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\title{Prevalence and Factors Associated with COVID-19 Vaccine Acceptance among Adult Population in Northern Uganda. A Cross-Sectional Study}
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\begin{abstract}
        


Background: When COVID-19 vaccines arrived in Uganda in March of 2021, there was inadequate information on vaccine acceptance in the population due to many factors, but mainly due to misinformation and disinformation circulating in Ugandan media. This study aimed to determine the prevalence and factors associated with COVID-19 vaccine acceptance among adult population in northern Uganda.Methods: We conducted a cross-sectional study on 723-adult populations in northern Uganda from March to April of 2022. Participants were selected by systematic sampling from twenty-four health facilities in Acholi sub-region. SPSS version 25.0 was used for data analysis at multivariable regression analysis and a p-value <0.05 was considered significant.

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\section[{I. INTRODUCTION}]{I. INTRODUCTION}\par
Coronavirus disease 2019 (COVID-19) is one of today's most significant public health worries world-wide \hyperref[b0]{[1,}\hyperref[b1]{2]}. As a result, much effort has been devoted to implementing control strategies for COVID-19 pandemic globally, for example, lockdown measures, travel bans, isolation of confirmed cases and close contacts, bans on mass gatherings, social distancing, wearing facemasks, COVID-19 vaccination, and other hygiene measures, but the transmission of the virus is likely to blowback when these strategies are lifted \hyperref[b1]{[2]}. Thus, many scholars, academicians, physicians, and public health specialists have observed that of the many approaches to control this pandemic, mass COVID-19 vaccination is one of the top priority interventions \hyperref[b2]{[3]}. It is now known that COVID-19 vaccines can potentially decrease the spread of coronavirus by reducing its incidence, risks of developing severe disease and hospitalization, and death in the general population; however, these have generated a lot of debate in the population \hyperref[b3]{[4]}.\par
Reports from Vaccine Alliance found that wealthier nations had hoarded so much of COVID-19 vaccines that it was predicted that many of the low-to-middle-income countries would most likely not receive COVID-19 vaccines in 2021 \hyperref[b4]{[5]}. In addition, in Africa, where most vaccines for many killer diseases have been very successful in reducing infant and child mortality rates and increased the lifespan of the current population, the population suffered from false rumors and conspiracy theories that have led to COVID-19 vaccine hesitancy; a factor jeopardizing critical efforts to stop the spread of severe -acute-respiratory-syndrome-coronavirus-2 (SAR S-CoV-2) on the continent \hyperref[b4]{[5]}. Also, vaccine safety and access to COVID-19 vaccines have been among the top concerns of most respondents in a survey conducted by GeoPoll in sub-Saharan Africa \hyperref[b4]{[5]}. The survey showed that 23\% of respondents believed that whoever paid for COVID-19 vaccines got it first, thus highlighting the inequity in healthcare resource distribution at critical moments, especially in sub-Saharan Africa \hyperref[b4]{[5]}.\par
Experts have described COVID-19 vaccine hesitancy as one of the top ten commonest threats to global health security in 2019 \hyperref[b5]{[6]}, and as defined by the World Health Organization (WHO), vaccine hesitancy is a reluctance or refusal by a person to get vaccinated despite availability of vaccines \hyperref[b5]{[6]}. Accordingly, WHO states that some reasons people choose not to get vaccinated include the lack of trust in the healthcare systems, complacency, and inconvenience in getting vaccines \hyperref[b5]{[6]}. On the other hand, vaccine acceptance is defined as the degree to which individuals accept, question, or refuse vaccination, and it determines vaccine uptake and distribution successes  {\ref [7]}.\par
As part of a broader process to prioritize frontline health workers' vaccination with limited COVID-19 vaccines in Uganda, a recent report from Amuru district local government in northern London Journal of Medical and Health Research Uganda showed that most COVID-19 vaccines sent for health workers were not used and were at risk of getting expired  {\ref [7]}. In response, the Resident District Commissioner (RDC) of Amuru issued an ultimatum to health workers to either get vaccinated with COVID-19 vaccines or quit their jobs \hyperref[b7]{[8]}.\par
So, looking broadly at vaccine acceptance in Uganda, it was found that approximately 60\% (600/1,000) of respondents were interested in getting COVID-19 vaccines \hyperref[b8]{[9]}. However, there were no comprehensive details on regional prevalence of COVID-19 vaccine acceptance as Uganda prepared to roll out mass COVID-19 vaccinations. As seen in many reports on the management of diseases with epidemic potential, population's education is part of the prevention and control strategies, particularly to inform people to change their habits and behaviors and holistically tackle the spread of the infection \hyperref[b9]{[10]}.\par
However, despite this vast knowledge on the role played by the population's goodwill in managing epidemics, some African governments still wanted to cut health education-related budgets during the COVID-19 pandemic \hyperref[b9]{[10]}. Such moves on health budget cuts during the pandemic could hamper efforts to effectively educate and vaccinate the general population in the African continent.\par
Remarkably, one study conducted among medical students in the United States of America (USA) showed that there was COVID-19 vaccine hesitancy and that 23\% were unwilling to take COVID-19 vaccines \hyperref[b11]{[11]}. Students raised concerns about COVID-19 vaccines, especially regarding the population's trust in public healthcare systems and side effects of COVID-19 vaccines \hyperref[b11]{[11]}.\par
Similarly, findings among university students in Italy, the United Kingdom, and Turkey showed a high COVID-19 vaccine hesitancy ranging from 14\% to 31\% \hyperref[b12]{[12]}.\par
On COVID-19 vaccine hesitancy, many scholars and experts view the many uncertainties surrounding the origin of the SAR-CoV-2 virus as the main underlying reason \hyperref[b13]{[13]}. In addition, a study found that COVID-19 vaccine hesitancy was associated with beliefs and suspicions about the origin of the SAR-CoV-2 virus \hyperref[b13]{[13]}. It is said that most people who believed in the natural evolution of SAR-CoV-2 virus were more likely to accept COVID-19 vaccines than those who thought the virus was manufactured \hyperref[b13]{[13]}.\par
In Jordan and Kuwait, a study investigating COVID-19 vaccine hesitancy found that misinformation and disinformation circulating in social media with numerous conspiracy theories extensively played a part in vaccine hesitancy in that population \hyperref[b14]{[14]}. In the same study, 28\% of participants believed COVID-19 vaccines were to introduce microchips into recipients' bodies, and 23\% thought COVID-19 vaccines were to reduce fertility in their population \hyperref[b14]{[14]}.\par
Also, a study on COVID-19 vaccine hesitancy in healthcare workers in two large academic centers in South Africa found that 90\% of the 1308 sampled population accepted COVID-19 vaccines \hyperref[b15]{[15]}. However, healthcare workers with lower educational status and those who previously refused other vaccines were less likely to take COVID-19 vaccines \hyperref[b15]{[15]}. In addition, Ahmed and colleagues researching COVID-19 vaccine acceptability in Somalia found that 23\% of their survey population were reluctant to take COVID-19 vaccines, and being a female was associated with vaccine hesitancy \hyperref[b16]{[16]}.\par
Not much is known or published on COVID-19 vaccine acceptance in the general Ugandan population. Because of this, several questions have been raised, and many more unanswered questions are being asked on the level of vaccine hesitancy/inquisitiveness or acceptance in the general Ugandan population as the country prepared to roll out COVID-19 vaccinations.\par
This study aimed to determine the prevalence and factors associated with COVID-19 vaccine acceptance among adult population of northern Uganda. 
\section[{II. METHODS}]{II. METHODS} 
\section[{Study design:}]{Study design:}\par
We conducted a cross-sectional study among adult population in northern Uganda from March to April 2022. 
\section[{London Journal of Medical and Health Research}]{London Journal of Medical and Health Research}\par
These health centers (HCs) were selected based on their participation in offering free COVID-19 vaccines to the region's population. 
\section[{Study Population}]{Study Population}\par
We recruited participants (adults/>18 years) who were attendees or attendants to outpatient clinics of the twenty-four health facilities in northern Uganda's nine districts of the Acholi sub-region. 
\section[{Selection Criteria}]{Selection Criteria}\par
The selection of participants was stratified at regional level into nine districts of the Acholi subregion and at district level to twenty-four health facilities (Hospitals, HCIVs, and HCIIIs).\par
The study was conducted in the Outpatient Department (OPD) in each of the twenty-four health facilities. Participants were selected by systematic sampling in that every third adult attendee or attendant aged 18 years and above who consented to the study were recruited. We excluded participants who were critically ill and those who were not willing to answer our research questions. 
\section[{Sample Size Estimation}]{Sample Size Estimation}\par
The sample size was calculated based on the Raosoft sample size calculation methods. The computation was built on a 50\% response distribution, 5\% margin of error, and 95\% Confidence Interval. The online software foundation uses a widely utilized descriptive sample size estimation formula \hyperref[b17]{[17,}\hyperref[b18]{18]}. The research team chose this software calculator because Raosoft, Inc. form and survey software comprises a database management system of great strength and reliability that communicates with other proprietary formats. In addition, the Raosoft database is a highly robust, proven system with high data integrity and security \hyperref[b17]{[17,}\hyperref[b18]{18]}.\par
The sample size was calculated using the formula = (z-score) 2\par
x StdDev x (1-StdDev) (Confidence Interval) 2   Based on the assumption of a population size of 45,000 clients and visitors in one month in all the health facilities in the Acholi subregion, the minimum sample size was calculated to be 396 participants. 
\section[{Sampling Technique}]{Sampling Technique}\par
