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International Journal for Equity in Health

Open Access

Effect of socioeconomic inequalities on cholecystectomy outcomes: a 10-year population-based analysis

  • Ping Lu1, 2,
  • Nan-Ping Yang3, 4,
  • Nien-Tzu Chang5,
  • K. Robert Lai6, 8,
  • Kai-Biao Lin7 and
  • Chien-Lung Chan2, 8Email author
International Journal for Equity in Health201817:22

https://doi.org/10.1186/s12939-018-0739-7

Received: 22 August 2017

Accepted: 6 February 2018

Published: 13 February 2018

Abstract

Background

Although numerous epidemiological studies on cholecystectomy have been conducted worldwide, only a few have considered the effect of socioeconomic inequalities on cholecystectomy outcomes. Specifically, few studies have focused on the low-income population (LIP).

Methods

A nationwide prospective study based on the Taiwan National Health Insurance dataset was conducted during 2003–2012. The International Classification of ICD-9-CM procedure codes 51.2 and 51.21–51.24 were identified as the inclusion criteria for cholecystectomy. Temporal trends were analyzed using a joinpoint regression, and the hierarchical linear modeling (HLM) method was used as an analytical strategy to evaluate the group-level and individual-level factors. Interactions between age, gender and SES were also tested in HLM model.

Results

Analyses were conducted on 225,558 patients. The incidence rates were 167.81 (95% CI: 159.78–175.83) per 100,000 individuals per year for the LIP and 123.24 (95% CI: 116.37–130.12) per 100,000 individuals per year for the general population (GP). After cholecystectomy, LIP patients showed higher rates of 30-day mortality, in-hospital complications, and readmission for complications, but a lower rate of routine discharge than GP patients. The hospital costs and length of stay for LIP patients were higher than those for GP patients. The multilevel analysis using HLM revealed that adverse socioeconomic status significantly negatively affects the outcomes of patients undergoing cholecystectomy. Additionally, male sex, advanced age, and high Charlson Comorbidity Index (CCI) scores were associated with higher rates of in-hospital complications and 30-day mortality. We also observed that the 30-day mortality rates for patients who underwent cholecystectomy in regional hospitals and district hospitals were significantly higher than those of patients receiving care in a medical center.

Conclusion

Patients with a disadvantaged finance status appeared to be more vulnerable to cholecystectomy surgery. This result suggested that further interventions in the health care system are necessary to reduce this disparity.

Keywords

Socioeconomic statusCholecystectomyGallbladder diseaseAcute cholecystitis

Background

Gallbladder disease remains one of the most common problems encountered in surgical intervention and, if managed incorrectly, can lead to high rates of morbidity and mortality [1]. Approximately 20 million people in the USA have gallstones, leading to over one million hospitalizations and 700,000 operative procedures annually [2, 3]. The overall prevalence of gallstone disease is 5.0% (4.6% in men, 5.4% in women) with no significant sex differences in Taiwan [4]. The hospital admission rate for severe gallstone disease in Taiwan increases with advancing age, and the age-standardized rate (95% CI) per 1000 population is 0.60 (0.59–0.60) for men and 0.59 (0.59–0.60) for women [5]. Symptomatic gallstones are treated by surgically removing the gallbladder, and cholecystectomy is largely accepted as the standard procedure for treating benign gallbladder disease [6, 7]. Although numerous epidemiological studies on cholecystectomy have been conducted worldwide, few studies have considered the effect of socioeconomic inequalities on cholecystectomy outcomes. To the best of our knowledge, relatively few studies have focused on the low-income population (LIP) in Asian settings.

In some Western countries, socioeconomic status (SES) has been reported to have a strong association with postoperative mortality in numerous studies [810]. For example, Andrew et al. [11] suggested that environmental factors associated with SES, such as level of education, income, occupational status, and neighborhood, play a role in gallstone pathogenesis. Vishnu et al. [12] reported that patients with Medicaid and lower SES had poorer outcomes after cholecystectomy. However, other researchers found no relationship between socioeconomic conditions and the prevalence of cholelithiasis [13, 14]. For example, Carbonell et al. [14] performed a nationwide study of 93,758 patients and demonstrated that income, insurance status, and race did not play a role in morbidity or mortality for patients who underwent cholecystectomy; moreover, academic or teaching status at the hospital did not influence patient outcomes. Therefore, previous studies have yielded conflicting results on the effects of socioeconomic inequalities on cholecystectomy outcomes. It is important to recognize patient populations that might be at a higher risk of mortality and postoperative complications to identify potentially modifiable risk factors [12]. Thus, in-depth population-based studies and analyses on the effects of socioeconomic inequalities on cholecystectomy must be conducted. The main purpose of this population-based study was to investigate the effect of socioeconomic inequalities on cholecystectomy outcomes in Taiwan. We also compared the temporal trends of cholecystectomy between the LIP and GP using a joinpoint regression and assessed the odds ratios of in-hospital complications and 30-day mortality between the LIP and GP for all enrolled patients using a multilevel analysis with HLM. We expected to be able to provide some valuable information to assist surgeons in decision-making and to make recommendations to health policy decision-makers with respect to the development of preventive strategies.

