Sonya Gamble, Tebitha Mawokomatanda, Fang Xu, Pranesh P Chowdhury, Carol Pierannunzi, David Flegel, William Garvin, Machell Town
<p><strong>Problem: </strong>Chronic diseases and conditions (e.g., heart diseases, stroke, arthritis, and diabetes) are the leading causes of morbidity and mortality in the United States. These conditions are costly to the U.S. economy, yet they are often preventable or controllable. Behavioral risk factors (e.g., excessive alcohol consumption, tobacco use, poor diet, frequent mental distress, and insufficient sleep) are linked to the leading causes of morbidity and mortality. Adopting positive health behaviors (e.g., staying physically active, quitting tobacco use, obtaining routine physical checkups, and checking blood pressure and cholesterol levels) can reduce morbidity and mortality from chronic diseases and conditions. Monitoring the health risk behaviors, chronic diseases and conditions, access to health care, and use of preventive health services at multilevel public health points (states, territories, and metropolitan and micropolitan statistical areas [MMSA]) can provide important information for development and evaluation of health intervention programs.</p><p><strong>Reporting period: </strong>2013 and 2014.</p><p><strong>Description of the system: </strong>The Behavioral Risk Factor Surveillance System (BRFSS) is an ongoing, state-based, random-digit-dialed telephone survey of noninstitutionalized adults aged ≥18 years residing in the United States. BRFSS collects data on health risk behaviors, chronic diseases and conditions, access to health care, and use of preventive health services and practices related to the leading causes of death and disability in the United States and participating territories. This is the first BRFSS report to include age-adjusted prevalence estimates. For 2013 and 2014, these age-adjusted prevalence estimates are presented for all 50 states, the District of Columbia, the Commonwealth of Puerto Rico, Guam, and selected MMSA.</p><p><strong>Results: </strong>Age-adjusted prevalence estimates of health status indicators, health care access and preventive practices, health risk behaviors, chronic diseases and conditions, and cardiovascular conditions vary by state, territory, and MMSA. Each set of proportions presented refers to the range of age-adjusted prevalence estimates of selected BRFSS measures as reported by survey respondents. The following are estimates for 2013. Adults reporting frequent mental distress: 7.7%-15.2% in states and territories and 6.3%-19.4% in MMSA. Adults with inadequate sleep: 27.6%-49.2% in states and territories and 26.5%-44.4% in MMSA. Adults aged 18-64 years having health care coverage: 66.9%-92.4% in states and territories and 60.5%-97.6% in MMSA. Adults identifying as current cigarette smokers: 10.1%-28.8% in states and territories and 6.1%-33.6% in MMSA. Adults reporting binge drinking during the past month: 10.5%-25.2% in states and territories and 7.2%-25.3% in MMSA. Adults with obesity: 21.0%-35.2% in states and territories and 12.1%-37.1% in MMSA. Adults aged ≥45 years w
{"title":"Surveillance for Certain Health Behaviors and Conditions Among States and Selected Local Areas - Behavioral Risk Factor Surveillance System, United States, 2013 and 2014.","authors":"Sonya Gamble, Tebitha Mawokomatanda, Fang Xu, Pranesh P Chowdhury, Carol Pierannunzi, David Flegel, William Garvin, Machell Town","doi":"10.15585/mmwr.ss6616a1","DOIUrl":"10.15585/mmwr.ss6616a1","url":null,"abstract":"<p><strong>Problem: </strong>Chronic diseases and conditions (e.g., heart diseases, stroke, arthritis, and diabetes) are the leading causes of morbidity and mortality in the United States. These conditions are costly to the U.S. economy, yet they are often preventable or controllable. Behavioral risk factors (e.g., excessive alcohol consumption, tobacco use, poor diet, frequent mental distress, and insufficient sleep) are linked to the leading causes of morbidity and mortality. Adopting positive health behaviors (e.g., staying physically active, quitting tobacco use, obtaining routine physical checkups, and checking blood pressure and cholesterol levels) can reduce morbidity and mortality from chronic diseases and conditions. Monitoring the health risk behaviors, chronic diseases and conditions, access to health care, and use of preventive health services at multilevel public health points (states, territories, and metropolitan and micropolitan statistical areas [MMSA]) can provide important information for development and evaluation of health intervention programs.</p><p><strong>Reporting period: </strong>2013 and 2014.</p><p><strong>Description of the system: </strong>The Behavioral Risk Factor Surveillance System (BRFSS) is an ongoing, state-based, random-digit-dialed telephone survey of noninstitutionalized adults aged ≥18 years residing in the United States. BRFSS collects data on health risk behaviors, chronic diseases and conditions, access to health care, and use of preventive health services and practices related to the leading causes of death and disability in the United States and participating territories. This is the first BRFSS report to include age-adjusted prevalence estimates. For 2013 and 2014, these age-adjusted prevalence estimates are presented for all 50 states, the District of Columbia, the Commonwealth of Puerto Rico, Guam, and selected MMSA.</p><p><strong>Results: </strong>Age-adjusted prevalence estimates of health status indicators, health care access and preventive practices, health risk behaviors, chronic diseases and conditions, and cardiovascular conditions vary by state, territory, and MMSA. Each set of proportions presented refers to the range of age-adjusted prevalence estimates of selected BRFSS measures as reported by survey respondents. The following are estimates for 2013. Adults reporting frequent mental distress: 7.7%-15.2% in states and territories and 6.3%-19.4% in MMSA. Adults with inadequate sleep: 27.6%-49.2% in states and territories and 26.5%-44.4% in MMSA. Adults aged 18-64 years having health care coverage: 66.9%-92.4% in states and territories and 60.5%-97.6% in MMSA. Adults identifying as current cigarette smokers: 10.1%-28.8% in states and territories and 6.1%-33.6% in MMSA. Adults reporting binge drinking during the past month: 10.5%-25.2% in states and territories and 7.2%-25.3% in MMSA. Adults with obesity: 21.0%-35.2% in states and territories and 12.1%-37.1% in MMSA. Adults aged ≥45 years w","PeriodicalId":48549,"journal":{"name":"Mmwr Surveillance Summaries","volume":"66 16","pages":"1-144"},"PeriodicalIF":37.3,"publicationDate":"2017-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5829867/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10264661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Katherine Kolor, Zhuo Chen, Scott D Grosse, Juan L Rodriguez, Ridgely Fisk Green, W David Dotson, M Scott Bowen, Julie A Lynch, Muin J Khoury
<p><strong>Problem/condition: </strong>Genetic testing for breast cancer 1 (BRCA1) and breast cancer 2 (BRCA2) gene mutations can identify women at increased risk for breast and ovarian cancer. These testing results can be used to select preventive interventions and guide treatment. Differences between nonmetropolitan and metropolitan populations in rates of BRCA testing and receipt of preventive interventions after testing have not previously been examined.</p><p><strong>Period covered: </strong>2009-2014.</p><p><strong>Description of system: </strong>Medical claims data from Truven Health Analytics MarketScan Commercial Claims and Encounters databases were used to estimate rates of BRCA testing and receipt of preventive interventions after BRCA testing among women aged 18-64 years with employer-sponsored health insurance in metropolitan and nonmetropolitan areas of the United States, both nationally and regionally.</p><p><strong>Results: </strong>From 2009 to 2014, BRCA testing rates per 100,000 women aged 18-64 years with employer-sponsored health insurance increased 2.3 times (102.7 to 237.8) in metropolitan areas and 3.0 times (64.8 to 191.3) in nonmetropolitan areas. The relative difference in BRCA testing rates between metropolitan and nonmetropolitan areas decreased from 37% in 2009 (102.7 versus 64.8) to 20% in 2014 (237.8 versus 191.3). The relative difference in BRCA testing rates between metropolitan and nonmetropolitan areas decreased more over time in younger women than in older women and decreased in all regions except the West. Receipt of preventive services 90 days after BRCA testing in metropolitan versus nonmetropolitan areas throughout the period varied by service: the percentage of women who received a mastectomy was similar, the percentage of women who received magnetic resonance imaging of the breast was lower in nonmetropolitan areas (as low as 5.8% in 2014 to as high as 8.2% in 2011) than metropolitan areas (as low as 7.3% in 2014 to as high as 10.3% in 2011), and the percentage of women who received mammography was lower in nonmetropolitan areas in earlier years but was similar in later years.</p><p><strong>Interpretation: </strong>Possible explanations for the 47% decrease in the relative difference in BRCA testing rates over the study period include increased access to genetic services in nonmetropolitan areas and increased demand nationally as a result of publicity. The relative differences in metropolitan and nonmetropolitan BRCA testing rates were smaller among women at younger ages compared with older ages.</p><p><strong>Public health action: </strong>Improved data sources and surveillance tools are needed to gather comprehensive data on BRCA testing in the United States, monitor adherence to evidence-based guidelines for BRCA testing, and assess receipt of preventive interventions for women with BRCA mutations. Programs can build on the recent decrease in geographic disparities in receipt of BRCA testing while sim
