Prevalence of Total and Untreated Dental Caries Among Youth: United States, 2015–2016

April 13, 2018

Eleanor Fleming, Ph.D., D.D.S., M.P.H., Dental Epidemiologist

Questions for Eleanor Fleming, Ph.D., D.D.S., M.P.H., Dental Epidemiologist and Lead Author of “Prevalence of Total and Untreated Dental Caries Among Youth: United States, 2015–2016

Q: What made you decide to focus on the prevalence of dental cavities in young children for this study, versus other dental conditions like gum disease or tooth grinding – or some other critical public health concerns today for America’s youth?

EF: Our intent in conducting this study was to provide up-to-date prevalence estimates for dental caries in children. We decided that our study would focus on dental caries because of the serious and negative impact untreated caries can have on children. By the way, dental “caries” is the scientific term for tooth decay or cavities. Dental caries are the most common chronic disease among youth aged 6-19 years. Untreated caries cause pain and infection. Children miss days from school and have their overall quality of life effected by untreated dental caries. This is an important public health concern for America’s youth. While dental conditions like gum disease or tooth grinding are important, the National Health and Nutrition Examination Survey (NHANES) Oral Health Component does not currently collect data on these dental conditions. The component focuses on collecting data on tooth loss, dental caries, and dental sealants.


Q: In your new report, you examine differences in the prevalence of tooth cavities by income level; what is the motivation to look at income, since many children’s dental care might be paid by either public or private health insurance?

EF: We examined family income in this study for a few reasons. One is that income is a significant social determinant of health. For our study, we decided to include family income in addition to age, race and Hispanic origin. We were curious about the differences in untreated and total caries (tooth decay) by family income level. For both total and untreated caries, prevalence decreased as family income level increased. There is also concern among the public health community that children who may have access to Medicaid dental benefits are not receiving the care that they need. The examination of income levels in our new report might offer some needed insight to this concern.

The prevalence of total dental caries decreased as family income levels increased, from 51.8% for youth from families living below the federal poverty level to 34.2% for youth from families with income levels greater than 300% of the federal poverty level.

The prevalence of untreated dental caries decreased from 18.6% for youth from families living below the federal poverty level to 7.0% for youth from families with incomes greater than 300% of the federal poverty level.


Q: Was there a result in your study that you hadn’t expected and that really surprised you?

EF: Because our motivation for this study was to provide updated national estimates on untreated and total caries (tooth decay) for 2015-2016, all of the results were very interesting in one way or another — and surprising. National estimates for age, race and Hispanic origin, and income are results that we need to understand for public health surveillance purposes. For me though, the overall estimates for youth by age were especially interesting.

While the untreated dental caries prevalence overall for youth is 13.0%, there were age differences that caught my eye. The low prevalence for 2-5 year-olds is an important and encouraging finding. While we don’t know if it is from prevention efforts, access to care, or other factors, the fact that our youngest youth have the lowest untreated and total caries prevalence shows they’re starting off their young lives with healthy teeth.

The prevalence was lowest in youth aged 2-5 years (8.8%) compared with youth aged 6-11 years (15.3%) and 12-19 years (13.4%). The prevalence of the 6-11 and 12-19 years-olds was significantly different from the prevalence of 2-5 year-olds.

The total caries experience was also lowest for youth aged 2-5 years (17.4%) compared to youth aged 6-11 years (45.2%) and 12-19 years (53.5%). As age increased, the total caries prevalence increased.


Q: What, if any, is the difference between the two terms you use in your report – primary teeth and permanent teeth?

EF: Primary teeth are baby teeth, or the first teeth that erupt, or come in, which are later shed and replaced by permanent teeth. Primary teeth erupt from around 6 months to age 2 or 3 years. The permanent teeth replace the primary teeth. These teeth start coming in around the age of 6 years and continue until the third molars, or wisdom teeth come in, somewhere between the ages of 17 to 21 years. In our analysis, we combined the two types of teeth in order to focus on dental caries (tooth decay) regardless of tooth type.


Q: In your report, are untreated dental cavities a subset of the number of total cavities, and therefore included in the total cavity statistics?

