Skip to main content
PMC home page
BMC Public Health logoLink to BMC Public Health
. 2016 May 26;16:443. doi: 10.1186/s12889-016-3113-y

The incidence risk of type 2 diabetes mellitus in female nurses: a nationwide matched cohort study

Hsiu-Ling Huang 1,2, Cheng-Chin Pan 3, Shun-Mu Wang 1, Pei-Tseng Kung 4, Wen-Yu Chou 5, Wen-Chen Tsai 5,✉
PMCID: PMC4881212  PMID: 27230065

Abstract

Background

Diabetes is one of the most common chronic illnesses worldwide. This study was to assess whether the incidence risk of type 2 diabetes mellitus between female nurses and female non-nurses.

Methods

Study data were obtained from the Longitudinal Health Insurance Research Database, and nurses were sampled from the Registry for medical personnel. Nurses and non-nurses with similar traits and health conditions were selected via 1:1 propensity score matching. A total of 111,670 subjects were selected (55,835 nurses and 55,835 non-nurses). Stages of diabetes development were monitored until December 31, 2009. The Cox proportional hazards model was used to discuss risks and influencing factors related to diabetes. Poisson distribution methods were used to examine the incidence rate of diabetes per 1,000 person-years.

Results

The propensity matching results show that on average, female nurses who were diagnosed with diabetes were younger compared with the non-nurses (46.98 ± 10.80 vs. 48.31 ± 10.43, p <0.05). However, the results of the Cox proportional hazards model show that the nurses showed a lower risk of developing diabetes compared with the non-nurses (Adj. HR = 0.84, 95 % CI: 0.79–0.90). Factors influencing diabetes development risks among the nurses include advanced age and high Charlson Comorbidity Index levels.

Conclusion

The low degree of diabetes development among the nurses may be attributable to the fact that nurses possess substantial knowledge on health care and on healthy behaviors. The results of this study can be used as a reference to assess occupational risks facing nursing staff, to prevent diabetes development, and to promote health education.

Keywords: Diabetes, Nurse, Occupational health, National health insurance, Cohort study

Background

Nurses work on the front lines in the medical industry and account for 45.2 % of all professional medical personnel in Taiwan [1]. Numerous medical orders and procedures rely on the work of nurses; therefore, nursing care is highly correlated with care quality levels and with patient treatment outcomes, and nurses play a crucial role in the medical industry.

Ongoing care services are essential to the medical industry. Shift work creates certain occupational problems for nurses [2, 3]. Relevant studies have shown that in addition to causing an increasing number of accidents [4] and higher rates of disease and cancer development [5], engagement in shift work increases one’s likelihood of developing metabolic diseases [6, 7]. Previous studies have revealed that individuals working in occupations that involve shift work show higher risks of developing diabetes compared with those who do not work shifts [8–10].

Diabetes is one of the most common chronic illnesses worldwide. Statistics [11] show that approximately 366 million people worldwide were diagnosed with diabetes in 2011. This number is estimated to increase to 552 million by 2030, accounting for 4.4 % of the global population. In the United States, research has been conducted on the health status of 67,420 nurses from 1976 to 1996, and the results show that 7,401 (11 %) of the nurses were diagnosed with type 2 diabetes mellitus (DM) [12]. Since 2003, diabetes has ranked third among the top 10 causes of death among women in Taiwan [13]. Nurses are exposed to an occupational environment that may be detrimental to their health, which may affect the quality of medical care. The present study compares type 2 DM development risk levels among female nurses to those of the general female citizen population to further identify influencing factors. These data may serve as a foundation for future discussions on occupational health in relation to health promotion, prevention and protection.

Methods

Study design

A retrospective and longitudinal method was adopted in this study, and data were sourced from the National Health Insurance Research Database. To comply with privacy measures, personal information was removed from the collected data.

The Taiwanese government created a compulsory National Health Insurance (NHI) system in 1995, a governmental insurance system that all citizens are mandated to be insured under. Since the end of 2012, the NHI coverage rate reached to as high as 99.85 % [14]. The NHI covers costs for outpatient procedures; emergency procedures; and inpatient prescriptions, treatments, and examinations. The NHI database details all medical claims records for the insured, including treatments for chronic illnesses such as diabetes [15, 16].

Data sources and study participants

Data were sourced from the Longitudinal Health Insurance Research Database of 2000 published by the NHI, which included one million population randomly selected to be representative of whole population in Taiwan and a sample of nurses was selected from the nationwide Registry for Medical Personnel published by the NHI. We used female nurses and female non-nurses as the study participants. Nurses were defined as the nurses were listed in the Registry for Medical Personnel before December 31, 2000. Non-nurses were defined as the participants who had not registered as a licensed medical professional, such as physicians, dentists, physical therapists, nutritionists, and so on before the end of study observation. Participants younger than 20 years and older than 90 years and those diagnosed with type 2 DM prior to December 31, 2000 were excluded. Male nurses were excluded from the study, as most nurses in Taiwan are female (98.92 %) [17]. A total of 374,173 participants were selected, 70,675 of whom were nurses and 303,498 of whom were female non-nurses.

