Evaluation of hospital quality of care outcomes in a teaching hospital in Ethiopia: a retrospective database study
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* Balew Arega
* Mekoya Mengistu
* Amdemeskel Mersha
* Asnake Agunie

## Abstract

**Objectives** We aimed to evaluate hospital mortality rates, readmission rates and length of hospital stay (LOS) among adult medical patients admitted to a teaching hospital in Ethiopia.

**Design** We performed a retrospective study using routinely collected electronic data.

**Setting** Data were collected from Yekatit 12 Hospital Medical College between January 2021 and July 2023.

**Participants** The analysis included 3499 (4111 admissions) adult medical patients with complete data.

**Outcome measures** We used mortality rates, readmission rates and LOS to measure the quality of the outcomes for the top 15 admission diagnoses. A multivariable Cox proportional hazard model was used to identify the statistically significant predictors of mortality with p values<0.05 and a 95% CI. The Kaplan-Meier curve was used to estimate the failure rate (mortality) of the admitted patients.

**Results** The median age of patients was 50 years and men accounted for 1827 (52.3%) of all admitted cases. Non-communicable diseases accounted for 2537 (72.5%) admissions. In descending order, stroke, 644 (18.29%); heart failure, 640 (18.41%); and severe pneumonia, 422 (12.06%) were the three most common causes of admission. The readmission rate was 25.67% (1056/411), and 61.9% of them were readmitted within 30 days of index discharge. The overall median LOS was 8 days. The median LOSs in the index admission (11 vs 8 days, p value=0.001) of readmitted patients was significantly higher than not readmitted. The in-hospital mortality rate was 438 (12.5%), with the highest number of deaths occurred between days 30 and 50 of admission. The mortality rate is significantly higher among patients with communicable diseases (adjusted HR, 1.64, 95% CI: 1.34, 2.10) and elderly patients (≥65 years) (adjusted HR, 1.79, 95% CI: 1.44, 2.22). Septicemia, chronic liver diseases with complications and HIV with complications were the three common causes of death with a proportional mortality rate of 55.2%, 27.93% and 22.46%, respectively.

**Conclusions** Mortality, median LOSs and readmission rate were comparable to other national and international studies. Multicentre compressive research using these three quality patient outcomes is required to establish national standards and evaluate institutional performance.

*   Health Equity
*   Mortality
*   Clinical audit

### STRENGTHS AND LIMITATIONS OF THIS STUDY

*   This is the first study in Ethiopia that evaluates the quality of hospital outcomes using three commonly used outcome measures: death, readmission and length of hospital stay (LOS), with a large sample based on patient characteristics and outcomes.

*   This composite measure provides a more comprehensive picture of care quality that is more reliable, and hence more effective for assessing hospital performance.

*   Since it was conducted in a referral hospital, there was a higher chance of mortality, readmission and long LOS.

*   We were only able to count readmissions to the same hospital, which led to an underestimation of the readmission rate.

## Background

Quality of care outcome measurements are increasingly being used to evaluate hospital performance to find areas for improvement. In-hospital mortality, readmissions and length of hospital stay (LOS) are three outcome metrics routinely used in various nations to evaluate the quality of care in hospitals.1 These three measures are connected, with a patient who has an extended hospital stay having a high death rate2 and recurrent hospitalisations potentially having a higher mortality risk.3 Furthermore, a patient with recurrent admissions has prolonged hospitalisation with the frequency of admissions increasing.4

Ethiopia is experiencing a double burden of illnesses due to the rising prevalence of non-communicable diseases (NCDs) and the persistent prevalence of pre-existing communicable diseases (CD).5 This puts Ethiopia’s already limited health resources at risk, impedes economic growth and lowers the standard of patient care.6 With the high burden of both NCD and CD in the community, increased rates of in-hospital mortality, recurrent hospital admissions because of recurrent infections and extended hospital stays because of severe index cases or healthcare-associated infections are to be expected.7 8

Previous studies conducted in Ethiopia mostly examined the death rate of patients in critical care units,9 single outcome (mortality) for different diseases10 or multiple outcomes of a single disease.4 These studies do not generate compressive data on the quality of hospital outcomes using the mortality rates, LOS and readmission rates for the common admission diagnoses in the hospital. Therefore, in this study, we evaluated the hospital quality of patient care outcomes for the top 15 common causes of admissions concerning hospital LOS, readmission rate and mortality rate in teaching hospitals in Ethiopia.

