| 5868 |
Centers for Disease Control and Prevention |
Html |
en |
Diisobutyl ketone - NIOSH Pocket Guide to Chemical Hazards |
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 |
|
CLICK HERE |
| 7565 |
Centers for Disease Control and Prevention |
Html |
en |
ADHD - PLAY Study Design (Project to Learn About ADHD in Youth) |
ADHD is one of the most common neurobehavioral disorders of childhood. It is usually first diagnosed in childhood and often lasts into adulthood. Children with ADHD have trouble paying attention, controlling impulsive behaviors (may act without thinking about what the result will be), and in some cases, are overly active. |
| high school | 0.581096 |
| diverse populations | 0.581488 |
| school-based samples | 0.580656 |
| in-depth assessment | 0.632878 |
| children | 0.716272 |
| multiple waves | 0.587026 |
| semi-annual data collection | 0.642536 |
| school districts | 0.659501 |
| ADHD Prevalence | 0.765312 |
| brief quarterly contacts | 0.637812 |
| ADHD diagnosis | 0.760637 |
| ADHD Criteria | 0.753996 |
| high number | 0.57914 |
| school elementary students | 0.650896 |
| Robert E. McKeown | 0.665058 |
| ADHD Change Depending | 0.815472 |
| high risk | 0.579002 |
| ADHD symptoms | 0.840908 |
| similar assessment protocols | 0.634809 |
| public health impact | 0.659507 |
| geographical settings | 0.58258 |
| role ADHD | 0.777014 |
| research sites | 0.588051 |
| low risk | 0.577366 |
|
| ADHD | 0.985559 |
| community study | 0.582031 |
| Oklahoma Health Sciences | 0.659832 |
| baseline assessment | 0.592337 |
| South Carolina | 0.659116 |
| additional waves | 0.576995 |
| comprehensive picture | 0.576304 |
| certain ADHD symptoms | 0.807307 |
| additional studies | 0.575668 |
| investigator Mark L. | 0.663381 |
| Data analysis | 0.57917 |
| low number | 0.577461 |
| study population | 0.591295 |
| annual in-depth interviews | 0.637352 |
| data collection | 0.646223 |
| school-based sample | 0.590547 |
| follow-up project | 0.576288 |
| children’s development | 0.605804 |
| baseline study | 0.607282 |
| core set | 0.574718 |
| principal investigator | 0.653524 |
| comparison group | 0.575956 |
| site-specific measures | 0.578368 |
| PLAY study methods | 0.674449 |
|
CLICK HERE |
| 7799 |
Centers for Disease Control and Prevention |
Html |
en |
Ovarian Cancer - Health Communication |
Gateway to Health Communication and Social Marketing Practice - Ovarian Cancer |
| oral contraceptives | 0.476965 |
| Corinne’s family | 0.451889 |
| common type | 0.439277 |
| appropriate tests | 0.433608 |
| stage ovarian cancer | 0.536231 |
| close relatives | 0.437362 |
| tumor marker | 0.434517 |
| hysterectomy | 0.415987 |
| rough time | 0.433748 |
| tubal ligation | 0.479928 |
| Germ cell tumors | 0.473814 |
| non-gynecologic conditions | 0.437911 |
| blood levels | 0.434447 |
| transvaginal ultrasound | 0.438566 |
| Corinne | 0.45838 |
| Pap test | 0.435075 |
| tumor markers | 0.43609 |
| ovarian cancer | 0.938794 |
| younger women | 0.441018 |
| history | 0.413965 |
| women age | 0.463933 |
| Corinne’s problem | 0.450261 |
| female hormones | 0.44307 |
| symptoms | 0.422295 |
| certain risk factors | 0.466735 |
|
| menstrual cycle | 0.443562 |
| annual pelvic exam | 0.464552 |
| egg-producing cells | 0.439084 |
| new cases | 0.439146 |
| women | 0.467667 |
| potential screening methods | 0.466102 |
| woman | 0.447747 |
| ovaries | 0.447775 |
| sooner ovarian cancer | 0.597286 |
| Epithelial carcinoma | 0.442524 |
| aggressive therapy | 0.434998 |
| familial predisposition | 0.438884 |
| abdominal pain | 0.436649 |
| ovarian cancer increases | 0.550335 |
| stromal tumors | 0.440698 |
| types | 0.414115 |
| disease | 0.477583 |
| greater chance | 0.440345 |
| colon cancer | 0.447536 |
| common form-epithelial-is | 0.438174 |
| better chance | 0.472992 |
| Corinne’s friends | 0.450356 |
| prophylactic oophorectomy | 0.439589 |
| screening test | 0.43422 |
|
CLICK HERE |
| 8087 |
Centers for Disease Control and Prevention |
Html |
en |
Preventing Chronic Disease | An Integrated Framework for Assessing the Value of Community-Based Prevention: A Report of the Institute of Medicine - CDC |
