The effect of shift work on different hematological parameters among healthcare workers

ABSTRACT

Shift workers frequently experience alterations in their circadian rhythms, which are correlated with variations in hematological parameters. Changes in blood cells may be related to an individual’s health status. Therefore, this study aimed to compare the relationship between shift work and changes in blood cells among a group of healthcare workers in Sri Lanka. A comparative cross-sectional study was conducted among healthcare workers, recruited by a stratified random sampling technique. Socio-demographic data were collected using a structured questionnaire. Venous blood samples were obtained and analyzed for the determination of total and differential blood cell counts. Descriptive statistics were used for the analysis of sociodemographic and hematological parameters. A sample of 37-day workers and 39 shift workers were included in the analysis. The mean ages (years) were not significantly different between the groups (36.8 ± 10.8 vs 39.1 ± 12.0; P = 0.371). Shift employees showed a significantly higher total mean white blood cell count (WBC) of 7548.75 mm⁻³ compared to day workers’ 6869.19 mm⁻³ (P = 0.027). They also had higher mean absolute counts for all different WBC types (Neutrophils: 3949.2 vs 3557.7  , Lymphocyte: 2756.5 vs 2614.2  , Eosinophil: 317.6 vs 233.4  , Monocytes: 491.63 vs 432.51  , Basophils: 31.68 vs 29.22  ). Shift employees exhibited higher WBC counts than day workers at the same level of work experience. The length of shift work exposure revealed a positive link with neutrophil (r = 0.225  ) and eosinophil counts (r = 0.262  ), whereas these correlations were negative for day workers. Shift workers were associated with higher WBC counts in healthcare workers compared to their day-working counterparts.

KEYWORDS:

• Hematological parameters
• white blood cells
• leukocytes
• shift work
• healthcare workers
• Sri Lanka

Introduction

Shift work is known to increase the risk for a number of acute and chronic health problems including cancer (Ijaz et al. 2013), cardiovascular disease (CVD) (Vyas et al. 2012), metabolic syndrome (Sooriyaarachchi et al. 2022), diabetes (Knutsson and Kempe 2014), and chronic lymphocytic leukemia (Costas et al. 2016). The normal sleep-wake cycle is forcefully disrupted by shift work, resulting in insufficient sleep and excessive fatigue (Sallinen and Kecklund 2010). There is strong evidence that lack of sleep can have negative effects on metabolism and the immune system (Irwin 2015). Born et al. discovered that individuals with sleep deprivation had higher levels of hemoglobin and hematocrit, leucocyte, red blood cells, platelet counts, monocytes, natural killer cells, and subgroups of lymphocytes (Born et al. 1997).

The immune system is an organization of cells and molecules with specialized roles in defending against infection. The macrophages and neutrophils of the innate immune system provide the first line of defence against many common microorganisms (Janeway et al. 2001). However, excessive white blood cell (WBC) production results in an accumulation of these cells along artery walls, which restricts blood flow and raises the risk of thrombi (Chang and Lin 2022). As to the literature, peripheral WBC count has been linked to insulin resistance, type 2 diabetes (Schmidt et al. 1999), coronary artery disease (CAD) (Kannel et al. 1992), stroke (Lee et al. 2001), blood cancers (Trujillo-Santos et al. 2008), and diabetic microvascular and macrovascular consequences (Cavalot et al. 2002). It has been discovered that shift workers had significantly higher peripheral total and differential leukocyte counts, which suggests that shift work may increase the risk of cardiovascular disease (Sooriyaarachchi et al. 2023).

In addition, red blood cell distribution width RDW has been identified as a new predictive marker and an independent risk factor that plays a significant role in assessing the severity and progression of CVDs (Li et al. 2017). RDW is a numerical measure of the variability in the size of circulating erythrocytes and is indicated as the coefficient of variation of the erythrocyte size. An increased RDW indicates more variability in RBC size (anisocytosis), which is generally caused by perturbation in erythrocyte maturation or degradation (Constantino 2013). The RDW is used as an auxiliary index to aid in the diagnosis of various types of anemia (Sultana et al. 2013), colonel cancers (Spell et al. 2004), and celiac diseases (Harmanci et al. 2012). Further, increased RDW was associated with higher mortality risk in middle-aged and older adults (Patel et al. 2009) as well as in individuals with cardiovascular diseases (Tonelli et al. 2008). In a nationwide sample of U.S. adults, women working rotational shifts had statistically significantly increased chances of having an elevated RDW than men who work shifts (Loprinzi 2015).