EF: Yes, untreated dental caries (tooth decay) are included in the total number of dental caries. When we describe total dental caries, we are focused on both untreated and treated dental caries. Essentially, the total of dental caries take into account any tooth decay experience that someone has had. Untreated dental caries represent tooth decay that has not been treated. Untreated dental caries are also known as cavities. What we capture in the untreated caries measure is the active disease of youth.


Q:  What differences or similarities did you see among race and ethnic groups, and various demographics, in this analysis?

EF: We noted a number of differences among youth by race and Hispanic origin in this analysis. Non-Hispanic black youth had the highest prevalence of untreated caries (tooth decay) (17.1%) compared to other race and Hispanic-origin groups. The prevalence for non-Hispanic black youth was significantly different from non-Hispanic whites (11.7%) and non-Hispanic Asians (10.5%). The prevalence of untreated dental caries in Hispanic youth was 13.5%.

Hispanic youth had the highest prevalence of total caries (52.0%) compared to other race and Hispanic-origin groups. The prevalence was also significantly different from non-Hispanic whites (39.0%) and non-Hispanic Asians (42.6%). The prevalence of total caries for non-Hispanic black youth was 44.3%.


Q: What sort of trend data do you have on this topic so we can see how prevalence has evolved over time?

EF: With six years of data, we can look at the trend in prevalence over time. Because dental caries (tooth decay) is the most common condition of childhood, we thought it was important to include trend analysis in our report.

The results show a significant linear decrease in total caries. From 2011-2012 to 2015-2016, the total caries prevalence decreased from 50.0% to 43.1%. The results show a different pattern for untreated dental caries. The prevalence of untreated dental caries increased from 2011-2012 (16.1%) to 2013-2014 (18.0%), and then decreased in 2015-2016 (13.05). There is significant quadratic trend – a single bend either upward or downward — in untreated dental caries from 2011-2012 to 2015-2016.


Q: What is the take-home message of this report?

EF: The take-home message from this report is that there are differences in untreated and total caries (tooth decay) by age group, race and Hispanic origin, and income. The trend analysis shows that the prevalence of untreated and total caries are decreasing. However, there are still disparities that exist. Because monitoring prevalence of untreated and total caries is key to preventing and controlling oral diseases, these disparities are important.

The prevalence of untreated dental caries in America’s youth is 13.0%. The prevalence decreased as family income increased, with youth with family incomes less than 100% of the federal poverty level having the highest prevalence. Disparities in untreated dental caries exist along race and Hispanic origin. Non-Hispanic black youth have the highest prevalence compared to Hispanic, non-Hispanic white, and non-Hispanic Asian youth.

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Emergency Department Visits by Patients aged 45 and over with Diabetes: United States, 2015

February 8, 2018

Questions for Pinyao Rui, Statistician and Author of, “Emergency Department Visits by Patients aged 45 and over with Diabetes: United States, 2015.”

Q: Why did you decide to examine emergency department (ED) visits made by patients aged 45 years older with diabetes?

PR: We decided to examine emergency department visits made by patients aged 45 years and older because we wanted to focus on visits made by older patients who are at higher risk of developing or having diabetes and who comprise a majority of all diabetes cases in the U.S.  Additionally, we wanted to use more recent data not currently available in the literature to examine characteristics of an ED visit for a condition that is projected to rise and contribute to increasing burden of medical care systems.


Q: How did the rate of emergency department visits by patients aged 45 and over with diabetes change with age?

PR: The rate of emergency department visits by patients aged 45 and over increased with age. The rate increased from 69 per 1,000 persons for those aged 45-64 years and more than doubled to 164 per 1,000 persons for those aged 75 years and over.


Q: Were there differences in the percentage of visits that ended in inpatient hospital admission by diabetes status?

PR: Yes, the percentage of ED visits with diabetes that ended in inpatient hospital admission was significantly higher than the percentage of ED visits without diabetes among visits made by patients aged 45-64 and 65 and over.


Q: Are there any findings that surprised you from this report?

PR: One finding from the report that surprised me was that among ED visits made by 45-64 year olds, a higher proportion of diabetes visits were paid by Medicare compared with visits made by patients without diabetes (24% versus 14%).


Q: What is the take home message in this report?

