| 6207 |
Centers for Disease Control and Prevention |
Html |
en |
General Information Features - Cancer Prevention and Control |
An overview of cancer topics that are appropriate for the season, or support a health awareness day or month. |
| specific kinds | 0.386211 |
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| Young Women | 0.400909 |
| Breast Cancer Awareness | 0.548237 |
| Colorectal Cancer Awareness | 0.516155 |
| regular physical activity | 0.459805 |
| Skin Cancer Awareness | 0.488351 |
| prostate cancers | 0.405624 |
| common non-skin cancer | 0.489976 |
|
| women | 0.421344 |
| United States | 0.46375 |
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| men | 0.414036 |
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| common cause | 0.386262 |
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| Cancer Prevention | 0.541076 |
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| 6289 |
Centers for Disease Control and Prevention |
Html |
en |
Affordable Care Act helps improve the health of the American workforce, increase workplace health programs - Press Release: September 30, 2011 |
Affordable Care Act helps improve the health of the American workforce, increase workplace health programs |
| Interested companies | 0.468238 |
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| Research Triangle Institute | 0.514695 |
| Disease Control | 0.478602 |
| Heart disease | 0.4712 |
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| Affordable Care Act | 0.536035 |
| u.s. department | 0.508036 |
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|
| employee participation | 0.46936 |
| workplace health program | 0.590183 |
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| worksite health programs | 0.592634 |
| project funds | 0.464682 |
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| chronic diseases | 0.473615 |
| significant cost savings | 0.522933 |
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| evidence–based initiatives | 0.462281 |
| comprehensive workplace health | 0.619933 |
| human services | 0.507915 |
| health costs | 0.496474 |
| employee productivity | 0.466331 |
| American businesses | 0.470868 |
| physical activity | 0.46545 |
| workplace health experience | 0.590684 |
| worksite capacity | 0.483019 |
| Thomas R. Frieden | 0.532381 |
| employee health | 0.505478 |
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| 6330 |
Centers for Disease Control and Prevention |
Html |
en |
Intention to seek care for symptoms associated with gynecologic cancers, HealthStyles Survey, 2008. |
null |
| logistic regression model | 0.441839 |
| demographic characteristics | 0.429316 |
| Gynecologic Cancer Education | 0.473991 |
| gynecologic cancer symptoms | 0.613692 |
| gynecologic cancer concern | 0.470489 |
| gynecologic cancer | 0.900396 |
| Women’s intention | 0.425797 |
| greater concern | 0.392053 |
| intention exist | 0.403872 |
| gynecologic cancers | 0.997036 |
| Disease Control | 0.413303 |
| Hispanic women | 0.401623 |
| certain symptoms | 0.403901 |
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| persistent symptoms | 0.428628 |
| gastrointestinal symptoms | 0.389978 |
| Introduction
Women | 0.379242 |
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| ovarian cancer | 0.558508 |
| menopausal status | 0.409693 |
| nongynecologic symptoms | 0.398912 |
| intention | 0.699667 |
| influenza-like symptoms | 0.478687 |
| ovarian cancer symptoms | 0.445469 |
| national mail survey | 0.424844 |
|
| symptoms | 0.932801 |
| white women | 0.410828 |
| survey | 0.470015 |
| black women | 0.434143 |
| women | 0.938313 |
| care | 0.720836 |
| Knowledge campaign | 0.383751 |
| generalized health care | 0.387224 |
| women’s intentions | 0.390187 |
| actual care-seeking behavior | 0.38641 |
| premenopausal women | 0.503322 |
| Facts About Gynecologic | 0.466537 |
| marital status | 0.387121 |
| gynecologic symptoms | 0.507053 |
| health care | 0.404549 |
| postmenopausal women | 0.506301 |
| nongynecologic cancer symptoms | 0.427938 |
| annual household income | 0.502624 |
| abnormal symptoms | 0.46361 |
