INTRODUCTION
During the Millennium Development Goal era (1990–2015), the mortality rate of children younger than 5 years of age declined by more than half in sub-Saharan Africa. 1 Much of this progress is attributed to the success of public health interventions, particularly those targeting malaria, malnutrition, and mother-to-child transmission of HIV. 2 – 4 However, as the world moves into the Sustainable Development Goal era and aims to end all preventable under-5 deaths by 2030, public health strategies alone will not be sufficient. 5 – 7 Child hospital mortality remains high, and challenges delivering high-quality hospital care may be responsible for over half of all deaths in low- and middle-income countries (LMICs). 8 , 9 Assessing and improving the quality of pediatric hospital care will be crucial in reducing childhood mortality.
Assessments of pediatric healthcare quality in sub-Saharan Africa are limited. Baseline data on births and deaths are often incomplete due to unreliable civil registration and vital statistics systems. 10 Many countries rely on cross-sectional surveys to evaluate healthcare quality measures, such as vaccination coverage. 11 Hospital-level quality measures are incompletely characterized. 6 , 7 Typically, hospital-level data describe aggregate mortality rates in children and diagnoses associated with the largest number of deaths. 8 , 12 – 15 The cause-specific mortality rate, or case fatality rate, is often not reported because it is challenging to attribute a primary diagnosis in patients with multiple comorbidities in settings with limited diagnostic testing. Studies recruiting patients with a single disease can report these measures but are often unable to place the single disease mortality rate in context with the mortality rates of other diseases. 16 An approach that approximates the mortality rates of multiple diseases at the same hospital may help clinicians and leaders evaluate the quality of healthcare and prioritize areas for improvement. 5 – 7 , 17 , 18
In this study, we investigated mortality by diagnosis in children less than 5 years of age who were admitted to a tertiary care government referral hospital in Malawi. Most children in this cohort were admitted with multiple diagnoses, reflecting a degree of uncertainty that is not uncommon in sub-Saharan African healthcare settings. Without a primary diagnosis or cause of death, we do not report a cause-specific mortality rate or a case fatality rate. Instead, our study gives each diagnosis equal consideration and primarily reports mortality rates in children with a diagnosis regardless of their comorbidities.
MATERIALS AND METHODS
Study design and population.
This study was a secondary analysis of a prospective routine database of children admitted to the Kamuzu Central Hospital’s (KCH) Pediatric Department between October 1, 2017, and June 30, 2020. 19 Before 2019, admissions of children aged 6–36 months were entered into the database. From January 2019, admissions of children aged 2 weeks to 60 months were entered. This analysis includes patients aged 1–60 months. Patients were excluded if they presented dead on arrival or had no documented admission diagnosis.
Setting.
This study was conducted at KCH, a tertiary care government referral hospital located in Lilongwe, Malawi. It serves the central region of the country and has a catchment area of 7.5 million people. 20 Most patients are referred to KCH from district health centers or regional hospitals, but they may also present directly for care. Therefore, KCH also provides services at the primary and secondary level.
During the study period, the pediatric department evaluated and admitted approximately 100,000 and 27,000 patients per year, respectively. 19 Rotating medical interns and permanently employed pediatric-trained clinical officers evaluated and treated most patients. Between one and three consultant pediatricians provided daily clinical supervision. National and expatriate pediatricians with primary responsibilities outside of KCH provided limited additional supervision.
Diagnostic studies immediately available upon admission included point-of-care testing for malaria, glucose, and hemoglobin. Anthropometric measurements and malnutrition screening occurred at triage. 21 Routine HIV testing was conducted after admission by a local partner. 22 Other laboratory and radiologic investigations were inconsistently available. 8 Inpatient therapies were provided free of charge and included oral rehydration fluids, intravenous fluids, pharmaceutical therapy, and ready-to-use therapeutic food. Critical care, including mechanical ventilation, was provided on a limited basis, and pediatric critical care specialists were generally unavailable.
Admission diagnoses.
Admission diagnoses were made by clinicians and written into paper charts. Data clerks reviewed paper charts and entered all documented diagnoses into an electronic database. Clinicians could give patients an unlimited number of diagnoses and did not indicate a primary diagnosis in the chart. Therefore, all diagnoses were given equal consideration, and none were labeled as comorbidities or consequences of a primary diagnosis. We considered only diagnoses at admission, as opposed to at discharge or death. Future references to diagnoses in this manuscript refer to admission diagnoses unless otherwise indicated.
All diagnoses were clinical diagnoses and not verified with the use of other recorded measures in the database, such as hemoglobin levels, malaria rapid diagnostic testing results, or anthropometric measures. We felt that our approach provided a pragmatic description of clinician diagnostic patterns at admission. Other analyses including these recorded measures have been and will be published separately. 21 , 23 Certain diagnoses were combined if considered clinically equivalent as follows. Pneumonia and bronchiolitis were combined as “pneumonia/bronchiolitis.” Dysentery and gastroenteritis were combined as “acute gastroenteritis (AGE).” “Malaria” included both uncomplicated and severe malaria. “Anemia” included anemia with and without malaria. Diagnoses that did not fit any category were labeled as “uncategorized diagnosis.” There were a total of 51 admission diagnoses including “uncategorized diagnosis.” Disposition was categorized as “discharged/home,” “absconded,” “death,” or “unknown.”
Data analysis.
Demographic data, HIV status on discharge, referral status, and disposition were reported both in aggregate and stratified by age (1–5 months; 6–36 months; and 37–60 months, inclusive). These age groups were chosen because the database initially only contained data on children aged 6–36 months and was later expanded.