PR: I think the take home message is that the percentage of ED visits by older patients with diabetes reported in the medical record has been increasing in recent years with the highest proportion observed in patients aged 65-74 (32% in 2015).


Q and As on “Mortality in the United States, 2016” and “Drug Overdose Deaths in the United States, 1999-2016”

December 21, 2017

Questions for Bob Anderson, Chief of the Mortality Statistics Branch, on the following reports, “Mortality in the United States, 2016” and “Drug Overdose Deaths in the United States, 1999-2016.”

Q: How significant is it that life expectancy¹ in the U.S. has declined two years in a row?

A:  This is the first time life expectancy for the U.S. as a whole has declined two years in a row since 1962 and 1963, years in which there were severe flu outbreaks – and an increase in deaths from flu and pneumonia – in the U.S.


Q:  Since this is very rare, do we have any idea why this decline in life expectancy has happened again?

A:  We first have to look at the leading causes of death and see what is happening there.  For 7 out of the 10 leading causes of death in the U.S., mortality actually declined between 2015 and 2016.  But mortality from 3 causes of deaths increased.  Suicide rates increased 1.5% in 2016, and mortality from Alzheimer’s disease increased 3.1%.  However, mortality from accidents/unintentional injuries increased at a rate over three times that the increase of Alzheimer’s disease mortality – a 9.7% increase between 2015 and 2016.   And many of these accidental/unintentional deaths were from drug overdoses.


Q: How many deaths in 2016 were attributed to opioids?

A: In 2016, 42,249 drug overdose deaths mentioned involvement of any type of opioid, including heroin and illicit opioids.


Q: Why is the 63,632 number of overdose deaths smaller than what CDC has previously reported for 2016?

A: The 63,632 number is a final, official number of overdose deaths among U.S. residents for 2016 whereas the previously reported (and slightly higher) numbers were provisional estimates.

BACKGROUND:  In August of this year, CDC began calculating monthly provisional data on counts of drug overdose deaths as a rapid response to this public health crisis, in order to provide a more accurate, closer to “real-time” look at what is happening both nationally and at the state level.  These monthly totals are provisional counts, and they include all deaths occurring in the U.S. – which include deaths among non-residents (i.e., visitors here on business or leisure, students from abroad, etc).  These counts also do not include deaths that are still under investigation.  As a result, the monthly numbers are provisional or very preliminary, and the final 2016 number of 63,632 deaths is an official number that only include deaths among U.S. residents and account for any previously unresolved deaths that were under investigation.


Q: In comparing the 2016 numbers with 2015 and past years, is the crisis of drug overdose deaths growing or about the same?

A: From 2015 to 2016, the number of drug overdose deaths increased from 52,404 deaths to 63,632 deaths, a 21% increase.  Over a longer period of time, from 1999 through 2016, the age-adjusted rate of drug overdose deaths increased on average by 10% per year from 1999 to 2006, by 3% per year from 2006 to 2014, and by 18% per year from 2014 to 2016.  So this is a continuing, disturbing upward trend.


Q: Are there any other trends of significance when looking at the types of drugs attributed to overdose deaths?

A: The rate of drug overdose deaths involving synthetic opioids other than methadone, which include drugs such as fentanyl, fentanyl analogs, and tramadol, doubled in one year, from 3.1 per 100,000 in 2015 to 6.2 per 100,000 in 2016. In 2016, 30% of all drug overdose deaths mentioned involvement of a synthetic opioid other than methadone.


Q: Has fentanyl overtaken heroin as a major cause of overdose death?

A: The data brief on drug overdose deaths does not specifically address fentanyl. However the rate of drug overdose deaths involving synthetic opioids other than methadone, which includes fentanyl, doubled between 2015 and 2016. In 2016, the rate of drug overdose deaths involving synthetic opioids other than methadone was 6.2 per 100,000 and the rate of drug overdose deaths involving heroin was 4.9 per 100,000.


Q: There is a lot of stark news in these two reports.  Are there any positives to report?

