Comorbidity in Older Patients Hospitalized with Cancer in ...

International Journal of Environmental Research and Public Health

Article

Comorbidity in Older Patients Hospitalized with Cancer in Northeast China based on Hospital Discharge Data

Xiao-Min Mu 1, Wei Wang 1, Fang-Yi Wu 2, Yu-Ying Jiang 1, Ling-ling Ma 1 and Jia Feng 1,* 1 Department of Medical Informatics, School of Public Health, Jilin University, Changchun 130021, China; muxm18@mails.jlu. (X.-M.M.); w_w@jlu. (W.W.); yyjiang18@mails.jlu. (Y.-Y.J.); mall19@mails.jlu. (L.-L.M.) 2 Information Research Center of Military Sciences, Academy of Military Sciences, Beijing 100039, China; wufy16@mails.jlu. * Correspondence: fengjia@jlu.

Received: 27 September 2020; Accepted: 27 October 2020; Published: 31 October 2020

Abstract: Patients with cancer often carry the dual burden of the cancer itself and other co-existing medical conditions. The problems associated with comorbidities among elderly cancer patients are more prominent compared with younger patients. This study aimed to identify common cancer-related comorbidities in elderly patients through routinely collected hospital discharge data and to use association rules to analyze the prevalence and patterns of these comorbidities in elderly cancer patients at different cancer sites. We collected the discharge data of 80,574 patients who were diagnosed with cancers of the esophagus, stomach, colorectum, liver, lung, female breast, cervix, and thyroid between 2016 and 2018. The same number of non-cancer patients were randomly selected as the control group and matched with the case group by age and gender. The results showed that cardiovascular diseases, metabolic diseases, digestive diseases, and anemia were the most common comorbidities in elderly patients with cancer. The comorbidity patterns differed based on the cancer site. Elderly patients with liver cancer had the highest risk of comorbidities, followed by lung cancer, gastrointestinal cancer, thyroid cancer, and reproductive cancer. For example, elderly patients with liver cancer had the higher risk of the comorbid infectious and digestive diseases, whereas patients with lung cancer had the higher risk of the comorbid respiratory system diseases. The findings can assist clinicians in diagnosing comorbidities and contribute to the allocation of medical resources.

Keywords: cancer; comorbidity; association rule; prevalence ratio

1. Introduction

The incidence of cancer (and cancer mortality rates) is rising rapidly worldwide due to the aging population [1]. Cancer is the leading cause of death and produces a heavy disease burden in China [2]. It has been reported that more than one-half of patients with cancer >65 years of age often carry the dual burden of cancer itself and other co-existing chronic conditions [3,4]. Individuals have multiple medical conditions referred to as comorbidity [5]. Comorbidities potentially affect the stages of the cancer spectrum from diagnosis, through treatment, to outcome [6]. Patients with comorbidity are substantially more likely to experience complicate treatment, increased cost of care, decreased quality of life and lower survival probabilities than those without comorbidity [3,7]. Therefore, understanding cancer comorbidities can help to comprehend the pathogenesis of comorbidities, promote the prevention and control of comorbidities, and assist the health administration department to rationally evaluate the status of comorbidities to better optimize the distribution and utilization of medical resources.

Int. J. Environ. Res. Public Health 2020, 17, 8028; doi:10.3390/ijerph17218028

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Many population-based surveys and clinical studies have attempted to explore the prevalence of comorbidities and the impact of comorbidities on health care or survival outcomes, such as identifying the comorbidity patterns of mental diseases [8,9], and assessing the impact of comorbidity on health care or outcomes of chronic diseases in the elderly [10,11]. Researchers have attempted to determine the risk of comorbidities in cancer patients [3,12], focusing on specific disorders related to cancers, such as cardiovascular and cerebrovascular diseases [13,14], hypertension [15,16], other associated complications, and/or specific populations with cancer, such as the elderly [17,18]. The overall pattern of cancer comorbidities has not been established to date because survey data are relatively small in size, usually focus on specific disorders, and sometimes include inadequate information on diagnosis and treatment. Therefore, there is a need for comprehensive information from large long-term datasets to improve our understanding of the prevalence of cancer-related comorbidities and analyze the comorbidity patterns.

