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Media ID	Source Name	Media Type	Language	Media Name	Media Description	Keywords	Keywords	Link to Article
4746	Centers for Disease Control and Prevention	Html	en	Tobacco Use - Secondhand Smoke - CDC Vital Signs	CDC Vital Signs links science, policy, and communications with the intent of communicating a call-to-action for the public. CDC Vital Signs provides the most recent, comprehensive data on key indicators of important health topics.	Mortality Weekly Report 0.3552 health insurance 0.284169 nonsmoking adults 0.297698 Tobacco Use Epidemic 0.415288 lung cancer 0.294637 number 0.236347 Smoke-free indoor air 0.369774 pregnant women 0.28818 children 0.347682 severe asthma 0.292336 homes 0.233508 tobacco 0.588208 cancer-causing chemicals 0.303073 health conditions 0.291057 state 0.229615 secondhand smoke 0.955726 private worksites 0.295897 comprehensive tobacco control 0.424788 World Health Organization 0.349256 preventable cause 0.302583 respiratory infections 0.295398 new CDC reports 0.383981 tobacco products 0.482488 nonsmokers 0.5139 parents 0.232853	toxic chemicals 0.295615 photo ID 0.282444 vehicles 0.247771 youth access 0.282965 Mexican-American nonsmokers 0.378908 especially children 0.290254 pediatric patients 0.278984 heart disease 0.460796 infant death syndrome 0.370907 tobacco marketing 0.365383 new FDA restrictions 0.350997 secondhand smoke exposure 0.642278 middle ear infections 0.370329 respiratory conditions 0.36559 company property 0.286539 people 0.237891 tobacco product indoors 0.443551 black nonsmokers 0.377625 home 0.250291 white nonsmokers 0.37726 CDC-recommended levels 0.279938 health risks 0.283074 time 0.231554 MPOWER strategies 0.286329	CLICK HERE
4972	Centers for Disease Control and Prevention	Html	en	Dietary intake of minerals and the risk of ischemic stroke in Guangdong Province, China, 2007-2008	null	ischemic stroke patients 0.594583 first-ever ischemic stroke 0.573245 previous stroke 0.490242 lifelong physical activity 0.488844 soy sauce 0.494993 weekly dietary calcium 0.516139 ischemic stroke cases 0.546361 Ischemic stroke status 0.5438 case patients 0.620204 hospital-based control patients 0.506698 logistic regression 0.499336 blood pressure 0.560806 lowest intake level 0.501946 iron intake 0.517643 dietary mineral intake 0.524479 body mass index 0.514924 primary author 0.481958 cumulative smoking 0.482622 dietary sodium intake 0.548876 southern Chinese population 0.547947 Dietary magnesium intake 0.528171 self-rated sodium intake 0.526617 red meat intake 0.486773 mineral intake 0.584605	stroke symptoms 0.482863 ischemic stroke risk 0.68806 sodium intake 0.708754 total energy intake 0.557498 higher weekly dietary 0.49906 H. Sodium intake 0.516245 mineral intake patterns 0.502096 dietary intake 0.567694 potassium intake 0.491079 Chinese adults 0.520814 incident stroke patients 0.529349 southern Chinese adults 0.515041 weekly intake 0.506132 ischemic stroke 0.937753 control patients 0.760061 energy intake 0.57272 High sodium intake 0.529608 food frequency questionnaire 0.492653 incident ischemic stroke 0.542159 Stroke research priorities 0.495888 physical activity 0.493402 eligible control patients 0.490511 et al 0.556182 Ischemic stroke accounts 0.553278	CLICK HERE
6474	Centers for Disease Control and Prevention	Html	en	Prevalence of asthma among adults in metropolitan versus nonmetropolitan areas in Montana, 2008	The objective of this study was to compare the prevalence of asthma among adults living in metropolitan versus nonmetropolitan counties in Montana.	potential respondents 0.32404 United States 0.48823 current asthma prevalence 0.463037 logistic regression analyses 0.354863 NMNA counties 0.352648 NMNA respondents 0.326313 Human Services 0.316175 Risk Factor Surveillance 0.355343 health insurance status 0.434816 Metro counties 0.316587 younger respondents 0.351386 nonwhite respondents 0.332844 rural areas 0.436078 asthma prevalence 0.7063 current asthma 0.618241 prevalence estimates 0.375534 public health 0.356965 Rural-Urban Continuum Codes 0.365209 lower annual household 0.343032 similar prevalence rates 0.342682 Montana 0.439779 metropolitan versus 0.327131 self-reported asthma 0.875559	multivariable logistic regression 0.36183 Asthma Call-back Survey 0.390825 versus nonmetropolitan counties 0.368869 self-reported current asthma 0.507405 metropolitan areas 0.319628 body mass index 0.366684 Obese respondents 0.35949 nonmetropolitan counties 0.518498 annual household income 0.704797 asthma 0.976911 urban areas 0.350002 potential geographic variation 0.342566 population 0.320873 respondents 0.590842 demographic risk factors 0.338256 Asthma Control Program 0.427773 current self-reported asthma 0.696858 sociodemographic characteristics 0.337352 metropolitan area 0.326743 American Indian/Alaska Native 0.34302 metropolitan county 0.435397 Behavioral Risk Factor 0.359453 self-reported asthma status 0.425227	CLICK HERE
