Factors Associated with Mortality in Hospitalized COVID-19 Patients with Non-Communicable Diseases in Dire Dawa, Ethiopia

doi:10.21203/rs.3.rs-5204296/v1

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Research Article

Factors Associated with Mortality in Hospitalized COVID-19 Patients with Non-Communicable Diseases in Dire Dawa, Ethiopia

https://doi.org/10.21203/rs.3.rs-5204296/v1

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Background: COVID-19 has disproportionately affected individuals with preexisting non-communicable diseases (NCDs). Despite the acknowledged risk, there is a notable gap in studies exclusively focusing on COVID-19 patients with underlying NCDs, particularly in the context of hospitalization. This study aimed to assess factors influencing mortality in COVID-19 patients with NCDs in Dire Dawa, Eastern Ethiopia.

Methods: A 1:2 case-control study was conducted from December 15, 2022, to January 15, 2023. Cases included COVID-19 hospital deaths (June 1, 2020–June 30, 2022); controls were recoveries. Data from randomly selected medical records were extracted and entered in to Epi-data v3.1 and analyzed using SPSS v22. Baseline vital signs and lab parameters were compared using t-test and Mann-Whitney U test. AOR alongside a 95% CI and p-values (<0.05) were computed via binary logistic analysis to identify factors associated with COVID-19 mortality.

Result: A total of 125 cases and 247 controls were included in the analysis. Cases (mean age: 60 ±14.7 years) were older than controls (53 ±15.9 years), with more females among cases (50.4%) and more males among controls (63.6%). Cases had higher vital signs and lab values. In multivariate analysis, men had lower COVID-19 mortality risk (AOR=0.5, 95% CI (0.32-0.92)).

Whereas, age ≥ 60 year (AOR=2.5, 95% CI (1.1-5.63)), hypertension (AOR = 2, 95% CI (1.14- 3.35)), diabetes mellitus (AOR = 1.9, 95% CI (1.1- 3.16)), severe COVID-19 (AOR = 4.9, 95% CI (2.25- 10.86)), critical COVID-19 (AOR = 6.2, 95% CI (2.38-16.06)) and ICU admission (AOR = 2.9, 95% CI (1.5-5.56)) were significant risk factors for COVID-19 mortality.

Conclusion: The study revealed that older age, hypertension, diabetes, disease severity, and ICU hospitalization were associated with increased odds of COVID-19 mortality. while male gender showed a lower mortality risk. Tailored interventions for vulnerable groups and enhanced critical care are crucial. Furthermore, public health strategies should address gender-specific disparities in COVID-19 outcomes and prioritize preventive measures accordingly.

Mortality

Hospitalized

COVID-19

NCD

Dire Dawa

Ethiopia.

Coronavirus disease 2019 (COVID-19) emerged as a highly contagious infectious disease. Originating in Hubei Province, China, and swiftly spread across nearly every continent globally (1). this highly contagious disease caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) prompted quick action from the World Health Organization (WHO)(2). Recognizing its severity, the WHO classified COVID-19 as a Public Health Emergency of International Concern (PHEIC) on January 30, 2020 (3). Subsequently, on March 11, 2020, the WHO declared the new coronavirus (COVID-19) a worldwide pandemic due to its alarming rates of transmission and severity (4).

Data show that the pandemic has had a significantly higher prevalence and death in higher income countries that represent 79% of all deaths (5), the majority of global deaths were attributed to the United States, Italy, the United Kingdom, Spain, and France which were responsible for 65% of fatalities (6).

The severity of the disease and patient outcomes vary from person to person and can range from asymptomatic infection to severe disease with complications and death (7).There is an evidence that older people and patients with non-communicable diseases are more likely to develop severe illness and subsequently die. COVID-19 and non-communicable diseases are closely related to the increased severity and mortality of COVID-19 (8, 9).

In response to the pandemic, the WHO established strategic goals aimed at controlling transmission, diagnosing and treating cases, with the ultimate objective of ending the global crisis by 2022 (10). Simultaneously, countries worldwide have implemented various strategies to promote vaccination uptake and address vaccine hesitancy, recognizing immunization as a pivotal tool in pandemic mitigation efforts (11).

The impact of COVID-19 extends to the African continent, with significant consequences (12). Although the COVID-19 pandemic in Africa started late and progress slowly, there was a growing worry about the impact of the pandemic (13). Ethiopia is among the most affected countries in Africa (14). Despite various measures being implemented, there was a consistent rise in both the number of cases and fatalities (15). Understanding the factors contributing to mortality among hospitalized COVID-19 patients with chronic NCDs is paramount. It informs targeted interventions and optimizes healthcare resource allocation to mitigate adverse outcomes in vulnerable populations.