## Methodology

### Study area, design and period

A hospital-based retrospective follow-up study was conducted among patients admitted to Yekatit 12 Hospital Medical College, a teaching hospital, in Addis Ababa, Ethiopia. The hospital has about 500 beds and it has treated approximately 310 000 patients annually from the city and its surrounding areas. It consists of more than 21 departments, wards and outpatient clinics. Internal medicine has the largest service and bed share. It includes 6 inpatient wards with an overall total of more than 90 beds and provides outpatient services daily in 2 referrals and 6 general clinics. Patients admitted to the hospital either from the referral institutions or from the hospital emergency or outpatient departments. The hospital started implementing electronic medical records (EMRs) around the end of 2019. Every inpatient and outpatient patient record, including those about clinical evaluation, imaging, laboratory and prescription services, is documented in an EMR. This study used the data of patients in internal medicine inpatient general wards between 1 January 2021 and 31 July 2023.

### Study population and eligibility criteria

We included the top 15 discharge diagnoses for this study. We used the discharge diagnosis since the patient’s diagnosis had been settled throughout the hospital stay, making classification simple. Patients with complete data including, sex, age, documented discharge diagnosis, documented outcome, date of discharge and date of admission were included. Patients missing any one of these data were excluded. Over the study periods, a total of 4936 admissions were recorded, of which 825 admission records were excluded based on the exclusion criteria. The majority were excluded due to incomplete data, 62.4% (515/825), followed by non-medical cases, 27.3% (225/825). Finally, 3449 patients with 4111 admissions were included in the analysis (figure 1).

![Figure 1](http://bmjopen.bmj.com/https://bmjopen.bmj.com/content/bmjopen/14/9/e082908/F1.medium.gif)

[Figure 1](http://bmjopen.bmj.com/content/14/9/e082908/F1)

Figure 1 
Flowchart describing the formation of the final study population.

### Data collection methods

Internal medicine residents collected the data from the EMRs using a data abstraction sheet. Daily checking by the supervisors was made to ensure that the data collected met the inclusion and exclusion criteria and was accurate, consistent and complete.

### Methods of statistical analysis

The collected data were entered into Epi Info V.7.0 and analysed by STATA software V.17.0. The type of diagnosis, treatment outcome, admission types and readmission rate were summarised by proportions. The LOS was summarised by the median and IQR. The significance of association with determinants was reported using the hazard rate for mortality rate. The baseline proportional hazard was assumed constant and the Cox proportional hazard assumption was tested for all included variables. All associations were tested at a 95% confidence level and a p value less than 0.05 was declared as a significant association between variables. Survival data were described by median time and Kaplan-Meier graphs.

### Definitions

*   Admission: it was defined as any unscheduled inpatient admission, emergency department visit or observation unit stay.

*   Readmission: it was the admission of patients for more than one time.

*   LOS: the period between the patient’s admission and discharge.

*   Mortality rate: the mortality was computed with in-hospital death as the event and censuring with discharged better or cured, left against medical advice or referred to another institution as the stopping point.

### Patient and public involvement

None.

## Results

### Admission and LOS

In this study, male patients accounted for 52.3% (1827) of all admitted cases. The median (IQR) age was 50.0 (25–75) years and 71.99% (2519) of the patients were younger than 65 years. NCD and CD accounted for 72.5% (2537) and 27.5% (962) of the admissions, respectively. Overall, in descending order, the three most common causes of admission were stroke, heart failure and severe pneumonia (table 1).

View this table:
[Table 1](http://bmjopen.bmj.com/content/14/9/e082908/T1)

Table 1 
Characteristics of total admitted (n=3499), not-readmitted (n=3056) and readmitted (n=1055) patients in a teaching hospital in Addis Ababa, Ethiopia, from 1 January 2021 to 11 August 2023

The median LOS for all admitted patients was 8 (IQR, 8) days, with a range of 1–59 days. The median LOS did not significantly differ between CD (8 days) and NCD (9 days) (p=0.35). The shortest median LOS was 5 days (IQR, 4) for patients treated for malaria/relapsing fever, while the longest was 13 days (IQR, 9) for meningitis (figure 2A).

