Background: There is paucity of data on health-related quality of life (HRQoL) among Health Information Managers/Health Record Officers (HROs) in the Nigeria health system. Hence, this study investigated the impact of the COVID-19 pandemic on health-related quality of life (QoL) among HROs in Obafemi Awolowo University Teaching Hospital Complex (OAUTHC), Ile-Ife, Nigeria. Methods: A cross-sectional study was conducted in the University Hospital, where a total of 52 health record officers were purposively sampled. Relevant data were collected using the Short Form survey (SF-36v2) questionnaire. One-way ANOVA was used to determine mean group differences across the nine and the two QoL (physical and mental) summary domains based on respondents’ socio-demographics, while level of significance was set at 0.05. Results: All the QoL sections of the instrument used yielded an α-Cronbach’s score of > 0.70. Analysis of some QoL physical component dimensions showed that; Bodily pain (BP) was found to be significantly (P=0.032) associated with marital status, Physical functioning (PF) with gender (P=0.023), and general health (GH) with age group (P=00.025) and highest level of education (P=0.023). On the other hand, mental health component analysis revealed that Social Functioning (SF) was associated with age group (P=014), Role limitation (RE) with marital status (P=0.048), highest level of education (P=0.048) and years of service (P=0.015) etc. Conclusion: The QoL among HROs studied was generally above average, and demographic characteristics such as age, gender and marital status significantly influence QoL. Health managers and stakeholders should consider some of the factors identified in managing HROs.

Since the outbreak of the novel Coronavirus (COVID-19) in Wuhan, China in late December 2019, the virus has spread to all continents of the world affecting above 170 million people with more than 3.8 million deaths (data till June 22, 2021) (World Health Organisation, WHO, 2021) [1]. As of the reference date, Nigeria has recorded about 167,000 confirmed cases with 2,117 deaths. The unique ability of the COVID 19 virus to mutate into different strains with higher infectivity resulted in concerns for a second and third wave of the virus across the world. Presently, governments in some countries around the world have started confinement measures to curtail the spread of the second wave of the COVID 19 (Hafeez et al., 2021) [2]. However, with the discovery and gradual acceptance of the COVID-19 vaccines globally, there seems to be a glimpse of hope to the end of the virus. There are also concerns on the acceptance of the COVID-19 vaccine and the emergence of strains that might be partly resistant to the vaccine.

The lockdown imposed in many countries of the world in 2020 had negative impact on their economy with developing countries grossly affected. The lockdown left the health care system overwhelmed with workers having to labour for long hours with meagre facilities. The World Health organization reported in late July that over 10,000 health workers in Africa tested positive for Covid-19 (WHO, 2021). This raised concerns about the ability of countries like Nigeria with only 0.4 doctors per 1,000 people (trAIDe Health, 2020) [3] to successfully control a pandemic that has overwhelmed better-resourced health systems. As the pressure increases on a handful of health workers and facilities across the country, many health workers have been reported to have contracted the disease with some mortality. There are tendencies for the increased workload to result in mental, physical, and emotional stress culminating in a sub-optimal quality of life for the health workers (Shigemura et al., 2020, Gao et al., 2020) [4, 5].

Against the backdrop of the foregoing, Health Information Managers (HIMs)/Health Record Officers (HROs) who, although constitute just one of the various cadres of health care professionals, remain a very critical components of hospital-based health professionals who were also exposed to the ravaging consequences of the pandemic with possible devastating bearing on their quality of life (QoL). By default, in the Nigerian setting, HIMs are meant to carry out the critical roles of organizing, overseeing, and protecting patient health information data which includes symptoms, diagnoses, medical histories, test results, and procedures as well as collect health care data at all levels of different specialist clinics/clinics within the hospital and compile statistic for administrative use and medical research (Oluwatoyin, 2022) [6].

Quality of life assessment is an individual’s assessment of their own health status based on their culture and value system in relation to their goals, expectations, standards, and concerns (WHO, 1996) [7]. The pandemic has undoubtedly projected some mental, psychological, and physical stress on the health workers (Alnazly et al, 2021) [8]. Health care professionals reported having the moral obligation to treat patients and save lives (Greenberg et al., 2020) [9]. Being the frontline workers to treat and be exposed to different patients, they are at risk of contracting the disease. In addition, Lai et al (2020) [10]., reported that health care professionals feared being infected, felt stigmatized, experienced high levels of anxiety, had symptoms of depression, and sleep problems. Poor quality of sleep (QoS) is associated with high levels of anxiety and symptoms of depression in the general population (Yeen & Ning, 2020) [11]. COVID-19 pandemic has been associated with impaired health-related quality of life (HRQoL) among local residents (Zhang & Ma, 2020) [12]. Health related quality of life are associated with cognitive assessments of well-being, as seen from the individual’s perspective. There is paucity of data on the health-related quality of life among health care professionals during the COVID-19 pandemic. What is the quality of life among HROs in the area of study? Are there associations between the different dimensions of QoL and socio-demographic factors of the respondents? This study will seek to respond to these questions and evaluate the health-related quality of life of health workers (health record officers) in a tertiary health institution (Obafemi Awolowo University Teaching Hospital Complex) in Nigeria.