The study was conducted in Dire Dawa City Administration, located in the eastern part of Ethiopia. Dire Dawa is the nation’s second-largest city, with a population of over 535,686 as of 2022, according to projections by the Central Statistics Agency. It is situated approximately 515 km from Addis Ababa, the capital city, 55 km from Harar, and 300 km from Djibouti. The city is bordered by the Oromia and Somali regions.

Dire Dawa’s healthcare infrastructure includes two public hospitals, 16 health centers, and 36 health posts. All health centers in the area serve as testing sites for COVID-19 antigen detection rapid diagnostic tests (Ag-RDT), while the regional laboratory is responsible for conducting reverse transcription polymerase chain reaction (RT-PCR) tests. There are three designated COVID-19 treatment centers in the city: Sabian, Dilchora, and Ethio-France Hospitals.

Data was collected over a period from December 15, 2022, to January 15, 2023.

Study Design

The study employed a hospital-based unmatched case-control design.

Source population

The source population consisted of all laboratory-confirmed COVID-19 patients aged 18 years and older with chronic non-communicable diseases (NCDs) who were admitted to the treatment centers in Dire Dawa.

Study Population

The study population included laboratory-confirmed COVID-19 patients aged 18 years and older with chronic NCDs who were admitted to the treatment centers in Dire Dawa between June 1, 2020, and June 30, 2022.

Inclusion and Exclusion Criteria

Inclusion Criteria

Patients aged 18 years and above with chronic NCD who were tested COVID-19 positive by real time polymerase chain reaction (RT-PCR) or Antigen Rapid Diagnostic Test (Ag-RDT) and who were on treatment between June 1, 2020 and June 30, 2022.

Case

Confirmed COVID-19 patients aged 18 years and above with chronic NCD admitted to treatment centers and whose treatment outcome was death.

Control

Confirmed COVID-19 patients aged 18 years and above with chronic NCD admitted to treatment centers and whose treatment outcome was recovered (discharged alive).

Exclusion criteria

Patients with incomplete medical records on very important variables. (such as: date of admission, date of discharge, outcome status).

Patients who were died on arrival.

Sample Size Determination and Sampling Procedure

The sample size was determined using a formula for double population proportions and computed with Epi Info version 7.2.5.0 statistical software package. This calculation considered that 62.2% of controls and 77.6% of cases were exposed to shortness of breath. Assumptions for the study included a 95% confidence interval, 80% power, and a case-to-control ratio of 1:2. After adjusting for 10% missing data, the final sample size was determined to be 381 (127 cases and 254 controls).

The sampling procedure began by purposively selecting all three available COVID-19 treatment centers. Subsequently, the total sample size was distributed proportionally among these centers according to their patient load during the study period. Within each treatment center, patient charts were randomly selected using a simple random sampling technique. This selection process was conducted using a computer random number generating system in Excel and adhered to predetermined eligibility criteria.

Data collection tools and procedure

A data abstraction tool, comprising key variables of interest, was developed from registry records and adapted from various sources (16, 18, 19). It was comprehensively structured to encompass essential categories such as socio-demographic data, pre-existing comorbidities, clinical details, baseline vital signs, and crucial laboratory parameters. Prior to the commencement of data collection activities, all relevant stakeholders received a comprehensive briefing regarding the study's objectives, purpose, and significance.

The actual data collection was carried out by a team of four highly trained clinical nurses, each equipped with the necessary skills and expertise to extract data. They systematically reviewed medical records, meticulously extracting pertinent information from both the COVID-19 treatment register and patient charts. This exhaustive review spanned a substantial period from June 1, 2020, to June 30, 2022, ensuring a comprehensive scope of data collection.

To maintain the highest standards of accuracy and consistency, two experienced supervisors were assigned to oversee the entire data collection process. Their roles encompassed providing ongoing oversight, guidance, and support to the data collectors. Each supervisor was responsible for directly managing and monitoring the activities of two data collectors, ensuring adherence to the study protocol and the attainment of rigorous data collection standards

Variables

Dependent variable

COVID-19 in-hospital mortality

Independent variables

Socio-demography characteristics (age and sex),

Clinical characteristics (Pre-existing comorbidity, type of comorbidity, number of comorbidities, Severity of illness, presenting symptom),

Baseline vital sign and laboratory parameters (complete blood count and chemistry), and

Patient management (ICU admission, mechanical ventilation, and length of hospital stay).

Operational Definition

Moderate Illness

Individuals who show evidence of lower respiratory disease during clinical assessment or imaging and who have an oxygen saturation (SpO2) ≥ 94% on room air at sea level (20).

Severe Illness

Individuals who have SpO2 < 94% on room air at sea level, a ratio of arterial partial pressure of oxygen to fraction of inspired oxygen (PaO2/FiO2) < 300 mm Hg, a respiratory rate > 30 breaths/min, or lung infiltrates > 50% (20).

