Journal of Investigative and Clinical Dentistry (2015), 6, 1–8
ORIGINAL ARTICLE Community Dentistry
Obesity and its association with comorbidities and hospital charges among patients hospitalized for dental conditions Veerasathpurush Allareddy1, Sankeerth Rampa2, Sindhura Anamali3, Min Kyeong Lee4, Veerajalandhar Allareddy5 & Romesh P. Nalliah6 1 2 3 4 5 6
Department Department Department Department Department Department
of of of of of of
Orthodontics, College of Dentistry, The University of Iowa, Iowa City, IA, USA Health Policy and Services Administration, University of Nebraska Medical Center, Omaha, NE, USA Preventive Dentistry, College of Dentistry, The University of Iowa, Iowa City, IA, USA Developmental Biology, Harvard School of Dental Medicine, Boston, MA, USA Pediatric Critical Care, Case Western Reserve University School of Medicine, Cleveland, OH, USA Global Health, Harvard School of Dental Medicine, Boston, MA, USA
Keywords comorbid burden, dental condition, hospitalization, Nationwide Inpatient Sample, obesity. Correspondence Dr Veerasathpurush Allareddy, Department of Orthodontics, College of Dentistry, The University of Iowa, 801 Newton Road, Iowa City, IA 52242, USA. Tel: +1-319-353-5806 Email: [email protected] Received 4 March 2014; accepted 15 November 2014. doi: 10.1111/jicd.12146
Abstract Aim: The aim of the present study was to examine the impact of obesity on hospitalization charges and comorbid burden following hospitalization due to dental conditions. Methods: The Nationwide Inpatient Sample for 2004–2010 was used. All hospitalizations due to dental conditions were selected. The prevalence of obesity was estimated among these hospitalizations. Multivariable linear regression models were used to examine the impact of obesity on outcomes. Results: A total of 11 965 hospitalizations were attributed to dental conditions; 5.6% were related to obesity. The proportion of those who were obese increased over the study period (ranging from 3.7% in 2004 to 7.3% in 2010). The mean age of those who were obese was 45 years (compared to 38.7 years for those who were not obese). Close to 41% of those who were obese were males (compared to 51% who were not obese). Whites comprised 62.4% of those who were obese (compared to 59.2% of those who were not obese). Those who were obese had a higher comorbid burden compared to those who were not obese (83.5% of those who were obese had at least one comorbid condition, whereas 56.4% of those who were not obese had at least one comorbid condition). Those who were obese had higher hospitalization charges ($US2225 more, P = 0.0001). Conclusions: Obesity is associated with high comorbid burden and hospital charges among patients hospitalized due to dental conditions.
Introduction Obesity is viewed as a major public health issue in the USA.1 The past few decades have witnessed an increase in the proportion of people that are obese.2,3 Close to 36% of US are obese.4 Several prior studies have documented the poor outcomes associated with obesity.5–8 These include high hospital costs, slower recovery time following hospitalizations, increased length of stay in hospital, ª 2015 Wiley Publishing Asia Pty Ltd
and a higher incidence of complication rates.5–8 It has been shown that obesity is related to a reduced quality of life and a higher prevalence of chronic pain comorbidities.9 Prior research estimates that between 280 000 and 325 000 deaths are from a cause related to obesity each year in the USA.10 The relative impact of obesity on hospitalization outcomes following admissions for several surgical procedures and acute illness has been examined.5–8 There is 1
V. Allareddy et al.
Obesity and its comorbidities
paucity of published data on the impact of obesity on outcomes following hospitalization due to dental conditions. We hypothesize that obese individuals tend to be associated with excess resource utilization in hospitals, and consequently, higher hospitalization charges when compared to non-obese individuals. The objective of the current study was to examine hospitalization charges related to obesity in those hospitalized primarily due to routine dental conditions, such as dental caries, pulpal and periapical lesions, gingival/periodontal conditions, and mouth cellulitis/abscess/Ludwig’s angina.