2.1. Study Design and Area of Study

This study is a descriptive cross-sectional survey carried out in Ife Hospital Unit of Obafemi Awolowo University Teaching Hospital Complex (OAUTHC), Ile-Ife. OAUTHC is one of the approved COVID-19 treatment and isolation centers in Nigeria.

2.2. Study Population, Sample, and sampling Technique

The study population comprised all the 70 health record officers working in OAUTH Ile-Ife, Nigeria. Though all of the 70 officers were targeted for inclusion in the study, only sixty of them who had been working for at least six months were willing to participate in the study. However, individuals with a prior history of mental illness and those absent during data collection were not included. Hence, 52 of them that met the aforementioned selection criteria eventually constituted the sample for this study.

2.3. Research Instrument

A two-part questionnaire consisting of sociodemographic characteristics and the short form health survey (SF-36 vs2) questionnaire developed as part of the RAND Medical Outcomes Study; was randomly administered, for study participants to respond to anonymously and voluntarily. The short form health survey form (SF-36vs2) was designed to measure functional health and well-being from the patient's point of view and often used as a measure of a person or population's quality of life (Ware and Sherbourne, 1992) [13]. It is composed of questions that covers the following eight major health domains: limitations in physical activities (PF) because of health problems, limitations in social activities because of physical or emotional problems (SF), limitations in usual role activities because of physical health problems (RP), bodily pain (BP), general mental health (psychological distress and well-being) (MH), limitations in usual role activities because of emotional problems (RE), vitality (energy and fatigue) (VT) and general health (GH) perceptions . The nineth single-item domain is health change – SF-36vs2 item number two. These domains have been further summarized into two major components based on the outcomes of factor analysis: physical component score (PCS) and the mental component score (MCS) (Farivar et al, 2007) [14].

2.4. Data analysis

Data obtained from this study were analyzed using IBM Statistical Package for Social Science (SPSS) version 21. The results were presented using frequency counts, percentage distribution, mean and standard deviation, and one-way analysis of variance (ANOVA). Continuous variables were reported as Mean ± standard deviation (SD), while categorical variables were presented in frequencies and percentages. Based on socio-demographic factors and QoL domains and summaries, one-way analysis of variance (ANOVA) was used to make comparisons among groups, and P-values < 0.05 were considered to be statistically significant. The responses to the 36 items were in the form of a Likert’s scale with options ranging from 1 to 5 such that, Excellent=5, Very Good=4, Good=3, Fair=2, Poor=1. These categories were converted to a score of 100 per option (5 = 100, 4 = 75, 3 = 50, 2 = 25, 1 = 0). Negatively worded items were then recoded reversely to ensure that higher scores reflect high quality of life or more favorable state of the study participants - and their scores were then averaged to form specific dimensions/ domains i.e. the score of each subscale (domain) is the sum score of its items divided by the number of items (Huang et al, 2020) [15].

The internal reliability of the questionnaire items was assessed according to sections, domains and the two major summaries (PCS and MCS). Alpha (α) Cronbach’s test coefficient scores of all sections of the instrument were > 0.70 and so were considered reliable.

2.5. Ethical Considerations

Ethical clearance was obtained from the Hospital's Research and Ethics Committee. During the data collection exercise proper introduction of the researcher (s) and topic of study were ensured among the health-care workers (health record officers) and their informed consents were obtained.

The result of data analysis for this study yielded an overall internal reliability coefficient of 0.792 based on Alpha (α) Cronbach’s test – with every domain or sub-scale having a minimum score of 7.0. The lowest score (0.702) was recorded in role limitations due to physical health (RP) which was measured on 4 items; while the highest (0.895) was in Physical Functioning (PF) which was measured using 10 items. The domains mean scores ranged from 52.1±26.7 for limitations due to emotional problems (RE) to 69.2±21.4 for Health Change (HC) – B2 (Table 1).

