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Assessment of Blood Lead Levels Among Children Aged ?5 Years— Zamfara State, Nigeria, June–July 2012 |
Muhammed Bashir, MD1, Nasir Umar-Tsafe, MSc1, Kabiru Getso, MBBS1, Ibrahim M. Kaita, MSc1, Abdulsalami Nasidi, PhD2; Nasir Sani-Gwarzo MD2; Patrick Nguku, MD3, Lora Davis, DVM3, Mary Jean Brown ScD4 (Author affiliations at end of text). |
| lead-containing gold ore | 0.679606 |
| family compound | 0.615677 |
| Public health strategies | 0.474305 |
| environmental lead | 0.502216 |
| chelation therapy | 0.541079 |
| different ore processing | 0.581065 |
| ore processing | 0.924975 |
| residual ore wastes | 0.599459 |
| low technology methods | 0.47646 |
| highest GM BLLs | 0.489321 |
| ore processing villages | 0.622067 |
| mothers | 0.477643 |
| toxic lead fumes | 0.472489 |
| children | 0.753495 |
| Zamfara State | 0.777229 |
| environmental lead contamination | 0.47825 |
| high-risk villages | 0.495832 |
| young children | 0.545212 |
| ore processing methods | 0.813914 |
| Zamfara State Ministry | 0.480027 |
| ore processing activities | 0.631337 |
| villages | 0.624498 |
| dangerous work practices | 0.519235 |
|
| gold ore | 0.685939 |
| ore processing techniques | 0.634656 |
| BLLs | 0.559123 |
| safer ore processing | 0.560187 |
| environmental cleanup | 0.498463 |
| fatal lead poisoning | 0.480364 |
| environmental lead levels | 0.486797 |
| lead poisoning | 0.724672 |
| Environ Health Perspect | 0.514837 |
| village ore activities | 0.544289 |
| non–dust-generating ore processing | 0.623244 |
| dry grinding ore | 0.55929 |
| Venous blood samples | 0.522646 |
| high GM BLLs | 0.490853 |
| dangerous ore processing | 0.598261 |
| ore processing practices | 0.585687 |
| environmental lead exposure | 0.478717 |
| childhood lead poisoning | 0.483395 |
| hazardous processing sites | 0.473441 |
| GM BLL | 0.561032 |
| dust-generating ore processing | 0.631014 |
| BLLs т‰Ѕ45 ТЕg/dL | 0.535293 |
| blood lead levels | 0.568791 |
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Centers for Disease Control and Prevention |
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5 Surprising Facts About High Blood Pressure | CDC Features |
What you don’t know about high blood pressure could hurt you. High blood pressure affects one in three Americans,1 yet many people with the condition don’t know they have it. |
| health insurance | 0.320857 |
| health care provider | 0.336356 |
| early delivery | 0.319007 |
| lower sodium food | 0.337456 |
| plain sight. | 0.320047 |
| health care team | 0.335482 |
| WISEWOMAN programs | 0.322482 |
| blood pressure numbers | 0.359112 |
| healthy lifestyle | 0.332957 |
| grocery stores | 0.319411 |
| women age | 0.321003 |
| chronic disease screenings | 0.332454 |
| WISEWOMAN program | 0.32148 |
| young adults | 0.320477 |
| Certain types | 0.318823 |
| higher rates | 0.332338 |
| women | 0.323836 |
| Sodium Reduction | 0.320979 |
| normal blood pressure | 0.356912 |
| United States | 0.322489 |
| direct result | 0.320371 |
| blood pressure.1Most people | 0.349346 |
| lower your risk | 0.3216 |
| lower blood pressure | 0.356479 |
| African American men | 0.334651 |
|
| sodium Americans | 0.320781 |
| heart disease | 0.3816 |
| uncontrolled blood pressure | 0.36323 |
| higher blood pressure | 0.356591 |
| Recent studies | 0.321721 |
| health care | 0.356163 |
| National Institute | 0.320845 |
| younger people | 0.339669 |
| higher risk | 0.334854 |
| undiagnosed high blood | 0.353087 |
| uncontrolled high blood | 0.417895 |
| U.S. adults | 0.319128 |
| CDC currently funds | 0.33531 |
