National Hospital Care Survey Demonstration Projects: Pneumonia Inpatient Hospitalizations and Emergency Department Visits

August 24, 2018

Sonja Williams, M.P.H., NCHS Statistician

Questions for Sonja Williams, M.P.H. and Lead Author of “National Hospital Care Survey Demonstration Projects: Pneumonia Inpatient Hospitalizations and Emergency Department Visits

Q: What is a demonstration project as mentioned in the title of your new study?

SW: A demonstration project is a report that exhibits the potential power of an up and coming national survey.  The National Hospital Care Survey is a survey collecting data from a nationally representative sample of hospitals across the United States. This survey data will allow linkage across settings and to outside data sources. Currently, with only a small number of the sampled hospitals reporting, we are not able to make national estimates. This project is an opportunity to tell researchers that although the data are not nationally representative yet, there are great insights that can be gleaned from the data we currently have.


Q: Why did you produce this report if the statistical results are not nationally representative?

SW: It is the dramatic potential of the National Hospital Care Survey data that motivated me to write this report. I want the U.S. Public Health Community to have information that will help them in their important work throughout America, and this survey could provide that. Although not yet nationally representative, we have millions of records that can demonstrate the power of the survey and still give us insight into what is happening in hospitals in the United States. This report also gives us an opportunity to demonstrate the ability to link to outside sources, such as the National Death Index, and examine what happens to patients after they leave the hospital.


Q: What type of trend data do you have on pneumonia hospitalizations and emergency room visits?

SW: We have extensive trend data from a number of surveys at the National Center for Health Statistics. For example, the National Hospital Discharge Survey, which is the predecessor to the National Hospital Care Survey, has trend data on pneumonia hospitalizations dating from the 1970s all the way to 2010—the last year the National Hospital Discharge Survey was fielded. For emergency room visits, we have trend data dating from 1992 to 2015 through the National Hospital Ambulatory Medical Care Survey. Once nationally representative, the National Hospital Care Survey will be able to produce trend data and possibly create trends for linked data.    


Q: Was there a finding in your new study that really surprised you?

SW: One finding that surprised me was that most pneumonia patients who died within 30 days after their discharge from the hospital, died of something other than pneumonia.

We were able to link our data to the National Death Index (NDI) and examine 30-, 60-, and 90 day- mortality along with looking at cause of death and average age of death after pneumonia hospitalizations. It was interesting to see that most patients lived past 90 days post-discharge, but of those who died, the number one cause of death was malignant neoplasm of an unspecified part of the bronchus or lung. Pneumonia was only the underlying cause of death for 5% of the patients who were hospitalized for pneumonia. Currently, with only a small number of the sampled hospitals reporting, we are not able to make national estimates.


Q: What differences or similarities did you see between or among various demographic groups in this analysis?

SW: There were some interesting differences among demographic groups. For example, there were several age distribution differences.

Among the records in our survey, most hospitalizations for pneumonia were aged 65 and over, while most of those being seen in the emergency department were under age 15. For those inpatients that stayed in the ICU, their average length of stay increased by 50% overall. Also, the gap between the average length of stay with and without time spent in the ICU, seemed to be the largest among those under age 15. For those under 15, their average length of stay was 3.1 days, while for the same age group—among those who stayed in the ICU—their average length of stay was 7.7 days. This is nearly a 5-day difference. The gap between ICU and non-ICU involved hospitalizations for other age groups did not have such a wide difference. Currently, with only a small number of the sampled hospitals reporting, we are not able to make national estimates.


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

SW: I think the real take-home message of this report is that once nationally representative, the National Hospital Care Survey will present an opportunity to look at hospital utilization—along with hospital care—across settings in the United States. The ability to link to outside sources of data, demonstrated in our current linkage to the National Death Index, will allow researchers to explore underlying cause of death and mortality details not previously available. This ability to link will also allow researchers to explore how surrounding social and economic factors can contribute to outcomes of hospital stays through linkage to other data sources such as U.S Census Bureau data. Also, the ability to look at key items of interest in greater detail, such as discharge status, and tracking ICU-involved hospitalizations, will give us a unique view into the care being conducted in hospitals across the United States.

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Identification of Substance-involved Emergency Department Visits Using Data From the National Hospital Care Survey

August 20, 2018

Questions for Amy M. Brown, Health Statistician and Lead Author of “Identification of Substance-involved Emergency Department Visits Using Data From the National Hospital Care Survey

Q: Why is this National Health Statistics Report (NHSR) important?

AB: The use of substances containing drugs or alcohol continues to be an important national health concern.  According to data from the Substance Abuse and Mental Health Services Administration (SAMHSA), in 2011, an estimated 2.5 million emergency department (ED) visits resulted from medical emergencies involving drug misuse or abuse.  This paper presents two approaches (algorithms) to identify substance-involved ED visits using administrative claims data submitted to the National Hospital Care Survey. The ability to identify substance-involved ED visits will allow the National Center for Health Statistics (NCHS) and researchers to track and characterize these visits, including services provided, demographics, and co-morbidities.  