Critical Illness

Individuals who have respiratory failure, septic shock, and/or multiple organ dysfunction (20).

Discharge

Patient diagnosed with COVID-19 can be discharged when the symptoms have subsided, patients get stable and able to feed, and the body temperature remains at a normal range for at least three days without antipyretics, and two consecutive laboratory tests are negative collected ≥ 24 hours apart (21).

Death

COVID-19 death is defined for surveillance purposes as a death resulting from a clinically compatible illness in a probable or confirmed COVID-19 case, unless there is a clear alternative cause of death that cannot be related to COVID-19 disease (e.g. trauma). There should be no period of complete recovery between the illness and death (22).

Confirmed case

A person with laboratory confirmation of COVID-19 infection, irrespective of clinical signs and symptoms (23).

Chronic non-communicable diseases

A medical condition or disease that may be longer or of longer duration and is not transmitted from person to person (hypertension, diabetes mellitus, cardiac disease, chronic kidney disease, chronic lung disease and cancer (24).

Comorbidity

The co-existence of any of the concomitant chronic non-communicable diseases (hypertension, diabetes mellitus, cardiac disease, chronic kidney disease, chronic lung disease and cancer) with COVID-19 at the time of admission (Pal and Bhadada, 2020).

Length of hospital stay

is a clinical metric that measures the length of time elapsed between a patient's hospital admittance and discharge (25).

Dead on arrival

Refer to patients those who were declared dead upon arrival to an emergency department with no resuscitation attempt or those who died after failed resuscitation, usually within the first hour of arrival (26)

Data quality management

Data collectors and supervisors received training over a two-day on the data abstraction tool, data collection methods, and study protocols to ensure uniform understanding and adherence to standardized procedures. The data has been cleaned. Each completed data abstraction form underwent thorough review to verify data completeness. Any missing or incomplete entries were identified and addressed promptly. Consistency checks were performed to ensure uniformity of data elements with the original source from which it was obtained. A pretest was conducted on a randomly selected sample of 5% of charts at the Sabian COVID-19 treatment center. The purpose of this pretest was to assess the effectiveness of data collection procedures and to identify any potential issues or challenges. Following the pretest, amendments were made to enhance both the data collection tool and process, addressing any issues that were identified. Continuous close supervision and daily checking of the data collection process were carried out by supervisors and the principal investigator.

Data analysis

The extracted data were entered into Epi-data version 3.1 and analyzed using SPSS version 22 software. Data cleaning procedures were performed to identify and rectify any errors or inconsistencies in the dataset. Descriptive statistics, including frequencies and percentages for categorical variables, and mean with standard deviation (SD) or median with interquartile range (IQR) for continuous data, were computed based on data distribution. Normality assumptions were verified.

Baseline vital signs and laboratory parameters between cases and controls were compared using independent t-tests for normally distributed data and Mann-Whitney U tests for non-normally distributed data. A significance level of p < 0.05 was used to determine statistical significance.

Multicollinearity among independent variables was assessed using the variance inflation factor (VIF), with all VIF values ranging between 1.1 and 2.3, indicating no issues with multicollinearity. The adequacy of the final model was evaluated using the Hosmer and Lemeshow goodness of fit test, with a significance value of 0.13 indicating a good fit of the model to the data.

Bivariable analysis was conducted for each independent variable, and those with a p-value less than 0.25 were considered candidates for inclusion in the multivariable logistic regression model. In the multivariable analysis, adjusted odds ratios (AOR) with corresponding 95% confidence intervals (CI) were calculated to assess the strength of association between hospital mortality and predictor variables. The backward selection approach was applied, and variables with a p-value of < 0.05 were considered statistically significant

Ethical Considerations

The study received ethical approval from the Institutional Health Research Ethics Review Committee (IHRERC) of Haramaya University’s College of Health and Medical Sciences (Ethical Approval Number: IHRERC/214/2022), ensuring compliance with ethical standards for research involving human data. A support letter was obtained from the regional health bureau and submitted to the treatment centers, which granted permission to carry out the study. The research was conducted in accordance with the principles outlined in the Declaration of Helsinki, which governs research involving human data. As the study involved secondary data analysis without direct participant interaction, individual informed consent was not required. However, signed information sheets and informed voluntary consent were obtained from the heads of the respective hospitals, authorizing the use of data collected from their facilities. Data extraction was performed anonymously without patient identifying information to maintain the confidentiality of patient information.

Socio-demographic characteristics

A total of 372 charts were included in the analysis, comprising 125 cases and 247 controls. Charts with incomplete data, accounting for 2.4%, were excluded from the analysis. The mean (± SD) age of cases was 60 years (± 14.7), compared to 53 years (± 15.9) for controls. Furthermore, nearly half of patients 166 (44.6%), were aged 60 years and older. In the study group, the majority, accounting for 219 individuals (58.9%), were male. Of the cases, 63 (50.4%) were female, whereas among the controls, 157 (63.6%) were male. (Table 1).

