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. 2021 Feb 26;100(8):e24766. doi: 10.1097/MD.0000000000024766

Non-apnea sleep disorder and its risk for all kinds of injuries

A 14-year follow-up for a nationwide population-based retrospective study

Zhu Wei Lim a, I-Duo Wang g, Feng-Cheng Liu b, Chi-Hsiang Chung c,d, Je-Ming Hu e, Pei-Yi Tsai f, Wu-Chien Chien c,d,h,∗
Editor: Mohammed Nader Shalaby
PMCID: PMC7909222  PMID: 33663092

Supplemental Digital Content is available in the text

Keywords: injury, longitudinal health insurance database, national health insurance research database, non-apnea sleep disorder

Abstract

Non-apnea sleep disorder (NASD) increases the risk of motor vehicle accidents. However, systemic review of NASD and its risk for all causes of injury is lacking. The aim of the present study was to provide a detailed demographic data on NASD and all causes of injury in a 14-year follow up.

Our study utilized outpatient and inpatient data from the Longitudinal Health Insurance Database between 2000 and 2013 in Taiwan. We enrolled 989,753 individuals aged ≥20 years who were diagnosed with NASD as outpatients ≥3 times or inpatients ≥1 time. We matched the study cohort with a comparison cohort by age, index date and comorbidities at a ratio of 1:4. We used Cox proportional hazards regression to analyze the association of NASD and the cause of injury.

In this 14-year follow up study, patients with NASD had 12.96% increased risk of injury compared to that of the control cohort. Fall was the first place of the cause of injury with 670.26 per 105 PYs. In the stratified age group, patients aged ≧65 years had the highest risk of injury (adjusted HR= 1.381; P < .001). Kaplan–Meier analysis showed that the incidence of injury between the with- and without-NASD cohorts started from the first year and persisted until the end of the follow-up.

Our study demonstrates that NASD patients were associated with higher risk of all causes of injuries, with falling being the most prevalent diagnosis. The general public should be more aware of this neglected issue of NASD.

1. Introduction

Sleep disorders (SDs) are prevalent among the worldwide general population. They are a group of diseases characterized by disturbances in the amount, quality or behaviors of physiological conditions associated with sleep.[1] According to the International Classification of Sleep Disorders (ICSD-3) (American Academy of Sleep Medicine [AASM], 2014), SDs can be divided into 7 categories: insomnia, sleep-related breathing disorders, central disorders of hypersomnolence, circadian rhythm sleep disorders, parasomnias, sleep-related movement disorders, and other sleep disorders.[1]

Sleep disorders affect physical activity and psychosocial and performance status.[2] Several studies have shown that SDs are associated with daytime fatigue or chronic fatigue syndrome and may lead to an increased risk of motor vehicle accidents.[2] Among the categories of SDs, sleep-breathing disorders, or the so-called obstructive sleep apnea syndrome, have been related to an increased risk of traffic accidents.[3,4] Nonetheless, studies regarding SDs and their correlation with injuries are rare, especially in non-apnea sleep disorder (NASD) patients. Smolensky et al[2] pointed out that a longitudinal study is needed to study the risk of injuries in patients with different SDs. One article, written by Lin et al,[5] showed an increasing risk of injuries in patients with sleep disorders. However, they did not show the demographic features of the injuries in NASD patients. Therefore, we utilized the Taiwan National Health Insurance Research Database (NHIRD) to perform a longitudinal study of data obtained over 14 years studying the type of injuries in NASD patients with detailed demographic features and common comorbidities, including age, sex, hypertension, type 2 diabetes, hyperlipidemia, coronary disease, etc.

2. Methods

2.1. Data sources

This study used Taiwan's National Health Insurance Research Database (NHIRD), which started in 1995. Taiwan administers its insurance-based health care system, which is characterized by good accessibility, high efficiency, comprehensive population coverage (99% of the 23.74 million residents in Taiwan), relatively low costs and short waiting times. [6,7] The NHIRD contains many health registration records from the general population in Taiwan from healthcare sectors including outpatient, inpatient and emergency departments. Physicians encode clinical diagnoses according to the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM). The NHIRD has demonstrated its high accuracy and validity of diagnoses in previous articles.[6,8] Therefore, it has been used as a source of representative data in medical and health care-related research fields. Our study design was approved by the Institutional Review Board of Tri-Service General Hospital (TSGHIRB No. 1-106-05-169).

