ASSESSMENT OF SOME HAEMATOLOGIC PARAMETERS AMONG TRICYCLE DRIVERS IN ABA, ABIA STATE, NIGERIA

ASSESSMENT OF SOME HAEMATOLOGIC PARAMETERS AMONG TRICYCLE DRIVERS IN ABA, ABIA STATE, NIGERIA BY EMEZUE, PRECIOUS CHINECHEREM 2018/116132/REGULAR PROJECT SUBMITTED TO DEPARTMENT OF HAEMATOLOGY AND BLOOD TRANSFUSION SCIENCE FACULTY OF MEDICAL LABORATORY SCIENCE, COLLEGE OF MEDICINE AND HEALTH SCIENCES ABIA STATE UNIVERSITY, UTURU IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE AWARD OF BACHELOR OF MEDICAL LABORATORY SCIENCE (B.MLS) DEGREE OF ABIA STATE UNIVERSITY, UTURU SUPERVISOR: DR. OKEZIE CALEB OKAMGBAI JULY, 2024. DECLARATION I, EMEZUE, PRECIOUS CHINECHEREM with matriculation number 2018/116132/REGULAR hereby declare on my honor, that this research study has not been previously presented either wholly or in parts for the award of any other Degree, Diploma certificate or publication in any university or higher institutions elsewhere. …………………………................... ………………… EMEZUE, PRECIOUS CHINECHEREM DATE 2018/116132/REGULAR CERTIFICATION This is to certify that I, EMEZUE, PRECIOUS CHINECHEREM with the Matriculation number 2018/116132/REGULAR from the department of Haematology and Blood Transfusion Sciences, Faculty of Medical Laboratory Science, College of Medicine and Health Science Abia State University, Uturu, carried out this project “Assessment of Some Haematologic Parameters Among Tricycle Drivers in Aba, Abia State, Nigeria” under the supervision of DR. OKEZIE CALEB OKAMGBA ….………………………. ………………………. DR. OKEZIE CALEB OKAMGBA Date (Supervisor) ………………………… ………………………… Dr. OKEZIE CALEB OKAMGBA Date (Dean Faculty of Medical Laboratory science) ….………………………. …….…………………… (External Examiner) Date DEDICATION This research work is dedicated to God Almighty. ACKNOWLEDGEMENT First of all, I give thanks to God Almighty for giving me an opportunity to excel in my effort to complete this project on time. I am very much thankful to my project supervisor who doubles as the dean faculty of Medical Laboratoratory Sciences, Abia State University, Uturu, DR. OKEZIE CALEB OKAMGBA, for his valuable guidance, keen interest, encouragement and constructive criticism at various stages of this research work. I appreciate all the lecturers of the department of Medical Laboratory Science, Abia State University Uturu who contributed to my academic growth I am extremely grateful to my parents……, for their unending love, moral, financial and all-round support. Finally, I will be failing in duty if I do not acknowledge with grateful thanks, the authors of the references and other literatures referred to in this research. TABLE OF CONTENTS Cover page Title page i Declaration ii Certification iii Dedication iv Acknowledgement v Table of Contents vi List of Tables viii List of Figures ix Abbreviations x Abstract xi CHAPTER ONE: INTRODUCTION 1.1 Background 1 1.2 Statement of Problem 4 1.3 Justification of Study 4 1.4 Aim of Study 5 1.5 Specific Objectives 5 CHAPTER TWO: LITERATURE REVIEW 2.1 Occupational Hazards 6 2.1.1 Prolonged Siting 6 2.1.2 Environmental Pollutants 7 2.2 Occupational Disorders in Transport Drivers 8 2.2.1 Cardiovascular System Disorders 8 2.2.2 Musculoskeletal Disorders 10 2.3 Haematologic Changes Associated with Tricycle Driving 12 2.3.1 Effect of Exposure to Volatile Organic Compounds 12 2.3.2 Effects of Stress 16 2.4 Laboratory Diagnostic Methods 17 CHAPTER THREE: MATERIALS AND METHODS 3.1 Study Area 22 3.2 Study Design 22 3.3 Sample Size 22 3.4 Sampling Technique 23 3.5 Inclusion Criteria 23 3.6 Exclusion Criteria 23 3.7 Ethical Approval 23 3.8 Sample Collection and Preparation 24 3.9 Laboratory Methods 24 3.10 Statistical Analysis 25 CHAPTER FOUR Result and Analysis 26 CHAPTER FIVE: DISCUSSION 5.1 Discussion 34 5.2 Conclusion 36 5.3 Recommendations 36 References 37 Appendix 42 LIST OF TABLES 4.1 Comparison of Mean±SD ofHB, PCV, RBC, PLT, TWBC, NEUT, LYM, MON, BAS, EOS, MCV, MCH, MCHC of Tricycle Drivers and Healthy Non-Tricycle Drivers Using Student T- Test. 27 4.2 Comparison of Mean±SD ofHB, PCV, RBC, PLT, TWBC, NEUT, LYM, MON, BAS, EOS, MCV, MCH, MCHC of Tricycle Drivers Based on Age, Using ANOVA and Post HOC. 29 4.3 Comparison of Mean±SDofofHB, PCV, RBC, PLT, TWBC, NEUT, LYM, MON, BAS, EOS, MCV, MCH, MCHC of Tricycle Drivers Based on Duration of Use, Using Student’s T- Test. 33 ABBREVIATIONS PMS- Premium Motor Spirit MetHb- Methaemoglobin VOCs- Volatile Organic Compunds CBC- Complete Blood Count PM- Particulate Matter CO- Carbon Monoxide NO2- Nitrogen Dioxide SHR- Standardized Hospitalization Ratio WBV- Whole Body Vibrations CFC- Colony Forming Cells HPAA- Hypothalamus–Pituitary–Adrenal Axis ROS- Reactive Oxygen Species WHO- World Health Organization ABSTRACT The nature of work and lifestyle of commercial cyclists predisposes them to various conditions that can lead to anemia. This condition, characterized by a deficiency in red blood cells or hemoglobin, adversely affects hematopoiesis and increases the risk of morbidity and mortality. Factors such as prolonged physical exertion, inadequate nutritional intake, and exposure to environmental pollutants further exacerbate their susceptibility. This study determined some haematological parameters among tricycle drivers. A total of 100 subjects; 50 tricycle drivers and 50 non-tricycle drivers, were recruited for the study. Ethical approval for the study was obtained from the ethics committee of the Faculty of Medical Laboratory Science Uturu, Abia State. Parameters studied were HB, PCV, RBC, PLT, TWBC, NEUT, LYM, MON, BAS, EOS, MCV, MCH, and MCHC. Parameters were determined using automated haematologyanalyser model and data generated wasanalysed statistically using Student's t-test. The mean±SD for HB, PCV, RBC, PLT (12.14±1.606g/dl, 0.38±4.292L/L, 4.31±0.262x1012/L, 177.13±46.286x109/L, respectively) of tricycle drivers were lower and statistically significant (p≤0.05) when compared with values of non- tricycle drivers (13.90±1.042g/dl, 0.42±3.723L/L, 6.12±0.832x1012/L, 268.94±39.971x109/L, respectively). Findings of this study showed that tricycle driving interferes with the haematological system. CHAPTER ONE INTRODUCTION 1.1 Background Transportation of persons, goods and services are inevitable necessities in every society. In developing countries like Nigeria, commercial transportation is a key element of economic growth (Atubi, 2015). For commercial purposes, the commonest means of transportation in developing countries like Nigeria are buses, taxi cabs, motorcycles and tricycles. The task of tricycling involves riding a three-wheeled vehicle (tricycle) which is also referred to as a kekenapep (Nigeria), three-wheeler, tuk-tuk, trishaw, autorick or bajaj (in Indonesia) and auto rickshaw (in India) (Ojukwuet al., 2020). Recently, tricycling is rapidly becoming more popular as a means of commercial transport in several developing countries, particularly in Asian and African countries (Grieshop et al., 2012). Tricycles exist in several types and designs. The commonest type is characterized by a sheet-metal body or open frame resting on three wheels, canvas roof with drop-down side curtains, small cabin at the front for the driver with handlebar controls, and a cargo, passenger, or dual purpose space at the bar (Ojukwuet al., 2020). Owing to the heavily congested nature of Nigerian roads, the small size and narrow configuration of tricycles are perfectly suited to navigate the roads. However, there are some possible health risks associated with commercial tricycling, relative to the configuration of tricycles and the environmental exposure of tricyclists to airborne pollutants. Studies have shown that inhalation of petroleum products are associated with reduced haematological indices which