The number of diagnoses that patients had at admission and death were reported. The frequency of individual diagnoses at admission and death were also reported. The mortality rate by diagnosis was calculated as the number of deaths among children admitted with a diagnosis divided by the number of children admitted with the same diagnosis. Therefore, children with multiple diagnoses were counted more than once when analyzing mortality rate. A 95% binomial CI was calculated for each mortality rate using the normal approximation method. Only diagnoses with at least 0.5% prevalence were reported in the tables.
For the seven diagnoses of greatest prevalence, the mortality rate in patients with combinations of two diagnoses were calculated. For all diagnoses with at least 0.5% prevalence, we reported mortality rate by age group (1–5 months, 6–36 months, and 37–60 months). Finally, the mortality rate by referral status was reported with a 95% binomial CI calculated using the normal approximation method. Fisher’s exact test was used in pairwise comparisons between groups with the two-tailed Type I error set at 0.05. All analyses were performed using Stata 13.0 (Stata Corp, College Station, TX), R 4.2.2 Statistical Software (R Foundation for Statistical Computing, Vienna, Austria), or Excel 14.7.2 (Microsoft, Redmond, WA).
RESULTS
Between October 1, 2017, and June 30, 2020, 25,016 admissions to the KCH Pediatric Department were recorded in the database. Patients were excluded if their age was outside of 1–60 months or unknown (N = 316), if they were dead on arrival (N = 112), or if they had no recorded admission diagnosis (N = 136) (Figure 1). Of the remaining 24,452 patients included in the analysis, 79.1% (N = 19,351) were in the 6- to 36-month age group (Table 1). The population was predominantly male (57.5%). The majority of children (80.5%, N = 19,669) had a known HIV status at discharge, with 1.7% (N = 409) confirmed as HIV infected. Most patients (76.1%, N = 18,619) initially presented to another facility before being referred to KCH. Disposition was recorded in 94.2% of patients (N = 23,043): 88.2% (N = 21,560) were discharged home, 2.1% (N = 506) absconded, and 4.0% (N = 977) died while in the hospital.
Distribution of patients in analysis.
Citation: The American Journal of Tropical Medicine and Hygiene 109, 2; 10.4269/ajtmh.22-0439
Characteristics of the patient cohort stratified by age
| Characteristic | Total | 1–5 Months | 6–36 Months | 37–60 Months |
|---|---|---|---|---|
| Patients, n (%) | 24,452 (100.0) | 3,079 (12.6) | 19,351 (79.1) | 2,022 (8.3) |
| Sex, n (%) | ||||
| Female | 10,360 (42.4) | 1,262 (41.0) | 8,209 (42.4) | 889 (44.0) |
| Male | 14,053 (57.5) | 1,815 (58.9) | 11,107 (57.4) | 1,131 (55.9) |
| Unknown | 39 (0.2) | 2 (0.1) | 35 (0.2) | 2 (0.1) |
| Age in months, median (Q1, Q3) | 16 (9, 26) | 3 (1, 4) | 17 (11, 25) | 48 (42, 52) |
| HIV status at discharge, n (%) | ||||
| Infected | 409 (1.7) | 59 (1.9) | 321 (1.7) | 29 (1.4) |
| Uninfected, ongoing exposure | 399 (1.6) | 126 (4.1) | 271 (1.4) | 2 (0.1) |
| Uninfected, unexposed | 18,861 (77.2) | 2,350 (76.3) | 14,917 (77.1) | 1,594 (78.8) |
| Unknown | 4,783 (19.6) | 544 (17.7) | 3,842 (19.9) | 397 (19.6) |
| Referral status, n (%) | ||||
| Referred | 18,619 (76.1) | 2,453 (79.7) | 14,618 (75.5) | 1,548 (76.6) |
| Self-presented | 5,575 (22.8) | 603 (19.6) | 4,529 (23.4) | 443 (21.9) |
| Unknown | 258 (1.1) | 23 (0.7) | 204 (1.1) | 31 (1.5) |
| Disposition, n (%) | ||||
| Home | 21,560 (88.2) | 2,708 (88.0) | 17,094 (88.3) | 1,758 (86.9) |
| Death | 977 (4.0) | 150 (4.9) | 737 (3.8) | 90 (4.5) |
| Absconded | 506 (2.1) | 33 (1.1) | 454 (2.3) | 19 (0.9) |
| Unknown | 1,409 (5.8) | 188 (6.1) | 1,066 (5.5) | 155 (7.7) |
There were 39,183 admission diagnoses assigned to the 24,452 admitted patients: 53.3% (N = 13,026) had one diagnosis, 35.4% (N = 8,664) had two diagnoses, and 11.3% (N = 2,762) had three or more diagnoses. There were 2,078 diagnoses assigned to the 977 patients who died: 28.7% (N = 280) had one diagnosis, 41.0% (N = 401) had two diagnoses, and 30.2% (N = 296) had three or more diagnoses.
The most frequent diagnoses among admissions and deaths were pneumonia/bronchiolitis, malaria, and sepsis (Tables 2 and 3). For diagnoses occurring in more than 0.5% of admissions, the highest mortality rates were found in surgical conditions (16.1%; 95% CI: 12.0–20.3), malnutrition (15.8%; 95% CI: 13.6–18.0), and congenital heart disease (14.5%; 95% CI: 9.9–19.2) (Table 4). When diagnoses of any prevalence were considered, the highest mortality rate were found in shock (57.1%; 95% CI: 36.0–78.3), respiratory failure (27.3%; 95% CI: 8.7–45.9), and hydrocephalus (23.7%; 95% CI: 15.0–32.2).