Methods

Study subjects and data source

We retrieved the inpatient data from Taiwan’s National Health Insurance Research Database (NHIRD), which is a nationwide medical claims database for Taiwan’s National Health Insurance (NHI) program. This program launched in 1995 and reached a 99.9% coverage rate by 2011. Various data subsets, such as inpatient expenditures, details of prescription orders, and clinics or ambulatory care expenditures, are included in the NHIRD. To protect privacy, all the subjects’ ID numbers were double encrypted. All researchers were properly trained and were required to declare and sign written agreement before using these data subsets.

Data definition

To examine the effects of socioeconomic inequalities on cholecystectomy outcomes in Taiwan, we used the diagnostic codes of the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM). Cholecystectomy was identified by ICD-9-CM procedure codes 51.2 and 51.21–51.24, and percutaneous cholecystostomy (PC) was identified as ICD-9-CM procedure code 51.01 [15]. To avoid the effects of PC on the outcome of patients who underwent cholecystectomy, we excluded patients who had initially undergone PC before cholecystectomy during a prior hospitalization. However, when patients had undergone both PC and cholecystectomy during the same hospitalization, they were classified as cholecystectomy patients. Only patients 18 years or older who had undergone cholecystectomy were included. To analyze the procedure causes, acute cholecystitis (AC) with a calculus/stone was defined as patients with ICD-9-CM diagnosis codes 574.0, 574.3, and 574.6; AC without a calculus/stone was defined as patients with the ICD-9-CM diagnosis code 575.0; calculus without AC referred to patients with ICD-9-CM diagnosis codes 574.1, 574.2, 574.4, 574.5, 574.7, 574.8, or 574.9; other disorders of the gallbladder or biliary tract were defined as patients with ICD-9-CM diagnosis codes 575 or 576, excluding diagnosis code 575.0; and malignant neoplasms of digestive organs and the peritoneum included patients with ICD-9-CM diagnosis codes 150–159, excluding diagnosis codes 574, 575, and 576.

Socioeconomic status

To evaluate the effects of SES, the enrolled subjects were classified into the LIP and the general population (GP) according to whether the subjects met the criteria of Taiwan’s Social Assistance Act and was registered in Taiwan’s NHI database. For easy recognition, Taiwan’s NHI database marked the LIP group as the fifth class beneficiary [16]. The GP refers to individuals who are not classified as LIP.

Outcome measurements

Length of hospital stays (LOS)

The period between admission and discharge was defined as the LOS (measured in days). The LOS was measured as 1 day for patients discharged on the same day that they were admitted to the hospital [17].

Hospital costs

Hospital costs were calculated by summing all items enumerated in the hospital discharge summary, including operation-associated costs and ward costs. Operation-associated costs included anesthesia and surgery fees and the costs of medical supplies used during an operation. Surplus costs were classified as ward costs. Costs are expressed in U.S. dollars (USD). In 2007, one USD dollar was equivalent to approximately 32.64 Taiwan dollars [17].

In-hospital complications

We examined all-cause, nonfatal in-hospital morbidity rates based on ICD-9-CM codes. Complications were grouped into 9 categories (mechanical wound complications, infections, urinary complications, pulmonary complications, systemic complications, complications arising during procedures, specific complications of the gallbladder/digestive system, respiratory complications, and others). Because the NHIRD inpatient dataset includes inpatient data only, complications occurring after hospital discharge were not considered in our analysis.

30-day mortality and in-hospital mortality

Thirty-day mortality was used to refer to patients who died within one month after undergoing cholecystectomy; this variable also included patients who died during hospitalization. In-hospital mortality was used to refer to patients undergoing cholecystectomy who died during hospitalization.