About a half (51.9%) of the respondents were below the age of 30, and about one-third (30.8%) aged from 31 to 40; while the rest were above 40 years. There was female gender preponderance in a male/female ratio of 1:3.3 where 76.9% of them were females. More than half (59.6%) of the study participants were unmarried (single), with half (50.0%) having attained a Higher National Diploma level of education (Table 2). Again, more than half (59.6%) of the health record officers have a working experience of not more than five years, while another good proportion (28.8%) have spent between 6 to 10 years in service.

A comparative analysis of the five QoL dimensions under physical component by socio-demographic characteristics of respondents shows that age group of respondents was significantly (P=0.025) associated with general health (GH) domain, where the highest mean (X=72.86, SD=8.09) of QoL was observed among respondents within the age group of 41-50 years. On the other hand, when all the five dimensions of the physical domain were compared by gender only physical functioning (PF) yielded statistically significant association (P=0.023) in which males had the highest mean QoL score of 72.08, SD=26.32. The result shows that singles had better QoL dimensions in terms of Bodily pain, BP (P=0.032) and Health Change, HC (P=0.011) with means scores of X=73.24, SD=26.84 and X=97.58, SD=7.51, respectively. According to the highest level of education general health (GH) was significantly (P=0.023) found to be associated with QoL in which OND and HND holders had the highest mean QoL scores of 67.00, SD=10.85 and 66.15, SD=9.83, respectively. On the other hand, study participants that had work experience of five years and below (0-5 yrs.) yielded a significantly (P=0.011) higher QoL health change mean of 98.39, SD=6.24.

The result for mental health component of QoL by socio-demographics of respondents shows that individuals under 30 years and those over 50 years old yielded the highest QoL (social functioning, SF) scores of 76.39, SD=18.78 and 75.00, SD=35.36 respectively (P=0.011) (Table 4). Marital status of respondents was significantly (P=0.048) associated with only emotional role limitation (RE) mental health component whereby singles fared well at mean QoL score of 58.10, SD=22.73. Similarly highest educational level of respondents was significantly (P=0.048) associated with only emotional role limitation (RE) mental health component whereby holders of lower educational certificates (secondary school certificate, OND and HND) scored higher mean QoL. Study participants’ years of work experience was found to be significantly associated with only emotional role limitation (RE) domain (P=0.015) and mental health (MH) domain (P=0.024) of the mental health component of QoL. While those with more service years (>20 years) appeared to have better mean QoL scores (X=98.67, SD=2.31) in the mental health (MH) domain, the reverse is the case in terms of emotional role limitation (RE) in which those in the lower rungs of years of service (0-5 years and 6-10 years) appears to have better mean QoL scores of 53.89, SD=23.83 and 60.16, SD=25.77 respectively. On the other hand, there were no statistically significant associations between the four mental health domains of QoL and gender of study participants.

A comparison of summaries of physical and mental components as well as overall QoL versus socio-demographics of respondents yielded no statistically significant (P>0.05) association (Table 5). Hence, based on the findings of this study, statistically significant associations between QoL and respondents’ socio-demographic variables appears only when QoL components are disaggregated into the various domains.

To the authors’ knowledge, this is the first Nigerian study evaluating the health-related quality of life among health record officers during the Covid-19 pandemic. Health record managers in the hospitals represents an at-risk population for Covid-19 because they are among officers with first contact with the patients. The health institution used for this research is one of the tertiary health facilities selected to cater for Covid-19 patients in the country. Hence, health care workers are at risk of contracting the disease if not well protected. The high response rate among the health record officers in the hospital gives a robust representative of the quality of life among the health record officers in the hospital.

While a strong correlation between health care work and occupational stress has been previously reported by some Nigerian studies (Ugwu et al., 2007, Azodo and Azeja, 2013) [16, 17], the way in which the unprecedented Covid-19 pandemic affects the quality of life of health workers have not been explored.

The quality of life of health record officers in this study were divided into different domains touching physical functioning, emotional stability and mental health. The overall Conbrach’s Alpha reliability score for all the domains evaluated in this study is 0.792 with the lowest reliability coefficient (0.702) recorded in role limitations due to physical health (RP) while the highest reliability coefficient (0.895) was in Physical Functioning (PF). This suggests a great internal consistency of the data with no measurement errors.

This study revealed that about half of the respondents are below age 30 with one-third between the age of 31-40. Also 59.6% of respondents which represents the majority are single. This statistics might influence the way they perceive stress. Health officers within the older age groups who are married with added responsibilities from the home front, will likely report a low quality of life in response to the stress posed by the Covid 19 pandemic compared to the youths. However, officers in the advanced age group would have achieved leadership position among their peers which implies they would be in the position of deploying personnels rather than attend to patients themselves. Differences could exist in individual perception of stress because workplace and individual factors as well as social situations could increase stress among health workers (Kheiraoui et al., 2012) [18].

