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Original Research
Diagnoses associated with the greatest years of potential life lost for in-hospital deaths in the United States, 1988e2010 B.P. Rosenbaum a,b,*, V.R. Kshettry a, M.L. Kelly a, R.J. Weil a,c,d a
Department of Neurosurgery, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA c Rose Ella Burkhardt Brain Tumor and Neuro-Oncology Center, Cleveland Clinic, Cleveland, OH, USA b
article info
abstract
Article history:
Objective: Premature mortality is a public health concern that can be quantified as years of
Received 1 May 2014
potential life lost (YPLL). Studying premature mortality from in-hospital mortality can help
Received in revised form
guide hospital initiatives and resource allocation. This paper identified the diagnosis cat-
7 November 2014
egories associated with in-hospital deaths that account for the highest YPLL and their
Accepted 12 November 2014
trends over time.
Available online 13 February 2015
Study design: Retrospective review of the Nationwide Inpatient Sample (NIS), 1988e2010. Methods: Using the NIS, YPLL on patients hospitalized in the United States from 1988 to 2010
Keywords:
was calculated. Hospitalizations were categorized by related principal diagnoses using the
Premature mortality
Healthcare Cost and Utilization Project (HCUP) single-level Clinical Classification Software
Nationwide inpatient sample
(CCS) definitions.
Public health
Results: Between 1988 and 2010, total in-hospital estimated mortality of 20,154,186 people accounted for 198,417,257 YPLL (9.84 YPLL per in-hospital mortality; 8,626,837 estimated annual mean YPLL). The ten highest YPLL diagnosis categories accounted for 51% of the overall YPLL. The liveborn disease category (i.e., in-hospital live births) was the most common principal diagnosis and accounted for the highest YPLL at 1,070,053. The septicemia category accounted for the second highest YPLL at 548,922. The highest in-hospital mortality rate (20.8%) was associated with adult respiratory failure/insufficiency/arrest. The highest estimated in-hospital annual mean deaths occurred in patients with pneumonia at 69,134. For all in-hospital mortality, the inflation adjusted total in-hospital charges per YPLL was highest for acute myocardial infarction at $9292 per YPLL. Conclusions: Using YPLL, a framework has been provided to compare the impact of premature in-hospital mortality from dissimilar diseases. The methodology and results may be used to help guide further investigation of hospital quality initiatives and resource allocation. © 2014 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.
* Corresponding author. Neurological Institute, S-40, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH 44195, USA. Tel.: þ1 011 216 444 5539; fax: þ1 011 216 636 0454. E-mail address:
[email protected] (B.P. Rosenbaum). d Current affiliation: Department of Neurosurgery, Geisinger Health System, Danville, PA 17822, USA. http://dx.doi.org/10.1016/j.puhe.2014.11.011 0033-3506/© 2014 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.
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Introduction Premature mortality, as defined by years of potential life lost (YPLL) and life expectancy, is a significant public health concern in the United States (U.S.) and beyond.1e3 Diagnoses or events that account for the highest YPLL may be an area for increased public health attention, resource allocation, or quality improvement. Several groups evaluated aspects of premature mortality in the U.S. from a socio-economic perspective, aside from YPLL. Cullen et al. investigated the variability in premature mortality based on geography and race.4 The authors found that the probability of survival to age seventy varies between the highest and lowest deciles of the healthiest U.S. counties. In addition, within counties, significant disparities in probability of survival exist between black and white race and by gender. Dowd et al. investigated mortality variability according to income in the United States.5 The authors found no reliable relationship between mortality and income throughout a distribution of incomes. Krieger et al. examined premature mortality data in the U.S. between 1960 and 2000. The authors demonstrated that health inequities do not change predictably as population health improves.6 YPLL has been specifically studied in several populations including unintentional child and adolescent injuries,7,8 traumatic populations,9 and alcohol-related deaths.10 Most recently, the Global Burden of Disease Study 2010 (GBD 2010) analyzed causes of death in 1990 and 2010 on a large scale in
multiple countries, including YPLL (termed years of life lost due to premature mortality [YLL]).11,12 A variety of related analyses, including by the U.S. Burden of Disease Collaborators, were since published.13e16 It has been noted that none of the prior studies are evaluated the scale of premature mortality limited to U.S. in-hospital mortality using the YPLL framework. According to the National Center for Health Statistics (NCHS), in 2000 and 2010, approximately one-third of all U.S. deaths occurred in shortstay, general hospitals.17 A longitudinal study focussing on inhospital mortality may provide important information for quality improvement efforts. In addition, the YPLL framework can help quantify lost potential beyond simple mortality rate. The principal diagnoses for inpatient hospitalizations in the U.S. was investigated that led to the highest YPLL due to in-hospital mortality. All the available data in Nationwide Inpatient Sample (NIS) from 1988 to 2010 were utilized to include a large, longitudinal U.S. population sample to compare diagnosis categories.