To objectively compare diabetes development risks among female nurses and female non-nurses, 1:1 propensity score matching was used to control for selection bias, and participants with similar characteristics and health conditions were selected from the two groups. After propensity score matching, we assigned 55,835 nurses to the observation group and 55,835 non-nurses to the control group (Table 1). Patterns of diabetes development were monitored until December 31, 2009, covering an average of 9.68 ± 1.06 years (9.72 ± 0.92 and 9.64 ± 1.19 y for nurses and non-nurses, respectively).

Table 1.

Participants demographics before and after propensity score (PS) matching

Variables Before PS Matching After PS Matching (1:1)
Total Non-nurses Nurse p -value Total Non-nurses Nurse p -value
N % N % N % N % N % N %
Total participants 374173 100.00 303498 81.11 70675 18.89 111670 100.00 55835 50.00 55835 50.00
Age <0.001 1.000
<25 59213 15.83 39267 12.94 19946 28.22 24912 22.31 12456 22.31 12456 22.31
25–34 107621 28.76 76758 25.29 30863 43.67 47271 42.33 23628 42.32 23643 42.34
35–44 88584 23.67 74351 24.50 14233 20.14 28221 25.27 14118 25.29 14103 25.26
45–54 54406 14.54 49631 16.35 4775 6.76 9550 8.55 4775 8.55 4775 8.55
55–64 32444 8.67 31771 10.47 673 0.95 1346 1.21 673 1.21 673 1.21
≧65 31905 8.53 31720 10.45 185 0.26 370 0.33 185 0.33 185 0.33
Average age (Mean, Std) 40.13 15.05 42.20 15.48 31.23 8.48 32.79 8.97 33.04 9.10 32.54 8.83
Monthly salary(NT$) <0.001 1.000
Low-income household 1326 0.35 1312 0.43 14 0.02 28 0.03 14 0.03 14 0.03
≦17280 47252 12.63 42573 14.03 4679 6.62 9358 8.38 4679 8.38 4679 8.38
17281 ~ 22800 141273 37.76 134297 44.25 6976 9.87 13952 12.49 6976 12.49 6976 12.49
22801 ~ 28800 71049 18.99 61248 20.18 9801 13.87 19602 17.55 9801 17.55 9801 17.55
28801 ~ 36300 34972 9.35 23113 7.62 11859 16.78 20061 17.96 10023 17.95 10038 17.98
36301 ~ 45800 44721 11.95 20443 6.74 24278 34.35 27366 24.51 13680 24.50 13686 24.51
45801 ~ 57800 24273 6.49 14628 4.82 9645 13.65 16136 14.45 8068 14.45 8068 14.45
≧57801 9291 2.48 5868 1.93 3423 4.84 5167 4.63 2594 4.65 2573 4.61
Missing data 16 16
CCI <0.001 0.980
0 93747 25.05 76960 25.36 16787 23.75 28218 25.27 14109 25.27 14109 25.27
1 ~ 3 127818 34.16 98810 32.56 29008 41.04 43511 38.96 21796 39.04 21715 38.89
4 ~ 6 87823 23.47 71246 23.47 16577 23.46 26529 23.76 13224 23.68 13305 23.83
7 ~ 9 42956 11.48 36932 12.17 6024 8.52 9815 8.79 4912 8.80 4903 8.78
≧10 21829 5.83 19550 6.44 2279 3.22 3597 3.22 1794 3.21 1803 3.23
Average CCI(Mean, Std) 3.38 3.34 3.48 3.43 2.93 2.89 2.93 2.90 2.92 2.89 2.93 2.92
Open in a new tab

CCI, Charlson Comorbidity Index; PS, propensity score

It’s 30 New Taiwan Dollar (NT$) per US dollar

p -value was considered significant at p < 0.05

Variables description

In this study, the type 2 diabetes mellitus was defined as a primary or secondary diagnosis with ICD-9-CM: 250 or A-code: A181 and the patients had made 3 or more clinic visits or been hospitalized at least once within 365 days [18], and this study excluded other types DM patients. We categorized residence areas into 7 levels, and level 1 was the most urbanized [19]. We used the modified Charlson Comorbidity Index (CCI) to classify the severity of comorbidity [20]. The CCI involved 17 comorbidities weighted based on severity. Higher score denoted greater comorbidity. Presence of catastrophic illnesses was defined as yes or no. The catastrophic illnesses or injuries were defined by National Health Insurance Administration in Taiwan including 30 categories of major illnesses (e.g., cancer, stroke, hemophilia, type I diabetes, autoimmune diseases, end-stage renal disease etc.) for which patients were exempted from copayment and thus avoided financial hardship [21].

