesota animal rescue organization to determine if our observations represented a localized or generalized disease issue among rescue dogs. Methods: A stratified random sample of stray and previously owned dogs entering the largest Minnesota animal rescue organization between November 1, 2016 and November 7, 2017, was tested for B. canis antibodies by the 2-Mercaptoethanol Rapid Slide Agglutination Test (2ME-RSAT)(Zoetis D-TEC (R) CB kit). Sample sizes for each strata were calculated using previously published seroprevalence estimates. Blood from selected dogs was collected, serum harvested, and transported to the Minnesota Veterinary Diagnostic Laboratory for testing. Positive samples in the 2ME-RSAT were shipped to Cornell University for confirmation by Agarose Gel Immunodiffusion (AGID)testing. Demographics, state and setting of origin, and health status were collected on study-dogs. Results: Of the 10,654 dogs accepted by AHS during the study period, 943 (8.9%)were selected for testing. Most study dogs arrived from Oklahoma (28%), Alabama (18%), and Minnesota (12%). The median age of study dogs was 1.5 years; 303 (32%) were intact males and 294 (31%) were intact females. Most study dogs were strays (n = 716, 76%). Of the total, 22 (3.1%)stray and eight (3.5%)owner-surrendered dogs were presumptively positive by RSAT; one (0.11%)of the stray dogs was positive by 2ME-RSAT and confirmed by AGID. The positive dog was a healthy-appearing 1 year-old neutered male beagle from Texas. Conclusions: The seroprevalence of canine brucellosis in dogs entering Minnesota for adoption from multiple states was low. Never-the-less, care must to be taken to consider all potential risks and outcomes of interstate and international dog trade, including the spread of infectious diseases such as canine brucellosis. |
Building U.S. capacity to review and prevent maternal deaths
Zaharatos J , St Pierre A , Cornell A , Pasalic E , Goodman D . J Womens Health (Larchmt) 2017 27 (1) 1-5 In the United States, the risk of death during and up to a year after pregnancy from pregnancy-related causes increased from approximately 10 deaths per 100,000 live births in the early 1990s to 17 deaths per 100,000 live births in 2013. While vital statistics-based surveillance systems are useful for monitoring trends and disparities, state and local maternal mortality review committees (MMRCs) are best positioned to both comprehensively assess deaths to women during pregnancy and the year after the end of pregnancy, and identify opportunities for prevention. Although the number of committees that exist has increased over the last several years, both newly formed and long-established committees struggle to achieve and sustain progress toward reviewing and preventing deaths. We describe the key elements of a MMRC; review a logic model that represents the general inputs, activities, and outcomes of a fully functional MMRC; and describe Building U.S. Capacity to Review and Prevent Maternal Deaths, a recent multisector initiative working to remove barriers to fully functional MMRCs. Increased standardization of review committee processes allows for better data to understand the multiple factors that contribute to maternal deaths and facilitates the collaboration that is necessary to eliminate preventable maternal deaths in the United States. |
Multidrug-resistant Salmonella Heidelberg associated with mechanically separated chicken at a correctional facility
Taylor AL , Murphree R , Ingram LA , Garman K , Solomon D , Coffey E , Walker D , Rogers M , Marder E , Bottomley M , Woron A , Thomas L , Roberts S , Hardin H , Arjmandi P , Green A , Simmons L , Cornell A , Dunn J . Foodborne Pathog Dis 2015 12 (12) 950-2 We describe multidrug-resistant (MDR) Salmonella Heidelberg infections associated with mechanically separated chicken (MSC) served at a county correctional facility. Twenty-three inmates met the case definition. All reported diarrhea, 19 (83%) reported fever, 16 (70%) reported vomiting, 4 (17%) had fever ≥103 degrees F, and 3 (13%) were hospitalized. A case-control study found no single food item significantly associated with illness. Salmonella Heidelberg with an indistinguishable pulsed-field gel electrophoresis pattern was isolated from nine stool specimens; two isolates displayed resistance to a total of five drug classes, including the third-generation cephalosporin, ceftriaxone. MDR Salmonella Heidelberg might have contributed to the severity of illness. Salmonella Heidelberg indistinguishable from the outbreak subtype was isolated from unopened MSC. The environmental health assessment identified cross-contamination through poor food-handling practices as a possible contributing factor. Proper hand-washing techniques and safe food-handling practices were reviewed with the kitchen supervisor. |
Alexander Duncan Langmuir