Table 1

Socio–demographic characteristics of hospitalized COVID-19 patients with chronic non-communicable diseases in Dire Dawa, Eastern Ethiopia, 2023.
Variables	Case (N = 125)	Control (N = 247)	Total (%) (N = 372)
Age (mean (± SD)	60 (± 14.7)	53 (± 15.9)	55 (± 15.8)
< 40	12 (9.6%)	53 (21.5%)	65 (17.5%)
40–59	36 (28.8% )	105 (42.5%)	141 (37.9%)
60+	77 (61.6%)	89 (36%)	166 (44.6%)
Sex
Male	62(49.6%)	157 (63.6%)	219 (58.9%)
Female	63 (50.4%)	90 (36.4%)	153 (41.1%)

Pre-existing comorbid illness characteristics

The most common comorbidities observed were diabetes mellitus 178 (47.8%), hypertension 176 (47.3%), and heart disease 70 (18.8%). One-third of patients 124 (33.3%), had a history of more than one pre-existing comorbid condition, with a higher proportion (50.4%) of cases presenting with multiple comorbidities compared to controls (24.7%). In comparing cases and controls, a higher percentage of hypertensive patients (59.2% vs. 41.3%) and diabetic patients (55.2% vs. 44.1%) experienced mortality. (Table 2)

Table 2

Pre-existing comorbid illness characteristics of hospitalized COVID-19 patients with chronic NCD in Dire Dawa, Eastern Ethiopia, 2023.
Variables	Case (N = 125)	Control (N = 247)	Total (%) (N = 372)
Hypertension
No	51 (40.8%)	145 (58.7%)	196 (52.7%)
Yes	74 (59.2%)	102 (41.3%)	176 (47.3%)
Diabetes mellitus
No	56 (44.8%)	138 (55.9%)	194 (52.2%)
Yes	69 (55.2%)	109 (44.1%)	178 (47.8%)
Heart disease
No	101 (80.8%)	201 (81.4%)	302 (81.2%)
Yes	24 (19.2%)	46 (18.6%)	70 (18.8%)
Chronic kidney disease
No	113 (90.4%)	230 (93.1%)	342 (91.9%)
Yes	12 (9.6%)	17 (6.9%)	30 (8.1%)
Chronic lung disease
No	116 (92.8%)	213 (86.2%)	329 (88.4%)
Yes	9 (7.2%)	34 (13.8%)	43 (11.6%)
Number of comorbidity
One	62 (49.6%)	186 (75.3%)	248 (66.7%)
More than one	63 (50.4%)	61 (24.7%)	124(33.3%)

Clinical Characteristics

The study found that cough (77.2%), fever (54.3%), and shortness of breath (54%) were the most common symptoms, with higher proportion of fever (64%), cough (86.4%), and shortness of breath (65.6%) were in the case group. Case patients also showed a higher percentage of severe cases (44.8%) and critical cases (46.4%). A greater percentage of case patients were admitted to the ICU (69.6%) and received mechanical ventilation (47.2%). Additionally, cases had a shorter duration of hospitalization, with a median (IQR) of 5 (2–11) days, compared to controls, which had a median duration of 10 (7–13) days. (Table 3)

Table 3

Clinical characteristics of hospitalized COVID-19 patients with NCDs in Dire Dawa, Eastern Ethiopia, 2023.
Variables	Case (N = 125)	Control (N = 247)	Total (N = 372)
Fever
No	45 (36.0%)	125 (50.6%)	170 (45.7%)
Yes	80 (64%)	122 (49.4%)	202 (54.3%)
Cough
No	17 (13.6%)	68 (27.5%)	85 (22.8%)
Yes	108 (86.4%)	179 (72.5%)	287 (77.2%)
Shortness of breath
No	43 (34.4%)	128 (51.8%)	171 (46%)
Yes	82 (65.6%)	119 (48.2%)	201 (54%)
Sore throat
No	81 (64.8%)	189 (76.5%)	270 (72.6%)
Yes	44 (35.2%)	58 (23.5%)	102 (27.4%)
Headache
No	64 (51.2%)	168 (68%)	232 (62.4%)
Yes	61 (48.8%)	79 (32%)	140 (37.6%)
Generalized body weakness
No	65 (52%)	159 (64.4%)	224 (60.2%)
Yes	60 (48.0%)	88 (35.6%)	148 (39.8%)
Severity of COVID-19
Moderate	11 (8.8%)	130 (52.6%)	141 (37.9%)
Severe	56 (44.8%)	80 (32.4%)	136 (36.6%)
Critical	58 (46.4%)	37 (15%)	95 (25.5%)
ICU admission
No	38 (30.4%)	186 (75.3%)	224 (60.2%)
Yes	87 (69.6%)	61 (24.7%)	148 (39.8%)
Mechanical ventilation
No	66 (52.8%)	210 (85.0%)	276 (74.2%)
Yes	59 (47.2%)	37 (15%)	96 (25.5%)
Length of stay (days), Median (IQR)	5(2–11)	10 (7–13)	9 (5–13)