Data analysis

A descriptive analysis was conducted to examine demographical traits of the research population (e.g., age, monthly salary, and CCI) and to classify the participants as nurses and non-nurses. Propensity matching (PS) methods were used, and a Chi-square test was subsequently employed to compare variations in diabetes development risks for the two groups. The Cox proportional hazards model was used to discuss relative risks and influencing factors related to diabetes. Hazard ratios (HRs) and 95 % CIs were derived from Cox proportional hazards models. Poisson distribution methods were employed to examine diabetes incidence rates for 1,000 person-years for the two groups. In this study, statistic significance was set at p < 0.05.

Results

Participants demographics

The data prior to propensity matching revealed significant differences (p <0.05) between age, monthly salary, and CCI levels for the two groups (Table 1). Before propensity score matching, the average age of the nurses was 31.23 ± 8.48 years, whereas that of the non-nurses was younger by 10.97 ± 7.00 years at 42.20 ± 15.48 years. Most of the nurses (34.35 %) earned a salary of NT$ 36,301–NT$ 45,800, and the non-nurses earned NT$ 17,281–NT$ 22,800 (44.25 %) on average. The CCI scores for the nurses and non-nurses were 2.93 ± 2.89 and 3.48 ± 3.43, respectively.

After performing the propensity score matching tasks, we did not find any significant differences in terms of age, monthly salaries, residence area urbanization levels, CCI levels, and in other catastrophic illness levels between the two groups (Table 1).

Stratified analysis on the risk of diabetes for the nurses and non-nurses

In the Table 2, the results of the bivariate analysis revealed a lower incidence of diabetes among the nurses (2.68 %) compared with that of the non-nurses (3.13 %), revealing a statistically significant difference (p <0.05). After controlling for other factors, Cox proportional hazards models were used to identify the risk of diabetes for the nurses and non-nurses (Table 3). The results of the nurses showed a lower risk of developing diabetes compared with the non-nurses (Adj. HR = 0.84, 95 % CI: 0.79–0.90). For age, in the 35–44- and 45–54-year age groups, the nurses exhibited a significantly lower risk of diabetes compared with the non-nurses (Adj. HR: 0.82 and 0.75, respectively). For the income groups of less than NT$ 17,280, NT$ 22,801–NT$ 28,800, and NT$ 36,301–NT$ 45,800, nurses presented significantly lower risks of diabetes development compared with those of the non-nurses (Adj. HR: 0.65, 0.77, 0.86, respectively). Regarding urbanization variables, with the exception of participants residing in Levels 4 and 5 urbanization areas, no significant differences in diabetes development risk were found between the nurses and non-nurses (p <0.05). In less urbanized regions, nurses showed lower risks of diabetes development than non-nurses (p <0.05).

Table 2.

Bivariate analysis on the incidence of diabetes

Variables Total Without Diabetes Diabetes p -value
N % N % N %
Total participants 111670 100.00 108422 97.09 3248 2.91
Nurses or non-nurses <0.001
Non-nurses 55835 50.00 54085 96.87 1750 3.13
Nurses 55835 50.00 54337 97.32 1498 2.68
Age <0.001
<25 24912 22.31 24695 99.13 217 0.87
25–34 47271 42.33 46626 98.64 645 1.36
35–44 28221 25.27 27085 95.97 1136 4.03
45–54 9550 8.55 8562 89.65 988 10.35
55–64 1346 1.21 1149 85.36 197 14.64
≧65 370 0.33 305 82.43 65 17.57
Average age (Mean, Std) 32.79 8.97 32.52 8.78 41.80 10.61
Monthly salary(NT$) <0.001
≦17280 9386 8.41 9087 96.81 299 3.19
17281 ~ 22800 13952 12.49 13558 97.18 394 2.82
22801 ~ 28800 19602 17.55 19117 97.53 485 2.47
28801 ~ 36300 20061 17.96 19626 97.83 435 2.17
36301 ~ 45800 27366 24.51 26466 96.71 900 3.29
45801 ~ 57800 16136 14.45 15660 97.05 476 2.95
≧57801 5167 4.63 4908 94.99 259 5.01
Urbanization of residence 0.662
Level 1 42449 38.01 41210 97.08 1239 2.92
Level 2 & 3 52585 47.09 51079 97.14 1506 2.86
Level 4 & 5 12212 10.94 11849 97.03 363 2.97
Level 6 & 7 4424 3.96 4284 96.84 140 3.16
Other catastrophic illnesses <0.001
No 109183 97.77 106068 97.15 3115 2.85
Yes 2487 2.23 2354 94.65 133 5.35
CCI <0.001
0 15926 14.26 15894 99.80 32 0.20
1 ~ 3 44302 39.67 43328 97.80 974 2.20
4 ~ 6 31181 27.92 30138 96.66 1043 3.34
7 ~ 9 14076 12.60 13342 94.79 734 5.21
≧10 6185 5.54 5720 92.48 465 7.52
Average CCI (Mean, Std) 3.75 3.16 3.69 3.13 5.71 3.48
Open in a new tab

CCI, Charlson Comorbidity Index; HR, hazard ratio; CI, confidence interval

It’s 30 New Taiwan Dollar (NT$) per US dollar

Urbanization level of residence area (overall 7 levels; Level 1 was the most urbanized)

p -value was considered significant at p < 0.05

Table 3.