Q: What are the differences between these algorithms?  

AB: The two algorithms are termed ‘general’ and ‘enhanced.’ Both use selected diagnoses and external cause of injury codes. The general algorithm can be used to monitor trends in the number of ED patients with any record of substance use (either recent or past history). The enhanced algorithm adds codes for substance use-related symptoms and procedures and was designed to meet a more specific case definition to identify ED visits involving recent substance use that was related to the reason for visit.


Q: Which substances can be identified by the algorithms? 

AB: The general and enhanced algorithms can be used to identify 10 substance categories:  alcohol (under age 21); antidepressants; antipsychotics; benzodiazepines or sedatives; cannabinoids; cocaine; hallucinogens; heroin; opiates or opioids; and pharmaceutical central nervous system stimulants. 


Q: What was found when these algorithms were applied to survey data?

AB: For demonstration purposes, both algorithms were applied to unweighted data from the 2013 National Hospital Care Survey. Overall, the general algorithm identified 81% more ED visits involving at least one of the priority substance categories compared with the enhanced algorithm.  However, the relative percent difference in the number of ED visits identified between the general and enhanced algorithms varied widely depending on the type of substance involved, ranging from 28% for antidepressants to 120% for cannabinoids.

The percent distributions of patient sex, age, and expected source of payment across all substances were similar between the general and enhanced algorithms.  In contrast, there were differences in discharge status distributions between both algorithms across all substances.


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

AB: Two algorithms are described that search for selected standard medical codes in administrative claims to identify ED visits involving the use of selected substances. NCHS plans to continue refining the algorithms to incorporate additional data elements available in the growing volume of submitted electronic health record (EHR) data, such as clinical notes capturing patient statements regarding events leading up to an ED visit, positive blood or urine tests for specific substances, and types of medication administered or prescribed during the encounter. Once refined and formally validated to ensure accuracy, they can be used with National Hospital Care Survey data to eventually generate national estimates of substance-involved ED visits.


QuickStats: Percentage Distribution of Adult Day Services Centers, by Type of Service — National Study of Long-Term Care Providers, 2016

August 20, 2018

In 2016, four in 10 adult day services centers had services that were designed to meet both the social and medical needs of their enrolled participants equally.

Approximately 31% of adult day services centers had services to meet primarily social needs and some medical needs of participants, 16% had services to meet only social needs, 13% had services to meet primarily medical needs and some social needs, and 1% had services to meet only medical needs.

Source: National Study of Long-Term Care Providers, 2016. https://www.cdc.gov/nchs/nsltcp/index.htm.

https://www.cdc.gov/mmwr/volumes/67/wr/mm6732a8.htm


Patient Health Information Shared Electronically by Office-based Physicians: United States, 2015

August 15, 2018

Questions for Brian Ward, Health Statistician and Lead Author of “Patient Health Information Shared Electronically by Office-based Physicians: United States, 2015

Q: Why did you decide to focus on office-based physicians who electronically share patient health information (PHI) in the United States?

BW: While previous research has looked at the extent to which office-based physicians electronically shared PHI, it has not provided details as to the types of PHI that are electronically sent, received, integrated, and searched for. Therefore, we decided to expand upon this previous research by describing the types of PHI that are shared electronically.


Q: How did you collect data for this report?

BW: Data from this report were from the 2015 National Electronic Health Records Survey (NEHRS). NEHRS is a nationally representative mixed-mode survey of office-based physicians, and asks about their adoption and use of electronic health records (EHRs). The different modes (or manners) NEHRS uses to collect data are via web, mail, and telephone.


Q: What were some of the most observed types of PHI electronically shared in physician offices?

BW: Among office-based physicians who sent PHI electronically, the most commonly observed types of PHI sent were referrals (67.9%), laboratory results (67.2%), and medication lists (65.1%). Among physicians who received PHI electronically, the most commonly observed types of PHI received were laboratory results (78.8%), imaging reports (60.8%), and medication lists (54.4%).

For physicians who integrated PHI electronically, the most commonly observed types of PHI integrated were laboratory results (73.2%), imaging reports (49.8%), and hospital discharge summaries (48.7%).

Finally, a large majority of physicians who searched for PHI electronically did so for medication lists (90.2%), medication allergy lists (88.2%), and hospital discharge summaries (80.4%).


Q: Do you have trend data that is older than 2015 or is this the first this data has been published?

BW: Older NEHRS data are available, dating back to 2008 (when it was a supplement to the National Ambulatory Medical Care Survey); however, these older data are not compatible with the measures examined in this report.