worsens with prolonged exposure (Olayanjuet al., 2021). This is seen among tricycle drivers due to frequent visits to fuel stations and to mechanic workshops. Crude oils and other refined oil products are both used in petrol (premium motor spirit (PMS). In terms of polycyclic aromatic hydrocarbons, it is a substantial source (Getuet al., 2020). Crude oil is delivered to an oil refinery after extraction, where complex hydrocarbon compounds are separated and processed into fuel-useable forms using various refining procedures. The presence, physico-chemical properties and toxicological characteristics of other important petroleum products like benzene, toluene, xylenes, ethyl-benzene, n-hexane, volatile hydrocarbons belonging to gasoline, kerosene, and diesel fuel (contents defined by the technology of the manufacturing processes), determine the extent of impacts of this industry on both the workers and environment (Ezejiofor, 2016). Benzene is one of the natural constituent of crude oil as well as a product of petrol refining. Benzene is one of the main carcinogens among more than 165 occupational carcinogens which are identified by the International Agency for Research on Cancer (IARC) (Ekpenyong and Asuquo, 2017). Few studies have reported haematological disorders associated with the exposure tobenzene in the environment in less developed countries (Ng and Newson-Smith, 2022; Sahb, 2011). However, there is evidence linking PMS and low dose exposures to volatile organic compounds, such as benzene, to adverse health effects (Udonwaet al., 2019). It is extremely hazardous when inhaled and can cause neurological problems, irritation of the mucous membranes, and other symptoms related to respiratory failure. Chronic exposure has been linked to heart attacks, cardiac irregularities, bone marrow depression, leukemia, stomach, lungs, and brain malignancies, among other conditions (Curtis et al., 2022; Udonwaet al., 2019). Following inhalation, benzene vapour is rapidly absorbed into the blood and distributed throughout the body (Cooper and Snyder, 2017). One of the effects of benzene in the body is the production of methaemoglobin (MetHb) which differs from haemoglobin. Haemoglobin accepts and transports oxygen only when the iron atom isin its ferrous form. When haemoglobin becomes oxidized, iron is converted to the ferric state (Fe3+) or MetHb. MetHb lacks the electron that is needed to form a bond with oxygen and, thus, is incapable of oxygen transport. Since red blood cells are continuously exposed to various oxidant stresses, blood normally contains MetHb levels of between 1and 3% (Udonwaet al., 2019). Considering that MetHb has little affinity for oxygen, transport of the later to cells is hindered resulting in functional anaemia. As the level of MetHb increases, symptoms like shortness of breath, palpitation, anxiety and confusion occur. Humans are commonly exposed to benzene and other VOCs through the pulmonary and dermal pathways (Pal et al., 2022). Studies have also shown that haemorrhoids are associated with tricyclists due to prolonged sitting (Valachi and Valachi, 2013; Olayanjuet al., 2021). Haemorrhoids cause gastrointestinal bleeding, which is an established cause of iron deficiency anaemia. Iron deficiency is one of the common nutritional deficiencies worldwide. The nature of work and lifestyle of commercial tricyclists predisposes them to various conditions that can lead to anaemia, which in turn affect haematopoiesis and cause morbidity and mortality. A complete blood count (CBC) is a test that provides information about the distribution and quantity of blood cells. This test provides valuable information about the kinds and the numbers of cells in the blood particularly red blood cells (RBC), white blood cells (WBC), and platelets that indicates health professionals to check symptoms, like weakness, fatigue, or bruising. In addition, it also diagnoses pathologic conditions such as anemia. It can reflect acute or chronic infection allergies and problems with clotting. So, CBC is an easy and readily available screening tool for evaluating hematotoxicity of benzene (Getuet al., 2020). 1.2 Statement of Problem Commercial tricyclists, due to the physical demands and lifestyle associated with their occupation, are particularly susceptible to developing conditions that can lead to anaemia. This vulnerability is exacerbated by factors such as prolonged periods of physical exertion, inadequate nutritional intake, and exposure to environmental pollutants. Anaemia, characterized by a deficiency in the number or quality of red blood cells, can severely impact hematopoiesis and lead to various health complications, including increased morbidity and mortality. Despite the critical role of commercial tricyclists in urban transportation systems, there is a paucity of research focusing on their haematologic health. This study aims to bridge this gap by investigating the changes in key haematologic parameters such as hemoglobin concentration among commercial tricyclists and propose potential interventions to improve their haematologic health. 1.3 Justification of Study Studies have shown that inhalation of petroleum products are associated with reduced haematological indices which worsens with prolonged exposure (Olayanjuet al., 2021). Studies have also shown that haemorrhoids are associated with tricyclists due to prolonged sitting (Valachi and Valachi, 2013; Olayanjuet al., 2021). Although a few studies have studied the implications of tricycle driving on haematologic parameters, no such study has been carried out in the present study area. This study was to assess some haematologic parameters in tricycle drivers, with a view to determining haematologic test(s) that could predict haematologic abnormalities early and thus allowing for prompt treatment. 1.4 Aim of Study To determine some haematologic parameters in tricycle drivers. 1.5 Specific Objectives 1. To assess HB, PCV, RBC, PLT, total WBC, differential WBC, MCV, and MCH, MCHC among tricycle drivers and non-tricycle drivers. 2. To investigate the effect of age on HB, PCV, RBC, PLT, total WBC, differential WBC, MCV, and MCH, MCHC among tricycle drivers. 3. To ascertain the duration of driving on HB, PCV, RBC, PLT, total WBC, differential WBC, MCV, and MCH, MCHC among tricycle drivers. CHAPTER TWO LITERATURE REVIEW 2.1 Occupational Hazards 2.1.1 Prolonged Sitting Some occupations such as office work and driving are associated with prevalent occupational hazardsand these have adverse health implications. Individuals engaged in such occupations require prolonged sitting and often, they experience a myriad of physiological and metabolic changes that may detrimentally and impact their health. Prolonged sitting has been consistently linked to an increased risk of cardiovascular diseases, including hypertension, coronary artery disease, and stroke. Sedentary behavior leads to reduced blood flow, endothelial dysfunction, and impaired vasodilation, predisposing individuals to atherosclerosis and cardiovascular events. Additionally, prolonged sitting promotes the accumulation of visceral adipose tissue, dyslipidemia, and insulin resistance, contributing to metabolic syndrome and cardiovascular risk factors. Sitting for prolonged periods leads to decreased muscle contraction and venous return, predisposing individuals to venous stasis and thrombotic events. Reduced lower limb blood flow, increased capillary permeability, and endothelial dysfunction contribute to venous thromboembolism and deep vein thrombosis. Moreover, prolonged sitting impairs arterial stiffness, augments sympathetic nervous system activity, and elevates blood pressure, further exacerbating cardiovascular risk (Ronchese and Bovenzi, 2012). Sitting for prolonged periods can exert excessive pressure on the musculoskeletal system, leading to musculoskeletal disorders such as low back pain, neck pain, and joint stiffness. Reduced muscle activation and prolonged static postures contribute to muscle imbalances, decreased flexibility, and spinal degeneration. Moreover, poor ergonomic design of workstations and seating arrangements may exacerbate musculoskeletal strain and increase the risk of occupational injuries. Sedentary behavior is associated with metabolic dysfunction, characterized by dysregulated glucose metabolism, insulin resistance, and impaired lipid metabolism. Prolonged sitting suppresses skeletal muscle glucose uptake, reduces lipoprotein lipase activity, and promotes adipocyte hypertrophy, contributing to obesity and metabolic syndrome. Furthermore, sedentary behavior disrupts circadian rhythms, alters appetite regulation, and promotes excessive calorie intake, exacerbating metabolic dysfunction and weight gain (Ronchese and Bovenzi, 2012). 