Baseline vital sign and laboratory parameters between case and control groups

Baseline vital signs and laboratory parameters were compared between the case and control groups. The independent t-test revealed that cases had a higher mean heart rate compared to controls (97 beats/min vs. 93 beats/min, P = 0.027). In the Mann-Whitney U test, cases demonstrated significantly higher median values for respiration rate (30 breaths/min vs. 24 breaths/min, P < .001), temperature (37°C vs. 36.7°C, P < .001), white blood cell count (11.3 x 10^9/L vs. 7.7 x 10^9/L, P < .001), neutrophil percentage (74% vs. 63.6%, P < .001), urea level (35.7 mg/dL vs. 25 mg/dL, P < .001), creatinine level (1.11 mg/dL vs. 0.9 mg/dL, P < .001), aspartate transaminase level (45 IU/L vs. 35 IU/L, P < .001), alanine transaminase level (40.3 IU/L vs. 33.4 IU/L, P < .001), and alkaline phosphatase level (110 IU/L vs. 96 IU/L, P < .001). Additionally, cases exhibited lower median percentages of SpO2 (84% vs. 93%, P < .001), hemoglobin (11.3 mg/dL vs. 12.6 mg/dL, P < .001), and lymphocytes (11% vs. 15%, P < .001). (Table 4).

Table 4

Baseline vital sign and laboratory parameters between case and control groups among hospitalized COVID-19 patients with chronic NCDs in Dire Dawa, Eastern Ethiopia, 2023.
Variables	Case (n = 125)	Control (n = 247)	p-value
Systolic BP (mmHg), Mean (SD)	125 (20.6)	126 (15.7)	0.66
Diastolic BP (mmHg), Mean (SD)	78 (14.7)	81 (9.6)	0.056
Heart rate (beat/m), Mean (SD)	97 (16.5)	93 (12.2)	0.027*
Respiratory rate (breath/m), Median (IQR)	30 (27–34)	24 (21–28)	< .001*
Temperature (℃), Median (IQR)	37.1 (36.6–37.3)	36.7(36.3–37)	< .001*
Spo2 (%),Median (IQR)	84 (75–88)	93 (87–96)	< .001*
Hemoglobin (mg/dl), Median (IQR)	11.3(10.3–12.4)	12.6(11.9–13.3)	< .001*
White blood cell (× 10⁹/L), Median (IQR)	11.3(9.1–13.1)	7.7(6.5–9.4)	< .001*
Neutrophil (%), Median (IQR	74(65-81.2)	63.6(60–68)	< .001*
Lymphocyte (%),Median (IQR)	11(6.7–13.5)	15(10–20)	< .001*
Urea (mg/dl), Median (IQR)	35.7(28–53)	25(22–32)	< .001*
Creatinine (mg/dl), Median (IQR)	1.11(0.9–1.24)	0.9(0.7–1.1)	< .001*
Aspartate transaminase (IU/L), Median (IQR)	45(35–45)	35(28–49)	< .001*
Alanine transaminase (IU/L), Median (IQR)	40.3(26.8–52.5)	33.4(26.4–47.5)	0.059
Alkaline phosphatase (IU/L), Median (IQR)	110(94-126.9)	96(83–114)	< .001*
* P-value less than 0.05
Mean with SD used for independent t-test

