| 753 |
Centers for Disease Control and Prevention |
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Preventing Chronic Disease | Clustering of Risk Factors With Smoking Habits Among Adults, Sousse, Tunisia - CDC |
In Tunisia, few studies have assessed the association between tobacco use and other lifestyle risk factors for chronic disease (eg, unhealthy diet, physical inactivity). We studied 1,880 adults to determine the association between tobacco use and other lifestyle risk factors in Tunisia. |
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| daily smokers | 0.2514 |
| smokers | 0.924563 |
| female smokers | 0.228426 |
| lifestyle risk factors | 0.374117 |
| alcohol consumption | 0.207769 |
|
| physical activity | 0.738305 |
| significant difference | 0.236163 |
| chronic disease risk | 0.548361 |
| current smokers | 0.441839 |
| nonsmokers | 0.423424 |
| male nonsmokers | 0.214095 |
| smoking | 0.351716 |
| smoking status | 0.21669 |
| male smokers | 0.42697 |
| tobacco | 0.333335 |
| mean age | 0.20343 |
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| 4483 |
Centers for Disease Control and Prevention |
Html |
en |
CDC A-Z Index - X |
CDC A-Z Index |
| MPEG | 0.741242 |
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| PDF | 0.544429 |
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| 5466 |
Centers for Disease Control and Prevention |
Html |
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Propargyl alcohol - NIOSH Pocket Guide to Chemical Hazards |
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| 5837 |
Centers for Disease Control and Prevention |
Html |
en |
Nitrogen dioxide - NIOSH Pocket Guide to Chemical Hazards |
null |
| MPEG | 0.378858 |
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| 8846 |
Centers for Disease Control and Prevention |
Html |
en |
Vital Signs: Listeria Illnesses, Deaths, and Outbreaks -United States, 2009-2011 |
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. |
| U.S. listeriosis incidence | 0.630214 |
| Listeria Initiative participation | 0.575792 |
| normally sterile site | 0.62973 |
| invasive Listeria monocytogenes | 0.634611 |
| Foodborne Pathog Dis | 0.581871 |
| territorial health departments | 0.556487 |
| soft cheese | 0.567674 |
| average annual incidence | 0.583148 |
| invasive listeriosis | 0.677314 |
| pregnant women | 0.745216 |
| food safety gaps | 0.599735 |
| Listeria Initiative data | 0.594504 |
| Listeria Initiative | 0.927176 |
| foodborne disease outbreaks | 0.582612 |
| foodborne disease outbreak | 0.74996 |
| soft cheeses | 0.645294 |
| Listeria monocytogenes contamination | 0.622593 |
| fetal losses | 0.572973 |
| pregnant Hispanic women | 0.572346 |
| Foodborne Diseases Active | 0.614684 |
| Disease Outbreak Surveillance | 0.640626 |
| L. monocytogenes | 0.639172 |
| foodborne illness | 0.569209 |
| listeriosis | 0.924571 |
| pregnancy-associated cases | 0.633316 |
|
| listeriosis identifies | 0.591569 |
| outbreaks | 0.665123 |
| listeriosis incidence | 0.641887 |
| listeriosis outbreak | 0.712657 |
| raw produce | 0.56592 |
| outbreak investigations | 0.643295 |
| laboratory-confirmed listeriosis | 0.588092 |
| Listeria contamination | 0.563624 |
| listeriosis outbreak vehicles | 0.664365 |
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| higher risk | 0.657262 |
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| cases | 0.712005 |
| Mexican-style cheese | 0.601746 |
| listeriosis outbreaks | 0.626604 |
| food vehicle | 0.552324 |
| unpasteurized milk | 0.608509 |
| listeriosis cases | 0.677102 |
| Older adults | 0.577496 |
| listeriosis prevention | 0.570172 |
| food safety | 0.79341 |
| Large listeriosis outbreak | 0.646387 |
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| Listeria monocytogenes infection | 0.782607 |
|
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| 8956 |
Centers for Disease Control and Prevention |
Html |
en |
Publications by Topic - Public Health Law |
null |
| MPEG | 0.741242 |
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| PDF | 0.544429 |
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| 12957 |
Centers for Disease Control and Prevention |
Html |
en |
Bacterial Enteric Infections Detected by Culture-IndependentDiagnostic Tests - FoodNet, United States, 2012-2014 |
Martha Iwamoto, MD1, Jennifer Y. Huang, MPH1, Alicia B. Cronquist, MPH2, Carlota Medus, PhD3, Sharon Hurd, MPH4, Shelley Zansky, PhD5, John Dunn, DVM6, Amy M. |
| negative culture | 0.284687 |
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| stool specimens | 0.297489 |
| clinical diagnostic testing | 0.292019 |
| positive CIDT reports | 0.943886 |
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| enteric pathogens | 0.353248 |
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| STEC | 0.313486 |
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| enhanced surveillance methods | 0.296736 |
| Foodborne Diseases Active | 0.327493 |
| clinical laboratories | 0.706956 |
| public health laboratory.§ | 0.290182 |
| FoodNet sites | 0.290327 |
| enhance surveillance methods | 0.284537 |
| public health laboratories | 0.506274 |
| culture-independent diagnostic tests | 0.3308 |
|
| bacterial enteric infections | 0.363284 |
| foodnet surveillance | 0.387375 |
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| CIDT methods | 0.28653 |
| bacterial enteric pathogen | 0.295479 |
| clinical laboratory practices | 0.284809 |
| specific public health | 0.283306 |
| FoodNet Clinical Laboratories | 0.358791 |
| culture-confirmed infections | 0.347957 |
| DNA-based syndrome panels | 0.531123 |
| public health | 0.726729 |
| Campylobacter | 0.350106 |
| surveillance area populations | 0.285985 |
| FoodNet surveillance area | 0.381118 |
| STEC infections | 0.297132 |
| positive CIDT result | 0.286408 |
| health surveillance programs | 0.293865 |
| CIDTs | 0.407538 |
| culture | 0.461328 |
| Diseases Active Surveillance | 0.344429 |
| positive CIDT report | 0.337094 |
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| 13020 |
Centers for Disease Control and Prevention |
Video |
en |
Marlene's Eye Injections - Tips From Former Smokers |
Marlene knows that she’s lucky to have found a treatment for her eye disease. But getting shots directly into her eyeballs is still upsetting each time she gets them. Marlene smoked and developed wet macular degeneration, a disease that can lead to blindness. In this video from CDC's Tips From Former Smokers campaign, Marlene talks about her monthly treatments and says, “Why did I ever smoke?”