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

BW: These are the first national estimates of PHI type according to the aspects of interoperability among physicians with EHR systems, and these estimates can be used as a benchmark for future studies. Combined with measures of electronic sharing of PHI by physicians, information on the specific type of PHI shared electronically among office-based physicians will assist in tracking progress outlined in the federal plan for achieving interoperability.


High-deductible Health Plan Enrollment Among Adults Aged 18-64 With Employment-based Insurance Coverage

August 9, 2018

Questions for Robin Cohen, Ph.D. and Lead Author of “High-deductible Health Plan Enrollment Among Adults Aged 18-64 With Employment-based Insurance Coverage

Q: What made you decide to put together a report about high and low deductible health plans for adults with employment-based coverage?

RC: We decided to produce an analysis focusing on high-deductible health plans (HDHPs) after observing how enrollment in HDHPs has increased over the past decade. In addition, HDHP enrollment growth has been faster among those with employment-based coverage than among those with directly-purchased coverage, so it also made sense to highlight employment-based insurance plans in this study. This report examines differences in the demographic characteristics for those with employment-based coverage by plan type.    


Q: Was there a finding in your new report that really surprised you?

RC: It was the dramatic increase in high-deductible health plan (HDHP) enrollment in recent years that really surprised us. We hadn’t expected to see such a large jump, which was most notable among those with a health savings account (HSA). The percentage of adults aged 18 to 64 enrolled in an HDHP with an HSA more than quadrupled in the past decade from 4.2% to 18.9%.


Q: What differences or similarities did you see between or among various demographic groups in this analysis?

RC: Both the differences among age groups and the lack of variance by sex in this study’s findings are notable. Among adults aged 18 to 64 with employment-based coverage, there were no differences in the type of health insurance plan by sex. Enrollment in a high-deductible health plan with a health savings account was higher among adults aged 30 to 44 than those aged 18 to 29 and 45 to 64.


Q: What is the significance of having a health savings account and not having one when you have a high-deductible health insurance plan?

RC: A health savings account (HSA) allows pretax income to be saved to help pay for the higher costs associated with a high-deductible health plan (HDHP). However, this report did not examine the association of having an HDHP — coupled with an HSA — on service use and financial burden for medical care.

 

Q: Is it a choice for Americans to have a health savings account? Can anyone have one?

 

RC: A health savings account (HSA) must be coupled with a high-deductible health plan (HDHP), but not everyone enrolled in an HDHP has an HSA. High-deductible health plans with HSAs are offered to individuals both by employers and in the direct-purchase health insurance market.

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

 

RC: I think the real take-home message in this Data Brief is the role that education and income play in health insurance coverage with these types of high-deductible health plans (HDHPs). More highly educated and affluent adults were more likely to enroll in an HDHP with a health savings account (HSA) and less likely to enroll in a traditional plan or an HDHP without an HSA — than their less educated and less affluent counterparts. The National Health Interview Survey will continue to monitor different types of private health insurance, and the survey can be used to examine further differences according to plan type.

Q: Do you have trend data on high-deductible health plans going back further than 2007?

RC: No, we don’t have earlier than 2007 trend data on high-deductible health plans (HDHPs). The National Health Interview Survey began to collect data on enrollment in HDHPs starting in 2007.


QuickStats: Age-Adjusted Death Rates from Lung Cancer by Race/Ethnicity — National Vital Statistics System, United States, 2001–2016

August 6, 2018

During 2001–2016, the lung cancer death rates for the total population declined from 55.3 to 38.3 as well as for each racial/ethnic group shown.

During 2001–2016, the death rate for the non-Hispanic black population decreased from 63.3 to 41.2, for the non-Hispanic white population from 57.7 to 41.5, and for the Hispanic population from 23.9 to 16.6.

Throughout this period, the Hispanic population had the lowest death rate.

SOURCE: CDC/National Center for Health Statistics, National Vital Statistics System, 2001–2016, Mortality. CDC Wonder online database. https://wonder.cdc.gov/ucd-icd10.html.

https://www.cdc.gov/mmwr/volumes/67/wr/mm6730a8.htm?


QuickStats: Percentage of Residential Care Community Residents with an Advance Directive by Census Division — National Study of Long-Term Care Providers, 2016

July 23, 2018

In 2016, 77.9% of residents in residential care communities had an advance directive documented in their files.

By Census division, the highest percentage (87.8%) of residents who had an advance directive were located in the Mountain division, followed by residents in East North Central (83.7%), New England (80.0%), West North Central (78.9%), Pacific (77.6%), South Atlantic (77.4%), East South Central (76.4%), Middle Atlantic (68.8%), and West South Central (64.9%).

Source: National Center for Health Statistics, National Study of Long-Term Care Providers, 2016. https://www.cdc.gov/nchs/nsltcp/index.htm.

https://www.cdc.gov/mmwr/volumes/67/wr/mm6728a7.htm