Rate of routine discharge

The NHIRD provides information on patient discharge statuses (1, treated and discharged; 2, continued to hospital; 3, transferred to outpatient treatment; 4, death; 5, discharge against medical advice; 6, referral; 7, change in status; 8, abscond; 9, suicide; 0, other; and A, discharged while dying). Patients were grouped into the following categories: routine discharge (1, 3) and non-routine discharge (0, 2, 4–9, and A).

Readmission due to complications

Readmission due to complications was designated when readmission occurred due to the diagnosis of a commonly encountered postoperative complication listed in Appendix within 1 month after the cholecystectomy.

Statistical analysis

Descriptive statistics for comparing baseline characteristics were determined based on the number of cases, percentages, and 95% confidence intervals (CIs) for the estimated rates. Chi-square tests and independent-t tests were used to evaluate the significance of frequency and continuous variable differences between two subgroups, respectively. Statistical significance was set at 0.05. Temporal trends were analyzed using a joinpoint regression, which is a statistical method that fits a series of joined straight lines between statistically significant changes in trends (joinpoints). We estimated the change between joinpoints using the National Cancer Institute’s Joinpoint Regression Program Version 4.3.1.0 [18, 19]. Long-term trends over the entire time series were designated average annual percentage changes (AAPCs) and were estimated as the weighted average of short-term annual percentage changes with weights equal to the length of the short-term line segment [20].

To evaluate the risk factors for 30-day mortality and in-hospital complications after cholecystectomy, a multiple logistic regression analysis was performed, and the adjusted odds ratio (AOR) was calculated. A multilevel analysis (or the hierarchical linear modeling (HLM) method) was used as an analytical strategy, which allowed the evaluation of group-level and individual-level factors [21]. Interactions between age, gender and SES were also tested in the HLM model. The hypothesis and formulas of the HLM analysis used in the present study were as follows.

Level 1 HLM model
$$ {\displaystyle \begin{array}{l} Yij=\beta 0+\beta 1\times (gender)+\beta 2\times \left(\mathrm{a} ge\; group\;1\right)+\beta 3\times \left( age\; group\;2\right)\\ {}+\beta 4\times \left( age\; group\;3\right)+\beta 5\times \left( age\; group\;4\right)+\beta 6\times \left( age\; group\;5\right)\\ {}+\beta 7\times \left( CCI\;1\right)+\beta 8\times \left( CCI\;2\right)+\beta 9\times \left( CCI\;3\right)\beta +\beta 10\times \left( AC\; without\;a\;C/S\right)+\beta 11\times \left( Calculus\kern0.17em without\; AC\right)+\beta 12\times (ODGBT)+\beta 13\times (MNDOP)+\beta 14\times (Others)+\gamma .\end{array}} $$
(1)
Level 2 HLM model
$$ {\displaystyle \begin{array}{l}\beta 0=\gamma 00+\gamma 01\times (SES)+\gamma 02\times \left( regional\kern0.5em hospital\right)+\gamma 03\times \left( district\kern0.5em hospital\right)\\ {}+\gamma 03\times \left({\mathrm{SES}}^{\ast}\kern0.5em gender\right)+\gamma 03\times \left({\mathrm{SES}}^{\ast}\kern0.5em age\kern0.5em group\kern0.5em 3\right)+\gamma 03\times \left({\mathrm{SES}}^{\ast}\kern0.5em age\kern0.5em group\kern0.5em 4\right)\\ {}+\gamma 03\times \left({\mathrm{SES}}^{\ast}\kern0.5em age\kern0.5em group\kern0.5em 5\right)+\mu 0.\end{array}} $$
(2)

All statistical analyses were performed using the Statistical Package for Social Sciences for Windows (SPSS for Windows Version 22.0).

Results

Of the 225,558 sampled patients who underwent cholecystectomy during 2003–2012, the incidence rate was 167.81 (95% CI: 159.78–175.83) per 100,000 individuals per year for the LIP and 123.24 (95% CI: 116.37–130.12) per 100,000 individuals per year for the GP. The mean ages were 54.9 ± 16.7 years (±standard deviation) and 56.8 ± 16.2 years for LIP and GP patients, respectively. Cholecystectomy was performed significantly more often and in slightly younger patients in the LIP than in the GP.