2.1.2 Environmental Pollutants Tricycle driving, a common occupation in many urban and peri-urban areas, exposes drivers to a variety of environmental pollutants that pose significant occupational health hazards (Marijaet al., 2022). Tricycle drivers, often navigating through congested traffic and busy streets, encounter elevated levels of air pollutants, including particulate matter, carbon monoxide, nitrogen dioxide, and volatile organic compounds (Marijaet al., 2022). Urban environments are characterized by high levels of air pollution resulting from vehicular emissions, industrial activities, and combustion processes. Tricycle drivers, operating in densely populated urban areas with heavy traffic congestion, are particularly susceptible to inhalation exposure to airborne pollutants emitted by motor vehicles and surrounding sources (Marijaet al., 2022). Tricycle drivers are exposed to a diverse array of environmental pollutants, including: Particulate Matter (PM): Fine particulate matter, PM2.5, and coarse particulate matter, PM10, are inhalable pollutants generated by vehicle exhaust, road dust, and industrial emissions. PM exposure has been linked to respiratory irritation, lung inflammation, and cardiovascular morbidity (Alemayehuet al., 2020). Carbon Monoxide (CO): Produced by incomplete combustion of fossil fuels, CO is a colorless, odorless gas that binds to hemoglobin, impairing oxygen delivery to tissues and organs. Chronic CO exposure can lead to headaches, dizziness, and cardiovascular complications (Marijaet al., 2022). Nitrogen Dioxide (NO2): A common air pollutant generated by motor vehicle emissions and combustion processes, NO2 contributes to respiratory symptoms, airway inflammation, and asthma exacerbations (Marijaet al., 2022). Volatile Organic Compounds (VOCs): Organic chemicals released from vehicle exhaust, fuel combustion, and industrial activities, VOCs contribute to ground- level ozone formation, smog, and respiratory irritation (Marijaet al., 2022). 2.2 Occupational Disorders in Transport Drivers 2.2.1 Cardiovascular System Disorders Transportation workers have an increased occurrence of cardiovascular pathologies, including myocardial infarction and other manifestations of coronary artery disease, in association with arterial hypertension and cerebrovascular pathologies (stroke) (Ronchese and Bovenzi, 2012). The etiopathogenetic mechanisms associated with the excess of cardiovascular diseases in transportation workers are multifactorial in nature, and the relative importance of various occupational risk factors and their interaction with extra- professional ones are not well defined (Ronchese and Bovenzi, 2012). Several studies conducted between the late 1980s and the early 1990s highlighted an excess risk for cardiovascular diseases among professional drivers. In a Danish study analyzing causes of hospitalizations and hospital deaths due to ischemic heart disease between 1981 and 1985, male urban and intercity bus drivers showed a high standardized hospitalization ratio for ischemic heart disease compared to the general population (standardized hospitalization ratio, SHR 136). The excess hospitalization was particularly evident for urban bus drivers (SHR 143) and male taxi drivers (SHR 168) (Ronchese and Bovenzi, 2012). In a study on the incidence of myocardial infarction among bus drivers in Sweden, a cohort of 9446 male drivers exhibited a 50% excess in mortality due to myocardial infarction compared to a control group recruited from other sectors over a 15-year period. Urban bus drivers had a relative risk (RR) of 1.6 (95% CI 1.1-1.9) for the first episode of myocardial infarction. Among the occupational risk factors, authors suggested the possible role of work-related stress, shift work patterns, sedentary work, vehicle exhaust fumes, and noise (Ronchese and Bovenzi, 2012). Analyzing the causes of increased ischemic heart disease risk in urban bus drivers, authors suggested the possible contribution of psychosocial stress factors, particularly high job demands associated with low job control, conditions reported by over 80% of the subjects surveyed (Ronchese and Bovenzi, 2012). 2.2.2 Musculoskeletal Disorders The relationship between driving activities and musculoskeletal disorders has long been documented. Several clinical-epidemiological studies have found an excess occurrence of disorders/pathologies of the cervical and lumbar spine in professional drivers compared to control groups or the general population. One limitation of these studies is that they are predominantly cross-sectional investigations, while studies with a longitudinal design are scarce (Ronchese and Bovenzi, 2012). Investigations in recent years tend to confirm an increased risk for low back pain in professional drivers. In 2005, Hoy et al. investigating a population of forklift drivers, reported a prevalence of low back pain almost double that of the control group (65% vs. 34.8%), corresponding to an OR of 3.5 (95% CI 1.04– 11.8). The intensity of low back pain was also significantly higher in forklift drivers compared to controls (44% vs. 22%). Among truck drivers, Andrusaitiset al. (2012) found a prevalence of low back pain of 59%, a figure comparable to that reported by Okunribidoet al. (2011) within a cohort of bus drivers (59%). The prevalence of low back symptoms was associated with driving duration expressed in hours (OR 1.07; 95% CI 1.01-1.13), confirming what has already been reported in other studies in the literature on a cohort of urban taxi drivers. Increased prevalences of musculoskeletal disorders in the upper and lower limbs have been associated with driving activity: the most frequently reported disorders were localized at the hip-thigh (22.2%) and knees (29.3%) for the lower limbs, and at the elbow (10.8%) and arms (17.5%) for the upper limbs. Most drivers who complained of these disorders associated them with driving conditions and the ergonomic characteristics of the workstation. Ergonomic factors associated with the onset of musculoskeletal disorders, both at the lumbar level and at the upper limb level, were uncomfortable seats (OR 2.2; 95% CI 1.2-4.3), inadequate lumbar support (OR 2.3; 95% CI 1.2-4.2) and steering wheel characteristics (OR 2.2; 95% CI 1.1-4.5). Another relevant factor was the frequent flexion/torsion movements during driving activity (OR 1.86; 95% CI 1.15–2.99) (Ronchese and Bovenzi, 2012). Numerous epidemiological studies have shown that the excess risk for low back pain and other musculoskeletal disorders (cervical pain, brachialgia, and knee pain) in professional drivers is associated with driving seniority, magnitude, and duration of exposure to whole-body