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. 2017 Mar 20;31(3):233–242. doi: 10.1111/ppe.12348

Mortality after Inpatient Treatment for Severe Pneumonia in Children: a Cohort Study

Moses M Ngari 1,2, Greg Fegan 1,3, Martha K Mwangome 1,2, Mwanajuma J Ngama 1, Neema Mturi 1, John Anthony Gerard Scott 1,4, Evasius Bauni 1, David James Nokes 1,5, James A Berkley 1,2,6,
PMCID: PMC5434848  PMID: 28317139

Abstract

Background

Although pneumonia is a leading cause of inpatient mortality, deaths may also occur after discharge from hospital. However, prior studies have been small, in selected groups or did not fully evaluate risk factors, particularly malnutrition and HIV. We determined 1‐year post‐discharge mortality and risk factors among children diagnosed with severe pneumonia.

Methods

A cohort study of children aged 1–59 months admitted to Kilifi County Hospital with severe pneumonia (2007–12). The primary outcome was death <1 year after discharge, determined through Kilifi Health and Demographic Surveillance System (KHDSS) quarterly census rounds.

Results

Of 4184 children (median age 9 months) admitted with severe pneumonia, 1041 (25%) had severe acute malnutrition (SAM), 267 (6.4%) had a positive HIV antibody test, and 364 (8.7%) died in hospital. After discharge, 2279 KHDSS‐resident children were followed up; 70 (3.1%) died during 2163 child‐years: 32 (95% confidence interval (CI) 26, 41) deaths per 1000 child years. Post‐discharge mortality was greater after admission for severe pneumonia than for other diagnoses, hazard ratio 2.5 (95% CI 1.2, 5.3). Malnutrition, HIV status, age and prolonged hospitalisation, but not signs of pneumonia severity, were associated with post‐discharge mortality. Fifty‐two per cent (95% CI 37%, 63%) of post‐discharge deaths were attributable to low mid‐upper arm circumference and 11% (95% CI 3.3%, 18%) to a positive HIV test.

Conclusions

Admission with severe pneumonia is an important marker of vulnerability. Risk stratification and better understanding of the mechanisms underlying post‐discharge mortality, especially for undernourished children, are needed to reduce mortality after treatment for pneumonia.

Keywords: children, severe pneumonia, post‐discharge, mortality


Worldwide, pneumonia is the leading cause of death for children <5 years and accounted for approximately 1.3 million deaths in 2011.1, 2 Several studies have examined inpatient treatment failure and death amongst children with severe pneumonia.3, 4, 5, 6 However, deaths are known to also occur after discharge from hospital. A systematic review of paediatric post‐discharge mortality in resource‐poor countries, identified 13 studies.7 Only three of these studies specifically reported outcomes among children admitted with pneumonia: from The Gambia (n = 118), Bangladesh (n = 162), and Tanzania (n = 666).8, 9, 10 Two of the studies excluded children with severe malnutrition or other co‐morbidities,9, 10 and one examined the effect of HIV prior to widespread availability of antiretroviral therapy.10

Since the systematic review in 2013,7 three studies reporting post‐discharge mortality among children admitted with pneumonia have been published.11, 12, 13 Two of these studies included hospitalised children with a diagnosis of pneumonia, but not necessarily severe pneumonia according to World Health Organization (WHO) guidelines, from The Gambia (n = 2725)11 and Uganda (n = 389).12 Only the Ugandan study12 examined the effect of HIV status. One other study, from Bangladesh (n = 369)13 used WHO diagnostic criteria for severe pneumonia, but only included HIV‐uninfected children with Severe Acute Malnutrition (SAM). The duration of follow‐up varied between 3 months and more than 12 months, and none adequately reported loss to follow‐up or expressed mortality as an incidence rate. Thus, current knowledge of post‐discharge mortality following treatment from severe pneumonia, and its risk factors, is limited.