We conducted a stratified sampling approach at regional and district levels, and a systematic sampling approach for selecting participants at each of the twenty-four health facility's outpatient departments \hyperref[b19]{[19]}. The Acholi subregion was stratified into the nine districts (Gulu City, Gulu, Nwoya, Amuru, Omoro, Pader, Agago, Kitgum, and Lamwo districts) and further into twenty-four selected health facilities (Hospitals, HCIVs, and HCIIIs) where COVID-19 vaccines were administered freely to the population. At each outpatient department, every day from morning to evening, a third attendee or attendant was selected from the OPD register by a systematic sampling method for one week until the required sample size was achieved \hyperref[b19]{[19,}\hyperref[b20]{20]}.\par
It was estimated that approximately 45,000 people receive health services in the twenty-four selected health facilities' outpatient departments in one month. We also defined systematic sampling as a probability sampling method where researchers select population members at regular  \hyperref[b19]{[19,}\hyperref[b20]{20]}, which was the ideal situation the research team had to achieve. 
\section[{Study Variables}]{Study Variables}\par
The dependent variable was COVID-19 vaccine acceptance ("Have you received a jab of COVID-19 vaccine? and the answer was either "yes" and coded as "1" or "no" and coded as "0" for the analysis).\par
The independent variables were the sociodemographic characteristics such as; age, sex, occupation, religion, level of education, tribe, marital status, districts, presence of comorbidities, nationality, race, health insurance coverage status, and whether participants "Strongly agree" ("SA"), "Agree," ("A"), "Neutral" ("N"), "Disagree" ("DA") or "Strongly Disagree," ("SD") that vaccines in health facilities in northern Uganda were safe. 
\section[{Data Collection Methods}]{Data Collection Methods} 
\section[{Data}]{Data}\par
were collected using face-to-face questionnaire interviews by our research team, strictly following Uganda's standard operating procedures (SOPs) and COVID-19 infection, prevention, and control (IPC) guidelines \hyperref[b21]{[21]}. We used a questionnaire constructed in English, consisting of questions on sociodemographic characteristics and participants' views on vaccine safety in health facilities in the Acholi sub-region (Additional file 1). The questionnaire was developed and grounded on literature reviews and discussions by our research team \hyperref[b22]{[22,}\hyperref[b23]{23]}.\par
Further, the questionnaire was pretested among out patients at Gulu Regional Referral Hospital with an internal validity of Cronbach's ?=0.772. Also, participants were assured of confidentiality and privacy of their responses to reduce potential bias introduced by self-reported data. In addition, the questionnaire was designed short to minimize lethargy in answering questions which made it easy for participants' responses. 
\section[{Data Analysis}]{Data Analysis}\par
Data analysis was performed using SPSS statistical software version 25.0. Continuous variables were presented in means, histograms, standard deviations, medians, and interquartile ranges. Categorical data were presented as frequencies and percentages. Chi-square and crosstabs tests were performed on categorical data when comparing two or more groups. Also, to assess associations of each independent variable with COVID-19 vaccine acceptance (dependent variable), a bivariable logistic regression analysis was conducted and Crude Odds Ratios (COR), at 95\% Confidence Intervals (CI) and P-values were presented.\par
Independent variables found insignificant at bivariable level but with (P-values ?0.20) were included in the final multivariable logistic regression analysis model together with significant independent variables and the dependent variable. However, independent variables that had P-values above 0.201 at bivariable level were not included in the final multivariable regression models. In the multivariable regression analysis, we constructed two models by categorizing independent variables into sociodemographic characteristics, comorbidity status, national insurance coverage status, The first multivariable logistic regression model included participant's socio-demographic characteristics (age, sex, level of education, districts, religion, occupation, and smoking status). After that, we constructed a second and final multivariable regression model adjusting for participants' comorbidity status, health insurance coverage status and views on the safety of vaccines in health facilities of northern Uganda.\par
The adjusted odds ratios (aOR) at 95\% Confidence Intervals (CI) and p-values were determined with a significant level set at a p-value <0.05. 
\section[{Ethical Approval}]{Ethical Approval}\par
This study was approved by St. Mary's Hospital, Lacor Institutional, Review and Ethics Committee (LHIREC No. 0193/10/2021) and administrative clearance from the twenty-four health facilities. In addition, each participant consented before being recruited to the study. The research team ensured that confidentiality of personal information was maintained during the investigation, and only participants' unique identifiers were retained on public records. During the study, only the Principal Investigator and supervisors accessed participants' database and at the end of the project, the database was archived at Gulu University, Faculty of Medicine, in the Department of Surgery. A map showing Acholi subregion and the nine districts is presented showing the health facilities where this study was conducted. A near-uniform distribution of health facilities in the region has been noted, indicating that findings from our study are representative of the region's population (Figure \hyperref[fig_1]{2}).          
\section[{IIII. RESULTS}]{IIII. RESULTS} 
\section[{Most}]{Most} 
\section[{IV. DISCUSSION}]{IV. DISCUSSION}\par
The most significant finding from this study population (Table \hyperref[tab_3]{1}, Figure \hyperref[fig_0]{1}, Figure \hyperref[fig_1]{2}) was that COVID-19 vaccine acceptance in northern Uganda was high at 580/723 (80.2\%, 95\% CI:78.9\%-83. 4\%). This finding contrasts with a study by Kabagenyi et al, in Uganda (2022) which observed a low COVID-19 vaccine acceptance at 41.4\% \hyperref[b26]{[24]}. However, that study noted substantial regional variations in vaccine hesitancy where a lower COVID-19 vaccine hesitancy was observed in participants from northern and eastern Uganda compared to western and central Uganda \hyperref[b26]{[24]}, a finding which is like our study findings (Table \hyperref[tab_4]{2}, Table \hyperref[tab_6]{3}, Table \hyperref[tab_8]{4}, Table \hyperref[tab_10]{5}). The authors argued that the lower vaccine hesitancy in northern Ugandan population compared to central and western Uganda was due to prior Ugandan Ministry of Health mobilization and roll out of information on COVID-19 vaccines, dispelling misconceptions, myths, and conspiracy theories about COVID-19 vaccines and thus the higher vaccine acceptance rate \hyperref[b26]{[24]}.\par
Thus, the high COVID-19 vaccine acceptance rate is likely because of the commendable work of health managers in northern Uganda for conducting consistent community sensitization, mobilization, and engagements using village health teams (VHTs), which helped turn a vaccine-hesitant/inquisitive population to the opposite. This finding is consistent with others that stakeholder engagement, social mobilization, and equitable distribution of vaccines increase vaccine acceptance in low-to-middle-income countries \hyperref[b27]{[25,}\hyperref[b28]{26,}\hyperref[b29]{27]}. Accordingly, we, the authors propose that the approach used to achieve this high COVID-19 vaccine acceptance rate in northern Uganda could be replicated in other parts of Uganda, especially using VHTs as agents of change.\par
The current study's finding that female gender was significantly associated with COVID-19 vaccine acceptance is not new (females 77.8\% versus males 82.2\%) (Table \hyperref[tab_12]{6}) but contrasts another observed in Kabagenyi, et al., (aOR=0.77,95\% CI:0.58-1.02) in Uganda \hyperref[b26]{[24]} but consistent with other studies elsewhere \hyperref[b30]{[28,}\hyperref[b31]{29]}.\par
For example, high COVID-19 vaccine acceptance rates were recorded among pregnant women in northwestern Ethiopia \hyperref[b30]{[28]} and Saudi Arabia \hyperref[b31]{[29]}.\par
Relatedly, many studies in Uganda show that females have better health-seeking behaviors than males \hyperref[b32]{[30,}\hyperref[b33]{31,}\hyperref[b34]{32,}\hyperref[b35]{33]}. Females' better healthseeking behaviors than males have been similarly observed during implementation of many health activities among communities in northern Uganda \hyperref[b33]{[31]}. In addition, experience from Uganda shows that females are more receptive to health messages from Ugandan government and have always been at the forefront of fighting against many infectious diseases, including malaria \hyperref[b34]{[32]}. Thus, their compliance with health messages from the Ugandan Ministry of Health has always been positive. This experience includes reproductive health services, vaccination of children, voluntary counseling, and testing (VCT) for HIV and AIDs, cancer screening, and many health prevention and promotion activities \hyperref[b35]{[33]}.\par
However, a systematic review and meta-analysis by Stephanie showed that males had more likely intentions of getting vaccinated against COVID-19 than females \hyperref[b36]{[34]}. In contrast, our current study showed that it was more likely for females to accept COVID-19 vaccines than males (Table \hyperref[tab_12]{6}). This finding is likely because of disinformation, misinformation, and numerous conspiracy theories circulating in the community through social and other media sources about COVID-19 vaccines that may have affected males more than females but also highlights deeper problems on health seeking behaviors among males in northern Uganda. For similar reasons, non-health workers were more likely to accept COVID-19 vaccines compared to health workers and this is consistent with the findings from Amuru district in Uganda  {\ref [7]}, sub-Saharan Africa \hyperref[b13]{[13]} and Kuwait and Jordan \hyperref[b14]{[14]}. Furthermore, the Baganda tribe who were resident in northern Uganda were more likely to accept COVID-19 vaccines (89.8\% versus 10.2\%) ( Baganda compared to the north (Table \hyperref[tab_4]{2} and Table \hyperref[tab_6]{3}). It is also important to note that participants in our study raised many issues regarding the reasons for accepting COVID-19 vaccines ranging from the fear of death, the fear of contracting the virus and infecting family members. We, the authors argue that the fear factor and experience of COVID-19 during the second wave may have in many ways contributed to the vaccine acceptance among this sector of the study population (Table \hyperref[tab_4]{2} and Table \hyperref[tab_6]{3}).\par
