Data sources and population.
For the purpose of this study, we utilized and merged two databases. Firstly, we accessed a nationwide claims database provided by VEKTIS. This database collects claims data from all healthcare insurance companies in the Netherlands, ensuring comprehensive coverage of over 99% of the Dutch population. It has been determined to be over 95% accurate when compared to hospital records16. Secondly, we obtained a database from Statistics Netherlands (CBS), which includes detailed patient characteristics17. The VEKTIS database contained data on all Dutch individuals aged 18 years and older who sought hospital care for lumbar degenerative disc disease between January 1, 2016, and December 31, 2019. The CBS databases encompassed information on all Dutch inhabitants aged 18 years and older.
Definitions and variables
In the VEKTIS database, we extracted specific data related to lumbar degenerative disc disease. This included diagnosis codes that are associated with sciatica, as well as surgical care products for discectomy, laminectomy, and fusion surgery (Supplemental files, Table 1 and Table 2). To ensure consistency in the registration process, we used multiple codes to capture indications for surgical treatment, minimizing potential variation in registration among physicians. We excluded patients who had cervical degenerative disc disease, spinal infections, fractures or trauma, malignancies, or congenital diseases. Additionally, individuals who had undergone back surgery within the past year were excluded from the analysis.
Table 1
Baseline characteristics for our study population between 2016 and 2019
Variable
|
All inhabitants
(n = 13,795,549)
|
Lumbar DDD patients
(n = 119,148)
|
Surgical patients
(n = 14,840)
|
Age (years), mean (SD)
|
49.5 (18.8)
|
57.5 (15.6)
|
58.1 (15.4)
|
Sex, n (%)
Male
Female
|
6,798,912 (49)
6,996,637 (51)
|
54,686 (46)
64,462 (54)
|
7,565 (51)
7,275 (49)
|
Comorbidities, n (%)
0
1
2
≥3
|
116,99719 (85)
838,777 (6)
787,544 (6)
469,509 (3)
|
83,156 (70)
14,339 (12)
11,725 (10)
8,936 (8)
|
10,386 (70)
1,725 (12)
1,453 (10)
1,286 (9)
|
Level of education, n (%)
Low
Middle
High
|
3,587,100 (26)
5,295,067 (38)
4,913,382 (36)
|
40,986 (34)
44,289 (37)
33,873 (28)
|
5,007 (34)
5,633 (38)
4,200 (28)
|
Household income, median (IQR)
|
27,182 (19,406 − 36,279)
|
25,938 (19,197 − 34,552)
|
27,786 (25,893 − 36,471)
|
Occupation, n (%)
Employee
Employer or entrepreneur
Retired
Unemployed
Student
|
6,423,232 (47)
1,209,288 (9)
3,183,242 (23)
1,866,463 (14)
1,113,324 (8)
|
43,710 (37)
8,280 (7)
42,160 (35)
23,639 (20)
1,359 (1)
|
5,700 (38)
920 (6)
5,553 (37)
2,518 (17)
149 (1)
|
Migration background, n (%)
Yes
No
|
2103826 (15)
11691723 (85)
|
17566 (15)
101582 (85)
|
1427 (10)
13431 (90)
|
Table 2
Baseline characteristics of hospital visitors for lumbar DDD between 2016 and 2019
Variables
|
Lumbar DDDa patients
(n = 119148b)
|
Surgical patients
(n = 14840b)
|
Hospital type, n (%)
University hospital
Teaching hospital
General hospital
Private clinic
|
3,962 (3)
55,603 (47)
43,744 (37)
15,839 (13)
|
609 (4)
7,338 (49)
4,331 (29)
2,563 (17)
|
Lumbar injection, n (%)
Yes
No
|
17,721 (15)
101,427 (85)
|
2,338 (16)
12,502 (84)
|
Prescription of opioids, n (%)
Yes
No
|
54,850 (46)
64,298 (54)
|
9,985 (67)
4,856 (33)
|
Referred patientsc, n (%)
|
10,116 (8)
|
4,261 (29)
|
Referral center, n (%)
|
17,987 (15)
|
3,586 (24)
|
a degenerative disc disease b average per year c Referral between hospitals
|
To account for physician registration differences and their potential impact on surgical rates, we also combined data on laminectomies and discectomies. We categorized patients into two age groups: those younger than 55 years and those older than 54 years. This distinction allowed us to differentiate between lumbar disc herniations, which are more prevalent in younger individuals, and lumbar spinal stenosis, which is commonly observed among the elderly. We separately analyzed the rates of instrumented fusion surgery.
