Mortality is a key indicator of the health status of populations and the performance of health systems. In hospitals, death is an outcome event that needs to be interpreted in light of the severity of the patients admitted, the types of illnesses treated, the timeliness of care, and the resources available. Hospital mortality is thus used as a quality indicator, in line with Donabedian's approach, which distinguishes between the structural, process, and organizational aspects of care quality [1].

Worldwide, non-communicable diseases are becoming an increasingly common cause of death, while infections, respiratory conditions, and acute illnesses still weigh heavily on mortality in low- and middle-income countries [2].

This change is part of an epidemiological transition that doesn’t mean infectious diseases are being replaced by chronic ones, but rather that they coexist. Health systems therefore need to meet the needs of managing infectious illnesses, cardiovascular diseases, cancers, neurological disorders, and injuries all at the same time [3].

In many African countries, hospital mortality is still influenced by late access to care, insufficient human and material resources, drug shortages, and the concentration of serious cases in referral facilities. So, when interpreting a mortality rate, you have to take into account the referral profile and the tertiary referral role of the facility being studied. This situation has been observed in many African hospital settings, especially in units dealing with life-threatening emergencies and severe conditions [3,4].

In Senegal, hospital mortality data is still underused, even though it can help identify potentially preventable deaths, improve medical records, and guide hospital policies [5,6].

The Fann National University Hospital Center, a public tertiary-level facility located in Dakar, receives patients from all over the country and is especially a reference center in several medical and surgical fields. Its activity has also been affected by the impact of the COVID-19 pandemic and by fluctuations in the demand for care.

Analyzing mortality in this facility is therefore interesting both from an epidemiological and institutional perspective. It helps to understand how the phenomenon changes over time, describe the profiles of patients who died, identify which departments are particularly affected, and look for factors associated with where the death occurred. The aim of this study was to analyze mortality at CHNU de Fann between 2021 and 2024.

Patients and methods

Study framework

The study was conducted at CHNU de Fann, a public university-affiliated health facility, ranked at level 3 in the Senegalese health system. The hospital serves as a national and sub-regional reference point, while also taking part in teaching, medical training, and clinical research.

The facility has around 384 functional beds spread across several medical and surgical departments, including neurology, neurosurgery, infectious diseases, pulmonology, medical and pediatric cardiology, plastic and cardiovascular thoracic surgery, ENT, psychiatry, geriatrics, and emergency services. It also includes related services, such as the Medical Imaging and Diagnostic Center, physical medicine, and reception units. The staff includes university hospital doctors, hospital practitioners, biologists, pharmacists, interns, nurses, technicians, and other medical-technical professionals.

Type and study period

It was a retrospective, cross-sectional, descriptive, and analytical study, focusing on deaths recorded at the CHNU of Fann from January 1, 2021, to December 31, 2024. Data collection was carried out from March 10 to July 31, 2025.

Study population and inclusion criteria

The study population consisted of all the patients whose deaths were recorded in the hospital records of the CHNU de Fann during the study period. Patients of all ages and sexes for whom a death was documented in the facility's records were included.

Incomplete or unavailable files, as well as files corresponding to undocumented out-of-hospital deaths, were not included. Recruitment was comprehensive for files meeting the inclusion criteria available in the records.

Data collection and variables studied

The data were collected from hospital death records using a structured form. The information gathered included sociodemographic characteristics, such as age, gender, region, department, and neighborhood of residence; details related to care, like the hospital ward and mode of admission; as well as variables directly linked to the death itself, including the date, place, length of hospital stay before death, any COVID-19 test results, and the cause recorded in the register.

The main dependent variable of the study was hospital mortality, defined as the occurrence of a death recorded within the hospital during the study period. For the multivariate analysis, a specific binary variable was created: death during hospitalization versus death in the emergency department. Deaths recorded outside the hospital were described in the descriptive analysis but are not included in this binary comparison as defined in the report.

Causes of death were grouped into non-communicable diseases, communicable diseases, injuries, and unspecified causes. Non-communicable diseases included things like tumors, cardiovascular diseases, chronic respiratory diseases, digestive diseases, neurological disorders, diabetes, kidney diseases, and other chronic conditions. Communicable diseases included respiratory infections, tuberculosis, HIV and sexually transmitted infections, malaria, intestinal infections, other infectious diseases, and maternal, neonatal, or nutritional conditions.

Statistical analysis

The initial data entry was done in KoboToolbox. The database was then exported to Microsoft Excel 2021 for completeness checking, cleaning, and recoding, and then analyzed with R software version 4.4.3.

The descriptive analysis included calculating counts, proportions, the mean, the median, and the standard deviation. The overall mortality rate was calculated as the ratio between the number of deaths and the total number of hospital and emergency visits. The 95% confidence interval for this proportion was calculated from the raw counts.

The relationships between qualitative variables were studied using the χ² test. Variables with epidemiological relevance or a p-value below 0.20 were considered for multivariate analysis. Binary logistic regression was used to identify factors independently associated with in-hospital death. The results are presented as adjusted Odds Ratios (aOR), along with their 95% confidence intervals. The α error risk was set at 5%.

Due to the lack of detailed data on all hospitalized patients, especially by age and sex, the bivariate and multivariate analyses were conducted on the deaths recorded. So, the interpretation mainly focuses on the distribution of causes and place of death among the patients who died, rather than on the individual risk of death calculated for each subgroup of hospitalized patients.