With the development and advances in information technology, the emergence of electronic medical record (EMR) systems has made it possible to use clinical information for disease relationship mining. Hospital discharge data, as a type of administrative data derived from EMR, are becoming one of the available data sources for assessing disease comorbidities [19,20], with the discharge diagnosis codes assigned by trained physicians following standard guidelines. Therefore, the use of EMR data for comorbidity analysis has gradually attracted the attention of researchers, such as in identifying important comorbidities among cancer [21], analyzing the impact of comorbidities on cancer care and outcomes [22], and assessing comorbidities of substance abuse [23,24]. Most previous studies used statistical analysis methods, such as relative risk and -correlation, to mine comorbidity patterns. However, both of these measures mainly consider pairwise relationships, which cannot demonstrate all of the comorbid associations [25]. To completely detect the co-occurrence relationships, association rule mining [26] (ARM) was used in the current study to identify the comorbidity patterns of cancer patients. ARM is an important data mining technology that is used to mine the association between valuable data items from a large amount of data [25,27]. ARM makes it possible to analyze the association between not only two diseases, but also among three or more comorbidities that can be calculated from existing statistics.

In this study, we used hospital discharge data derived from 16 tertiary hospitals in northeast China between 2016 and 2018 to identify important cancer-related comorbidities and estimate the prevalence and patterns of these comorbidities among elderly cancer patients with diverse cancer sites.

2. Materials and Methods

2.1. Study Population and Data Source

The 10th revision of the International Classification of Diseases (ICD-10) [28] is used in the public hospital diagnosis system of Jilin Province. All categories of comorbidities in the current study followed the original categories of the ICD-10 system. The selection of cancers for this study included cancers of the esophagus (ICD 10 code C15), stomach (ICD 10 code C16), colorectum (ICD 10 codes C18-C20), liver (ICD 10 code C22), lung (ICD 10 code C34), female breast (ICD 10 code C50), cervix (ICD 10 code C53), and thyroid (ICD 10 code C73), which are the predominant malignancies based on incidence and mortality [29]. Jilin Province is located in northeast China and had 27.04 million permanent residents in 2018. Because of environmental factors and dietary habits, such as serious air pollution, a love of pickled cabbage, smoking and drinking, northeast China has a high incidence of cancer, and the incidence of respiratory, digestive, and reproductive tract cancers ranks among the highest in China. The data used in this study were obtained from the hospital discharge medical records of patients >60 years of age who were diagnosed with the above cancers in Jilin Province, China between 2016 and 2018. In addition, we randomly selected 80,574 elderly patients without cancer as the control group, which was matched to the case group by age and gender.

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The hospital discharge medical records included the following data: demographic, such as age and gender; disease diagnosis; and medication. The diagnostic data consisted of one primary diagnosis and up to 15 secondary diagnoses, which were coded by trained coders using ICD 10. We used demographic and diagnostic data to analyze the co-morbid relationships to cancers. To ensure data quality, we only included the medical records of patients from tertiary hospitals. Ethical approval to conduct this study was obtained from the Ethics Committee of the School of Public Health at Jilin University (Jilin, China) (grant number: ethical review 2020-02-01).

2.2. Statistical Analysis

The characteristics of patients were summarized using frequency distributions and proportions. We used quartiles to describe the number of comorbidities in patients, presented as median (interquartile range (IQR)). The prevalence ratios (PRs) and 95% confidence intervals (CIs) for categories of co-morbid diseases were calculated, which were based on the categories of ICD 10. Distributions of categories of co-morbid diseases were compared for cancer and non-cancer patients using chi-square tests. Differences were considered significant if the p-value was 0.01.

We adopted the Apriori algorithm, which is the best-known ARM algorithm to extract and analyze the patterns of liver cancer comorbidities. The Apriori algorithm is a frequent itemset algorithm for mining association rules. The association rules are evaluated by support (the number of occurrences of disease A and disease B among all patients) and confidence (the number of occurrences of disease A co-occurring with disease B). The formulas for support and confidence are presented below.

Number o f patients with X and Y Support(X Y) = Total number o f patients

The basic premise of the algorithm is to first find all frequency sets, the frequency of which is at least as frequent as the pre-defined minimum support, then generate strong association rules from the frequency sets that satisfy the minimum support and minimum confidence. The advantage of the Apriori algorithm is that the structure is simple, easy to understand, and there is no complicated derivation, which greatly improves the efficiency of the algorithm. The result of the algorithm is a list of patterns between two sets of diseases in the form of "XY," which indicates that if disease X exists, disease Y co-exists. Although each pattern is directed with an arrow, it does not mean causation between diseases, but only represents co-occurrences. To avoid confusion, we ignored the directions of the patterns, and considered all diseases in set X and Y to be associated. Herein, support >0.01 and confidence >0.5 were used according to performance of validation diseases.