| gynecologic cancer signs | 0.454324 |
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| public health initiatives | 0.398572 |
| survey participants | 0.410325 |
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| 6371 |
Centers for Disease Control and Prevention |
Html |
en |
Understanding Test Results for Infectious Diseases |
Information on Lyme disease. Provided by the U.S. Centers for Disease Control and Prevention. |
| area | 0.262287 |
| risk factors | 0.514859 |
| figure | 0.264408 |
| disease | 0.681917 |
| false negative test | 0.932771 |
| false positives | 0.62051 |
| prior probability | 0.591391 |
| population | 0.277927 |
| symptoms | 0.261306 |
| signs | 0.261417 |
|
| likelihood | 0.587807 |
| Clinicians | 0.286039 |
| patient | 0.448444 |
| true positives | 0.56223 |
| illustration | 0.270954 |
| populations | 0.272419 |
| results | 0.270286 |
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| high resolution | 0.499305 |
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| 10614 |
Centers for Disease Control and Prevention |
Html |
en |
World Health Day | Digital Press Kit | CDC Online Newsroom |
World Health Day |
| 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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| 10687 |
Centers for Disease Control and Prevention |
Html |
en |
Verification Analysis'Research' Communicating in the First Hours |
The CERC training program educates people on the principles and application of crisis and emergency risk communication when responding to a public health emergency. |
| 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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| 12183 |
Centers for Disease Control and Prevention |
Html |
en |
Preventing Chronic Disease | The Role of Fear and Disgust inPredicting the Effectiveness of Television Advertisements ThatGraphically Depict the Health Harms of Smoking - CDC |
Antismoking television advertisements that depict the graphic health harms of smoking are increasingly considered best practices, as exemplified by the Centers for Disease Control and Prevention’s current national campaign. Evaluation of responses to these widely used advertisements is important to determine advertisements that are most effective and their mechanisms of action. Our study tested the hypothesis that advertisements rated highest in fear- and disgust-eliciting imagery would be rated as the most effective. |
| effective antismoking advertisements | 0.364742 |
| study | 0.225846 |
| high disgust | 0.220181 |
| CDC antismoking advertisements | 0.373415 |
| emotional responses | 0.230045 |
| national antismoking media | 0.21922 |
| fear–disgust interaction | 0.816145 |
| health harms | 0.373489 |
| advertisements | 0.900893 |
| greater perceived effectiveness | 0.296139 |
| graphic health harms | 0.218084 |
| ratings | 0.305547 |
| antismoking health-harms advertisements | 0.40066 |
| smoking status | 0.386693 |
| good antismoking ad. | 0.203733 |
| Suzy’s Tip | 0.389595 |
| North Dakota | 0.224984 |
| antismoking mass media | 0.224364 |
| participants | 0.210072 |
|
| effectiveness | 0.653985 |
| Terrie’s Tip | 0.603997 |
| effective advertisements | 0.226864 |
| positive advertisements | 0.244713 |
| graphic imagery | 0.481644 |
| disgust responses | 0.295107 |
| graphic advertisements | 0.261291 |
| antismoking advertisements | 0.499758 |
| Tob Control | 0.273749 |
| fear responses | 0.244642 |
| widely used advertisements | 0.313979 |
| new CDC advertisements | 0.281538 |
| Introduction
Antismoking television | 0.238448 |
| recent antismoking campaign | 0.230714 |
| young adults | 0.240991 |
| advertisement effectiveness ratings | 0.287336 |
| graphic antismoking advertisements | 0.426738 |
| antismoking television advertisements | 0.581872 |
|
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| 13289 |
Centers for Disease Control and Prevention |
Html |
en |
Notes from the Field: Hepatitis E Outbreak Among Refugeesfrom South Sudan - Gambella, Ethiopia, April 2014-January2015 |