2.2. Study population

We enrolled 989,753 outpatients and inpatients from the Longitudinal Health Insurance Database between 2000 and 2013. We included patients 20 years old and above who were diagnosed with NASD as outpatients ≥3 times or were admitted as inpatients with NASD. We defined the index date as the time NASD was diagnosed, and 7671 patients were included. We excluded patients with NASD who were diagnosed before the index date, had injuries before tracking, were <20 years old and had unknown gender, and 6648 participants remained as the study cohort. They were matched by age, gender, index date, and comorbidities to a comparison cohort at a ratio of 1:4. We compared the baseline comorbidities between the study and comparison cohorts, including Type 2 diabetes mellitus, hypertension, hyperlipidemia, cerebrovascular disease, stroke, obesity, anxiety and depression. In addition, we included categories of injury according to the ICD-9 diagnosis, such as fracture, dislocation, sprains and strains, intracranial/internal injury, open wound, injury to blood vessels, superficial injury/contusion, crushing, foreign body entering through orifice, burn, injury to nerve and spinal cord, poisoning, and other injuries. Moreover, we listed the mechanisms of injuries in Table S3: traffic, poisoning (drugs/medicaments/biologicals), poisoning (solid and liquid substances/gases/vapors), surgical/medical care, abnormal reaction to medical procedure, falls, burns, and fires, environment, drowning, suffocation, other unintentional injuries, late effects, adverse drug reaction, suicide and homicide/abuse. Both our study and comparison cohorts were tracked until the end of 2013.12.31.

2.3. Statistical analysis

The comparison of demographic features and comorbidities in both cohorts was performed with the Chi-Squared test. Continuous data, such as the mean age, years of follow-up and years to injury, were compared by Student t test. We calculated the incidence (per 105 person-years) of NASD according to age, sex and concomitant comorbidities and used multivariable analysis for the adjustment. Multivariable Cox proportional hazards regression models were used to calculate the adjusted hazard ratios and the 95% confidence intervals. We evaluated the cumulative risk of injury and year of follow-up by using Kaplan–Meier analysis and the log-rank test. SPSS 22.0 software was used for all analyses. We considered a two-sided P value to be statistically significant at P < .05.

3. Results

The study included 6648 patients with newly diagnosed NASD and 26,592 patients in the control cohort during the period between 2000 and 2013. The mean follow-up time was 10.78 ± 11.17 years in the NASD cohort and 11.78 ± 11.12 years in the control cohort (see Supplemental Digital Content Table 1 which showed years of follow up in both cohorts). The mean number of years to injury was 4.40 ± 3.88 years in the NASD population and 5.03 ± 3.96 years in the control cohort (see Supplemental Digital Content Table 1 which showed years to injuries in patients with NASD). At the end of follow-up, 2446 participants in the NASD cohort had injuries compared with 6738 participants in the control cohort (Fig. 1).

Figure 1.

Figure 1

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Flowchart of study group selection from the National Health Insurance Research Database in Taiwan.

Table 1 shows the baseline characteristics of the study, with the results of follow-up shown in Table 2. The average ages were 68.14 ± 19.25 and 69.03 ± 20.11 years for the study and comparison cohorts, respectively (Table 2). In the age-stratified NASD group, patients aged ≥65 years had the highest injury rate (49.37%) compared to non-NASD group, followed by patients aged 45 to 64 years (35.05%) (Table 2). Table 2 shows the results after 14 years of follow-up, and we found a higher incidence of injury in the NASD groups with comorbidities of diabetes mellitus, hypertension, cerebrovascular disease, anxiety and depression. Kaplan–Meier analysis showed that the incidence of injury started to branch off in the first year in the NASD cohort and persisted until the end of the follow-up (log-rank; P < .001; Fig. 2).

Table 1.

Study characteristics at baseline.