We aimed to determine 1‐year post‐discharge mortality rate and its risk factors amongst children consecutively admitted to a rural Kenyan hospital with syndromically diagnosed severe pneumonia.14

Methods

Study setting

This was an observational cohort study. The study was conducted at Kilifi County Hospital (KCH) in a rural area on the Kenyan coast. Haemophilus influenzae type b and pneumococcal conjugate vaccines were introduced in November 2001 and February 2011, respectively.15, 16 The antenatal HIV prevalence is 4.9%.17 KCH provides inpatient and outpatient services for HIV and malnutrition. Exposures were clinical, laboratory, and demographic features at hospitalisation. The primary outcome was death within 1 year after discharge from hospital. The study was approved by the Kenya Medical Research Institute (KEMRI) National Ethical Review Committee (SCC 2778).

Study population

We analysed systematically collected surveillance data from all children aged 1 to 59 months admitted to KCH between January 2007 and December 2012, and followed up those resident in the Kilifi Health and Demographic Surveillance System (KHDSS) until April 2014. We initially included non‐KHDSS residents in order to better understand the characteristics and generalisability of the children who were followed up in the context of all children served by the hospital. Trained clinicians provided care according to WHO guidelines.14

Clinical definitions and care

Severe pneumonia was defined by WHO (2005)14 as cough or difficulty breathing plus either lower chest wall indrawing or inability to breast feed/drink/vomiting everything, impaired consciousness, central cyanosis or peripheral oxygen saturation <90% by pulse oximetry (Nelcor USA).3, 14 Impaired consciousness was defined as ‘prostration’ (inability to sit unassisted at ≥1 year; inability to drink or breast feed at <1 year) or ‘coma’ (Blantyre coma score ≤2). We classified children as having the syndromes of severe or very severe pneumonia, hereafter called ‘severe pneumonia’ (including children who had an additional diagnosis), or not having severe pneumonia.

As previously described,18, 19 trained clinical assistants measured Mid‐upper arm circumference (MUAC) with a non‐stretch measuring tape (TALC, St Albans, UK), weight with an electronic scale (Seca, Birmingham, UK) that was checked weekly for consistency, and length using a measuring board of standard United Nation Children's Fund (UNICEF) design (for children younger than 2 years or those who could not stand) or height using a wall‐mounted stadiometer (Seca, Birmingham, UK). Inpatient management of SAM was based on one or more of weight‐for‐length z‐score, MUAC or the presence of kwashiorkor, and followed WHO guidelines.14 Children with SAM were discharged to therapeutic and/or supplementary feeding programmes as per national guidelines.

HIV testing using two rapid antibody tests, Determine (Inverness Medical, Florida, USA) and Unigold (Trinity Biotech, Bray, Ireland), was systematically offered to all paediatric admissions according to national guidelines.20 Families of patients with a positive test were counselled and referred for comprehensive care. Blood culture was systematically undertaken by methods previously published.18 We defined severe anaemia as haemoglobin <5 g/dL. As part of an ongoing study, nasopharyngeal specimens were systematically collected from children with severe pneumonia and tested for respiratory syncytial virus (RSV) using a Direct Immunofluorescent Antibody Test (Chemicon, Temecula, CA).21, 22 Nasopharyngeal samples were not collected in a high proportion of the most severely ill children (i.e., those in the high dependency ward), as previously reported.21 RSV was the only viral pathogen more frequently detected in severe pneumonia compared to controls without pneumonia.21

Follow‐up after discharge was done through the KHDSS. Within the KHDSS, the community of approximately 262 000 residents in an area of 891 km2 immediately surrounding the hospital has been allocated unique identifiers that are matched when patients are admitted to hospital. Households are enumerated by trained fieldworkers for vital events every 4 months, as described elsewhere.17

Statistical analysis

Participants were stratified either as residents of KDHSS or non‐residents. Outlying anthropometric z‐score values were excluded if their values were ±6 from the median z‐score for each anthropometric parameter. This method differs from data cleaning methods commonly applied to community surveys (usually ±3 z‐score from the observed survey mean)23 because very low values were typically confirmed as genuine within this range amongst this population of children who were sick enough to be admitted to hospital.