Frequency of diagnoses among admissions
| Rank | Diagnosis | Frequency, n (%)* |
|---|---|---|
| 1 | Pneumonia/bronchiolitis | 10,680 (43.7) |
| 2 | Malaria | 8,871 (36.3) |
| 3 | Sepsis | 4,629 (18.9) |
| 4 | Anemia | 3,375 (13.8) |
| 5 | Acute gastroenteritis | 3,328 (13.6) |
| 6 | Dehydration | 1,656 (6.8) |
| 7 | Malnutrition | 1,054 (4.3) |
| 8 | Convulsions (without malaria) | 954 (3.9) |
| 9 | Meningitis | 694 (2.8) |
| 10 | Asthma | 489 (2.0) |
| 11 | Uncategorized diagnosis | 425 (1.7) |
| 12 | Surgical condition | 304 (1.2) |
| 13 | Fever | 268 (1.1) |
| 14 | Congenital heart disease | 220 (0.9) |
| 15 | Dermatologic condition | 204 (0.8) |
| 16 | Upper respiratory tract infection | 172 (0.7) |
| 17 | Poisoning | 132 (0.5) |
Number of admissions with diagnosis and percentage of admissions with diagnosis. Percentages do not add up to 100% because of multiple diagnoses per admission.
Frequency of diagnoses among deaths
| Rank | Diagnosis | Frequency, n (%)* |
|---|---|---|
| 1 | Malaria | 390 (39.9) |
| 2 | Pneumonia/bronchiolitis | 288 (29.5) |
| 3 | Sepsis | 227 (23.2) |
| 4 | Anemia | 222 (22.7) |
| 5 | Acute gastroenteritis | 221 (22.6) |
| 6 | Dehydration | 171 (17.5) |
| 7 | Malnutrition | 167 (17.1) |
| 8 | Meningitis | 62 (6.3) |
| 9 | Surgical condition | 49 (5.0) |
| 10 | Uncategorized diagnosis | 36 (3.7) |
| 11 | Congenital heart disease | 32 (3.3) |
| 12 | Convulsions (without malaria) | 25 (2.6) |
| 13 | Dermatologic condition | 19 (1.9) |
| 14 | Upper respiratory tract infection | 5 (0.5) |
| 15 | Fever | 3 (0.3) |
| 16 | Asthma | 3 (0.3) |
| 17 | Poisoning | 0 (0.0) |
Number of deaths with diagnosis and percentage of deaths with diagnosis. Percentages do not add up to 100% because of multiple diagnoses per death.
Mortality rate by diagnosis
| Rank | Diagnosis | Mortality rate, %* (95% CI) |
|---|---|---|
| 1 | Surgical condition | 16.1 (12.0–20.3) |
| 2 | Malnutrition | 15.8 (13.6–18.0) |
| 3 | Congenital heart disease | 14.5 (9.9–19.2) |
| 4 | Dehydration | 10.3 (8.9–11.8) |
| 5 | Dermatologic condition | 9.4 (5.4–13.4) |
| 6 | Meningitis | 8.9 (6.8–11.1) |
| 7 | Uncategorized diagnosis | 8.5 (5.8–11.1) |
| 8 | Acute gastroenteritis | 6.6 (5.8–7.5) |
| 9 | Anemia | 6.6 (5.7–7.4) |
| 10 | Sepsis | 4.9 (4.3–5.5) |
| 11 | Malaria | 4.4 (4.0–4.8) |
| 12 | Upper respiratory tract infection | 2.9 (0.4–5.4) |
| 13 | Pneumonia/bronchiolitis | 2.7 (2.4–3.0) |
| 14 | Convulsions (without malaria) | 2.6 (1.6–3.6) |
| 15 | Fever | 1.1 (0.0–2.4) |
| 16 | Asthma | 0.6 (0.0–1.3) |
| 17 | Poisoning | 0.0 (0.0–0.0) |
Percentages do not add up to 100% because of multiple diagnoses per admission.
The most frequent combinations of diagnoses were malaria and anemia (N = 2,965), AGE and dehydration (N = 1,551), and malaria and sepsis (N = 1,363). Children with pneumonia/bronchiolitis, malaria, sepsis, and AGE had lower mortality rates when admitted with these diagnoses alone than when admitted with multiple diagnoses (Table 5). Anemia, dehydration, and malnutrition were notable for particularly high mortality rates when in combination with other diagnoses. For example, the mortality rate of children admitted with AGE and no other diagnosis was 1.3% (95% CI: 0.2–2.4). However, the mortality rates of children admitted with AGE and anemia, AGE and dehydration, and AGE and malnutrition were 14.9% (95% CI: 8.5–21.2), 9.5% (95% CI: 8.1–11.0), and 22.5% (95% CI: 17.5–27.6), respectively.