Also, our study found that participants with comorbidities were less likely to accept COVID-  \hyperref[tab_12]{6}). This finding among persons with comorbidities is inconsistent with many studies in Uganda, which showed participants with comorbidities, particularly diabetes, hyper tension, obesity, heart diseases, chronic obstructive pulmonary diseases (COPD), HIV, and AIDS, were more at risk of developing severe COVID-19 illness, and higher chances of hospitalization, and death \hyperref[b37]{[35]}\hyperref[b38]{[36]}\hyperref[b39]{[37]}\hyperref[b40]{[38]} {\ref [39]} {\ref [40]}.\par
Note that despite persistent messages on the increased risks and susceptibility to coronavirus, with higher chances of acquiring the more severe form of the disease, higher chances of hospitalization, and death among the most at-risk population which the mainstream and social media had widely covered and that most people had become aware, the COVID-19 vaccine acceptance was less likely in participants with comorbidity in our study population (Table \hyperref[tab_12]{6}). In addition, the Ugandan Ministry of Health had prioritized vaccination of the elderly and those with comorbidities in the early phases of COVID-19 vaccine roll-out in Uganda \hyperref[b38]{[36]}.\par
Of special interest was a finding in Kabagambe, et al., that a significant proportion of Ugandan population had misconceptions that COVID-19 vaccines could spread coronavirus in the body, that the virus kills people with underlying conditions, and that the COVID-19 vaccine could make them infertile \hyperref[b26]{[24]}. In addition, others doubted the existence of the virus and the safety of the vaccine itself \hyperref[b26]{[24]}. This information could have likely been responsible in part for the COVID-19 vaccine hesitancy among the comorbid population, age-group of 20-29 years, and graduates in our study (Table \hyperref[tab_12]{6}).\par
Meanwhile, in other participants in this study population, COVID-19 vaccine acceptance was high for many reasons, including the fear of getting infected, the fear of infecting family members, the fear of death, and worries that COVID-19 medications would be forced on them if they did not get vaccinated (Table \hyperref[tab_6]{3} and Table \hyperref[tab_8]{4}).\par
Most notable was, however that the COVID-19 vaccine preferred by each participant in our study population was provided by the Government of Uganda through the Ministry of Health, and choices on the type of COVID-19 vaccine were participant's decision (Table \hyperref[tab_10]{5}).\par
Furthermore, some participants and their associates had tested positive for coronavirus and had experienced the disease, which perhaps impacted their decision to get vaccinated (Table \hyperref[tab_4]{2}, Table \hyperref[tab_6]{3}, and Table \hyperref[tab_8]{4}). So, we, the authors argue that whereas COVID-19 vaccination was a timely intervention by the Ugandan Ministry of Health, participants with comorbidities were less likely to accept COVID-19 vaccines (  \hyperref[tab_12]{6}). On this finding, studies show that vaccine acceptance is linked to community's confidence in healthcare systems, health workers, cultural backgrounds, attitudes, beliefs, perceptions, political, environmental, personal factors, and compliance with face mask-wearing guidelines \hyperref[b11]{[11,} {\ref 41,} {\ref 42,} {\ref 43]}.\par
We, the authors, found that the three districts, just like others in Uganda, set up COVID-19 district task forces layered to the village health teams (VHTs) who promoted COVID-19 vaccinations at local levels  {\ref [43]}. The village health teams are vital in connecting communities to the Ugandan healthcare system  {\ref [43]}. We, the Authors, argue that VHTs' roles in disease prevention, promotion and control in Ugandan healthcare system need to be rated more by policymakers.\par
Nevertheless, VHTs are critical change agents, and their position in Ugandan health delivery system should be promoted to enhance their contributions to the healthcare system [44]. This finding implies that for the Ugandan Ministry of Health to achieve higher COVID-19 vaccine acceptance rates, layered task forces up to the village level and using VHTs for campaigns could be adopted  {\ref [44]}. The authors argue that VHTs played a considerable role in convincing the community to accept COVID-19 vaccines in the three districts  {\ref [44]}.\par
Further, the finding that smokers in this study population were more likely to accept COVID-19 vaccines than non-smokers and ex-smokers have attracted much interest (Table \hyperref[tab_12]{6}). These participants could have been more confident in COVID-19 vaccines' ability to reduce the virus's chances of infecting them. More so, this virus being a respiratory disease could have swayed them by the fear factor and worries about getting infected or being forced to take medications if they missed out on their COVID-19 jabs (Table \hyperref[tab_6]{3}).\par
This finding is like one in a refugee camp in Bidibidi in Uganda where the authors found that COVID-19 vaccine acceptance rate among refugees was 78\% and was associated with beliefs that COVID-19 vaccines could stop the spread of coronavirus [45] as similarly seen in these groups of smokers (Table \hyperref[tab_12]{6}). In addition, findings show that respondents who were uncertain whether COVID-19 vaccines would stop transmissions were less likely to get the vaccine (aOR=0.70; 95\%CI=0.51-0.96) than confident respondents. In that study, respondents who did not want to go to health facilities (aOR=0.61;95\%CI=0.44-0.84) were also less likely to accept COVID-19 vaccines than counterparts who wanted to go to health facilities  {\ref [45]}.\par
Lastly, our finding that participants that strongly disagree and disagree on the safety of vaccines in health facilities in northern Uganda were three and two times more likely to accept COVID-19 vaccines compared to participants who strongly agree, respectively, raised our interests (Table \hyperref[tab_12]{6}). This finding is unique as most previous studies show that the confidence and trust in healthcare systems were among the most likely reasons for vaccine acceptance \hyperref[b5]{[6,}\hyperref[b14]{14,}\hyperref[b15]{15,} {\ref [41]} {\ref [42]} {\ref [43]}. We, the authors, intend to explore these responses from our participants in a future qualitative study. Could it have been that this finding was an isolated response or specifically seen with COVID-19 vaccines in northern Uganda? We, the authors argue that it may be too early to determine what exactly it is until a comprehensive analysis has been completed in future studies.\par
In summary, our current study found a high COVID-19 vaccine acceptance rate of 580/723(80.2\%) in an adult population in northern Uganda. This survey was conducted after the second wave of COVID-19 in Uganda when many high-profile persons had lost their lives compared to the first wave. In addition, this current acceptance rate in northern Uganda was lower than a South African study at 90\% \hyperref[b15]{[15]} but higher than a Somali study at 77\% \hyperref[b16]{[16]} and another Ugandan study at 60\% \hyperref[b7]{[8]}  
\section[{Strengths and Limitations of this Study}]{Strengths and Limitations of this Study}\par
Our study has many strengths. First, this data is vital as it is one of the few well-documented and completed data on 723 participants from the Acholi sub-region regarding COVID-19 vaccine acceptance in the recent period. Second, findings from this study show a higher COVID-19 vaccine acceptance rate despite differing results from other parts of Uganda. Third, we used a systematic sampling method, a probability sampling method which is vital for the study's results. Finally, using a validated questionnaire helped us obtain this information which is generalizable in the context.\par
However, this study had limitations in the design, a cross-sectional study where one-time information from participants is gathered and analyzed. These have shortcomings in that, views and opinions of participants are dynamic; they vary according to prevailing environmental situations. In this, we suggest a need for future prospective or a longitudinal assessment of COVID-19 vaccine acceptance in future, ensuring that all data are measured and recorded accordingly. 
\section[{Generalizability of Results}]{Generalizability of Results}\par
These findings should be cautiously interpreted and generalized to regions with low-resource settings in Uganda and other sub-Saharan African countries. 
\section[{V. CONCLUSION}]{V. CONCLUSION}\par
COVID-19 vaccine acceptance rate among the study population was encouragingly high despite misinformation and disinformation in Ugandan media. Participants were more likely to accept COVID-19 vaccines among those who strongly disagree and disagree that vaccines in northern Uganda's health facilities were safe than those who strongly agree; smokers compared to non-smokers, and participants from Gulu, Kitgum, and Pader districts compared to Lamwo district. However, it was less likely for participants with comorbidities to accept COVID-19 vaccines compared to participants without comorbidities. The fear of contracting coronavirus and death if not vaccinated contributed substantially to COVID-19 vaccine acceptance in northern Uganda. There is a need for health managers to engage, sensitize and mobilize the population on COVID-19 vaccines and vaccination using VHTs and other structures, which remain critically important if the high COVID-19 vaccine acceptance rate in the subregion is maintained or improved.  
\section[{Statements and Declarations}]{Statements and Declarations} 
\section[{Ethics approval and consent to participate}]{Ethics approval and consent to participate}\begin{figure}[htbp]
\noindent\textbf{1}\includegraphics[]{image-2.png}
\caption{\label{fig_0}Figure 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2}\includegraphics[]{image-3.png}
\caption{\label{fig_1}Figure 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{P{0.37698744769874476\textwidth}P{0.14581589958158997\textwidth}P{0.32719665271966525\textwidth}}
\tabcellsep \tabcellsep London Journal of Medical and Health Research\\
Variables\tabcellsep Frequency (N=723)\tabcellsep Percent (\%)\\
Sex\tabcellsep \tabcellsep \\
Female\tabcellsep 329\tabcellsep 45.5\\
Male\tabcellsep 394\tabcellsep 54.5\\
Age (years)\tabcellsep \tabcellsep \\
Less than 20\tabcellsep 80\tabcellsep 11.1\\
20-29\tabcellsep 279\tabcellsep 38.6\\
30-39\tabcellsep 225\tabcellsep 31.1\\
40-49\tabcellsep 95\tabcellsep 13.1\\
50 and above\tabcellsep 44\tabcellsep 6.1\\
Marital status\tabcellsep \tabcellsep \\
Never married\tabcellsep 316\tabcellsep 43.7\\
Married\tabcellsep 377\tabcellsep 52.1\end{longtable} \par
 