This study revealed an association between age group and general health domain of respondents. The highest mean (72.86), signifying the highest quality of life was reported among respondents between the age group of 41-50 years. Health officers within this age group would have attained leadership positions in their respective units. They assign duties and give instructions to other staffs, hence they have fewer exposure to patients and stress compared to officers in the younger age group.

As regards the physical domains of the quality-of-life assessment, a seemingly statistically significant correlation may appear to exists between the male gender and the physical functioning aspect of the physical domain in which males had a high mean score of 72.08 which may seem to suggests a positive perception of quality of life among males. In the context of the present study, this could as well be a pointer that males may have a better capacity to maintain their physical functioning even in the face of stress when compared with females.

Officers who are single (unmarried) and young had better quality of life dimension in terms of bodily pain (X=73.24, SD=26.84) and health change (X=97.58, SD= 7.51) than the married officers. This suggests that the strength and vigour of youthfulness reflects in their perceived quality of life in terms of coping with bodily pain and overall health change. Similarly, younger officers with work experience of five years or less reported a significantly high quality of life change than health officers who are older with more work experience.

As regards the mental health component of the quality-of-life assessment, respondents who are under 30 years and over 50 years old yielded the highest quality of life in terms of social functioning. The younger age group are more socially inclined with tendency to flow with what is in vogue. Also, the older age groups who are leaders in their respective units will be less stressed from work and with the required affluence to keep up their social status. This explains why these two age groups reported a good quality of life as regards social functioning in the mental health component of the quality of lie assessment.

Married health officers experienced emotional role limitation compared to singles. This suggests extra emotional stress and responsibility attached to being married when compared with singles. A study conducted in Korea reported an association between marital status and quality of life, however, this relationship differ by gender and age (Han et al., 2014) [19].

Health record officers with higher qualification also had emotional role limitation compared with holders of lower educational certificates. The respondents with more work experience have better quality of life domain scores in terms of mental health when compared with others, whereas emotional role limitation is prevalent amongst health officers with lower service years. This suggests that health record officers with lower number of service year or who one can refer to as junior staffs are more stressed as a result of more duties while officers with high number of service year, who are senior officers take leadership roles and oversee the junior staffs. No statistically significant difference exists between the four mental health domains and gender of respondents in the study. Generally, individual’s ability to cope with stress contributes to their perceived quality of life (Kheiraoui et al., 2012) [18].

The comparative analysis of summaries of physical and mental components with the sociodemographic characteristics of respondents shows no significant association. However, when each of these domains (physical and mental domains) are disaggregated, there exists some statistically significant association. This suggests that in order to better understand the quality of life of respondents, attention must be placed on how respondents fare based on the individual domains of the quality-of-life components. Evaluating quality of life from this view will help reveal and understand specific ways to improve the quality of life of health workers.

The present study has attempted to make a modest effort to explore into the QoL among a hitherto overlooked cadre of health workers. It provides a baseline information that future studies can leverage on for possible scale up. However, a number of inherent limitations to the study needs to acknowledged and highlighted.

The first and most obvious weakness of the study is the apparently small sample size of 52. This may have been imposed by the uniquely less prominent nature of this cadre of health professionals, though. Nevertheless, it is an unfortunate weakness of the study that must be pointed out. The study design being cross sectional, places an obvious constrain on generalizability of the findings. As such the authors would want to advise that a lot of caution should be exercised in the interpretation of the results, especially in an attempt to disaggregate elements of the different domains of QoL in light of alpha significance value outputs. It must also be noted that the method of purposive sampling as contrary to other acceptable probability sampling techniques are capable of introducing bias in the selection of the study participants.

In conclusion, the quality of life among health record officers in Obafemi Awolowo University Teaching Hospital, Ile-ife can be generally described as above average. It is noteworthy to state that most of the respondents are youths below 30 which perhaps influenced the outcome of the study because of the energy, vigour and strength in the youth. This study also portrays that while carrying out quality of life studies, one must be careful to consider evidences from domains of each quality of health component which could help to better understand different aspects of individual’s quality of life.

Gender, age and marital status are some sociodemographic characteristics that could influence some specific quality of life domains like physical and social functioning which perhaps might not be clearly apparent when considering the physical and mental components of quality-of-life assessment as a whole.