![Figure 2](http://bmjopen.bmj.com/https://bmjopen.bmj.com/content/bmjopen/14/9/e082908/F2.medium.gif)

[Figure 2](http://bmjopen.bmj.com/content/14/9/e082908/F2)

Figure 2 
(A and B) Median length of hospital stay by admission and readmission status of patients with different diagnoses in teaching hospital, Addis Ababa, Ethiopia (2021–2023). CKD, chronic kidney disease; CLD, chronic liver disease; COPD, chronic obstructive pulmonary disease; DM, diabetes mellitus; DVT, deep vein thrombosis; LOS, length of hospital stay; PTE, pulmonary thromboembolism; RF, relapsing fever.

### Readmission and LOS

The proportion of readmission was 25.67% (1056/4111) among 443 patients within 28 months. The median time of readmission was 54.8 (IQR, 117) days. Within the study time, the number of readmissions among survivors of the index admission ranged from 1 to 7 readmissions. Nearly one-third (38.1%) of the readmission episodes occurred within 30 days and 61.9% of readmissions occurred after 30 days of the index discharge. The most common readmission diagnosis was chronic kidney disease with complications (13/49 (26.53%)), chronic obstructive pulmonary disease (16/63 (25.4%)) and asthma, (38/162 (23.46%)) (table 1). The median LOS (IQR) was 11 (10) among patients who had readmissions, which is significantly higher than the corresponding who had no readmissions, 8 (8) with a median difference of 3 (95% CI: 2, 4, p value=0.001). The median LOS increases when the number of readmissions increases (figure 2B).

### Mortality rate of admitted patients

Of the total admitted patients, 3036 (86.8%) were discharged, 438 (12.5%) died in the hospital and the remaining 25 (0.7%) were referred or left against medical advice. The total person-time observation was 34 667 person-days in this study. The incidence of death among medical patients, in general, was found to be 12.34 cases (95% CI, 11.51 to 13.87) per 1000-person-day observation. The proportion of deaths among CDs and NCDs was 17.9% (173/963) and 10.44% (265/2536), respectively.

According to the Kaplan-Meier failure estimates curve, the overall hazard rate was 61.6% at 59 days of follow-up. The estimated cumulative death rates for patients on day 1, 10, 20, 30, 40 and 50 were 1.3%, 9.5%, 17.2%, 24.5%, 33.3% and 42.97%, respectively (figure 3A). The likelihood of hazard is lowest on the first day of admission, but it increases later as the length of the hospital increases. In the present study, we found that the highest mortality rate between 30-day and 50-day LOS (figure 3A). The mortality rate was higher among males than females but did not reach a significant level (adjusted hazard ratio (AHR), 1.11, 95% CI: 0.912, 1.33, p value=0.31) (figure 3B).

![Figure 3](http://bmjopen.bmj.com/https://bmjopen.bmj.com/content/bmjopen/14/9/e082908/F3.medium.gif)

[Figure 3](http://bmjopen.bmj.com/content/14/9/e082908/F3)

Figure 3 
(A–D) Kaplan Meier overall mortality rate and the mortality rate among sex groups, age groups and disease categories in teaching hospital, Addis Ababa, Ethiopia (2021–2023).

The mortality rate among elderly patients (age>65 years) is significantly higher than that of patients under the age of 65 (AHR, 1.79, 95% CI:1.44, 2.22, p value=0.001) (figure 3C). A significant linear increase in mortality rate with increasing age was observed. For every year increase in age in-hospital mortality increases by about 2.2% (AHR, 1.022, 95% CI: 1.02, 1.03, p value=0.001). The mortality rate was significantly higher among with CD than patients with NCDs (AHR, 1.64, 95% CI:1.34, 98, p value=0.001) (figure 3D).

The proportion of mortality was higher among patients with septicemia (55.2%), followed by chronic liver disease (CLD) with complications (27.93%), HIV with complications (22.46%) and meningitis (16.67%). No patient died in the hospital due to asthma exacerbation and acute febrile illness (malaria and relapsing fever) (figure 4).

![Figure 4](http://bmjopen.bmj.com/https://bmjopen.bmj.com/content/bmjopen/14/9/e082908/F4.medium.gif)

[Figure 4](http://bmjopen.bmj.com/content/14/9/e082908/F4)

Figure 4 
Proportion of mortality rate across the specific causes among patients admitted to teaching hospital, Addis Ababa, Ethiopia (2021–2023). CKD, chronic kidney disease; CLD, chronic liver disease; COPD, chronic obstructive pulmonary disease; DM, diabetes mellitus; DVT, deep vein thrombosis; PE, pulmonary embolism.

