Effect of Human Immunodeficiency Virus (HIV) Infection on Mortality Among Hospitalised COVID-19 Patients at Levy Mwanawasa University Teaching Hospital, Lusaka, Zambia

Abstract

Emerging but limited evidence suggests that HIV infection does not affect in-hospital COVID-19 mortality, regardless of the prevalence of HIV infection in most parts of sub-Saharan Africa, especially the southern Africa region, and Zambia, Lusaka District in particular, is not an exception. Therefore, this study aimed to determine the effect of HIV infection, demographics, and clinical factors on mortality among hospitalized COVID-19 patients at Levy Mwanawasa University Teaching Hospital (LMUTH). A cross-sectional study was conducted with a sample size of 698 adults admitted for COVID-19 at LMUTH from 18 March 2020 to 31 December 2021. For all statistical analysis of data, STATA statistical software, version 15 MP (College Station, TX 77845, USA) was used—ensuring that appropriate statistical techniques were applied to the data. Unadjusted and adjusted logistic regressions were conducted to model COVID-19 mortality among COVID-19 patients based on their HIV status while controlling for five predictor variables. Based on the results, the best predictors of in-hospital COVID-19 mortality were HIV status, number of comorbidities, age in years, smoking, and alcohol intake. The results suggest that COVID-19 mortality among those with HIV and those without HIV infection was different. People living with HIV infection had increased odds of COVID-19 mortality compared to those without HIV. The results further suggested that a unit increase in age was associated with increased odds of COVID-19 mortality. Furthermore, drinking alcohol and having two or more comorbidities increased the odds of COVID-19 mortality compared to not drinking alcohol, having no comorbidity, or having a comorbidity. This study, therefore, concludes that HIV infection has a significant effect on COVID-19 mortality among patients hospitalized at LMUTH and that the proportion of COVID-19 mortality in the HIV-infected group is relatively higher than in the uninfected group. Therefore, there is a need for close monitoring of COVID-19 patients with HIV infection.

1. Introduction

The outbreak of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) was initially identified in Wuhan, a city in Hubei province of China, in December 2019 [1,2]. The virus causes coronavirus disease (COVID-19) [2], which was earlier named 2019-nCov but after it rapidly spread, resulting in an epidemic throughout China, experts from the International Committee on Taxonomy of Viruses associated the outbreak with SARS-CoVs [3], and the disease COVID-19 was eventually designated in February 2020 by WHO. After several continuous significant public health challenges, and subsequent rapid spread to other areas across the globe, in particular to over 180 countries [4], a global pandemic was later declared by WHO on 11 March 2020 [5], and on 18 March 2020, Zambia recorded the first COVID-19 case [6].

As of July 2021, official statistics from the WHO reported over 197 million confirmed cases with 4.2 million deaths, representing about a 2.1% case fatality rate globally [5]. In Zambia, over 196,000 confirmed cases had been reported as of July 2021, with a recorded case fatality of 1.7%, and with most cases being in Lusaka City [7]. Although most cases identified at the beginning of the epidemic were imported cases from overseas travellers, increasing numbers of cases are due to community transmission [6].

Moreover, Zambia is a landlocked country situated in south-central Africa with an estimated total population of 17.8 million. The prevalence of the HIV/AIDS epidemic among adults aged ≥15 years is 12.1%, with over 17,000 adults and child deaths due to HIV as of 2020 [8]. From the time Zambia recorded its first COVID-19 cases, patients with COVID-19 were mostly referred to Levy Mwanawasa University Teaching Hospital, in Lusaka Zambia, which was designated as a COVID-19 treatment center. The hospital has a bed capacity of over 730, with approximately 150 beds allocated for critical and severe cases of COVID-19, and an estimated catchment population of over three million people. Additionally, the hospital attends to about 90 HIV patients weekly, with more than 1500 attending ART clinics and over 10 referrals monthly [9].