vibrations (WBV), as well as, as mentioned above, assuming incongruous postures. A meta-analysis of epidemiological studies on musculoskeletal pathologies in professional drivers has shown significant associations between exposure to WBV and the onset of low back pain, sciatica, and early degeneration, not related to age, of the spine. Some studies have also reported an excess prevalence of spondylolisthesis and higher rates of hospitalization for cervical and lumbar discopathy in professional drivers compared to the general population. These conditions were significantly associated with driving seniority (OR 3.4; 95% CI 1.1 - 10.7, for work seniority > 15 years vs< 5 years) (Ronchese and Bovenzi, 2012). Like vascular pathologies, musculoskeletal pathologies, especially those affecting the spine, have a multifactorial origin. In addition to exposure to WBV and unfavorable ergonomic factors, the contribution of work-related stress and adverse psychosocial factors has been reported. It has been observed that the occurrence of disorders/pathologies of the lumbar spine is associated with heavy traffic conditions (OR 1.8; 95% CI 1.2–2.7) and with a limitation of leisure time beyond working hours (OR 1.6; 95% CI 1.0–2.6). The role of psychosocial factors in the workplace, such as "job demand-control," "job support," and "job satisfaction" in Anglo-Saxon terminology, is still a matter of debate as various epidemiological studies and meta-analyses of literature data offer a non- univocal interpretation of their contribution to the etiopathogenesis of musculoskeletal disorders/pathologies in professional drivers (Ronchese and Bovenzi, 2012). 2.3 Haematologic Changes Associated with Tricycle Driving 2.3.1 Effect of Exposure to Volatile Organic Compounds Benzene Benzene (C6H6) is the parent hydrocarbon of the aromatic group, the resonant cyclic compound consisting of only carbon and hydrogen (Aksoy, 2017). Benzene can be found in products such as gasoline, nail polish, acrylic spray paint, airplane glue, cleaning products, and paint thinners. Benzene exposure, whether environmental, accidental or intentional, can cause toxicity throughout the body, specifically affecting the pulmonary system, central and peripheral nervous system, gastrointestinal, cardiovascular, renal, hepatic, and haematological systems (Ekpenyong and Asuquo, 2017). Acute benzene toxicity usually occurs following inhalation of concentrated benzene vapour, usually in poorly ventilated rooms. The clinical findings, the severity of symptoms and outcome of acute benzene toxicity depend on the amount of toxic vapour inhaled and the duration of the exposure (Aksoy, 2017). Benzene is absorbed through the lungs and approximately 40 or 50 percent of it is retained. Due to the high lipid solubility, benzene accumulates in fat and nervous system and about 40-50 percent of benzene with some carbon dioxide is excreted unchanged from the lungs and a small percentage of benzene is exhaled unchanged even after 24 hours. As it is known the major organ system implicated for benzene metabolism are the liver and the bone marrow. After inhalation, apart from insignificant traces of benzene in urine, most of the absorbed benzene remaining is ultimately metabolized into derivatives which are more water soluble and thus more removable by the kidneys. The fact that the benzene metabolizing system is located in the cell microsomal fraction of the liver is demonstrated by differential centrifugation of the liver homogenate. The mixed function oxidases (mono-oxidases) in the endoplasmotic reticulum of the parenchym cells perform epoxidation of the nucleus to form arene oxides which decompose spontaneously to form phenol. Benzene oxide or epoxide, an unstable chemical which can attach to DNA or RNA giving cause to mutation. Furthermore, recently some studies found out experimentally that high doses of benzene such as 4680 or 4000 ppm cause rapid decrease of colony forming cells (CFC). This disclosed that benzene inhibits the self renewal of pluripotent stem cells and that of the cells of microenvironment and by this phenomenon causes aplastic anaemia (Aksoy, 2017). Benzene is a suspected Leukemogenic agent and intake of high doses of benzene causes malignant tumours in several tissues. If an agent is toxic to the pluripotent stem cell or to microenvironment, it can cause an alteration in the two important capabilities of the colony forming cells namely; Self renewal and Differentiation to produce a variety of lineage progenitor cells. Any block or disturbance in these capabilities will cause either aplastic anaemia (a block in self renewal) or leukemia (a block in differentiation). Benzene is known to cause haematological disorders. Rarely, can result in death. Benzene being one of the main constituent of most spray paints is a major cause of hazards associated with spray painter’s job (Ekpenyong and Asuquo, 2017). Solvents, after being absorbed into the intercellular fluid, may enter main bloodstream and get distributed throughout the body (Baynes and Hodgson, 2004). Blood production is affected by benzene, because the bone marrow is affected. There is an excessive risk of haematopoietic diseases because of relatively high past exposure to benzene has been reported in Korean industries Thus, occupational exposure to benzene leads to haematotoxicity and other blood disorders such as blood cancer (leukaemia), aplastic anaemia, and dysplastic bone marrow conditions are the most common results of exposure to aromatics such as benzene (Ekpenyong and Asuquo, 2017). . The effects of benzene include leukocytopenia, thrombocytopenia, anaemia, transitory leukocytosis, lymphopenia, rarely lymphocytosis. Human exposure to benzene in working environment is a global occupational health problem. It has been established that benzene requires to be metabolized to induce its effects. Benzene has been associated with various haematotoxins and carcinogens. Benzene, also known as benzol, is a volatile, colorless, highly flammable liquid, with a characteristic sickly sweet odor and was discovered in 1825 by Faradat who isolated it from a liquid condensed by compressing oil gas (Ekpenyong and Asuquo, 2017). Lead Lead is a pigment in petrol. Lead (Pb) is one of the most widely scattered toxic metals in the world. An overview of lead exposure can be obtained from a number of tissues and fluids in human body, such as blood, hair, bone, and urine. Generally, it is known that lead exposure could have health risks, such as damage to central nervous and peripheral nerves, cardiovascular system, haematopoietic system, kidneys, digestive system and reproductive system (Shukla et al., 2018). It is also stated as carcinogenic element. Lead interferes with heme biosynthesis by altering the activity of three enzymes namely; aminolevulinic acid synthetase (ALAS), aminolevulinic acid dehydratase (ALAD) and ferrochelatase. Lead indirectly stimulates the mitochondrial enzyme ALAS, which catalyzes the condensation of glycine and succinyl Co-A to form aminolevulinic acid or ALA. Lead also inhibits the zinc containing cytosolic enzyme ALAD, which catalyzes the condensation of two units of ALA to form porphobilinogen. This inhibition is non competitive and occurs through the binding of lead to vicinal sulfhydryls at the active sites of ALAD. Lead bridges the vicinal sulfhydryls. The end results of these changes in enzyme activities are Coproporphyrin, d-aminolevulinic acid (ALA), increased urinary porphyrins and increased erythrocyte protoporphyrin (Patilet al., 2017). Lead has been known to alter the haematological system. The anaemia induced by lead is microcytic and hypochromic and results primarily from both inhibition of heme and globin