Median with IQR used for Mann-Whitney U test

Table 5

Bivariate analysis of Factors Associated with Mortality in Hospitalized COVID-19 Patients with Chronic Non-Communicable Diseases in Dire Dawa, Eastern Ethiopia, 2023.
Variables	Category	Case (N = 125)	Control (N = 247)	COR (95%CI)	P-value
Age (Years)	< 40	12 (9.6%)	53 (21.5%)	Ref
	40–59	36 (28.8% )	105 (42.5%)	1.5 ( 0.73–3.15)3.149)	0.267
	60+	77 (61.6%)	89 (36%)	3.8 ( 1.9–7.7)7.671)	< .001*
Sex	Male	62 (49.6%)	157 (63.6%)	0.6 (0.37–0.87)	0.010*
Sex	Female	63 (50.4%)	90 (36.4%)	Ref
hypertension	No	51 (40.8%)	145 (58.7%)	Ref
hypertension	Yes	74 (59.2%)	102 (41.3%)	2.1 ( 1.33–3.2)	0.001*
Diabetes mellitus	No	56 (44.8%)	138 (55.9%)	Ref
Diabetes mellitus	Yes	69 (55.2%)	109 (44.1%)	1.6 (1.01–2.41 )	0.044*
Heart disease	No	101 (80.8%)	201 (81.4%)	Ref
Heart disease	Yes	24 (19.2%)	46 (18.6%)	1.04 (0.6–1.8)	0.89
Chronic kidney disease	No	113 (90.4%)	230 (93.1%)	Ref
Chronic kidney disease	Yes	12 (9.6%)	17 (6.9%)	1.4 (0 .66 − 3.1)	0.36
Chronic lung disease	No	116 (92.8%)	213 (86.2%)	Ref
Chronic lung disease	Yes	9 (7.2%)	34 (13.8%)	0.5 (0.23–1.05)	0.07*
Fever	No	45 (36.0%)	125 (50.6%)	Ref
Fever	Yes	80 (64%)	122 (49.4%)	1.8 ( 1.17–2.84)	0.008*
Cough	No	17 (13.6%)	68 (27.5%)	Ref
Cough	Yes	108 (86.4%)	179 (72.5%)	2.4 ( 1.35–4.32)	0.003*
Shortness of breath	No	43 (34.4%)	128 (51.8%)	Ref
Shortness of breath	Yes	82 (65.6%)	119 (48.2%)	2.1 ( 1.31–3.2)	0.002*
Sore throat	No	81 (64.8%)	189 (76.5%)	Ref
Sore throat	Yes	44 (35.2%)	58 (23.5%)	1.8 ( 1.11–2.83)	0.017*
Headache	No	64 (51.2%)	168 (68%)	Ref
Headache	Yes	61 (48.8%)	79 (32%)	2.03 ( 1.3–3.15)	0.002*
Generalized body weakness	No	65 (52%)	159 (64.4%)	Ref
Generalized body weakness	Yes	60 (48.0%)	88 (35.6%)	1.7 ( 1.08–2.58)	0.022*
Severity of COVID-19	Moderate	11 (8.8%)	130 (52.6%)	Ref
	Severe	56 (44.8%)	80 (32.4%)	8.3 ( 4.1–16.7)	< .001*
	Critical	58 (46.4%)	37 (15%)	18.5( 8.8–38.9)	< .001*
ICU admission	No	38 (30.4%)	186 (75.3%)	Ref
ICU admission	Yes	87 (69.6%)	61 (24.7%)	7 (4.33–11.26)	< .001*
Mechanical ventilation	No	66 (52.8%)	210 (85.0%)	Ref
Mechanical ventilation	Yes	59 (47.2%)	37 (15%)	5.1 (3.1–8.33)	< .000*

*P-value less than 0.25

Factors associated with COVID-19 mortality

The selected independent variables were tested for their individual association with COVID-19 mortality. In bivariate logistic regression analysis age, sex, hypertension, diabetes mellitus,

chronic lung disease, fever, cough, shortness of breath, sore throat, headache, generalized body weakness, severe disease, critical disease, ICU admission, and mechanical ventilation were found to be statistically significant with COVID-19 mortality at a significant level of (P < 0.25) and candidates for multivariate analysis. (Table 5)

In multivariable analysis, we found that being male had 50% less chance of COVID-19 mortality compared to female (AOR = 0.5, 95% CI (0.32–0.92), P = 0.023). The odds of mortality due to COVID-19 was 2.5 times higher among patients aged 60 years and above compared to patients aged < 40 year (AOR = 2.5, 95% CI (1.1–5.63), P = 0.028). Moreover, patients with hypertension were twice as likely to die from COVID-19 compared to those without hypertension (AOR = 2, 95% CI (1.14–3.35), P = 0.015). Similarly, patients with diabetes mellitus had a 1.9 times higher likelihood of dying from COVID-19 compared to those without diabetes mellitus (AOR = 1.9, 95% CI (1.1–3.16), P = 0.021). The odds of mortality was 4.9 times higher among sever cases (AOR = 4.9, 95% CI (2.25–10.86), P = < .001) and 6.2 times higher among critical cases (AOR = 6.2, 95% CI (2.38–16.06), P = < .001), compared to moderately ill patients. Being admitted to ICU had 2.9 times higher odds of COVID-19 mortality compared to those who were admitted in the ward (AOR = 2.9, 95% CI (1.5–5.56), P = 0.002). (Table 6)