Based on multiple permutation tests that kept the overall level of type I errors to less than 0.05, the final selected model detected 2 joinpoints in 2005 and 2010 for LIP patients and 2 joinpoints in 2007 and 2009 for GP patients (Fig. 1). During the entire retrospective period, the incidence for the GP increased slowly (AAPC = 2.1%) from 106.86 to 130.79 per 100,000 individuals. Meanwhile, the incidence for the LIP remained constant at approximately 170.00 per 100,000 individuals (AAPC = 0%). The short-term trends (APCs) between LIP and GP patients were different, although similar trends were observed after 2004.
Fig. 1

Comparison of cholecystectomy incidence in the LIP and GP in Taiwan

As shown in Table 1, the differences in gender, age stratum, Charlson Comorbidity Index (CCI) score, cause of procedure and hospital level were significant between the LIP and GP (p < 0.05). Male and female patients were essentially evenly distributed in both the LIP and GP. In the LIP, there was a higher proportion of young patients (aged 30–39 and aged 40–49) and a lower proportion of elderly patients (aged over 50) than in the GP. Patients aged 40–49 years comprised the highest proportion (27.79%) of LIP patients, whereas patients aged over 70 years comprised the highest proportion (25.32%) of GP patients (Table 1).
Table 1

Demographic characteristic of patients undergoing cholecystectomy in Taiwan, 2003–2012

Variables

Low-income population (n = 2454)

General population (n = 223,104)

p-value

Total no.

Percent (%)

Total no.

Percent (%)

Gender

  

0.028

 Female

1192

48.57%

113,346

50.80%

 

 Male

1262

51.43%

109,758

49.20%

Age stratum

  

< 0.001

 18–29 y/o

79

3.22%

10,438

4.68%

 

 30–39 y/o

353

14.38%

26,965

12.09%

 40–49 y/o

682

27.79%

37,976

17.02%

 50–59 y/o

428

17.44%

48,679

21.82%

 60–69 y/o

312

12.71%

42,557

19.07%

 70 y/o or more

600

24.45%

56,489

25.32%

CCI score

  

< 0.001

 0

1711

69.72%

155,823

69.84%

 

 1

405

16.50%

28,752

12.89%

 2

185

7.54%

21,703

9.73%

  ≥ 3

153

6.23%

16,826

7.54%

Cause of procedure

  

< 0.001

 AC with a C/S

786

32.03%

63,605

28.51%

 

 AC without a C/S

154

6.28%

9019

4.04%

 Calculus without AC

1216

49.55%

113,825

51.02%

 ODGBT

131

5.34%

13,521

6.06%

 MNDOP

104

4.24%

18,289

8.20%

 Others

63

2.57%

4845

2.17%

Hospital level

  

< 0.001

 Medical center

730

29.75%

105,542

47.31%

 

 Regional hospital

1345

54.81%

99,729

44.70%

 District hospital

379

15.44%

17,833

7.99%

AC with a C/S: Acute cholecystitis with a calculus/stone

AC without a C/S: Acute cholecystitis without a calculus/stone

Calculus without AC: Calculus without acute cholecystitis

ODGBT: Other disorders of the gallbladder or biliary tract

MNDOP: Malignant neoplasm of digestive organs and peritoneum

CCI: Charlson Comorbidity Index

The primary reason for a cholecystectomy was a calculus without AC (LIP: 49.55%, GP: 51.02%), followed by AC with a calculus/stone (LIP: 32.03%, GP: 28.51%) for both the LIP and GP. The proportion of malignant neoplasms of digestive organs and peritoneum (MNDOP) was nearly twice as high in the GP as in the LIP. Nearly 30% of patients who underwent cholecystectomy had CCI scores of 1 or more for both the LIP and the GP (LIP: 30.28%, GP 30.16%). Unlike the LIP, GP patients preferred to undergo cholecystectomy at medical centers and were less likely to choose regional or district hospitals.

The overall 30-day mortality for patients who underwent cholecystectomy was 2.21%. LIP patients showed higher rates of 30-day mortality (LIP: 4.65% vs. GP: 2.18%, p < 0.001), in-hospital complications (LIP: 5.62% vs. GP: 4.01%, p = 0.008), and readmission for complications (LIP: 1.83% vs. GP: 1.09%, p < 0.001) but showed a lower rate of routine discharge (LIP: 94.91% vs. GP: 97.67%, p < 0.001) than GP patients (Table 2).
Table 2

Characteristics of 30-day mortality, in-hospital complications, rate of routine discharge and readmission for complications, and length of hospital stay

Variables

All No. (%)

