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Media ID	Source Name	Media Type	Language	Media Name	Media Description	Keywords	Keywords	Link to Article
4763	Centers for Disease Control and Prevention	Html	en	The governance of standard-setting to improve health.	null	public health interventions 0.315714 American foreign policy 0.269687 review organizations 0.380693 health care policy 0.427043 european community atlas 0.23408 industrial countries 0.261744 National Health Service 0.250166 Nations Health Organization 0.261779 evidence-based health research 0.311449 global health 0.264067 health professionals 0.228556 British health official 0.265762 population health 0.513665 World Health Organization 0.247484 countries 0.287972 public health officials 0.296239 health expenditures 0.244303 standards 0.384124 health status 0.266418 dysfunctional health systems 0.243334 health care infrastructure 0.282945 public officials 0.2712 Health Protection Agency 0.245238 public health policy 0.359486 United States 0.319802	health districts 0.230729 national security policy 0.227956 conventional governance 0.274368 policy set standards 0.294363 multiple determinants 0.228509 health policy 0.372482 health improvement 0.227689 health services 0.392429 population health status 0.250121 Milbank Memorial Fund 0.267764 foreign policy 0.547237 health care 0.762323 public health 0.362213 advocacy groups 0.298127 global health evince 0.246937 health 0.911416 best available evidence 0.248352 policy making 0.235384 policy makers 0.687611 population health policy 0.327636 organizational governance 0.25399 systematic reviews 0.435301 health risks 0.227329 governance 0.712183	CLICK HERE
4921	Centers for Disease Control and Prevention	Html	en	2010 Haiti Cholera Outbreak: CDC Responds to Cholera Outbreak in Haiti	CDC - Global Health	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	CLICK HERE
6739	Centers for Disease Control and Prevention	Html	en	Global Health - Kenya - Blog: A Promising New Career in the Slums of Kibera	In Kenya, the CDC is addressing the region's toughest health problems at their source, directly working with vulnerable families and communities in local hospitals, clinics, and laboratories.	Field Coordinator 0.546454 U.S. CDC Kenya 0.879957 largest slums 0.569468 Community Interviewer team 0.719193 right medical services 0.67179 rigorous training course 0.675217 unique collaboration 0.542659 undergraduate degrees 0.55361 surveillance activities 0.545481 graduation 0.430584 Tabitha clinic management 0.731773 HIV status 0.562516 graduate school 0.54768 commitments 0.434328 local church function 0.707135 right opportunity 0.558453 Carolina 0.442427 Community Interviewers 0.574607 current team 0.544701 Social Studies 0.559845 home base 0.551723 community health workers 0.717209 large urban slum 0.748135 slum neighborhood 0.624854 job 0.450811	Kibera 0.5679 mismanagement 0.434262 sustainable contact 0.55067 prosperous Nairobi locality 0.724823 CDC-Kenya infectious disease 0.679905 Aurum Institute 0.581016 earliest members 0.546265 flagship project 0.551415 western Kenya 0.611584 local community 0.562316 Kennedy Odero 0.58416 Public Administration 0.559728 Medical Sociology 0.535474 CDC Community Interviewer 0.801344 data collection 0.543835 pm ET 0.572448 KENYA BLOG 0.63948 life-saving healthcare services 0.688854 Tabitha clinic 0.901376 school uniforms 0.565514 ideal career 0.551829 quality control systems 0.664372 prestigious Moi University 0.7117 time 0.449957	CLICK HERE
9496	Centers for Disease Control and Prevention	Html	en	Surveillance for Foodborne Disease Outbreaks — UnitedStates, 2009–2010	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.	illnesses 0.392947 Population-based outbreak 0.35594 L. Hannah Gould 0.361628 Foodborne Outbreak Online 0.39142 disease outbreak reporting 0.374175 outbreak-related illnesses 0.381287 foodborne illnesses 0.385058 STEC O121 0.355005 multiple etiologies percentages 0.364547 disease outbreak surveillance 0.482583 foodborne disease outbreaks 0.804985 total etiology percentages 0.385779 foodborne disease outbreak 0.560935 etiology category total 0.386333 public health officials 0.366045 food industry professionals 0.377879 major etiology group 0.357579 Salmonella serotype Enteritidis 0.378762 multistate outbreaks 0.519808 etiologic agent 0.550415 nonfoodborne norovirus outbreaks 0.480962 outbreaks 0.97678 foodborne outbreaks 0.518039 Puerto Rico 0.374706	etiology total 0.377025 United States 0.359086 shellfish poisoning outbreaks 0.487037 dairy-associated outbreaks 0.471185 safe food-handling practices 0.374077 public health 0.39657 unpasteurized dairy products 0.393841 suspected outbreaks 0.494016 food vehicle 0.434635 laboratory-confirmed etiologic agent 0.358615 unknown etiology 0.384842 single confirmed etiologic 0.394453 average annual rate 0.393349 Outbreaks identifying foods 0.494533 foodborne norovirus outbreaks 0.508444 single-etiology outbreaks 0.513676 waterborne outbreaks 0.464065 National Outbreak Reporting 0.368714 Thirty-eight multistate outbreaks 0.48787 sporadic foodborne illnesses 0.372799 Salmonella outbreaks 0.496511 STEC O157 0.474071 fewer foodborne disease 0.367105 outbreak surveillance data 0.365094	CLICK HERE