Clinically, COVID-19 presents with prevalent complications of pneumonia, acute respiratory distress syndrome, acute kidney injury, and acute cardiac injury, among others. The group at most risk of developing severe disease includes individuals over the age of 60 years, patients with immunosuppression, and patients with other comorbidities such as diabetes, coronary heart disease, chronic kidney disease, autoimmune conditions, cancer, and hypertension [10,11]. Zambia’s population is generally younger but potentially at higher risk of the same comorbidities as it harbors the so-called colliding epidemics of HIV, tuberculosis (TB), lung disease, and chronic obstructive pulmonary disease [8]. Furthermore, SARS-CoV-2 poses significant challenges for patients with pre-existing cardiovascular conditions, and such patients have an increased risk of severe disease and death [11]. Nevertheless, many features of the disease (fever, raised inflammatory markers, and low lymphocytes) in severe cases overlap with those of HIV infection, making the treatment challenging [11]. Although, data are limited on the outcome of COVID-19 in patients with HIV, being immunocompromised with an altered cytokine profile could raise the risk for COVID-19 mortality [10,12].

Data are scarce on the effect of HIV in the population with COVID-19, and Zambia is not an exception. Therefore, this research aimed to achieve a better understanding of the association of COVID-19 in-hospital mortality with HIV infection in patients who were admitted for COVID-19. This is of great importance for the evaluation of treatment outcomes among this population, especially in low-resource settings such as Zambia where the population is at the highest risk for both HIV and COVID-19 morbidity and mortality [1].

2. Materials and Methods

2.1. Study Design

A cross-sectional study design was applied to determine the effect of HIV, demographics, and clinical factors on mortality among hospitalized COVID-19 patients at Levy Mwanawasa University Teaching Hospital.

2.2. Study Site

This study was carried out at Levy Mwanawasa University Teaching Hospital, a specialized COVID-19 treatment centre located in the capital city of Lusaka, with a catchment population area estimated to be just over three million people. The hospital attends to about 90 HIV patients (visits) weekly, and over 1500 patients are on ART, with about 10 monthly referrals [9].

2.3. Study Populations

The frame for this study was people admitted for COVID-19 (those living with and without HIV infection, and those with unknown HIV status) at Levy Mwanawasa University Teaching Hospital.

2.3.1. Inclusion Criteria

All adult patients aged ≥18 years hospitalized for COVID-19, confirmed using positive reverse transcription polymerase chain reaction (PCR) or rapid antigen test results for SARS-CoV-2, or radiologic evidence suggestive of COVID-19 with acute respiratory symptoms if diagnosed as a probable case.

2.3.2. Exclusion Criteria

Patients with confirmed COVID-19 who were admitted for less than half a day (under twelve hours), and those with incomplete records.

2.4. Sample Size Determination and Sampling Methods

2.4.1. Sample Size Determination

The formula for prevalence studies was used to determine the sample size for this study. Equation (1): Formula for sample size calculation:

$$\mathbf{n}={\displaystyle \frac{{\mathrm{z}}^2\times \mathrm{p}\left(1-\mathrm{p}\right)}{{\mathrm{e}}^2}}$$

(1)

where z = 1.96 (5% significance level was used), and p = prevalence to be detected = 50% because no historical prevalence for COVID-19 was available when the protocol was being developed. E = precision error around the prevalence = 5%. Therefore, a minimum sample size of 384 was required in this study.

2.4.2. Sampling Methods

A probability-based, simple random sampling (SRS) method was applied in this study. Medical files of patients who were admitted for COVID-19 between 18 March 2020 and 31 December 2021 were listed, and then a sample of 698 files were randomly selected for this study out of over 1000 eligible medical files.

2.5. Data Collection

Secondary data were manually extracted from paper-based medical records of patients who met the inclusion criteria between the dates of 18 March 2020 and 31 December 2021 for the following variables: sex, age, alcohol drinking, smoking status, comorbidities, HIV status, cluster of differentiation-4 (CD-4), antiretroviral therapy (ART) and C-reactive protein (CRP). The primary outcomeCOVID-19 in-hospital mortality and length of hospital stay were also extracted. These data were directly entered into Microsoft Office Excel Software 2013 version as part of the data entry process.

2.6. Statistical Analysis

Analysis of data was performed using STATA statistical software, version 15 MP (College Station, TX 77845, USA), and the primary outcome of this study was COVID-19 in-hospital mortality, measured as yes (if a patient died) or no (if a patient was discharged).

Descriptive statistics were generated to summarise the characteristics of the study participants. The normality of continuous variables was tested statistically using the Shapiro–Wilk test, and graphically using box and whisker plots, q-q plots, and histograms. The median and interquartile range were reported for all continuous variables because their distributions were not normally distributed, and the Wilcoxon rank-sum test was used to test for associations. Categorical variables were summarised using frequencies and percentages, and then the Chi-squared test or Fischer’s exact test was used to test for associations after checking the expected frequency assumptions.

