| 883 |
Centers for Disease Control and Prevention |
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H7N9: In the News |
News and spotlight articles related to Avian Influenza A (H7N9) - CDC |
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| 970 |
Centers for Disease Control and Prevention |
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Alcohol-Attributable Deaths and Years of Potential Life Lost- 11 States, 2006-2010 |
Katherine Gonzales, MPH1, Jim Roeber, MSPH2, Dafna Kanny, PhD3, Annie Tran, MPH4, Cathy Saiki, MS5, Hal Johnson, MPH6, Kristin Yeoman, MD7, Tom Safranek, MD8, Kathleen Creppage, MPH9, Alicia Lepp10, Tracy Miller, MPH10, Nato Tarkhashvili, MD11, Kristine E. Lynch, PhD12, Joanna R. Watson, DPhil13, Danielle Henderson, MPH14, Megan Christenson, MS, MPH15, Sarah Dee Geiger, PhD16 (Author affiliations at end of text). |
| fewer AAD | 0.577619 |
| alcohol retailers | 0.534878 |
| median YPLL rate | 0.783244 |
| Routine monitoring | 0.527712 |
| alcohol-attributable health outcomes | 0.617988 |
| national AAF data | 0.497815 |
| median AAD rates | 0.746599 |
| working-age adults | 0.524325 |
| Preventive Services Task | 0.624883 |
| evidence-based prevention strategies | 0.624266 |
| deaths | 0.567801 |
| annual state rates | 0.502868 |
| state median YPLL | 0.684113 |
| AAD | 0.950524 |
| AAF | 0.503929 |
| state median AAD | 0.745975 |
| median YPLL rates | 0.623872 |
| average annual AAD | 0.63273 |
| dram shop liability | 0.627912 |
| sex-specific AAD estimates | 0.646694 |
| related harms | 0.556952 |
| United States | 0.49811 |
| 100,000 | 0.640641 |
| age-adjusted median AAD | 0.785541 |
| alcohol consumption | 0.569551 |
|
| alcoholic beverages | 0.570887 |
| Alcohol-Related Disease Impact | 0.502979 |
| states | 0.582809 |
| excessive alcohol | 0.812668 |
| ARDI application | 0.517935 |
| excessive drinking | 0.674088 |
| Excessive alcohol consumption | 0.563924 |
| average annual rates | 0.506226 |
| intoxicated patrons | 0.526733 |
| alcohol-attributable deaths | 0.504636 |
| alcohol outlet density | 0.635833 |
| median age-adjusted YPLL | 0.618198 |
| ARDI | 0.546216 |
| South Dakota | 0.537946 |
| Services Task Force | 0.626178 |
| median rate | 0.57038 |
| median AAD rate | 0.935375 |
| median state rate | 0.645471 |
| rate range | 0.502 |
| age-adjusted AAD rate | 0.807925 |
| New Mexico | 0.69002 |
| Community Preventive Services | 0.628051 |
| ARDI Custom Data | 0.531106 |
| median alcohol-attributable death | 0.585212 |
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CLICK HERE |
| 7989 |
Centers for Disease Control and Prevention |
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Notes from the Field: Multistate Outbreak of HumanSalmonella Typhimurium Infections Linked to Contact with PetHedgehogs - United States, 2011-2013 |
Persons using assistive technology might not be able to fully access information in this file. For assistance, please send e-mail to: mmwrq@cdc.gov. |
| mmwrq@cdc.gov. | 0.713675 |
| State Dept | 0.710579 |
| Human Services | 0.752775 |
| assistive technology | 0.706811 |
| Casey Barton Behravesh | 0.756849 |
| infected animals | 0.705931 |
| human isolates | 0.716121 |
| U.S. Department | 0.853217 |
| MMWR HTML versions | 0.76514 |
| Salmonella infection | 0.726862 |
| Health Inspection Service | 0.829933 |
| direct contact | 0.7203 |
| Michigan Dept | 0.706467 |
| electronic PDF version | 0.764777 |
| state health departments | 0.967851 |
| Zoonotic Infectious Diseases | 0.762819 |
| Salmonella transmission | 0.714289 |
| ill persons | 0.702588 |
| foodborne disease surveillance | 0.802802 |
| hedgehog care | 0.70732 |
| Contact GPO | 0.722289 |
| safe handling instructions | 0.772038 |
| Disease Epidemiology | 0.775744 |
| Communicable Disease Epidemiology | 0.774625 |
|