Table 6

Multivariate analysis of Factors Associated with Mortality in Hospitalized COVID-19 Patients with Chronic Non-Communicable Diseases in Dire Dawa, Eastern Ethiopia, 2023.
Variables	Category	Case (N = 125)	Control (N = 247)	AOR (95%CI)	P-value
Age (Years)	< 40	12 (9.6%)	53 (21.5%)	Ref
	40–59	36 (28.8% )	105 (42.5%)	1.4 (0.58–3.18)	0.48
	60+	77 (61.6%)	89 (36%)	2.5 (1.1–5.63)	0.028*
Sex	Male	62 (49.6%)	157 (63.6%)	0.5(0.32–0.92)	0.023*
Sex	Female	63 (50.4%)	90 (36.4%)	Ref
Hypertension	No	51 (40.8%)	145 (58.7%)	Ref
Hypertension	Yes	74 (59.2%)	102 (41.3%)	2 (1.14–3.35)	0.015*
Diabetes mellitus	No	56 (44.8%)	138 (55.9%)	Ref
Diabetes mellitus	Yes	69 (55.2%)	109 (44.1%)	1.9 (1.1–3.16)	0.021*
Chronic lung disease	No	116 (92.8%)	213 (86.2%)	Ref
Chronic lung disease	Yes	9 (7.2%)	34 (13.8%)	0.57 (0.22–1.47	0.25
Fever	No	45 (36.0%)	125 (50.6%)	Ref
Fever	Yes	80 (64%)	122 (49.4%)	0.95 (0.5–1.81)	0.87
Cough	No	17 (13.6%)	68 (27.5%)	Ref
Cough	Yes	108 (86.4%)	179 (72.5%)	1.12 (0.55–2.28)	0.75
Shortness of breath	No	43 (34.4%)	128 (51.8%)	Ref
Shortness of breath	Yes	82 (65.6%)	119 (48.2%)	1.03 (0.57–1.85)	0.93
Sore throat	No	81 (64.8%)	189 (76.5%)	Ref
Sore throat	Yes	44 (35.2%)	58 (23.5%)	1.01 (0.5–2.04)	0.98
Headache	No	64 (51.2%)	168 (68%)	Ref
Headache	Yes	61 (48.8%)	79 (32%)	1.15 (0.64–2.08)	0.65
Generalized body weakness	No	65 (52%)	159 (64.4%)	Ref
Generalized body weakness	Yes	60 (48.0%)	88 (35.6%)	0.86 (0.5–1.49)	0.6
Severity of COVID-19	Moderate	11 (8.8%)	130 (52.6%)	Ref
	Severe	56 (44.8%)	80 (32.4%)	4.9 (2.25–10.86)	< .001*
	Critical	58 (46.4%)	37 (15%)	6.2 (2.38–16.06)	< .001*
ICU admission	No	38 (30.4%)	186 (75.3%)	Ref
ICU admission	Yes	87 (69.6%)	61 (24.7%)	2.9 (1.5–5.56)	0.002*
*P-value less than 0.05

This study aimed to assess determinants of mortality among hospitalized COVID-19 patients with chronic NCDs at Dire Dawa treatment centers. to assess determinants of mortality. Variables identified as significant predictors of COVID-19 mortality were older age, sex, hypertension, diabetes, disease severity, and ICU hospitalization.

Our findings underscored that COVID-19 mortality was notably influenced by older age, with mortality being 2.5 times higher among patients aged 60 years and above compared to those aged under 40 years. This observation aligns with previous studies conducted in various regions, including Addis Ababa (27), North Ethiopia(16), Democratic Republic of the Congo (28), sub-Saharan (29), Burkina Faso and Guinea (30) and Kenya (31). The susceptibility of older individuals to infectious diseases and their predisposition to chronic NCDs likely contribute to this trend. (32, 33).

This study revealed a lower probability of COVID-19 mortality among men. This finding was in line with studies from Pakistan (34) and (35), which found that the risk of COVID-19 mortality was significantly higher among women than men. However, studies, from North Ethiopia (16), Democratic Republic of the Congo (28), South Africa (36) and Zambia (37), revealed that male patients had significantly higher mortality rates than female patients. On the other hand, study from USA, Italy, India (38) and Saudi Arabia revealed that there was no significant differences in COVID-19 mortality between genders (39–41). One potential explanation for the higher mortality rate among women with COVID-19 is their typically stronger immune responses compared to men. While women's robust immune systems are generally beneficial, in the context of COVID-19, an excessively strong response—manifested as cytokine storms and heightened inflammation—can result in more severe disease outcomes (42). Additionally, disparities in nutrition between males and females could be another contributing factor, with males who maintain good nutrition possibly experiencing reduced mortality rates. (43).

Our study highlighted that hypertension is a significant predictor of COVID-19 mortality, patients with hypertension were found to have twice the odds of COVID-19 mortality compared to those without hypertension. This association was consistent with findings from various regions, including North Ethiopia (16), South Africa (36), Kenya (44), Sub-Saharan Africa Zimbabwe (45), and India (46).

The elevated risk of COVID-19 death in hypertensive patients could potentially be linked to high blood pressure's known impact on cytokine levels. Hypertension, or high blood pressure, is associated with chronic low-grade inflammation and dysregulation of the immune system, which can lead to an imbalance in cytokine production. (47, 48). Additionally, hypertension is often associated with other comorbidities such as cardiovascular disease, diabetes, and obesity, which further contribute to the risk of severe COVID-19 outcomes (49).