Ethical considerations

The study was carried out after obtaining administrative approval from the CHNU of Fann. The data was collected anonymously and used solely for scientific purposes. The principles of confidentiality and protection of individual information were respected.

Results

Descriptive

Folder selection

Between 2021 and 2024, 37,196 hospitalization and emergency visit cases were recorded at the CHNU of Fann. Among them, 3,309 deaths were recorded. After excluding 11 cases that did not meet the inclusion criteria, 3,298 death cases were kept for analysis (figure 1).

Figure 1: Flowchart of collected deaths

Inclusion flow: 37, 196 saved files → 3, 309 recorded deaths → 11 excluded files → 3, 298 analyzed deaths.

Distribution of deaths by age:

The study involved 3,298 patients. The 60 to 74 age group was the most represented, with 950 patients (28.8%).

The average age of the patients was 53.4 ± 21.8 years, with extremes ranging from 0 to 94 years. The median age was 57 years.

Figure 2: Distribution of deaths by age group

Distribution of deaths by sex:

Death was observed in 1,917 male subjects (58.1%) and 1,381 female subjects (41.9%), giving a male-to-female ratio of 1.3.

Figure 3: Distribution of deaths by sex

Annual change in mortality  

The overall death rate was 8.87% among the 37,196 cases recorded. Yearly trends showed a decrease in 2022, followed by a sharp increase in 2023. The rate calculated from the raw numbers was 9.00% in 2021, 5.66% in 2022, 11.13% in 2023, and 9.58% in 2024.

Table I: Distribution of deaths by annual evolution

Most of the deaths happened in the hospital, about 68.7% of them, compared to 30.9% in the emergency room. Eleven deaths, or 0.3%, were recorded outside the hospital (table II).

Table II: Distribution by place and service where the death occurred   

Among the deaths broken down by hospital department, neurosurgery had the highest proportion, with 615 deaths (27.15%), followed by infectious diseases with 505 deaths (22.30%), cardiology with 431 deaths (19.03%), and pulmonology with 290 deaths (12.80%). Neurology had 200 deaths (8.83%), geriatrics 101 (4.46%), oto-rhino-laryngology (ENT) 115 (5.08%), and psychiatry 8 (0.35%).

Figure 4: Distribution of mortality rate by department

The table III shows that the neurosurgery service recorded the highest number of deaths, with 615 deaths, representing 27.15% of all deaths for which the hospitalization service was documented. It was followed by the infectious diseases service, which accounted for 505 deaths (22.30%), and the cardiology service, with 431 deaths (19.03%). Together, these three services accounted for 1,551 deaths, representing 68.48% of all deaths distributed by hospitalization service.

Table III: Distribution of mortality by service

Distribution of mortality rate by location :

Most of the deaths happened in the hospital with 2,265 cases (68.8%), while 1,020 deaths (30.9%) were recorded in the emergency room. 11 deaths were recorded outside the hospital, which is 0.3%.

Figure 5 : Distribution of mortality rates by location

Men accounted for 1,917 deaths (58.1%) and women 1,381 deaths (41.9%). The male-to-female ratio was 1.3.

Distribution by causes of deaths

Noncommunicable diseases were the top category of causes recorded, with 2,156 deaths (65.37%). Communicable diseases accounted for 826 deaths (25.04%), while the cause wasn’t listed in 316 cases (9.58%).

Among non-communicable diseases, neurological disorders were the leading cause, with 912 deaths (42.3% of non-communicable diseases), followed by tumors, with 362 deaths (16.8%). Among communicable diseases, tuberculosis was the most common cause, with 283 deaths (34.2%), followed by respiratory infections, with 232 deaths (28.1%). Other documented infectious causes included tetanus, toxoplasmosis, infectious syndrome, non-necrotizing dermohypodermatitis, hepatitis, and rabies.

Table IV : Distribution by causes of deaths

Neurological disorders were the leading cause of death among non-communicable diseases, with a count of n = 912, or 42.3%, followed by tumors with n = 362 (16.8%).

Communicable diseases :

 Tuberculosis was the leading cause of death, with a total of n = 283, or 34.2% of cases, followed by respiratory infections with n = 232 (28.1%). Other infectious diseases accounted for 20% of deaths related to communicable diseases. These mainly included tetanus with n = 53 cases (6.4%), toxoplasmosis with n = 40 cases (4.8%), infectious syndrome with n = 33 cases (4.0%), non-necrotizing dermohypodermitis with n = 19 cases (2.3%), hepatitis with n = 11 cases (1.3%), and rabies with n = 9 cases (1.1%).

Analytical

Bivariate analysis

A statistically significant association was observed between the year of death and the category of cause (χ² = 67.47; p < 0.001), indicating a variation in the distribution of communicable diseases, non-communicable diseases, and unrecorded causes over the study period.

A significant association was also found between sex and cause category (χ² = 19.25; p < 0.001). Non-communicable diseases were more common in both men and women, while the proportion of communicable diseases was higher in men in the analyzed data.

Finally, the place of death varied significantly depending on the year it occurred (χ² = 165.10; p < 0.001), which reflects a temporal change in the distribution of deaths between hospitalization, emergency rooms, and deaths recorded outside the hospital.

Multivariate analysis

The binary logistic regression model had hospital death as the dependent variable, as opposed to death in the emergency room. The reference categories were female sex, other regions of residence, non-communicable diseases, the 30 to 44 age group, and the year 2021.

Table V: Multivariable binary logistic regression analysis of factors associated with hospital mortality