| Illness onset dates | 0.797248 |
| median patient age | 0.783686 |
| Health Inspection Svc | 0.793571 |
| Minnesota Dept | 0.706925 |
| Acute Disease Investigation | 0.768952 |
| original paper copy | 0.758622 |
| illness onset | 0.798934 |
| Tara Creel Anderson | 0.894936 |
| original MMWR paper | 0.77188 |
| U.S. Government Printing | 0.768821 |
| indistinguishable pulsed-field gel | 0.802216 |
| various hedgehog breeders | 0.789395 |
| human Salmonella Typhimurium | 0.840466 |
| indirect contact | 0.730947 |
| pet hedgehogs | 0.911899 |
| Community Health | 0.714854 |
| outbreak strain | 0.810966 |
| Sally A. Bidol | 0.761698 |
| subject line | 0.707465 |
| Infectious Disease Epidemiology | 0.770539 |
| elderly persons | 0.701277 |
| national molecular subtyping | 0.790515 |
| indirect transmission | 0.704991 |
| infected hedgehogs | 0.814797 |
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| 8353 |
Centers for Disease Control and Prevention |
Html |
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Quiz: Autism- Alt Text Version - NCBDDD |
null |
| MPEG | 0.378858 |
| search | 0.263099 |
| PDF | 0.261307 |
| PPT | 0.446092 |
|
| DOC | 0.368812 |
| information | 0.262482 |
| different file formats | 0.938484 |
| page | 0.276773 |
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| 9272 |
Centers for Disease Control and Prevention |
Html |
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Preventing Chronic Disease - CDC: Volume 10, 2013: 12_0270 |
Volumen 10 — el 30 de mayo de 2013. |
| comunidad métodos | 0.899312 |
| observaciones soparc | 0.935792 |
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| 10007 |
Centers for Disease Control and Prevention |
Html |
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Preventing Chronic Disease | Obesity Prevalence by Occupation in Washington State, Behavioral Risk Factor Surveillance System - CDC |
Data that estimate the prevalence of and risk factors for worker obesity by occupation are generally unavailable and could inform the prioritization of workplace wellness programs. The aims of this study were to estimate the prevalence of obesity by occupation, examine the association of occupational physical activity and a range of health behaviors with obesity, and identify occupations in which workers are at high risk of obesity in Washington State. |
| workplace health intervention | 0.491295 |
| study | 0.48417 |
| Washington workers | 0.483159 |
| prevalence ratios | 0.515989 |
| data | 0.494936 |
| health intervention programs | 0.490994 |
| occupation-specific obesity prevalence | 0.550289 |
| state-level prevalence estimates | 0.488644 |
| Risk Factor Surveillance | 0.483726 |
| obesity prevention programs | 0.534199 |
| protective services | 0.581028 |
| occupations | 0.518977 |
| higher obesity prevalence | 0.566768 |
| lower obesity prevalence | 0.556049 |
| BRFSS data | 0.489385 |
| workplace wellness programs | 0.560854 |
| abdominal obesity | 0.494928 |
| truck drivers | 0.581759 |
| Washington State workers | 0.48176 |
| health diagnosing occupations | 0.517796 |
| obesity prevalence | 0.763133 |
| workers | 0.636448 |
| health behaviors | 0.600073 |
| obesity | 0.809742 |
|
| occupational physical activity | 0.987168 |
| Overall obesity prevalence | 0.587574 |
| highest obesity prevalence | 0.559513 |
| health | 0.635042 |
| occupational physical activities | 0.510531 |
| annual household income | 0.501672 |
| occupational groups | 0.526402 |
| worker obesity | 0.516562 |
| significantly lower prevalence | 0.520258 |
| health care costs | 0.493679 |
| Washington State Department | 0.5193 |
| physical activity | 0.993791 |
| obesity risk factors | 0.585809 |
| significantly higher prevalence | 0.492509 |
| obesity epidemic | 0.494323 |
| Washington State | 0.85263 |