| stroke | 0.34506 |
| physical activity | 0.319052 |
| Neurological Disorders | 0.32071 |
| health care practitioners | 0.333215 |
| public health efforts | 0.334429 |
| high blood pressure | 0.988307 |
| silent killer. | 0.319846 |
| older adults | 0.319916 |
| risk | 0.343928 |
| low birth weight | 0.335108 |
| tribal organizations | 0.319206 |
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Centers for Disease Control and Prevention |
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C diff - Questions and Answers - HAI |
null |
| additional toxin | 0.270918 |
| Clostridium difficile infection | 0.910113 |
| alcohol-based hand rubs | 0.261047 |
| toxin production | 0.280835 |
| historical gold standard | 0.262542 |
| toxin detection | 0.278874 |
| two-step testing algorithms | 0.264919 |
| patient-care areas | 0.266535 |
| Clostridium difficile | 0.921041 |
| early experimental data | 0.259941 |
| C. difficile infections | 0.425017 |
| environmental surface disinfection | 0.306914 |
| infection control experts | 0.286389 |
| detects toxin | 0.308842 |
| current epidemic strain | 0.264464 |
| severe disease | 0.262832 |
| Environmental Protection Agency | 0.261235 |
| Clostridium difficile testing | 0.540952 |
| slow turn-around time | 0.262268 |
| Clostridium difficile colitis | 0.510471 |
| Clostridium difficile-infected patients | 0.376835 |
| toxin-producing Clostridium | 0.346628 |
| infection control | 0.304293 |
| Clostridium difficile  transmission | 0.591125 |
| Clostridium difficile spores | 0.715892 |
|
| toxin B. | 0.287492 |
| proton pump inhibitors | 0.261389 |
| Environmental Infection Control | 0.290866 |
| C. difficile infection | 0.533391 |
| Clostridium difficile  antigen | 0.558876 |
| clinical symptoms | 0.30127 |
| exhibits clinical symptoms | 0.270164 |
| Clostridium difficile  strains | 0.563558 |
| toxin degrades | 0.278343 |
| C. difficile spores | 0.443649 |
| clinical significant disease | 0.271864 |
| infection control practices | 0.289294 |
| Gram-positive anaerobic bacillus | 0.269517 |
| patient tests | 0.265329 |
| electronic rectal thermometers | 0.259892 |
| appropriate culture environment | 0.267793 |
| C. diff infection | 0.379518 |
| tissue culture cytotoxicity | 0.327683 |
| binary toxin | 0.268661 |
| EPA-registered hospital disinfectants | 0.265543 |
| toxigenic culture | 0.318039 |
| Clostridium difficile organism | 0.664515 |
| Clostridium difficile toxin | 0.592005 |
| FDA-approved PCR assays | 0.271418 |
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Centers for Disease Control and Prevention |
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Phenol - 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 |
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| 8534 |
Centers for Disease Control and Prevention |
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Preventing Chronic Disease | Supermarket and Grocery Store-Based Interventions to Promote Healthful Food Choices and Eating Practices: A Systematic Review - CDC |
Increasingly high rates of obesity have heightened interest among researchers and practitioners in identifying evidence-based interventions to increase access to healthful foods and beverages. |
| food stores | 0.437077 |
| healthful food choices | 0.720922 |
| evidence | 0.567543 |
| healthful eating practices | 0.367116 |
| demand-side interventions | 0.414895 |
| public health practitioners | 0.355921 |
| clinical screening interventions | 0.47561 |
| food store interventions | 0.681948 |
| points | 0.488842 |
| health outcomes | 0.373411 |