Stratified Cox proportional hazard model analysis on the risk of diabetes for the nurses and non-nurses

Variables Non-nurses Nurses Adj. HR*
(Nurse:GP)		 95 % CI p -value
Total Diabetes (N) Diabetes (%) Total Diabetes (N) Diabetes (%)
Total 55835 1750 3.13 55835 1498 2.68 0.84 0.79 0.90 <0.001
Age(y/o)
<35 36084 434 2.23 36099 428 2.24 0.97 0.85 1.11 0.628
35–44 14118 617 4.37 14103 519 3.68 0.82 0.73 0.92 <0.001
45–54 4775 563 11.79 4775 425 8.90 0.75 0.66 0.85 <0.001
55–64 673 104 15.45 673 93 13.82 0.85 0.64 1.13 0.260
≧65 185 32 17.30 185 33 17.84 0.98 0.60 1.62 0.943
Monthly salary(NT$)
≦17280 4693 176 3.75 4693 123 2.62 0.65 0.52 0.82 <0.001
17281 ~ 22800 6976 211 3.02 6976 183 2.62 0.84 0.69 1.03 0.089
22801 ~ 28800 9801 273 2.79 9801 212 2.16 0.77 0.64 0.92 0.004
28801 ~ 36300 10023 218 2.17 10038 217 2.16 0.97 0.81 1.18 0.779
36301 ~ 45800 13680 479 3.50 13686 421 3.08 0.86 0.76 0.99 0.030
45801 ~ 57800 8068 251 3.11 8068 225 2.79 0.93 0.78 1.12 0.433
≧57801 2594 142 5.47 2573 117 4.55 0.80 0.63 1.03 0.080
Urbanization of residence
Level 1 22883 695 3.04 19566 544 2.78 0.86 0.76 0.96 0.006
Level 2 & 3 25499 804 3.15 27086 702 2.59 0.84 0.76 0.93 <0.001
Level 4 & 5 5544 181 3.26 6668 182 2.73 0.85 0.69 1.05 0.133
Level 6 & 7 1909 70 3.67 2515 70 2.78 0.71 0.50 1.00 0.050
Other catastrophic illnesses
No 54530 1680 3.08 54653 1435 2.63 0.84 0.78 0.90 <0.001
Yes 1305 70 5.36 1182 63 5.33 0.93 0.66 1.31 0.667
CCI
≦3 30433 552 2.70 29795 454 2.10 0.82 0.73 0.93 0.002
4 ~ 6 15428 551 3.57 15753 492 3.12 0.87 0.77 0.98 0.026
7 ~ 9 6912 389 5.63 7164 345 4.82 0.85 0.74 0.99 0.030
≧10 3062 258 8.43 3123 207 6.63 0.80 0.66 0.96 0.015
Open in a new tab

*The stratified Cox proportional hazards models have controlled for age, monthly salary, urbanization of residence, other catastrophic illnesses, CCI

CCI, Charlson Comorbidity Index; HR, hazard ratio; CI, confidence interval

It’s 30 New Taiwan Dollar (NT$) per US dollar

p -value was considered significant at p < 0.05

An analysis on the comorbidity of other catastrophic illnesses indicated that risks of diabetes development facing those nurses who did not have other catastrophic illnesses were significantly lower than those of non-nurses (Adj. HR: 0.84; p <0.05). While risks of diabetes development among the nurses with catastrophic illnesses were lower than those for the non-nurses, the results were non-significant (p <0.05). The results of the CCI analysis show that the nurses presented significantly lower susceptibility to diabetes development overall compared with the non-nurses (p <0.05).

Relative factors and the incidence of diabetes among nurses

The analysis results presented in Table 4 show that age and CCI serves as critical factors that influence diabetes development risks among nurses. Regarding age variables, a 10-year period was used as an interval to create age groups. The older nurses presented relatively higher risks of diabetes development compared with the nurses of less than 25 years (Adj. HR: 1.56–16.85). Nurses who were older than 65 years of age showed a 16.85-fold higher risk of developing diabetes compared with nurses who were younger than 25 years of age (95 % CI: 11.47–24.75).

Table 4.