问题/状况:乳腺癌1号(BRCA1)和乳腺癌2号(BRCA2)基因突变的基因检测可以识别出乳腺癌和卵巢癌风险增加的女性。这些检测结果可用于选择预防干预措施和指导治疗。非大都市人群和大都市人群在BRCA检测率和检测后接受预防性干预措施方面的差异此前未被研究过。涵盖期间:2009-2014年。系统描述:来自Truven Health Analytics MarketScan Commercial claims和Encounters数据库的医疗索赔数据被用于估计美国大都市和非大都市地区18-64岁雇主赞助的健康保险女性的BRCA检测率和BRCA检测后预防性干预的接受率，包括国家和地区。结果:2009 - 2014年，大城市地区每10万名18-64岁雇主赞助医疗保险女性的BRCA检测率增加了2.3倍(102.7至237.8)，非大城市地区增加了3.0倍(64.8至191.3)。大都市和非大都市地区BRCA检测率的相对差异从2009年的37%(102.7对64.8)下降到2014年的20%(237.8对191.3)。随着时间的推移，年轻女性和非大都市地区BRCA检测率的相对差异比老年女性下降得更多，除西部地区外，所有地区都有所下降。在大都市地区和非大都市地区，BRCA检测后90天内接受预防服务的情况因服务而异:妇女接受乳房切除术的比例是相似的,女性的比例接受核磁共振成像的乳房nonmetropolitan地区较低(低至2014年的5.8%到2011年高达8.2%)比大城市(低至2014年的7.3%到2011年高达10.3%),和妇女接受乳房x光检查的百分比低nonmetropolitan地区早些年但在晚年很相似。解释:在研究期间，BRCA检测率的相对差异降低了47%，可能的解释包括非大都市地区获得遗传服务的机会增加，以及由于宣传而导致全国需求增加。与老年妇女相比，年轻妇女在大都市和非大都市BRCA检测率上的相对差异较小。公共卫生行动:需要改进数据来源和监测工具，以收集美国BRCA检测的综合数据，监测BRCA检测循证指南的遵守情况，并评估BRCA突变妇女预防性干预措施的接受情况。项目可以建立在最近接受BRCA检测的地域差异减少的基础上，同时教育公众和卫生保健提供者关于美国预防服务工作组的建议和其他BRCA检测和咨询的临床指南。
{"title":"BRCA Genetic Testing and Receipt of Preventive Interventions Among Women Aged 18-64 Years with Employer-Sponsored Health Insurance in Nonmetropolitan and Metropolitan Areas - United States, 2009-2014.","authors":"Katherine Kolor, Zhuo Chen, Scott D Grosse, Juan L Rodriguez, Ridgely Fisk Green, W David Dotson, M Scott Bowen, Julie A Lynch, Muin J Khoury","doi":"10.15585/mmwr.ss6615a1","DOIUrl":"https://doi.org/10.15585/mmwr.ss6615a1","url":null,"abstract":"<p><strong>Problem/condition: </strong>Genetic testing for breast cancer 1 (BRCA1) and breast cancer 2 (BRCA2) gene mutations can identify women at increased risk for breast and ovarian cancer. These testing results can be used to select preventive interventions and guide treatment. Differences between nonmetropolitan and metropolitan populations in rates of BRCA testing and receipt of preventive interventions after testing have not previously been examined.</p><p><strong>Period covered: </strong>2009-2014.</p><p><strong>Description of system: </strong>Medical claims data from Truven Health Analytics MarketScan Commercial Claims and Encounters databases were used to estimate rates of BRCA testing and receipt of preventive interventions after BRCA testing among women aged 18-64 years with employer-sponsored health insurance in metropolitan and nonmetropolitan areas of the United States, both nationally and regionally.</p><p><strong>Results: </strong>From 2009 to 2014, BRCA testing rates per 100,000 women aged 18-64 years with employer-sponsored health insurance increased 2.3 times (102.7 to 237.8) in metropolitan areas and 3.0 times (64.8 to 191.3) in nonmetropolitan areas. The relative difference in BRCA testing rates between metropolitan and nonmetropolitan areas decreased from 37% in 2009 (102.7 versus 64.8) to 20% in 2014 (237.8 versus 191.3). The relative difference in BRCA testing rates between metropolitan and nonmetropolitan areas decreased more over time in younger women than in older women and decreased in all regions except the West. Receipt of preventive services 90 days after BRCA testing in metropolitan versus nonmetropolitan areas throughout the period varied by service: the percentage of women who received a mastectomy was similar, the percentage of women who received magnetic resonance imaging of the breast was lower in nonmetropolitan areas (as low as 5.8% in 2014 to as high as 8.2% in 2011) than metropolitan areas (as low as 7.3% in 2014 to as high as 10.3% in 2011), and the percentage of women who received mammography was lower in nonmetropolitan areas in earlier years but was similar in later years.</p><p><strong>Interpretation: </strong>Possible explanations for the 47% decrease in the relative difference in BRCA testing rates over the study period include increased access to genetic services in nonmetropolitan areas and increased demand nationally as a result of publicity. The relative differences in metropolitan and nonmetropolitan BRCA testing rates were smaller among women at younger ages compared with older ages.</p><p><strong>Public health action: </strong>Improved data sources and surveillance tools are needed to gather comprehensive data on BRCA testing in the United States, monitor adherence to evidence-based guidelines for BRCA testing, and assess receipt of preventive interventions for women with BRCA mutations. Programs can build on the recent decrease in geographic disparities in receipt of BRCA testing while sim","PeriodicalId":48549,"journal":{"name":"Mmwr Surveillance Summaries","volume":"66 15","pages":"1-11"},"PeriodicalIF":24.9,"publicationDate":"2017-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5879728/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35382016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S Jane Henley, Robert N Anderson, Cheryll C Thomas, Greta M Massetti, Brandy Peaker, Lisa C Richardson
<p><strong>Problem/condition: </strong>Previous reports have shown that persons living in nonmetropolitan (rural or urban) areas in the United States have higher death rates from all cancers combined than persons living in metropolitan areas. Disparities might vary by cancer type and between occurrence and death from the disease. This report provides a comprehensive assessment of cancer incidence and deaths by cancer type in nonmetropolitan and metropolitan counties.</p><p><strong>Reporting period: </strong>2004-2015.</p><p><strong>Description of system: </strong>Cancer incidence data from CDC's National Program of Cancer Registries and the National Cancer Institute's Surveillance, Epidemiology, and End Results program were used to calculate average annual age-adjusted incidence rates for 2009-2013 and trends in annual age-adjusted incidence rates for 2004-2013. Cancer mortality data from the National Vital Statistics System were used to calculate average annual age-adjusted death rates for 2011-2015 and trends in annual age-adjusted death rates for 2006-2015. For 5-year average annual rates, counties were classified into four categories (nonmetropolitan rural, nonmetropolitan urban, metropolitan with population <1 million, and metropolitan with population ≥1 million). For the trend analysis, which used annual rates, these categories were combined into two categories (nonmetropolitan and metropolitan). Rates by county classification were examined by sex, age, race/ethnicity, U.S. census region, and cancer site. Trends in rates were examined by county classification and cancer site.</p><p><strong>Results: </strong>During the most recent 5-year period for which data were available, nonmetropolitan rural areas had lower average annual age-adjusted cancer incidence rates for all anatomic cancer sites combined but higher death rates than metropolitan areas. During 2006-2015, the annual age-adjusted death rates for all cancer sites combined decreased at a slower pace in nonmetropolitan areas (-1.0% per year) than in metropolitan areas (-1.6% per year), increasing the differences in these rates. In contrast, annual age-adjusted incidence rates for all cancer sites combined decreased approximately 1% per year during 2004-2013 both in nonmetropolitan and metropolitan counties.</p><p><strong>Interpretation: </strong>This report provides the first comprehensive description of cancer incidence and mortality in nonmetropolitan and metropolitan counties in the United States. Nonmetropolitan rural counties had higher incidence of and deaths from several cancers related to tobacco use and cancers that can be prevented by screening. Differences between nonmetropolitan and metropolitan counties in cancer incidence might reflect differences in risk factors such as cigarette smoking, obesity, and physical inactivity, whereas differences in cancer death rates might reflect disparities in access to health care and timely diagnosis and treatment.</p><p><strong>Public h