NASD
Total With Without
Variables n % n % n % P
Total 33,240 6648 20.00 26,592 80.00
Gender
 Male 15,425 46.40 3085 46.40 12,340 46.40 .999
 Female 17,815 53.60 3563 53.60 14,252 53.60
Age (years) 57.27 ± 17.64 57.36 ± 17.74 57.25 ± 17.61 .649
Age group (years)
 20–44 6410 19.28 1282 19.28 5128 19.28 .999
 45–64 12,370 37.21 2474 37.21 9896 37.21
 ≥65 14,460 43.50 2892 43.50 11,568 43.50
DM
 Without 28,475 85.66 5614 84.45 22,861 85.97 .002
 With 4765 14.34 1034 15.55 3731 14.03
HTN
 Without 27,027 81.31 5121 77.03 21,906 82.38 <.001
 With 6213 18.69 1527 22.97 4686 17.62
Hyperlipidemia
 Without 32,216 96.92 6355 95.59 25,861 97.25 <.001
 With 1024 3.08 293 4.41 731 2.75
CVD
 Without 30,062 90.44 6008 90.37 24,054 90.46 .835
 With 3178 9.56 640 9.63 2538 9.54
Stroke
 Without 30,290 91.13 5996 90.19 24,294 91.36 .003
 With 2950 8.87 652 9.81 2298 8.64
Obesity
 Without 33,216 99.93 6636 99.82 26,580 99.95 .001
 With 24 0.07 12 0.18 12 0.05
Anxiety
 Without 32,895 98.96 6398 96.24 26,497 99.64 <.001
 With 345 1.04 250 3.76 95 0.36
Depression
 Without 32,872 98.89 6,425 96.65 26,447 99.45 <.001
 With 368 1.11 223 3.35 145 0.55
Season
 Spring 9048 27.22 1892 28.46 7,156 26.91 <.001
 Summer 7598 22.86 1363 20.50 6235 23.45
 Autumn 6962 20.94 1221 18.37 5741 21.59
 Winter 9632 28.98 2172 32.67 7460 28.05
Location
 Northern Taiwan 12,769 38.41 1852 27.86 10,917 41.05 <.001
 Middle Taiwan 9912 29.82 2538 38.18 7374 27.73
 Southern Taiwan 8433 25.37 1759 26.46 6674 25.10
 Eastern Taiwan 1994 6.00 468 7.04 1526 5.74
 Outlet islands 132 0.40 31 0.47 101 0.38
Urbanization level
 1 (highest) 11,064 33.29 1746 26.26 9318 35.04 <.001
 2 14,111 42.45 2567 38.61 11,544 43.41
 3 2830 8.51 910 13.69 1,920 7.22
 4 (lowest) 5235 15.75 1425 21.44 3810 14.33
Level of care
 Hospital center 10,800 32.49 1379 20.74 9421 35.43 <.001
 Regional hospital 11,596 34.89 2347 35.30 9249 34.78
 Local hospital 10,844 32.62 2922 43.95 7922 29.79
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P = Chi-Squared/Fisher exact test on category variables and t-test on continuous variables.

Data are from Health and Welfare Data Science Center, Ministry of Health and Welfare (HWDC, MOHW).

Table 2.

Study characteristics at the endpoint.

NASD
Total With Without
Variables n % n % n % P
Total 33,240 6648 20.00 26,592 80.00
Injury
 Without 24,056 72.37 4202 63.21 19,854 74.66 <.001
 With 9,184 27.63 2446 36.79 6738 25.34
Gender
 Male 15,425 46.40 3085 46.40 12,340 46.40 .999
 Female 17,815 53.60 3563 53.60 14,252 53.60
Age (years) 68.85 ± 19.94 68.14 ± 19.25 69.03 ± 20.11 .001
Age group (years)
 20–44 6170 18.56 1036 15.58 5134 19.31 <.001
 45–64 11,242 33.82 2,330 35.05 8912 33.51
 ≧65 15,828 47.62 3,282 49.37 12,546 47.18
DM
 Without 27,969 84.14 5496 82.67 22,473 84.51 <.001
 With 5271 15.86 1152 17.33 4119 15.49
HTN
 Without 27,505 82.75 5401 81.24 22,104 83.12 <.001
 With 5735 17.25 1247 18.76 4488 16.88
Hyperlipidemia
 Without 32,562 97.96 6498 97.74 26,064 98.01 .174
 With 678 2.04 150 2.26 528 1.99
CVD
 Without 30,433 91.56 6016 90.49 24,417 91.82 .001
 With 2807 8.44 632 9.51 2175 8.18
Stroke
 Without 30,526 91.84 6104 91.82 24,422 91.84 .962
 With 2714 8.16 544 8.18 2170 8.16
Obesity
 Without 33,219 99.94 6641 99.89 26,578 99.95 .166
 With 21 0.06 7 0.11 14 0.05
Anxiety
 Without 33,018 99.33 6524 98.13 26,494 99.63 <.001
 With 222 0.67 124 1.87 98 0.37
Depression
 Without 32,865 98.87 6441 96.89 26,424 99.37 <.001
 With 375 1.13 207 3.11 168 0.63
Season
 Spring 8168 24.57 1624 24.43 6544 24.61 .072
 Summer 8279 24.91 1636 24.61 6643 24.98
 Autumn 8659 26.05 1812 27.26 6847 25.75
 Winter 8134 24.47 1576 23.71 6558 24.66
Location
 Northern Taiwan 12,750 38.36 1976 29.72 10,774 40.52 <.001
 Middle Taiwan 9904 29.80 2401 36.12 7503 28.22
 Southern Taiwan 8459 25.45 1786 26.87 6673 25.09
 Eastern Taiwan 1991 5.99 452 6.80 1539 5.79
 Outlet islands 136 0.41 33 0.50 103 0.39
Urbanization level
 1 (highest) 10,743 32.32 1841 27.69 8902 33.48 <.001
 2 14,429 43.41 2767 41.62 11,662 43.86
 3 2641 7.95 708 10.65 1933 7.27
 4 (lowest) 5427 16.33 1332 20.04 4095 15.40
Level of care
 Hospital center 11,201 33.70 1764 26.53 9437 35.49 <.001
 Regional hospital 12,866 38.71 2720 40.91 10,146 38.15
 Local hospital 9173 27.60 2164 32.55 7009 26.36
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P = Chi-Squared/Fisher exact test on category variables and t-test on continuous variables.