To determine the mortality among COVID-19 patients with HIV; the case fatality rate (CFR) stratified by HIV status was calculated, and then only the case fatality rate among people living with HIV was reported per 100 population.

For inferential statistics, unadjusted and adjusted logistics regression analyses were employed. An unadjusted analysis with all single explanatory variables involved in the study was performed first to identify variables that independently affected COVID-19 mortality. Then later on, in the adjusted analysis, the full model with six explanatory variables was fitted to help identify variables that explained COVID-19 mortality while adjusting for other variables. The choice of explanatory variables for the adjusted (multiple) logistic regression model was determined through an investigator-led stepwise approach, as opposed to machine-led, while treating HIV status as a priori explanatory variable. This was guided by model-fit selection statistics indices, such as the lowest Akaike information criterion (AIC = 436.0) and Bayesian information criterion (BIC = 467.4), as well as a likelihood ratio test after estimation of the nested model by adding and eliminating variables one at a time. A null or nested model with no predictor variable gender (LRχ², p-value = 0.5260) indicated that a nested model with five explanatory variables provided a better fit than a full model with six variables. Furthermore, model performance was assessed using sensitivity (93.0%), specificity (60.1%), and the area under the ROC statistics (0.8875), which suggested that the model predicted mortality better than survival and that the classification of outcomes was not due to chance because the ROC value is closer to 1 than 0.5. The study’s significance level was defined at 0.05 with a 95% confidence interval. The results were presented using graphs and tables.

2.7. Handling of Missing Data

Missingness in the variables which were part of the analysis was observed; the covariates with missing data had at least 90% non-missing observations, meaning that only less than 10% missingness was observed with no obvious pattern or traceable source; hence, a complete case analysis was adopted. Furthermore, CRP and CD-4 were dropped in their respective regression analyses because they had missing data of more than 75%.

2.8. Ethical Considerations

All standard ethical procedures were followed, with particular sensitivity to issues of confidentiality and anonymity, before, during, and after conducting this research. The ethical approval was granted from UNZABREC (Ref. No. 2100–2021) and the National Health Research Authority (Ref. No. NHRA00029-7-12-2021). Permission was obtained from the Ministry of Health (MoH) through LMUTH management. Furthermore, no physical interaction with participants was involved as secondary data were used. However, the researcher ensured that no names or any personal identifying information were collected from the participants’ medical files to protect their identity and ensure confidentiality.

3. Results

3.1. Participants’ Baseline Characteristics

From the sample of 698 patients who were hospitalised with confirmed COVID-19 at LMUTH, 654 (93.7%) had a known HIV status, among which 158 (24.2%) were HIV positive. Among the hospitalised patients, 453 (64.9%) were males, the median age was 56 years, IQR (44, 67 years), and the median CRP level was 4.4, IQR (2.7, 12.4). Regarding social habits, 366 (52.5%) consumed alcohol and 127 (18.4%) smoked. The overall proportion of comorbidity was 543 (77.8%), of which hypertension (64.2%), diabetes (38.1%) and tuberculosis (35.8) were the three most prevalent COVID-19 comorbidities.

3.2. Mortality Among COVID-19 Patients Stratified by HIV Status

To determine the mortality among COVID-19 patients with HIV, the case fatality rate (CFR) stratified by HIV status was calculated and then reported per 100 in a stacked bar chart, as follows.

As shown in Figure 1, mortality among COVID-19 patients with HIV infection was 66 (41.8%) and 70 (14.1%) among the HIV-negative patients.

3.3. Baseline Characteristics Univariate Comparison

This section gives a summary of the baseline statistics for COVID-19 mortality and survival using the Wilcoxon rank-sum test and Chi-squared test for association.

Table 1. Comparisons of baseline characteristics between COVID-19 outcomes.