Relative factors and the incidence risk of diabetes among nurses

Variables Unadj.
HR		 P-value Adj.
HR		 95 % CI p -value
Age
<25 (reference)
25–34 1.57 <0.001 1.56 1.30 1.87 <0.001
35–44 4.52 <0.001 4.19 3.50 5.01 <0.001
45–54 11.29 <0.001 9.59 7.97 11.55 <0.001
55–64 18.10 <0.001 13.85 10.67 17.99 <0.001
≧65 25.34 <0.001 16.85 11.47 24.75 <0.001
Monthly salary(NT$)
≦17280(reference)
17281 ~ 22800 1.00 0.991 1.23 0.98 1.55 0.077
22801 ~ 28800 0.83 0.090 1.03 0.82 1.28 0.830
28801 ~ 36300 0.76 0.015 1.11 0.89 1.38 0.350
36301 ~ 45800 0.81 0.035 1.06 0.87 1.29 0.588
45801 ~ 57800 0.92 0.449 0.98 0.79 1.23 0.884
≧57801 1.42 0.006 1.08 0.84 1.39 0.537
Urbanization of residence
Level 1(reference)
Level 2 & 3 0.98 0.672 0.97 0.87 1.08 0.572
Level 4 & 5 1.05 0.545 0.97 0.83 1.14 0.719
Level 6 & 7 1.11 0.407 0.91 0.72 1.16 0.441
Other catastrophic illnesses
No(reference)
Yes 2.18 <0.001 1.24 0.97 1.59 0.090
CCI
0 (reference)
1 ~ 3 12.75 <0.001 12.25 6.74 22.28 <0.001
4 ~ 6 21.44 <0.001 18.66 10.27 33.90 <0.001
7 ~ 9 34.13 <0.001 24.85 13.64 45.30 <0.001
≧10 48.23 <0.001 28.48 15.51 52.29 <0.001
Open in a new tab

CCI, Charlson Comorbidity Index; HR, hazard ratio; CI, confidence interval

It’s 30 New Taiwan Dollar (NT$) per US dollar

Urbanization level of residence area (overall 7 levels; Level 1 was the most urbanized)

p -value was considered significant at p < 0.05

CCI significantly influences diabetes development risks among nurses. The analysis results indicate that those nurses with a higher CCI were at a higher risk of developing diabetes than the reference group (CCI = 0). Those nurses with a CCI of ≧10 presented 28.48-fold higher relative risks of diabetes development than the reference group (95 % CI: 15.51–52.29). The research results shown in Table 5 indicate that diabetes incidence rates per 1,000 person-years for the nurses were lower than those of the non-nurses (2.76 ‰ vs. 3.25 ‰). The analysis results on the various variables reveal that with the exception of the ≦25-year age group, the incidence rate of diabetes per 1,000 person-years for nurses of all of the age groups was lower than that of the non-nurses.

Table 5.

The incidence rate of diabetes per 1000 person-years

Variables Non-nurses Nurses p -value
Diabetes (N) Incidence rate(%) Diabetes (N) Incidence rate(%)
Total participants 1750 3.25 1498 2.76 <0.001
Age
<25 104 0.87 113 0.94 0.574
25–34 330 1.44 315 1.36 0.492
35–44 617 4.53 519 3.78 0.003
45–54 563 12.63 425 9.41 <0.001
55–64 104 17.25 93 14.97 0.322
≧65 32 21.99 33 20.62 0.794
Average age (Mean, Std) 48.31 10.43 46.98 10.80 <0.001
Monthly salary(NT$)
≦17280 176 4.07 123 2.71 <0.001
17281 ~ 22800 211 3.14 183 2.70 0.139
22801 ~ 28800 273 2.89 212 2.23 0.005
28801 ~ 36300 218 2.25 217 2.23 0.924
36301 ~ 45800 479 3.61 421 3.16 0.045
45801 ~ 57800 251 3.20 225 2.86 0.221
≧57801 142 5.68 117 4.68 0.120
Urbanization of residence
Level 1 695 3.14 544 2.85 0.094
Level 2 & 3 804 3.28 702 2.67 <0.001
Level 4 & 5 181 3.40 182 2.82 0.077
Level 6 & 7 70 3.81 70 2.87 0.093
Other catastrophic illnesses
No 1680 3.19 1435 2.70 <0.001
Yes 70 5.72 63 5.63 0.928
CCI
0 22 0.26 10 0.14 0.107
1 ~ 3 530 2.52 444 2.01 <0.001
4 ~ 6 551 3.69 492 3.22 0.028
7 ~ 9 389 5.89 345 5.01 0.029
≧10 258 9.06 207 7.01 0.006
Open in a new tab

CCI, Charlson Comorbidity Index

It’s 30 New Taiwan Dollar (NT$) per US dollar

Urbanization level of residence area (overall 7 levels; Level 1 was the most urbanized)

p -value was considered significant at p < 0.05

Discussion

After the research population was collected from the NHI database, 1:1 propensity score matching was employed. The results of the bivariate analysis shown in Table 2 indicate that the nurses presented a lower risk of diabetes development than the non-nurses (2.68 % vs. 3.13 %), which is consistent with that of the Cox proportional hazards model (Table 3 and Fig. 1; Adj. HR: 0.83, 95 % CI: 0.78–0.89).