Data are from Health and Welfare Data Science Center, Ministry of Health and Welfare (HWDC, MOHW).

Figure 2.

Figure 2

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The Kaplan–Meier analysis for cumulative risk of injury in aged 20 years and older between with- and without-non-apnea sleep disorders (NASD) with log-rank test.

NASD patients had a 32.8% increased risk of injury compared with the control cohort (crude HR = 1.328 [95% CI, 1.238–1.391]; P < .001; Table 3). After adjusting for age and other concomitant comorbidities, the HR of injury in patients with NASD decreased to 1.296 (95% CI = 1.236–1.358; P < .001; Table 3). Females were predominant in our study (53.6%, Table 2); however, when adjusted for age and other comorbidities, males had a 12% higher risk of injury than females (adjusted HR = 1.120 [95% CI, 1.074–1.168]; P < .001; Table 3). We further stratified common comorbidities and the age groups by using the Cox regression model and studied the impact of NASD on injuries in the study group and the control group (Table 4). The incidence of injuries in the NASD and non-NASD groups were 3122.76 and 2351.34 per 105 person-years, respectively (Table 4). When stratified into gender groups, in patients with NASD, male patients had an incidence of 3466.14 injuries per 105 person-years, while the incidence was 2784.65 per 105 person-years in female patients. Moreover, male patients with NASD had a 39.7% increased risk of injury compared to those who did not, and women with NASD had a 20.4% increased risk of injury than the control group. When stratified into age groups, patients in the 20 to 44-year group had the highest incidence of injury at 4191.61 per 105 person-years, whereas patients aged ≥65 years had the lowest incidence rate at 2943.54 per 105 person-years. However, NASD had the greatest impact on patients aged ≥65 years, with an adjusted HR of 1.381 (95% CI = 1.318–1.448; P < .001), followed by those aged 45 to 64 years (adjusted HR = 1.265), then aged 20 to 44 years (adjusted HR = 1.207), compared to non-NASD patients. In patients with NASD, the spring and summer seasons had the highest risk of injuries (adjusted HR = 1.311 and 1.326, respectively) (Table 4).

Table 3.

Injury factors by using Cox regression.

Variables Crude HR 95% CI 95% CI P Adjusted HR 95% CI 95% CI P
NASD
 Without Reference Reference
 With 1.328 1.268 1.391 <.001 1.296 1.236 1.358 <.001
Gender
 Male 1.094 1.049 1.140 <.001 1.120 1.074 1.168 <.001
 Female Reference Reference
Age group (years)
 20–44 Reference Reference
 45–64 1.001 0.956 1.047 .657 1.081 1.010 1.157 .025
 ≧65 1.014 0.953 1.080 .986 1.125 1.054 1.202 <.001
DM
 Without Reference Reference
 With 1.028 0.881 1.297 .725 1.015 0.961 1.073 .586
HTN
 Without Reference Reference
 With 1.766 1.737 1.817 <.001 1.845 1.800 1.892 <.001
Hyperlipidemia
 Without Reference Reference
 With 1.310 1.250 1.384 <.001 1.382 1.308 1.474 <.001
CVD
 Without Reference Reference
 With 1.614 1.509 1.697 <.001 1.714 1.656 1.776 <.001
Stroke
 Without Reference Reference
 With 1.725 1.668 1.788 <.001 1.755 1.694 1.821 <.001
Obesity
 Without Reference Reference
 With 0.619 0.258 1.488 .284 0.629 0.261 1.513 .300
Anxiety
 Without Reference Reference
 With 1.001 0.772 1.299 .991 1.071 0.748 1.261 .826
Depression
 Without Reference Reference
 With 1.196 1.016 1.408 .031 1.160 0.984 1.367 .076
Season
 Spring Reference Reference
 Summer 0.941 0.888 0.998 .041 0.953 0.899 1.010 .105
 Autumn 0.821 0.775 0.870 <.001 0.818 0.771 0.867 <.001
 Winter 0.961 0.906 1.019 .186 0.965 0.910 1.024 .238
Location
 Northern Taiwan Reference Multicollinearity with urbanization level
 Middle Taiwan 1.272 1.210 1.338 <.001 Multicollinearity with urbanization level
 Southern Taiwan 1.235 1.171 1.302 <.001 Multicollinearity with urbanization level
 Eastern Taiwan 1.305 1.200 1.418 <.001 Multicollinearity with urbanization level
 Outlet islands 1.066 0.711 1.475 .698 Multicollinearity with urbanization level
Urbanization level
 1 (highest) 1.128 0.686 1.508 .602 1.155 0.800 1.319 .554
 2 1.071 0.769 1.373 .585 1.020 0.869 1.175 .801
 3 1.007 0.931 1.090 .853 1.008 0.931 1.091 .849
 4 (lowest) Reference Reference
Level of care
 Hospital center 0.619 0.587 0.653 <.001 0.684 0.644 0.726 <.001
 Regional hospital 0.683 0.651 0.717 <.001 0.701 0.668 0.737 <.001
 Local hospital Reference Reference
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Adjusted HR = adjusted variables listed in the table, CI = confidence interval, HR = hazard ratio.