Characteristic	COVID-19 Outcome		Sig.
	Died	Survived
Continuous Variables
Age (years)
Median (IQR)	67.5 (61, 75.5)	53 (41, 63)	<0.0001 W
CRP
Median (IQR)	26.8 (18, 34)	3.4 (2.3, 5.1)	<0.0001 W
Categorical Variables, n (%)
Sex
Female	51 (20.8)	194 (79.2)	0.513 C
Male	85 (18.8)	368 (81.2)
HIV Status
Negative	70 (14.1)	426 (85.9)	<0.0001 C
Positive	66 (41.8)	92 (58.2)
Smoking
No	95 (16.9)	467 (83.1)	<0.0001 C
Yes	39 (30.7)	88 (69.3)
Alcohol drinking
No	49 (14.8)	282 (85.2)	0.003 C
Yes	87 (23.8)	279 (76.2)
Comorbidity
None	3 (1.9)	152 (98.1)	<0.0001 C
One	20 (6.7)	280 (93.3)
Two and more	113 (46.5)	130 (53.5)

HIV = human immunodeficiency virus, ^W = Wilcoxon rank-sum test, ^C = Chi-squared test, CRP = C-reactive protein, IQR = interquartile range.

below shows that there was overwhelming evidence to suggest that median age distribution (p < 0.0001) and median CRP (p < 0.0001) significantly differed between patients who died with COVID-19 and those who did not die with COVID-19. Similarly, the proportions of mortality were statistically different across the levels of HIV status (p < 0.0001), smoking status (p < 0.0001), alcohol drinking (p = 0.003), and number of underlying conditions (p < 0.0001). However, the proportions of COVID-19 mortality were not significantly different across the levels of gender (p = 0.513).

For the subgroup analysis involving COVID-19 patients living with HIV infection,

Table 2. Comparisons of HIV patients’ baseline characteristics between COVID-19 outcomes.

Characteristic	COVID-19 Outcome		Sig.
	Died	Survived
Continuous Variables
CD4 (Cells/µL)
Median (IQR)	217.6 (167, 270.5)	393.4 (347.9, 509.3)	<0.0001 W
Categorical Variables, n (%)
ART
No	4 (80.0)	1 (20)	0.163 F
Yes	61 (40.9)	88 (59.1)

CD4 = cluster of differentiation-4, ART = antiretroviral therapy, ^F = Fischer’s exact test, ^W = Wilcoxon rank-sum test, IQR = interquartile range.

below shows that there was overwhelming evidence to suggest that median CD-4 (p < 0.0001) significantly differed between patients who died with COVID-19 and those who did not die with COVID-19. Not only that, the proportions of COVID-19 mortality did not differ by ART status (p = 0.163).

As shown in Figure 2 below, the prevalence of comorbidity among COVID-19 patients with HIV infection was 151 (95.6%) and 349 (70.4%) among the HIV-negative patients, while among patients with two or more comorbidities, 100 (63.3%) were HIV positive and 125 (25.2%) were HIV negative.

3.3.1. Univariable and Multivariable Logistic Regression Analysis

The results in this section summarise findings from the logistics regression analysis, for both the unadjusted and adjusted analyses, taking HIV status as the a priori explanatory variable.

Table 3. Univariable and multivariable logistic regression analysis for COVID-19 mortality.

Variables	Univariable Estimates			Multivariable Estimates
	cOR	CI (95%)	p-Value	aOR	CI (95%)	p-Value
Age in years	1.08	1.07, 1.10	<0.0001	1.07	1.04, 1.09	<0.0001
HIV Status
Negative	Ref			Ref
Positive	4.37	2.91, 6.54	<0.0001	2.02	1.21, 3.37	0.007
Smoking
No	Ref			Ref
Yes	2.18	1.41, 3.37	<0.0001	1.38	0.76, 2.51	0.288
Drinking alcohol
No	Ref			Ref
Yes	1.79	1.22, 2.64	0.003	2.46	1.42, 4.24	0.001
Comorbidity
None	Ref			Ref
One	3.62	1.06, 12.37	0.040	1.92	0.54, 6.82	0.312
Two or more	44.04	13.67, 141.92	<0.0001	12.97	3.77, 44.58	<0.0001
Sex
Female	Ref
Male	0.88	0.60, 1.30	0.514

cOR = crude odds ratio, aOR = adjusted odds ratio, CI = confidence interval, Ref = reference.