Mortality rates of combinations of diagnoses, % (95% CI)
| Diagnosis | Co-diagnosis | ||||||
|---|---|---|---|---|---|---|---|
| No other diagnosis | Malaria | Sepsis | Anemia | AGE | Dehydration | Malnutrition | |
| PNA/B | 1.3 (1.0–1.5) | 6.1 (4.6–7.5) | 5.0 (3.5–6.6) | 14.7 (10.3–19.1) | 8.6 (6.2–11.0) | 18.6 (12.8–24.4) | 16.8 (11.7–21.9) |
| Malaria | 2.1 (1.6–2.6) | 4.8 (3.6–5.9) | 6.0 (5.1–6.9) | 9.8 (7.4–12.3) | 16.6 (11.6–21.6) | 13.1 (9.0–17.2) | |
| Sepsis | 2.4 (1.5–3.4) | 10.1 (7.0–13.2) | 6.6 (5.1–8.2) | 12.1 (8.9–15.3) | 17.5 (12.5–22.5) | ||
| Anemia | 5.7 (0.8–10.5) | 14.9 (8.5–21.2) | 28.9 (15.6–42.1) | 10.5 (5.5–15.5) | |||
| AGE | 1.3 (0.2–2.4) | 9.5 (8.1–11.0) | 22.5 (17.5–27.6) | ||||
| Dehydration | 12.5 (0.0–28.7) | 29.2 (22.0–36.4) | |||||
| Malnutrition | 16.3 (10.5–22.2) | ||||||
AGE = acute gastroenteritis; PNA/B = pneumonia/bronchiolitis. Normal type = 0.0–9.9%; italic type = 10.0–19.9%; bold type = ≥ 20.0%.
When stratified by age group, the overall mortality rate was higher in the 1- to 5-month group (4.9%; 95% CI: 4.1–5.6) compared with the 6- to 36-month group (3.8%; 95% CI: 3.5–4.1; P = 0.006) (Table 6). There were no other differences in overall mortality rate in pairwise comparisons by age group. The mortality rates for malaria, sepsis, and anemia were higher in the 1- to 5-month group compared with both the 6- to 36-month and 37- to 60-month age groups. The mortality rates of pneumonia/bronchiolitis and meningitis were higher in the 1- to 5-month group compared with the 6- to 36-month group. The mortality rate of AGE was higher in the 1- to 5-month group compared with the 37- to 60-month group, whereas the mortality rate of surgical conditions was higher in the 37- to 60-month group compared with the 1- to 5-month group. The mortality rate of dermatologic conditions was higher in the 37- to 60-month group compared with the 6- to 36-month group. All other pairwise comparisons of mortality rate by diagnosis between age groups were not statistically significant.
Mortality rates of diagnoses by age
| Diagnosis | Mortality rate, % (95% CI) | Age group comparisons, P value* | ||||
|---|---|---|---|---|---|---|
| 1–5 Months | 6–36 Months | 37–60 Months | 1–5 vs. 6–36 | 1–5 vs. 37–60 | 6–36 vs. 37–60 | |
| All diagnoses | 4.9 (4.1–5.6) | 3.8 (3.5–4.1) | 4.5 (3.6–5.4) | 0.006 | 0.500 | 0.163 |
| Pneumonia/bronchiolitis | 3.5 (2.7–4.2) | 2.5 (2.2–2.8) | 2.2 (0.9–3.6) | 0.009 | 0.194 | 0.876 |
| Malaria | 8.0 (4.4–11.7) | 4.3 (3.9–4.8) | 4.3 (3.1–5.4) | 0.016 | 0.024 | 1.000 |
| Sepsis | 9.1 (6.4–11.8) | 4.5 (3.8–5.1) | 4.3 (2.3–6.3) | < 0.001 | 0.006 | 1.000 |
| Anemia | 14.2 (7.5–20.8) | 6.5 (5.6–7.4) | 5.3 (3.1–7.4) | 0.005 | 0.005 | 0.390 |
| Acute gastroenteritis | 9.2 (5.6–12.7) | 6.6 (5.7–7.5) | 2.7 (0.0–5.6) | 0.117 | 0.027 | 0.116 |
| Dehydration | 11.9 (6.4–17.3) | 10.2 (8.7–11.8) | 9.1 (0.6–17.6) | 0.555 | 0.785 | 1.000 |
| Malnutrition | 22.6 (11.4–33.9) | 15.6 (13.3–18.0) | 14.1 (6.7–21.5) | 0.178 | 0.249 | 0.875 |
| Convulsions (without malaria) | 2.9 (0.0–8.6) | 2.6 (1.5–3.7) | 2.5 (0.0–5.2) | 0.605 | 1.000 | 1.000 |
| Meningitis | 18.6 (7.0–30.2) | 8.2 (5.9–10.5) | 8.8 (3.3–14.3) | 0.045 | 0.155 | 0.845 |
| Asthma | 0.0 (0.0–0.0) | 0.5 (0.0–1.3) | 0.8 (0.0–2.5) | 1.000 | 1.000 | 0.575 |
| Uncategorized diagnosis | 6.3 (3.2–9.3) | 9.1 (7.4–10.8) | 7.7 (4.4–11.0) | 0.624 | 1.000 | 1.000 |
| Surgical condition | 12.2 (8.6–15.8) | 17.8 (15.0–20.6) | 29.0 (20.9–37.2) | 0.285 | 0.047 | 0.147 |
| Fever | 0.0 (0.0–0.0) | 1.3 (0.5–2.0) | 0.0 (0.0–0.0) | 1.000 | 1.000 | 1.000 |
| Congenital heart disease | 16.9 (12.5–21.3) | 13.0 (10.0–15.9) | 16.7 (7.9–25.5) | 0.529 | 1.000 | 0.712 |
| Dermatologic condition | 5.9 (1.8–9.9) | 7.5 (5.3–9.7) | 25.0 (16.2–33.8) | 1.000 | 0.055 | 0.018 |
| Upper respiratory tract infection | 10.0 (3.3–16.7) | 2.2 (0.9–3.4) | 0.0 (0.0–0.0) | 0.122 | 0.496 | 1.000 |
| Poisoning | 0.0 (0.0–0.0) | 0.0 (0.0–0.0) | 0.0 (0.0–0.0) | 1.000 | 1.000 | 1.000 |
Fisher’s exact test.