Data are from Health and Welfare Data Science Center, Ministry of Health and Welfare (HWDC, MOHW).

Table 4.

Injury factors stratified by variables listed in the table by using Cox regression.

NASD
With Without With vs. Without
Stratified Event PYs Rate (per 105 PYs) Event PYs Rate (per 105 PYs) Ratio Adjusted HR 95% CI 95% CI P
Total 2446 78,328.06 3,122.76 6738 286,559.40 2351.34 1.328 1.296 1.236 1.358 <.001
Gender
 Male 1347 38,861.68 3,466.14 3991 164,836.85 2421.18 1.432 1.397 1.332 1.464 <.001
 Female 1099 39,466.38 2,784.65 2747 121,722.55 2256.77 1.234 1.204 1.148 1.262 <.001
Age group (years)
 20–44 383 9137.29 4,191.61 1349 39,799.81 3389.46 1.237 1.207 1.151 1.265 <.001
 45–64 815 26,792.87 3,041.85 2162 91,483.77 2363.26 1.287 1.265 1.198 1.316 <.001
 ≧65 1248 42,397.90 2,943.54 3227 155,275.82 2078.24 1.416 1.381 1.318 1.448 <.001
DM
 Without 2041 64,358.45 3,171.30 5537 230,527.98 2401.88 1.320 1.288 1.224 1.350 <.001
 With 405 13,969.61 2,899.15 1201 56,031.42 2143.44 1.353 1.320 1.259 1.383 <.001
HTN
 Without 1963 60,054.40 3,268.70 5390 218,142.85 2470.86 1.323 1.291 1.231 1.351 <.001
 With 483 18,273.66 2,643.15 1348 68,416.55 1970.28 1.342 1.309 1.249 1.372 <.001
Hyperlipidemia
 Without 2424 76,375.69 3,173.78 6672 278,014.05 2399.88 1.322 1.291 1.230 1.342 <.001
 With 22 1952.37 1,126.84 66 8545.35 772.35 1.459 1.424 1.358 1.492 <.001
CVD
 Without 2,295 71,075.07 3,228.98 6339 258,441.28 2452.78 1.316 1.284 1.221 1.346 <.001
 With 151 7252.99 2,081.90 399 28,118.12 1419.01 1.467 1.432 1.365 1.501 <.001
Stroke
 Without 2282 71,756.39 3,180.20 6280 261,087.73 2405.32 1.322 1.290 1.230 1.351 <.001
 With 164 6571.67 2,495.56 458 25,471.67 1798.08 1.388 1.354 1.292 1.491 <.001
Obesity
 Without 2445 78,178.29 3,127.47 6734 286,396.96 2351.28 1.330 1.299 1.238 1.364 <.001
 With 1 149.77 667.69 4 162.44 2462.45 0.271 0.304 0.201 0.472 .001
Anxiety
 Without 2410 77,349.34 3,115.73 6,717 285,334.27 2,354.08 1.324 1.292 1.224 1.353 <.001
 With 36 978.72 3,678.27 21 1,225.13 1,714.10 2.146 2.094 1.997 2.198 <.001
Depression
 Without 2358 76,101.87 3,098.48 6679 283,937.31 2352.28 1.317 1.284 1.226 1.347 <.001
 With 88 2226.19 3,952.94 59 2,622.09 2250.11 1.757 1.726 1.635 1.799 <.001
Season
 Spring 617 18,635.02 3,310.97 1607 65,216.09 2464.12 1.344 1.311 1.251 1.374 <.001
 Summer 618 19,172.81 3,223.31 1718 72,435.96 2371.75 1.359 1.326 1.265 1.392 <.001
 Autumn 635 22,652.02 2,803.28 1739 80,829.34 2151.45 1.303 1.272 1.213 1.332 <.001
 Winter 576 17,868.21 3,223.60 1674 68,078.01 2458.94 1.311 1.279 1.220 1.341 <.001
Urbanization level
 1 (highest) 622 21,755.55 2,859.04 1871 88,777.04 2107.53 1.357 1.324 1.267 1.389 <.001
 2 1001 32,454.11 3,084.36 2911 127,062.36 2291.00 1.346 1.314 1.253 1.377 <.001
 3 281 7,683.45 3,657.21 598 21,842.45 2737.79 1.336 1.304 1.243 1.360 <.001
 4 (lowest) 542 16,434.95 3,297.85 1358 48,877.55 2778.37 1.187 1.158 1.105 1.214 <.001
Level of care
 Hospital center 524 21,643.70 2,421.03 1988 97,040.37 2048.63 1.182 1.153 1.100 1.209 <.001
 Regional hospital 1005 35,027.74 2,869.15 2657 122,357.80 2171.50 1.321 1.288 1.230 1.350 <.001
 Local hospital 917 21,656.62 4,234.27 2093 67,161.23 3116.38 1.359 1.327 1.267 1.398 <.001
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Adjusted HR = adjusted hazard ratio: adjusted for the variables listed in Table 3, CI = confidence interval, PYs = person-years.