Low-income population No. (%), mean ± SE

General population No. (%), mean ± SE

p-valuec

30-day mortality

4987 (2.21%)

114 (4.65%)

4873 (2.18%)

< 0.001

 In-hospital mortality

3602 (1.60%)

81 (3.30%)

3521 (1.58%)

< 0.001

In-hospital complicationsa

9089 (4.03%)

138 (5.62%)

8951 (4.01%)

0.008

 Specific complications of the gallbladder/digestive system

4674 (2.07%)

62 (2.53%)

4612 (2.07%)

0.112

 Infections

2850 (1.26%)

53 (2.16%)

2797 (1.25%)

< 0.001

 Mechanical wound complications

1320 (0.59%)

20 (0.81%)

1300 (0.58%)

0.133

 Complications during procedure

882 (0.39%)

15 (0.61%)

867 (0.39%)

0.079

 Pulmonary complications

310 (0.14%)

5 (0.20%)

305 (0.14%)

0.373

 Systemic complications

156 (0.07%)

3 (0.12%)

153 (0.07%)

0.314

 Respiratory complications

74 (0.03%)

1 (0.04%)

73 (0.03%)

0.827

 Urinary complications

22 (0.01%)

1 (0.04%)

21 (0.01%)

0.118

 Other

40 (0.02%)

1 (0.04%)

39 (0.02%)

0.389

Rate of routine discharge

220,244 (97.64%)

2329 (94.91%)

217,915 (97.67%)

< 0.001

 Treatment and discharge

12,469 (5.53%)

94 (3.83%)

12,375 (5.55%)

< 0.001

 Transferred to outpatient treatment

207,775 (92.12%)

2235 (91.08%)

205,540(92.13%)

0.055

Readmission for complicationsb

2475 (1.10%)

45 (1.83%)

2430 (1.09%)

< 0.001

Length of hospital stay

12.13 ± 0.03

9.49 ± 0.02

< 0.001

Hospital costs

3263.82 ± 65.89

2832.17 ± 6.00

< 0.001

aTwo or more complications occurred for the same patient; therefore, the total number of patients with complications was less than the sum of the number of patients with each independent complication

bReadmission for complications was defined as readmission with the diagnosis of a commonly encountered postoperative complication within 1 month after the cholecystectomy

cp-values for length of hospital stay and hospital costs were analyzed with independent t-tests; other variables were analyzed with Chi-square tests

The mean LOS for LIP patients was significantly longer than that of GP patients (12.13 ± 0.28 vs. 9.49 ± 0.02, p < 0.001). Correspondingly, the mean cost for LIP patients was much higher than that for GP patients (3263.82 ± 65.89 vs. 2832.17 ± 6.00 USD, p < 0.001) (Table 2). The detailed LOS distribution for the GP and LIP is shown in Fig. 2. More GP patients were hospitalized for 1–6 days, whereas more LIP patients were hospitalized for over 14 days. Figure 3 illustrates that the mean LOS increased with advancing age for both LIP and GP patients, and the mean LOS was higher in LIP patients than in GP patients for every age group.
Fig. 2

Frequency distributions of length of hospital stay for patients who underwent cholecystectomy

Fig. 3

Mean length of hospital stay for patients who underwent cholecystectomy according to age group

A multilevel analysis with HLM was used to evaluate the individual effects (i.e., gender, age, cause of procedure, CCI Score) and the group effects (i.e., SES and hospital level) on in-hospital complications and 30-day mortality. As shown in Table 3, male patients showed higher rates of 30-day mortality (AOR = 1.341, p < 0.001) and in-hospital complications (AOR = 1.470, p < 0.001). Compared to the 18-to-29-year-olds, patients aged 30–39 exhibited a lower rate of in-hospital complications (AOR = 0.842, p = 0.026). However, patients aged 40–49 showed a higher rate of 30-day mortality (AOR = 1.410, p = 0.013), and patients aged 60–69 and 70 years or older displayed much higher rates of both in-hospital complications and 30-day mortality. In particular, the AORs of patients aged 70 years or older were 2.369 for in-hospital complications and 7.831 for 30-day mortality. In general, aging was associated with a higher risk of complications and mortality. Additionally, different reasons for the procedure showed significantly different complications and mortality risks compared to cases of AC with a calculus/stone. In particular, patients with the cause ‘other’ had a significantly higher risk of 30-day mortality (AOR = 9.799, p < 0.001). Lower hospital levels were associated with a higher risk of 30-day mortality, and an adverse trend was also reported for in-hospital complications. More importantly, the LIP patients faced clearly higher rates of 30-day mortality (AOR = 2.197, p < 0.001) and increased in-hospital complications (AOR = 1.599, p < 0.001). The only significant interaction factor was SES*70 y/o or more, which was identified as a protective factor associated with reduced in-hospital complications. No significant interaction terms between age, gender and SES were identified for all other factors in the HLM analysis.
Table 3