Lauren B. Browne, MD1,2; Zeray Menkir, MPH3; Vincent Kahi, MD4; Gidraf Maina, MPH4; Solomon Asnakew, MPH5; Michelle Tubman, MD5; Hajir Z. Elyas, MD6; Alemayehu Nigatu, MPH4; David Dak, MPH4; U Aye Maung, MD4; Jolene H. |
| acute liver infection | 0.672653 |
| HEV outbreak | 0.723049 |
| outbreak response training | 0.67326 |
| real-time polymerase chain | 0.672828 |
| community hygiene education | 0.655515 |
| active community screening | 0.660571 |
| temporary transit sites | 0.654025 |
| Lauren B. Browne | 0.839151 |
| pregnant woman | 0.61143 |
| malnourished boy | 0.608206 |
| community screening efforts | 0.643219 |
| daily household visits | 0.651351 |
| HEV infection | 0.683953 |
| 6MУЉdecins Sans FrontiУЈres-France | 0.658009 |
| Confirmatory HEV testing | 0.762917 |
| HEV transmission | 0.763565 |
| health care facilities | 0.664903 |
| Gidraf Maina | 0.608689 |
| AJS cases | 0.73313 |
| western Ethiopia | 0.615313 |
| case fatality rate | 0.823009 |
| drinking water supplies | 0.658014 |
| Low level transmission | 0.664508 |
| 1Epidemic Intelligence Service | 0.653754 |
| active AJS case | 0.722728 |
|
| border entry points | 0.651843 |
| overall case counts | 0.657995 |
| water quality monitoring | 0.655724 |
| Aye Maung | 0.607599 |
| Nations High Commissioner | 0.798855 |
| Jolene H. Nakao | 0.698967 |
| acute jaundice syndrome | 0.711026 |
| joint multi-sectoral response | 0.656346 |
| Sans FrontiУЈres-Operational Centre | 0.658124 |
| acute onset | 0.61335 |
| drinking water | 0.667129 |
| South Sudanese refugees | 0.943596 |
| United Nations High | 0.657424 |
| current outbreak | 0.606459 |
| Global Health Protection | 0.658537 |
| routine prenatal clinic | 0.683635 |
| Leitchour refugee camp | 0.705374 |
| hepatitis e virus | 0.677139 |
| Gambella region | 0.919499 |
| Tierkidi refugee camp | 0.713107 |
| mass food distributions | 0.657945 |
| passive AJS surveillance | 0.707107 |
| Hajir Z. Elyas | 0.697469 |
| overall case fatality | 0.685222 |
|
CLICK HERE |
| 13357 |
Centers for Disease Control and Prevention |
Html |
en |
List of Selected Multistate Foodborne Outbreak Investigations |
List of Selected Multistate Foodborne Outbreak Investigations. |
| similar outbreaks | 0.785124 |
|
|
CLICK HERE |
| 15970 |
Centers for Disease Control and Prevention |
Html |
en |
Clinical Guidance for Healthcare Providers for Prevention of Sexual Tranmission of Zika Virus |
Information on Zika virus. Provided by the U.S. Centers for Disease Control and Prevention |
| genital secretions | 0.482612 |
| Zika response | 0.266596 |
| Zika virus | 0.854527 |
| longer precautionary period | 0.271188 |
| possible sexual exposure | 0.269207 |
| possible Zika virus | 0.336875 |
| potential sexual exposure | 0.28515 |
| pregnant women | 0.405593 |
| anal sex | 0.26538 |
| infectious virus | 0.273953 |
| Female-to-female sexual transmission | 0.293841 |
| infected men | 0.293247 |
| asymptomatically infected male | 0.337876 |
| area | 0.293848 |
| Zika illness | 0.268859 |
| female sex partners | 0.350201 |
| Zika virus symptoms | 0.32916 |
| transmitted Zika infection | 0.313448 |
| oral sex | 0.278698 |
| body fluids | 0.281937 |
| male sex partners | 0.294348 |
| Zika virus transmission | 0.379954 |
| sexual Zika transmission | 0.313642 |
| asymptomatic pregnant women | 0.287604 |
| menstrual blood | 0.278124 |
|
| pregnant sex partner | 0.296775 |
| illness onset | 0.277127 |
| semen | 0.312667 |
| condoms | 0.339392 |
| sex partners | 0.726948 |
| vaginal secretions | 0.266734 |
| asymptomatic Zika infection | 0.322407 |
| widespread Zika virus | 0.37839 |
| Zika virus infection | 0.383631 |
| sexual transmission cases | 0.279982 |
| Zika | 0.957392 |
| different body fluids | 0.27623 |
| concerned STD/HIV clients | 0.265976 |
| sexual transmission | 0.656719 |
| vaginal sex | 0.277248 |
| Zika testing | 0.265704 |
| female-to-male sex partners | 0.304923 |
| people | 0.324764 |
| sex toys | 0.292152 |
| viral shedding | 0.274981 |
| male-to-male sexual transmission | 0.290897 |
| risk | 0.361814 |
| Healthcare providers | 0.282439 |
| vaginal fluids | 0.492531 |
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