Fig. 1.

Fig. 1

Open in a new tab

Relative risk of diabetes in nurses and non-nurses

Previous studies [8–10] have determined that individuals who work in professions that require shift work present higher risks of developing diabetes. After propensity score matching, our nurses cohort and non-nurses control group were similar in terms of demographics, socioeconomic status, and health conditions. However, the nurses showed lower risks of developing diabetes than the non-nurses, and this may be attributed to nurses’ medical knowledge and educational training. Furthermore, nurses are assume health promotion and education responsibilities [22] and are thus likely to live healthy lifestyles and to invest in their long-term health. This phenomenon is known as the healthy worker effect [23, 24]. Nurses must be in excellent health to achieve effective performance; therefore, nurses are generally healthier than non-nurses.

The aforementioned results may be correlated with knowledge, attitudes, and practice (KAP) theory principles [25, 26]. Self-care levels depend on patient behaviors, and a patient’s knowledge influences his or her attitudes, thereby affecting his or her practices. Acquiring correct knowledge about a disease and adopting positive and proactive attitudes are essential for motivating self-care behaviors, which directly or indirectly affect health outcomes. Nurses are equipped with comprehensive medical knowledge and thus possess a favorable view of personal health and diseases and a positive attitude toward medical care, resulting in superior self-care [27]. However, further study is needed to examine the effect of KAP theory on the nurses’ incidence risk of developing diabetes.

The results presented in Tables 3 and 4 show that for the entire sample population, age and CCI variables most heavily influenced diabetes development risks. The older participants presented higher risks of diabetes development than the reference group (<25 y), complementing the results of previous studies [28–30], and this shows that glucose tolerance levels decrease with age; therefore, diabetes development risks increase rapidly among individuals who are older than 45 years of age. As shown in Table 5, a rapid increase in the incidence rate of diabetes per 1,000 person-years was observed among both nurses and non-nurses of 45 years of age and older. As shown in Tables 3 and 4, a higher CCI involves an increased relative risk of diabetes development, complementing the results of Huang [31] and Monami et al. [32]. Thus, CCI serves as a critical variable for predicting diabetes development risks.

Study limitations

There are several limitations to our analyses. According to the International Statistical Classification of Diseases (ICD), DM diagnosis is based on an ICD-9 diagnosis code. Therefore, clinical diagnostic data could not be acquired and verified. To optimize the accuracy of this study while compensating for this limitation, diabetes occurrence was identified when patients visited an outpatient department three times or when they were hospitalized more than once within 365 days of receiving a primary or secondary diabetes diagnosis (ICD-9-CM: 250 or A-code: A181) [17]. Some risk factors are not present in analysis. For instance, information on lifestyle, health behaviors and clinical testing data were not available and thus could not be used in variable analyses. So, we used the propensity score matching to control for selection bias. The propensity score adjustment is an important statistical technique to reduce the bias from confounding variables in observational studies and mimic the results of a randomized controlled trial [33]. In addition, the long follow-up and national design provided adequate power.

Conclusion

This study is the first to use a nationwide database to compare risks of diabetes development among female nurses and female non-nurses. The results show that nurses present a lower risk of developing diabetes than non-nurses. This result may be attributable to the fact that nurses possess superior medical knowledge and thus have lower incidence risk of developing diabetes.

Abbreviations

CCI, Charlson Comorbidity Index; CI, confidence interval; DM, diabetes mellitus; HR, hazard ratio; ICD, International Statistical Classification of Diseases; KAP, knowledge, attitudes, and practice; NHI, National Health Insurance; NT$, New Taiwan Dollar; PS, propensity score.

Acknowledgments

We are grateful for use of the National Health Insurance Research Database provided by the National Health Research Institutes. The interpretations and conclusions contained herein do not represent those of the Ministry of Health and Welfare, Taiwan, R.O.C.

Funding

This study was supported by the grant (CMU102-ASIA-12) from China Medical University and Asia University as well as the grant (DOH 10216) from Ministry of Health and Welfare.

Availability of data and materials

Data are available from the National Health Research Institutes (NHRI), Taiwan. All interested researchers can obtain the databases published and managed by the NHRI. All researchers are allowed to use the databases for their studies if they are interested in using the data. Before using the databases for research, all studies should get the IRB permission.

Authors’ contributions

HLH and WCT drafted the manuscript. WCT and PTK designed the study. CCP, SMW, PTK and WYC collected data. HLH, PTK and WCT were responsible for study conceptualization and developing the analytical plan. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics statement

The institutional review board of China Medical University approved this study (IRB No.: CMUH 20130326C).