To determine post‐discharge mortality, we used KDHSS census data from January 2007 to April 2014. The time at risk considered was from discharge to 365 days later, or the date of out‐migration or death. We performed a single discharge analysis, where only the first admission during the study period was considered. Data from children with missing outcomes were excluded. Absent HIV antibody and RSV test data were analysed as separate categories as they were assumed not to have been missed randomly. Post‐discharge mortality was reported as the incidence rate per 1000 child‐years of observation.

Variables were investigated as potential risk factors for post‐discharge mortality based on previous work.3, 24 A binary variable for previous admission was used in the analysis since only 0.9% (22/2461) of KHDSS‐residents who were discharged alive had >1 prior admission. We plotted Kaplan—Meier curves and used Cox proportional hazards models to test associations with post‐discharge mortality, retaining all variables in the multivariable model. We tested for interactions by comparing models using likelihood‐ratio tests. Survival distributions were compared using the log‐rank test. Goodness of fit was assessed using correlation coefficient, R‐squared and Akaike information criterion.

Because of the anticipated role of undernutrition in driving mortality, that stunting as well as thinness may be associated with increased mortality risk, and debate over which indices are most informative,25 we built three multivariable models using different anthropometric parameters: (i) using MUAC, which captures elements of age, is less affected by dehydration than weight‐based indices and appears to be more reliable than calculated length‐based z‐ scores in young children;25, 26, 27 (ii) using both of the traditional indicators of acute and chronic malnutrition, wasting (weight‐for‐height z‐score) and stunting (length or height‐for‐age z‐score); and (iii) using weight‐for‐age z‐score, which captures all factors affecting bodyweight.

Internal validation was done by calculating bootstrapped area under the receiver operating curve (AUROC) and bias‐corrected 95% confidence intervals estimated using a probit model, resampled 200 times (with replacement). Population attributable fractions (PAF) were calculated28 and adjusted for age, gender, and HIV status.

Results

Overall, 4184 children (1–59 months) were admitted with severe pneumonia, comprising 32% of all admissions within the study age range (Figure 1). Their median age (IQR) was 8.9 (4 to 19) months; 578 (14%) were hypoxic at admission, 1041 (25%) were severely malnourished, and 267 (6.4%) had a positive HIV antibody test (Table 1). Three hundred and sixty‐four (8.7%) children with severe pneumonia died in hospital (55% of inpatient deaths within this age range) compared with 296/8908 (3.3%) amongst all children admitted without severe pneumonia (age‐adjusted relative risk (RR) 2.6, 95% CI 2.3, 3.1). Within the KHDSS, children admitted with severe pneumonia had more severe disease and co‐morbidities than children admitted without severe pneumonia. There were 137/2461 (5.6%) inpatient deaths among children admitted with severe pneumonia compared to 125/5270 (2.4%) among children admitted without severe pneumonia within KHDSS [age‐adjusted relative risk [RR] 2.3, (95% CI 1.8, 2.9)].

Figure 1.

Figure 1

Recruitment and follow‐up.

Table 1.