Multilevel analysis (with HLM) of the risk factors for in-hospital mortality and 30-day mortality among patients who underwent cholecystectomy in Taiwan, 2003–2012

Variable

In-hospital complications

30-day mortality

β-value

AOR

p-value

β-value

AOR

p-value

Gendera

 Femalec

 

1.0

  

1.0

 

 Male

0.381

1.464 (1.399, 1.532)

< 0.001

0.290

1.337 (1.257, 1.422)

< 0.001

Age stratuma

 18–29 y/oc

 

1.0

  

1.0

 

 30–39 y/o

−0.173

0.841 (0.722, 0.980)

0.025

0.030

1.030 (0.767, 1.385)

0.843

 40–49 y/o

−0.017

0.983 (0.852, 1.133)

0.808

0.335

1.398 (1.066, 1.835)

0.016

 50–59 y/o

0.276

1.318 (1.149, 1.511)

< 0.001

0.524

1.689 (1.298, 2.198)

< 0.001

 60–69 y/o

0.506

1.658 (1.446, 1.902)

< 0.001

1.045

2.842 (2.192, 3.687)

< 0.001

 70 y/o or more

0.869

2.383 (2.085, 2.725)

< 0.001

2.065

7.884 (6.111, 10.171)

< 0.001

CCI scorea

 0c

 

1.0

  

1.0

 

 1

0.064

1.067 (0.996, 1.142)

0.065

0.142

1.152 (1.052, 1.261)

0.003

 2

0.378

1.459 (1.354, 1.572)

< 0.001

0.848

2.336 (2.119, 2.575)

< 0.001

  ≥ 3

0.604

1.829 (1.694, 1.974)

< 0.001

1.343

3.831 (3.488, 4.207)

< 0.001

Cause of procedurea

 AC with a C/Sc

 

1.0

  

1.0

 

 AC without a C/S

0.247

1.280 (1.151, 1.423)

< 0.001

0.975

2.650 (2.374, 2.958)

< 0.001

 Calculus without AC

−0.122

0.885 (0.836, 0.937)

< 0.001

−0.178

0.837 (0.772, 0.908)

< 0.001

 ODGBT

1.019

2.770 (2.562, 2.995)

< 0.001

0.458

1.581 (1.394, 1.792)

< 0.001

 MNDOP

0.389

1.476 (1.348, 1.615)

< 0.001

0.379

1.461 (1.303, 1.637)

< 0.001

 Others

0.988

2.686 (2.393, 3.015)

< 0.001

2.282

9.793 (8.625, 11.120)

< 0.001

Hospital levelb

 Medical centerc

 

1.0

  

1.0

 

 Regional hospital

−0.108

0.898 (0.857, 0.940)

< 0.001

0.551

1.734 (1.624, 1.852)

< 0.001

 District hospital

−0.228

0.796 (0.724, 0.874)

< 0.001

0.950

2.585 (2.335, 2.861)

< 0.001

SESb

 General populationc

 

1.0

  

1.0

 

 Low-income population

0.430

1.537 (1.039, 2.274)

0.023

0.910

2.484 (1.543, 4.001)

< 0.001

SES*Genderb

0.400

1.492 (0.993, 2.240)

0.058

0.187

1.205 (0.773, 1.879)

0.410

SES*50–59 y/ob

−0.217

0.805 (0.474, 1.367)

0.421

−0.167

0.846 (0.424, 1.689)

0.635

SES*60–69 y/ob

−0.073

0.930 (0.547, 1.582)

0.785

−0.224

0.800 (0.400, 1.598)

0.526

SES*70 y/o or moreb

−0.552

0.576 (0.364, 0.910)

0.020

−0.384

0.681 (0.409, 1.135)

0.141

aIndividual level. bCluster level. cReference group. AOR: adjusted odds ratio. SES: Socioeconomic status