Data are from Health and Welfare Data Science Center, Ministry of Health and Welfare (HWDC, MOHW).

In our study, we analyzed injury diagnosis and mechanisms of injury according to the International Classification of Diseases Ninth Revision, Clinical Modification (ICD-9-CM) list (see Supplemental Digital Content Table 2 which showed injury diagnosis and mechanisms of injury according to the ICD-9-CM list). We further stratified the patients into male and female and studied their associated risk of injury. In the categories of “total causes of injuries,” falls had the highest incidence with 670.26 injuries per 105 person-years, followed by traffic, with an incidence of 434.07 per 105 person-years. In NASD patients, drowning had the highest risk of injury (adjusted HR = 4.760 [95% CI, 4.540–5.012]; P < .001), followed by suicide (adjusted HR = 3.184 [95% CI, 3.037–3.339]; P < .001) (see Supplemental Digital Content Table 3 for adjusted HR in each injury diagnosis when having NASD). The incidence of injury in male patients with NASD was 3466.14 per 105 person-years and 2784.65 per 105 person-years in female patients (Table 4). In male patients with NASD, falls and traffic had the highest incidence, 643.31 and 488.91 per 105 person-years, respectively. We also ranked the cause of injury in Table S4.

However, in regard to the risk of injury, suicide was the highest, with an adjusted HR of 3.198 (95% CI, 3.050–3.352; P < .001), followed by accidental poisoning by other solid and liquid substances, gases, and vapors (adjusted HR = 3.000 [95% CI, 2.908–3.199]; P < .001; Table S3). In female patients with NASD, the highest risk of injury was for suicide (adjusted HR = 3.210 [95% CI, 3.016–3.365]; P < .001; see Supplemental Digital Content Table 3 for adjusted HR in each injury diagnosis when having NASD), which was the same as in male patients, followed by accidental poisoning by other solid and liquid substances, gases, and vapors (adjusted HR = 3.009 [95% CI, 2.836–3.154]; P < .001; see Supplemental Digital Content Table 3 for adjusted HR in each injury diagnosis when having NASD).

4. Discussion

Our study is the largest and the longest longitudinal cohort to date discussing the association between NASD and factors of all injuries by analyzing the ICD-9-CM code. The mean follow-up time in the NASD group was 10.78 ± 11.17 years. Previous studies only focused on the association between sleep disorders and motor vehicle accidents[2]. Uehli et al[9] published a meta-analysis article about workers with sleep problems, and the results showed a 62% increased risk of injuries compared to workers without sleep problems. However, there was no article to date demonstrating NASD and all causes of injury.