Methods Description of database The Nationwide Inpatient Sample (NIS) for 2004–2010 was used for the current study. The NIS is a 20% stratified sample of all acute care hospitals in the USA.11 Each selected hospital provides information on all hospitalizations occurring in the selected years. Each hospitalization in the NIS database is assigned a discharge weight that can be used to provide nationally-representative estimates of all hospitalizations occurring in the USA in the selected years. The NIS database is part of the Healthcare Cost and Utilization Project (HCUP) group of datasets, which also include the State Inpatient Database, Nationwide Emergency Department Sample, and State Ambulatory Databases. The NIS–HCUP is sponsored by the Agency for Healthcare Research and Quality (AHRQ). Each hospitalization unit in the NIS database has information on age, sex, race, type of admission, primary reason for admission, presence of comorbid conditions (obtained from NIS disease severity measurements), procedures performed during hospitalization, disposition status, length of stay in hospital, hospitalization charges, and hospital attributes (including teaching status, bed size, and hospital region). Institutional review board approval One of the authors completed the data-user agreement with HCUP–AHRQ and obtained the datasets. According to this data-user agreement, any individual cell count less than or equal to 10 cannot be presented to preserve patient confidentiality. We used the term “discharge information suppressed” in the Tables whenever there were such low numbers. The current study was exempt from institutional review board approval. Selection of hospitalizations According to the NIS documentation, the primary diagnosis field represents the reason for hospitalization. All 2
hospitalizations with a primary diagnosis of dental caries, pulp/periapical lesions, gingival lesions, periodontal lesions, and mouth cellulitis/abscess/Ludwig’s angina were selected. During these hospitalizations, obesity was identified by using the NIS disease severity files.12 Comorbid burden A total of 29 comorbid conditions identified according to the NIS disease severity files were examined for each hospitalization, and a comorbid burden severity score was computed. The NIS provides information on 29 chronic comorbid conditions, and these are identified by using a complex algorithm. Based on the comorbidity algorithm report, those with International Classification of Diseases Ninth Revision, Clinical Modification (ICD-9-CM) codes for obesity/overweight (ICD-9-CM codes 278.0, 278.00, 278.01, 278.03) and high body mass index (ICD-9-CM codes V85.30–V85.39, V85.41–V85.45, and V85.54) are categorized as being obese.13 Several prior reports have validated this algorithm.14–18 Hospitalization charges The outcome variable of interest was hospitalization charges. All hospitalization charges across the study period were inflation adjusted to 2010 $US values based on the Bureau of Labor Statistics hospital inpatient care inflation rates.19 Statistical approach Two multivariable linear regression models were used to examine comorbid burden and hospitalization charges. The primary independent variable of interest in the current study was obesity. This was used as a binomial variable (presence or absence). Other independent variables whose effects were adjusted included age, sex, race, type of dental condition that resulted in hospitalization, comorbid burden (for hospitalization charges model), year of hospitalization, hospital teaching status, and hospital region. The simultaneous association of all independent variables and outcome variable (separate models for comorbid burden and hospitalization charges) was examined by multivariable linear regression model. Because hospitalization charges were highly skewed, it was log transformed. The log-transformed value was used as the dependent variable in the regression model. For each level of independent variable, the estimates of change in hospitalization charges from the mean were computed. The log estimates were exponentiated and retransformed to a $US value. Taylor linearization method was used to compute the estimates in the multivariable linear regression models. The effects of clustering of ª 2015 Wiley Publishing Asia Pty Ltd
V. Allareddy et al.
outcomes within hospitals were adjusted in the regression model. The complex sampling strata and sampling frame of the NIS database was taken into consideration while fitting the regression model. Each individual hospitalization was the unit of analysis. All statistical tests were two sided, and a P-value of
ORIGINAL ARTICLE Community Dentistry
Obesity and its association with comorbidities and hospital charges among patients hospitalized for dental conditions Veerasathpurush Allareddy1, Sankeerth Rampa2, Sindhura Anamali3, Min Kyeong Lee4, Veerajalandhar Allareddy5 & Romesh P. Nalliah6 1 2 3 4 5 6
Department Department Department Department Department Department
of of of of of of
Orthodontics, College of Dentistry, The University of Iowa, Iowa City, IA, USA Health Policy and Services Administration, University of Nebraska Medical Center, Omaha, NE, USA Preventive Dentistry, College of Dentistry, The University of Iowa, Iowa City, IA, USA Developmental Biology, Harvard School of Dental Medicine, Boston, MA, USA Pediatric Critical Care, Case Western Reserve University School of Medicine, Cleveland, OH, USA Global Health, Harvard School of Dental Medicine, Boston, MA, USA
Keywords comorbid burden, dental condition, hospitalization, Nationwide Inpatient Sample, obesity. Correspondence Dr Veerasathpurush Allareddy, Department of Orthodontics, College of Dentistry, The University of Iowa, 801 Newton Road, Iowa City, IA 52242, USA. Tel: +1-319-353-5806 Email: [email protected] Received 4 March 2014; accepted 15 November 2014. doi: 10.1111/jicd.12146
Abstract Aim: The aim of the present study was to examine the impact of obesity on hospitalization charges and comorbid burden following hospitalization due to dental conditions. Methods: The Nationwide Inpatient Sample for 2004–2010 was used. All hospitalizations due to dental conditions were selected. The prevalence of obesity was estimated among these hospitalizations. Multivariable linear regression models were used to examine the impact of obesity on outcomes. Results: A total of 11 965 hospitalizations were attributed to dental conditions; 5.6% were related to obesity. The proportion of those who were obese increased over the study period (ranging from 3.7% in 2004 to 7.3% in 2010). The mean age of those who were obese was 45 years (compared to 38.7 years for those who were not obese). Close to 41% of those who were obese were males (compared to 51% who were not obese). Whites comprised 62.4% of those who were obese (compared to 59.2% of those who were not obese). Those who were obese had a higher comorbid burden compared to those who were not obese (83.5% of those who were obese had at least one comorbid condition, whereas 56.4% of those who were not obese had at least one comorbid condition). Those who were obese had higher hospitalization charges ($US2225 more, P = 0.0001). Conclusions: Obesity is associated with high comorbid burden and hospital charges among patients hospitalized due to dental conditions.