9577	Centers for Disease Control and Prevention	Html	en	Preventing Chronic Disease | Disability, Health, and Multiple Chronic Conditions Among People Eligible for Both Medicare and Medicaid, 2005"2010 - CDC	People who are eligible for both Medicare and Medicaid (dual eligibles) and who have disabilities and multiple chronic conditions (MCC) present challenges for treatment, preventive services, and cost-effective access to care within the US health system.	Medical Expenditure Panel 0.368966 high school 0.398011 dual eligible population 0.477619 disability 0.501732 mental health 0.370199 functional limitations 0.417625 cognitive disabilities 0.386234 multiple chronic conditions 0.415244 high school education 0.357197 assistive device 0.365539 national health care 0.355329 poor health status 0.48842 daily living 0.367252 angina pectoris 0.36938 health care providers 0.355451 Expenditure Panel Survey 0.369308 age category 0.35839 health care services 0.378431 cognitive limitations 0.646022 Human Services Strategic 0.365363 physical disability 0.398585 health status 0.520935 dual eligible populations 0.388692 significant odds 0.356843	usual source 0.41414 disability category 0.401862 MEPS 0.381462 long-term need 0.381315 heart attack 0.364688 functional disability limitations 0.418447 physical disabilities 0.551844 long-term walking limitations 0.395756 health care 0.449938 dual eligibles accounts 0.39977 federal poverty guidelines 0.359654 health characteristics 0.367627 health 0.53696 cognitive limitation 0.477456 MCC 0.553736 health care coverage 0.355696 medical care 0.388813 people 0.379193 chronic conditions 0.793564 Services Strategic Initiative 0.364767 memory loss 0.370952 high school diploma 0.358356 coronary heart disease 0.418923 dual eligibles 0.984161	CLICK HERE
10366	Centers for Disease Control and Prevention	Html	en	Preventing Chronic Disease | Differences in the Delivery of Health Education to Patients With Chronic Disease by Provider Type, 2005"2009 - CDC	Health education provided to patients can reduce mortality and morbidity of chronic disease. Although some studies describe the provision of health education by physicians, few studies have examined how physicians, physician assistants, and nurse practitioners differ in the provision of health education. The objective of our study was to evaluate the rate of health education provision by physicians, physician assistants, and nurse practitioners/certified midwives.	health education 0.991991 data 0.359573 Physician assistant training 0.346299 ischemic heart disease 0.389045 training programs 0.34959 physicians 0.394422 Hospital Ambulatory Medical 0.36051 health care providers 0.382811 health counseling 0.348825 Introduction Health education 0.38745 health professionals 0.349274 nurse practitioner training 0.363074 patients 0.447357 Ann Fam Med 0.349891 weight reduction education 0.34635 health care teams 0.366581 outpatient department 0.360703 Acad Nurse Pract 0.355079 primary care physicians 0.351043 National Hospital Ambulatory 0.358878 nurse practitioner programs 0.357828 chronic condition–specific health 0.380283 condition-specific education 0.352667 stress management 0.359589 obstructive pulmonary disease 0.416457	health care needs 0.366429 Nebraska Medical Center 0.362194 physician training programs 0.346517 study 0.354219 stress management education 0.347792 health education provision 0.437305 nurse practitioner 0.442604 provider types 0.347757 Ambulatory Medical Care 0.369932 Veterans Administration health 0.350077 quality health education 0.381301 diabetes care 0.350495 nurse practitioners 0.813726 provider type 0.421801 physician assistant practices 0.34671 ambulatory care 0.349863 condition–specific health education 0.395998 chronic conditions 0.401074 Medical Care Survey 0.370146 physician assistant 0.416146 health education elements 0.372503 physician assistants 0.667022 primary care 0.424284 condition-specific health education 0.371672	CLICK HERE
11338	Centers for Disease Control and Prevention	Html	en	Newport Infections Linked to Organic Sprouted Chia Powder (Final Update)	Epi Curve Multistate Outbreak of Salmonella Newport Infections Linked to Organic Sprouted Chia Powder	average 0.823049 Illnesses 0.784543 Salmonella Newport 0.740337 person 0.743937 Persons 0.256387	ill 0.774024 information 0.731178 time 0.661522 outbreak strain 0.751508 illness 0.9567	CLICK HERE