| leisure-time physical activity | 0.509334 |
| physical activity level | 0.68034 |
| higher obesity rates | 0.519736 |
| multivariable prevalence ratios | 0.48578 |
| Behavioral Risk Factor | 0.483297 |
| Washington State BRFSS | 0.56123 |
| workplace obesity prevention | 0.578693 |
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Centers for Disease Control and Prevention |
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Disease Detective: Kelsey - CDC Responds to the 2014 Ebola Outbreak |
CDC works 24/7 saving lives, protecting people from health threats, and saving money to have a more secure nation. A US federal agency, CDC helps make the healthy choice the easy choice by putting science and prevention into action. CDC works to help people live longer, healthier and more productive lives. |
| community workers | 0.575719 |
| Ebola symptoms | 0.721612 |
| frightened local residents | 0.755979 |
| boatmen | 0.444208 |
| coffee plant | 0.560338 |
| Ebola outbreak | 0.792949 |
| symbolic gift | 0.555264 |
| remote area | 0.599912 |
| largely uneducated workers | 0.73455 |
| different backgrounds | 0.559447 |
| crazy time | 0.586527 |
| Crammed | 0.452615 |
| CDC training | 0.58011 |
| muddy stream | 0.576816 |
| fragile public health | 0.71695 |
| West African nations | 0.729189 |
| person | 0.526597 |
| community health workers | 0.980757 |
| Ebola patients | 0.730973 |
| village leaders | 0.575352 |
| Ebola | 0.955242 |
| nerve wracking | 0.600085 |
| small hotel | 0.585146 |
| Guinea | 0.508355 |
| post-natal care | 0.57616 |
|
| Kelsey | 0.799407 |
| Sierra Leone | 0.578544 |
| treatment center | 0.561743 |
| important key | 0.569044 |
| CDC disease detective | 0.763551 |
| important health messages | 0.71889 |
| awful hemorrhagic fever | 0.749355 |
| Sierra Leonean | 0.575334 |
| dirt runway | 0.595205 |
| epicenter | 0.442322 |
| tiny plane | 0.595043 |
| official language | 0.57249 |
| worst roads | 0.590193 |
| narrow river | 0.575338 |
| three-hour drive | 0.616093 |
| pickup truck | 0.60022 |
| landing path | 0.594922 |
| people | 0.632365 |
| healthcare resources | 0.570275 |
| international aid workers | 0.749511 |
| grateful village chief | 0.688616 |
| local language | 0.568438 |
| area villages | 0.574212 |
| suspected patient | 0.566466 |
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| 11962 |
Centers for Disease Control and Prevention |
Html |
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Inside Knowledge Campaign Gynecologic Cancer Comprehensive Brochure |
This 32-page brochure provides detailed information about each of the most common gynecologic cancers. |
| Knowledge campaign | 0.672333 |
| non-CDC entities | 0.62835 |
| CDC campaign | 0.776698 |
| endorsement | 0.396364 |
| independently created materials | 0.873349 |
| Knowledge print ads | 0.92736 |
| contractual agreements | 0.661941 |
| Human Services | 0.616221 |
| questions | 0.357885 |
| Knowledge images | 0.61733 |
| CDC’s Web | 0.885446 |
| contact CDC-INFO | 0.635697 |
| logos | 0.354854 |
| federal policy | 0.624364 |
| company | 0.353903 |
| symptoms diaries | 0.649825 |
|
| Knowledge fact sheets | 0.922763 |
| buttons | 0.361569 |
| brochures | 0.355848 |
| individual | 0.353963 |
| posters | 0.357216 |
| campaign logo | 0.599364 |
| way | 0.41315 |
| PSAs | 0.42008 |
| badges | 0.366232 |
| third-party organizations | 0.634204 |
| site | 0.362457 |
| public service announcements | 0.909814 |
| campaign resources | 0.644105 |
| print disks | 0.654773 |
| Content | 0.362916 |
| online | 0.362097 |
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Centers for Disease Control and Prevention |
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National, Regional, State, and Selected Local AreaVaccination Coverage Among Adolescents Aged 13-17 Years - UnitedStates, 2014 |
