| 1199 |
Centers for Disease Control and Prevention |
Html |
en |
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 |
| page options Skip | 0.505178 |
| Form Controls TOPIC | 0.460415 |
| different file formats | 0.474838 |
| Mortality Data | 0.332655 |
| Public Affairs | 0.325558 |
| Cat Flea | 0.459136 |
| 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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| 6596 |
Centers for Disease Control and Prevention |
Html |
en |
CDC Grand Rounds: Prescription Drug Overdoses - a U.S. Epidemic |
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. |
| prescription drug diversion | 0.498476 |
| Health Services Administration | 0.535745 |
| mg morphine | 0.463306 |
| prescription drug overdoses | 0.538525 |
| prescription drug abuse | 0.777071 |
| prescription drugs | 0.544747 |
| fastest growing drug | 0.508671 |
| prescription opioid overdoses | 0.687721 |
| opioid prescribing patterns | 0.518772 |
| State Drug Laws | 0.454108 |
| Drug Abuse Crisis | 0.451079 |
| high-dose opioid therapy | 0.53205 |
| proper medication disposal | 0.44027 |
| report drug abuse | 0.490777 |
| prescription drug monitoring | 0.575576 |
| state prescription drug | 0.527842 |
| persons | 0.448405 |
| unintentional overdose death | 0.536774 |
| Drug Control Policy | 0.497602 |
| substance abuse treatment | 0.488735 |
| opioid overdose deaths | 0.646524 |
| chronic pain | 0.462503 |
| United States | 0.63293 |
| Prescription opioid overdose | 0.661418 |
| opioid analgesic misuse | 0.559299 |
|
| overdose death rates | 0.556455 |
| prescription drug overdose | 0.844338 |
| opioid dose | 0.505717 |
| opioid overdose-related deaths | 0.520078 |
| unintentional pharmaceutical overdose | 0.484878 |
| substance abuse | 0.524435 |
| prescription drug | 0.965749 |
| drug overdose deaths | 0.706634 |
| opioid prescriptions | 0.533909 |
| drug overdose death | 0.604808 |
| morphine equivalent dose | 0.688338 |
| law enforcement agencies | 0.451616 |
| Drug Enforcement Administration | 0.440466 |
| opioid analgesics | 0.665605 |
| National Prescription Drug | 0.513714 |
| long-term opioid therapy | 0.509631 |
| unintentional drug overdose | 0.829629 |
| drug monitoring programs | 0.508544 |
| Mental Health Services | 0.536892 |
| opioid therapy | 0.533753 |
| high daily doses | 0.470727 |
| Fulton-Kehoe D. Opioid | 0.50835 |
| National Drug Control | 0.551912 |
| opioids | 0.567725 |
|
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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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| 8809 |
Centers for Disease Control and Prevention |
Html |
en |
Naegleria fowleri - Organ Transplantation - Info for Health Professionals |
Information for health professionals about Naegleria fowleri. Education and information about the brain eating ameba Naegleria fowleri that causes encephalitis and death including frequently asked questions, biology, sources of infection, diagnosis, treatment, prevention and control, and other publications and pertinent information for the public and medical professionals. |
| tissue cross-contamination | 0.650288 |
| Trans R Soc | 0.679433 |
| generalized amoebiasis | 0.677607 |
| Engl J Med | 0.651407 |
| shipping instructions | 0.648017 |
| CDC Emergency Operations | 0.692967 |
| CDC Free-Living Ameba | 0.697448 |
| organ transplantation | 0.808426 |
| da Silva AJ | 0.672207 |
| solid organ transplantation | 0.796203 |
| primary amoebic meningoencephalitis | 0.877887 |
| specimen collection guidance | 0.683891 |
| Morb Mortal Wkly | 0.746019 |
| CDC. Balamuthia | 0.668577 |
| B. Diagnostic review | 0.670854 |
| free-living ameba | 0.698663 |
| historical case | 0.651788 |
| Visvesvara GS | 0.728969 |
| Tuppeny M. Primary | 0.684381 |
| Public Health | 0.686108 |
| primary meningoencephalitis | 0.684738 |
| Duma RJ | 0.686934 |
| individual organ | 0.683419 |
| extra-CNS organs | 0.654355 |
| donor organs | 0.653768 |
|
| MMWR Morb Mortal | 0.74604 |
| suitable organ | 0.679978 |
| experimental pathological changes | 0.675391 |
| organ recipient serology | 0.716055 |
| Naegleria fowleri | 0.996022 |
| organ transplantation—Mississippi | 0.680204 |
| Mortal Wkly Rep. | 0.73665 |
| blood-brain barrier | 0.647835 |
| Transplant-transmitted Balamuthia mandrillaris—Arizona | 0.702388 |
| Soc Trop Med | 0.768134 |
| organ recipients | 0.689078 |
| Trop Med Public | 0.701363 |
| Crit Rev Clin | 0.679978 |
| Primary amebic meningoencephalitis | 0.712562 |
| guide clinical management | 0.676219 |
| Clin Microbiol. | 0.651517 |
| potentially greater risk | 0.682091 |
| subsequent organ procurement | 0.713096 |
| deceased PAM cases | 0.686603 |
| Annu Rev Microbiol | 0.675549 |
| Trop Med Hyg | 0.764552 |
| diagnostic assistance | 0.648926 |
| Roy SL | 0.676483 |
| Naegleria sp. | 0.786321 |
|
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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 |
Html |
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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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| 12500 |
Centers for Disease Control and Prevention |
Video |
en |
February 2014 ACIP Meeting -- ACIP Influenza |
Novel influenza vaccine work group, Influenza activity update, Interim estimates of 2013-2014 seasonal influenza vaccine effectiveness, Effectiveness of Live-Attenuated vs. Inactivated Influenza Vaccines for Healthy Children (GRADE), and Interim Influenza Vaccine Safety Update.
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=b7a051f069c7a138c56228d20547986320140410080816714 |
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CLICK HERE |
| 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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