7040	Centers for Disease Control and Prevention	Html	en	Treating Children's Flu Illness Costly	A new study by the Centers for Disease Control and Prevention in the journal Vaccine puts a dollar figure on treating influenza illness in young children.	lead author 0.367196 lab-confirmed influenza 0.382412 flu-stricken children 0.434166 Vaccine study 0.404787 emergency room patients 0.842914 relatively low price 0.481216 emergency rooms 0.607824 U.S. cities 0.368939 flu spending 0.466505 economic burden 0.362161 Disease Control 0.366949 children 0.877597 young children 0.390224 high-risk children 0.389499 good health habits 0.440761 surveillance network 0.446174 flu complications—meaning parents 0.577461 little ones 0.528893 new study 0.486122 flu illness 0.726327 travel costs 0.454296 medical visits 0.365208 journal Vaccine 0.409808 flu-related expenses 0.38105 Dr. Ismael Ortega-Sanchez 0.505695	2003-2004 influenza season 0.49998 flu risk factors 0.543922 parents 0.638489 outpatient settings 0.483419 out-of-pocket expenses 0.55134 CDC-funded data surveillance 0.451065 annual flu vaccine 0.571884 especially children 0.392279 time parents 0.402524 sleepless nights 0.364291 influenza-related indirect costs 0.590775 medical expenses 0.422079 similar costs 0.431389 New Vaccine Surveillance 0.484813 dollar figure 0.359761 medical plan 0.376861 emergency departments 0.47124 whopping average 0.365735 out-of-pocket costs 0.471774 flu shot 0.466871 sick children 0.424291 medical costs 0.950728 preventive flu vaccine 0.635227 medical records 0.361995	CLICK HERE
9023	Centers for Disease Control and Prevention	Video	en	Cancer in the Family	A news segment about individuals with a family member whose cigarette smoking led to a cancer diagnosis. 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/basic_information/health_effects/cancer/index.htm	Family 0.365045	YouTube 0.930567	CLICK HERE
11159	Centers for Disease Control and Prevention	Html	en	CDC's Transplant-Transmitted Infection Toolkit	CDC – Information to help make organ and tissue transplants the highest quality possible so that recipients have the best outcomes.	standard public health 0.347035 National Organ Transplant 0.449761 UNOS patient safety 0.386903 Transplant-Transmitted Infections form 0.240167 broadly applicable framework 0.228725 public health authorities 0.750351 public health departments 0.40894 transplant community 0.363039 transplant transmission 0.280276 transplant investigations 0.235086 unusual notification process 0.277663 Organ Sharing 0.200338 Disease Transmission Advisory 0.278431 organ procurement organizations 0.521694 transplant centers 0.50117 working agreement 0.290513 diagnostic test results 0.228891 health departments 0.474343	disease transmission 0.365777 public health 0.924053 U.S. transplant centers 0.381755 state health authorities 0.340373 organ recipients 0.26575 donor derived disease 0.259519 potential transplant-transmitted cases 0.240191 highlight important aspects 0.235994 potential transplant-associated transmission 0.314117 local public health 0.459736 Transplant-Transmitted Infections 0.272993 state public health 0.345236 UNOS staff 0.265881 public health agencies 0.28285 unified transplant network 0.384273 potentially impacted recipients 0.273784 relevant health departments 0.274389	CLICK HERE
11999	Centers for Disease Control and Prevention	Html	en	Disease Detective: Neil - 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.	practice 0.320291 Liberia’s capital 0.434697 terrible outbreak 0.402411 Ambulance chasing 0.412551 source 0.320165 consistency 0.318193 better track 0.39819 international partners 0.395745 Social Welfare 0.392955 Ebola transmission 0.580505 contact 0.38409 Bomi County 0.527175 Neil 0.546542 lax 0.318834 ongoing Ebola epidemic 0.65641 Global Health 0.411016 wife 0.333891 short time 0.39201 lapse 0.322719 corrections 0.318482 community care center. 0.487531 Ebola patients 0.663449 Ebola 0.90943 limited access 0.390935 biggest risks 0.389525	new chain 0.40307 distance 0.319951 infection control 0.506097 quality emergency care 0.469806 rural area 0.405457 major strides 0.403753 suspected Ebola cases 0.708424 virus 0.335036 Ebola cases 0.74543 CDC disease detective 0.678311 key component 0.40475 major progress 0.387645 local phones 0.389124 local health officials 0.477154 Ebola-free patients 0.403686 safety standards 0.39236 ambulances 0.322532 spread 0.337396 tough situation 0.388796 community care centers 0.922863 community care center 0.484075 horrible car crash 0.466956 West Africa 0.392219 investigation 0.318793	CLICK HERE