shows the univariable and multivariable logistic regression analysis results. In the univariable analysis, a unit (a year) increase in age (cOR = 1.08, 95% CI = 1.07, 1.10, p < 0.0001) increased the odds of in-hospital COVID-19 mortality. Also, being HIV positive compared to being HIV negative (cOR = 4.37, 95% CI = 2.91, 6.54, p < 0.0001), smoking versus not smoking (cOR = 2.18, 95% CI = 1.41, 3.37, p < 0.0001), taking alcohol versus not taking alcohol (cOR = 1.79, 95% CI = 1.22, 2.64, p = 0.003), and having one, or more than one underlying condition (apart from HIV) compared to having none (cOR = 3.62, 95% CI = 1.06, 12.37, p = 0.04; cOR = 44.04, 95% CI = 13.67, 141.92, p < 0.0001) increased the odds of in-hospital COVID-19 mortality. The effects of all the five variables were statistically significant. However, for gender, the results show that there was no evidence (cOR = 0.88, 95% CI = 0.60, 1.30, p = 0.514) of a difference in odds between males and females for in-hospital COVID-19 mortality.

However, adjusting for other variables, a unit (a year) increase in age (aOR = 1.07, 95% CI = 1.04, 1.09, p < 0.0001) increased the odds of in-hospital COVID-19 mortality. Also, being HIV positive compared to being HIV negative (aOR = 2.02, 95% CI = 1.21, 3.37, p = 0.007), smoking versus not smoking (aOR = 1.38, 95% CI = 0.76, 2.51, p = 0.288), alcohol drinking versus non-alcohol drinking (aOR = 2.46, 95% CI = 1.42, 4.24, p = 0.001), and having one, or more than one underlying condition (apart from HIV) compared to having none (aOR = 1.92, 95% CI = 0.54, 6.82, p = 0.312; aOR = 12.97, 95% CI = 3.77, 44.58, p < 0.0001) increased the odds of in-hospital COVID-19 mortality. However, smoking status (p = 0.288) and having one comorbidity had no significant effect on mortality.

3.3.2. The Predicted Probabilities of COVID-19 Mortality by HIV Status Across Age

Generally, Figure 3 above shows that the probability of mortality increased with increasing age, more so for the individuals with positive HIV status compared to those with negative HIV status. And the difference in COVID-19 mortality probability was statistically significant (p = 0.007).

4. Discussion

According to ZAMPHIA study [13], based on this study sample size of 698 patients, the HIV status reported among patients who were hospitalised with confirmed COVID-19 at Levy Mwanawasa University Teaching Hospital was below a Joint United Nations programme on HIV/AIDS-UNAIDS 95% target. Further, 158 (24.2%) of cases hospitalised for COVID-19 with their HIV status known were HIV positive, and this is in line with the district prevalence range (from as low as 4.0% to as high as 24%), which mostly, is slightly over or double the national prevalence level [13,14]. Not only that, the mortality among COVID-19 patients with HIV was relatively higher at 66 (41.8%) than in those without HIV, which was 70 (14.1%).

Based on the best-fit model, the best predictors of in-hospital COVID-19 mortality were HIV status, number of underlying medical conditions (having at least two underlying medical conditions), smoking and drinking alcohol status, and age in years. As for age in years, our findings suggest that for each unit (years) increase in age (aOR = 1.07, 95% CI = 1.04, 1.09, p < 0.0001) there were increased odds of in-hospital COVID-19 mortality. This is similar to the findings of many studies that have been conducted both locally and globally [8,10,15,16,17,18,19,20,21,22,23,24,25]. The increase effect size of age in this study was quite minimal, which is because the median (interquartile range) of age in years of the whole sample was lower [56 (44, 67)]. The sample comprised younger adults, and yet, COVID-19 most commonly affects the older age group. Therefore, the risk of developing poor outcomes like mortality, increases with age, especially among the elderly, hence the effect size [18,25]. Also, to avoid loss of power, residuals confounding and bias, the continuous predictor variable age was not categorized.