## Discussion

This study was designed to evaluate the hospital quality of care outcomes using the readmission rate, mortality rate and LOS among admitted medical patients in the internal medicine ward in a teaching hospital in Addis Ababa, Ethiopia. We found that the most common diagnosis was stroke (18.4%) followed by heart failure (18.3%). The readmission rate among medical patients during the study period was 25.6%. The median LOS differed significantly between patients who had been readmitted and those who were not. The overall rate of mortality was 12.5%, with the highest risk of death occurring between 30 and 50 days of LOS. The mortality rate is significantly higher among elderly (>65 years) and female patients.

This study showed that NCDs accounted for about three-fourths of admissions to the hospital. This is consistent with similar studies on hospital admission in Northern Ethiopia, Mekelle (68.2%),11 Nigeria (60.3%)12 and Sudan (71.8%).13 However, the CD was the most prevalent reason for medical admission in Africa,14 and many other previous studies were done in Ethiopia’s Central15 and Northern regions.16 This might be the referral bias or might be due to the changing pattern of disease epidemiology from CD to NCDs. The present study was carried out in a capital city, and previous investigations indicate that individuals living in urban areas are at a higher risk of developing NCDs due to factors including altered food habits, reduced physical activity and rising obesity rates.17 This change or predominance of NCDs in epidemiology poses a challenge to Ethiopia’s future. First, most NCDs are incurable diseases that require lifelong care and follow-up at a high expense. Second, it is difficult to deliver the specialised and subspecialty-level care that NCDs require due to a shortage of human resources in our country. Third, because of the persistent presence of CD, NCDs put a burden on healthcare systems through recurrent admission and readmission. It is important to conduct multicentre, nationwide studies to fully comprehend the scope of the illness pattern.

The top specific causes of hospitalisation were stroke and heart failure in our study. This is consistent with another study in Northern Ethiopia,11 where heart failure and stroke were major reasons for hospitalisation, but this differs from a study in Uganda,18 where HIV/AIDS, hypertension, tuberculosis (TB), non-TB pneumonia and heart failure were the most prevalent reasons for admission. These differences could be due to differences in primary diagnosis recorded in patients with multiple comorbidities and the difference in the study setting. Inconsistencies in classification may contribute to differences in the epidemiology of some diseases

In our study, about one-fourth (25.6%) were readmitted within 28 months and nearly one-third (38.1%) of the readmission episodes occurred within 30 days after the index admission. Our finding is lower than the 30-day readmission rate in Hong Kong (40.8%)19 and the USA (55%),20 but higher than several other studies conducted in Spain (23.9%),21 Switzerland (12.3%)22 and Italy (10.2%).23 In Ethiopia, the 30-day readmission rate was conducted among specific diseases with 26.4%24 for heart failure and 18.1%25 among obstructive chronic lung disease patients. It should be noted that comparing hospital readmission and possibly avoidable readmission across different geographical areas should be done with caution, because many factors, such as the healthcare delivery system, variation in physician practice styles and the methodology and data analysis used across studies, can all have an impact on hospitalisation rates. Unplanned readmission within 30 days of discharge is an important indicator of the cost and quality of healthcare service26 and should be the priority for our hospitals and clinicians to improve the quality of healthcare and reduce costs.

The median LOS in our study is 8 days. This is shorter than the average LOS compared with the study done in Addis Ababa (12.3 days),27 Northern Ethiopia (11 days)11 and Nigeria (10.3 days).28 The difference might be attributed to the presence of extreme values in the LOSs in our study; we used a median in our study, whereas the aforementioned studies used the mean. Furthermore, the variation was also attributed to the type and severity of diseases handled by a specific institution, the institutional administration structure and quality efforts performed across various institutions. In our study, the median LOS was similar to NCD and CDs, which was comparable to the other study in Addis Ababa.27

A significant number of patients were discharged with improvement (more than two-thirds), while the in-hospital mortality rate was 12.5%. This is similar to the study conducted in Southeast Ethiopia (12.6%),29 Nigeria (12.90%)28and Northern Ethiopia (14.9%),11 but lower than the study in teaching hospital in Addis Ababa (24.2%),27 Nigeria teaching hospital (24%)30and Uganda (17.1%).18 The differences might be attributed to variations in the types and severity of accepted patients. Patients with CDs had a higher mortality rate than those with NCDs, but the proportionate mortality rate was higher for NCDs than CDs. This implies that comparatively, CDs continue to take the lives of the productive segment of the population of the nation, similar to other studies in Ethiopia.11 27 In our analysis, however, the leading causes of mortality were septicemia, CLD with complications, HIV with complications and meningitis. This is comparable to a study undertaken in Uganda,27 a teaching hospital in Addis Ababa, Ethiopia29 and another teaching hospital in Mekelle,11 where infectious diseases were the leading cause of mortality.