A: As mentioned, mortality from 7 out of the 10 leading causes of death in the U.S. declined in 2016; in fact, the overall mortality rate for the U.S. actually dropped despite the decline in life expectancy.  As for drug overdose deaths, the rate of increase in drug overdose deaths involving natural and semi-synthetic opioids, such as oxycodone and hydrocodone, is slowing. From 1999 to 2009, the rate increased on average by 13% per year but from 2009 to 2016, the rate increased by 3% per year.  And from 2006 to 2016, the rate of drug overdose deaths involving methadone decreased from 1.8 per 100,000 to 1.0 per 100,000.

The 2015 life expectancy estimate was revised to 78.7 years, down from 78.8 years, which was reported a year ago.

¹BACKGROUND: As a routine matter, for the highest degree of accuracy we blend Medicare data for people ages 66 and over with our vital statistics data to get a final, official estimate of life expectancy.  However, the two data sets are released on different schedules and occasionally, as with the 2015 estimates, the Medicare data for that year isn’t available at the time we release our final mortality statistics.


Sleep Duration and Quality Among Women Aged 40-59, by Menopausal Status

September 7, 2017

Questions for Anjel Vahratian, Ph.D., Author of “Sleep Duration and Quality Among Women Aged 40-59, by Menopausal Status

Q: What made you decide to conduct this study on sleep duration and sleep quality for this group of women?

AV: My research focuses on the health of women as they age and transition from the childbearing period. During this time, women may be at increased risk for chronic health conditions such as diabetes and cardiovascular disease. As insufficient sleep is a modifiable behavior that is associated with these chronic health conditions, I wanted to examine how sleep duration and quality varies by menopausal status.


Q: Was there a finding in your new study that surprised you, and if so, why?

AV: I was surprised to learn that nearly one in two women aged 40-59 did not wake up feeling well rested four times or more in the past week and that postmenopausal women aged 40-59 were more likely to experience disruptions in sleep quality compared with premenopausal women in the same age group.


Q: How did the women from your survey track their sleep behavior; for example, did they use a wearable sleep tracker?

AV: In this report, information on sleep duration and quality are based on self-report. Trained interviewers asked survey participants on average, how many hours of sleep did they get in a 24-hour period. In addition, they asked participants to recall how many times they had problems falling asleep and staying asleep and how many days they woke up not feeling well rested in the past week.


Q: In addition to menopausal status, do you have any other lifestyle information that could impact women’s sleep quality for this age group; for example, shift work employment or having infants or very young children in the home?

AV: While this report did not specifically look at other lifestyle factors that could affect women’s sleep duration and quality – other than age and menopausal status — my colleagues released a report in January 2016 on sleep duration and quality by sex and family type. This report looked at the presence of young children in the household. In addition, we have produced estimates of sleep duration and quality across several sociodemographic characteristics such as race and ethnicity, education, poverty status, marital status, and region.


Q: This report seems to offer just a single year of data – 2015; do you have any trend data to compare these findings to previous years, or any newer data?

AV: Unfortunately, we do not have any long-term trend data on sleep duration and quality among women aged 40-59 by menopausal status. The National Health Interview Survey, or NHIS, has included questions on sleep duration and quality since 2013, while the questions on menopausal status were a part of the 2015 NHIS cancer control supplement.


Q: What is the take-home message from this report?

AV: I think the real take-home message of this report is that sleep is critical for optimal health and wellbeing, and it is a modifiable risk factor for diabetes and cardiovascular disease. As sleep duration and quality vary by menopausal status, it is an area for targeted health promotion for women at midlife.


Infant Mortality Rates in Rural and Urban Areas in the United States, 2014

September 6, 2017

Questions for Danielle Ely, Ph.D., Statistician and Lead Author of “Infant Mortality Rates in Rural and Urban Areas in the United States, 2014

Q: What is the most significant finding in your study?

DE: The most significant finding in this study was the consistency with which infants in rural areas have significantly higher mortality rates than infants in urban places. Higher rural infant mortality was generally observed by race and Hispanic origin, mother’s age, and by infant age at death.


Q: Why are infant mortality rates higher in rural areas vs urban areas?

DE: Generally, previous research shows that health outcomes are poorer in rural places compared with urban places and this study is consistent with those findings. This study did not examine the factors that might be influencing the higher rural infant mortality in comparison with urban infant mortality.


Q: Is this surprising, or are problems with poverty, substance abuse, and health care that much worse in rural areas?