Patient characteristics at admission

All severe pneumonia admissions (n = 4184) Severe pneumonia admissions among KHDSS Residents (n = 2461) Admission without severe pneumonia among KHDSS Residents (n = 5270)
Demographic characteristicsa
Age in months: median (IQR) 8.9 (4, 19) 9.3 (3.9, 20.4) 19.5 (11, 28.5)
Female 1846 (44) 1064 (43) 2297 (44)
Reported preterm/LBW 166 (4.0) 74 (3.0) 108 (2.1)
Hospitalization time (days): median (IQR) 4 (2, 6) 3 (2, 5) 3 (2, 6)
Previous hospital admission 109 (2.6) 69 (2.8) 128 (2.4)
Clinical characteristics at admission
Hypoxia (SaO2 <90%) 578 (14) 304 (12) 73 (1.4)
Capillary refill >2 s 202 (4.8) 89 (3.6) 116 (2.2)
Impaired consciousnessb 329 (7.9) 161 (6.5) 234 (4.4)
Wheezing 671 (16) 432 (18) 27 (0.5)
Cough for >14 days 185 (4.8) 68 (3.0) 117 (6.9)
Jaundice 34 (0.8) 15 (0.6) 73 (1.4)
Severe anaemia 114 (3.4) 59 (3.0) 174 (3.3)
Axillary temperature <36°C 112 (2.7) 71 (2.9) 172 (3.3)
Axillary temperature 36–39°C 3355 (80) 1988 (81) 4159 (79)
Axillary temperature >39°C 717 (17) 402 (16) 939 (18)
HIV antibody test positive 267 (6.4) 110 (4.5) 145 (2.8)
HIV test not performed 307 (7.3) 180 (7.3) 634 (12)
RSV test positive 805 (19) 485 (20) 9 (0.2)
RSV test not performed 1109 (27) 723 (29) 5146 (98)
Malaria slide positive 401 (9.6) 240 (9.8) 1203 (23)
Bacteraemia 189 (4.5) 113 (4.6) 308 (5.8)
Nutritional characteristics at admission
WHZ Mean (sd)c −1.1 (1.8) −0.9 (1.7) −1.1 (1.5)
HAZ Mean (sd)d −1.6 (1.7) −1.5 (1.6) −1.7 (1.6)
WAZ Mean (sd)e −1.9 (1.7) −1.7 (1.6) −1.7 (1.4)
HCAZ Mean (sd)f −0.5 (1.7) −0.4 (1.6) −0.5 (1.5)
MUAC cm Median (IQR)g 13.0 (11.8, 14.0) 13.0 (12.0, 14.2) 14.0 (12.6, 14.9)
Kwashiorkor (nutritional oedema) 68 (1.6) 25 (1.0) 234 (4.5)
Outcome
Inpatient death 364 (8.7) 137 (5.6) 126 (2.4)

adata given as N (%) unless otherwise indicated, IQR: interquartile range, LBW: low birthweight (<2500 g), bconscious level classified as ‘prostrate’ or ‘unconscious’, RSV: Respiratory Syncytial Virus, e115 missing, d378 missing, c454 missing, f168 missing, sd: Standard deviation, g161 missing, WHZ: weight for length/height z‐score, HAZ: length/height for age z‐score, WAZ: weight for‐age z‐score, HCAZ: head circumference for age z‐score, MUAC: Mid‐upper arm circumference, and KHDSS: Kilifi Health and Demographic Surveillance System.

Amongst children admitted with severe pneumonia, non‐KHDSS residents had more severe disease and more co‐morbidities than KHDSS residents (Table S1). Overall, 137/2461 (5.6%) KHDSS‐residents died in hospital compared to 227/1723 (13.2%) non‐KHDSS residents (RR 0.4, 95% CI 0.34, 0.52).

Two thousand two hundred and seventy‐nine KHDSS‐resident children with severe pneumonia were discharged alive and followed up for 1 year, giving 2163 child‐years, during which 70 (3.1%) children died (Figure 1). Twenty‐six (37%) of the 70 deaths occurred during a subsequent hospital admission. Post‐discharge deaths comprised 70/207 (34%) of all inpatient and post‐discharge deaths amongst KHDSS‐resident children admitted with severe pneumonia. Six (8.6%), 19 (27%), 31 (44%), and 52 (74%) post‐discharge deaths occurred within 7, 30, 90, and 180 days, respectively. The post‐discharge mortality rates from discharge to months 3, 6, and 12 were 144 (95% CI 101, 205), 65 (95% CI 49, 86), and 32 (95% CI 26, 41) deaths per 1000 child‐years, respectively.

For comparison, of 5099 KHDSS‐resident children admitted during the same period without severe pneumonia, 65 (1.3%) children died within 1‐year post‐discharge (4898 child years): 13 (95% CI 10, 17) deaths per 1000 child years. Thus, severe pneumonia comprised 70/135 (52%) of all post‐discharge deaths. Children admitted with severe pneumonia had a higher risk of post‐discharge mortality than children admitted without severe pneumonia (age adjusted hazard ratio 2.50 (95% CI 1.17, 5.32) (Figure 2a).

Figure 2.