Methods The Nationwide Inpatient Sample (NIS) All inpatient hospitalizations from 1988 to 2010 were examined using hospital discharge data from the Nationwide Inpatient Sample (NIS), Healthcare Cost and Utilization Project
Fig. 1 e Frequency histogram of estimated YPLL for NIS hospitalizations between 1988 and 2010. *Estimate created using NIS variable DISCWT. Non-integer YPLL values placed in integer histogram bins using standard rounding logic. YPLL of 0 is not pictured with an overall estimated number of in-hospital deaths of 9,502,889.
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Table 1 e Ten principal diagnosis CCS categories causing the highest YPLL. Principal diagnosis CCS category [Category#]
Liveborn [218] Septicemia (except in labor) [2] Acute cerebrovascular disease [109] Respiratory failure/ insufficiency/arrest (adult) [131] Secondary malignancies [42] Pneumonia (except that caused by tuberculosis or sexually transmitted disease) [122] Intracranial injury [233] Acute myocardial infarction [100] HIV infection [5] Cancer of bronchus; lung [19]
Rank
Estimated Prevalence Estimated annual In-hospital Inflation adjusted annual mean YPLL mean in-hospital Mortality rate hospital charges for in-hospital death deaths per YPLL for (% total YPLL) in-hospital deathsa
1 2 3
1,070,053 (12.4) 548,922 (6.4) 450,025 (5.2)
11.0% 1.2% 1.5%
14,026 72,133 64,362
0.4% 16.2% 11.2%
$ 596 $ 6984 $ 4751
4
420,043 (4.9)
0.7%
50,189
20.8%
$ 7181
5 6
415,342 (4.8) 413,030 (4.8)
0.7% 3.2%
34,074 69,134
13.0% 5.7%
$ 2898 $ 6957
7 8
357,446 (4.1) 276,804 (3.2)
0.5% 1.9%
14,736 60,168
8.3% 8.6%
$ 2422 $ 9292
9 10
219,574 (2.5) 218,127 (2.5)
0.2% 0.4%
6473 24,028
9.8% 15.4%
$ 2057 $ 3861
CCS ¼ clinical classification software. HIV ¼ human immunodeficiency virus. YPLL ¼ years of potential life lost. a In hospitalizations where the patient died, this represents the total inflation adjusted total in-hospital charges per total YPLL.
(HCUP), Agency for Healthcare Research and Quality (Rockville, MD). The NIS is the largest publicly available, all-payer inpatient care database of non-federal hospitals in the U.S. The NIS contains data from approximately five to eight million hospital stays each year and represents an approximately twenty-percent stratified sample of U.S. community (nonfederal) hospitals. NIS data are represented by diagnosis and procedure codes from the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM). Hospitalizations were categorized by related principal diagnoses (NIS data element DX1) using the HCUP single-level Clinical Classification Software (CCS) diagnosis definitions, updated in 2013. Constituent ICD-9-CM codes in each CCS category are readily available (see http://www.hcup-us.ahrq.gov/ toolssoftware/ccs/ccs.jsp). CCS diagnosis categories are herein referred to as diagnosis categories. Hospitalizations with a missing or invalid age were excluded (0.08%). Total hospitalization charges were corrected for inflation to the 2010 value using the United States Bureau of Labor Statistics Consumer Price Index Inflation Calculator.18 In addition, the NIS discharge weight was used to extrapolate to estimates of annual U.S. volume (NIS variable DISCWT). Corrected discharge weight data for years 1988e1997, as suggested and published by HCUP, were used (see http://www. hcup-us.ahrq.gov/db/nation/nis/trendwghts.jsp).
