| 1199 |
Centers for Disease Control and Prevention |
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Notes from the Field: Increase in Fentanyl-Related OverdoseDeaths - Rhode Island, November 2013-March 2014 |
Melissa C. Mercado-Crespo, PhD1, Steven A. Sumner, MD1, M. |
| time-limited active surveillance | 0.367989 |
| Acetyl fentanyl | 0.459937 |
| CDC | 0.377252 |
| Melissa C. Mercado-Crespo | 0.617751 |
| unintentional overdose deaths | 0.689876 |
| illegally produced fentanyl | 0.58412 |
| fentanyl-related overdose deaths | 0.788992 |
| overdose deaths | 0.98534 |
| synthetic opioid | 0.275807 |
| injection-drug users | 0.246739 |
| Preliminary analyses | 0.230667 |
| National Center | 0.228914 |
| program records | 0.235543 |
| risk factors | 0.232119 |
| detection limit | 0.231703 |
| drug overdose patients | 0.541404 |
| active fentanyl prescriptions | 0.547164 |
| Unintentional Injury Prevention | 0.363439 |
| fentanyl-related deaths | 0.394947 |
| nonfatal opioid overdose | 0.562136 |
| fentanyl levels | 0.442833 |
| northern Rhode Island | 0.424978 |
| Steven A. Sumner | 0.385704 |
| additional data analyses | 0.334451 |
|
| fentanyl-related overdose death | 0.782597 |
| prescription monitoring program | 0.588323 |
| local staff members | 0.351776 |
| Rhode Island Department | 0.608473 |
| M. Bridget Spelke | 0.40874 |
| drug overdose deaths | 0.772832 |
| prescription drug | 0.256662 |
| Rhode Island | 0.89142 |
| Christina Stanley | 0.24962 |
| Rhode Island emergency | 0.39148 |
| Author affiliations | 0.255671 |
| Drug Enforcement Administration | 0.37177 |
| urban areas | 0.229319 |
| illicit fentanyl | 0.481638 |
| illicit sources | 0.246928 |
| New Jersey | 0.242799 |
| official cause | 0.230674 |
| Vital Records | 0.239665 |
| toxicology reports | 0.232953 |
| enzyme-linked immunosorbent assay | 0.348234 |
| David E. Sugerman | 0.405704 |
| medical records | 0.238672 |
| State Medical Examiners | 0.584034 |
| large percentage | 0.246853 |
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| 4415 |
Centers for Disease Control and Prevention |
Html |
en |
Diseases & Conditions A-Z Index - D |
CDC Diseases and Conditions A-Z Index |
| Error processing SSI | 0.968415 |
| Microsoft PowerPoint file | 0.472728 |
| ePub file | 0.388096 |
| Audio/Video file | 0.375696 |
| Dengue Hemorrhagic Fever | 0.728753 |
| Hymenolepis Infection | 0.395291 |
| Workforce Development | 0.644468 |
| Microsoft Word file | 0.47615 |
| Dengue Fever | 0.602329 |
| Corynebacterium diphtheriae Infection | 0.512 |
| Disease Control | 0.42408 |
| Public Health Systems | 0.957903 |
| Microsoft Excel file | 0.470219 |
| [Trisomy 21] | 0.330865 |
| Contact CDC | 0.478314 |
| list Skip | 0.384095 |
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| different file formats | 0.474838 |
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| Public Affairs | 0.325558 |
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| Dermatophyte Infection | 0.400441 |
| Apple Quicktime file | 0.467474 |
| Dog Bites | 0.347602 |
|
| CDC A-Z | 0.648492 |
| RealPlayer file | 0.377893 |
| Adobe PDF file | 0.468107 |
| Guinea Worm Disease | 0.422168 |
| Diphtheria Vaccination | 0.348252 |
| Developmental Disabilities | 0.328797 |
| Deep Vein Thrombosis | 0.671772 |
| processing SSI file | 0.934422 |
| Search Form Controls | 0.695156 |
| Dog Heartworm | 0.562463 |
| new entry | 0.32733 |
| Diphyllobothrium Infection | 0.504283 |
| A-Z Index | 0.952079 |
| Conditions A-Z Index | 0.789537 |
| CDC Topics | 0.59372 |
| Search The CDC | 0.527585 |
| Search Controls | 0.374158 |
| Clifton Road Atlanta | 0.412255 |
| Dog Flea Tapeworm | 0.535687 |
| Text file | 0.379815 |
| Antimicrobial Resistance | 0.32345 |