References

  • 1.National Statistic , R.O.C (Taiwan). Health, Medical Facilities and Medical Personnel. 2013. http://eng.stat.gov.tw/mp.asp?mp=5. Accessed 20 May 2015.
  • 2.Triolo P. Occupational health hazards of hospital staff nurses. Part I: Overview and psychosocial stressors. AAOHN J. 1989;37(6):232–237. [PubMed] [Google Scholar]
  • 3.Triolo P. Occupational health hazards of hospital staff nurses. Part II: Physical, chemical, and biological stressors. AAOHN J. 1989;37(7):274–279. [PubMed] [Google Scholar]
  • 4.Plaimee P, Mititraporn R, Nuangjumnong W, Johns NP, Johns J. Comparison of Melatonin Levels, Sleep Quality and Alertness between Nurses Working Night Shift and Day Shift. Srinagarind Med J. 2013;27(4):393–400. [Google Scholar]
  • 5.Schernhammer ES, Feskanich D, Liang G, Han J. Rotating night-shift work and lung cancer risk among female nurses in the United States. Am J Epidemiol. 2013;178(9):1434–1441. doi: 10.1093/aje/kwt155. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Sookoian S, Gemma C, Fernandez Gianotti T, Burgueno A, Alvarez A, Gonzalez C, Pirola C. Effects of rotating shift work on biomarkers of metabolic syndrome and inflammation. J Intern Med. 2007;261(3):285–292. doi: 10.1111/j.1365-2796.2007.01766.x. [DOI] [PubMed] [Google Scholar]
  • 7.Esquirol Y, Bongard V, Mabile L, Jonnier B, Soulat J-M, Perret B. Shift work and metabolic syndrome: respective impacts of job strain, physical activity, and dietary rhythms. Chronobiol Int. 2009;26(3):544–559. doi: 10.1080/07420520902821176. [DOI] [PubMed] [Google Scholar]
  • 8.Pan A, Schernhammer ES, Sun Q, Hu FB. Rotating night shift work and risk of type 2 diabetes: two prospective cohort studies in women. PLoS Med. 2011;8(12):e1001141. doi: 10.1371/journal.pmed.1001141. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Karlsson B, Knutsson A, Lindahl B. Is there an association between shift work and having a metabolic syndrome? Results from a population based study of 27 485 people. Occup Environ Med. 2001;58(11):747–752. doi: 10.1136/oem.58.11.747. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Suwazono Y, Sakata K, Okubo Y, Harada H, Oishi M, Kobayashi E, Uetani M, Kido T, Nogawa K. Long-term longitudinal study on the relationship between alternating shift work and the onset of diabetes mellitus in male Japanese workers. J Occup Environ Med. 2006;48(5):455–461. doi: 10.1097/01.jom.0000214355.69182.fa. [DOI] [PubMed] [Google Scholar]
  • 11.Whiting DR, Guariguata L, Weil C, Shaw J. IDF diabetes atlas: global estimates of the prevalence of diabetes for 2011 and 2030. Diabetes Res Clin Pract. 2011;94(3):311–321. doi: 10.1016/j.diabres.2011.10.029. [DOI] [PubMed] [Google Scholar]
  • 12.Al-Delaimy WK, Willett WC, Manson JE, Speizer FE, Hu FB. Smoking and Mortality Among Women With Type 2 Diabetes The Nurses’ Health Study cohort. Diabetes Care. 2001;24(12):2043–2048. doi: 10.2337/diacare.24.12.2043. [DOI] [PubMed] [Google Scholar]
  • 13.Ministry of Health and Welfare. 2012 statistics of causes of death. 2014. http://www.mohw.gov.tw/EN/Ministry/Statistic. Accessed 31 Dec 2015.
  • 14.National Health insurance Administration, Ministry of Health and Welfare. Statistics & Surveys. 2013. http://www.nhi.gov.tw. Accessed 13 May 2015.
  • 15.Cheng T-M. Taiwan’s new national health insurance program: genesis and experience so far. Health Aff. 2003;22(3):61–76. doi: 10.1377/hlthaff.22.3.61. [DOI] [PubMed] [Google Scholar]
  • 16.Lu J-FR, Hsiao WC. Does universal health insurance make health care unaffordable? Lessons from Taiwan. Health Aff. 2003;22(3):77–88. doi: 10.1377/hlthaff.22.3.77. [DOI] [PubMed] [Google Scholar]
  • 17.Hsu Y, Tang W, Chang Y, Maa S. [A preliminary study of the work values of male nurses in Taiwan and related factors] Hu Li Za Zhi. 2013;60(2):50–60. doi: 10.6224/JN.60.2.50. [DOI] [PubMed] [Google Scholar]
  • 18.Chang C, Shau W, Jiang Y, Li H, Chang T, H‐H Sheu W, Kwok C, Ho L, Chuang L. Type 2 diabetes prevalence and incidence among adults in Taiwan during 1999–2004: a national health insurance data set study. Diabet Med. 2010;27(6):636–643. doi: 10.1111/j.1464-5491.2010.03007.x. [DOI] [PubMed] [Google Scholar]