Years of potential life lost (YPLL) YPLL is a measure of premature mortality and calculates the number of years a person would have lived had he or she not died prematurely. YPLL was calculated for each individual hospitalization by determining the age of patients with inhospital mortality and subtracting from a reference age. The reference age was determined for each hospitalization by
year and gender according to the National Vital Statistics Reports: Deaths: Final Data for 2010 life expectancy.19 If the patient's age at the time of death was greater than the reference age, YPLL was set to zero. Other methods exist to calculate YPLL, such as a single global reference age.20 Given the long time span in the study, an annual, gender-based reference age was chosen given the rise in U.S. life expectancy over time. No age-weighting or discounting methods were employed.
Results Between 1988 and 2010, the NIS contains data on 163,849,665 inpatient hospitalizations with valid ages. From those hospitalizations, the authors identified principal diagnoses belonging to 268 diagnosis categories. During the entire time period, total in-hospital mortality of an estimated 20,154,186 people resulted in an estimated 198,417,257 YPLL or 9.84 YPLL per in-hospital mortality. The annual mean was 8,626,837 YPLL. Fig. 1 illustrates the frequency histogram of estimated YPLL between 1988 and 2010. Table 1 illustrates the principal diagnosis categories accounting for the ten highest YPLL between 1988 and 2010. Combined, the ten highest YPLL principal diagnoses accounted for 51% of the overall YPLL between 1988 and 2010. The absolute and relative prevalence of each diagnosis category appears relatively stable, with the exception of septicemia and adult respiratory failure/insufficiency/ arrest, which have been increasing since 2002 (Fig. 2A and B). The mortality rate for the top five diagnoses has also been decreasing over the study period, with the exception of septicemia (Fig. 2C). In addition, the estimated YPLL for septicemia over the same time period has increased more than three-fold since 2002 (Fig. 2D).
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Fig. 2 e Principal diagnosis CCS category according to: A) Prevalence in total hospitalizations B) Relative prevalence according to 1988 to 2010 maxima. *The maximum prevalence (100%) was determined for each CCS diagnosis category during the 1988 to 2010 period. All other annual prevalence data, by CCS diagnosis category, are referenced to that maximum C) For all hospitalizations within each CCS diagnosis category, the in-hospital mortality rate D) Annual estimated YPLL. Estimate created using NIS variable DISCWT.
The liveborn diagnosis category (i.e., in-hospital live births) accounted for the highest YPLL (1,070,053) and overall prevalence (11.0%) (Table 1). Despite a very low inhospital mortality rate (0.4%), the liveborn YPLL burden resulted from high prevalence and very low mean age at inhospital death (0 years). The septicemia category had the second highest estimated annual mean YPLL at 548,922 and second highest in-hospital mortality rate (16.2%). Pneumonia had the second highest overall prevalence at 3.2%. Even though pneumonia had the second lowest in-hospital mortality rate (5.7%) of the top ten diagnosis categories, given its prevalence it resulted in the highest estimated number of in-hospital annual mean deaths at 69,134. Adult respiratory failure/insufficiency/arrest had the highest inhospital mortality rate at 20.8%. For all in-hospital mortality, the inflation adjusted total in-hospital charges per YPLL was highest for acute myocardial infarction ($9292 per YPLL) and lowest for liveborn ($596 per YPLL) diagnosis categories. For the top five diagnosis categories, inflation adjusted total in-hospital charges per YPLL increased over the study period (Fig. 3).
Table 2 illustrates the mean length of stay, age, and inhospital death along with proportion of male gender for the principal diagnosis categories accounting for the ten highest YPLL between 1988 and 2010. Of those categories, the highest mean length of stay of in-hospital deaths was 11.0 days for secondary malignancies. Length of stay for all categories declined steadily and relatively uniformly over the time period, except septicemia which rose steadily after a relative minimum was reached in 1998 and the liveborn category, which was relatively constant. Excluding the liveborn category, the highest and lowest mean age of in-hospital deaths was 73.7 years for septicemia and 41.2 years for HIV infection, respectively. The highest mean YPLL per in-hospital death was 76.3 for the liveborn diagnosis category and lowest at 4.6 for the acute myocardial infarction diagnosis category.