Autumn had the highest percentage of injuries (27.26%), followed by summer (24.61%) (Table 2). However, in patients with NASD, the spring and summer seasons had the highest risk of injury (adjusted HR= 1.311 and 1.326, respectively) (Table 4). Our results are similar to those in the United States, when more injuries occurred during the summer months and less reported in winter.[10]

In our study, falls and traffic had the highest incidence of 670.26 and 434.07 per 105 person-years, respectively. In the NASD patients, the highest risk for injuries was drowning (adjusted HR = 4.760 [95% CI, 4.540–5.012]; P < .001), followed by suicide (adjusted HR = 3.184 [95% CI, 3.037–3.339]; P < .001) (Table S3).[11–15] Scientists have demonstrated that unintentional gun injuries occur while playing with, handling, loading, unloading, cleaning and firing a gun. It may occur in rural areas without parental supervision or permission and while hunting.[16–23] In addition, scientists have concluded that firearm-related homicide and suicide are associated with the use of crack cocaine and binge drinking, changes in the lethality and types of firearms, and urban poverty in the United States.[16,23,24] However, because firearms are not available in Taiwan, the different patterns of injuries may reflect sociocultural preferences and socioeconomic differences between the 2 regions.[25] We suggest that personal medical problems and mental status may be possible reasons for intentional suicide and homicide in Taiwan.[26] Therefore, we need a more thorough investigation of this explanation for further studies.

The 5 leading causes of nonfatal injuries in the United States are unintentional fall, unintentional stuck by/against, unintentional overexertion, unintentional motor vehicles occupant and unintentional cut/pierce, which unintentional injury are more than intentional injury. Traffic is the most prevalence unintentional injury in Taiwan. This is because of the distracted driving with cellphones, such as sending text messages and email, chatting, and playing mobile games while driving.[27,28] In addition, playing with cell phones right before sleep may cause NASD and increase the risk of injuries during the daytime.

The injuries from accidental poisoning by other solid and liquid substances, gases, and vapors in our study, were similar to those in the United States in 2017 and were among the high-risk injuries among male and female patients with NASD. These substances included alcohol, cleansing and polishing agents, disinfectants, paints and varnishes, petroleum products, other solvents and their vapors, agricultural and horticultural chemical and pharmaceutical preparations other than plant foods and fertilizers, corrosives and caustics, foodstuffs and poisonous plants, gas distributed by pipeline, and other utility gas and other carbon monoxide (ICD code E860-E869).

Adverse drug effects and accidental poisoning by drugs and medicinal and biological substances are other causes of injuries in Taiwan and the United States due to the inappropriate prescribing for elderly patients without knowing the drug-drug interactions.[2] In contrast, for teenagers and young adults, accidental poisoning by narcotics and hallucinogen overdoses are the most concerning problems to the public, as they may experience lifelong addiction from these drugs.[29]

There are several possibilities for why NASD were associated with high risk of injuries. First, elderly individuals undergo changes in brain morphology. These changes result in more and shorter REM episodes and less total REM sleep, which result in poor quality of sleep and lower tolerance and threshold to fatigue.[30] Second, compared to elderly people, younger patients have less experience with driving exposure and lower accident rates.[2] Third, elderly people have many comorbidities and more concomitant medications than younger people. Abad et al[31] demonstrated that arthritis-related symptoms and chronic painful physical ailments are the leading causes of sleep problems, such as difficulty in initiating sleep, maintaining sleep continuity, early morning awakenings and non-restorative sleep in individuals aged ≥55 years in the United States. Smolensky et al[2] demonstrated certain classes of drugs and sleep disorders as risk factors for driving incidents. However, non-prescription medications and other medical conditions may also be potential risk factors in road crashes, especially for at-fault drivers ≥65 years of age. Scientists have demonstrated a list of drugs that are associated with an increased risk for injurious motor vehicle collisions: non-steroidal anti-inflammatory drugs (NSAIDs), angiotensin converting enzyme inhibitors, antihypertensive drugs, antidepressants,[32] opioid analgesics, benzodiazepines, antihistamines, psychoactive drugs, hypnotics, anxiolytics, beta agonists, and sleep medications.[33–41] However, the neglecting and complexity of the potential adverse effects, doses and drug-drug interactions of the above drugs lead to an increased drowsy-driving risk that the public needs to recognize.

The mechanism of NASD and its association with different types of injuries remain unknown. Scientists have demonstrated that NASD is a disorder associated with the activation of the sympathetic nervous system and hypothalamic-pituitary-adrenal (HPA) axis. In addition to melatonin acting as an important sleep-regulating hormone, several regulatory neurotransmitters, such as acetylcholine, serotonin and norepinephrine, control our sleep cycles and daily activities.[42] The changes in the neural activation of sympathetic tone disrupt the daytime and nighttime activity and thus increase the occurrence of various types of injury, as shown in Table S3. In addition, NASD may also be responsible for the dysregulation of mood and even suicide, homicide or abuse, as both mood and sleep cycles are derived from the same monoamine neurotransmitters in brain.[43]