Mortality was significantly higher in the age group greater than 65 years. Advancing age has been reported as an immutable risk factor for mortality in hospitalised older patients, as shown in Africa,30 South America18 and Nigeria. Men died at higher rates than women in the present study, although this difference was not statistically significant. The result was not a surprise, given male sex has already been documented to be an independent risk factor related to inpatient mortality in medical wards in other studies.31–33 This might be due to older women having a longer life expectancy, better health-seeking behaviour, healthier lifestyle practices and reduced stress exposure. In addition, cardiocirculatory protection of women due to the effect of hormones during their fertile years is a major contribution.34

Our study has some limitations. First, since we conducted the study at a referral hospital, more critically ill patients with a higher risk of mortality, readmission and prolonged LOS were admitted. This led to an overestimation of our results. Second, we were only able to count readmissions to the same hospital, which led to an underestimation of the readmission rate. Third, our study carries the limitations of a retrospective study design, including the potential for misclassification and incomplete data.

In conclusion, we produced comprehensive data that evaluated the quality of care using three commonly used outcome measures for quality of care: mortality, readmission and LOS in large clinical data with information on patient characteristics and outcomes. The findings will be used by hospital leaders and others as baseline figures for future measures of service quality improvement of the hospital and related sister hospitals in the country.

## Data availability statement

All data relevant to the study are included in the article or uploaded as online supplemental information.

## Ethics statements

### Patient consent for publication

Not applicable.

### Ethics approval

We obtained ethical approval from the Yekatit 12 Hospital Medical College ethical review committee. The institutional review board waived the need for written informed consent from participants, since the study required no direct contact with human subjects (no interview or sample collection) and only used a pooled program/health information system.

## Footnotes

*   Contributors BA and MM contributed to the design of the study, collected, entered, analysed and interpreted the data and prepared the paper. AM and AA contributed to the interpretation of the results and in drafting and critically reviewing the paper. All authors read and approved the final paper. BA is responsible for the overall content (as guarantor).

*   Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

*   Competing interests None declared.

*   Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

*   Provenance and peer review Not commissioned; externally peer reviewed.

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## References

1.  Bottle A, Middleton S, Kalkman CJ, et al. Global comparators project: international comparison of hospital outcomes using administrative data. Health Serv Res 2013;48:2081–100. [doi:10.1111/1475-6773.12074](http://dx.doi.org/10.1111/1475-6773.12074)
    
    [CrossRef](http://bmjopen.bmj.com/lookup/external-ref?access_num=10.1111/1475-6773.12074&link_type=DOI) 
    
    [PubMed](http://bmjopen.bmj.com/lookup/external-ref?access_num=23742025&link_type=MED&atom=%2Fbmjopen%2F14%2F9%2Fe082908.atom) 

2.  da Costa Sousa V, da Silva MC, de Mello MP, et al. Factors associated with mortality, length of hospital stay and diagnosis of COVID-19: Data from a field hospital. J Infect Public Health 2022;15:800–5. [doi:10.1016/j.jiph.2022.06.010](http://dx.doi.org/10.1016/j.jiph.2022.06.010)
    
    

3.  Fry CH, Fluck D, Han TS. Frequent identical admission-readmission episodes are associated with increased mortality. Clin Med (Lond) 2021;21:e351–6. [doi:10.7861/clinmed.2020-0930](http://dx.doi.org/10.7861/clinmed.2020-0930)
    
    

4.  Regassa LD, Tola A. Magnitude and predictors of hospital admission, readmission, and length of stay among patients with type 2 diabetes at public hospitals of Eastern Ethiopia: a retrospective cohort study. BMC Endocr Disord 2021;21. [doi:10.1186/s12902-021-00744-3](http://dx.doi.org/10.1186/s12902-021-00744-3)
    
    