Introduction Obesity is viewed as a major public health issue in the USA.1 The past few decades have witnessed an increase in the proportion of people that are obese.2,3 Close to 36% of US are obese.4 Several prior studies have documented the poor outcomes associated with obesity.5–8 These include high hospital costs, slower recovery time following hospitalizations, increased length of stay in hospital, ª 2015 Wiley Publishing Asia Pty Ltd
and a higher incidence of complication rates.5–8 It has been shown that obesity is related to a reduced quality of life and a higher prevalence of chronic pain comorbidities.9 Prior research estimates that between 280 000 and 325 000 deaths are from a cause related to obesity each year in the USA.10 The relative impact of obesity on hospitalization outcomes following admissions for several surgical procedures and acute illness has been examined.5–8 There is 1
V. Allareddy et al.
Obesity and its comorbidities
paucity of published data on the impact of obesity on outcomes following hospitalization due to dental conditions. We hypothesize that obese individuals tend to be associated with excess resource utilization in hospitals, and consequently, higher hospitalization charges when compared to non-obese individuals. The objective of the current study was to examine hospitalization charges related to obesity in those hospitalized primarily due to routine dental conditions, such as dental caries, pulpal and periapical lesions, gingival/periodontal conditions, and mouth cellulitis/abscess/Ludwig’s angina.
Methods Description of database The Nationwide Inpatient Sample (NIS) for 2004–2010 was used for the current study. The NIS is a 20% stratified sample of all acute care hospitals in the USA.11 Each selected hospital provides information on all hospitalizations occurring in the selected years. Each hospitalization in the NIS database is assigned a discharge weight that can be used to provide nationally-representative estimates of all hospitalizations occurring in the USA in the selected years. The NIS database is part of the Healthcare Cost and Utilization Project (HCUP) group of datasets, which also include the State Inpatient Database, Nationwide Emergency Department Sample, and State Ambulatory Databases. The NIS–HCUP is sponsored by the Agency for Healthcare Research and Quality (AHRQ). Each hospitalization unit in the NIS database has information on age, sex, race, type of admission, primary reason for admission, presence of comorbid conditions (obtained from NIS disease severity measurements), procedures performed during hospitalization, disposition status, length of stay in hospital, hospitalization charges, and hospital attributes (including teaching status, bed size, and hospital region). Institutional review board approval One of the authors completed the data-user agreement with HCUP–AHRQ and obtained the datasets. According to this data-user agreement, any individual cell count less than or equal to 10 cannot be presented to preserve patient confidentiality. We used the term “discharge information suppressed” in the Tables whenever there were such low numbers. The current study was exempt from institutional review board approval. Selection of hospitalizations According to the NIS documentation, the primary diagnosis field represents the reason for hospitalization. All 2
hospitalizations with a primary diagnosis of dental caries, pulp/periapical lesions, gingival lesions, periodontal lesions, and mouth cellulitis/abscess/Ludwig’s angina were selected. During these hospitalizations, obesity was identified by using the NIS disease severity files.12 Comorbid burden A total of 29 comorbid conditions identified according to the NIS disease severity files were examined for each hospitalization, and a comorbid burden severity score was computed. The NIS provides information on 29 chronic comorbid conditions, and these are identified by using a complex algorithm. Based on the comorbidity algorithm report, those with International Classification of Diseases Ninth Revision, Clinical Modification (ICD-9-CM) codes for obesity/overweight (ICD-9-CM codes 278.0, 278.00, 278.01, 278.03) and high body mass index (ICD-9-CM codes V85.30–V85.39, V85.41–V85.45, and V85.54) are categorized as being obese.13 Several prior reports have validated this algorithm.14–18 Hospitalization charges The outcome variable of interest was hospitalization charges. All hospitalization charges across the study period were inflation adjusted to 2010 $US values based on the Bureau of Labor Statistics hospital inpatient care inflation rates.19 Statistical approach Two multivariable linear regression models were used to examine comorbid burden and hospitalization charges. The primary independent variable of interest in the current study was obesity. This was used as a binomial variable (presence or absence). Other independent variables whose effects were adjusted included age, sex, race, type of dental condition that resulted in hospitalization, comorbid burden (for hospitalization charges model), year of hospitalization, hospital teaching status, and hospital region. The simultaneous association of all independent variables and outcome variable (separate models for comorbid burden and hospitalization charges) was examined by multivariable linear regression model. Because hospitalization charges were highly skewed, it was log transformed. The log-transformed value was used as the dependent variable in the regression model. For each level of independent variable, the estimates of change in hospitalization charges from the mean were computed. The log estimates were exponentiated and retransformed to a $US value. Taylor linearization method was used to compute the estimates in the multivariable linear regression models. The effects of clustering of ª 2015 Wiley Publishing Asia Pty Ltd
V. Allareddy et al.
outcomes within hospitals were adjusted in the regression model. The complex sampling strata and sampling frame of the NIS database was taken into consideration while fitting the regression model. Each individual hospitalization was the unit of analysis. All statistical tests were two sided, and a P-value of