| (Dog Heartworm) | 0.526402 |
| RIS file | 0.386044 |
| SAS file | 0.376842 |
|
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| 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 |
|
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| 7075 |
Centers for Disease Control and Prevention |
Html |
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Notifiable Diseases and Mortality Tables - May 25, 2012 |
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. |
| H. influenzae | 0.376572 |
| National Center | 0.293729 |
| cases | 0.354859 |
| CDC | 0.287887 |
| novel influenza | 0.635704 |
| Total case counts | 0.443245 |
| pandemic influenza | 0.93758 |
| measles cases | 0.335474 |
| ** Data | 0.337913 |
|
| Cumulative total E. | 0.463016 |
| Influenza Division | 0.441593 |
| case reports | 0.281924 |
| 2009 pandemic | 0.744245 |
| probable cases | 0.321713 |
| Respiratory Diseases | 0.300603 |
| rubella cases | 0.304484 |
| human infection | 0.323499 |
| influenza A virus | 0.480032 |
|
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| 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 |
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| 10567 |
Centers for Disease Control and Prevention |
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Preventing Chronic Disease | Models for Count Data With an Application to Healthy Days Measures: Are You Driving in Screws With a Hammer? - CDC |
Volume 11 — March 27, 2014. |
| logistic regression model | 0.599977 |
| data | 0.693577 |
| data distribution | 0.562542 |
| mental health | 0.597178 |
| relevant financial relationships | 0.593676 |
| Healthy Days data | 0.568421 |
| Risk Factor Surveillance | 0.703971 |
| Poisson regression model | 0.624847 |
| Prev Chronic Dis | 0.617334 |
| AMA PRA | 0.574025 |
| unhealthy day count | 0.571416 |
| homeownership question | 0.565906 |
| number | 0.590643 |
| Disease Control | 0.639293 |
| multivariate regression models | 0.618718 |
| alternative regression models | 0.595912 |
| William W. Thompson | 0.594605 |
| logistic regression | 0.615487 |
| health-related quality | 0.686096 |
| Paul Z. Siegel | 0.597397 |
| Poisson regression models | 0.646249 |
| Poisson data | 0.562441 |
| chronic disease | 0.599278 |
| linear regression models | 0.621163 |
|
| Behavioral Risk Factor | 0.70647 |
| regression models | 0.707003 |
| logistic regression analysis | 0.580074 |
| AMA PRA Category | 0.563022 |
| homeownership | 0.607374 |
| logistic regression analyses | 0.570452 |
| Epidemiol Community Health | 0.561264 |
| exact Poisson distribution | 0.562603 |
| Hong Zhou | 0.567031 |
| negative binomial models | 0.633667 |
| unhealthy days questions | 0.572365 |
| Charles P. Vega | 0.56568 |
| count data | 0.621902 |
| negative binomial model | 0.833214 |
| Rashid S. Njai | 0.598107 |
| binomial regression model | 0.644886 |
| negative binomial regression | 0.936392 |
| simplest regression model | 0.595946 |
| negative binomial component | 0.603142 |
| Poisson distribution | 0.571208 |
| observed percentage distribution | 0.563263 |
| Poisson regression | 0.687421 |
| Chronic Disease Prevention | 0.571757 |
| percentage distribution | 0.584319 |
|
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| 11573 |
Centers for Disease Control and Prevention |
Html |
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NER - Peer-Reviewed Biomonitoring Articles | Environmental Phenols: Triclosan |
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 |
|
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| 12295 |
Centers for Disease Control and Prevention |
Html |
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Non-Polio Enterovirus | Transmission Non-Polio Enterovirus Infection | Picornavirus | CDC |