{"title":"Invasive Cancer Incidence, 2004-2013, and Deaths, 2006-2015, in Nonmetropolitan and Metropolitan Counties - United States.","authors":"S Jane Henley, Robert N Anderson, Cheryll C Thomas, Greta M Massetti, Brandy Peaker, Lisa C Richardson","doi":"10.15585/mmwr.ss6614a1","DOIUrl":"https://doi.org/10.15585/mmwr.ss6614a1","url":null,"abstract":"<p><strong>Problem/condition: </strong>Previous reports have shown that persons living in nonmetropolitan (rural or urban) areas in the United States have higher death rates from all cancers combined than persons living in metropolitan areas. Disparities might vary by cancer type and between occurrence and death from the disease. This report provides a comprehensive assessment of cancer incidence and deaths by cancer type in nonmetropolitan and metropolitan counties.</p><p><strong>Reporting period: </strong>2004-2015.</p><p><strong>Description of system: </strong>Cancer incidence data from CDC's National Program of Cancer Registries and the National Cancer Institute's Surveillance, Epidemiology, and End Results program were used to calculate average annual age-adjusted incidence rates for 2009-2013 and trends in annual age-adjusted incidence rates for 2004-2013. Cancer mortality data from the National Vital Statistics System were used to calculate average annual age-adjusted death rates for 2011-2015 and trends in annual age-adjusted death rates for 2006-2015. For 5-year average annual rates, counties were classified into four categories (nonmetropolitan rural, nonmetropolitan urban, metropolitan with population <1 million, and metropolitan with population ≥1 million). For the trend analysis, which used annual rates, these categories were combined into two categories (nonmetropolitan and metropolitan). Rates by county classification were examined by sex, age, race/ethnicity, U.S. census region, and cancer site. Trends in rates were examined by county classification and cancer site.</p><p><strong>Results: </strong>During the most recent 5-year period for which data were available, nonmetropolitan rural areas had lower average annual age-adjusted cancer incidence rates for all anatomic cancer sites combined but higher death rates than metropolitan areas. During 2006-2015, the annual age-adjusted death rates for all cancer sites combined decreased at a slower pace in nonmetropolitan areas (-1.0% per year) than in metropolitan areas (-1.6% per year), increasing the differences in these rates. In contrast, annual age-adjusted incidence rates for all cancer sites combined decreased approximately 1% per year during 2004-2013 both in nonmetropolitan and metropolitan counties.</p><p><strong>Interpretation: </strong>This report provides the first comprehensive description of cancer incidence and mortality in nonmetropolitan and metropolitan counties in the United States. Nonmetropolitan rural counties had higher incidence of and deaths from several cancers related to tobacco use and cancers that can be prevented by screening. Differences between nonmetropolitan and metropolitan counties in cancer incidence might reflect differences in risk factors such as cigarette smoking, obesity, and physical inactivity, whereas differences in cancer death rates might reflect disparities in access to health care and timely diagnosis and treatment.</p><p><strong>Public h","PeriodicalId":48549,"journal":{"name":"Mmwr Surveillance Summaries","volume":"66 14","pages":"1-13"},"PeriodicalIF":24.9,"publicationDate":"2017-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5879727/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35148803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
<p><strong>Problem/condition: </strong>The places in which persons live, work, and play can contribute to the development of adverse health outcomes. Understanding the differences in risk factors in various environments can help to explain differences in the occurrence of these outcomes and can be used to develop public health programs, interventions, and policies. Efforts to characterize urban and rural differences have largely focused on social and demographic characteristics. A paucity of national standardized environmental data has hindered efforts to characterize differences in the physical aspects of urban and rural areas, such as air and water quality.</p><p><strong>Reporting period: </strong>2008-2012 for air quality and 2010-2015 for water quality.</p><p><strong>Description of system: </strong>Since 2002, CDC's National Environmental Public Health Tracking Program has collaborated with federal, state, and local partners to gather standardized environmental data by creating national data standards, collecting available data, and disseminating data to be used in developing public health actions. The National Environmental Public Health Tracking Network (i.e., the tracking network) collects data provided by national, state, and local partners and includes 21 health outcomes, exposures, and environmental hazards. To assess environmental factors that affect health, CDC analyzed three air-quality measures from the tracking network for all counties in the contiguous United States during 2008-2012 and one water-quality measure for 26 states during 2010-2015. The three air-quality measures include 1) total number of days with fine particulate matter (PM<sub>2.5</sub>) levels greater than the U.S. Environmental Protection Agency's (EPA's) National Ambient Air Quality Standards (NAAQS) for 24-hour average PM<sub>2.5</sub> (PM<sub>2.5</sub> days); 2) mean annual average ambient concentrations of PM<sub>2.5</sub> in micrograms per cubic meter (mean PM<sub>2.5</sub>); and 3) total number of days with maximum 8-hour average ozone concentrations greater than the NAAQS (ozone days). The water-quality measure compared the annual mean concentration for a community water system (CWS) to the maximum contaminant level (MCL) defined by EPA for 10 contaminants: arsenic, atrazine, di(2-ethylhexyl) phthalate (DEHP), haloacetic acids (HAA5), nitrate, perchloroethene (PCE), radium, trichloroethene (TCE), total trihalomethanes (TTHM), and uranium. Findings are presented by urban-rural classification scheme: four metropolitan (large central metropolitan, large fringe metropolitan, medium metropolitan, and small metropolitan) and two nonmetropolitan (micropolitan and noncore) categories. Regression modeling was used to determine whether differences in the measures by urban-rural categories were statistically significant.</p><p><strong>Results: </strong>Patterns for all three air-quality measures suggest that air quality improves as areas become more rural (or less urb
{"title":"Rural and Urban Differences in Air Quality, 2008-2012, and Community Drinking Water Quality, 2010-2015 - United States.","authors":"Heather Strosnider, Caitlin Kennedy, Michele Monti, Fuyuen Yip","doi":"10.15585/mmwr.ss6613a1","DOIUrl":"https://doi.org/10.15585/mmwr.ss6613a1","url":null,"abstract":"<p><strong>Problem/condition: </strong>The places in which persons live, work, and play can contribute to the development of adverse health outcomes. Understanding the differences in risk factors in various environments can help to explain differences in the occurrence of these outcomes and can be used to develop public health programs, interventions, and policies. Efforts to characterize urban and rural differences have largely focused on social and demographic characteristics. A paucity of national standardized environmental data has hindered efforts to characterize differences in the physical aspects of urban and rural areas, such as air and water quality.</p><p><strong>Reporting period: </strong>2008-2012 for air quality and 2010-2015 for water quality.