| multilevel interventions | 0.45301 |
| intervention development | 0.35485 |
| food purchasing | 0.361638 |
| study design suitability | 0.356916 |
| positive intervention effect | 0.370789 |
| 12-week child-focused intervention | 0.366955 |
| healthful eating | 0.393951 |
| intervention categories | 0.374891 |
| study design | 0.742232 |
| successful interventions | 0.407775 |
| community-based interventions | 0.424455 |
| healthful food purchases | 0.366738 |
| health promotion interventions | 0.470444 |
| intervention participants | 0.376001 |
| intervention | 0.837503 |
|
| healthful dietary choices | 0.364319 |
| POP interventions | 0.479779 |
| intervention strategy | 0.392292 |
| summary score | 0.395946 |
| store sales data | 0.370344 |
| sales data | 0.479771 |
| studies | 0.440569 |
| grocery store interventions | 0.807642 |
| public health | 0.505349 |
| food store intervention | 0.417336 |
| effectiveness | 0.392457 |
| newer intervention strategies | 0.379988 |
| original research articles | 0.439384 |
| strategies | 0.413144 |
| social cognitive theory | 0.573402 |
| process measures | 0.363865 |
| Innovative supply-side interventions | 0.451839 |
| intervention category | 0.433598 |
| nutrition benefit interventions | 0.435615 |
| interventions | 0.999882 |
| grocery interventions | 0.450324 |
| store-based interventions | 0.441771 |
| concurrent comparison group | 0.392856 |
| evidence-based interventions | 0.481443 |
|
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MMWR News Synopsis: December 19, 2013 | CDC Media Relations | CDC |
CDC Media Relations - MMWR News Synopsis: December 19, 2013 |
| Influenza activity | 0.628987 |
| general public | 0.631024 |
| trivalent influenza vaccine | 0.692083 |
| overall sodium consumption | 0.698694 |
| influenza vaccine viruses | 0.76647 |
| preschool children | 0.56867 |
| influenza-positive respiratory specimens | 0.615769 |
| sodium reduction | 0.61792 |
| Northern Hemisphere | 0.613463 |
| influenza activity. | 0.629304 |
| quadrivalent influenza vaccine | 0.691258 |
| Excess sodium | 0.622736 |
| food poisoning | 0.5726 |
| similarly rare pathogens | 0.630671 |
| MMWR articles | 0.578847 |
| staphylococcal intoxication | 0.575231 |
| new CDC study | 0.673941 |
| food handling practice | 0.632367 |
| better food safety | 0.630005 |
| often-identified pathogen | 0.566687 |
| American diet | 0.571931 |
| raw fenugreek sprouts. | 0.624644 |
| older children | 0.568294 |
| CDC Media Relations | 0.944545 |
| in-depth look | 0.568797 |
|
| sodium intake | 0.62022 |
| United States | 0.76096 |
| major contributor | 0.571587 |
| restaurant foods | 0.570316 |
| major public health | 0.637682 |
| S. aureus | 0.569429 |
| heart disease | 0.570914 |
| public health outbreak | 0.657262 |
| influenza B viruses | 0.645692 |
| novel pathogens | 0.56836 |
| STEC O104 | 0.580562 |
| public health | 0.693925 |
| food workers | 0.631151 |
| months. Health-care providers | 0.617752 |
| High-quality public health | 0.630056 |
| hemolytic uremic syndrome. | 0.625957 |
| influenza vaccination | 0.62872 |
| systematic disease surveillance | 0.615002 |
| large outbreak | 0.587941 |
| influenza season | 0.628219 |
| STEC O157 | 0.581317 |
| moderate reductions | 0.571291 |
| military lunch party | 0.624164 |
| salt. Researchers | 0.57182 |
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Preventing Chronic Disease | Improving Blood Pressure Control in a Large Multiethnic California Population Through Changes in Health Care Delivery, 2004"2012 - CDC |