\begin{quote}
London Journal of Medical and Health Research 8 Table 1 Shows That Most Participants Were Males 394/723(54.5\%); in the Age-Group of 20-29 Years 279/723 (38.6\%); Married 377/723 (52.1\%); Catholics 354/723 (49.0\%); Acholi by Tribe 446/723 (61.7\%); From Gulu District 364/723 (50.4\%), Had Secondary Level of Education 237/723 (32.8\%), Non-Health Workers 518/723 (71.7\%); Ugandans by Nationality 720/723 (99.6\%); Africans by Race 721/723 (99.7\%); Did Not Smoke Cigarettes 699/723 (96.7\%); Never Drank Alcohol 521/723 (72.1\%); Had No Comorbidities 520/723 (71.9\%); Agreed That Vaccines in Health Facilities in the Region Were Safe 250/723 (34.6\%); and Had No Health Insurance Coverage 666/723(92.1\%).\end{quote}

\caption{\label{tab_3}Table 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2} \par 
\begin{longtable}{P{0.5810623556581986\textwidth}P{0.17176674364896075\textwidth}P{0.09717090069284064\textwidth}}
The fear of getting infected with coronavirus\tabcellsep 491(67.9)\tabcellsep 232(32.1)\\
The fear of a family member getting infected with coronavirus\tabcellsep 440(60.9)\tabcellsep 283(39.1)\\
The fear of death\tabcellsep 462(63.9)\tabcellsep 261(36.1)\\
Financial related worries\tabcellsep 325(45.0)\tabcellsep 398(55.0)\\
Food insecurity related worries\tabcellsep 163(22.5)\tabcellsep 560(77.5)\\
Unavailability of vaccines\tabcellsep 114(15.8)\tabcellsep 609(84.2)\\
Coronavirus is a plot or a conspiracy theory\tabcellsep 62(8.6)\tabcellsep 661(91.4)\\
I may be forced to take medicines for coronavirus\tabcellsep 59(8.16)\tabcellsep 664(91.8)\\
I may be forced to take COVID-19 vaccines\tabcellsep 158(21.9)\tabcellsep 565(78.1)\\
I am not worried about any COVID-19 issues\tabcellsep 34(4.7)\tabcellsep 689(95.3)\\
Have you got a jab of the COVID-19 vaccine?\tabcellsep 580(80.2)\tabcellsep 143(19.8)\\
Which COVID-19 vaccine have you received?\tabcellsep \tabcellsep \\
AstraZeneca\tabcellsep 414(57.3)\tabcellsep \\
Johnson and Johnson\tabcellsep 17(2.4)\tabcellsep \\
Modena\tabcellsep 117(16.2)\tabcellsep \\
Pfizer\tabcellsep 14(2.0)\tabcellsep \\
Sinovac\tabcellsep 13(1.8)\tabcellsep \\
Sputnik\tabcellsep 7(1.0)\tabcellsep \\
None\tabcellsep 141(19.5)\tabcellsep \\
How many doses of the COVID-19 vaccine have you received?\tabcellsep \tabcellsep \\
One\tabcellsep 189(26.1)\tabcellsep \\
Two\tabcellsep 392(54.2)\tabcellsep \\
None\tabcellsep 142(19.6)\tabcellsep \end{longtable} \par
 
\begin{quote}
' Views on COVID-19 and Vaccinations among Participants in Northern Uganda Variables Yes (n, \%) No (n, \%) Have you been exposed to coronavirus? 407(56.3) 316(43.7) What are you most worried about during this COVID-19 pandemic? London Journal of Medical and Health Research 10\end{quote}

\caption{\label{tab_4}Table 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2} \par 
\begin{longtable}{}
\end{longtable} \par
 
\caption{\label{tab_5}Table 2}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3} \par 
\begin{longtable}{P{0.45674698795180724\textwidth}P{0.13825301204819276\textwidth}P{0.1208433734939759\textwidth}P{0.0655421686746988\textwidth}P{0.06861445783132529\textwidth}}
\tabcellsep \multicolumn{2}{l}{Participants' Responses}\tabcellsep \tabcellsep \\
Variables\tabcellsep Yes (n, \%)\tabcellsep No (n, \%)\tabcellsep ? 2\tabcellsep p-value\\
Those who were exposed to coronavirus\tabcellsep 337(46.6)\tabcellsep 243(33.6)\tabcellsep 5.183\tabcellsep 0.023\\
The fear of getting infected with coronavirus\tabcellsep 413(57.1)\tabcellsep 167(23.1)\tabcellsep 14.614\tabcellsep 0.000\\
The fear of death\tabcellsep 382(52.8)\tabcellsep 198(27.1)\tabcellsep 4.892\tabcellsep 0.027\\
The fear of a family member getting infected\tabcellsep 363(50.2)\tabcellsep 217(30.0)\tabcellsep 3.679\tabcellsep 0.055\\
Financial worries\tabcellsep 268(37.1)\tabcellsep 312(43.2)\tabcellsep 1.867\tabcellsep 0.172\\
Job-related worries\tabcellsep 152(21.0)\tabcellsep 428(59.2)\tabcellsep 0.183\tabcellsep 0.669\\
Food insecurity worries\tabcellsep 134(18.5)\tabcellsep 446(61.7)\tabcellsep 0.524\tabcellsep 0.469\\
Worries about unavailability of vaccines\tabcellsep 96(13.3)\tabcellsep 484(66.9)\tabcellsep 1.357\tabcellsep 0.244\\
Worries that COVID-19 is a plot or conspiracy theory\tabcellsep 47(6.5)\tabcellsep 533(73.7)\tabcellsep 0.833\tabcellsep 0.361\\
Worries of being forced to take COVID-19\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep 41(5.7)\tabcellsep 539(74.6)\tabcellsep 4.661\tabcellsep 0.031\\
medications\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Worries about being forced to take COVID-19\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep 114(15.8)\tabcellsep 466(64.5)\tabcellsep 8.297\tabcellsep 0.004\\
vaccines\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
No worries on issues of COVID-19 vaccines\tabcellsep 19(2.6)\tabcellsep 561(77.6)\tabcellsep 13.32\tabcellsep 0.000\end{longtable} \par
 
\caption{\label{tab_6}Table 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3} \par 
\begin{longtable}{}
\end{longtable} \par
 
\caption{\label{tab_7}Table 3}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{P{0.0692867540029112\textwidth}P{0.13176855895196507\textwidth}P{0.051965065502183404\textwidth}P{0.13362445414847163\textwidth}P{0.04701601164483261\textwidth}P{0.07856622998544395\textwidth}P{0.19115720524017465\textwidth}P{0.006186317321688501\textwidth}P{0.006804949053857351\textwidth}P{0.07237991266375546\textwidth}P{0.061244541484716156\textwidth}}
Variables\tabcellsep Age groups\tabcellsep Marital status\tabcellsep Religion\tabcellsep Tribe\tabcellsep Districts\tabcellsep Level of\tabcellsep Occupation\tabcellsep Nationality\tabcellsep Race\tabcellsep Comorbidities\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Education\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Fever\tabcellsep 3.146(p=0.534)\tabcellsep 4.786(p=0.310)\tabcellsep \multicolumn{2}{l}{2.453(p=0.653) 2.148(p=0.709)}\tabcellsep 7.582(p=0.371)\tabcellsep \multicolumn{3}{l}{4.694(p=0.454) 0.072(p=0.789) 0.224(p=0.974)}\tabcellsep 0.149(p=0.699)\tabcellsep 0.442(p=0.506 )\\
Joint pains\tabcellsep 2.069(p=0.723)\tabcellsep 1.355(p=0.852)\tabcellsep \multicolumn{3}{l}{0.353(p=0.983) 5.094(p=0.278) 13.633(p=0.058)}\tabcellsep \multicolumn{5}{l}{8.871(p=0.114) 0.264(p=0.607) 0.090(p=0.993) 0.060(p=0.8007) 2.037(p=0.153)}\\
Loss of\tabcellsep 3.927(p=0.416)\tabcellsep 1.311(p=0.846)\tabcellsep 1.592(p=0.810)\tabcellsep 6.284(p=0.179)\tabcellsep 16.573(p=0.020)\tabcellsep \multicolumn{3}{l}{3.593(p=0.609 1.395(p=0.237) 0.025(p=0.999)}\tabcellsep 0.017(p=0.899)\tabcellsep 1.440(p=0.230)\\
appetite\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Steven-John\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
son ' s\tabcellsep \multicolumn{2}{l}{4.980(p=0.289) 0.657(p=0.957)}\tabcellsep 2.455(p=0.653)\tabcellsep 7.494(p=0.112)\tabcellsep 6.137(p=0.524)\tabcellsep \multicolumn{3}{l}{2.722(p=0.743 0.336(p=0.562) 0.021(p=0.999)}\tabcellsep 0.014(p=0.906)\tabcellsep 1.966(p=0.161)\\
reaction\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Blot clot\tabcellsep 6.509(p=0.164)\tabcellsep 4.895(p=0.298)\tabcellsep \multicolumn{6}{l}{6.335(p=0.176) 16.284(p=0.003) 22.710(p=0.002) 18.431(p=0.002) 8.656(p=0.003) 6.108(p=0.106)}\tabcellsep 1.971(p=0.160)\tabcellsep 7.878(p=0.005)\\
Feeling\tabcellsep 0.691(p=0.952)\tabcellsep 0.461(p=0.977)\tabcellsep 1.549(p=0.818)\tabcellsep \multicolumn{2}{l}{8.880(p=0.064) 3.060(p=0.879)}\tabcellsep \multicolumn{3}{l}{5.532(p=0.354) 0.890(p=0.346) 0.108(p=0.99)}\tabcellsep 0.072(p=0.789)\tabcellsep 1.870(p=0.171)\\
dizzy\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Death\tabcellsep \multicolumn{4}{l}{16.608(p=0.002) 8.350(p=0.080) 6.892(p=0.142) 4.099(p=0.393)}\tabcellsep \multicolumn{4}{l}{35.083(p=0.000) 14.298(p=0.014) 4.936(p=0.026) 0.307(p=0.946)}\tabcellsep 0.246(p=0.620)\tabcellsep 15.454(p=0.000)\\
Feeling\tabcellsep \multicolumn{3}{l}{4.402(p=0.354) 0.786(p=0.940) 1.762(p=0.779)}\tabcellsep 3.335(p=0.503)\tabcellsep 4.855(p=0.678)\tabcellsep \multicolumn{3}{l}{3.971(p=0.554) 1.023(p=0.312) 0.064(p=0.994)}\tabcellsep \multicolumn{2}{l}{0.042(p=0.837) 1.078(p=0.299)}\\
uncomfortable\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\begin{quote}
London Journal of Medical and Health Research 15 11 © 2023 Great ] Britain Journals Press Volume 23 | Issue 7 | Compilation 1.0 7 Prevalence and Factors Associated with COVID-19 Vaccine Acceptance among Adult Population in Northern Uganda. A Cross-Sectional Study\end{quote}

\caption{\label{tab_8}Table 4 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{P{0.85\textwidth}}
Shows Significant Differences in Signs and Symptoms Shared Among Age-Groups, Especially\\
Excessive Sweating ?2=10.163; p=0.038 and the Fear of Death ?2=16.608; p=0.002, Among Older\\
Age-Groups. Joint Pains ?2=13.633; p=0.058, Loss of Appetite ?2=16.573; p=0.020, Blood Clots\\
?2=22.710; p=0.002, the Fear of Death ?2=35.083; p=0.000, and Excessive Sweating ?2=24.316;\\
p=0.001 Were Common in Districts. Blood Clots ?2=18.431;p=0.002 and the Fear of Death ?2=14.298;\\
p=0.014 Were Reported at Levels of Education. for Occupation, Blood Clots ?2=8.656; p=0.003 and\\
the Fear of Death ?2=4.936; p=0.02 and Finally, Blood Clots ?2=7.878; p=0.005and the Fear of Death\\
?2=15.454; p<0.000 Were Reported Among Participants With Comorbidities.\end{longtable} \par
 