</p><p><strong>Description of system: </strong>Since 2002, CDC's National Environmental Public Health Tracking Program has collaborated with federal, state, and local partners to gather standardized environmental data by creating national data standards, collecting available data, and disseminating data to be used in developing public health actions. The National Environmental Public Health Tracking Network (i.e., the tracking network) collects data provided by national, state, and local partners and includes 21 health outcomes, exposures, and environmental hazards. To assess environmental factors that affect health, CDC analyzed three air-quality measures from the tracking network for all counties in the contiguous United States during 2008-2012 and one water-quality measure for 26 states during 2010-2015. The three air-quality measures include 1) total number of days with fine particulate matter (PM<sub>2.5</sub>) levels greater than the U.S. Environmental Protection Agency's (EPA's) National Ambient Air Quality Standards (NAAQS) for 24-hour average PM<sub>2.5</sub> (PM<sub>2.5</sub> days); 2) mean annual average ambient concentrations of PM<sub>2.5</sub> in micrograms per cubic meter (mean PM<sub>2.5</sub>); and 3) total number of days with maximum 8-hour average ozone concentrations greater than the NAAQS (ozone days). The water-quality measure compared the annual mean concentration for a community water system (CWS) to the maximum contaminant level (MCL) defined by EPA for 10 contaminants: arsenic, atrazine, di(2-ethylhexyl) phthalate (DEHP), haloacetic acids (HAA5), nitrate, perchloroethene (PCE), radium, trichloroethene (TCE), total trihalomethanes (TTHM), and uranium. Findings are presented by urban-rural classification scheme: four metropolitan (large central metropolitan, large fringe metropolitan, medium metropolitan, and small metropolitan) and two nonmetropolitan (micropolitan and noncore) categories. Regression modeling was used to determine whether differences in the measures by urban-rural categories were statistically significant.</p><p><strong>Results: </strong>Patterns for all three air-quality measures suggest that air quality improves as areas become more rural (or less urb","PeriodicalId":48549,"journal":{"name":"Mmwr Surveillance Summaries","volume":"66 13","pages":"1-10"},"PeriodicalIF":24.9,"publicationDate":"2017-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.15585/mmwr.ss6613a1","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35112794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
<p><strong>Problem/condition: </strong>Malaria in humans is caused by intraerythrocytic protozoa of the genus Plasmodium. These parasites are transmitted by the bite of an infective female Anopheles mosquito. The majority of malaria infections in the United States occur among persons who have traveled to regions with ongoing malaria transmission. However, malaria is occasionally acquired by persons who have not traveled out of the country through exposure to infected blood products, congenital transmission, laboratory exposure, or local mosquitoborne transmission. Malaria surveillance in the United States is conducted to identify episodes of local transmission and to guide prevention recommendations for travelers.</p><p><strong>Period covered: </strong>This report summarizes cases in persons with onset of illness in 2014 and trends during previous years.</p><p><strong>Description of system: </strong>Malaria cases diagnosed by blood film, polymerase chain reaction, or rapid diagnostic tests are reported to local and state health departments by health care providers or laboratory staff. Case investigations are conducted by local and state health departments, and reports are transmitted to CDC through the National Malaria Surveillance System, National Notifiable Diseases Surveillance System, or direct CDC consultations. CDC conducts antimalarial drug resistance marker testing on blood samples submitted by health care providers or local or state health departments. Data from these reporting systems serve as the basis for this report.</p><p><strong>Results: </strong>CDC received reports of 1,724 confirmed malaria cases, including one congenital case and two cryptic cases, with onset of symptoms in 2014 among persons in the United States. The number of confirmed cases in 2014 is consistent with the number of confirmed cases reported in 2013 (n = 1,741; this number has been updated from a previous publication to account for delayed reporting for persons with symptom onset occurring in late 2013). Plasmodium falciparum, P. vivax, P. ovale, and P. malariae were identified in 66.1%, 13.3%, 5.2%, and 2.7% of cases, respectively. Less than 1.0% of patients were infected with two species. The infecting species was unreported or undetermined in 11.7% of cases. CDC provided diagnostic assistance for 14.2% of confirmed cases and tested 12.0% of P. falciparum specimens for antimalarial resistance markers. Of patients who reported purpose of travel, 57.5% were visiting friends and relatives (VFR). Among U.S. residents for whom information on chemoprophylaxis use and travel region was known, 7.8% reported that they initiated and adhered to a chemoprophylaxis drug regimen recommended by CDC for the regions to which they had traveled. Thirty-two cases were among pregnant women, none of whom had adhered to chemoprophylaxis. Among all reported cases, 17.0% were classified as severe illness, and five persons with malaria died. CDC received 137 P. falciparum-positive sam
{"title":"Malaria Surveillance - United States, 2014.","authors":"Kimberly E Mace, Paul M Arguin","doi":"10.15585/mmwr.ss6612a1","DOIUrl":"10.15585/mmwr.ss6612a1","url":null,"abstract":"<p><strong>Problem/condition: </strong>Malaria in humans is caused by intraerythrocytic protozoa of the genus Plasmodium. These parasites are transmitted by the bite of an infective female Anopheles mosquito. The majority of malaria infections in the United States occur among persons who have traveled to regions with ongoing malaria transmission. However, malaria is occasionally acquired by persons who have not traveled out of the country through exposure to infected blood products, congenital transmission, laboratory exposure, or local mosquitoborne transmission. Malaria surveillance in the United States is conducted to identify episodes of local transmission and to guide prevention recommendations for travelers.</p><p><strong>Period covered: </strong>This report summarizes cases in persons with onset of illness in 2014 and trends during previous years.</p><p><strong>Description of system: </strong>Malaria cases diagnosed by blood film, polymerase chain reaction, or rapid diagnostic tests are reported to local and state health departments by health care providers or laboratory staff. Case investigations are conducted by local and state health departments, and reports are transmitted to CDC through the National Malaria Surveillance System, National Notifiable Diseases Surveillance System, or direct CDC consultations. CDC conducts antimalarial drug resistance marker testing on blood samples submitted by health care providers or local or state health departments. Data from these reporting systems serve as the basis for this report.</p><p><strong>Results: </strong>CDC received reports of 1,724 confirmed malaria cases, including one congenital case and two cryptic cases, with onset of symptoms in 2014 among persons in the United States. The number of confirmed cases in 2014 is consistent with the number of confirmed cases reported in 2013 (n = 1,741; this number has been updated from a previous publication to account for delayed reporting for persons with symptom onset occurring in late 2013). Plasmodium falciparum, P. vivax, P. ovale, and P. malariae were identified in 66.1%, 13.3%, 5.2%, and 2.7% of cases, respectively. Less than 1.0% of patients were infected with two species. The infecting species was unreported or undetermined in 11.7% of cases. CDC provided diagnostic assistance for 14.2% of confirmed cases and tested 12.0% of P. falciparum specimens for antimalarial resistance markers. Of patients who reported purpose of travel, 57.5% were visiting friends and relatives (VFR). Among U.S. residents for whom information on chemoprophylaxis use and travel region was known, 7.8% reported that they initiated and adhered to a chemoprophylaxis drug regimen recommended by CDC for the regions to which they had traveled. Thirty-two cases were among pregnant women, none of whom had adhered to chemoprophylaxis. Among all reported cases, 17.0% were classified as severe illness, and five persons with malaria died. CDC received 137 P. falciparum-positive sam","PeriodicalId":48549,"journal":{"name":"Mmwr Surveillance Summaries","volume":"66 12","pages":"1-24"},"PeriodicalIF":37.3,"publicationDate":"2017-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5829864/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35026354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Walter W Williams, Peng-Jun Lu, Alissa O'Halloran, David K Kim, Lisa A Grohskopf, Tamara Pilishvili, Tami H Skoff, Noele P Nelson, Rafael Harpaz, Lauri E Markowitz, Alfonso Rodriguez-Lainz, Amy Parker Fiebelkorn