The Kaiser Permanente Southern California (Kaiser) health care system succeeded in improving hypertension control in a multiethnic population by adopting a series of changes in health care delivery. Data from the Healthcare Effectiveness Data and Information Set (HEDIS) was used to assess blood pressure control from 2004 through 2012. Hypertension control increased overall from 54% to 86% during that period, and 80% or more in every subgroup, regardless of race/ethnicity, preferred language, or type of health insurance plan. Health care delivery changes improved hypertension control across a large multiethnic population, which indicates that health care systems can achieve a clinical target goal of 70% for hypertension control in their populations. |
| major risk factor | 0.449216 |
| usual blood pressure | 0.45997 |
| Kaiser health plan | 0.48792 |
| Permanente Southern California | 0.544839 |
| hypertension registry | 0.490619 |
| Human Services | 0.449794 |
| clinical target goal | 0.449098 |
| Kaiser Permanente Southern | 0.524868 |
| Kaiser patient population | 0.485817 |
| care delivery changes | 0.68221 |
| health care delivery | 0.989232 |
| blood pressure measure | 0.472046 |
| Disease Control | 0.491539 |
| health care plans | 0.481291 |
| simple drug treatment | 0.448577 |
| HEDIS CBP measurement | 0.484638 |
| fully integrated health | 0.460635 |
| HEDIS measure | 0.446867 |
| blood pressure | 0.797611 |
| large multiethnic population | 0.504478 |
| hypertension control | 0.823084 |
| preferred language | 0.450973 |
| blood pressure control | 0.664533 |
| similar CBP measurements | 0.465106 |
|
| health care settings | 0.485903 |
| multiethnic population | 0.5118 |
| overall Kaiser membership | 0.470105 |
| HEDIS data | 0.445421 |
| heart attacks | 0.448131 |
| United States | 0.47463 |
| specific health care | 0.48182 |
| hearts–where population health | 0.469253 |
| non-Hispanic black patients | 0.464144 |
| hypertension population registry | 0.534609 |
| HEDIS CBP measure | 0.488048 |
| Hearts population goal | 0.454225 |
| CBP measurement | 0.484944 |
| specific delivery changes | 0.461817 |
| blood pressure monitoring | 0.469292 |
| health care systems | 0.488869 |
| large integrated health | 0.468048 |
| health insurance plan | 0.507731 |
| Healthcare Effectiveness Data | 0.494807 |
| Controlling High Blood | 0.457179 |
| Kaiser population | 0.461654 |
| Kaiser’s population | 0.46537 |
| commercial health insurance | 0.501383 |
| high blood pressure | 0.558416 |
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Ebola Epidemic - Liberia, March-October 2014 |
Please note: An erratum has been published for this article. To view the erratum, please click here. |
| rapid response | 0.656311 |
| large number | 0.665397 |
| James Dorbor Falla | 0.615318 |
| critically ill patients | 0.619237 |
| 2World Health Organization | 0.620416 |
| national health care | 0.649208 |
| MMWR Early Release | 0.620714 |
| situation report data | 0.618634 |
| unique identifier | 0.614111 |
| laboratory results | 0.611777 |
| consecutive negative Ebola | 0.734902 |
| positive RT-PCR result | 0.61658 |
| World Health Organization | 0.708176 |
| contact tracing | 0.661023 |
| Liberian Ministry | 0.621809 |
| longest Ebola epidemic | 0.790603 |
| community engagement | 0.646423 |
| safe burial teams | 0.800406 |
| health care facilities | 0.6491 |
| Bong County ETU | 0.63315 |
| Ebola patients | 0.811283 |
| Ebola | 0.929238 |
| social mobilization | 0.644073 |
| Ebola virus disease | 0.755785 |
| Ebola laboratory test | 0.764023 |
|
| Frank Mahoney | 0.613136 |
| Ebola virus infection | 0.728604 |
| endemic Ebola transmission | 0.718508 |