Comments on this video are allowed in accordance with our comment policy: http://www.cdc.gov/SocialMedia/Tools/CommentPolicy.html
This video can also be viewed at
http://www.cdc.gov/tobacco/campaign/tips/videos/src/MARLENE_NEEDLE_IN_MY_EYE_VIGNETTE_VCDC0265000H.mp4 |
| Marlene | 0.783221 |
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| CDC | 0.7486 |
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| YouTube | 0.73058 |
|
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| 13903 |
Centers for Disease Control and Prevention |
Html |
en |
World AIDS Day - December 1, 2015 |
The Morbidity and Mortality Weekly Report (MMWR) Series is prepared by the Centers for Disease Control and Prevention (CDC). Often called 'the Voice of CDC,' the MMWR series is the agency's primary vehicle for scientific publication of timely, reliable, authoritative, accurate, objective, and useful public health information and recommendations. MMWR readership predominately consists of physicians, nurses, public health practitioners, epidemiologists and other scientists, researchers, educators, and laboratorians. |
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| identification | 0.431796 |
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| Emergency Plan | 0.600874 |
| middle-income countries | 0.616162 |
| issue | 0.464296 |
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| persons | 0.616009 |
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| original paper copy | 0.716366 |
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| human immunodeficiency | 0.660637 |
| Puerto Rico | 0.588698 |
| United States | 0.595196 |
|
| original MMWR paper | 0.730119 |
| U.S. Government Printing | 0.726212 |
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| Superintendent | 0.435793 |
| Time | 0.436602 |
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| character translation | 0.581106 |
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| end | 0.434097 |
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| attention | 0.432592 |
| electronic conversions | 0.577481 |
| HIV infection | 0.941511 |
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| 15945 |
Centers for Disease Control and Prevention |
Html |
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Real-Time Monitoring of Vaccination Campaign PerformanceUsing Mobile Phones - Nepal, 2016 | MMWR |
The Morbidity and Mortality Weekly Report (MMWR) Series is prepared by the Centers for Disease Control and Prevention (CDC). |
| supplementary immunization activities | 0.718761 |
| SIA administrative coverage | 0.670274 |
| data | 0.763443 |
| vaccination campaigns | 0.687667 |
| mobile phones | 0.833936 |
| Global Positioning | 0.672236 |
| RCM mechanism | 0.648463 |
| service delivery coverage | 0.626003 |
| Mobile data collection | 0.642047 |
| RCM-MP | 0.669978 |
| district supervisors | 0.711528 |
| World Health Organization | 0.841476 |
| rapid convenience monitoring | 0.810884 |
| Measles elimination strategies | 0.643138 |
| immunization service delivery | 0.693139 |
| SIA | 0.744974 |
| mobile networks | 0.626576 |
| real-time data visualization | 0.631102 |
| RCM results | 0.68033 |
| public health practice | 0.633659 |
| SIA coverage | 0.720774 |
| nationwide catch-up SIA | 0.649913 |
| high risk | 0.663333 |
| rubella elimination worldwide | 0.624909 |
| monitors | 0.68375 |
|
| national supervisors | 0.944621 |
| paper-based RCM | 0.927606 |
| corrective vaccination activities | 0.754097 |
| overall SIA performance | 0.660299 |
| mass vaccination campaigns | 0.654506 |
| measles-rubella vaccination campaign | 0.755586 |
| unvaccinated children | 0.715482 |
| electronic data collection | 0.63724 |
| phone screen size | 0.723002 |
| public health | 0.635001 |
| small phone screen | 0.723011 |
| mop-up vaccination activities | 0.739136 |
| SIA quality | 0.66763 |
| out-of-house RCM form§§ | 0.708011 |
| data collection forms | 0.635097 |
| Future RCM implementation | 0.699539 |
| faster data transmission | 0.727305 |
| Electronic data visualization | 0.631222 |
| data collection | 0.665009 |
| SIA implementation performance | 0.665653 |
| nationwide measles-rubella vaccination | 0.644494 |
| paper reporting systems | 0.625449 |
| follow-up sias | 0.639595 |
| paper-based RCM data | 0.739213 |
|
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