synthesis and shortening serum level of erythropoietin; a hormone that regulates erythrocyte formation which might also affect the reserve capacity for erythropoiesis. Lead causes oxidative stress by inducing the generation of reactive oxygen species (ROS), reducing the antioxidant defence system of cells through depleting glutathione, interfering with some essential metals, inhibiting sulfhydryls, dependent enzymes or antioxidant enzymes activities and/or increasing susceptibility of cells to oxidative attack by altering membrane integrity and fatty acid composition. Research of lead exposure in haematology is very important because the erythrocytes in bone marrow and blood is the main lead toxicity target in adults. Lead can cause erythrocytes haemolysis and inhibit the formation of haemoglobin (van-Avondtet al., 2019). Patrick (2016) stated that lead can cause G-6PD enzyme deficiency pyrimidine-5’- nucleotidase enzyme inhibition in mature erythrocytes. This leads to the decline of the erythrocytes lifespan and increase the vulnerability of erythrocytes membrane, thus reduce the number of erythrocytes. Enzyme deficiency is characterized by the presence of basophilic stripling of erythrocytes. Lead could inhibit haem biosynthesis through the inhibition of δ-ALAD, coproporphyrinogen, and ferrochelatase enzyme, thus cause the reduction of haemoglobin concentration in blood. Anaemia may develop with lead poisoning due to impaired synthesis of haem, haemolysis of red cells and shortened red cell survival. Anaemia is usually mild and is more commonly seen in adults. Lead inhibits the body's ability to make haemoglobin by interfering with several enzymatic steps in the haem synthesis pathway (Ray, 2016). 2.3.2 Effects of Stress Stress, defined as the physiological response to adverse stimuli, encompasses a multitude of psychological, environmental, and physiological factors. The impact of stress on various bodily systems has been extensively studied, yet its intricate relationship with the haematologic system remains a subject of ongoing research and interest. Stress can lead to many health disorders via many of the behavioral and physiological pathways due to some essential biochemical, metabolic, and hormonal pathways (Al-Hatamlehet al., 2020). The dramatic elevation of the chemical mediators which have been release by body during stressful event produced many physiological effects (Al-Hatamlehet al., 2020). To add on, stress reaction will lead to neuroendocrine response which involved the sympathetic nervous system, and the hypothalamus–pituitary–adrenal axis (HPAA) stimulation that will result in the secretion of glucocorticoids and catecholamine. Subsequently, this response will be followed by a sharp increase in oxidative stress (Al-Hatamlehet al., 2020). Oxidative stress describes the imbalance status between free radicals and antioxidants in the body, which resulting in the damage of important biomolecules and cells that consequently will give impact on the whole body (Al-Hatamlehet al., 2020). Meanwhile, free radicals are unstable molecules, or atoms having an unpaired valence electron that are eligible to stay in independent existence status. These free radicals may cause damage of fatty acids and proteins in the cell membrane, which lead to the destruction of cells due to its characteristics of having an odd number of electrons, thus make it short-lived and highly reactive. Noteworthily, reactive oxygen species (ROS) are classified as the most common and harmful types of free radicals (Al- Hatamlehet al., 2020). In addition to the well-established mechanisms of initial coordination of pro- inflammatory cytokine production, signals provided by ROS also play a significant role in increasing complexity. Furthermore, slightly elevated white blood cells (WBCs) count has been reported to be significantly associated with stressful lifestyles (Al-Hatamlehet al., 2020). On the other hand, the red blood cells (RBCs) are continuously facing ROS from exogenous and endogenous sources, which can lead to insufficient RBCs functions and destruction. Stress by its correlation with oxidative stress, can result to malabsorption of hematinic factors such as iron, vitamin B12, and folic acid, which in turn, may lead to RBC integrity and erythropoiesis disorders (Al-Hatamlehet al., 2020). 2.4 Laboratory Diagnostic Methods 2.4.1 Haemoglobin Concentration and Erythrocyte Indices: Haemoglobin concentration and erythrocyte indices, including mean corpuscular volume (MCV), mean corpuscular haemoglobin (MCH), and mean corpuscular haemoglobin concentration (MCHC), serve as primary indicators of erythropoietic activity and oxygen-carrying capacity. Chronic stress may result in anaemia secondary to suppression of erythropoiesis, leading to decreased haemoglobin levels and microcytic or normocytic erythrocyte morphology. Monitoring changes in haemoglobin concentration and erythrocyte indices enables the assessment of stress-induced alterations in red blood cell production and maturation (Joseph et al., 2024). 2.4.2 White Blood Cell Count and Leukocyte Differential: White blood cell count and leukocyte differential provide valuable insights into the immune response to chronic stress. Stress-induced activation of the immune system may manifest as leukocytosis, characterized by an elevation in total white blood cell count and differential leukocyte counts. Differential analysis allows for the identification of specific leukocyte subsets, including neutrophils, lymphocytes, monocytes, eosinophils, and basophils, each of which plays a distinct role in the inflammatory and immune response to stressors (Joseph et al., 2024). 2.4.3 Laboratory Determination of Lead Poisoning Several laboratory methods can be employed to determine the amount of lead in whole blood. These methods may include the following Atomic Absorption Spectrometry (AAS) The principle of the AAS is that free atoms absorb light at specific wavelengths. The amount of absorbed light can be correlated in a linear fashion to theconcentration of the analyte present in the sample. In order to perform an AAS measurement for lead, the sample must first be processed to generate ground-state atoms as vapour within the light path of the instrument. This process is known as atomization and can be done with a flame, (flame atomic absorption spectrometry, FAAS) or electro-thermal source, most often a graphite furnace (graphite furnace atomic absorption spectrometry, GFAAS) (World Health Organization, 2018). The flame atomic absorption spectrometry makes use of an acetylene–air or a nitrous oxide–acetylene–air laminar flame to atomize lead at temperatures ranging between 2000–3000 °C (World Health Organization, 2018). This method is relatively cheaper, simple to use and free from interferences. The GFAAS uses an electrically heated graphite tube to vaporize and atomize the analyte at temperatures up to 3000 °C prior to its detection. It is a very sensitive method but subject to interferences. These interferences have been reduced by enhanced instrument design in addition to using matrix modifiers. Improved instrument design also makes it possible to detect BLLs as low as 1μg/dl with the AAS. Smaller sample volumes between 10–50 μl can also be measured by this method (World Health Organization, 2018). Anodic Stripping Voltammetry (ASV) With the ASV method of estimating blood lead, a standard electrode and a thin- film mercury graphite electrode are placed in the blood sample, followed by a negative potential application to the mercury electrode for few seconds causing the lead in the blood sample and other cations present to accumulate on the surface of the mercury electrode which is negatively charged (World Health