Since the early 1900s, the major causes of illness and death in the United States have changed from infectious disease to chronic disease. Recognition is growing that nonclinical community- and population-based prevention has a large role in improving the public’s health and well-being. |
| intelligent decision making | 0.606034 |
| HIV transmission rates | 0.605175 |
| state policy implications | 0.611171 |
| nonclinical community | 0.619096 |
| senior program assistant | 0.607237 |
| IOM study committee | 0.63172 |
| population-based prevention | 0.613818 |
| Prev Chronic Dis | 0.619046 |
| California Endowment | 0.616615 |
| important step | 0.611631 |
| Wood Johnson Foundation | 0.715076 |
| W.K. Kellogg Foundation | 0.714115 |
| new framework | 0.722082 |
| Beaumont Foundation | 0.614742 |
| Integrated Framework | 0.653411 |
| health inequalities | 0.60412 |
| community process | 0.825324 |
| Lyla M. Hernandez | 0.687797 |
| health sector | 0.606567 |
| public’s health | 0.64594 |
| Hopkins Bloomberg School | 0.677103 |
| nonclinical prevention policies | 0.65943 |
| overall health | 0.613302 |
| decision makers | 0.661718 |
| community benefit | 0.637129 |
|
| quality-adjusted life | 0.614429 |
| community-based prevention interventions | 0.72263 |
| Angeles County Department | 0.603404 |
| health-adjusted life expectancy | 0.710539 |
| community health promotion | 0.705255 |
| Good-quality cost data | 0.607758 |
| Johns Hopkins Bloomberg | 0.67225 |
| IOM study staff | 0.624196 |
| community well-being | 0.863956 |
| Robert Wood Johnson | 0.713966 |
| community-based prevention intervention | 0.703066 |
| Public Health Practice | 0.666281 |
| community-based prevention | 0.993555 |
| value | 0.760566 |
| public health | 0.856233 |
| Robert S. Lawrence | 0.675955 |
| Nicolaas P. Pronk | 0.707933 |
| Health Promotion Department | 0.65004 |
| Health risks | 0.620418 |
| public health workers | 0.686132 |
| Public Health Service | 0.663653 |
| Health behaviors | 0.621106 |
| potential legitimacy problem | 0.604097 |
| Non-Clinical Prevention Programs | 0.746654 |
|
CLICK HERE |
| 8981 |
Centers for Disease Control and Prevention |
Video |
en |
CDC: Tips from Former Smokers - Nathan: "I never smoked a day in my life!" |
Nathan was Lakota, a member of the Oglala Sioux tribe, and never smoked. However, he worked in a facility where smoking was allowed, and experienced health problems as a result. In this video from CDC's Tips From Former Smokers campaign, Nathan describes his health problems—including asthma—triggered by exposure to secondhand smoke. He had to give up many activities he loved, including tribal dancing, because of damage to his lungs. That damage led to his early death at age 54.
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://streaming.cdc.gov/vod.php?id=cffaad4ff59ef8601e66780918669cef20130605130611015 |
| Smokers | 0.923068 |
| CDC | 0.901732 |
| Nathan | 0.85422 |
|
| YouTube | 0.846462 |
| life | 0.430486 |
|
CLICK HERE |
| 10683 |
Centers for Disease Control and Prevention |
Html |
null |
Business Improvements |
Centers for Disease Control and Prevention's (CDC) Business Improvements |
|
|
CLICK HERE |
| 10815 |
Centers for Disease Control and Prevention |
Html |
en |
QuickStats: Percentage of Adults Aged ?18 Years Who Looked Up Health Information on the Internet, by Age Group and Sex--- National Health Interview Survey, United States, January--September 2009 |
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. | 0.484187 |
| endorsement | 0.368145 |
| adult core component | 0.611048 |
| Alternate Text | 0.45837 |
| Human Services | 0.577895 |
| assistive technology | 0.492977 |
| health information | 0.962191 |
| National Health Interview | 0.632915 |
| final data files | 0.604885 |
| MMWR HTML versions | 0.576873 |
| U.S. Department | 0.57138 |
| electronic PDF version | 0.57166 |
| percentage | 0.734187 |
| e-mail | 0.365978 |
| estimates | 0.402779 |
| household interviews | 0.464027 |
| higher percentage | 0.729355 |
| Contact GPO | 0.477677 |
| commercial sources | 0.458537 |
| age groups | 0.620898 |
| current prices | 0.453321 |
| final weighting | 0.473327 |
| persons | 0.443063 |
| original paper copy | 0.576347 |
|
| Internet | 0.523924 |
| women | 0.429288 |
| earlier public access | 0.602151 |
| original MMWR paper | 0.572679 |