Based on the priori variable HIV infection, our findings suggest that HIV infection increased the odds of in-hospital COVID-19 mortality. This was consistently shown by both univariable and multivariable logistic regression (cOR = 4.37, 95% CI = 2.91, 6.54, p < 0.0001; aOR = 2.02, 95% CI = 1.21, 3.37, p = 0.007). Also, the proportions of in-hospital COVID-19 mortality between the two groups of HIV status were significantly different. The rate of COVID-19 mortality among those with HIV infection, 66 (41.8%), was higher than among those without HIV infection, 70 (14.1%), suggesting high mortality of COVID-19 in the HIV-positive group compared to the HIV-negative group. These results are consistent with other studies which have been conducted outside Zambia [17,18], and a recent retrospective cohort multi-center study conducted in Zambia with a large sample size [25], although an earlier study suggested the opposite, and this could be due to small sample size [8]. Moreover, the HIV prevalence, 158 (24.2%), among COVID-19 patients was higher, and this is slightly similar to the latest reported district HIV prevalence range (as low as 4.2 to as high as 23.5%) in Zambia [14], but above the national prevalence rate of 11.4% among people aged 15 years plus [13,25]. Also, similar studies have confirmed that the district HIV prevalence is mostly higher than the national prevalence, with the prevalence in some districts almost twice as high as the national level. This could be the cause for the increased odds of in-hospital COVID-19 mortality among HIV-positive patients in this study, which was performed mainly at the district level. For this reason, HIV infection had a significant association with increased odds of COVID-19 mortality in this research, which is important for this study setup as there is high burden of people living with HIV, especially in countries from the sub-Saharan Africa region. Zambia is not an exception, and in particular, Lusaka District has double the national HIV prevalence [18,25,26]. Also, this significant association of HIV infection with increased odds of in-hospital COVID-19 mortality could be related to active dysfunction of the immune system. This study found lower average absolute CD-4 counts [354.8 (±166.7)] which was lower than the normal reference range (<500 cells/micro liters of blood), although it was not adjusted for and its sample size was very minimal. In addition, it could be because of poorly controlled HIV infection, which presented with other comorbidity complications, leaving the body vulnerable to attack by any foreign body pathogens [12]. Nevertheless, all this comes about due to low diagnosis rates or a failure to determine the HIV status when the infection is in its initial stages, which results in delays for HIV-positive people to be on medication to control and maintain their HIV infection [10,12,13,27]. Also, due to poor retention in care and adherence to ART treatment, as well as drug toxicity, there are high levels of viral load, which contribute to immunosuppression and opportunistic infection, as well as residual confounding related to higher risks for non-communicable diseases in people living with HIV infection [12,17,18,28]. However, in this study, based on the data available, the undetermined HIV status in 654 patients (93.7%) was the primary challenge to the three UNAIDS target goals for HIV patients (regardless of whether the HIV status was not known or it was just the health workers failure to report the patients’ HIV status in the file) as the target is 95% [13].

Among the social risk behaviors, drinking alcohol, 366 (52.5%), was the main social habit, followed by smoking, 127 (18.4%). This is because during the COVID-19 period, a large proportion of people drank more, and there was an increase in alcohol consumption [29]. Also, the medical histories suggest that more than half of this sample, 366 (52.5%), used to drink alcohol, and 127 (18%) previously smoked. Although other studies have found that there is significant dependence on alcohol and smoking, the alcohol drinking socio-habits are mostly double the rate of smoking habits in Zambia, especially in urban areas, and Lusaka District is not an exception [30,31,32]. Among adults in the Lusaka District, almost half drink alcohol [33]. Thus, alcohol drinking verses non-alcohol drinking had consistently increased odds of in-hospital COVID-19 mortality. The results are shown only for the adjusted model (aOR = 2.46, 95% CI = 1.42, 4.24, p = 0.001), and the findings are similar to those of a study which was conducted at a global level [34,35]. Also, studies have shown that alcohol consumption, regardless of how it is consumed, is associated with multiple diseases; it intensifies COVID-19 severity, which results in deteriorating COVID-19 outcomes [29,34,35]. However, increased odds for the smoking variable did not reveal a significant association with in-hospital COVID-19 mortality compared to non-smoking, although smoking is considered to be a risk factor increasing COVID-19 mortality. Also, the medical histories suggest that more than half of this sample, 366 (52.5%), used to drink alcohol, and 127 (18%) previously smoked. Although other studies have found that there is significant dependence on alcohol and smoking, the alcohol drinking socio-habits are mostly double the rate of smoking in Zambia, especially in urban areas, and Lusaka District is not an exception [30,31,32]. Among adults in the Lusaka District, almost half drink alcohol [33].