| infection control | 0.657945 |
| suspected Ebola patients | 0.772842 |
| Ebola cases | 0.723706 |
| Montserrado County outbreak | 0.658277 |
| ETU admission records | 0.617832 |
| Ebola case | 0.697081 |
| case counts | 0.643624 |
| health care workers | 0.858179 |
| densely populated Montserrado | 0.615939 |
| Liberia counties | 0.657877 |
| health care | 0.88648 |
| negative RT-PCR test | 0.61517 |
| multiple outbreaks | 0.613842 |
| Lofa County | 0.626591 |
| Montserrado County | 0.769001 |
| Liberia | 0.775733 |
| Ebola treatment units | 0.742774 |
| MOHSW field teams | 0.619132 |
| West Africa | 0.624864 |
| rapid response teams | 0.638297 |
| negative RT-PCR results | 0.616895 |
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CDC Telebriefing: New Vital Signs Report - Secondhand smoke exposure kills |
CDC is releasing the first national estimates of doctor visits and healthcare costs associated with keratitis |
| media availability | 0.807264 |
| half | 0.503494 |
| Non-Media | 0.577019 |
| Tom Frieden | 0.947676 |
| M.D. | 0.486713 |
| briefing | 0.507735 |
| blacks | 0.5242 |
| Noon ET | 0.914315 |
| transcript | 0.599629 |
| children | 0.493107 |
| PASSCODE | 0.563604 |
| M.P.H | 0.5486 |
| Tuesday | 0.496104 |
| poverty | 0.512061 |
| secondhand smoke | 0.965252 |
|
| Important Instructions | 0.874893 |
| SHS | 0.741088 |
| dial | 0.505908 |
| CDC Media | 0.878349 |
| question | 0.696431 |
| Declines | 0.552196 |
| start | 0.486382 |
| touchtone phone | 0.896021 |
| people | 0.494832 |
| exposure | 0.803199 |
| nonsmokers | 0.559478 |
| time | 0.483088 |
| press conference | 0.81309 |
| rental housing | 0.906711 |
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Health Status of Older US Workers and Nonworkers, National Health Interview Survey, 1997-2011 |
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. |
| high school | 0.449723 |
| better health outcomes | 0.501449 |
| blue collar workers | 0.480881 |
| stronger healthy worker | 0.380462 |
| blue collar | 0.523899 |
| multimorbidity | 0.408801 |
| fair/poor self-rated health | 0.381196 |
| functional limitations | 0.922588 |
| National Health Interview | 0.428524 |
| alcohol consumption | 0.478658 |
| complementary health status | 0.382654 |
| high school education | 0.432969 |
| white collar | 0.555952 |
| poor health | 0.481872 |
| physically demanding occupations | 0.382811 |
| health outcomes | 0.541341 |
| major health status | 0.39815 |
| poor/fair self-rated health | 0.380314 |
| functional limitations measures | 0.389704 |
| self-rated health | 0.444201 |
| poorer health outcomes | 0.407113 |
| marital status | 0.397874 |
| Miami Miller School | 0.418392 |
| previous studies | 0.406772 |
|
| Activities Limitation Index | 0.390059 |
| non-Hispanic whites | 0.398108 |
| health behaviors | 0.419395 |
| white collar workers | 0.525564 |
| health care resources | 0.40821 |
| multiple functional limitations | 0.751384 |
| poor health outcomes | 0.45994 |
| multivariable logistic regression | 0.383558 |
| health | 0.775259 |
| Health Interview Survey | 0.428523 |
| Public Health Sciences | 0.418654 |
| white collar occupations | 0.415293 |
| fair/poor health | 0.502597 |
| chronic conditions | 0.389077 |
| health status variables | 0.383222 |
| lowest HALex quintile | 0.520673 |
| multiple health outcomes | 0.401715 |
| health status | 0.610972 |
| older workers | 0.404008 |
| poor health behaviors | 0.380874 |
| older adults | 0.66017 |
| health status measures | 0.594216 |
| various health | 0.380341 |
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