In patients who were referred, self-presented, or had an unknown referral status, the mortality rates were 3.8% (95% CI: 3.7–3.9), 3.4% (95% CI: 3.2–3.6), and 29.1% (95% CI: 26.3–31.9), respectively. Patients with an unknown referral status accounted for 1.1% (258/24,452) of all admitted patients but 7.6% of all deaths (75/977). Pairwise comparison of the mortality rate between referred and self-presenting patients was not statistically significant (P = 0.136). Pairwise comparisons between patients with an unknown referral status and either referred or self-presenting patients were statistically significant (P < 0.001).
DISCUSSION
In our study, we found that the mortality rate of hospitalized Malawian children varies widely depending on their admission diagnoses. The overall mortality rate and prevalence of diagnoses approximate the results of prior studies from Malawi. 8 , 12 , 13 , 24 Children with surgical conditions, malnutrition, congenital heart disease, and dehydration had the highest mortality rates in our analysis. Children with the co-diagnoses of anemia, dehydration, and malnutrition also had higher mortality rates, which may indicate that these diagnoses are pathogenic consequences of other diagnoses and are markers of illness severity.
Our study differs from other studies of child hospital mortality in sub-Saharan Africa in its approach to diagnostic uncertainty. Most studies describing the mortality rates of hospitalized children report a single cause of death or final diagnosis. 13 , 25 – 36 However, children in this population have comorbidities, such as HIV and severe acute malnutrition, that affect mortality. Furthermore, due to limited testing, imaging, and subspeciality consultation, many diagnoses can be challenging to confirm. Our study considers each diagnosis to have equal impact on mortality. We first report the mortality rate of children with a diagnosis regardless of co-diagnoses. We also then report the mortality rate of children admitted with combinations of diagnoses. Only one prior study has reported mortality rates in children with combinations of diagnoses. 14 Our study also relies on clinical diagnoses. Single disease studies typically ensure the accuracy of the diagnosis by enrolling patients with strict inclusion and exclusion criteria meeting the case definition of a disease. 16 , 37 However, they are costly to perform and do not depict the mortality burden of the entire hospitalized cohort. Our approach may provide a greater breadth of information, thereby helping leaders more quickly respond to the needs of the population.
Our study also differs from others because it reports mortality rates based on diagnoses at admission instead of at final disposition. Because treatment decisions in resource-limited settings are usually made at admission, our approach may provide clinicians a practical estimation of risk at the time of clinical decision-making. Furthermore, diagnoses at final disposition are not necessarily more accurate than admission diagnoses, and the concordance between the two remains unstudied in resource-limited settings. 38 – 43
When examining the diagnoses with the highest mortality rates, they appear alike in their need for significant human and material resources for medical care. Malnutrition and dehydration both have standardized treatment plans that are effective in LMICs. 44 However, in settings with inconsistent nursing coverage, the feeding or rehydration plan may be informally task shifted to guardians. 45 – 47 Formal training of guardians and increased nursing coverage could improve adherence to these plans, thereby improving outcomes. Task-shifting of asthma education to lay educators, for example, has already been demonstrated in Malawi. 48 For children with surgical conditions, both limited surgical capacity nationally and inconsistent clinical coverage of surgical patients in the hospital may contribute to their high mortality. 49 In response, our department has considered increasing clinical coverage of the surgical wards with pediatric staff to help monitor these patients. Similarly, for patients with congenital heart disease, the inconsistent availability of subspecialists likely drives lethality. At our institution, only a few children have access to a pediatric cardiologist, and even fewer can be referred for definitive surgical care taking place outside of the country. 50 , 51 Finally, when examining diagnoses of high mortality regardless of prevalence (i.e., shock, respiratory failure, and hydrocephalus), the need for consistent specialized physician and nursing care is again clear. Patients with these conditions typically require admission to intensive care units, which are lacking throughout sub-Saharan Africa. 52
Late referral of patients may also drive the high mortality rates of some of these conditions. Although many children can obtain sufficient care at a local center, others require prompt recognition by the local clinician to refer the patient to a tertiary care facility. Unfortunately, delays in referral are frequent due to barriers in diagnostic testing, local expertise, and transportation. 53 – 56 When these patients do arrive, they often present with advanced disease. Such patients would likely have high mortality rates even in resource-abundant settings. Our study did not find that referred patients and self-presenting patients had a difference in mortality. However, a disproportionate share of patients who died had an unknown referral status, which could affect our results. Therefore, further research on the impact of referral systems on child hospital mortality is necessary.
There are several limitations to our study. First, due to our methodology, our estimates of mortality rate by diagnosis may be less accurate compared with those reported by studies with strict case definitions. However, our study’s reported mortality rate of pneumonia/bronchiolitis is consistent with case fatality rates reported by cohort studies from Malawi. 16 , 57 , 58 Unfortunately, there are few other cohort studies of hospitalized Malawian children with which to compare the accuracy of our other results. Second, our study is unable to identify deaths after discharge. 59 – 61 Our reported mortality rates probably underestimate the true mortality burden of disease. Finally, 5.8% of patients in our study had an unknown disposition. Although this is a degree of data loss seen in other studies from sub-Saharan African hospitals, the possibility of missed deaths may cause our study to underestimate the true mortality rate. 15
CONCLUSION
Child hospital mortality in resource-limited settings is unacceptably high but is not insurmountable. 62 , 63 By measuring the mortality rates of all prevalent conditions, hospitals can evaluate the quality of their care and track the impact of interventions. Throughout sub-Saharan Africa and within our own hospital, quality improvement initiatives, audits, post-graduate training programs, and clinical information systems are being established to reduce child hospital mortality. 12 , 14 , 64 – 67 Accurate measurement of the impacts of these interventions will be essential in allocating the limited resources in these settings. 5
ACKNOWLEDGMENTS
We thank the patients and families at KCH for their involvement in the study. We also thank the clinical staff in the Department of Pediatrics at KCH for providing care for these patients as well as the data clerks for their management of the database.