Discussion Despite differing methodologies, the diagnosis categories causing the ten highest YPLL from in-hospital mortality from
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Fig. 3 e For in-hospital mortality NIS records, annual inflation adjusted total in-hospital charges per YPLL. 1988 to 2010 overlap with the U.S. Burden of Disease publication utilizing the GBD 2010 data (top five: ischaemic heart disease, lung cancer, stroke, chronic obstructive pulmonary disease, and road injury).13 Of note, GBD 2010 used overall mortality as opposed to the present analysis of in-hospital mortality. The combined analyses may provide perspective on the most important diagnosis categories to target in public health efforts. The liveborn diagnosis category (i.e., in-hospital live births) comprises the highest YPLL over the time period studied. Because no age discounting methodologies were used in calculating YPLL, liveborn in-hospital deaths are expected to contribute considerably to YPLL (overall, 12.4%). The authors did not limit the methods to adult patients in the NIS database, rather they urge the reader to interpret the liveborn data in the overall context of inpatient hospitalizations. From the in-hospital perspective, it may be more difficult to improve YPLL for liveborn patients than in other diseases, such as acute myocardial infarction. It is likely that the strategies for improving YPLL in various diseases will differ considerably. For example, gains in YPLL for liveborn patients may be greater with resources directed toward out-of-hospital prenatal care, whereas gains in YPLL in other conditions, such as septicemia, might be better achieved with in-hospital preventative measures. The data provide a reference benchmark of in-hospital premature mortality as a public health burden. Of note, in data not presented, when limiting the analysis to adult patients (age eighteen years or greater), the diagnosis categories aside from the liveborn category accounting for the highest YPLL are unchanged. As reported, the diagnosis categories causing the ten highest YPLL over the 1988 to 2010 time period account for 51% of the overall YPLL calculated from in-hospital mortality. It was given that 268 diagnosis categories has been identified in the data set, a disproportionate contribution to YPLL exists in
several diagnosis categories over others. As a result, the focus of public health resource allocation may be biased toward the more common diagnosis categories. Fig. 2B and D illustrates a remarkable increase in the relative prevalence and estimated YPLL of septicemia since 2002. Others have investigated similar trends in the increase in sepsis and in-hospital mortality.21e23 The present analysis highlights the importance of focusing efforts on improving outcomes in diagnosis categories contributing the most to YPLL such as septicemia, as others have done.24 Interestingly, the inflation-adjusted total charges per YPLL for septicemia is one of the highest (overall mean $6984; Fig. 3) and trending upwards over the same time period. For a selected disease or condition, further investigation is needed to determine whether increased hospital expenditures (estimated by total in-hospital charges) provide more or less benefit than less costly interventions or preventative measures.25e35 Inflation adjusted in-hospital charges per YPLL may be a challenging quantity to consider at first glance. By comparing the value between diagnosis categories, a frame of reference for a relative amount of money consumed (by way of total inhospital charges) per YPLL is provided. Charge data are available in the NIS and, as opposed to costs, they do limit the absolute interpretation of the results, but still provide an appropriate relative framework. A diagnosis category that causes less overall YPLL with a higher in-hospital charge per YPLL may be a consideration for redistribution of resources. The authors provide the data as another way to frame possible constraints surrounding resource distribution. Moving forward, the reader is challenged to consider YPLL, based on in-hospital mortality and beyond, to help determine how economic and health care resources are utilized. From Table 1, it is clear that lung cancer is a significant contributor to YPLL but public health efforts aimed at reducing premature mortality should be more heavily focused on preventive,
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Male gender (in-hospital deaths)
56% 46% 44%
49%
45% 52%
67% 50% 75% 60%
51% 46% 46%
46%
44% 49%
64% 60% 70% 56%
smoking cessation, and prehospital care than in-hospital care. Likewise, septicemia and pneumonia are conditions where more direct in-hospital resource allocation is likely to have a positive effect on YPLL than preventative or prehospital care. Resource allocation must consider the preventative, prehospital, in-hospital, postacute, and research funding directed at diseases. The National Institutes of Health (NIH) publishes data on annual categorical disease spending.36 Along with charges per YPLL (Table 1), such research spending can help frame a discussion of which diseases may receive too little or require more (or less) funding for in-hospital care or otherwise.