Discussion

In Taiwan, low-income households are defined as those with an average per-person gross monthly income of less than the monthly minimum living expense standard of that residence region. The minimum living expense standard is defined as 60% of the average monthly disposable income for each region. In addition, family property is not permitted to exceed a certain amount, as determined by the central or municipal authorities in the corresponding year [22]. The Taiwan Ministry of the Interior has conducted six surveys regarding living conditions in the LIP and moderate LIP. The first survey was conducted in 1990, and the latest was performed in 2013 [23]. The survey results showed that long-term illness is the second leading cause of people becoming poor (the first leading cause is all family members being unable to work), and the LIP was more subject to serious diseases than the GP in Taiwan [2426]. Hence, conducting in-depth research and analyses of the effects of socioeconomic inequalities on cholecystectomy in LIP patients is necessary; such studies may lead to treatment suggestions for medical research institutions and may assist surgeons in making decisions concerning the management of LIP patients with gallbladder disease and the judicious use of cholecystectomy.

In our analysis, we found that the overall incidence of cholecystectomy in the LIP was 36.17% higher than that in the GP. Thus, the risk of gallbladder disease in the LIP was higher than that in the GP. Moreover, LIP patients differed significantly from GP patients in hospital choice; they were more likely to undergo cholecystectomy in regional or district hospitals, which accounted for 70.25% of their total procedures. However, nearly half of the GP patients underwent cholecystectomy in medical centers (Table 1). Therefore, LIP patients are at a disadvantage in accessing medical resources compared to GP patients; they tend to live in more remote areas than GP patients, and they may need to travel further than GP patients to obtain premium medical care.

As shown in Table 2, LIP patients showed higher rates of 30-day-mortality, in-hospital complications and readmission for complications, but they had a slightly lower rate of routine discharge than GP patients. Thus, the overall situation after surgery is worse for LIP patients than for GP patients. In addition, the hospital costs and LOS for the LIP patients were higher than those of the GP patients (Table 2), which occurred primarily because more LIP patients were hospitalized for over 14 days compared to GP patients (Fig. 2). Furthermore, LIP patients constitute a more vulnerable population than GP patients.

A multilevel analysis using HLM was performed with data from all enrolled patients to assess the odds ratios of in-hospital complications and 30-day mortality. As shown in Table 3, male sex, elderly patients, and CCI score were associated with mortality, which has been reported in previous studies [12, 27]. Notably, 30-day mortality differed greatly in patient groups separated by reason for the procedure. When we used the patient group that underwent cholecystectomy because of ‘AC with a calculus/stone’ as a reference group and excluded the patient group requiring cholecystectomy because of ‘calculus without AC’, the 30-day mortality of the remaining patient groups was significantly higher than that of the reference group. For procedures classified as ‘other’, such as uncommon gallbladder-related diseases or digestive-related diseases, the 30-day mortality was much higher than that of other diseases (AOR = 9.799, p < 0.001). Common sense dictates that more serious diseases exhibit increased mortality rates. However, the mortality increased dramatically when the cause of the procedure was ‘other’, and this finding should be taken more seriously when making surgical decisions. Surprisingly, the interaction term “SES*70 y/o or more” was identified as a protective factor of reduced in-hospital complications. More investigations are needed to analyze the possible cause, such as a similar healthy worker effect (HWE) [28] and/or other effects.

In addition, our analysis found that the 30-day cholecystectomy mortality in regional hospitals was higher than that in medical centers, and regional hospitals also showed higher mortality rates than district hospitals. Thus, fewer hospital resources lead to higher mortality rates. Nevertheless, as previously mentioned, the LIP has poorer access to health care, making them more prone to requiring surgery for an acute process such as cholecystectomy in a regional or district hospital; poorer access is also responsible for higher mortality in LIP patients than in GP patients. Therefore, LIP patients requiring surgery should select better hospitals to the best of their abilities, particularly for serious illnesses. Meanwhile, the government must balance medical resources, enhance the overall quality of medical care in remote areas, and improve patient care for the LIP. Finally, we found that the 30-day mortality of LIP patients was higher than that of GP patients (AOR = 2.197, p < 0.001, Table 3), which confirms that socioeconomic inequalities are a risk factor for cholecystectomy outcomes.

Similar to the results of 30-day mortality, male sex, elderly patients, and CCI score were associated with the rate of in-hospital complications. In addition, the rate of in-hospital complications also differed greatly in different patient groups divided by different causes of procedure. Interestingly, the lower the hospital level patients were admitted to, the lower the risk of in-hospital complications, indicating a reversal of the association reported for 30-day mortality; however, additional research is necessary to confirm this relationship. We also observed that the rate of in-hospital complications in LIP patients was higher than that in GP patients (AOR = 1.599, p < 0.001, Table 3), which is consistent with the 30-day mortality association.