  • 19.Liu C-Y, Hung Y, Chuang Y, Chen Y, Weng W, Liu J, Liang K. Incorporating development stratification of Taiwan townships into sampling design of large scale health interview survey. J Health Manag. 2006;4(1):1–22. [Google Scholar]
  • 20.Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol. 1992;45(6):613–619. doi: 10.1016/0895-4356(92)90133-8. [DOI] [PubMed] [Google Scholar]
  • 21.National Health insurance Administration, Ministry of Health and Welfare. Reglations governing the exemption of the national heaelth insurance beneficiaries from co-payment. 2015. http://www.nhi.gov.tw. Accessed 20 Apr 2016.
  • 22.Carpenter, H. Nurses. Role models, advocates and educators of health, safety, and wellness. Paper presented at the 141st APHA Annual Meeting (November 2-November 6, 2013). Boston.
  • 23.Naimi AI, Richardson DB, Cole SR. Causal Inference in Occupational Epidemiology: Accounting for the Healthy Worker Effect by Using Structural Nested Models.American journal of epidemiology. 2013. doi:10.1093/aje/kwt215. [DOI] [PMC free article] [PubMed]
  • 24.Radon K, Goldberg M, Becklake M. Healthy worker effect in cohort studies on chronic bronchitis. Scand J Work Environ Health. 2002;28(5):328–332. doi: 10.5271/sjweh.682. [DOI] [PubMed] [Google Scholar]
  • 25.Bhargava A, Mishra B, Thakur A, Dogra V, Loomba P, Gupta S. Assessment of knowledge, attitude and practices among healthcare workers in a tertiary care hospital on needle stick injury. Int J Health Care Qual Assur. 2013;26(6):549–558. doi: 10.1108/IJHCQA-04-2012-0035. [DOI] [PubMed] [Google Scholar]
  • 26.Tobin EA, Okojie P-W, Isah EC. Community knowledge and attitude to pulmonary tuberculosis in rural Edo state, Nigeria. Ann Afr Med. 2013;12(3):148. doi: 10.4103/1596-3519.117623. [DOI] [PubMed] [Google Scholar]
  • 27.Andersen R, Newman JF. Societal and individual determinants of medical care utilization in the United States. Milbank Q. 2005;83(4). doi:10.1111/j.1468-0009.2005.00428.x. [PubMed]
  • 28.Campbell AJ, Busby WJ, Horwath CC, Robertson MC. Relation of age, exercise, anthropometric measurements, and diet with glucose and insulin levels in a population aged 70 years and over. Am J Epidemiol. 1993;138(9):688–696. doi: 10.1093/oxfordjournals.aje.a116906. [DOI] [PubMed] [Google Scholar]
  • 29.Mizuno S, Nakai Y, Isayama H, Yanai A, Takahara N, Miyabayashi K, Yamamoto K, Kawakubo K, Mohri D, Kogure H. Risk factors and early signs of pancreatic cancer in diabetes: screening strategy based on diabetes onset age. J Gastroenterol. 2013;48(2):238–246. doi: 10.1007/s00535-012-0622-z. [DOI] [PubMed] [Google Scholar]
  • 30.International Diabetes Federation. About diabetes. Risk factors. 2014. http://www.idf.org. Accessed 2 Jan 2015.
  • 31.Huang Y-Q, Gou R, Diao Y-S, Yin Q-H, Fan W-X, Liang Y-P, Chen Y, Wu M, Zang L, Li L. Charlson comorbidity index helps predict the risk of mortality for patients with type 2 diabetic nephropathy. Science B. 2014;15(1):58–66. doi: 10.1631/jzus.B1300109. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Monami M, Vitale V, Lamanna C, Bartoli N, Martelli D, Zannoni S, Antenore A, Toffanello G, Marchionni N, Mannucci E. HbA1c levels and all-cause mortality in type 2 diabetic patients: Epidemiological evidence of the need for personalised therapeutic targets. Nutr Metab Cardiovasc Dis. 2013;23(4):300–306. doi: 10.1016/j.numecd.2012.01.003. [DOI] [PubMed] [Google Scholar]
  • 33.Austin PC. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivar Behav Res. 2011;46(3):399–424. doi: 10.1080/00273171.2011.568786. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

Data are available from the National Health Research Institutes (NHRI), Taiwan. All interested researchers can obtain the databases published and managed by the NHRI. All researchers are allowed to use the databases for their studies if they are interested in using the data. Before using the databases for research, all studies should get the IRB permission.

Articles from BMC Public Health are provided here courtesy of BMC

RESOURCES