Organization, 2018). The potential’s direction is then reversed to yield a progressively higher potential over several minutes. All other ions are released from the electrode when a specific characteristic lead voltage is reached. A measureable current which is directly proportional to the number of lead ions released is produced. This is then compared with calibration solutions to evaluate the lead concentration in the blood sample (World Health Organization, 2018). This method requires sample preparation because the lead has to be available as free Pb2+ aqueous cation. These ASV devices have a detection limit between 1–100μg/dl. Portable ASV devices (example, lead care device) are available for use on the field and also as a point of care device. It is recommended that BLLs greater than 10μg/dl are confirmed with either the AAS or ICP-MS. The presence of co-reducible metals and chelating agents or elevated copper concentrations can create interferences. Small sample volumes of 50μl are used (World Health Organization, 2018). Inductively Coupled Plasma Mass Spectrometry (ICP-MS) This method involves the use of inductively coupled plasma to atomize and subsequently ionize the atoms of interest in the sample. Extraction of the ions from the plasma is done and they are passed through a mass spectrometer. In the mass spectrometer, the ions are separated and measured based on their mass-to- charge ratio. The ICP-MS has a high selectivity of filters of ions in addition to highly amplified ionic signals that strike the detector. It also has low background interferences with noise and offers very minute detection limits (part per trillion to part per billion) for majority of elements (World Health Organization, 2018). The ICP-MS method has a detection limit of nearly 0.2 μg/dl for direct analysis of lead in blood. Blood sample dilution before aspiration into the plasma is very vital because ICP-MS is less tolerable relative to AAS of heavy matrices. Based on this, highest standard of operation by skilled personnel is required for the operation of ICP-MS. The ICP-MS can evaluate several elements from a lone sample volume as low as 50–100 μl and also permits the measurement of the lead isotope ratio present in the blood sample. This makes it feasible in making out whether the lead was from a specific location. The ICP-MS is expensive though highly productive and therefore, becomes relatively economical in analysing many samples or elements (World Health Organization, 2018). The AAS method for the detection of blood lead was adopted for this study. The reason being that it is relatively cheaper and available and also has a high detection limit as well as a low detection limit of above 100μg/dl and 1μg/dl respectively. It can also analyse smaller sample volumes of 10 to 50μl. Improvement in instrument designs has also reduced the problem with interferences. CHAPTER THREE MATERIALS AND METHODS 3.1 Study Area The study was carried out at the Haematology Laboratory of Abia State Teaching Hospital (ABSUTH) located at Umueze Road Abayi, in the town of Aba. Aba is a city found in the Southeast geopolitical zone of Nigeria and lie between Latitude 5̊ 25'N 7˚30'E and longitude 5̊ 45'N 7˚52’E and about 48km from Umuahia, also it is the commercial centre of Abia state. It is a major urban settlement and surrounded by small villages and towns. The indigenous people are the Ngwa. 3.2 Study Design This was a cross-sectional study involving tricycle drivers in Aba metroplolis. 3.3 Sample Size The study size was determined by using the Fischers’ Formula, with the prevalence rate of 93% giving a sample size of one hundred (100) subjects. A total sample size of 100 subjects were recruited for this study. Sample size was calculated using the formula below: n = = where: n = the minimum sample size Z = the standard normal deviation, usually set at 1.96, which corresponds to the 95% confidence interval p = the prevalence rate of 93% (0.93) q = 1- P d = degree of accuracy desired usually set at 0.05 The required sample size was calculated as follows: n = n = n = n = = 100 n = 100 The total sample size of 100(50 subjects and 50 controls) were recruited for the study. 3.4Sampling Technique A Simple random sampling technique was adopted for this study. 3.5Inclusion Criteria This study included; apparently healthy tricycle drivers with no adverse medical history, consenting tricycle drivers, and healthy non-tricycle drivers as control. 3.6Exclusion Criteria This study excluded subjects with; denial of consent, febrile illness in the last four weeks, features or laboratory diagnosis of haemoglobinopathy or bleeding disorder, chronic medical ailment, record of recent blood transfusion, and acute renal, liver or systemic diseases. 3.7Ethical Approval Ethical approval to carry out the study was obtained from the Ethical committee of the Department of Medical Laboratory Science, Abia State Teaching Hospital. 3.8Sample Collection and Preparation Five milliliters of blood (5 ml) was collected. The 5ml of blood was used for the determination of haematological parameters count using an Automated- Full blood count analyzer. 3.9Laboratory Methods Determination of Complete blood Count Using Automated Analyser. Principle: Hemia 5D is a quantitative automated hematology analyzer for in vitro diagnostic use for determining 17 hematological parameters. Examination of the numerical and/or morphologic findings of the complete blood count are useful in diagnosis of such disease states as anemias, leukemias, allergic reactions, viral, bacterial, and parasitic infections. Hemia 5D directly measures the WBC, RBC, HGB, HCT, PLT, LYM, MIXED and NEUT. The KX-21N counts and sizes red blood cells (RBC) and platelets (PLT) using electronic resistance detection. Hematocrit (HCT) is measured as the ratio of the total RBC volume to whole blood using cumulative pulse height detection. Hemoglobin (HGB) is converted to methemoglobin, and read photometrically at 555 nm. White blood cells (WBC) are analyzed by direct current and discriminated into a three-part differential using Particle Distribution Analysis (PDA). The resulting WBC histogram is discriminated into lymphocyte, neutrophil and mixed cell populations. The mixed cell population contains monocytes, basophils and eosinophils Procedure: The automatic voltage regulator, the uninterrupted power supply unit and the power button on the Hemmia 5D Haematologyanalyser were turned on. The “ready” for analysis was allowed to appear and there was a temporary wait for the analyzer to come to “ready state”. Then, the sample numbers of the subject entered into the device. Five millilitres (5 ml) of blood collected in K3EDTA vacutainer was mixed gently. The stopper of the vacutainer was opened to start analysis. The tube was set to the sample probe and the start switch was pressed so that the device can aspirate the required volume. The tube was held against the sample probe until the buzzer sounds twice. At the end of each analysis, the device displays and prints a hard copy of the report for onward documentation. After the printing, it goes to ready state so that it can analyze another sample. 3.10Statistical Analysis The data analysis was performed using Statistical Package for Social Science (SPSS) version 25.0. The parameters were expressed in mean and standard deviation. Comparison of groups was done using ANOVA and Student’s t-test. Level of significance was set at p0.05- Not Significant. 