\caption{\label{tab_9}Table 4}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{5} \par 
\begin{longtable}{P{0.002324521422060164\textwidth}P{0.09608021877848677\textwidth}P{0.10731540565177758\textwidth}P{0.10886508659981768\textwidth}P{0.10692798541476756\textwidth}P{0.09879216043755697\textwidth}P{0.09569279854147676\textwidth}P{0.09569279854147676\textwidth}P{0.10421604375569735\textwidth}P{0.01472196900638104\textwidth}P{0.005036463081130355\textwidth}P{0.01433454876937101\textwidth}}
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{3}{l}{Chi Square Test}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep Variables\tabcellsep AZ\tabcellsep J\&J\tabcellsep Moderna\tabcellsep Pfizer\tabcellsep Sinovac\tabcellsep Sputnik\tabcellsep None\tabcellsep Chi\tabcellsep df\tabcellsep p-value\\
1\tabcellsep Sex\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep Female\tabcellsep 91(12.6\%)\tabcellsep 109(15.1\%)\tabcellsep 34(4.7\%)\tabcellsep 6(0.8\%)\tabcellsep 0(0.0\%)\tabcellsep 1(0.1\%)\tabcellsep 88(12.2\%)\tabcellsep 22.362\tabcellsep 6\tabcellsep 0.001\\
\tabcellsep Male\tabcellsep 115(15.9\%)\tabcellsep 141(19.5\%)\tabcellsep 26(3.6\%)\tabcellsep 30(4.1\%)\tabcellsep 3(0.4\%)\tabcellsep 2(0.3\%)\tabcellsep 77(10.7\%)\tabcellsep \tabcellsep \\
2\tabcellsep \multicolumn{2}{l}{Age groups (years)}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep <20\tabcellsep 21(2.9\%)\tabcellsep 28(3.9\%)\tabcellsep 8(1.1\%)\tabcellsep 4(0.6\%)\tabcellsep 0(0.0\%)\tabcellsep 0(0.0\%)\tabcellsep 19(2.6\%)\tabcellsep 52.877\tabcellsep 24\tabcellsep 0.001\\
\tabcellsep 20-29\tabcellsep 77(10.7\%)\tabcellsep 82(11.3\%)\tabcellsep 25(3.5\%)\tabcellsep 14(1.9\%)\tabcellsep 1(0.1\%)\tabcellsep 1(0.1\%)\tabcellsep 78(10.84\%)\tabcellsep \tabcellsep \\
\tabcellsep 30-39\tabcellsep 73(10.19\%)\tabcellsep 79(10.9\%)\tabcellsep 12(1.7\%)\tabcellsep 15(2.1\%)\tabcellsep 0(0.0\%)\tabcellsep 0(0.0\%)\tabcellsep 45(6.2\%)\tabcellsep \tabcellsep \\
\tabcellsep 40-49\tabcellsep 21(2.9\%)\tabcellsep 42(5.8\%)\tabcellsep 15(2.1\%)\tabcellsep 2(0.3\%)\tabcellsep 0(0.0\%)\tabcellsep 0(0.0\%)\tabcellsep 15(2.1\%)\tabcellsep \tabcellsep \\
\tabcellsep >50\tabcellsep 14(1.9\%)\tabcellsep 19(2.6\%)\tabcellsep 0(0.0\%)\tabcellsep 1(0.1\%)\tabcellsep 2(0.3\%)\tabcellsep 2(0.3\%)\tabcellsep 8(1.1\%)\tabcellsep \tabcellsep \\
3\tabcellsep Marital status\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep Divorced\tabcellsep 4(0.6\%)\tabcellsep 11(1.5\%)\tabcellsep 1(0.1\%)\tabcellsep 0(0.0\%)\tabcellsep 0(0.0\%)\tabcellsep 0(0.0\%)\tabcellsep 7(1.0\%)\tabcellsep 20.763\tabcellsep 24\tabcellsep 0.653\\
\tabcellsep Married\tabcellsep 99(13.7\%)\tabcellsep 141(19.5\%)\tabcellsep 37(5.1\%)\tabcellsep 16(2.2\%)\tabcellsep 2(0.3\%)\tabcellsep 3(0.4\%)\tabcellsep 79(10.9\%)\tabcellsep \tabcellsep \\
\tabcellsep Separated\tabcellsep 1(0.1\%)\tabcellsep 0(0.0\%)\tabcellsep 0(0.0\%)\tabcellsep 0(0.0\%)\tabcellsep 0(0.0\%)\tabcellsep 0(0.0\%)\tabcellsep 1(0.1\%)\tabcellsep \tabcellsep \\
\tabcellsep Single\tabcellsep 100(13.8\%)\tabcellsep 96(13.3\%)\tabcellsep 21(2.9\%)\tabcellsep 20(2.8\%)\tabcellsep 1(0.1\%)\tabcellsep 0(0.0\%)\tabcellsep 1(0.1\%)\tabcellsep \tabcellsep \\
\tabcellsep widowed\tabcellsep 2(0.3\%)\tabcellsep 2(0.3\%)\tabcellsep 1(0.1\%)\tabcellsep 0(0.0\%)\tabcellsep 0(0.0\%)\tabcellsep 0(0.0\%)\tabcellsep 78(10.8\%)\tabcellsep \tabcellsep \\
4\tabcellsep Religion\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep Born Again\tabcellsep 28(3.9\%)\tabcellsep 31(4.3\%)\tabcellsep 7(1.0\%)\tabcellsep 9(1.2\%)\tabcellsep 0(0.0\%)\tabcellsep 1(0.1\%)\tabcellsep 36(5.0\%)\tabcellsep 36.56\tabcellsep 24\tabcellsep 0.048\\
\tabcellsep Catholics\tabcellsep 98(13.6\%)\tabcellsep 142(19.6\%)\tabcellsep 21(2.9\%)\tabcellsep 16(2.2\%)\tabcellsep 2(0.3\%)\tabcellsep 2(0.3\%)\tabcellsep 73(10.1\%)\tabcellsep \tabcellsep \\
\tabcellsep Muslims\tabcellsep 7(1.0\%)\tabcellsep 8(1.1\%)\tabcellsep 4(0.6\%)\tabcellsep 1(0.1\%)\tabcellsep 0(0.0\%)\tabcellsep 0(0.0\%)\tabcellsep 6(0.8\%)\tabcellsep \tabcellsep \\
\tabcellsep Protestants\tabcellsep 69(9.5\%)\tabcellsep 69(9.5\%)\tabcellsep 28(3.9\%)\tabcellsep 9(1.2\%)\tabcellsep 1(0.1\%)\tabcellsep 0(0.0\%)\tabcellsep 50(6.9\%)\tabcellsep \tabcellsep \\
\tabcellsep Others\tabcellsep 4(0.6\%)\tabcellsep 0(0.0\%)\tabcellsep 0(0.0\%)\tabcellsep 1(0.1\%)\tabcellsep 0(0.0\%)\tabcellsep 0(0.0\%)\tabcellsep 0(0.0\%)\tabcellsep \tabcellsep \\
5\tabcellsep Tribes\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep Acholi\tabcellsep 130(18.0\%)\tabcellsep 163(22.3\%)\tabcellsep 38(5.3\%)\tabcellsep 10(1.4\%)\tabcellsep 3(0.4\%)\tabcellsep 3(0.4\%)\tabcellsep 99(13.7\%)\tabcellsep 43.666\tabcellsep 24\tabcellsep 0.008\\
\tabcellsep Itesot\tabcellsep 7(1.0\%)\tabcellsep 9(1.2\%)\tabcellsep 1(0.1\%)\tabcellsep 2(0.4\%)\tabcellsep 0(0.0\%)\tabcellsep 0(0.0\%)\tabcellsep 3(0.4\%)\tabcellsep \tabcellsep \\
\tabcellsep Lango\tabcellsep 25(3.5\%)\tabcellsep 24(3.3\%)\tabcellsep 9(1.2\%)\tabcellsep 2(0.3\%)\tabcellsep 0(0.0\%)\tabcellsep 0(0.0\%)\tabcellsep 0(0.0\%)\tabcellsep \tabcellsep \\
\tabcellsep Baganda\tabcellsep 15(2.1\%)\tabcellsep 16(2.2\%)\tabcellsep 2(0.3\%)\tabcellsep 8(1.1\%)\tabcellsep 0(0.0\%)\tabcellsep 0(0.0\%)\tabcellsep 0(0.0\%)\tabcellsep \tabcellsep \\
\tabcellsep Others\tabcellsep 29(4.0\%)\tabcellsep 38(5.3\%)\tabcellsep 10(1.4\%)\tabcellsep 14(1.9\%)\tabcellsep 0(0.0\%)\tabcellsep 0(0.0\%)\tabcellsep 33(4.6\%)\tabcellsep \tabcellsep \\
6\tabcellsep Districts\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep Agago\tabcellsep 28(3.8\%)\tabcellsep 22(3.0\%)\tabcellsep 8(1.1\%)\tabcellsep 0(0.0\%)\tabcellsep 1(0.1\%)\tabcellsep 0(0.0\%)\tabcellsep 24(3.3\%)\tabcellsep 83.912\tabcellsep 42\tabcellsep 0.000\\
\tabcellsep Amuru\tabcellsep 4(0.6\%)\tabcellsep 1(0.1\%)\tabcellsep 1(0.1\%)\tabcellsep 1(0.1\%)\tabcellsep 0(0.0\%)\tabcellsep 0(0.0\%)\tabcellsep 5(0.7\%)\tabcellsep \tabcellsep \\
\tabcellsep Gulu\tabcellsep 105(14.5\%)\tabcellsep 111(15.4\%)\tabcellsep 19(2.6\%)\tabcellsep 28(3.9\%)\tabcellsep 2(0.3\%)\tabcellsep 0(0.0\%)\tabcellsep 99(13.7\%)\tabcellsep \tabcellsep \\
\tabcellsep Kitgum\tabcellsep 20(2.8\%)\tabcellsep 19(2.6\%)\tabcellsep 8(1.1\%)\tabcellsep 2(0.3\%)\tabcellsep 0(0.0\%)\tabcellsep 0(0.0\%)\tabcellsep 8(1.1\%)\tabcellsep \tabcellsep \\
\tabcellsep Lamwo\tabcellsep 17(2.4\%)\tabcellsep 26(3.6\%)\tabcellsep 10(1.4\%)\tabcellsep 2(0.3\%)\tabcellsep 0(0.0\%)\tabcellsep 1(0.1\%)\tabcellsep 6(0.8\%)\tabcellsep \tabcellsep \\
\tabcellsep Nwoya\tabcellsep 1(0.1\%)\tabcellsep 0(0.0\%)\tabcellsep 0(0.0\%)\tabcellsep 0(0.0\%)\tabcellsep 0(0.0\%)\tabcellsep 0(0.0\%)\tabcellsep 3(0.4\%)\tabcellsep \tabcellsep \\
\tabcellsep Omoro\tabcellsep 4(0.6\%)\tabcellsep 5(0.7\%)\tabcellsep 0(0.0\%)\tabcellsep 0(0.0\%)\tabcellsep 0(0.0\%)\tabcellsep 0(0.0\%)\tabcellsep 0(0.0\%)\tabcellsep \tabcellsep \\
\tabcellsep Pader\tabcellsep 27(3.7\%)\tabcellsep 66(9.1\%)\tabcellsep 14(1.9\%)\tabcellsep 3(0.4\%)\tabcellsep 0(0.0\%)\tabcellsep 2(0.3\%)\tabcellsep 20(2.8\%)\tabcellsep \tabcellsep \end{longtable} \par
 