NASD and chronic insomnia may cause dysregulation of the HPA axis because of the frequent negative feedback of stress hormones to the hypothalamus and pituitary gland.[44] Cortisol is an important stress hormone that has wide-ranging effects, such as influencing immunologic status, inflammatory reactions, and arousal and increasing the catabolic properties of proinflammatory cytokines, glucocorticoids and stress peptides.[45] The increased levels of stress hormone in NSAD that are involved in the inflammatory and oxidative pathways affect endothelial function and coagulability in the cardiovascular system, causing delayed healing of wounds and late effects of accidental injury, accidental poisoning and accidental fall.[46,47]

The possible mechanism of NASD and drug metabolism is the cumulative cortisol burden, which can influence the development of metabolic disorders and the catabolic reaction of drugs.[45] Liver cytochrome P450 (CYP) is the key enzyme in the metabolism of xenobiotics and other drugs.[48] Cortisol is a type of human glucocorticoid that also acts as an enzyme inhibitor of hepatic metabolism in xenobiotics.[49] Monostory et al[50] demonstrated that NR3C1, a transcription factor mediates glucocorticoid-responsive genes within regulatory DNA regions and modulates the induction of CYP1A1 and CYP1A2 according to the physiological levels of glucocorticoids in human and rodent cells. Therefore, NASD, which leads to a dysfunctional HPA axis and the production of cortisol, may participate in drug metabolism and cause accidental poisoning by drugs, medicines, biological substances, and other solid and liquid substances, gases, and vapors.

Our study has several limitations. First, the NHIRD database lacks laboratory data, clinical images and information about the lifestyles of the patients. Second, there were no objective measurements of NASD in this study. Therefore, we cannot connect the severity of NASD to the different kinds of injuries. Third, our study only identified patients with sleep disorders who visited the OPD or were hospitalized. However, there are moderate numbers of people who have sleep problems and do not seek medical help. They may have had injuries and added some bias to our study. In conclusion, our study is the largest retrospective study to date discussing the association of NASD with all kinds of injuries by using the NHIRD, which has a large sample size and is capable of longitudinal tracking. NASD has a higher prevalence than obstructive sleep apnea syndrome in the general population, especially in the elderly population. Therefore, the general public should be more aware of this neglected issue of NASD, and healthcare providers should do more to prevent the possibility of all kinds of injuries.

Author contributions

Conceptualization: Feng-Cheng Liu, Je-Ming Hu, I-Duo Wang.

Data curation: Chi-Hsiang Chung, Pei-Yi Tsai, I-Duo Wang, Wu-Chien Chien.

Formal analysis: Zhu Wei Lim, Feng-Cheng Liu, Pei-Yi Tsai, I-Duo Wang, Wu-Chien Chien.

Funding acquisition: Chi-Hsiang Chung, Wu-Chien Chien.

Investigation: Zhu Wei Lim, I-Duo Wang, Wu-Chien Chien.

Methodology: Feng-Cheng Liu, Chi-Hsiang Chung, Je-Ming Hu, Pei-Yi Tsai, Wu-Chien Chien.

Project administration: Zhu Wei Lim, Feng-Cheng Liu, Wu-Chien Chien.

Resources: Wu-Chien Chien.

Software: Chi-Hsiang Chung, Pei-Yi Tsai, Wu-Chien Chien.

Supervision: I-Duo Wang, Wu-Chien Chien.

Validation: Chi-Hsiang Chung, Je-Ming Hu, Pei-Yi Tsai, I-Duo Wang, Wu-Chien Chien.

Visualization: I-Duo Wang.

Writing – original draft: Zhu Wei Lim.

Writing – review & editing: I-Duo Wang, Wu-Chien Chien.

Supplementary Material

Supplemental Digital Content

Footnotes

Abbreviations: HPA = hypothalamic- pituitary-adrenal, ICD-9-CM = International Classification of Diseases Ninth Revision, Clinical Modification, NASD = Non-apnea sleep disorder, NHIRD = National Health Insurance Research Database, SDs = Sleep disorders.

How to cite this article: Lim ZW, Wang ID, Liu FC, Chung CH, Hu JM, Tsai PY, Chien WC. Non-apnea sleep disorder and its risk for all kinds of injuries: a 14-year follow-up for a nationwide population-based retrospective study. Medicine. 2021;100:8(e24766).

ZWL and I-DW contribute equally in this article.

This study was supported by the Tri-Service General Hospital Research Foundation (TSGH-B-110012), and the sponsor has no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

The authors have no conflicts of interests to disclose.

The datasets generated during and/or analyzed during the current study are publicly available.

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