Furthermore, human red blood cells display robust, temperature-entrainable, and temperature-compensated circadian rhythms, consistent with the presence of a circadian clock within these cells (O’Neill and Reddy 2011). It is widely known that erythropoietin (EPO), a hormone that increases the formation of erythrocytes, has a distinct circadian rhythm (Wide et al. 1989). Additionally, the changes in lipid profiles, blood sugar levels, and blood pressure have all been associated with the osmotic stability of the erythrocyte membrane (OSEM). However, shift workers frequently experience changes in these parameters, probably because of the lack of synchronization of biological rhythms, which causes social jetlag.

A full blood count (FBC) test is possibly the single most common investigation that measures several components of the blood and can help diagnose a broad range of conditions, from anemia to cancer (George-Gay and Parker 2003). Hematologic alterations on the FBC may often be the only indications present in cases when symptomatology of a medical illness is absent (George-Gay and Parker 2003). Therefore, in this study, we aimed to evaluate changes in FBC results between shift workers and regular day workers.

Methods

Study population

This cross-sectional study was carried out using the FBC reports of the day and shift working healthcare workers at Nawaloka Hospital, Colombo, Sri Lanka between July, and August 2021. The study design and population have been described in detail previously (Sooriyaarachchi et al. 2020). After matching for age and gender, a sample of day and shift workers who met the eligibility requirements was chosen by stratified sampling. The study was approved by the Ethics Review Committee of the Faculty of Medicine, University of Colombo, Sri Lanka (EC-20-066) and the Queensland University of Technology (QUT), Human Research Ethics Committee (Ref. no: 2000000831), and written informed consent was obtained from all participants before any data collection.

Inclusion and exclusion criteria

To be included in the study sample, a respondent must be permanently employed, aged between 18–65 years, have worked the same shift schedule for the previous year, and has to meet one of the following two criteria: (1) Working shifts (any type of shift work involving at least 3-night shifts per week) and (2) Working in day shift (including day shifts only without weekends or day shifts only with weekends). The following were the exclusion criteria: being pregnant or nursing, having a chronic illness like cancer or renal disease, having had a minor or major operation within the previous six months, being already on a diet or participating in an exercise program, and having problematic drinking disorders.

Blood cell parameters

The normal reference range for different hematological parameters was defined as follows: red blood cell (RBC), 4.0–6.2 × 10⁶/µL; hematocrit (PCV), 36–48%; hemoglobin (Hb), 11.0–16.5 g/dL; mean corpuscular volume (MCV), 78–100 fL; mean corpuscular haemoglobin (MCH), 27–31 pg; mean corpuscular haemoglobin concentration (MCHC), 33–37 g/dL; red blood cell distribution width, 10–14%; platelet count, 1.5–4.5 × 10⁵ mm⁻³; WBC count, 4.0–11.0 × 10³ mm⁻³; neutrophil %, 40–80%; lymphocyte %, 20–40%; monocyte %, 2–10%; eosinophil count, 0.0–0.4 × 10⁹/L, and; basophil count, 0.0–0.1 × 10⁹/L. Hematological parameters below or above these pre-stated reference ranges were considered as decreased or increased in accordance.

Data collection

Socio-demographic and lifestyle data

Participants’ socio-demographic and occupational information was collected using a pre-tested structured questionnaire. To evaluate dietary habits, physical activity, and sleep quality, respectively, the Pittsburgh Sleep Quality Index (PSQI) (Anandakumar et al. 2016), the International Physical Activity Questionnaire (IPAQ-short form) (Lee et al. 2011), and the Sri Lankan food frequency questionnaire (FFQ) (Jayawardena et al. 2016) were administered by an investigator during a face-to-face interview with the participant.

Laboratory analysis

For FBC measurements, whole-blood venous samples from individuals were collected in EDTA K2 Vacutainer tubes and mixed gently before testing for the FBC in the accredited laboratory of the Nawaloka Hospital PLC, Colombo, Sri Lanka, using the Sysmex XN-10 analyzer (Sysmex, Kobe, Japan). Blood was drawn between 7:30 am and 9:00 am after confirming that the participants fasted for at least 12 hours. In particular, the blood tests on shift workers were only conducted when they did not have an overnight shift on the previous day. All FBC tests were performed within 8 h post-collection following acceptable laboratory protocols.