13015	Centers for Disease Control and Prevention	Video	en	Mark's Story - Tips From Former Smokers	Mark picked up his first cigarette as a teenager to fit in with friends. Life moved quickly and he continued to smoke until age 42, when doctors told him he had rectal cancer, one of many cancers that are linked to smoking. In this video from CDC's Tips From Former Smokers campaign, Mark talks about the harsh treatments he endured to save his life. 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/MARK_THE_RACE_WAS_ON_VIGNETTE_VCDC0255000H.mp4	Smokers 0.932887 CDC 0.92604	Mark 0.658455 YouTube 0.871802	CLICK HERE
13680	Centers for Disease Control and Prevention	Html	en	A Sex-Specific Analysis of Nutrition Label Use and Health, Douglas County, Nebraska, 2013	Preventing Chronic Disease (PCD) is a peer-reviewed electronic journal established by the National Center for Chronic Disease Prevention and Health Promotion. PCD provides an open exchange of information and knowledge among researchers, practitioners, policy makers, and others who strive to improve the health of the public through chronic disease prevention.	higher probability 0.35517 Douglas County 0.394203 higher odds 0.418943 nutrition education 0.376667 body weight 0.367686 high cholesterol 0.41242 weight change 0.390214 self-rated health 0.393706 health behaviors 0.366252 health care access 0.420425 health insurance coverage 0.381059 selected health variables 0.361967 sex-specific nutrition education 0.35902 nutrition education efforts 0.35623 self-reported health status 0.353918 health status 0.385629 respondents 0.349877 self-rated health categories 0.351469 Reducing Health Disparities 0.347546 various health needs 0.354458 women 0.535545 highest nutrition label 0.382106 Nebraska Medical Center 0.461686 nutrition facts 0.353111	food choices 0.37276 association 0.452009 heart disease 0.428957 men 0.565929 health behavior 0.367547 targeted nutrition education 0.356202 Nutrition Labeling 0.347776 standardized nutrition information 0.366092 Health status variables 0.356818 weight reported nutrition 0.370252 Similar findings 0.350147 nutrition label 0.964844 label reading 0.362497 Community Health Survey 0.35902 nutrition labels 0.633835 men vs women 0.364788 health 0.536328 U-shaped relationship 0.412291 total sample 0.378189 chronic conditions 0.453836 random sample survey 0.348749 leisure-time physical activity 0.438073 close association 0.3607 personal doctor 0.414013	CLICK HERE
15555	Centers for Disease Control and Prevention	Html	en	Testimonials from Participants - National Diabetes Prevention Program	National Diabetes Prevention Program	poor eating habits.I 0.454915 exercise routine 0.397619 Cynthia Johnson 0.403736 Tim Enfinger 0.387849 glucose levels 0.487362 different ideas 0.383812 favorite thing 0.38348 type 0.3953 entire adult life 0.470121 program participants 0.418501 high cholesterol 0.382612 healthy recipes 0.418149 friend 0.332981 Jan Booker 0.39379 group 0.331544 Suzi Gomez 0.410393 blood pressure 0.398193 hard time 0.391487 different diets 0.392153 emotional eating habits 0.449756 grandchildren 0.34658 Program Participant 0.896336 lifestyle change program 0.943566 lifestyle coach 0.520718 food labels 0.38954	sugary drinks 0.395001 lunch-break rides 0.396177 Suchada Jin-Jin Hantragoon 0.452413 Bruce Wheeler 0.400001 Phyllis Perkins 0.385819 great information 0.401841 stationary bike 0.387961 medical research 0.392749 pictures 0.318673 red meat 0.393473 online lifestyle change 0.501778 healthy lifestyle changes 0.512922 hospital 0.330504 high glucose levels 0.452956 daily routine 0.389926 standard couch potato 0.458162 Online Program Participant 0.527922 medical librarian 0.391526 people 0.333157 video series 0.388838 exercise habits 0.386679 healthy changes 0.450686 difficult time 0.3908 family 0.318662	CLICK HERE