24.3 4.6 33.9 9.1 54.0 76.0 41.2 68.3 CCS ¼ clinical classification software. HIV ¼ human immunodeficiency virus. YPLL ¼ years of potential life lost.
5.9 5.8 14.3 9.6 6.9 6.1 9.6 8.4
43.8 67.7 39.8 67.3
12.2 6.0 65.9 75.7 11.0 9.8 8.2 6.4
64.1 58.9
8.4 71.5 10.1 10.6
65.7
7.2 7.9 7.5 3.1 8.9 7.7
0 66.0 71.5
0 73.7 73.6
76.3 7.6 7.0
Limitations
Liveborn [218] Septicemia (except in labor) [2] Acute cerebrovascular disease [109] Respiratory failure/insufficiency/ arrest (adult) [131] Secondary malignancies [42] Pneumonia (except that caused by tuberculosis or sexually transmitted disease) [122] Intracranial injury [233] Acute myocardial infarction [100] HIV infection [5] Cancer of bronchus; lung [19]
Mean age of in-hospital deaths (years) Overall mean age (years) Mean length of stay of in-hospital deaths (days) Overall mean length of stay (days) Principal diagnosis CCS category [Category#]
Table 2 e Length of stay, age, gender, and in-hospital death for the top ten principal diagnosis CCS categories.
Mean YPLL per in-hospital death
Male gender
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There are limitations to this investigation. Traditional limits of population-based, retrospective data sets relying on codified billing data exist. The annual variation in prevalence of each principal diagnosis may be due, in part, to documentation, coding, and/or billing practices or incentives that have changed over time. Incentives to document and code higher value diagnoses may bias the principal diagnoses coded over time. In addition, the current analysis does not consider other epidemiologic markers of disease severity and impact on public health, such as healthy life expectancy (HALE), years lived with disability (YLD), disability-adjusted life years (DALY; calculated as the sum of YPLL and YLD), or potential gains in life expectancy (PGLE). HALE, YLD, and DALY incorporate non-fatal measures using disability weighting. In the GBD 2010 study, 68.8% of DALYs were contributed by YPLL.14 In addition, PGLE requires selected disease states or population subsets to be disregarded, potentially biasing an analysis focused on overall comparisons. Examples of analyses using such markers, some including assumptions and value-based weights, are readily available elsewhere and outside the scope of the current work.13e15,37,38 This paper focused on YPLL given the overall perspective provided when comparing multiple disease states over time. Choosing a common reference age, even by year and gender, is another limitation of the current study. Diseasespecific life expectancy is available for some conditions. Unfortunately, without complete, rigorous, and disease-specific life expectancy data for all diseases obtained in the current analysis, establishing accurate, meaningful, and comparable YPLL across diseases (via diagnosis categories) is impossible to calculate. Moreover, submitting that a disease inherently limits life expectancy based on historical data, to some degree, relinquishes the potential for diseases where more resources (e.g., research, hospital quality improvement, or public health) could be devoted to increase the life expectancy despite historical trends. Finally, the current analysis focused solely on in-hospital mortality, not overall mortality. The in-hospital limitation is a result of utilizing the NIS database as the data source. Despite the limitation, the NIS is a large, robust data source that reflects the trends in U.S. hospitalizations over a long time period. In addition, according to the NCHS, in 2000 and 2010, approximately one-third of all U.S. deaths occurred in short-stay, general hospitals.17 The actual national hospital-
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related mortality rate is likely higher if dying patients, fully treated in hospital but who are transferred out-of-hospital, are considered.
Conclusions YPLL provides a method to compare the effects of dissimilar diseases on public health and longevity of life. The current work highlights the most common principal diagnoses, by way of CCS diagnosis categories, seen in hospitalized patients accounting for the highest in-hospital mortality YPLL. Such principal diagnoses and their associated prevalence, death rates, lengths of stay, mean ages, and total in-hospital charges may be targets for increased investigation or resource allocation.
Author statements Acknowledgements RJW was supported in part by the Melvin Burkhardt chair in neurosurgical oncology, and by the Karen Colina Wilson research endowment within the Brain Tumor and Neurooncology Center at the Cleveland Clinic. The funders of this philanthropic support had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Ethical approval None sought.
Funding The authors report no personal or institutional financial interest in drugs, materials, or devices described in the manuscript.
Competing interests All authors confirm that they have no conflicts to report.
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