The overall proportion of underlying medical conditions or comorbidity in this sample was 543 (77.8%), suggesting hypertension (448, 64.2%), diabetes (265, 38.1%) and tuberculosis (250, 35.8%) to be among the top three most prevalent COVID-19 comorbidities. These results are similar to the national findings of the research conducted by the CDC and MOH locally in Zambia on the top two levels [25]. However, in this study, there was no evidence to suggest that having one comorbidity, apart from HIV infection, was different from not having any comorbidity at all, even though the odds suggested there was an increased direction (cOR = 1.92, 95% CI = 0.54, 6.82, p = 0.312). As for having more than one comorbidity, apart from HIV, compared to not having any comorbidity apart from HIV (aOR = 12.97, 95% CI = 3.77,44.58, p < 0.0001), there was overwhelming evidence to suggest there were increased odds of in-hospital COVID-19 mortality. This is similar to what many studies have found, although the approach was different. Mostly, the approach of other studies was based on whether there is a presence of comorbidity or not [27,36,37]. Not only that, in one study which was found to be very similar to this study, the scaling of the number of comorbidities was different, yet it also concluded that the presence of comorbidity and the higher number of comorbidities among COVID-19 patients increased the odds of their in-hospital COVID-19 mortality [25]. Also, based on this research, we can suggest that, for other comorbidities apart from HIV infection, having at least two comorbidities has a significant effect of increased COVID-19 mortality.

Consequently, the mortality among COVID-19 patients with HIV was relatively higher at 66 (41.8%) than in those without HIV (70, 14.1%). This is because of the high prevalence of comorbidities (543, 77.8%) in this particular study subgroup of interest, not excluding the high prevalence of the priori variable, HIV infection [18,26]. However, this was contrary to the overall national COVID-19 mortality rate of 4058 (1.2%) as of April 2023, and this is because mortality varies by location, time and how it is measured [26]. Nevertheless, a global systematic review performed over 74 countries across the world suggested that the mortality impact from the COVID-19 pandemic has been more devastating than the situation documented; there is COVID-19 excess mortality, and this estimated difference between excess mortality and reported COVID-19 death is far larger in sub-Saharan Africa countries, among which Zambia is not an exception. This is because of reporting challenges, higher than expected mortality from other diseases due to pandemic-related changes in behaviors or reduced access to health care and other essential services [26].

5. Conclusions

In view of the research questions and objectives posed for the research, this study investigated the effects of HIV infection, demographics and clinical factors on mortality among hospitalized COVID-19 patients at Levy Mwanawasa University Teaching Hospital.

The findings from this study suggest that COVID-19 mortality among those with and without HIV infection is different. People living with HIV infection have increased odds of dying with COVID-19 compared to those without HIV infection, and the proportion of COVID-19 mortality in the HIV group was higher at 66 (41.8%) than in the group without HIV (70, 14.1%). Hence, the risk of death among COVID-19 patients is high in regions/areas with the highest burden of HIV infection, especially countries in the sub-Saharan Africa region, among which Zambia is not an exception [13,18,25]. In particular, in Lusaka District, the HIV prevalence is almost double the national level.

In addition, having more than one comorbidity had consistently increasing odds of COVID-19 mortality compared to having none or one comorbidity, except for HIV infection. Also, having two or more comorbidities, alcohol drinking and age in years had increasing effects on COVID-19 mortality. Having two or more comorbidities had the highest odds of dying with COVID-19, followed by drinking alcohol, HIV infection, and smoking, while the age of patients in years had the lowest increase in odds for in-hospital COVID-19 mortality among the five variables in the best-fit model.

Furthermore, the rate of COVID-19 mortality in the HIV group was relatively higher at 66 (41.8%) than in the group without HIV, 70 (14.1%). This is because of the high prevalence of comorbidities in this particular study group of interest, not excluding the high prevalence of the priori variable, HIV infection.

From this study we can conclude that, among other things, COVID-19 and HIV comorbidity is still a public health concern. Therefore, continued efforts are essential to ensure that people living with HIV infection maintain control of their HIV infection through achieving the 95 × 95 × 95 target, a Joint United Nations programme on HIV/AIDS—UNAIDS. These efforts include early determination of an individual’s HIV status and immediate introduction of ART treatment or retention in care, as well as adherence to treatment, in order to achieve viral load suppression while addressing and managing their comorbidity conditions. Such efforts could help minimize COVID-19 mortality, especially among HIV-positive patients, and end morbidity resulting from the association of COVID-19 and HIV. Hence, this study concludes that HIV infection had effects on mortality among hospitalized COVID-19 patients at Levy Mwanawansa University Teaching Hospital (LMUTH).