Figure 2

Hazards of post‐discharge death associated with (a) severe pneumonia vs. other diagnoses; (b) age at admission; (c) MUAC at admission; and (d) HIV status.

Within the severe pneumonia cohort, young age, nutritional status, HIV status, and prolonged duration of hospitalisation, were associated with post‐discharge mortality (Table 2, Tables S2, S3, S4 and Figure 2b, c and d). Severe underweight (weight‐for‐age z‐score <−3) and a positive HIV test were present in 38 (54%) and 16 (23%) of the 70 children who died post‐discharge. Eight (11%) children who died had both severe underweight and a positive HIV test. Distance from the hospital was associated with post‐discharge mortality in the univariable model, but the effect was not evident in the multivariable model (Table 2, Tables S2, S3). We found interaction between HIV status and weight‐for‐height z‐score (P < 0.01) but no interaction with height‐for‐age z‐score (P = 0.42), weight‐for‐age z‐score (P = 0.31), or MUAC (P = 0.63). The effect of nutritional status on post‐discharge mortality was attenuated slightly after adjusting for HIV status (Table S4). There was no evidence of interaction between age and MUAC (P = 0.27), weight‐for‐height z‐score (P = 0.09) or HIV status (P = 0.47) on post‐discharge mortality. However, we found interaction between age and height‐for‐age z‐score (P < 0.01) on post‐discharge mortality. In the model with wasting and stunting, the predominant effect was from stunting (Table S3). The bootstrapped AUROCs for the final models were similar to the original models based on MUAC (Table 2), wasting and stunting, and weight‐for‐age z‐score (Table S3).

Table 2.

Multivariable analysis of factors associated with 1‐year post‐discharge mortality among KHDSS residents (n = 2279)

(Child years of observation (cyo)=2163) Number (Mortality rate per 1000cyo) Adjusted HR (95% CI)
Demographic characteristics
Age in months
≥24 months 401 (12.4) 1.0 (Reference)
12–23 months 546 (13.8) 1.0 (0.1, 9.6)
6–11 months 519 (27.2) 5.8 (0.8, 40.5)
<6 months 813 (57.2) 4.8 (0.7, 34.1)
Female 976 (29.1) 0.5 (0.3, 1.1)
Reported preterm/LBW 64 (99.5) 0.7 (0.2, 2.8)
Residence distance from KCH per km 2117 1.0 (0.9, 1.1)
Duration of hospitalization per day 2276 1.1 (1.0, 1.2)
Clinical characteristics at admission
Hypoxia (SaO2 <90%) 247 (87.9) 1.9 (0.7, 5.4)
Capillary refill >2 s 48 (188.3) 2.4 (0.5, 12.1)
Impaired consciousnessa 103 (71.6) 1.1 (0.2, 7.8)
Wheezing 424 (9.6) 0.5 (0.1, 2.4)
Cough for >14 days 55 (92.7) 0.2 (0.1, 5.5)
Jaundice 13 (75.5) 12.5 (1.1, 13.7)
Severe anaemia (Hb <5 g/dL) 48 (42.7) 0.8 (0.1, 7.5)
Axillary temperature <36°C 58 (35.4) 0.3 (0.1, 2.8)
Axillary temperature 36 to 39°C 1858 (30.9) 1.0 (Reference)
Axillary temperature >39°C 363 (24.5) 1.1 (0.4, 3.0)
HIV antibody test negative 2035 (25.1) 1.0 (Reference)
HIV antibody test positive 85 (202.6) 6.5 (2.3, 18.4)
HIV test not performed 159 (17.3) 0.4 (0.1, 3.6)
RSV test negative 1174 (32.5) 1.0 (Reference)
RSV test positive 477 (13.1) 0.3 (0.1, 1.2)
RSV test not performed 628 (34.5) 2.7 (1.2, 6.3)
Malaria slide negative 2073 (32.6) 1.0 (Reference)
Malaria slide positive 206 (5.3) 0.5 (0.1, 5.2)
No bacteraemia 2187 (29.2) 1.0 (Reference)
Bacteraemia 92 (43.5) 0.8 (0.1, 5.2)
MUAC per cm 2218 0.6 (0.5, 0.8)
Year of admission
2007 480 (27.0) 1.0 (Reference)
2008 381 (31.4) 0.9 (0.3, 3.1)
2009 392 (19.9) 0.5 (0.1, 2.1)
2010 392 (28.5) 0.7 (0.2, 2.5)
2011 319 (35.2) 1.7 (0.5, 5.3)
2012 315 (41.6) 1.8 (0.2, 15.7)
Model performance
AIC 526.9
Raw AUC (95% CI) 0.91 (0.88, 0.95)
Bootstrapped AUC (95% CI) 0.92 (0.88, 0.96)
a