3. Results

3.1. Patient Statistics

Figure 1 and Table 1 presents the age, gender, and number of comorbidities in the cancer and non-cancer study groups, each of which was comprised of 80,574 patients. There were 80,574 patients shown to have one of the specific cancers, as follows: esophagus, 3088; gastric, 8220; colorectal, 16,961; liver, 8710; lung, 28,282; breast, 11,231; cervix, 2623; and thyroid, 1459. Except for malignant tumors of the reproductive system, thyroid cancer patients were more likely to be female, while cancer patients with cancers other than thyroid cancer were more likely to be male; the distributions were consistent with the expected results. The patients in the cancer and non-cancer study groups were stratified using the following age brackets (in years): 60?69; 70?79; and 80. The largest age group was 60?69 years, and the proportion of each type of cancer was > 60%. In addition, the same analysis was performed on the control group.

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Figure 1. The age and gender distribution in the cancer and non-cancer study groups. (A) Gender diFstirgiubrueti1o.nT; h(Be)aaggeeadnidstgriebnudteiornd.istribution in the cancer and non-cancer study groups. (A) Gender

distribution; (B) age distribution.

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Variables

All By gender

Male Female By age 60?70 70?80

80

Table 1. Number of comorbidities of patients with and without cancer.

All (n = 80,574)

3 [1?6]

Esophagus (n = 3088)

2 [1?5]

Stomach (n = 8220)

2 [1?5]

Colorectum (n = 16,961)

2 [1?5]

Cancers

Liver (n = 8710)

4 [2?6]

Lung (n = 28,282)

4 [2?7]

Breast (n = 11,231)

2 [1?4]

Cervix (n = 2623)

2 [1?4]

Thyroid (n = 1459)

2 [1?4]

Without Cancer (n = 80,574)

2 [1?4]

3 [1?6] 3 [1?5]

2 [1?5] 3 [1?5]

2 [1?5] 3 [1?5]

2 [1?5] 3 [1?5]

4 [2?6] 4 [2?6]

4 [2?7] 4 [2?7]

---- 2 [1?4]

---- 2 [1?4]

2 [1?5] 2 [1?4]

2 [1?4] 2 [1?5]

3 [1?5] 3 [1?6] 4 [2?8]

2 [1?5] 2 [1?5] 3 [1?5]

2 [1?4] 3 [1?5] 4 [2?7]

2 [1?5] 3 [1?5] 4 [1?7]

4 [2?6] 4 [2?6] 5 [2?8]

4 [2?7] 4 [2?7] 5 [2?8]

2 [1?4] 3 [1?6] 4 [1?6]

Number of comorbidities presented as median (interquartile range).

2 [1?3] 2 [1?4] 4 [2?5]

2 [1?4] 2 [1?5] 5 [2?8]

2 [1?4] 2 [1?5] 3 [1?5]

Table 2. Comorbidity prevalence and prevalence ratios in elderly patients with and without cancer.

Comorbid Disease Categories

ICD-10 Codes

Patients with Cancer Patients without Cancer

n (%)

n (%)

PRs(95%CI)

Infectious diseases Blood system diseases

Metabolic diseases Mental, Behavioral and Neurodevelopmental disorders

Diseases of the nervous system Diseases of the eye, adnexa, ear and mastoid process

Cardiovascular diseases Diseases of the respiratory system Diseases of the digestive system Diseases of the skin and subcutaneous tissue Diseases of the musculoskeletal system Diseases of the genitourinary system

Congenital anomalies Symptoms, signs and ill-defined conditions Injury, poisoning and certain other consequences of external causes

A00-B99 D50-D89 E00-E89 F01-F99 G00-G99 H00-H95 I00-I99 J00-J99 K00-K95 L00-L99 M00-M99 N00-N99 Q00-Q99 R00-R99 S00-T88

5501(6.79) 8037(9.92) 19,617(24.20) 58(0.07) 1354(1.67) 238(0.29) 28,457(35.11) 19,083(23.54) 23,992(29.60) 455(0.56) 2283(2.82) 9705(11.97) 343(0.42) 7931(9.79) 620(0.76)

1823(2.26) 3913(4.86) 29,110(36.13) 388(0.48) 3920(4.87) 4143(5.14) 46,245(57.39) 12,548(15.57) 13,421(16.66) 884(1.10) 5685(7.06) 9476(11.76) 658(0.82) 6191(7.68) 2429(3.01)

3.00(2.85?3.16) 2.04(1.97?2.12) 0.67(0.66?0.68) 0.15(0.11?0.20) 0.34(0.32?0.37) 0.08(0.07?0.09) 0.61(0.61?0.62) 1.51(1.48?1.54) 1.78(1.74?1.81) 0.51(0.46?0.57) 0.40(0.38?0.42) 1.02(0.99?1.05) 0.52(0.46?0.59) 1.27(1.23?1.32) 0.25(0.23?0.28)

PR, prevalence ratio. CI, confidence intervals. The code number of each system was found at .

p Value

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