11748	Centers for Disease Control and Prevention	Html	en	Preventing Chronic Disease | Association Between Living Alone and Physical Inactivity Among People With and Without Disability, Florida Behavioral Risk Factor Surveillance System, 2009 - CDC	People with disability may be at risk of developing diseases due to physical inactivity; social support from family and friends is positively related to engaging in regular physical activity. We compared the association between living alone and engagement in physical activity among people with and without disability in Florida. We used multivariate logistical regression to analyze 2009 Florida Behavioral Risk Factor Surveillance System data (n = 10,902) to assess differences in physical activity in disability levels for respondents who lived alone versus those who did not.	disability levels 0.57065 personal care needs 0.546779 disability 0.704774 health problem 0.566503 Human Services 0.544527 Risk Factor Surveillance 0.627979 Prev Chronic Dis 0.567185 ADL 0.563848 B. Physical activity 0.577683 moderate physical activity 0.589505 multivariate logistical regression 0.544415 Disease Control 0.568339 disability classification 0.553572 daily living 0.612346 Health care providers 0.543858 vigorous physical activity 0.648294 social support 0.600542 everyday household chores 0.543413 ADL needs 0.557344 Public health efforts 0.584325 instrumental activities 0.564351 physical activity interventions 0.593686 respondents 0.56531 independent living 0.547734 Population Health Sciences 0.543418	regular physical activity 0.61506 vigorous activity 0.613404 Behavioral Risk Factor 0.628094 ADL assistance 0.558616 regular physical activities 0.570464 Health Services Research 0.547175 IADL assistance 0.54514 telephone household survey 0.546704 4-level disability severity 0.562243 activity limitations 0.573788 disability status 0.559279 heavy yard work 0.620466 public health 0.589931 territorial health agencies 0.551437 moderate activity 0.559016 people 0.604128 physical activity 0.909148 Florida Behavioral Risk 0.627994 CDC-defined physical activity 0.585683 Public Health Service 0.545826 disability type 0.546653 physical activity measures 0.583704 household composition 0.580766 physical inactivity 0.560451	CLICK HERE
12779	Centers for Disease Control and Prevention	Html	en	Interim Guidance for U.S. Businesses, Employers, andBusiness Travelers to Prevent Exposures to Ebola | EbolaHemorrhagic Fever | CDC	null	uncertain control measures 0.82689 health care provider 0.287127 Ebola webpage 0.413161 Ebola infection 0.403415 public health authorities 0.450544 Ebola symptoms 0.409065 U.S. Department 0.299417 Ebola outbreak 0.501547 pack health supplies 0.339487 emergency health needs 0.281603 evacuation insurance coverage 0.330625 monitoring procedures 0.299137 countries 0.391851 body fluids 0.366131 Ebola outbreaks 0.727299 Travel Health Notices 0.398191 CDC’s Ebola 0.423138 employees 0.419963 active monitoring 0.304608 Travel insurance information 0.282639 State Ebola Protocols 0.427228 medical evacuation 0.288171 Travel health topic 0.292408 Postpone employee travel 0.284438 United States 0.645304	Ebola exposure 0.49076 ongoing Ebola transmission 0.488332 Ebola cases 0.41499 public health monitoring 0.293139 local health department 0.314161 CDC Travel Health 0.376199 travel health kit 0.305279 practice health behaviors 0.288886 confirmed Ebola 0.422842 Ebola websites 0.45664 Check CDC Travel 0.3094 nonessential travel 0.362169 medical care 0.368715 people 0.282818 local public health 0.284668 flexible work arrangements 0.309187 widespread Ebola transmission 0.991234 business travelers 0.325316 Ebola Virus 0.398469 guidance 0.291132 CDC Ebola website 0.508858 information 0.288349 risk 0.283429 U.S. citizens 0.299251	CLICK HERE
15532	Centers for Disease Control and Prevention	Html	en	CDC Collaborative Study: Influenza Seasonality in the Tropics and Sub-tropics	Spotlight highlighting a new collaborative study by CDC	South America 0.527029 new collaborative study 0.543042 peak flu activity 0.54805 southern hemisphere formulation 0.532636 vaccination campaigns 0.501568 influenza virological surveillance 0.691399 southern hemispheres 0.47907 maternal influenza immunization 0.732942 sub-tropics 0.498231 local flu seasonality 0.563212 production cycle 0.475501 seasonal influenza vaccination 0.855201 optimal timing 0.483248 influenza vaccination campaign 0.733347 Health Organization 0.477 middle-income countries 0.498416 different statistical approaches 0.511796 influenza activity 0.908284 increased activity 0.474132 countries 0.577653 National surveillance data 0.514632 influenza virus circulation 0.757724 alternate vaccination times 0.537348 influenza surveillance 0.675252 distinct influenza peak 0.727416	influenza activity data 0.701135 influenza 0.940297 year-round influenza activity 0.704239 temperate regions 0.473078 northern hemisphere formulation 0.532259 bi-annual World Health 0.540926 Health Services Research 0.515329 local seasonality 0.503147 influenza vaccine composition 0.762081 influenza vaccine formulation 0.779496 immunization programs 0.474179 sub-Saharan countries 0.495087 largest global database 0.512607 multiple peaks 0.484435 journal PLOS 0.48994 additional secondary peak 0.523743 distinct peaks 0.479263 study results 0.475316 virus vaccine recommendation 0.518819 influenza seasonality patterns 0.805131 global health partners 0.542063 Thirty-seven countries 0.496392 Latin American region 0.528463 secondary peaks 0.47365	CLICK HERE