\begin{quote}
London Journal of Medical and Health Research 12 © 2023 Great ] Britain Journals Press Volume 23 | Issue | Compilation 1.0 7 7 Prevalence and Factors Associated with COVID-19 Vaccine Acceptance among Adult Population in Northern Uganda. A Cross-Sectional Study\end{quote}

\caption{\label{tab_10}Table 5 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{5} \par 
\begin{longtable}{P{0.85\textwidth}}
Between Males and Females ?2=22.362; p=0.001; Age-Groups ?2=52.887; p=0.001; Religious Groups\\
?2=36.560; p=0.048; Districts ?2=83.192; p=0.000; Tribal Groups ?2=43.666;p=0.008; Those With\\
and Without Comorbidities ?2=23.532;p=0.001\end{longtable} \par
 
\caption{\label{tab_11}Table 5}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{6} \par 
\begin{longtable}{P{0.85\textwidth}}
Participants\end{longtable} \par
 
\begin{quote}
Prevalence and Factors Associated with COVID-19 Vaccine Acceptance among Adult Population in Northern Uganda. A Cross-Sectional Study\end{quote}

\caption{\label{tab_12}Table 6 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{6} \par 
\begin{longtable}{P{0.04607843137254902\textwidth}P{0.7676470588235293\textwidth}P{0.036274509803921565\textwidth}}
London Journal of Medical and Health Research\tabcellsep \multicolumn{2}{l}{shows factors associated with COVID-19 vaccine acceptance among participants in northern}\\
\tabcellsep \multicolumn{2}{l}{Uganda. Participants who disagree that vaccines in health facilities in northern Uganda were safe,}\\
\tabcellsep \multicolumn{2}{l}{aOR=1.98,95\% CI:1.01-3.89; p=0.046 and participants who strongly disagree that vaccines in health}\\
\tabcellsep \multicolumn{2}{l}{facilities in northern Uganda were safe aOR=3.31,95\% CI:1.49-7.36; p=0.003 compared to those who}\\
\tabcellsep \multicolumn{2}{l}{strongly agree; participants from Gulu district aOR=5.19,95\% CI:1.71-15.80; p=0.004; Kitgum district}\\
\tabcellsep \multicolumn{2}{l}{aOR=6.05,95\% CI:1.76-20.80; p=0.004; Pader district aOR=3.45,95\% CI:1.07-11.14; p=0.038}\\
\tabcellsep \multicolumn{2}{l}{compared to Lamwo district, smokers aOR=7.75,95\% CI:2.06-29.23; p=0.002 compared to}\\
\tabcellsep \multicolumn{2}{l}{non-smokers; females aOR=1.95,95\% CI:1.04-2.42; p=0.032 compared to males; Baganda tribe}\\
14\tabcellsep Volume 23 | Issue | Compilation 1.0 7 7\tabcellsep © 2023 Great ] Britain Journals Press\end{longtable} \par
 
\begin{quote}
Prevalence and Factors Associated with COVID-19 Vaccine Acceptance among Adult Population in Northern Uganda. A Cross-Sectional Study\end{quote}

\caption{\label{tab_13}Table 6}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.27878440366972473\textwidth}P{0.09357798165137615\textwidth}P{0.08772935779816514\textwidth}P{0.05653669724770642\textwidth}P{0.09942660550458716\textwidth}P{0.0389908256880734\textwidth}P{0.05653669724770642\textwidth}P{0.10137614678899083\textwidth}P{0.03704128440366972\textwidth}}
Strongly Agree\tabcellsep 108 (83.7)\tabcellsep 21 (16.3)\tabcellsep Reference\tabcellsep \tabcellsep \tabcellsep Reference\tabcellsep \tabcellsep \\
Agree\tabcellsep 216 (86.4)\tabcellsep 34 (13,6)\tabcellsep 0.810\tabcellsep (0.448-1.462)\tabcellsep 0.483\tabcellsep 0.824\tabcellsep (0.436-1.558)\tabcellsep 0.552\\
Neutral\tabcellsep 146 (81.8)\tabcellsep 34 (18.9)\tabcellsep 1.198\tabcellsep (.658-2.178)\tabcellsep 0.555\tabcellsep 1.060\tabcellsep (0.552-2.038)\tabcellsep 0.86\\
Disagree\tabcellsep 77 (68.1)\tabcellsep 36 (31.9)\tabcellsep 2.404\tabcellsep (1.303-4.436)\tabcellsep 0.005\tabcellsep 1.984\tabcellsep (1.011-3.894)\tabcellsep 0.046\\
Strongly Disagree\tabcellsep 33 (64.7)\tabcellsep 18 (35.3)\tabcellsep 2.805\tabcellsep (1.338-5.882)\tabcellsep 0.006\tabcellsep 3.308\tabcellsep (1.487-7.360)\tabcellsep 0.003\\
\multicolumn{9}{l}{aOR=5.19,95\% CI:1.71-15.80; p=0.004; and Other tribes (Alur, Basoga, Banyoro) aOR=6.05,95\%}\end{longtable} \par
 
\begin{quote}
CI:1.76-20.80; p=0.004 compared to Itesot; and non-health-workers aOR=1.74,95\% CI:1.03-2.96; p=0.040 compared to health workers. However, participants with comorbidities aOR=0.42,95\% CI:0.24-0.71;p=0.001 were less likely to accept COVID-19 vaccines than those who did not have; Graduates were less likely to accept COVID-19 vaccines aOR=0.42,95\% CI:0.18-0.99; p=0.049 than participants with primary education; and age-group of 20-29 years aOR=0.52,95\% CI:0.31-0.86; p=0.011 than 30-39 year age-group. London Journal of Medical and Health Research 15 15 © 2023 Great ] Britain Journals Press Volume 23 | Issue 7 | Compilation 1.0 7 Prevalence and Factors Associated with COVID-19 Vaccine Acceptance among Adult Population in Northern Uganda. A Cross-Sectional Study\end{quote}

\caption{\label{tab_14}in our health facilities are safe}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{6} \par 
\begin{longtable}{P{0.85\textwidth}}
© 2023 Great ] Britain Journals Press\end{longtable} \par
 
\begin{quote}
Prevalence and Factors Associated with COVID-19 Vaccine Acceptance among Adult Population in Northern Uganda. A Cross-Sectional Study\end{quote}

\caption{\label{tab_15}Table 6 )}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{6} \par 
\begin{longtable}{P{0.85\textwidth}}
). Could they\end{longtable} \par
 
\caption{\label{tab_17}Table 6}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.85\textwidth}}
Therefore, we, the authors propose that the most\\
effective strategy for reducing COVID-19 vaccine\\
hesitancy in the Ugandan setting should include\\
educating the population on COVID-19 and\\
vaccines. The authors propose that educating\\
people through a community engagement strategy\\
is the most optimum way of dispelling myths,\\
misconceptions, rumors, conspiracy theories, and\\
fears about coronavirus. Thus, we propose that\\
encouraging healthy behaviors towards corona-\\
virus will keep Ugandans safe, a virus that has\\
ravaged the world so much.\\
Finally, findings from other Ugandan studies\\
indicate a high COVID-19 vaccine hesitancy in the\\
general population. However, our findings are\\
inconsistent with theirs and have a higher\\
COVID-19 vaccine acceptance rate. Therefore, we,\\
the authors, question and continue to ask more\\
questions whether the suspected high COVID-19\\
vaccine hesitancy among the Ugandan population\\
could have been a vaccine inquisitiveness rather\\
than vaccine hesitancy. The higher COVID-19\\
vaccine acceptance among this study population\\
in northern Uganda compared to others favors the\\
understanding that the situation was more of\\
COVID-19 vaccine inquisitiveness rather than\\
COVID-19 vaccine hesitancy.\end{longtable} \par
 
\caption{\label{tab_18}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{}
\end{longtable} \par
 
\begin{quote}
© 2023 Great ] Britain Journals Press Volume 23 | Issue | Compilation 1.0 7 7\end{quote}