<p><strong>Problem/condition: </strong>Overall, the prevalence of illness attributable to vaccine-preventable diseases is greater among adults than among children. Adults are recommended to receive vaccinations based on their age, underlying medical conditions, lifestyle, prior vaccinations, and other considerations. Updated vaccination recommendations from CDC are published annually in the U.S. Adult Immunization Schedule. Despite longstanding recommendations for use of many vaccines, vaccination coverage among U.S. adults is low.</p><p><strong>Period covered: </strong>August 2014-June 2015 (for influenza vaccination) and January-December 2015 (for pneumococcal, tetanus and diphtheria [Td] and tetanus and diphtheria with acellular pertussis [Tdap], hepatitis A, hepatitis B, herpes zoster, and human papillomavirus [HPV] vaccination).</p><p><strong>Description of system: </strong>The National Health Interview Survey (NHIS) is a continuous, cross-sectional national household survey of the noninstitutionalized U.S. civilian population. In-person interviews are conducted throughout the year in a probability sample of households, and NHIS data are compiled and released annually. The survey objective is to monitor the health of the U.S. population and provide estimates of health indicators, health care use and access, and health-related behaviors.</p><p><strong>Results: </strong>Compared with data from the 2014 NHIS, increases in vaccination coverage occurred for influenza vaccine among adults aged ≥19 years (a 1.6 percentage point increase compared with the 2013-14 season to 44.8%), pneumococcal vaccine among adults aged 19-64 years at increased risk for pneumococcal disease (a 2.8 percentage point increase to 23.0%), Tdap vaccine among adults aged ≥19 years and adults aged 19-64 years (a 3.1 percentage point and 3.3 percentage point increase to 23.1% and to 24.7%, respectively), herpes zoster vaccine among adults aged ≥60 years and adults aged ≥65 years (a 2.7 percentage point and 3.2 percentage point increase to 30.6% and to 34.2%, respectively), and hepatitis B vaccine among health care personnel (HCP) aged ≥19 years (a 4.1 percentage point increase to 64.7%). Herpes zoster vaccination coverage in 2015 met the Healthy People 2020 target of 30%. Aside from these modest improvements, vaccination coverage among adults in 2015 was similar to estimates from 2014. Racial/ethnic differences in coverage persisted for all seven vaccines, with higher coverage generally for whites compared with most other groups. Adults without health insurance reported receipt of influenza vaccine (all age groups), pneumococcal vaccine (adults aged 19-64 years at increased risk), Td vaccine (adults aged ≥19 years, 19-64 years, and 50-64 years), Tdap vaccine (adults aged ≥19 years and 19-64 years), hepatitis A vaccine (adults aged ≥19 years overall and among travelers), hepatitis B vaccine (adults aged ≥19 years, 19-49 years, and among travelers), herpes zoster vaccine (adult
{"title":"Surveillance of Vaccination Coverage among Adult Populations - United States, 2015.","authors":"Walter W Williams, Peng-Jun Lu, Alissa O'Halloran, David K Kim, Lisa A Grohskopf, Tamara Pilishvili, Tami H Skoff, Noele P Nelson, Rafael Harpaz, Lauri E Markowitz, Alfonso Rodriguez-Lainz, Amy Parker Fiebelkorn","doi":"10.15585/mmwr.ss6611a1","DOIUrl":"10.15585/mmwr.ss6611a1","url":null,"abstract":"<p><strong>Problem/condition: </strong>Overall, the prevalence of illness attributable to vaccine-preventable diseases is greater among adults than among children. Adults are recommended to receive vaccinations based on their age, underlying medical conditions, lifestyle, prior vaccinations, and other considerations. Updated vaccination recommendations from CDC are published annually in the U.S. Adult Immunization Schedule. Despite longstanding recommendations for use of many vaccines, vaccination coverage among U.S. adults is low.</p><p><strong>Period covered: </strong>August 2014-June 2015 (for influenza vaccination) and January-December 2015 (for pneumococcal, tetanus and diphtheria [Td] and tetanus and diphtheria with acellular pertussis [Tdap], hepatitis A, hepatitis B, herpes zoster, and human papillomavirus [HPV] vaccination).</p><p><strong>Description of system: </strong>The National Health Interview Survey (NHIS) is a continuous, cross-sectional national household survey of the noninstitutionalized U.S. civilian population. In-person interviews are conducted throughout the year in a probability sample of households, and NHIS data are compiled and released annually. The survey objective is to monitor the health of the U.S. population and provide estimates of health indicators, health care use and access, and health-related behaviors.</p><p><strong>Results: </strong>Compared with data from the 2014 NHIS, increases in vaccination coverage occurred for influenza vaccine among adults aged ≥19 years (a 1.6 percentage point increase compared with the 2013-14 season to 44.8%), pneumococcal vaccine among adults aged 19-64 years at increased risk for pneumococcal disease (a 2.8 percentage point increase to 23.0%), Tdap vaccine among adults aged ≥19 years and adults aged 19-64 years (a 3.1 percentage point and 3.3 percentage point increase to 23.1% and to 24.7%, respectively), herpes zoster vaccine among adults aged ≥60 years and adults aged ≥65 years (a 2.7 percentage point and 3.2 percentage point increase to 30.6% and to 34.2%, respectively), and hepatitis B vaccine among health care personnel (HCP) aged ≥19 years (a 4.1 percentage point increase to 64.7%). Herpes zoster vaccination coverage in 2015 met the Healthy People 2020 target of 30%. Aside from these modest improvements, vaccination coverage among adults in 2015 was similar to estimates from 2014. Racial/ethnic differences in coverage persisted for all seven vaccines, with higher coverage generally for whites compared with most other groups. Adults without health insurance reported receipt of influenza vaccine (all age groups), pneumococcal vaccine (adults aged 19-64 years at increased risk), Td vaccine (adults aged ≥19 years, 19-64 years, and 50-64 years), Tdap vaccine (adults aged ≥19 years and 19-64 years), hepatitis A vaccine (adults aged ≥19 years overall and among travelers), hepatitis B vaccine (adults aged ≥19 years, 19-49 years, and among travelers), herpes zoster vaccine (adult","PeriodicalId":48549,"journal":{"name":"Mmwr Surveillance Summaries","volume":"66 11","pages":"1-28"},"PeriodicalIF":24.9,"publicationDate":"2017-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.15585/mmwr.ss6611a1","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34967314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Stephanie A Rutledge, Svetlana Masalovich, Rachel J Blacher, Magon M Saunders
<p><strong>Problem/condition: </strong>Diabetes self-management education (DSME) is a clinical practice intended to improve preventive practices and behaviors with a focus on decision-making, problem-solving, and self-care. The distribution and correlates of established DSME programs in nonmetropolitan counties across the United States have not been previously described, nor have the characteristics of the nonmetropolitan counties with DSME programs.</p><p><strong>Reporting period: </strong>July 2016.</p><p><strong>Description of systems: </strong>DSME programs recognized by the American Diabetes Association or accredited by the American Association of Diabetes Educators (i.e., active programs) as of July 2016 were shared with CDC by both organizations. The U.S. Census Bureau's census geocoder was used to identify the county of each DSME program site using documented addresses. County characteristic data originated from the U.S. Census Bureau, compiled by the U.S. Department of Agriculture's Economic Research Service into the 2013 Atlas of Rural and Small-Town America data set. County levels of diagnosed diabetes prevalence and incidence, as well as the number of persons with diagnosed diabetes, were previously estimated by CDC. This report defined nonmetropolitan counties using the rural-urban continuum code from the 2013 Atlas of Rural and Small-Town America data set. This code included six nonmetropolitan categories of 1,976 urban and rural counties (62% of counties) adjacent to and nonadjacent to metropolitan counties.</p><p><strong>Results: </strong>In 2016, a total of 1,065 DSME programs were located in 38% of the 1,976 nonmetropolitan counties; 62% of nonmetropolitan counties did not have a DSME program. The total number of DSME programs for nonmetropolitan counties with at least one DSME program ranged from 1 to 8, with an average of 1.4 programs. After adjusting for county-level characteristics, the odds of a nonmetropolitan county having at least one DSME program increased as the percentage insured increased (adjusted odds ratio [AOR] = 1.10, 95% confidence interval [CI] = 1.08-1.13), the percentage with a high school education or less decreased (AOR = 1.06, 95% CI = 1.04-1.07), the unemployment rate decreased (AOR = 1.19, 95% CI = 1.11-1.23), and the natural logarithm of the number of persons with diabetes increased (AOR = 3.63, 95% CI = 3.15-4.19).</p><p><strong>Interpretation: </strong>In 2016, there were few DMSE programs in nonmetropolitan, socially disadvantaged counties in the United States. The number of persons with diabetes, percentage insured, percentage with a high school education or less, and the percentage unemployed were significantly associated with whether a DSME program was located in a nonmetropolitan county.</p><p><strong>Public health action: </strong>Monitoring the distribution of DSME programs at the county level provides insight needed to strategically address rural disparities in diabetes care and outcomes. The