4.2 The mean and standard deviation of haemoglobin concentration (HB), packed cell volume (PCV), red blood cell (RBC), platelet (PLT), total white blood cell (TWBC), neutrophil (NEUT), lymphocyte (LYM), monocyte (MON), basophil (BAS), eosinophil (EOS), mean cell volume (MCV), mean cell haemoglobin (MCH), and mean cell haemoglobin concentration (MCHC) of tricycle drivers based on age recruited into the study was shown in table 4.2, using ANOVA and Post HOC. The mean ± standard deviation values of HB (12.93±1.449, 12.23±1.354, 12.36±1.287, respectively) g/dl of tricycle drivers aged 20-29, 30-39, & 40 years above, respectively, were not statistically significant (P>0.05). The mean ± standard deviation values of PCV (0.40±4.089, 0.38±3.652, 0.39±6.118, respectively)L/L of tricycle drivers aged 20-29, 30-39, & 40 years above, respectively, were not statistically significant (P>0.05). The mean ± standard deviation values of RBC (4.57±0.550, 4.46±0.499, 4.51±0.827, respectively) x1012 /L of tricycle drivers aged 20-29, 30-39, & 40 years above, respectively, were not statistically significant (P>0.05). The mean ± standard deviation values of PLT (190.25±48.716, 188.20±33.184, 199.21±42.403, respectively) x109/L of tricycle drivers aged 20-29, 30-39, & 40 years above, respectively, were not statistically significant (P>0.05). The mean ± standard deviation values of TWBC (4.77±0.982, 5.26±1.284, 4.80±0.845, respectively) x109 /L of tricycle drivers aged 20-29, 30- 39, & 40 years above, respectively, were not statistically significant (P>0.05). The mean ± standard deviation values of NEUT (2.15±0.667, 2.55±0.607, 2.21±0.981, respectively) x109/L of tricycle drivers aged 20-29, 30-39, & 40 years above, respectively, were not statistically significant (P>0.05). The mean ± standard deviation values of LYM (1.75±0.359, 1.93±0.037, 1.83±0.524, respectively) x109 /L of tricycle drivers aged 20-29, 30-39, & 40 years above, respectively, were not statistically significant (P>0.05). The mean ± standard deviation values of MON (0.58±0.033, 0.64±0.124, 0.61±0.077respectively) x109 /L of tricycle drivers aged 20-29, 30-39, & 40 years above, respectively, were not statistically significant (P>0.05). The mean ± standard deviation values of BAS (0.21±0.231, 0.33±0.421, 0.22±0.012, respectively) x109 /L of tricycle drivers aged 20-29, 30-39, & 40 years above, respectively, were not statistically significant (P>0.05). The mean ± standard deviation values of EOS (0.11±0.023, 0.12±0.043, 0.12±0.064, respectively)x109 /L of tricycle drivers aged 20-29, 30-39, & 40 years above, respectively, were not statistically significant (P>0.05). The mean ± standard deviation values of MCV (86.83±4.355, 85.40±2.074, 86.51±6.455, respectively) fl of tricycle drivers aged 20-29, 30-39, & 40 years above, respectively, were not statistically significant (P>0.05). The mean ± standard deviation values of MCH (29.33±1.366, 27.60±2.302, 30.25±0.957, respectively) pg of tricycle drivers aged 20-29, 30- 39, & 40 years above, respectively, were not statistically significant (P>0.05). The mean ± standard deviation values of MCHC (32.01±1.41, 32.17±0.983, 30.53±2.944, respectively) g/dl of tricycle drivers aged 20-29, 30-39, & 40 years above, respectively, were not statistically significant (P>0.05). TABLE 4.2: COMPARISON OF MEAN±SD OF HB, PCV, PLT, TWBC, NEUT, LYM, MON, BAS, EOS, MCV, MCH, MCHC OF TRICYCLE DRIVERS BASED ON AGE . Parameters 20-29 years 30-39 years 40 and above F ratio P value HB (g/dl) 12.93±1.449 12.23±1.354 12.36±1.287 0.465 0.639 PCV (L/L) 0.40±4.089 0.38±3.652 0.39±6.118 0.210 0.814 RBC (x1012/L) 4.57±0.550 4.46±0.499 4.51±0.827 0.345 0.786 PLT (x109/L) 190.25±48.716 188.20±33.184 199.21±42.403 2.307 0.142 TWBC (x109/L) 4.77±0.982 5.26±1.284 4.80±0.845 0.338 0.719 NEUT (x109/L) 2.15±0.667 2.55±0.607 2.21±0.981 0.084 0.920 LYM (x109/L) 1.75±0.359 1.93±0.037 1.83±0.524 0.387 0.687 MON 0.58±0.033 0.64±0.124 0.61±0.077 1.038 0.369 (x109/L) BAS (x109/L) 0.21±0.231 0.33±0.421 0.22±0.012 0.835 0.458 EOS (x109/L) 0.11±0.023 0.12±0.043 0.12±0.064 0.238 0.962 MCV (fl) 86.83±4.355 85.40±2.074 86.51±6.455 0.352 0.710 MCH (pg) 29.33±1.366 27.60±2.302 30.25±0.957 3.014 0.087 MCHC (g/dl) 32.01±1.414 32.17±0.983 30.53±2.944 2.510 0.123 Key: P≤0.05 – Significant, P>0.05- Not Significant. Multiple Comparisons LSD Dependent Variable (I) Variable (J) Variable Mean Difference (I-J) Std. Error Appendix. 95% Confidence Interval Lower Bound Upper Bound HB 20-29 30-39 .91333 1.01191 .385 -1.2914 3.1181 40 above .08333 1.07870 .940 -2.2669 2.4336 30-39 20-29 -.91333 1.01191 .385 -3.1181 1.2914 40 above -.83000 1.12102 .473 -3.2725 1.6125 PCV 20-29 30-39 1.78000 2.75953 .531 -4.2325 7.7925 40 above .65000 2.94167 .829 -5.7593 7.0593 30-39 20-29 -1.78000 2.75953 .531 -7.7925 4.2325 40 above -1.13000 3.05707 .718 -7.7908 5.5308 RBC 20-29 30-39 -.00667 .37320 .986 -.8198 .8065 40 above -.00667 .39783 .987 -.8735 .8601 30-39 20-29 .00667 .37320 .986 -.8065 .8198 40 above .00000 .41344 1.000 -.9008 .9008 PLT 20-29 30-39 45.80000 25.72897 .100 -10.2586 101.8586 40 above -9.00000 27.42718 .748 -68.7587 50.7587 30-39 20-29 -45.80000 25.72897 .100 -101.8586 10.2586 40 above -54.80000 28.50316 .079 -116.9030 7.3030 TWBC 20-29 30-39 -.49167 .64346 .460 -1.8936 .9103 40 above -.03417 .68593 .961 -1.5287 1.4603 30-39 20-29 .49167 .64346 .460 -.9103 1.8936 40 above .45750 .71284 .533 -1.0956 2.0106 NEUT 20-29 30-39 .70667 6.44964 .915 -13.3459 14.7592 40 above -2.13333 6.87534 .762 -17.1134 12.8467 30-39 20-29 -.70667 6.44964 .915 -14.7592 13.3459 40 above -2.84000 7.14506 .698 -18.4078 12.7278 LYM 20-29 30-39 -3.06667 4.39629 .499 -12.6454 6.5120 40 above -3.66667 4.68647 .449 -13.8776 6.5443 30-39 20-29 3.06667 4.39629 .499 -6.5120 12.6454 40 above -.60000 4.87032 .904 -11.2115 10.0115 MON 20-29 30-39 -2.13333 1.64799 .220 -5.7240 1.4573 40 above -2.08333 1.75677 .259 -5.9110 1.7443 30-39 20-29 2.13333 1.64799 .220 -1.4573 5.7240 40 above .05000 1.82568 .979 -3.9278 4.0278 BAS 20-29 30-39 -.30000 .56100 .603 -1.5223 .9223 40 above .50000 .59803 .419 -.8030 1.8030 30-39 20-29 .30000 .56100 .603 -.9223 1.5223 40 above .80000 .62149 .222 -.5541 2.1541 EOS 20-29 30-39 .02667 .37390 .944 -.7880 .8413 40 above -.08333 .39858 .838 -.9518 .7851 30-39 20-29 -.02667 .37390 .944 -.8413 .7880 40 above -.11000 .41422 .795 -1.0125 .7925 MCV 20-29 30-39 .43333 2.69122 .875 -5.4303 6.2970 40 above 2.33333 2.86885 .432 -3.9174 8.5840 30-39 20-29 -.43333 2.69122 .875 -6.2970 5.4303 40 above 1.90000 2.98140 .536 -4.5959 8.3959 MCH 20-29 30-39 1.73333 1.00846 .111 -.4639 3.9306 40 above -.91667 1.07502 .411 -3.2589 1.4256 30-39 20-29 -1.73333 1.00846 .111 -3.9306 .4639 40 above -2.65000 1.11720 .055 -5.0842 -.2158 MCHC 20-29 30-39 .16667 .97063 .867 -1.9482 2.2815 40 above 2.16667 1.03470 .058 -.0877 4.4211 30-39 20-29 -.16667 .97063 .867 -2.2815 1.9482 40 above 2.00000 1.07529 .088 -.3429 4.3429 4.3 The mean and standard deviation of haemoglobin concentration (HB), packed cell volume (PCV), red blood cell (RBC), platelet (PLT), total white blood cell (TWBC), neutrophil (NEUT), lymphocyte (LYM), monocyte (MON), basophil (BAS), eosinophil (EOS), mean cell volume (MCV), mean cell haemoglobin (MCH), and mean cell haemoglobin concentration (MCHC) of tricycle drivers based on duration of use recruited into the study was shown in table 4.3, using student’s t-test. The mean ± standard deviation values of HB, PCV, RBC, PLT, MON, EOS, MCV, MCHC (12.68±1.884g/dl, 0.39±4.915L/L, 4.57±0.656x1012/L, 185.32±58.581x109/L, 0.76±0.321x109/L, 0.15±0.051x109/L, 86.56±3.468fl, 31.67±1.414g/dl, respectively) of subjects that have been driving tricycle for 1-4 years were higher but not statistically significant (P>0.05) when compared with the values of subjects who have been driving tricycle for 5 years and above (12.56±1.238g/dl, 0.38±3.591L/L, 4.41±0.450x1012/L, 175.33±45.148x109/L, 0.65±0.265x109/L, 0.13±0.043x109/L, 85.33±2.465fl, 31.33±2.338g/dl, respectively). The mean ± standard deviation values of