REFERENCES
- 1.↑
United Nations , 2015. The Millennium Development Goals Report 2015. New York, NY: United Nations. Available at: https://www.un.org/millenniumgoals/2015_MDG_Report/pdf/MDG%202015%20rev%20(July%201).pdf. Accessed April 19, 2022.
- 2.↑
Kanyuka M et al., 2016. Malawi and millennium development goal 4: a countdown to 2015 country case study. Lancet Glob Health 4: e201–e214.
- 3.↑
Requejo JH et al., 2015. Countdown to 2015 and beyond: fulfilling the health agenda for women and children. Lancet 385: 466–476.
- 4.↑
Ruducha J et al., 2017. How Ethiopia achieved Millennium Development Goal 4 through multisectoral interventions: a countdown to 2015 case study. Lancet Glob Health 5: e1142–e1151.
- 5.↑
National Academies of Sciences, Engineering, and Medicine , 2018. Crossing the Global Quality Chasm: Improving Health Care Worldwide. Washington, DC: The National Academies Press.
- 6.↑
English M , Mwaniki P , Julius T , Chepkirui M , Gathara D , Ouma PO , Cherutich P , Okiro EA , Snow RW , 2018. Hospital mortality – a neglected but rich source of information supporting the transition to higher quality health systems in low and middle income countries. BMC Med 16: 32.
- 7.↑
Kruk ME et al., 2018. High-quality health systems in the Sustainable Development Goals era: time for a revolution. Lancet Glob Health 6: e1196–e1252.
- 8.↑
Fitzgerald E , Mlotha-Mitole R , Ciccone EJ , Tilly AE , Montijo JM , Lang HJ , Eckerle M , 2018. A pediatric death audit in a large referral hospital in Malawi. BMC Pediatr 18: 75.
- 9.↑
Kruk ME , Gage AD , Joseph NT , Danaei G , García-Saisó S , Salomon JA , 2018. Mortality due to low-quality health systems in the universal health coverage era: a systematic analysis of amenable deaths in 137 countries. Lancet 392: 2203–2212.
- 10.↑
Setel PW , Macfarlane SB , Szreter S , Mikkelsen L , Jha P , Stout S , AbouZahr C , 2007. A scandal of invisibility: making everyone count by counting everyone. Lancet 370: 1569–1577.
- 11.↑
National Statistical Office , 2015. Malawi Demographic and Health Survey 2015–16. Zomba, Malawi: National Statistical Office. Available at: http://dhsprogram.com/pubs/pdf/FR319/FR319.pdf. Accessed March 15, 2021.
- 12.↑
Robison JA et al., 2012. Decreased pediatric hospital mortality after an intervention to improve emergency care in Lilongwe, Malawi. Pediatrics 130: e676–e678.
- 13.↑
Harris C , Mills R , Seager E , Blackstock S , Hiwa T , Pumphrey J , Langton J , Kennedy N , 2019. Paediatric deaths in a tertiary government hospital setting, Malawi. Paediatr Int Child Health 39: 240–248.
- 14.↑
Ayieko P et al., 2016. Characteristics of admissions and variations in the use of basic investigations, treatments and outcomes in Kenyan hospitals within a new Clinical Information Network. Arch Dis Child 101: 223–229.
- 15.↑
The Child-PIP Group , The MRC Research Unit for Maternal and Infant Health Care Strategies, 2011. Saving Children 2004: A Survey of Child Healthcare in South Africa. Krug A & Pattinson R Pretoria, South Africa: University of Pretoria.
- 16.↑
Lazzerini M et al., 2016. Mortality and its risk factors in Malawian children admitted to hospital with clinical pneumonia, 2001–12: a retrospective observational study. Lancet Glob Health 4: e57–e68.
- 17.↑
Stewart K , Choudry MI , Buckingham R , 2016. Learning from hospital mortality. Clin Med (Lond) 16: 530–534.
- 18.↑
Marchant T , Bryce J , Victora C , Moran AC , Claeson M , Requejo J , Amouzou A , Walker N , Boerma T , Grove J , 2016. Improved measurement for mothers, newborns and children in the era of the Sustainable Development Goals. J Glob Health 6: 010506.
- 19.↑
Ciccone EJ et al., 2020. Lessons learned from the development and implementation of an electronic paediatric emergency and acute care database in Lilongwe, Malawi. BMJ Glob Health 5: e002410.
- 20.↑
National Statistical Office , 2018. 2018 Malawi Population and Housing Census. Zomba, Malawi: National Statistical Office. Available at: https://malawi.unfpa.org/sites/default/files/resource-pdf/2018%20Census%20Preliminary%20Report.pdf. Accessed March 15, 2021.
- 21.↑
Vonasek BJ , Chiume M , Crouse HL , Mhango S , Kondwani A , Ciccone EJ , Kazembe PN , Gaven W , Fitzgerald E , 2020. Risk factors for mortality and management of children with complicated severe acute malnutrition at a tertiary referral hospital in Malawi. Paediatr Int Child Health 40: 148–157.