问题/状况:糖尿病自我管理教育(DSME)是一种临床实践，旨在改善预防措施和行为，重点是决策，解决问题和自我保健。在美国非大都市县建立的DSME项目的分布和相关关系以前没有被描述过，也没有非大都市县的DSME项目的特征。报告期:2016年7月。系统描述:截至2016年7月，由美国糖尿病协会认可或由美国糖尿病教育者协会认可的DSME项目(即活跃项目)由两个组织与CDC共享。使用美国人口普查局的人口普查地理编码来确定每个DSME项目站点使用记录地址的县。县特征数据来自美国人口普查局，由美国农业部经济研究局汇编成2013年美国农村和小城镇地图集。各县诊断出的糖尿病患病率和发病率水平，以及诊断出糖尿病的人数，以前是由疾病预防控制中心估计的。本报告使用2013年美国农村和小城镇地图集中的农村-城市连续体代码定义了非大都市县。该代码包括六个非大都市类别，1976个城市和农村县(62%的县)与大都市县相邻或不相邻。结果:2016年，共有1,065个DSME项目位于1976个非大都市县的38%;62%的非大都市县没有DSME项目。非大都市县至少有一个DSME项目的DSME项目总数在1 ~ 8个之间，平均1.4个项目。在调整了县级特点,nonmetropolitan县有至少一个的几率DSME程序增加投保比例增加(调整优势比(AOR) = 1.10, 95%可信区间[CI] = 1.08 - -1.13),高中教育或更少的比例降低(优势比= 1.06,95% CI = 1.04 - -1.07),失业率下降(优势比= 1.19,95% CI = 1.11 - -1.23),和自然对数患有糖尿病的人的数量增加(优势比= 3.63,95% ci = 3.15-4.19)。解读:2016年，美国非大都市、社会弱势县的DMSE项目很少。糖尿病患者的数量、参保比例、高中以下教育程度的比例和失业比例与DSME项目是否位于非大都市县有显著关联。公共卫生行动:监测DSME项目在县一级的分布，为战略性地解决农村糖尿病护理和结果的差异提供了必要的见解。这些发现提供了必要的信息，以评估DSME项目的可用性，并探索以证据为基础的战略和创新技术，在服务不足的农村社区提供DSME项目。
{"title":"Diabetes Self-Management Education Programs in Nonmetropolitan Counties - United States, 2016.","authors":"Stephanie A Rutledge, Svetlana Masalovich, Rachel J Blacher, Magon M Saunders","doi":"10.15585/mmwr.ss6610a1","DOIUrl":"https://doi.org/10.15585/mmwr.ss6610a1","url":null,"abstract":"<p><strong>Problem/condition: </strong>Diabetes self-management education (DSME) is a clinical practice intended to improve preventive practices and behaviors with a focus on decision-making, problem-solving, and self-care. The distribution and correlates of established DSME programs in nonmetropolitan counties across the United States have not been previously described, nor have the characteristics of the nonmetropolitan counties with DSME programs.</p><p><strong>Reporting period: </strong>July 2016.</p><p><strong>Description of systems: </strong>DSME programs recognized by the American Diabetes Association or accredited by the American Association of Diabetes Educators (i.e., active programs) as of July 2016 were shared with CDC by both organizations. The U.S. Census Bureau's census geocoder was used to identify the county of each DSME program site using documented addresses. County characteristic data originated from the U.S. Census Bureau, compiled by the U.S. Department of Agriculture's Economic Research Service into the 2013 Atlas of Rural and Small-Town America data set. County levels of diagnosed diabetes prevalence and incidence, as well as the number of persons with diagnosed diabetes, were previously estimated by CDC. This report defined nonmetropolitan counties using the rural-urban continuum code from the 2013 Atlas of Rural and Small-Town America data set. This code included six nonmetropolitan categories of 1,976 urban and rural counties (62% of counties) adjacent to and nonadjacent to metropolitan counties.</p><p><strong>Results: </strong>In 2016, a total of 1,065 DSME programs were located in 38% of the 1,976 nonmetropolitan counties; 62% of nonmetropolitan counties did not have a DSME program. The total number of DSME programs for nonmetropolitan counties with at least one DSME program ranged from 1 to 8, with an average of 1.4 programs. After adjusting for county-level characteristics, the odds of a nonmetropolitan county having at least one DSME program increased as the percentage insured increased (adjusted odds ratio [AOR] = 1.10, 95% confidence interval [CI] = 1.08-1.13), the percentage with a high school education or less decreased (AOR = 1.06, 95% CI = 1.04-1.07), the unemployment rate decreased (AOR = 1.19, 95% CI = 1.11-1.23), and the natural logarithm of the number of persons with diabetes increased (AOR = 3.63, 95% CI = 3.15-4.19).</p><p><strong>Interpretation: </strong>In 2016, there were few DMSE programs in nonmetropolitan, socially disadvantaged counties in the United States. The number of persons with diabetes, percentage insured, percentage with a high school education or less, and the percentage unemployed were significantly associated with whether a DSME program was located in a nonmetropolitan county.</p><p><strong>Public health action: </strong>Monitoring the distribution of DSME programs at the county level provides insight needed to strategically address rural disparities in diabetes care and outcomes. The","PeriodicalId":48549,"journal":{"name":"Mmwr Surveillance Summaries","volume":"66 10","pages":"1-6"},"PeriodicalIF":24.9,"publicationDate":"2017-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5829897/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34948254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lara R Robinson, Joseph R Holbrook, Rebecca H Bitsko, Sophie A Hartwig, Jennifer W Kaminski, Reem M Ghandour, Georgina Peacock, Akilah Heggs, Coleen A Boyle
<p><strong>Problem/condition: </strong>Mental, behavioral, and developmental disorders (MBDDs) begin in early childhood and often affect lifelong health and well-being. Persons who live in rural areas report more health-related disparities than those in urban areas, including poorer health, more health risk behaviors, and less access to health resources.</p><p><strong>Reporting period: </strong>2011-2012.</p><p><strong>Description of system: </strong>The National Survey of Children's Health (NSCH) is a cross-sectional, random-digit-dial telephone survey of parents or guardians that collects information on noninstitutionalized children aged <18 years in the United States. Interviews included indicators of health and well-being, health care access, and family and community characteristics. Using data from the 2011-2012 NSCH, this report examines variations in health care, family, and community factors among children aged 2-8 years with and without MBDDs in rural and urban settings. Restricting the data to U.S. children aged 2-8 years with valid responses for child age and sex, each MBDD, and zip code resulted in an analytic sample of 34,535 children; MBDD diagnosis was determined by parent report and was not validated with health care providers or medical records.</p><p><strong>Results: </strong>A higher percentage of all children in small rural and large rural areas compared with all children in urban areas had parents who reported experiencing financial difficulties (i.e., difficulties meeting basic needs such as food and housing). Children in all rural areas more often lacked amenities and lived in a neighborhood in poor condition. However, a lower percentage of children in small rural and isolated areas had parents who reported living in an unsafe neighborhood, and children in isolated areas less often lived in a neighborhood lacking social support, less often lacked a medical home, and less often had a parent with fair or poor mental health. Across rural subtypes, approximately one in six young children had a parent-reported MBDD diagnosis. A higher prevalence was found among children in small rural areas (18.6%) than in urban areas (15.2%). In urban and the majority of rural subtypes, children with an MBDD more often lacked a medical home, had a parent with poor mental health, lived in families with financial difficulties, and lived in a neighborhood lacking physical and social resources than children without an MBDD within each of those community types. Only in urban areas did a higher percentage of children with MBDDs lack health insurance than children without MBDDs. After adjusting for race/ethnicity and poverty among children with MBDDs, those in rural areas more often had a parent with poor mental health and lived in resource-low neighborhoods than those in urban areas.</p><p><strong>Interpretation: </strong>Certain health care, family, and community disparities were more often reported among children with MBDDS than among children with