TWBC, NEUT, LYM, BAS, MCH (4.84±0.898x109/L, 2.12±0.697x109/L, 0.41±0.237x109/L, 28.89±2.315 x109/L, respectively) of subjects that have been driving tricycle for 1-4 years were lower but not statistically significant (P>0.05) when compared with the values of subjects who have been driving tricycle for 5 years and above (5.09±1.236x109/L, 2.95±0.496x109/L, 1.45±0.676x109/L, 0.43±0.124x109/L, 29.17±1.169pg, respectively). TABLE 4.3: COMPARISON OF MEAN±SD OF HB, PCV, PLT, TWBC, NEUT, LYM, MON, BAS, EOS, MCV, MCH, MCHC OF TRICYCLE DRI VERS BASED ON DURATION OF DRIVING USING STUDENT T-TEST. Parameters 1-4 years 5 years above Calculated t P value HB (g/dl) 12.68±1.884 12.56±1.238 0.146 0.886 PCV (L/L) 0.39±4.915 0.38±3.591 0.095 0.926 RBC (x1012/L) 4.57±0.656 4.41±0.450 0.544 0.596 PLT (x109/L) 185.32±58.581 175.33±45.148 1.796 0.441 TWBC (x109/L) 4.84±0.898 5.09±1.236 -0.457 0.655 NEUT (x109/L) 2.12±0.697 2.95±0.496 -0.215 0.833 LYM (x109/L) 1.23±0.322 1.45±0.676 -0.443 0.665 MON (x109/L) 0.76±0.321 0.65±0.265 1.085 0.298 BAS (x109/L) 0.41±0.237 0.43±0.124 -1.840 0.092 EOS (x109/L) 0.15±0.051 0.13±0.043 1.928 0.076 MCV (fl) 86.56±3.468 85.33±2.465 0.534 0.603 MCH (pg) 28.89±2.315 29.17±1.169 -0.269 0.792 MCHC (g/dl) 31.67±1.414 31.33±2.338 0.346 0.735 Key: P≤0.05 – Significant, P>0.05- Not Significant. CHAPTER FIVE DISCUSSION, CONCLUSION, RECOMMENDATION 5.1 Discussion The nature of work and lifestyle of commercial cyclists predisposes them to various conditions that can lead to anaemia, which in turn affect haematopoiesis and cause morbidity and mortality (Olayanjuet al., 2021). The findings of this present study showed a statistically significant (P≤0.05) reduction in haemoglobin concentration, packed cell volume and red blood cells of tricycle drivers when compared with non-tricycle drivers. This is similar with the findings of Olayanjuet al. (2021) which found haemoglobin to be significantly lower among tricycle drivers. Similarly, a study carried out in Owerri, Nigeria on the effect of chronic alcohol consumption on haematologicaland cardio hepatic function markers among commercial motorcyclists, showed that Haemoglobin was significantly lower in motorcyclists (Igbohet al., 2015). Tricycle drivers are often exposed to higher levels of environmental pollutants such as carbon monoxide, lead, and other particulate matter. These pollutants can cause oxidative stress, leading to haemolysis (destruction of red blood cells) and a consequent reduction in haemoglobin levels and RBC count. Tricycle drivers may have less access to a balanced diet due to economic constraints, leading to deficiencies in essential nutrients such as iron, vitamin B12, and folic acid, which are crucial for erythropoiesis (the production of red blood cells). This can result in lower haemoglobin, PCV, and RBC levels. In this study, platelet count was statistically lower (P≤0.05) in tricycle drivers when compared with non-tricycle drivers. This is consistent with the findings of Olayanjuet al. (2021). Tricycle drivers are constantly exposed to high levels of vehicular emissions, including particulate matter (PM), carbon monoxide (CO), nitrogen oxides (NOx), and sulfur dioxide (SO2) (Ulakpa and Ulakpa, 2022). Chronic exposure to these pollutants can lead to systemic inflammation and oxidative stress (Gangwaret al., 2020). Oxidative stress and inflammation can impair megakaryocytes (the precursor cells of platelets), leading to reduced platelet production (Noh, 2022). This may also be associated with nutritional deficiency. ROS is known to deplete antioxidants and depletion of antioxidants render blood cells very fragile, thus leading to accelerated destruction of the blood cells (Igbohet al., 2015). In this study, white blood cells were higher among tricycle drivers compared to non-tricycle drivers but this was not statistically significant (P>0.05). This was in contrast with the findings of Olayanjuet al. (2021) which observed lower white blood cells among tricycle drivers, although without a statistical significance (P>0.05). Igbohet al. (2015) also found lower levels of white blood cells in motorcyclists. Chnages in haematological parameters among tricyclists were not associated with age and duration of use of tricycle. Tricycle drivers are consistently exposed to similar levels of pollutants and physical strain, which may result in relatively uniform haematological profiles over different durations of exposure. 5.2 Conclusion This study revealed significant hematological differences between tricycle drivers and non-tricycle drivers, particularly in parameters such as haemoglobin concentration (HB), packed cell volume (PCV), red blood cell (RBC) count, platelet (PLT) count, and mean cell volume (MCV). Tricycle drivers exhibited lower levels of these parameters, which were statistically significant. 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(2018). Brief guide to analytical methods for measuring lead in blood. https://www.who.int/publications/i/item/9789240009776. APPENDIX I INFORMED CONSENT FORM I…………………………………………………………………. of…………… …………………………………………………………………………………… ……….. do hereby consent to participate in this study titled: “Assessment of Some Haematologic Parameters Among Tricycle Drivers in Aba, Abia State, Nigeria” I acknowledge that I have been properly counseled on the implications of my involvement. I understand that the investigations that would be carried out in the course of this study are solely for the purpose of medical research and I express my willingness to participate on the understanding that I shall be entitled to withdraw this consent at any time I choose to do so and that my withdrawal will not affect the quality of care I received in the hospital. Date…………………… Signed ………………… Witness…………………... I confirm that I have explained to the patient the purpose and nature of this study and his/her refusal to participate will not in any way affect the normal care by me or any member of this institution. I know the consequences of any false declaration on this or any other form. Date……………………… Signed…………………… Witness…………………... APPENDIX II Equipment Model Manufacturer General laboratory glass wares and Test tube Racks Pyrex Corning England Automatic pipettes and tips Full blood count automated analyser Sysmex KX-21N Sysmex Germany Sample Containers (K3EDTA 5ml vacutainer tube) and disposable needle KRIS17-759G KRIS-ALOY Spirit alcohol and Cotton wool Reagents Product Number Description Sysmex Reagents • Diluent) Sodium Chloride 6.38g/L Sysmex F820/KX- 21N/ Zhejiang Xinke Medical Technology co., Ltd. Boric Acid 1.00g/L Sodium Tetraborate 0.20g/L EDTA-2K 0.20g/L • HaemoglobinLyser Organic quanternary ammonium salt 8.5g/L Sodium Chloride 0.6g/L • Cell cleaning Solution-Bleach Sodium Hypochlorite 8.25% APPENDIX III APPENDIX IV STATISTICAL ANALYSIS Arithmetic Mean x = mean = summation Different values that make up the set N = Number of data (sample size) Standard deviation SD= SD = Standard Deviation = mean = summation Different values that make up the set (n1 + n2) – 2 = absolute degree of freedom Student’s T-test “t” = Where “t” = Student’s T-test = Mean of group one = Mean of group two SD1 Standard Deviation of regular donors SD2 = Standard Deviation of control. ANOVA (Analysis of Variance) F = MST/MSE MST = SST/ p-1 MSE = SSE/N-p SSE = ∑ (n−1) 2 Where, F = Anova Coefficient MSB = Mean sum of squares between the groups MSW = Mean sum of squares within the groups MSE = Mean sum of squares due to error SST = total Sum of squares p = Total number of populations n = The total number of samples in a population SSW = Sum of squares within the groups SSB = Sum of squares between the groups SSE = Sum of squares due to error s = Standard deviation of the samples 55