Statistical analysis

Parametric and non-parametric statistical tests were applied using IBM SPSS version 23 (SPSS Inc., Chicago, IL, USA) statistical software package for the data analysis. The analyses included descriptive analysis of the demographics and FBC testing results among the two groups of workers. Continuous variables are reported as the mean and standard deviation and categorical variables are described as frequency and percentage. The categorical variables were compared using the Chi-square or Fisher’s exact tests. For normally distributed data, the independent t-test was used to compare differences in the mean of hematological parameters between cases and controls. For non-normally distributed data the Mann-Whitney U test was used. To measure the correlation of hematological parameters with the duration of shift work, the Pearson correlation for normally distributed data and Spearman’s rank-order correlation analysis for non-normally distributed data were used. A P-value <0.05 was considered significant.

Results

A sample of 37 day working employees and 40 shift working employees were included in the final analysis. Table 1 shows the characteristics of the study population. The mean ages (±SD) of day and shift working groups were 36.8 ± 10.8 and 39.1 ± 12.0 years, respectively, with no statistically significant differences between the groups. Females dominated the sample representing 51.4% (n = 19) in the day group and 52.5% (n = 21) in the shift group. Shift employees were more likely to be less educated than day shift workers, with 50.0% of shift workers having completed only up to the secondary level of education. In contrast, 64.9% (n = 24) of daytime workers were having a degree or higher-level educational qualifications. The daytime employees had an average job experience of 11.0 ± 10.9 years, whereas shift employees had an average exposure to shift work of 8.6 ± 8.6 years. There was no difference in the average daily calorie intake across the groups. The physical activity levels of shift employees were significantly lower, with 90% of them having lower levels (p = 0.008). Additionally, there was a significant difference in sleep quality with 90% of shift workers having poor sleep compared to 51.4% in the day group (p < 0.001) (Table 1).

Table 1. Sample characteristics.

	Day workers (N = 37)	Shift workers (N = 40)	p-value
Age (years)
18–25	5 (13.5)	5 (12.5)	0.482^b
26–35	17 (45.9)	11 (27.5)
36–45	7 (18.9)	13 (32.5)
46–55	6 (16.2)	8 (20.0)
>55	2 (5.4)	3 (7.5)
Gender, n (%)			0.920^c
Male	18 (48.6)	19 (47.5)
Female	19 (51.4)	21 (52.5)
Education level, n (%)			<0.001^c
Secondary (up to O/L)	2 (5.4)	20 (50.0)
Tertiary (up to A/L)	11 (29.7)	12 (30.0)
Degree or above	24 (64.9)	8 (20.0)
Exposure to shift work (years)
<3	-	12 (30.8)
4–6	-	9 (23.1)
7–10	-	7 (17.9)
>10	-	11 (28.2)
Dietary intake, mean (±SD)
Total energy intake (kcal/day)	2037 ± 398.36	1922 ± 431.31	0.231^a
Physical activity, n (%)
Low	23 (62.2)	36 (90.0)	0.008^b
Moderate	12 (32.4)	4 (10.0)
High	2 (5.4)	0
Sleep Quality, n (%)
Good sleepers (Global PSQI score<5)	18 (48.6)	4 (10.0)	<0.001^c
Poor sleepers (Global PSQI score ≥5)	19 (51.4)	36 (90.0)

^aThe results of the independent T-test; ^bThe results of the Fisher’s exact test; ^cThe results of the Chi-square test; SD-standard deviation; PSQI- Pittsburgh Sleep Quality Index.

Table 2 shows the hematological parameters of day and night shift workers. The mean RBC count, Hb level, PCV, MCV, MCH, MCHC, RDW, and platelet count were not different between the groups (p > 0.05). However, the mean WBC counts in shift workers were significantly higher than those in daytime employees (7548.8 vs 6869.2 mm⁻³, p = 0.027). Although the differences were not statistically significant, the shift working group also had higher mean absolute counts for neutrophils, lymphocytes, eosinophils, monocytes, and basophils.

Table 2. Hematological parameters of study participants.