{"title":"Differences in Health Care, Family, and Community Factors Associated with Mental, Behavioral, and Developmental Disorders Among Children Aged 2-8 Years in Rural and Urban Areas - United States, 2011-2012.","authors":"Lara R Robinson, Joseph R Holbrook, Rebecca H Bitsko, Sophie A Hartwig, Jennifer W Kaminski, Reem M Ghandour, Georgina Peacock, Akilah Heggs, Coleen A Boyle","doi":"10.15585/mmwr.ss6608a1","DOIUrl":"https://doi.org/10.15585/mmwr.ss6608a1","url":null,"abstract":"<p><strong>Problem/condition: </strong>Mental, behavioral, and developmental disorders (MBDDs) begin in early childhood and often affect lifelong health and well-being. Persons who live in rural areas report more health-related disparities than those in urban areas, including poorer health, more health risk behaviors, and less access to health resources.</p><p><strong>Reporting period: </strong>2011-2012.</p><p><strong>Description of system: </strong>The National Survey of Children's Health (NSCH) is a cross-sectional, random-digit-dial telephone survey of parents or guardians that collects information on noninstitutionalized children aged <18 years in the United States. Interviews included indicators of health and well-being, health care access, and family and community characteristics. Using data from the 2011-2012 NSCH, this report examines variations in health care, family, and community factors among children aged 2-8 years with and without MBDDs in rural and urban settings. Restricting the data to U.S. children aged 2-8 years with valid responses for child age and sex, each MBDD, and zip code resulted in an analytic sample of 34,535 children; MBDD diagnosis was determined by parent report and was not validated with health care providers or medical records.</p><p><strong>Results: </strong>A higher percentage of all children in small rural and large rural areas compared with all children in urban areas had parents who reported experiencing financial difficulties (i.e., difficulties meeting basic needs such as food and housing). Children in all rural areas more often lacked amenities and lived in a neighborhood in poor condition. However, a lower percentage of children in small rural and isolated areas had parents who reported living in an unsafe neighborhood, and children in isolated areas less often lived in a neighborhood lacking social support, less often lacked a medical home, and less often had a parent with fair or poor mental health. Across rural subtypes, approximately one in six young children had a parent-reported MBDD diagnosis. A higher prevalence was found among children in small rural areas (18.6%) than in urban areas (15.2%). In urban and the majority of rural subtypes, children with an MBDD more often lacked a medical home, had a parent with poor mental health, lived in families with financial difficulties, and lived in a neighborhood lacking physical and social resources than children without an MBDD within each of those community types. Only in urban areas did a higher percentage of children with MBDDs lack health insurance than children without MBDDs. After adjusting for race/ethnicity and poverty among children with MBDDs, those in rural areas more often had a parent with poor mental health and lived in resource-low neighborhoods than those in urban areas.</p><p><strong>Interpretation: </strong>Certain health care, family, and community disparities were more often reported among children with MBDDS than among children with","PeriodicalId":48549,"journal":{"name":"Mmwr Surveillance Summaries","volume":"66 8","pages":"1-11"},"PeriodicalIF":24.9,"publicationDate":"2017-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.15585/mmwr.ss6608a1","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34818950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Christopher A Taylor, Jeneita M Bell, Matthew J Breiding, Likang Xu
<p><strong>Problem/condition: </strong>Traumatic brain injury (TBI) has short- and long-term adverse clinical outcomes, including death and disability. TBI can be caused by a number of principal mechanisms, including motor-vehicle crashes, falls, and assaults. This report describes the estimated incidence of TBI-related emergency department (ED) visits, hospitalizations, and deaths during 2013 and makes comparisons to similar estimates from 2007.</p><p><strong>Reporting period: </strong>2007 and 2013.</p><p><strong>Description of system: </strong>State-based administrative health care data were used to calculate estimates of TBI-related ED visits and hospitalizations by principal mechanism of injury, age group, sex, and injury intent. Categories of injury intent included unintentional (motor-vehicle crashes, falls, being struck by or against an object, mechanism unspecified), intentional (self-harm and assault/homicide), and undetermined intent. These health records come from the Healthcare Cost and Utilization Project's National Emergency Department Sample and National Inpatient Sample. TBI-related death analyses used CDC multiple-cause-of-death public-use data files, which contain death certificate data from all 50 states and the District of Columbia.</p><p><strong>Results: </strong>In 2013, a total of approximately 2.8 million TBI-related ED visits, hospitalizations, and deaths (TBI-EDHDs) occurred in the United States. This consisted of approximately 2.5 million TBI-related ED visits, approximately 282,000 TBI-related hospitalizations, and approximately 56,000 TBI-related deaths. TBIs were diagnosed in nearly 2.8 million (1.9%) of the approximately 149 million total injury- and noninjury-related EDHDs that occurred in the United States during 2013. Rates of TBI-EDHDs varied by age, with the highest rates observed among persons aged ≥75 years (2,232.2 per 100,000 population), 0-4 years (1,591.5), and 15-24 years (1,080.7). Overall, males had higher age-adjusted rates of TBI-EDHDs (959.0) compared with females (810.8) and the most common principal mechanisms of injury for all age groups included falls (413.2, age-adjusted), being struck by or against an object (142.1, age-adjusted), and motor-vehicle crashes (121.7, age-adjusted). The age-adjusted rate of ED visits was higher in 2013 (787.1) versus 2007 (534.4), with fall-related TBIs among persons aged ≥75 years accounting for 17.9% of the increase in the number of TBI-related ED visits. The number and rate of TBI-related hospitalizations also increased among persons aged ≥75 years (from 356.9 in 2007 to 454.4 in 2013), primarily because of falls. Whereas motor-vehicle crashes were the leading cause of TBI-related deaths in 2007 in both number and rate, in 2013, intentional self-harm was the leading cause in number and rate. The overall age-adjusted rate of TBI-related deaths for all ages decreased from 17.9 in 2007 to 17.0 in 2013; however, age-adjusted TBI-related death rates attributable to
{"title":"Traumatic Brain Injury-Related Emergency Department Visits, Hospitalizations, and Deaths - United States, 2007 and 2013.","authors":"Christopher A Taylor, Jeneita M Bell, Matthew J Breiding, Likang Xu","doi":"10.15585/mmwr.ss6609a1","DOIUrl":"10.15585/mmwr.ss6609a1","url":null,"abstract":"<p><strong>Problem/condition: </strong>Traumatic brain injury (TBI) has short- and long-term adverse clinical outcomes, including death and disability. TBI can be caused by a number of principal mechanisms, including motor-vehicle crashes, falls, and assaults. This report describes the estimated incidence of TBI-related emergency department (ED) visits, hospitalizations, and deaths during 2013 and makes comparisons to similar estimates from 2007.</p><p><strong>Reporting period: </strong>2007 and 2013.</p><p><strong>Description of system: </strong>State-based administrative health care data were used to calculate estimates of TBI-related ED visits and hospitalizations by principal mechanism of injury, age group, sex, and injury intent. Categories of injury intent included unintentional (motor-vehicle crashes, falls, being struck by or against an object, mechanism unspecified), intentional (self-harm and assault/homicide), and undetermined intent. These health records come from the Healthcare Cost and Utilization Project's National Emergency Department Sample and National Inpatient Sample. TBI-related death analyses used CDC multiple-cause-of-death public-use data files, which contain death certificate data from all 50 states and the District of Columbia.