| age group | 0.461969 |
| adults | 0.383199 |
| assistance | 0.332287 |
| percentage points | 0.586822 |
| MMWR readers | 0.45836 |
| character translation | 0.452376 |
| format errors | 0.449026 |
| final data editing | 0.607433 |
| survey data | 0.477908 |
| civilian noninstitutionalized population | 0.600635 |
| question | 0.359383 |
| subject line | 0.489966 |
| typeset documents | 0.455553 |
| trade names | 0.458629 |
| official text | 0.448153 |
| sample | 0.35278 |
| non-CDC sites | 0.454384 |
| confidence interval | 0.48167 |
| Accommodation | 0.3321 |
| electronic conversions | 0.446729 |
|
CLICK HERE |
| 10851 |
Centers for Disease Control and Prevention |
Html |
en |
Preventing Chronic Disease | Comparing Farmers™ Market Revenue Trends Before and After the Implementation of a Monetary Incentive for Recipients of Food Assistance - CDC |
We examined the influence of an intervention to increase fruit and vegetable purchases at farmers’ markets for recipients of food assistance, Shop N Save (SNS), on revenue trends at a farmers’ market located at a federally qualified health center (FQHC) in rural South Carolina. We compared revenue trends for 20 weeks before the intervention (2011) and 20 weeks after (2012). |
| sales receipts | 0.440234 |
| SNS participants | 0.471808 |
| Fresh Start Farmers | 0.434934 |
| Supplemental Nutrition Program | 0.388698 |
| rural South Carolina | 0.430713 |
| market food assistance | 0.411678 |
| SNS unique identification | 0.434407 |
| revenue trends | 0.481239 |
| WIC FMNP vouchers | 0.430663 |
| healthy food retailers | 0.391936 |
| produce-only farmers | 0.433078 |
| food assistance dollars | 0.417285 |
| incentive programs | 0.412394 |
| FQHC-based farmers | 0.437493 |
| SNS enrollment | 0.412091 |
| Market Nutrition Program | 0.421963 |
| assistance incentive programs | 0.3894 |
| FMNP vouchers | 0.491434 |
| Total food assistance | 0.389458 |
| market | 0.615487 |
| food assistance incentive | 0.407508 |
| food assistance benefits | 0.38815 |
| federal food assistance | 0.541446 |
| Senior FMNP vouchers | 0.439128 |
| Senior FMNP | 0.486233 |
|
| food assistance programs | 0.433845 |
| WIC FMNP | 0.465725 |
| rural farmers | 0.424958 |
| SNS intervention | 0.4472 |
| future farmers | 0.415949 |
| farmers | 0.761089 |
| WIC Farmers | 0.504542 |
| farmers markets | 0.462669 |
| South Carolina | 0.645889 |
| Prevention Research Center | 0.391047 |
| SNS program | 0.411385 |
| SNS | 0.658585 |
| monetary incentive | 0.549748 |
| SNS coupon | 0.415282 |
| SNS customer | 0.418486 |
| nutrition assistance program | 0.411444 |
| federally qualified health | 0.388886 |
| food assistance revenue | 0.518808 |
| food assistance | 0.919698 |
| SNAP | 0.454864 |
| food assistance recipients | 0.448357 |
| monetary incentive programs | 0.393378 |
| Senior FMNP voucher | 0.389265 |
| small-scale farmers | 0.448888 |
|
CLICK HERE |
| 11923 |
Centers for Disease Control and Prevention |
Html |
en |
Epidemiologic Risk Factors to Consider when Evaluating a Person for Exposure to Ebola Virus |
null |
| country | 0.209281 |
| Laboratory processing | 0.306309 |
| uncertain control measures | 0.413823 |
| appropriate PPE | 0.908847 |
| Ebola cases | 0.310604 |
| Ebola survivors | 0.305018 |
| public health authorities | 0.206456 |
| urban settings | 0.274645 |
| widespread transmission | 0.565445 |
| Ebola virus | 0.504839 |
| n’t showing symptoms | 0.2196 |
| following epidemiologic risk | 0.238557 |
| Brief direct contact | 0.226557 |
| direct contact | 0.515096 |
|
| close contact | 0.215942 |
| potential exposure | 0.210455 |
| disease. Unprotected contact | 0.222236 |
| high risk exposures | 0.219511 |
| public health | 0.237921 |
| personal protective equipment | 0.229067 |
| public health actions | 0.233676 |
| body fluids | 0.553286 |
| symptoms | 0.31816 |
| mucous membrane exposure | 0.249157 |
| person | 0.632177 |
| Ebola | 0.800545 |
| standard biosafety precautions | 0.35679 |
|
CLICK HERE |
| 14017 |
Centers for Disease Control and Prevention |
Html |
null |
International Cancer Research |
Selected articles that are authored or coauthored by scientists in CDC's Division of Cancer Prevention and Control are cited. |
|
|
CLICK HERE |