- 22.↑
Preidis GA , McCollum ED , Kamiyango W , Garbino A , Hosseinipour MC , Kazembe PN , Schutze GE , Kline MW , 2013. Routine inpatient provider-initiated HIV testing in Malawi, compared with client-initiated community-based testing, identifies younger children at higher risk of early mortality. J Acquir Immune Defic Syndr 63: e16–e22.
- 23.↑
Keating EM et al., 2021. Blood transfusion and mortality in children with severe anaemia in a malaria-endemic region. Paediatr Int Child Health 41: 129–136.
- 24.↑
Ngwalangwa F , Phiri CHA , Dube Q , Langton J , Hildenwall H , Baker T , 2019. Risk factors for mortality in severely ill children admitted to a tertiary referral hospital in Malawi. Am J Trop Med Hyg 101: 670–675.
- 25.↑
Afolabi BM , Clement CO , Ekundayo A , Dolapo D , 2012. A hospital-based estimate of major causes of death among under-five children from a health facility in Lagos, Southwest Nigeria: possible indicators of health inequality. Int J Equity Health 11: 39.
- 26.↑
Aluvaala J , Collins GS , Maina B , Mutinda C , Wayiego M , Berkley JA , English M , 2019. Competing risk survival analysis of time to in-hospital death or discharge in a large urban neonatal unit in Kenya. Wellcome Open Res 4: 96.
- 27.↑
Bohn JA et al., 2016. Demographic and mortality analysis of hospitalized children at a referral hospital in Addis Ababa, Ethiopia. BMC Pediatr 16: 168.
- 28.↑
Campbell JD , Sow SO , Levine MM , Kotloff KL , 2004. The causes of hospital admission and death among children in Bamako, Mali. J Trop Pediatr 50: 158–163.
- 29.↑
Gordon DM , Frenning S , Draper HR , Kokeb M , 2013. Prevalence and burden of diseases presenting to a general pediatrics ward in Gondar, Ethiopia. J Trop Pediatr 59: 350–357.
- 30.↑
Huerga H , Vasset B , Prados E , 2009. Adult and paediatric mortality patterns in a referral hospital in Liberia 1 year after the end of the war. Trans R Soc Trop Med Hyg 103: 476–484.
- 31.↑
Mitiku K , Abera D , 2007. Analysis of pediatric admissions at Tefera Hailu Hospital, Sekota, Amhara Region. Ethiopian J Pediatr Child Health 3: 1–9.
- 32.↑
Onyiriuka A , 2009. Morbidity and mortality patterns of post-neonatal paediatric medical admissions in a large mission hospital in Benin City, Nigeria. J Med Biomed Res 4: 49–58.
- 33.↑
Osano BO , Were F , Mathews S , 2017. Mortality among 5–17 year old children in Kenya. Pan Afr Med J 27: 121.
- 34.↑
Tsai C , Walters C , Sampson J , Kateh F , Chang M , 2017. Pediatric mortality in a rural tertiary care center in Liberia. Children (Basel) 4: 8.
- 35.↑
van den Boogaard W , Manzi M , Harries AD , Reid AJ , 2012. Causes of pediatric mortality and case-fatality rates in eight Médecins Sans Frontières-supported hospitals in Africa. Public Health Action 2: 117–121.
- 36.↑
Veirum JE , Biai S , Jakobsen M , Sandström A , Hedegaard K , Kofoed PE , Aaby P , Sodemann M , 2007. Persisting high hospital and community childhood mortality in an urban setting in Guinea-Bissau. Acta Paediatr 96: 1526–1530.
- 37.↑
Guinovart C et al., 2022. The epidemiology of severe malaria at Manhiça District Hospital, Mozambique: a retrospective analysis of 20 years of malaria admissions surveillance data. Lancet Glob Health 10: E873–E881.
- 38.↑
Hautz WE , Kämmer JE , Hautz SC , Sauter TC , Zwaan L , Exadaktylos AK , Birrenbach T , Maier V , Müller M , Schauber SK , 2019. Diagnostic error increases mortality and length of hospital stay in patients presenting through the emergency room. Scand J Trauma Resusc Emerg Med 27: 54.
- 39.↑
Avelino-Silva TJ , Steinman MA , 2020. Diagnostic discrepancies between emergency department admissions and hospital discharges among older adults: secondary analysis on a population-based survey. Sao Paulo Med J 138: 359–367.
- 40.↑
Leske MC , Sorensen AA , Zimmer JG , 1978. Discrepancies between admission and discharge diagnoses in a university hospital. Med Care 16: 740–748.
- 41.↑
Lim G , Seow E , Koh G , Tan D , Wong H , 2002. Study on the discrepancies between the admitting diagnoses from the emergency department and the discharge diagnoses. Hong Kong J Emerg Med 9: 78–82.
- 42.↑
McNutt R , Johnson T , Kane J , Ackerman M , Odwazny R , Bardhan J , 2012. Cost and quality implications of discrepancies between admitting and discharge diagnoses. Qual Manag Health Care 21: 220–227.
- 43.↑
Johnson T , McNutt R , Odwazny R , Patel D , Baker S , 2009. Discrepancy between admission and discharge diagnoses as a predictor of hospital length of stay. J Hosp Med 4: 234–239.
- 44.↑
WHO , 2013. Pocket Book of Hospital Care for Children, 2nd edition. Geneva, Switzerland: World Health Organization.
- 45.↑
North N , Leonard A , Bonaconsa C , Duma T , Coetzee M , 2020. Distinctive nursing practices in working with mothers to care for hospitalised children at a district hospital in KwaZulu-Natal, South Africa: a descriptive observational study. BMC Nurs 19: 28.