A huge difficulty of socioeconomic inequality studies is how to reasonably distinguish the LIP group and the GP group. A key strength of our current study is that the government of Taiwan has strict standards and procedures for the identification of low-income households, and there are special departments and staff in charge of these works. Thus, the quality of the current data can be deemed reliable. Meanwhile, this study was based on a population-based database, which allowed us to study the LIP with large sample sizes. However, the present data also have limitations. First, as in many national-level databases, we lack detailed clinical data and examination information. Second, data on postoperative conditions were not included. Third, we could not review individual medical records to ensure that the records were coded precisely; deviations may exist between the codes and the actual severity of disease conditions. Even so, this national population-based claims database can be recognized as reliable because it has been adopted in many research fields and numerous high-impact publications.

Conclusions

In conclusion, our study confirms that socioeconomic inequalities have a negative effect on the outcomes of patients undergoing cholecystectomy. Compared to GP patients, LIP patients show higher rates of 30-day mortality, in-hospital complications, and readmission for complications, as well as a slightly lower rate of routine discharge. In addition to the factors of male sex, advanced age, CCI score, cause of procedure, and hospital level, among others, adverse SES plays an important role in the risk of mortality and complications for patients who undergo cholecystectomy. We recommend surgeons fully consider a patient’s SES and other features when performing cholecystectomy to design adequate plans to reduce the rates of mortality and complications for LIP patients. In addition, governments should balance medical resources, enhance the overall quality of medical care in remote areas, and limit disparities in access to health care for LIP patients.

Abbreviations

AAPC: 

Average annual percentage change

AC: 

Acute cholecystitis

AOR: 

Adjusted odds ratio

APC: 

Annual percentage change

CCI: 

Charlson Comorbidity Index

CI: 

Confidence interval

GP: 

General population

HLM: 

Hierarchical linear modeling

LIP: 

Low-income population

LOS: 

Length of hospital stay

MNDOP: 

Malignant neoplasm of digestive organs and peritoneum

NHI: 

National Health Insurance

NHIRD: 

National Health Insurance research database

ODGBT: 

Other disorders of gallbladder or biliary tract

SES: 

Socioeconomic status

Declarations

Acknowledgements

This study is based in part on data from the National Health Insurance Research Database provided by the National Health Insurance Administration and Ministry of Health and Welfare and was managed by the National Health Research Institutes.

Funding

This study was partially supported by the Ministry of Science and Technology, MOST-104-2218-E-155-004 and MOST-105-2221-E-155-041-MY3, and the Research Project of Xiamen Overseas Students (XRS201631401).

Availability of data and materials

The data that support the findings of this study are available from the Taiwan National Health Research Institutes, but restrictions apply to the availability of these data, which were used under license for the current study and thus are not publicly available. Data are available from the authors upon reasonable request and with permission of the Taiwan National Health Research Institutes.

Authors’ contributions

The study was designed by CLC, PL and NPY; data were gathered and analyzed by NTC and CLC; the initial draft of the manuscript was written by PL, CLC and LKB; and the accuracy of the data and analyses was ensured by KRL. All authors helped prepare the manuscript and read and approved the final version.

Ethics approval and consent to participate

This study was approved by the research ethics committee of the Taoyuan General Hospital (Approval Number: TYGH103015), which has been certified by the Ministry of Health & Welfare of Taiwan, and the research protocol was reviewed by the National Health Research Institutes (Agreement Number: NHIRD-104-081).

Consent for publication

Not applicable.

Competing interests

All authors declare that they have no conflicts of interest, including directorships, stock holdings or contracts.

Publisher’s Note

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
School of Economics and Management, Xiamen University of Technology, Xiamen, China
(2)
Department of Information Management, Yuan Ze University, Taoyuan, Taiwan
(3)
Department of Surgery, Keelung Hospital, Ministry of Health and Welfare, Keelung, Taiwan
(4)
Institute of Public Health, National Yang-Ming University, Taipei, Taiwan
(5)
School of Nursing, College of Medicine, National Taiwan University, Taipei, Taiwan
(6)
Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, Taiwan
(7)
School of Computer & Information Engineering, Xiamen University of Technology, Xiamen, China
(8)
Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Taoyuan, Taiwan

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Copyright

© The Author(s). 2018

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