5.  Noncommunicable diseases country profiles. 2014.
    
    

6.  Fikru T. Epidemiology of cardiovascular disease risk factors in Ethiopia: the rural-urban gradient. 2008.
    
    

7.  Owusu AY, Kushitor SB, Ofosu AA, et al. Institutional mortality rate and cause of death at health facilities in Ghana between 2014 and 2018. PLoS One 2021;16:e0256515. [doi:10.1371/journal.pone.0256515](http://dx.doi.org/10.1371/journal.pone.0256515)
    
    

8.  Gouda HN, Charlson F, Sorsdahl K, et al. Burden of non-communicable diseases in sub-Saharan Africa, 1990-2017: results from the Global Burden of Disease Study 2017. Lancet Glob Health 2019;7:e1375–87. [doi:10.1016/S2214-109X(19)30374-2](http://dx.doi.org/10.1016/S2214-109X(19)30374-2)
    
    [CrossRef](http://bmjopen.bmj.com/lookup/external-ref?access_num=10.1016/S2214-109X(19)30374-2&link_type=DOI) 

9.  Endeshaw AS, Tarekegn F, Bayu HT, et al. The magnitude of mortality and its determinants in Ethiopian adult intensive care units: A systematic review and meta-analysis. Ann Med Surg (Lond) 2022;84:104810:104810. [doi:10.1016/j.amsu.2022.104810](http://dx.doi.org/10.1016/j.amsu.2022.104810)
    
    

10. Misganaw A, Mariam DH, Araya T, et al. Patterns of mortality in public and private hospitals of Addis Ababa, Ethiopia. BMC Public Health 2012;12:1–9:1007. [doi:10.1186/1471-2458-12-1007](http://dx.doi.org/10.1186/1471-2458-12-1007)
    
    [CrossRef](http://bmjopen.bmj.com/lookup/external-ref?access_num=10.1186/1471-2458-12-1&link_type=DOI) 
    
    [PubMed](http://bmjopen.bmj.com/lookup/external-ref?access_num=22214479&link_type=MED&atom=%2Fbmjopen%2F14%2F9%2Fe082908.atom) 

11. Hailu A, Gidey K, Ebrahim MM, et al. Patterns of Medical Admissions and Predictors of Mortality in Ayder Comprehensive Specialized Hospital, Northern Ethiopia: A Prospective Observational Study. Int J Gen Med 2023;16:243–57. [doi:10.2147/IJGM.S385578](http://dx.doi.org/10.2147/IJGM.S385578)
    
    

12. The pattern of admissions into the medical wards of the University of Nigeria Teaching Hospital, Enugu (2). Available: [https://www.researchgate.net/publication/23783876\_The\_pattern\_of\_admissions\_into\_the\_medical\_wards\_of\_the\_University\_of\_Nigeria\_Teaching\_Hospital\_Enugu\_2](https://www.researchgate.net/publication/23783876\_The\_pattern\_of\_admissions\_into\_the\_medical\_wards\_of\_the\_University\_of\_Nigeria\_Teaching_Hospital_Enugu_2) [Accessed 24 Nov 2023].
    
    

13. Noor SK, Elmadhoun WM, Bushara SO, et al. The Changing Pattern of Hospital Admission to Medical Wards: Burden of non-communicable diseases at a hospital in a developing country. Sultan Qaboos Univ Med J 2015;15:e517–22. [doi:10.18295/squmj.2015.15.04.013](http://dx.doi.org/10.18295/squmj.2015.15.04.013)
    
    

14. Etyang AO, Scott JAG. Medical causes of admissions to hospital among adults in Africa: a systematic review. Glob Health Action 2013;6:1–14. [doi:10.3402/gha.v6i0.19090](http://dx.doi.org/10.3402/gha.v6i0.19090)
    
    [CrossRef](http://bmjopen.bmj.com/lookup/external-ref?access_num=10.3402/gha.v6i0.19923&link_type=DOI) 
    
    [PubMed](http://bmjopen.bmj.com/lookup/external-ref?access_num=23490302&link_type=MED&atom=%2Fbmjopen%2F14%2F9%2Fe082908.atom) 