Hematological parameters	Day workers (N = 37)	Shift workers (N = 40)	p-value
RBC (×10^6/µL)	4.82 (0.67)	4.81 (0.59)	0.966
Hb (g/dL)	13.74 (1.75)	13.69 (1.39)	0.888
PCV (%)	40.60 (4.45)	40.34 (3.41)	0.776
MCV (fL)	84.05 (7.05)	83.67 (6.01)	0.799
MCH (Pg)	28.50 (3.03)	28.38 (2.41)	0.895
MCHC (g/dL)	33.80 (1.31)	33.90 (1.17)	0.674
RDW-CV (%)	13.37 (1.62)	13.23 (1.65)	0.724
WBC (per mm^3)	6869.19 (1195.29)	7548.75 (1434.30)	0.027
Neutrophil (%)	51.55 (6.70)	52.05 (9.03)	0.786
Neutrophils absolute (per mm^3)	3557.73 (864.58)	3949.15 (1173.73)	0.102
Lymphocyte (%)	38.34 (7.63)	36.64 (8.57)	0.361
Lymphocyte absolute (per mm^3)	2614.22 (640.16)	2756.53 (891.40)	0.427
Eosinophil (%)	3.36 (1.66)	4.27 (3.27)	0.133
Eosinophil absolute (per mm^3)	233.43 (133.70)	317.58 (265.42)	0.087
Monocytes (%)	6.28 (2.79)	6.59 (2.24)	0.590
Monocytes absolute (per mm^3)	432.51 (203.89)	491.63 (179.13)	0.180
Basophils (%)	0.43 (0.33)	0.42 (0.25)	0.849
Basophils absolute (per mm^3)	29.22 (21.16)	31.68 (19.87)	0.601
Platelets (per mm^3)	267459.46 (57719.43)	280375.00 (60837.19)	0.342

The variation of total WBC count and differential WBC counts with the work experience of shift workers and day workers are illustrated in Figure 1. Shift employees showed a mild negative correlation between WBC counts (r = −0.026; P = 0.876) and the duration of shift work exposure, but day workers showed a significant negative correlation between WBC counts and the work experience (r = −0.395; P = 0.023). However, at the same level of work experience, shift workers always had higher WBC counts than day workers (Figure 1a). Additionally, the duration of shift work exposure showed a positive correlation with neutrophils (r = 0.225; P = 0.169) (Figure 1b) and eosinophil (r = 0.262; P = 0.107) (Figure 1d) counts of shift employees, whereas these correlations were negative for day workers. Other WBC types such as lymphocytes (Figure 1c), monocytes (Figure 1e), and basophils demonstrated negative correlations with the duration of shift work exposure as well as daytime work. Variation of several other physiological parameters are provided in supplementary file 1.

Figure 1. Variation of total WBC and differential WBC counts among day and shift employees along with their work experience.

Discussion

This study compares the hematological parameters between day and shift working employees employed in the healthcare sector. The results of this study showed that shift workers have significantly higher WBC counts when compared to day workers.

Our findings regarding shift work and WBC counts are consistent with those of previous studies conducted among different occupational groups of shift workers, such as steel workers, police officers, office workers, and healthcare professionals (Hanprathet et al. 2019; Lu et al. 2016; Wirth et al. 2017b). Further, the duration of shift work exposure seemed to have a positive correlation with neutrophil and eosinophil counts. Among the prior shift work studies, two studies have reported that shift work was associated with an increased level of neutrophil and lymphocyte counts (Khosro et al. 2011; Wirth et al. 2017a). Another study among steelworkers found an association with increased monocyte and eosinophil counts (Lu et al. 2016). Several other investigations conducted among previous shift employees in addition to studies concentrating on present shift workers also identified a relationship between elevated WBC levels (Kim et al. 2016). Conversely, some studies have not discovered a connection between shift employment and WBC counts (Buss et al. 2018).

Circadian disruption may explain the rise in white blood cells among shift workers (Morris et al. 2017). Circadian rhythms are endogenous oscillators with periods of approximately 24 h, which are generated by networks of central and peripheral clocks (Logan and Sarkar 2012). The presence of peripheral clocks has been detected in peripheral blood mononuclear cells (Boivin et al. 2003) and other blood cells (Kusanagi et al. 2004). Therefore, circadian misalignment caused by poor sleep among shift workers causes an increase in the number of most white blood cell subpopulations at night, with a peak occurring between 6 p.m. and 3 a.m (Lasselin et al. 2015). Although all leukocyte subpopulations are subjected to circadian rhythm control, only the total WBC count showed a significant increase in shift workers compared to the day employees in our study. Previous research looking into the leukocyte subpopulations of shift workers has produced mixed results, which may be because different leukocyte subpopulations have different diurnal cycles (Lasselin et al. 2015).