\caption{\label{tab_19}}\end{figure}
 			\label{foot_0}\footnote{\label{foot_0} © 2023 Great ] Britain Journals Press Volume 23 | Issue 7 | Compilation 1.0} 			\label{foot_1}\footnote{\label{foot_1} Prevalence and Factors Associated with COVID-19 Vaccine Acceptance among Adult Population in Northern Uganda. A Cross-Sectional Study} 		 		\backmatter   			 
\subsection[{ACKNOWLEDGMENT}]{ACKNOWLEDGMENT}\par
We acknowledge with many thanks the assistance from the administration of health facilities for the information obtained. Financial support from UMA Acholi branch members, which enabled the team to conduct this study successfully, is most appreciated. 			   			 
\subsection[{Availability of data and materials}]{Availability of data and materials}\par
All datasets supporting this article's conclusion are within this article and are accessible by a reasonable request to the corresponding author. 			   			 \par
Prevalence and Factors Associated with COVID-19 Vaccine Acceptance among Adult Population in Northern Uganda. A Cross-Sectional Study 			   			 \par
Funding: All funds for this study were contributions from individual research members of the Uganda Medical Association (UMA) Acholi branch.\par
Authors' contributions: This study was designed by DLK, JNO, JA, SB, PA, and FWDO. JA, JNO, PA, FWDO, and DLK supervised data collection. ENI and DLK conducted data analysis and interpretation. SB, CO, PA, NOA, DA, JNO, DO, POO, SGO, FPP, ENI, FWDO, JA, and DLK wrote and revised the manuscript. All Authors approved the manuscript and attested they met the ICMJE criteria for authorship. 			  			 			 			  				\begin{bibitemlist}{1}
\bibitem[ Africa, Pan African Medical Journal ()]{b10}\label{b10} 	 		\textit{},  		 \xref{http://dx.doi.org/10.1186/s12879-021-06863-5\#auth-Roongruedee-Chaiteerakij}{10.1186/s12879-021-06863-5\#auth-Roongruedee-Chaiteerakij}.  	 	 		\textit{Africa, Pan African Medical Journal}  		2021. p. 38.  	 
\bibitem[ London Journal of Medical and Health Research ()]{b24}\label{b24} 	 		\textit{},  		 Compilation 1.0.  	 	 		\textit{London Journal of Medical and Health Research}  		2023. Britain Journals Press. 15  (7) .  	 
\bibitem[Nzirakaindi Ikoona and Kitara]{b9}\label{b9} 	 		\textit{A proposed framework to limit post-lockdown community transmission of},  		 			Eric Nzirakaindi Ikoona 		,  		 			David Lagoro Kitara 		.  		 \xref{http://dx.doi.org/10.1186/s12879-021-06863-5\#auth-Roongruedee-Chaiteerakij}{10.1186/s12879-021-06863-5\#auth-Roongruedee-Chaiteerakij}.  		 COVID-19.  		 	 
\bibitem[Nzaji et al. ()]{b2}\label{b2} 	 		‘Acceptability of vaccination against COVID-19 among healthcare workers in the Democratic Republic of the Congo’.  		 			Kabamba Nzaji 		,  		 			M 		,  		 			Kabamba Ngombe 		,  		 			L 		,  		 			Ngoie Mwamba 		,  		 			G 		,  		 			Banza Ndala 		,  		 			DB 		,  		 			Mbidi Miema 		,  		 			J 		,  		 			Luhata Lungoyo 		,  		 			C 		.  		 \xref{http://dx.doi.org/10.2147/por.s271096}{10.2147/por.s271096}.  	 	 		\textit{Pragmatic Obes Res}  		2020. 11 p. .  	 
\bibitem[El-Elimat et al. ()]{b22}\label{b22} 	 		‘Acceptance and attitudes toward COVID-19 vaccines: A cross-sectional study from Jordan’.  		 			T El-Elimat 		,  		 			M M Abualsamen 		,  		 			B A Almomani 		,  		 			Al - Sawalha 		,  		 			N A Alali 		,  		 			FQ 		.  	 	 		\textit{PloS one}  		2021. 16  (4)  p. 250555.  	 
\bibitem[Adeniyi et al. ()]{b15}\label{b15} 	 		‘Acceptance of COVID-19 Vaccine among the Healthcare Workers in the Eastern Cape, South Africa: A Cross-Sectional Study’.  		 			O V Adeniyi 		,  		 			D Stead 		,  		 			M Singata-Madliki 		,  		 			J Batting 		,  		 			M Wright 		,  		 			E Jelliman 		.  	 	 		\textit{Vaccines}  		2021. 9  (6)  p. 666.  	 
\bibitem[Ranya A Ghamri et al. ()]{b31}\label{b31} 	 		\textit{Acceptance of COVID-19 Vaccine and Associated Factors Among Pregnant Women in Saudi Arabia},  		 			Ranya A Ghamri 		,  		 			Sahar S Othman 		,  		 			H Mudhawi 		,  		 			Alhiniah 		,  		 			Rakan H Alelyani 		,  		 			M Atheer 		,  		 			Asma A Badawi 		,  		 			Alshahran 		.  		2022. 101713. Dove press. 26.  	 
\bibitem[Marvin Kanyike et al. ()]{b37}\label{b37} 	 		‘Acceptance of the coronavirus disease 2019 vaccine among medical students in Uganda’.  		 			Andrew Marvin Kanyike 		,  		 			Ronald Olum 		,  		 			Jonathan Kajjimu 		,  		 			Daniel Ojilong 		,  		 			Gabriel Madut Akech 		,  		 			Rhoda Dianah 		,  		 			Nassozi 		.  		 \xref{http://dx.doi.org/10.1186/s12936-022-04046-4}{10.1186/s12936-022-04046-4}.  		 \url{https://doi.org/10.1186/s41182-021-00331-1}  	 	 		\textit{Tropical Medicine and Health}  		2021. 49 p. 37.  	 
\bibitem[Acharya et al. ()]{b29}\label{b29} 	 		\textit{Access to and equitable distribution of COVID-19 vaccine in low-income countries. npj Vaccines},  		 			K P Acharya 		,  		 			T R Ghimire 		,  		 			S H Subramanya 		.  		 \xref{http://dx.doi.org/10.1038/s41541-021-00323-6}{10.1038/s41541-021-00323-6}.  		 \url{https://doi.org/10.1038/s41541-021-00323-6}  		2021. 6.  	 
\bibitem[Amuru RDC orders compulsory COVID-19 vaccination for frontline workers. Uganda Radio Network (URN) (2022)]{b7}\label{b7} 	 		\textit{Amuru RDC orders compulsory COVID-19 vaccination for frontline workers. Uganda Radio Network (URN)},  		 \url{https://www.independent.co.ug}  		12 th June 2022.  		 			The Independent 		 	 
\bibitem[Dahab et al. ()]{b28}\label{b28} 	 		‘COVID-19 control in low-income settings and displaced populations: what can realistically be done?’.  		 			M Dahab 		,  		 			K Van Zandvoort 		,  		 			S Flasche 		.  		 \xref{http://dx.doi.org/10.1186/s13031-020-00296-8}{10.1186/s13031-020-00296-8}.  	 	 		\textit{Conflict and Health}  		2020. 14  (1)  p. 54.  	 
\bibitem[COVID-19 Prevention Standard Operating Procedures (SOPS) for Uganda ()]{b21}\label{b21} 	 		\textit{COVID-19 Prevention Standard Operating Procedures (SOPS) for Uganda},  		 \url{https://www.igg.go.ug/media/files/publications/INSPECTORATE\textunderscore OF\textunderscore GOVERNMEnt\textunderscore covid\textunderscore 19\textunderscore sops.pdf}  		2020. Inspectorate of Government (IG).  	 
\bibitem[Ahmed et al. ()]{b16}\label{b16} 	 		‘COVID-19 Vaccine Acceptability and Adherence to Preventive Measures in Somalia: Results of an Online Survey’.  		 			M Ahmed 		,  		 			R Colebunders 		,  		 			A A Gele 		,  		 			A A Farah 		,  		 			S Osman 		,  		 			I A Guled 		.  	 	 		\textit{Vaccines}  		2021. 9  (6)  p. 543.  	 
\bibitem[Bongomin et al. ()]{b38}\label{b38} 	 		‘COVID-19 vaccine acceptance among high-risk populations in Uganda’.  		 			Felix Bongomin 		,  		 			Ronald Olum 		,  		 			Irene Andia-Biraro 		,  		 			Frederick Nelson Nakwagala 		,  		 			Khalid Hudow Hassan 		,  		 			Dianah Rhoda Nassozi 		.  		 \xref{http://dx.doi.org/10.1177/20499361211024376}{10.1177/20499361211024376}.  	 	 		\textit{Ther Adv Infectious Dis}  		2021. 8 p. .  	 
\bibitem[Lucia et al. ()]{b11}\label{b11} 	 		‘COVID-19 vaccine hesitancy among medical students’.  		 			V C Lucia 		,  		 			A Kelekar 		,  		 			N M Afonso 		.  	 	 		\textit{Journal of public health}  		2020. Oxford, England.  	 	 (fdaa230. Advance online publication) 
\bibitem[Salali and Uysal ()]{b13}\label{b13} 	 		‘COVID-19 vaccine hesitancy is associated with beliefs on the origin of the novel coronavirus in the UK and Turkey’.  		 			G D Salali 		,  		 			M S Uysal 		.  	 	 		\textit{Psychological medicine}  		2020. p. .  	 
\bibitem[David ()]{b33}\label{b33} 	 		‘Determinants of Health Seeking Behaviour in Uganda -Is It Just Income and Important User Fees?’.  		 			Lawson David 		.  	 	 		\textit{Development Economics and Public Policy Working Papers},  		2004. 30553.  		 			University of Manchester, Institute for Development Policy, and Management (IDPM 		 	 
\bibitem[Kumari et al. ()]{b23}\label{b23} 	 		‘Development and validation of a questionnaire to assess knowledge, attitude, practices, and concerns regarding COVID-19 vaccination among the general population’.  		 			A Kumari 		,  		 			P Ranjan 		,  		 			S Chopra 		,  		 			D Kaur 		,  		 			A D Upadhyay 		,  		 			T Kaur 		.  	 	 		\textit{Diabetes \& metabolic syndrome}  		2021. 15  (3)  p. .  	 