15. The pattern of adult medical admissions in Addis Ababa, Ethiopia. 2023. Available: [https://www.semanticscholar.org/paper/The-pattern-of-adult-medical-admissions-in-Addis-Ft-Tsega/98c3a9ef831c392aeed753ce893fb8ac3fb56d8b](https://www.semanticscholar.org/paper/The-pattern-of-adult-medical-admissions-in-Addis-Ft-Tsega/98c3a9ef831c392aeed753ce893fb8ac3fb56d8b)
    
    

16. Analysis of admissions to Gondar Hospital in North-Western Ethiopia, 1971–1972–PubMed. Ethiop Med J 1976;49–59. Available: [https://pubmed.ncbi.nlm.nih.gov/976249/](https://pubmed.ncbi.nlm.nih.gov/976249/)
    
    

17. Girum T, Mesfin D, Bedewi J, et al. The Burden of Noncommunicable Diseases in Ethiopia, 2000-2016: Analysis of Evidence from Global Burden of Disease Study 2016 and Global Health Estimates 2016. Int J Chronic Dis 2020;2020:3679528. [doi:10.1155/2020/3679528](http://dx.doi.org/10.1155/2020/3679528)
    
    

18. Kalyesubula R, Mutyaba I, Rabin T, et al. Trends of admissions and case fatality rates among medical in-patients at a tertiary hospital in Uganda; A four-year retrospective study. PLoS One 2019;14:e0216060. [doi:10.1371/journal.pone.0216060](http://dx.doi.org/10.1371/journal.pone.0216060)
    
    

19. Yam CHK, Wong ELY, Chan FWK, et al. Avoidable readmission in Hong Kong--system, clinician, patient or social factor? BMC Health Serv Res 2010;10:1–11:311. [doi:10.1186/1472-6963-10-311](http://dx.doi.org/10.1186/1472-6963-10-311)
    
    [CrossRef](http://bmjopen.bmj.com/lookup/external-ref?access_num=10.1186/1472-6963-10-1&link_type=DOI) 
    
    [PubMed](http://bmjopen.bmj.com/lookup/external-ref?access_num=20044945&link_type=MED&atom=%2Fbmjopen%2F14%2F9%2Fe082908.atom) 

20. Friedman B, Basu J. The rate and cost of hospital readmissions for preventable conditions. Med Care Res Rev 2004;61:225–40. [doi:10.1177/1077558704263799](http://dx.doi.org/10.1177/1077558704263799)
    
    [CrossRef](http://bmjopen.bmj.com/lookup/external-ref?access_num=10.1177/1077558704263799&link_type=DOI) 
    
    [PubMed](http://bmjopen.bmj.com/lookup/external-ref?access_num=15155053&link_type=MED&atom=%2Fbmjopen%2F14%2F9%2Fe082908.atom) 
    
    [Web of Science](http://bmjopen.bmj.com/lookup/external-ref?access_num=000221488200005&link_type=ISI) 

21. Jiménez-Puente A, García-Alegría J, Gómez-Aracena J, et al. Readmission rate as an indicator of hospital performance: the case of Spain. Int J Technol Assess Health Care 2004;20:385–91. [doi:10.1017/s0266462304001230](http://dx.doi.org/10.1017/s0266462304001230)
    
    [PubMed](http://bmjopen.bmj.com/lookup/external-ref?access_num=15446771&link_type=MED&atom=%2Fbmjopen%2F14%2F9%2Fe082908.atom) 
    
    [Web of Science](http://bmjopen.bmj.com/lookup/external-ref?access_num=000223311900019&link_type=ISI) 

22. Maurer PP, Ballmer PE. Hospital readmissions--are they predictable and avoidable? Swiss Med Wkly 2004;134:606–11. [doi:10.4414/smw.2004.10706](http://dx.doi.org/10.4414/smw.2004.10706)
    
    [PubMed](http://bmjopen.bmj.com/lookup/external-ref?access_num=15592954&link_type=MED&atom=%2Fbmjopen%2F14%2F9%2Fe082908.atom) 
    
    [Web of Science](http://bmjopen.bmj.com/lookup/external-ref?access_num=000225762100003&link_type=ISI) 

23. Bianco A, Molè A, Nobile CGA, et al. Hospital readmission prevalence and analysis of those potentially avoidable in southern Italy. PLoS One 2012;7:e48263. [doi:10.1371/journal.pone.0048263](http://dx.doi.org/10.1371/journal.pone.0048263)
    
    