- 46.↑
Phiri PGMC , Kafulafula U , Chorwe-Sungani G , 2017. Registered nurses’ experiences pertaining to family involvement in the care of hospitalised children at a tertiary government hospital in Malawi. Afr J Nurs Midwifery 19: 131–143.
- 47.↑
Söderbäck M , Christensson K , 2007. Care of hospitalized children in Mozambique: nurses’ beliefs and practice regarding family involvement. J Child Health Care 11: 53–69.
- 48.↑
Nkhalamba L , Rylance S , Muula AS , Mortimer K , Limbani F , 2021. Task-shifting to improve asthma education for Malawian children: a qualitative analysis. Hum Resour Health 19: 28.
- 49.↑
Henry JA , Frenkel E , Borgstein E , Mkandawire N , Goddia C , 2015. Surgical and anaesthetic capacity of hospitals in Malawi: key insights. Health Policy Plan 30: 985–994.
- 50.↑
Sanyahumbi AS , Sable CA , Karlsten M , Hosseinipour MC , Kazembe PN , Minard CG , Penny DJ , 2017. Task shifting to clinical officer-led echocardiography screening for detecting rheumatic heart disease in Malawi, Africa. Cardiol Young 27: 1133–1139.
- 51.↑
Selman A , Kennedy NEB , 2013. Defining the burden of paediatric cardiac disease in Malawi – the experience from a tertiary referral centre. Arch Dis Child 98 (Suppl 1 ):A69.
- 52.↑
Vukoja M , Riviello E , Gavrilovic S , Adhikari NKJ , Kashyap R , Bhagwanjee S , Gajic O , Kilickaya O , 2014. A survey on critical care resources and practices in low- and middle-income countries. Glob Heart 9: 337–342.e1-5.
- 53.↑
Gyedu A , Baah EG , Boakye G , Ohene-Yeboah M , Otupiri E , Stewart BT , 2015. Quality of referrals for elective surgery at a tertiary care hospital in a developing country: an opportunity for improving timely access to and cost-effectiveness of surgical care. Int J Surg 15: 74–78.
- 54.↑
Grimes CE , Bowman KG , Dodgion CM , Lavy CBD , 2011. Systematic review of barriers to surgical care in low-income and middle-income countries. World J Surg 35: 941–950.
- 55.↑
Levine AC , Presser DZ , Rosborough S , Ghebreyesus TA , Davis MA , 2007. Understanding barriers to emergency care in low-income countries: view from the front line. Prehosp Disaster Med 22: 467–470.
- 56.↑
Simoes EAF , Peterson S , Gamatie Y , Kisanga FS , Mukasa G , Nsungwa-Sabiiti J , Were MW , Weber MW , 2003. Management of severely ill children at first-level health facilities in sub-Saharan Africa when referral is difficult. Bull World Health Organ 81: 522–531.
- 57.↑
Hooli S , Colbourn T , Lufesi N , Costello A , Nambiar B , Thammasitboon S , Makwenda C , Mwansambo C , McCollum ED , King C , 2016. Predicting hospitalised paediatric pneumonia mortality risk: an external validation of RISC and mRISC, and local tool development (RISC-Malawi) from Malawi. PLoS One 11: e0168126.
- 58.↑
Mvalo T et al., 2022. Antibiotic treatment failure in children aged 1 to 59 months with World Health Organization-defined severe pneumonia in Malawi: a CPAP IMPACT trial secondary analysis. PLoS One 17: e0278938.
- 59.↑
Wiens MO , Pawluk S , Kissoon N , Kumbakumba E , Ansermino JM , Singer J , Ndamira A , Larson C , 2013. Pediatric post-discharge mortality in resource poor countries: a systematic review. PLoS One 8: e66698.
- 60.↑
Ngari MM , Berkley JA , 2022. Severe anaemia and paediatric mortality after hospital discharge in Africa. Lancet Child Adolesc Health 6: 447–449.
- 61.↑
Childhood Acute Illness and Nutrition (CHAIN) Network , 2022. Childhood mortality during and after acute illness in Africa and south Asia: a prospective cohort study. Lancet Glob Health 10: e673–e684.
- 62.↑
Amouzou A , Kozuki N , Gwatkin DR , 2014. Where is the gap? The contribution of disparities within developing countries to global inequalities in under-five mortality. BMC Public Health 14: 216.
- 63.↑
Victora CG , Wagstaff A , Schellenberg JA , Gwatkin D , Claeson M , Habicht JP , 2003. Applying an equity lens to child health and mortality: more of the same is not enough. Lancet 362: 233–241.
- 64.↑
Irimu G , Ogero M , Mbevi G , Agweyu A , Akech S , Julius T , Nyamai R , Githang’a D , Ayieko P , English M , 2018. Approaching quality improvement at scale: a learning health system approach in Kenya. Arch Dis Child 103: 1013–1019.
- 65.↑
Rule ARL , Maina E , Cheruiyot D , Mueri P , Simmons JM , Kamath-Rayne BD , 2017. Using quality improvement to decrease birth asphyxia rates after ‘Helping Babies Breathe’ training in Kenya. Acta Paediatr 106: 1666–1673.
- 66.↑
South Africa Every Death Counts Writing Group , 2008. Every death counts: use of mortality audit data for decision making to save the lives of mothers, babies, and children in South Africa. Lancet 371: 1294–1304.
- 67.↑
Singh K , Brodish P , Speizer I , Barker P , Amenga-Etego I , Dasoberi I , Kanyoke E , Boadu EA , Yabang E , Sodzi-Tettey S , 2016. Can a quality improvement project impact maternal and child health outcomes at scale in northern Ghana? Health Res Policy Syst 14: 45.