Schultz MG , Schaffner W . Emerg Infect Dis 2015 21 (9) 1635-1637 Alex Langmuir was born in Santa Monica, California, and grew up in New Jersey. His uncle, Irving Langmuir, a physicist and chemist, won the Nobel Prize in Chemistry in 1932. At Harvard College, Alex Langmuir tried to follow in his uncle’s footsteps, but he found that the mathematics of advanced physics was beyond him and thus decided to pursue a career in medicine. He received his AB (cum laude) in 1931 from Harvard and his MD in 1935 from Cornell University Medical College. As a college student, Langmuir was inspired by Massachusetts Commissioner of Health George Hoyt Bigelow to enter the field of public health. His first 2 jobs were with the New York State Health Department; he began as a medical consultant and then became an assistant district health officer in Albany. After graduating with an MPH from the Johns Hopkins School of Hygiene and Public Health in 1940, Langmuir became a deputy commissioner of health in Westchester County, New York. His family was dismayed that he chose a career in public health rather than clinical medicine, but Langmuir expressed in his later years that his time in local public health taught him lessons that were fundamental to his achievements. From 1942 to 1946, he served as an epidemiologist with the Armed Forces Epidemiologic Board’s Commission on Acute Respiratory Diseases, stimulating his lifelong interest in influenza. In 1946, Langmuir returned to Johns Hopkins University as an associate professor of epidemiology. However, by 1949 he was restive in academia and was attracted to the challenge of becoming the first chief epidemiologist of the newly established Communicable Disease Center (now the Centers for Disease Control and Prevention [CDC]) in Atlanta, Georgia, a position he held for over 20 years. When Langmuir retired from CDC, he became a visiting professor of epidemiology at Harvard Medical School and, later, a visiting professor of epidemiology at Johns Hopkins School of Hygiene and Public Health. He wrote extensively on all phases of epidemiology and public health surveillance on a global basis and was recognized internationally as an assertive public health authority. |
From theory to measurement: recommended state MCH life course indicators
Callahan T , Stampfel C , Cornell A , Diop H , Barnes-Josiah D , Kane D , McCracken S , McKane P , Phillips G , Theall K , Pies C , Sappenfield W . Matern Child Health J 2015 19 (11) 2336-47 PURPOSE: In May 2012, the Association of Maternal and Child Health (MCH) Programs initiated a project to develop indicators for use at a state or community level to assess, monitor, and evaluate the application of life course principles to public health. DESCRIPTION: Using a developmental framework established by a national expert panel, teams of program leaders, epidemiologists, and academicians from seven states proposed indicators for initial consideration. More than 400 indicators were initially proposed, 102 were selected for full assessment and review, and 59 were selected for final recommendation as Maternal and Child Health (MCH) life course indicators. ASSESSMENT: Each indicator was assessed on five core features of a life course approach: equity, resource realignment, impact, intergenerational wellness, and life course evidence. Indicators were also assessed on three data criteria: quality, availability, and simplicity. CONCLUSION: These indicators represent a major step toward the translation of the life course perspective from theory to application. MCH programs implementing program and policy changes guided by the life course framework can use these initial measures to assess and influence their approaches. |
A Community Health Advisor Program to reduce cardiovascular risk among rural African-American women
Cornell CE , Littleton MA , Greene PG , Pulley L , Brownstein JN , Sanderson BK , Stalker VG , Matson-Koffman D , Struempler B , Raczynski JM . Health Educ Res 2009 24 (4) 622-33 The Uniontown, Alabama Community Health Project trained and facilitated Community Health Advisors (CHAs) in conducting a theory-based intervention designed to reduce the risk for cardiovascular disease (CVD) among rural African-American women. The multiphased project included formative evaluation and community organization, CHA recruitment and training, community intervention and maintenance. Formative data collected to develop the training, intervention and evaluation methods and materials indicated the need for programs to increase knowledge, skills and resources for changing behaviors that increase the risk of CVD. CHAs worked in partnership with staff to develop, implement, evaluate and maintain strategies to reduce risk for CVD in women and to influence city officials, business owners and community coalitions to facilitate project activities. Process data documented sustained increases in social capital and community capacity to address health-related issues, as well as improvements in the community's physical infrastructure. This project is unique in that it documents that a comprehensive CHA-based intervention for CVD can facilitate wide-reaching changes in capacity to address health issues in a rural community that include improvements in community infrastructure and are sustained beyond the scope of the originally funded intervention. |
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