24. Ayenew B, Kumar P, Hussein A. Heart failure drug classes and 30-day unplanned hospital readmission among patients with heart failure in Ethiopia. J Pharm Health Care Sci 2023;9:49. [doi:10.1186/s40780-023-00320-y](http://dx.doi.org/10.1186/s40780-023-00320-y)
    
    

25. Anbesse ZK, Mega TA, Tesfaye BT, et al. Early readmission and its predictors among patients treated for acute exacerbations of chronic obstructive respiratory disease in Ethiopia: A prospective cohort study. PLoS One 2020;15:e0239665. [doi:10.1371/journal.pone.0239665](http://dx.doi.org/10.1371/journal.pone.0239665)
    
    

26. Zamir D, Zamir M, Reitblat T, et al. Readmissions to hospital within 30 days of discharge from the internal medicine wards in southern Israel. Eur J Intern Med 2006;17:20–3. [doi:10.1016/j.ejim.2005.10.004](http://dx.doi.org/10.1016/j.ejim.2005.10.004)
    
    [PubMed](http://bmjopen.bmj.com/lookup/external-ref?access_num=16378880&link_type=MED&atom=%2Fbmjopen%2F14%2F9%2Fe082908.atom) 

27. Bane A, Bayisa T, Adamu F, et al. Medical Admissions and Outcomes at Saint Paul’s Hospital, Addis Ababa, Ethiopia: a retrospective study. Eth J Health Dev 2016;30:50–6.
    
    

28. Odenigbo CU, Oguejiofor OC. Pattern of medical admissions at the Federal Medical Centre, Asaba-a two year review. Niger J Clin Pract 2009;12:395–7.
    
    [PubMed](http://bmjopen.bmj.com/lookup/external-ref?access_num=20329679&link_type=MED&atom=%2Fbmjopen%2F14%2F9%2Fe082908.atom) 

29. Ali E, Woldie M. Reasons and Outcomes of Admissions to the Medical Wards of Jimma University Specialized Hospital, Southwest Ethiopia. Ethiop J Health Sci 2010;20. [doi:10.4314/ejhs.v20i2.69437](http://dx.doi.org/10.4314/ejhs.v20i2.69437)
    
    

30. Ike SO. The pattern of admissions into the medical wards of the University of Nigeria Teaching Hospital, Enugu (2). Niger J Clin Pract 2008;11:185–92.
    
    [PubMed](http://bmjopen.bmj.com/lookup/external-ref?access_num=19140351&link_type=MED&atom=%2Fbmjopen%2F14%2F9%2Fe082908.atom) 

31. Adebusoye L, Owolabi M, Kalula S, et al. All-cause mortality among elderly patients admitted to the medical wards of hospitals in Africa: A systematic review. Niger J Health Sci 2015;15:45. [doi:10.4103/1596-4078.171372](http://dx.doi.org/10.4103/1596-4078.171372)
    
    

32. Silva TJA, Jerussalmy CS, Farfel JM, et al. Predictors of in-hospital mortality among older patients. Clinics (Sao Paulo) 2009;64:613–8. [doi:10.1590/S1807-59322009000700002](http://dx.doi.org/10.1590/S1807-59322009000700002)
    
    [PubMed](http://bmjopen.bmj.com/lookup/external-ref?access_num=19606235&link_type=MED&atom=%2Fbmjopen%2F14%2F9%2Fe082908.atom) 

33. Myer L, Smith E, Mayosi BM. Medical inpatient mortality at Groote Schuur Hospital, Cape Town, South Africa, 2002-2009. S Afr Med J 2012;103:28–31. [doi:10.7196/samj.6285](http://dx.doi.org/10.7196/samj.6285)
    
    [PubMed](http://bmjopen.bmj.com/lookup/external-ref?access_num=23237120&link_type=MED&atom=%2Fbmjopen%2F14%2F9%2Fe082908.atom) 

34. Maia F de O, Duarte YAO, Lebrão ML. Risk factors for mortality among elderly people. Rev Saude Publica 2006;40:1049–56. [doi:10.1590/S0034-89102006005000009](http://dx.doi.org/10.1590/S0034-89102006005000009)
    
    [CrossRef](http://bmjopen.bmj.com/lookup/external-ref?access_num=10.1590/S0034-89102006005000009&link_type=DOI) 
    
    [PubMed](http://bmjopen.bmj.com/lookup/external-ref?access_num=17173162&link_type=MED&atom=%2Fbmjopen%2F14%2F9%2Fe082908.atom)