You can get infected with non-polio enteroviruses by having close contact with an infected person. Also spread by touching objects or surfaces that have the virus on them and then touching your mouth, nose, or eyes. |
| stool | 0.418736 |
| hands | 0.447013 |
| objects | 0.334489 |
| sputum | 0.335759 |
| babies | 0.335386 |
| eyes | 0.383484 |
| Mothers | 0.336983 |
| navigation Skip | 0.615467 |
| coughing | 0.344144 |
| sneezing | 0.350418 |
| diapers | 0.345993 |
| mouth secretions | 0.551455 |
| close contact | 0.491626 |
| Infected people | 0.593088 |
| nasal mucus | 0.535609 |
| nose | 0.464758 |
| delivery | 0.332956 |
|
| non-polio enteroviruses | 0.720976 |
| list Skip | 0.616861 |
| pass | 0.337555 |
| page options Skip | 0.781747 |
| blister fluid | 0.5119 |
| Non-Polio Enterovirus Infection | 0.684831 |
| water | 0.335221 |
| infected person | 0.912067 |
| saliva | 0.352754 |
| eye | 0.336001 |
| doctor | 0.33318 |
| Pregnancy | 0.332548 |
| Pregnant women | 0.48825 |
| respiratory tract | 0.479622 |
| non-polio enteroviruses infection | 0.680301 |
| information | 0.332592 |
| feces | 0.36251 |
|
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| 13351 |
Centers for Disease Control and Prevention |
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Progress Toward Measles Elimination - South-East AsiaRegion, 2003-2013 |
Arun Thapa, MD1; Sudhir Khanal, MPH1; Umid Sharapov, MD2; Virginia Swezy, MPH1; Tika Sedai, MA1; Alya Dabbagh, PhD3; Paul Rota, PhD4; James L. Goodson, MPH2; Jeffrey McFarland, MD1 (Author affiliations at end of text). |
| measles elimination platform | 0.469388 |
| endemic measles virus | 0.440691 |
| measles elimination | 0.837069 |
| measles incidence | 0.584168 |
| measles laboratory network | 0.50276 |
| aggregate measles surveillance | 0.474146 |
| measles genotypes | 0.406827 |
| routine immunization | 0.340674 |
| measles outbreaks | 0.561745 |
| MCV1 coverage | 0.308762 |
| timely case-based measles | 0.498258 |
| routine immunization services | 0.30856 |
| measles control | 0.415283 |
| World Health Organization | 0.4085 |
| measles virus genotypes | 0.446291 |
| Rubella Laboratory Network | 0.318499 |
| countries | 0.372906 |
| measles elimination activities | 0.461883 |
| Rubella Strategic Plan | 0.303569 |
| laboratory-confirmed measles outbreaks | 0.543037 |
| measles epidemiology | 0.411126 |
| congenital rubella syndrome | 0.466683 |
| rubella syndrome control | 0.459724 |
| measles case-based surveillance | 0.507425 |
| MCV2 coverage | 0.335589 |
|
| laboratory-confirmed measles cases | 0.453367 |
| Global Measles | 0.463257 |
| south-east asia region | 0.771863 |
| regional measles mortality | 0.451228 |
| laboratory-confirmed rubella outbreaks | 0.325887 |
| South-East Asia Region* | 0.31777 |
| laboratory-confirmed mixed measles | 0.457113 |
| measles elimination goal | 0.576663 |
| measles cases | 0.461191 |
| Asia Regional Office | 0.347612 |
| measles SIAs | 0.457287 |
| Regional Committee | 0.324839 |
| rubella/congenital rubella syndrome | 0.303718 |
| coverage | 0.351446 |
| South-East Asia Regional | 0.361093 |
| measles surveillance data | 0.506908 |
| target population | 0.312665 |
| Sri Lanka | 0.324549 |
| annual measles incidence | 0.461823 |
| rubella outbreaks | 0.343385 |
| case-based measles surveillance | 0.574895 |
| periodic high-quality SIAs | 0.307003 |
| routine immunization program | 0.301291 |
| measles deaths | 0.482338 |
|
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| 13680 |
Centers for Disease Control and Prevention |
Html |
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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 |
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