Similarly, diabetes mellitus was found to be significantly associated with COVID-19 mortality, with diabetic patients being 1.9 times more likely to die from the disease compared to non-diabetic individuals. The result of this study is in line with studies conducted in North Ethiopia (16), Addis Ababa (19), Democratic Republic of the Congo (28), Uganda (50) and Sub-Saharan Africa (51). However, contradictory findings were reported in studies from Saudi Arabia (41) and China (52, 53). Elevated blood glucose levels and related complications, often seen in patients with diabetes, may compromise immunity and contribute to adverse outcomes among individuals with COVID-19, patients with higher blood sugar levels often exhibit an increase in inflammatory cytokines, further exacerbating the severity of the disease. (54–56). The observed variability between studies could arise from differences in population characteristics, their level of exposure to risk factors, and variations in sample sizes utilized across studies.

The severity of COVID-19 disease emerged as a strong predictor of mortality, with severe and critical cases exhibiting a higher likelihood of mortality compared to moderate cases. This report is consistent with earlier research from India (57), Northern Ethiopia (58), Ethiopia (59), Kenya (44), Tanzania (60), and Indonesia (61). The heightened risk of mortality in severe and critical cases may be attributed to the rapid progression and complications of the disease in severely as well as critically ill patient that may require high-level respiratory support and could lead to progressive respiratory failure (62).

Furthermore, ICU hospitalization was associated with higher COVID-19 mortality, with patients admitted to the ICU having 2.9 times higher odds of mortality compared to those admitted to the ward. The results of research carried out in Ethiopia (63), Tanzania (60), Germany (64) and India (38) were in line with this study finding. The increased mortality risk observed in severe and critical cases of COVID-19 may be attributed to the critical nature of the disease itself, limited intensive care unit capacity, and the provision of substandard critical care.

This study is limited by its reliance on secondary data, with key variables such as residence, occupation, duration of non-communicable disease (NCD) illness, adherence to NCD follow-up, and health system-related factors not well-documented, hindering thorough evaluation. Additionally, the absence of a similar study for reference limits comparative analysis.

However, the study's multicenter design is a notable strength, enhancing generalizability and reliability by incorporating data from multiple sources and settings, the study can capture a more diverse population and provide a broader perspective on the topic. Despite limitations, this approach strengthen the robustness of the findings. Furthermore, the matching of participants by their underlying condition or involving those exclusively with NCDs is another strength, controlling for confounding variables and ensuring findings are directly applicable to this population, thereby enhancing the relevance of the study's conclusions.

The study revealed that older age, hypertension, diabetes, disease severity, and ICU hospitalization were associated with increased odds of COVID-19 mortality, while male gender showed a lower mortality risk. Tailored interventions for vulnerable groups, timely critical care intervention and enhanced ICU capacities is crucial to improve patient outcomes. Public health strategies need to address gender-specific disparities in COVID-19 outcomes and prioritize preventive measures accordingly. Additionally, further prospective studies integrating qualitative data are needed to address missed crucial variables in secondary data and understand gender-based differences in COVID-19 outcomes.

Acknowledgements: I extend my heartfelt gratitude to the study participants and data collectors for their invaluable contributions. I would also like to express my appreciation to the Dire Dawa Administration Health Bureau, Haramaya University, and the COVID-19 treatment centers for their support and collaboration, which were instrumental in the successful completion of this study.

Funding

Haramaya University provided financial support for this study. However, the funding organization had no role in the collection, analysis, interpretation of the data, and publication of this research work.

Conflict of interest: The authors declare no competing conflicts of interest.

Authors’ contributions

D.A: Developed the proposal, conducted data analysis, wrote up the results, and made significant contributions to the manuscript. N.A and A.T: participated in the critical review of the study, reviewed and approved the final manuscript, and agreed to submit the current manuscript.

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The authors declare no competing interests.

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Factors Associated with Mortality in Hospitalized COVID-19 Patients with Non-Communicable Diseases in Dire Dawa, Ethiopia

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Abstract

Introduction

Methods and materials

Study Design

The study employed a hospital-based unmatched case-control design.

Source population

Study Population

Inclusion and Exclusion Criteria

Inclusion Criteria

Exclusion criteria

Sample Size Determination and Sampling Procedure

Data collection tools and procedure

Variables

Dependent variable

Independent variables

Operational Definition

Data quality management

Data analysis

Ethical Considerations

Result

Socio-demographic characteristics

Pre-existing comorbid illness characteristics

Clinical Characteristics

Baseline vital sign and laboratory parameters between case and control groups

Factors associated with COVID-19 mortality

Discussion

Conclusion

Declarations

References

Additional Declarations

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