In addition to leukocytes, erythrocytes are also well known to possess rhythmic properties. In the study by Loprinzi, it was revealed that shift-working women had a 46% increased risk of having elevated RDW (Loprinzi 2015). But in our study, no significant difference was observed in RDW values between shift and day working groups. Additionally, no statistically significant differences were seen across several other measures, including RBC, Hb, PCV, MCV, MCH, MCHC, and platelet. Similar findings were reported in a study of women who worked 32-hour shifts continuously, where the authors discovered no significant variations in RBC, MCH, MCHC, RD, MCV, and platelet between the groups (ÇAKAN & YILDIZ).

Multiple chronic disorders have been linked to altered WBC levels. For instance, excessive levels of leukocytes might result in an unusual accumulation of early atherosclerotic plaques, which may eventually contribute to the thickening of the arterial wall. These accumulating leukocytes release pro-inflammatory cytokines that could lead to an increase in systemic, chronic inflammation (Pritchett and Reddy 2015). It is now widely accepted that inflammation plays a role in the development of many chronic diseases, such as type II diabetes, obesity, and cardiovascular disease (CVD) (Lordan et al. 2019). These chronic diseases are highly prevalent among shift workers (Sooriyaarachchi et al. 2021; Wang et al. 2011)

However, there are several limitations associated with this study. First, the study consisted of a small sample size and targeted healthcare workers at a single hospital. Therefore, results among this population may not apply to other shift working populations. Second, we were unable to measure the impact of confounding factors such as smoking, and alcohol consumption, which have been identified as potential confounders affecting the WBC count (Ruggiero et al. 2007). Also, we were unable to measure the feeding times and core circadian gene expression levels of workers. Yet studies have demonstrated that disruptions in the timing of food intake can perturb the circadian rhythm, potentially leading to immune dysregulation and alterations in WBC counts (Feriel et al. 2021). Third, the capacity to directly capture the “subjective night” of shift employees may be hampered by the collection of blood samples from both day and shift workers during the daytime. Lastly, the cross-sectional nature of the study does not allow the establishment of causal relationships. However, it is possible to propose a causal link between shift work and WBC given the prior studies showing a correlation between shift and elevated WBC levels. Therefore, to better understand the true impact of shift work on WBC counts and other hematological parameters, it is crucial to undertake large-scale follow-up studies among shift workers.

Considering the above limitations, future studies should aim to include a larger and more diverse sample size to enhance the generalizability of the findings. Also, the studies should prioritize measuring feeding times and core circadian gene expression levels in shift workers. This would enable a more comprehensive understanding of the interplay between circadian rhythm, food intake timing, and WBC counts, potentially uncovering additional factors that contribute to immune dysregulation in shift workers. On the other hand, blood sample collection time should be aligned precisely with shift workers’ circadian rhythms. Considering the subject’s “subjective night” are vital for understanding the impact of shift work on physiological processes. Further, to establish causal relationships, future research should incorporate a longitudinal design. Besides, WBC may be a helpful diagnostic tool for estimating risk for chronic diseases and all-cause mortality because it is the most widely available, relatively inexpensive laboratory test. Additionally, annual health screening programs at workplaces are strongly recommended to further monitor the health consequences caused by the employee’s shift work schedules.

Conclusion

The results indicated that shift work is associated with an elevated WBC count. But none of the other hematological parameters were significantly different between the day and shift employees. Regular health screenings are necessary to identify any early health impacts that could bring in by shift work.

Authors’ contributions

PS devised the conceptual idea. PS involved in data collection and data analysis. PS and RJ drafted the manuscript. NK and TP revised the manuscript. All authors provided critical feedback on the manuscript. All authors read and approved the final manuscript

Ethics approval

The study was approved by the Ethics Review Committee of the Faculty of Medicine, University of Colombo, Sri Lanka (EC-20-066) and the Queensland University of Technology (QUT), Human Research Ethics Committee (Ref. no: 2000000831).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Data are available from the corresponding author upon reasonable request.

Supplemental data

Supplemental data for this article can be accessed online at https://doi.org/10.1080/07420528.2023.2231079.

Additional information

Funding

The authors declare that they received no funding for the conduction of this study.