</p><p><strong>Results: </strong>In 2013, a total of approximately 2.8 million TBI-related ED visits, hospitalizations, and deaths (TBI-EDHDs) occurred in the United States. This consisted of approximately 2.5 million TBI-related ED visits, approximately 282,000 TBI-related hospitalizations, and approximately 56,000 TBI-related deaths. TBIs were diagnosed in nearly 2.8 million (1.9%) of the approximately 149 million total injury- and noninjury-related EDHDs that occurred in the United States during 2013. Rates of TBI-EDHDs varied by age, with the highest rates observed among persons aged ≥75 years (2,232.2 per 100,000 population), 0-4 years (1,591.5), and 15-24 years (1,080.7). Overall, males had higher age-adjusted rates of TBI-EDHDs (959.0) compared with females (810.8) and the most common principal mechanisms of injury for all age groups included falls (413.2, age-adjusted), being struck by or against an object (142.1, age-adjusted), and motor-vehicle crashes (121.7, age-adjusted). The age-adjusted rate of ED visits was higher in 2013 (787.1) versus 2007 (534.4), with fall-related TBIs among persons aged ≥75 years accounting for 17.9% of the increase in the number of TBI-related ED visits. The number and rate of TBI-related hospitalizations also increased among persons aged ≥75 years (from 356.9 in 2007 to 454.4 in 2013), primarily because of falls. Whereas motor-vehicle crashes were the leading cause of TBI-related deaths in 2007 in both number and rate, in 2013, intentional self-harm was the leading cause in number and rate. The overall age-adjusted rate of TBI-related deaths for all ages decreased from 17.9 in 2007 to 17.0 in 2013; however, age-adjusted TBI-related death rates attributable to","PeriodicalId":48549,"journal":{"name":"Mmwr Surveillance Summaries","volume":"66 9","pages":"1-16"},"PeriodicalIF":37.3,"publicationDate":"2017-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5829835/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34818952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Catherine A Okoro, Guixiang Zhao, Jared B Fox, Paul I Eke, Kurt J Greenlund, Machell Town
<p><strong>Problem/condition: </strong>As a result of the 2010 Patient Protection and Affordable Care Act, millions of U.S. adults attained health insurance coverage. However, millions of adults remain uninsured or underinsured. Compared with adults without barriers to health care, adults who lack health insurance coverage, have coverage gaps, or skip or delay care because of limited personal finances might face increased risk for poor physical and mental health and premature mortality.</p><p><strong>Period covered: </strong>2014.</p><p><strong>Description of system: </strong>The Behavioral Risk Factor Surveillance System (BRFSS) is an ongoing, state-based, landline- and cellular-telephone survey of noninstitutionalized adults aged ≥18 years residing in the United States. Data are collected from states, the District of Columbia, and participating U.S. territories on health risk behaviors, chronic health conditions, health care access, and use of clinical preventive services (CPS). An optional Health Care Access module was included in the 2014 BRFSS. This report summarizes 2014 BRFSS data from all 50 states and the District of Columbia on health care access and use of selected CPS recommended by the U.S. Preventive Services Task Force or the Advisory Committee on Immunization Practices among working-aged adults (aged 18-64 years), by state, state Medicaid expansion status, expanded geographic region, and federal poverty level (FPL). This report also provides analysis of primary type of health insurance coverage at the time of interview, continuity of health insurance coverage during the preceding 12 months, and other health care access measures (i.e., unmet health care need because of cost, unmet prescription need because of cost, medical debt [medical bills being paid off over time], number of health care visits during the preceding year, and satisfaction with received health care) from 43 states that included questions from the optional BRFSS Health Care Access module.</p><p><strong>Results: </strong>In 2014, health insurance coverage and other health care access measures varied substantially by state, state Medicaid expansion status, expanded geographic region (i.e., states categorized geographically into nine regions), and FPL category. The following proportions refer to the range of estimated prevalence for health insurance and other health care access measures by examined geographical unit (unless otherwise specified), as reported by respondents. Among adults with health insurance coverage, the range was 70.8%-94.5% for states, 78.8%-94.5% for Medicaid expansion states, 70.8%-89.1% for nonexpansion states, 73.3%-91.0% for expanded geographic regions, and 64.2%-95.8% for FPL categories. Among adults who had a usual source of health care, the range was 57.2%-86.6% for states, 57.2%-86.6% for Medicaid expansion states, 61.8%-83.9% for nonexpansion states, 64.4%-83.6% for expanded geographic regions, and 61.0%-81.6% for FPL categories. Among adu
{"title":"Surveillance for Health Care Access and Health Services Use, Adults Aged 18-64 Years - Behavioral Risk Factor Surveillance System, United States, 2014.","authors":"Catherine A Okoro, Guixiang Zhao, Jared B Fox, Paul I Eke, Kurt J Greenlund, Machell Town","doi":"10.15585/mmwr.ss6607a1","DOIUrl":"10.15585/mmwr.ss6607a1","url":null,"abstract":"<p><strong>Problem/condition: </strong>As a result of the 2010 Patient Protection and Affordable Care Act, millions of U.S. adults attained health insurance coverage. However, millions of adults remain uninsured or underinsured. Compared with adults without barriers to health care, adults who lack health insurance coverage, have coverage gaps, or skip or delay care because of limited personal finances might face increased risk for poor physical and mental health and premature mortality.</p><p><strong>Period covered: </strong>2014.</p><p><strong>Description of system: </strong>The Behavioral Risk Factor Surveillance System (BRFSS) is an ongoing, state-based, landline- and cellular-telephone survey of noninstitutionalized adults aged ≥18 years residing in the United States. Data are collected from states, the District of Columbia, and participating U.S. territories on health risk behaviors, chronic health conditions, health care access, and use of clinical preventive services (CPS). An optional Health Care Access module was included in the 2014 BRFSS. This report summarizes 2014 BRFSS data from all 50 states and the District of Columbia on health care access and use of selected CPS recommended by the U.S. Preventive Services Task Force or the Advisory Committee on Immunization Practices among working-aged adults (aged 18-64 years), by state, state Medicaid expansion status, expanded geographic region, and federal poverty level (FPL). This report also provides analysis of primary type of health insurance coverage at the time of interview, continuity of health insurance coverage during the preceding 12 months, and other health care access measures (i.e., unmet health care need because of cost, unmet prescription need because of cost, medical debt [medical bills being paid off over time], number of health care visits during the preceding year, and satisfaction with received health care) from 43 states that included questions from the optional BRFSS Health Care Access module.</p><p><strong>Results: </strong>In 2014, health insurance coverage and other health care access measures varied substantially by state, state Medicaid expansion status, expanded geographic region (i.e., states categorized geographically into nine regions), and FPL category. The following proportions refer to the range of estimated prevalence for health insurance and other health care access measures by examined geographical unit (unless otherwise specified), as reported by respondents. Among adults with health insurance coverage, the range was 70.8%-94.5% for states, 78.8%-94.5% for Medicaid expansion states, 70.8%-89.1% for nonexpansion states, 73.3%-91.0% for expanded geographic regions, and 64.2%-95.8% for FPL categories. Among adults who had a usual source of health care, the range was 57.2%-86.6% for states, 57.2%-86.6% for Medicaid expansion states, 61.8%-83.9% for nonexpansion states, 64.4%-83.6% for expanded geographic regions, and 61.0%-81.6% for FPL categories. Among adu","PeriodicalId":48549,"journal":{"name":"Mmwr Surveillance Summaries","volume":"66 7","pages":"1-42"},"PeriodicalIF":24.9,"publicationDate":"2017-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5829627/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34759224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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