We collected additional information on opioid prescriptions, which were recorded during the same year as the Diagnosis Treatment Combination (DBC) code registration. These prescription data were based on mandatory basic health insurance records for all Dutch inhabitants. Furthermore, we obtained details on lumbar injection therapy, including the corresponding DBC codes and procedure codes (found in the supplemental files). Lastly, we gathered data on the type of hospital (general hospital, teaching hospital, university hospital, private clinic), the number of hospitals visited (indicating referrals from other hospitals if more than one), and the specific medical specialists involved, such as orthopedic surgeons and neurosurgeons.
From the CBS database, we extracted patient-specific information, including age, sex, comorbidities assessed using the Charlson Comorbidity Index, ZIP code, average household income, level of education (categorized as low, middle, high), type of income (employer, employee, retired, student, unemployed), and migration background (whether they were born in the Netherlands or another country).
Calculation of indirect adjusted surgical rates.
To account for potential confounding factors, surgical rates were adjusted for age, sex, Charlson Comorbidity Index (CCI), household income, and level of education18. Adjusted rates were calculated per 10,000 inhabitants in two-digit postal code areas and per 10,000 patients in neurosurgical spine clusters, which represent hospital service areas (HSAs) in the Netherlands. Referrals within these clusters were considered in the analysis. Private clinics were analyzed separately. The calculation of indirectly adjusted surgical rates per 10,000 inhabitants or hospital visitors followed the following formula:
Hereafter, we will refer to surgical or referral rates per 10,000 inhabitants in two-digit postal code areas as ‘regional surgical rates’ and to surgical rates per 10,000 inhabitants in neurosurgical clusters as HSA surgical rates.
Analysis
For all rates, we calculated the Extreme Quotient (EQ, Highest/Lowest rate), EQ5 − 95 (95th percentile/5th percentile), the interquartile range (IQR, 75th percentile/25th percentile), the Coefficient of Variation (CoV, Mean/SD (Standard Deviation)), the Systematic Component of Variation (SCV). The SCV estimated the systematic variation between areas that cannot be account for by the random variation within each area and is calculated by the following formula:
An SCV below 5 was considered ‘low variation’, an SCV between 5 and 10 was considered high variation, and an SCV of 10 or higher was considered very high variation.19
We utilized mixed-effects logistic regression analyses to investigate the factors underlying the observed variation in regional surgical rates and HSA surgical rates. The analysis of regional rates included patient characteristics such as age, sex, CCI, level of education, household income, type of income, and migration background. For the HSA rates analysis, we further considered variables including referral from another hospital, referral center, neurosurgical regions, prescription of opioids, and lumbar injection therapy. Two-digit postal code areas and HSAs were treated as random effects in the analyses.
Analytic approach
All analyses were conducted at the patient level, ensuring that each patient was included only once per year. Continuous variables were presented as mean (SD) or median (IQR) for nonparametric data. Missing data were examined, and multiple imputation with 10 imputation sets was employed to address missing values. Age, sex, CCI, level of education, household income, and postal code area were utilized as predictors. Statistical significance was defined as p-value < 0.05. SPSS Statistics (version 26) was employed for all statistical analyses, while Python was used to generate maps illustrating the two-digit postal code areas.
Ethical approach
The current study adheres to the principles outlined in the Declaration of Helsinki. Our study protocol (N20.075) underwent thorough review and approval by the Medical Ethics Committee Leiden Den Haag and Delft. The committee determined that official approval was not required because participants were not directly involved in the study and patient anonymity was safeguarded in the database. Thereby, the informed consent was waived by the Medical Ethics Committee of Leiden Den Haag and Delft.