DE: Higher infant mortality in rural places compared with urban places is not necessarily surprising based on the number of other poor health outcomes (such as higher overall mortality rates, higher rates of disability) that rural residents have in comparison to urban residents.


Q: Are there any theories in the literature as to why this infant mortality disparity exists between rural and urban?

DE: Given there are some poorer health outcomes in rural areas, it is possible more pregnant women in rural areas have poorer general health than pregnant women in urban areas that can lead to poor infant outcomes. Further, there is generally less access to health care due to distance and number of providers available in rural areas, which can impact health outcomes.


Q: Any other findings of note that you find significant?

DE: These findings highlight the importance of place for infant survival and suggests the need for including place in research on health outcomes, as well as a need for further research on the greater risk of infant death in rural settings.


Selected Health Conditions Among Native Hawaiian and Pacific Islander Adults: United States, 2014

March 15, 2017

Questions for Adena M. Galinsky, Statistician and Lead Author on “Selected Health Conditions Among Native Hawaiian and Pacific Islander Adults: United States, 2014

Q: What factors led you to undertake this analysis on Native Hawaiian/Pacific Islanders?

AG: NHPI became a race group separate from Asians nearly 20 years ago, but there are still few reliable national NHPI health statistics, because the population is numerically small and hard to include in sufficient numbers in national health surveys.

While NCHS as an agency is committed to collecting and reporting health information about all Americans, our goal with this new survey is to fill the gaps in the country’s knowledge about the health of Native Hawaiian and Pacific Islanders in the United States so that others can make decisions based on accurate, reliable, up to date information.

This research is just the beginning of the exciting work that will be coming out using this data.


Q: What do you feel was the most interesting finding in the study?

AG: The pattern of results was the most interesting finding: that for a whole range of outcomes, from serious psychological distress, to arthritis, to asthma, the NHPI population had a higher prevalence than the Asian population.


Q: Is there any comparable data at the moment? Have there been any other studies done on this population and if so, what conclusions were drawn from those studies?

AG: The annual National Health Interview Survey (NHIS) has been publishing NHPI statistics for a while now (since 2003, when data from the 1999 NHIS were published), but because of small sample sizes many of the statistics were unreliable, and not useful for comparing to other populations’ statistics, such as Asian. A few NHPI statistics have been reliable over the years, such as the high prevalence of diabetes in the NHPI population. But trend data has been hard to come by, because even when an NHPI statistic for a given health condition is reliable one year, it’s generally unreliable or suppressed the next.


Q: So the bottom line here is that NHPIs are in poorer health than the U.S. population as a whole?

AG: That’s suggested here but it’s not really the bottom line. The bottom line here is that the NHPI population differs in many ways from the Asian population, and any analysis that presents combined API statistics will likely only tell the story of the Asian population, since that population is so much larger.

Also, it’s crucial that more work is done using the data file that was just released today. This data source is unprecedented and will allow a much more thorough understanding of the health of the NHPI population. We plan to do more research and we are hopeful that many researchers will do the same.


Q: Why would this population lag behind the rest of the population in certain health indicators?

AG: The data in this report do not address that. Other research has shown that there are socioeconomic differences between the NHPI population and the rest of the population. But our report does not answer this question.


Q: Why is it important to compare this group to single-race Asian adults?

AG: The NHPI population has traditionally been subsumed into the “Asian and Pacific Islander” category. The Asian population is much larger than the NHPI population and the question has been whether API statistics were really telling the story of both the Asian and NHPI population, or just the Asian population.

Of course, even within the Asian population there is variation/heterogeneity, but these results, which show the pattern of differences between the NHPI and Asian populations illustrate the danger of assuming that statistics that describe the Asian population also describe the NHPI population.


Mortality in the United States, 2015

December 8, 2016

Questions for Jiaquan Xu, Epidemiologist and Lead Author on “Mortality in the United States, 2015.”

Q: Is it true that death rates in the U.S. have been increasing over the past few years?