Sarah Reagan-Steiner, MD1; David Yankey, MS1; Jenny Jeyarajah, MS1; Laurie D. Elam-Evans, PhD1; James A. Singleton, PhD1; C. |
| HPV exposure | 0.255696 |
| routinely recommended vaccines | 0.223478 |
| strong vaccination recommendations | 0.220954 |
| age-specific HPV vaccination | 0.347992 |
| adequate provider data | 0.347936 |
| HPV vaccine | 0.471592 |
| HPV vaccine dose | 0.361403 |
| cell phone | 0.230416 |
| medical records.** Coverage | 0.201671 |
| routine HPV vaccination | 0.349126 |
| percentage points | 0.26062 |
| Indian/Alaska Native adolescents | 0.219385 |
| vaccination provider contact | 0.244813 |
| black female adolescents | 0.218031 |
| HPV vaccine administration | 0.290527 |
| MenACWY dose | 0.241917 |
| HPV dose | 0.488429 |
| HPV vaccine safety | 0.281503 |
| HPV vaccination coverage | 0.907725 |
| percentage point increases | 0.306001 |
| MenACWY vaccination coverage | 0.280086 |
| nine-valent HPV vaccine | 0.276888 |
|
| estimates | 0.231508 |
| HPV doses | 0.31464 |
| white female adolescents | 0.218332 |
| provider-reported vaccination histories | 0.276706 |
| adolescents | 0.443552 |
| females | 0.273135 |
| on-time HPV vaccination | 0.35033 |
| lower HPV series | 0.284386 |
| т‰Ѕ3-dose HPV coverage | 0.366902 |
| vaccination coverage data | 0.250956 |
| coverage estimates | 0.21067 |
| vaccination coverage differences | 0.271887 |
| HPV vaccination services | 0.353922 |
| reduced-dose HPV vaccination | 0.332748 |
| т‰Ѕ3-dose HPV vaccination | 0.428918 |
| local areas | 0.225042 |
| HPV vaccine doses | 0.285096 |
| vaccination providers | 0.215744 |
| non-Hispanic white adolescents | 0.258638 |
| national hpv vaccination | 0.39938 |
| high HPV vaccination | 0.355313 |
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About Lead in Drinking Water |
The Childhood Lead Poisoning Prevention Program is committed to the Healthy People goal of eliminating elevated blood lead levels in children by 2020. CDC continues to assist state and local childhood lead poisoning prevention programs, to provide a scientific basis for policy decisions, and to ensure that health issues are addressed in decisions about housing and the environment. |
| lead levels | 0.686233 |
| CDC | 0.583906 |
| large number | 0.475338 |
| blood lead tests | 0.56933 |
| service line | 0.477253 |
| Lead concentrations | 0.500235 |
| DC-area children | 0.482916 |
| water service pipes | 0.621804 |
| children | 0.496597 |
| U.S. children | 0.485903 |
| available blood lead | 0.575079 |
| elevated blood lead | 0.567322 |
| young children | 0.493671 |
| residential tap water | 0.555419 |
| test results | 0.806255 |
| surveillance data | 0.54129 |
| older homes | 0.482528 |
| DC Water | 0.496588 |
| safe blood level | 0.543108 |
| Environmental Protection Agency | 0.673963 |
| leaded solder | 0.48433 |
| Sewer Authority | 0.475527 |
| lead exposure | 0.561171 |
| health effects | 0.478201 |
|
| water supply pipe | 0.568497 |
| lead-in-water testing program | 0.54626 |
| water pipes | 0.58148 |
| Lead-based paint | 0.500198 |
| blood samples | 0.480935 |
| connect homes | 0.482047 |
| U.S. Environmental Protection | 0.54048 |
| water lead levels | 0.591639 |
| lead service pipes | 0.986163 |
| major contributors | 0.481482 |
| Morbidity Mortality Weekly | 0.542443 |
| drinking water | 0.485826 |
| federal agencies | 0.489866 |
| elevated lead levels | 0.57483 |
| previously reported results | 0.550438 |
| children’s health | 0.495292 |
| old lead-based pipes | 0.617196 |
| blood lead level | 0.575316 |
| DC Department | 0.47701 |
| U. S. Environmental | 0.532601 |
| missing test data | 0.536375 |
| childhood lead poisoning | 0.592762 |
| effective programs | 0.489806 |
| action level | 0.60309 |
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