\bibitem[Aloyo et al.]{b40}\label{b40} 	 		\textit{Distinct Characteristics of the COVID-19 among Children and Young Adolescents Treated at Gulu Regional Referral Hospital},  		 			Judith Aloyo 		,  		 			Denis Acullu 		,  		 			Nelson Onira Alema 		,  		 			Freddy Wathum Drinkwater 		,  		 			Eric Oyat 		,  		 			David Nzirakaindi Ikoona 		,  		 			Lagoro Kitara 		.  		 \xref{http://dx.doi.org/10.1186/s41182-021-00331-1}{10.1186/s41182-021-00331-1}.  		 	 
\bibitem[Scott and Smith ()]{b17}\label{b17} 	 		‘Estimation in multi-stage surveys’.  		 			Aj \& Scott 		,  		 			Tmf Smith 		.  	 	 		\textit{Journal of the American Statistical Association}  		1969. 64 p. .  	 
\bibitem[Rutten et al. ()]{b0}\label{b0} 	 		‘Evidence-based strategies for clinical organizations to address COVID-19 vaccine hesitancy’.  		 			Ljf Rutten 		,  		 			X Zhu 		,  		 			A L Leppin 		,  		 			J L Ridgeway 		,  		 			M D Swift 		,  		 			J M Griffin 		.  		 \xref{http://dx.doi.org/10.1016/J.MAYOCP.2020.12.024}{10.1016/J.MAYOCP.2020.12.024}.  	 	 		\textit{Mayo Clin Proc}  		2021. 96  (3)  p. 699.  	 
\bibitem[Mouhamadou et al. ()]{b1}\label{b1} 	 		\textit{Factors associated with COVID-19 vaccine hesitancy in Senegal: a mixed study, Human Vaccines \& Immunotherapeutics},  		 			Faly Mouhamadou 		,  		 			Adama Ba 		,  		 			Babacar Faye 		,  		 			Amadou Kane 		,  		 			Amandine Ibra Diallo 		,  		 			Ibrahima Junot 		,  		 			Gaye 		.  		 \xref{http://dx.doi.org/10.1080/21645515.2022.2060020}{10.1080/21645515.2022.2060020}.  		2022.  	 
\bibitem[Kabagenyi et al. ()]{b26}\label{b26} 	 		‘Factors Associated with COVID-19 Vaccine Hesitancy in Uganda: A Population-Based Cross-Sectional Survey’.  		 			Allen Kabagenyi 		,  		 			Ronald Wasswa 		,  		 			Atek Kagirita 		,  		 			Juliet Nabirye 		,  		 			Leonard Atuhaire 		,  		 			Peter Waiswa 		.  	 	 		\textit{Intern J General Medicine}  		2022. 15 p. .  	 
\bibitem[Baguma et al. ()]{b39}\label{b39} 	 		\textit{Factors associated with mortality among the COVID-19 patients treated at Gulu Regional Referral Hospital: A retrospective study},  		 			Steven Baguma 		,  		 			Denis Acullu 		,  		 			Freddy Wathum Drinkwater 		,  		 			Judith Oyat 		,  		 			Eric Nzirakaindi Aloyo 		,  		 			David Ikoona 		,  		 			Lagoro Kitara 		.  		 \xref{http://dx.doi.org/10.1007/s10389-021-01677-w}{10.1007/s10389-021-01677-w}.  		2022. Front. Public Health. 10 p. 841906.  	 
\bibitem[Okiring et al. ()]{b34}\label{b34} 	 		‘Gender differencein the incidence of malaria diagnosedin public health facilities in Uganda’.  		 			Jafer Okiring 		,  		 			Adrienne Epstein 		,  		 			Jane F Namuganga 		,  		 			Emmanuel V Kamya 		,  		 			Isaiah Nabende 		,  		 			Martha Nassali 		.  		 \xref{http://dx.doi.org/10.1186/s12936-022-04046-4}{10.1186/s12936-022-04046-4}.  		 \url{https://doi.org/10.1186/s12936-022-04046-4}  	 	 		\textit{Malaria Journal}  		2022. 21 p. 22.  	 
\bibitem[Zintel et al. ()]{b36}\label{b36} 	 		‘Gender differences in the intention to get vaccinated against COVID-19: a systematic review and meta-analysis’.  		 			Stephanie Zintel 		,  		 			Charlotte Flock 		,  		 			Anna Lisa Arbogast 		,  		 			Alice Forster 		,  		 			Monika Christian Von Wagner 		,  		 			Sieverding 		.  		 \xref{http://dx.doi.org/10.1007/s10389-021-01677-w}{10.1007/s10389-021-01677-w}.  		 \url{https://doi.org/10.1007/s10389-021-01677-w}  	 	 		\textit{Journal of Public Health}  		2022.  	 
\bibitem[Ateghang-Awankem et al. ()]{b27}\label{b27} 	 		‘Good participatory practice, clinical trials awareness, and COVID-19 vaccine acceptance in Sub-Sahara Africa’.  		 			B Ateghang-Awankem 		,  		 			L Deluca 		,  		 			E Shadzeka 		,  		 			K Y Anchang 		.  	 	 		\textit{Am J Public Health Research}  		2021. 9  (3)  p. .  	 
\bibitem[Bishawtaye and Wasietaye ()]{b30}\label{b30} 	 		‘Haymanot AlemMuche, Nuhamin TesfaTsega, Tsion TadesseHaile, Agumas EskeziaTiguh. COVID-19 vaccine acceptance and associated factors among women attending antenatal and postnatal care in Central Gondar Zone public hospitals, Northwest Ethiopia’.  		 			Eden Bishawtaye 		,  		 			Zewdu Wasietaye 		.  	 	 		\textit{Clinical Epidemiology and Global Health}  		2022. 14 p. 100993.  	 
\bibitem[Sallam et al. ()]{b14}\label{b14} 	 		‘High Rates of COVID-19 Vaccine Hesitancy and Its Association with Conspiracy Beliefs: A Study in Jordan and Kuwait among Other Arab Countries’.  		 			M Sallam 		,  		 			D Dababseh 		,  		 			H Eid 		,  		 			K Al-Mahzoum 		,  		 			A Al-Haidar 		,  		 			D Taim 		.  	 	 		\textit{Vaccines}  		2021. 9  (1)  p. 42.  	 
\bibitem[Lagoro and Aloyo ()]{b35}\label{b35} 	 		‘HIV/AIDS Stigmatization, the reason for poor access to HIV Counseling and Testing (HCT) among the youths in Gulu (Uganda)’.  		 			David Lagoro 		,  		 			Kitara 		,  		 			Judith Aloyo 		.  	 	 		\textit{Afr J Infect Dis}  		2012. 6  (1)  p. .  	 
\bibitem[Mahmutovic ()]{b20}\label{b20} 	 		 			Jasko Mahmutovic 		.  		 \url{https://www.surveylegend.com/sampling/systematic-sampling/}  		\textit{What is Systematic Sampling? Pros, Cons, and Examples. Survey Legend},  		2023.  	 
\bibitem[Sirikalyanpaiboon et al. ()]{b6}\label{b6} 	 		‘Panyavee Pitisuttithum, Watsamon Jantarabenjakul, Roongruedee Chaiteerakij et al. COVID-19 vaccine acceptance, hesitancy, and determinants among physicians in a university-based teaching hospital in Thailand’.  		 			May Sirikalyanpaiboon 		,  		 			Krittin Ousirimanee Chai 		,  		 			Jeerath Phannajit 		.  	 	 		\textit{BMC Infectious Diseases}  		2021. 2021. 21 p. 1174.  	 
\bibitem[Prevalence and Factors Associated with COVID-19 Vaccine Acceptance among Adult Population in Northern Uganda. A Cross-Sectional Study]{b25}\label{b25} 	 		\textit{Prevalence and Factors Associated with COVID-19 Vaccine Acceptance among Adult Population in Northern Uganda. A Cross-Sectional Study},  		 	 
\bibitem[Sathian et al. ()]{b18}\label{b18} 	 		‘Relevance of Sample Size Determination in Medical Research’.  		 			B Sathian 		,  		 			J Sreedharan 		,  		 			S Baboo 		,  		 			K Sharan 		,  		 			E Abhilash 		,  		 			E Rajesh 		.  	 	 		\textit{Nepal Journal of Epidemiology}  		2010. 1  (1)  p. .  	 
\bibitem[Thomas ()]{b19}\label{b19} 	 		\textit{Systematic Sampling | A Step-by-Step Guide with Examples. Scribbr},  		 			Lauren Thomas 		.  		 \url{https://www.scribbr.com/methodology/systematic-sampling/}  		2022.  	 
\bibitem[Ten threats to global health in 2019]{b5}\label{b5} 	 		\textit{Ten threats to global health in 2019},  		 \url{https://www.who.int/news-room/spotlight/ten-threats-to-global-health-in-2019}  		World Health Organization (WHO).  	 
\bibitem[Seyed M Moghadas et al. ()]{b3}\label{b3} 	 		‘The Impact of Vaccination on Coronavirus Disease 2019 (COVID-19) Outbreaks in the United States’.  		 			Thomas N Seyed M Moghadas 		,  		 			Kevin Vilches 		,  		 			Chad R Zhang 		,  		 			Affan Wells 		,  		 			Burton H Shoukat 		,  		 			Singer 		.  	 	 		\textit{Clinical Infectious Diseases}  		2021. 73  (12)  p. .  	 
\bibitem[Ashley E Thompson et al. ()]{b32}\label{b32} 	 		‘The influence of gender and other patient characteristics on healthcare-seeking behavior a QUALICOPC study’.  		 			Yvonne Ashley E Thompson 		,  		 			Baukje Anisimowicz 		,  		 			William Miedema 		,  		 			Walter P Hogg 		,  		 			Kris Wodchis 		,  		 			Aubrey-Bassler 		.  		 \xref{http://dx.doi.org/10.1038/s41541-021-00323-6}{10.1038/s41541-021-00323-6}.  	 	 		\textit{BMC Family Practice}  		2016. 17 p. 38.  	 
\bibitem[Agaba (2021)]{b8}\label{b8} 	 		\textit{Ugandans shed their vaccine hesitancy as COVID-19 cases spike},  		 			John Agaba 		.  		2021. 16 th July 2021.  	 
\bibitem[Barello et al. ()]{b12}\label{b12} 	 		‘Vaccine hesitancy among university students in Italy during the COVID-19 pandemic’.  		 			S Barello 		,  		 			T Nania 		,  		 			F Dellafiore 		,  		 			G Graffigna 		,  		 			R Caruso 		.  	 	 		\textit{European journal of epidemiology}  		2020. 35  (8)  p. .  	 
\bibitem[Lansell (2020)]{b4}\label{b4} 	 		\textit{Vaccine Hesitancy and COVID-19 Vaccine Acceptance in sub-Saharan Africa. GeoPoll},  		 			S Lansell 		.  		2020. 11 th Dec 2020.  	 
\end{bibitemlist}
 			 		 	 
\end{document}