JX: Not exactly. The age-adjusted death rate for total US population increased 1.2% from 724.6 per 100,000 standard population in 2014 to 733.1 in 2015. This was the first significant increase since 1999. We have seen the decrease in mortality for most race/ethnic groups in most of years since 2006. Especially the rates decreased significantly for all male, all female, non-Hispanic white male, non-Hispanic white female, non-Hispanic black male, non-Hispanic black female, Hispanic male, and Hispanic female in 2014 from 2013.


Q: What are some of the reasons why the death rate increased between 2014 and 2015?

JX: We don’t know exactly what caused the increase in mortality in the United States from 2014 to 2015. The results have shown that the age-adjusted death rates increased for 8 (heart disease, chronic lower respiratory, unintentional injuries, stroke, Alzheimer’s disease, diabetes, kidney disease, and suicide) of the 10 leading causes of death. Only decrease in mortality among 10 leading causes of death in 2015 from 2014 was for cancer. Death rates increased significantly for 20 states and decreased for 1. The change for the rest of states were not significant.


Q: Do your findings for 2015 suggest we have reached a peak as far as increases in life expectancy goes?

JX: We don’t think we have reached a peak in life expectancy. Many people died of non-age-related causes because they have aged. Those deaths are preventable. For example, there are 146,571 deaths caused by accidents which accounted for 5.4% of total deaths in 2015. About 65% of deaths from these unintentional injuries were those aged under 65. Among accidental deaths, unintentional poisoning accounted for 32.4 % and motor vehicle traffic accidents accounted for 24.5%. We also don’t know if the increase in mortality in 2015 will continue in 2016. But preliminary data have shown that the mortality for most of the 10 leading causes of death in 2015 went down in second quarter from first quarter, 2016 (http://www.cdc.gov/nchs/products/vsrr/mortality-dashboard.htm#trends). But it is too early to say that the mortality in 2016 will go down or continue going up. We will see what happens when the 2016 final file is available.


Q: What accounts for the decline in life expectancy at birth in 2015 from 2014?

JX: For the total US population, life expectancy decreased 0.1 year from 78.9 years in 2014 to 78.8 in 2015, mainly because of increases in mortality from the 13 causes of death among the 15 leading causes of death, such as heart disease, chronic lower respiratory disease, unintentional injuries, stroke, Alzheimer’s disease, diabetes, kidney disease, suicide, septicemia, , chronic liver disease, hypertension, Parkinson’s disease, and pneumonitis due to solids and liquids. From 2014 to 2015, life expectancy decreased 0.1 year for females largely because of increases in mortality from 12 of 15 leading causes of death such as heart disease, chronic lower respiratory disease, stroke, Alzheimer’s disease, unintentional injuries, influenza and pneumonia, septicemia, hypertension, chronic liver disease, Parkinson’s disease, suicide, and pneumonitis due solids and liquids. The deaths from those 12 leading causes of death accounted for 52.9% of total female deaths.

Life expectancy declined 0.2 year for males largely because of increases in mortality from 11 of 15 leading causes of death such as unintentional injuries, chronic lower respiratory disease, stroke, diabetes, suicide, Alzheimer’s disease, chronic liver disease, septicemia, Parkinson’s disease, Homicide, and hypertension. And about 65% of accidental deaths were under 65 years old, while 81% of suicides were aged 15-64, and 95% of homicides were under 65 years. More young people dying from preventable causes drags life expectancy down.


Q: Is it unusual that mortality rates for so many leading causes of death increased in 2015?

JX: We haven’t seen the increase in mortality from so many leading causes of death for a long time. The age-adjusted death rates increased significantly for 3 of 10 leading causes of death in 2014, 2 in 2013, 1 in 2012, and 5 in 2011. It is an unusual year. Again we don’t know why.


Q: Does the increase in mortality among white females suggest another drop in life expectancy for that group?

JX: We don’t have life expectancy numbers for white females yet. It is possible that the life expectancy numbers in 2015 for white women will drop again in 2015 since the life expectancy decreased 0.1 year for all females in 2015 from 2014 and mortality from 12 of 15 leading causes of death for white females increased significantly in 2015 from 2014 (heart disease, chronic lower respiratory diseases, Alzheimer’s disease, stroke, unintentional injuries, diabetes, influenza and pneumonia, hypertension, chronic liver disease, Parkinson’s disease, suicide, pneumonitis due to solids and liquids).