conscious level classified as ‘prostrate’ or ‘unconscious’, RSV: Respiratory Syncytial Virus, MUAC: mid‐upper arm circumference, LBW: Low birthweight (<2.5 kg), AIC: Akaike information criterion, AUC: area under the receiver operating characteristics curve.

For individual anthropometric indices, the performance of MUAC and weight‐for‐age did not differ (Table S4), whilst weight‐for‐length/height and length/height‐for‐age performed less well than MUAC in predicting post‐discharge mortality.

Among the 1979 children with no SAM, a total of 14 (0.7%), 20 (1.0%), and 24 (1.2%) children died during months 3, 6, and 12 of follow‐up, respectively. While 22 (7.3%), 32 (11%), and 46 (15%) children died within months 3, 6, and 12 respectively among SAM children. MUAC <11.5 cm alone (indicative of severe malnutrition) was strongly associated with post‐discharge mortality (Figure 2c). Among children with MUAC <11.5 cm, 14.0% died within 1 year. This contrasts with non‐malnourished children with MUAC ≥13.5 cm, among whom 1.0% died within 1 year (adjusted HR 11.8, 95% CI 5.7, 24.5) (Table S4).

After adjusting for age, HIV status, and gender, the fractions of post‐discharge mortality attributable to MUAC <13.5 cm; weight‐for‐length/height z‐score <−1; weight‐for‐age z‐score <−1; and height/length‐for‐age z‐score <−1 were 52% (95% CI 37, 63); 13% (95% CI 3.2, 22); 57% (95% CI 42, 68); and 34% (95% CI 18, 46), respectively. The fraction of post‐discharge deaths accounted for by a positive HIV test was 11% (95% CI 3.3, 18) after adjusting for age, gender, and MUAC.

Comment

Amongst Kenyan children (1–59 months), admitted to hospital, approximately a third of deaths in children admitted with severe pneumonia and followed up for 12 months occurred after discharge. The risk of post‐discharge mortality amongst children admitted with severe pneumonia was more than twice that of those admitted with other conditions. We had previously reported data from a large clinical trial showing a similar relationship between clinical syndrome at the index admission and post‐discharge mortality amongst HIV‐uninfected children with SAM,29 the current results extend this phenomenon to all admissions. Thus, admission with severe pneumonia can be regarded as an important marker of vulnerability, partly because it captures effects of common co‐morbidities. Malnutrition, rather than HIV, was the predominant risk factor for post‐discharge mortality, despite the provision of outpatient treatment services for malnutrition.

We found an inpatient case fatality ratio for severe pneumonia of 8.7%, which is much higher than the most recent regional estimate of case fatality for Africa, derived from 11 studies, of 3.9%.30 This was despite an increase in staffing, an ensured supply of essential drugs, and careful adherence to WHO treatment guidelines because of the presence of the research centre. These findings concord with the inpatient case fatality ratio of 9.8% reported at district hospitals throughout Malawi following an intervention to improve case management,31 and suggest the need to investigate new treatment strategies.

Nutritional status was the major driver of post‐discharge mortality. MUAC was an efficient single marker of mortality risk, performing better than weight‐for‐height z‐score, the traditional marker of acute malnutrition, including after adjustment for age and gender which are known confounders of MUAC.25, 27 It could be argued that because MUAC changes with age, it predominantly captures the youngest children, rather than nutritional status. However, mortality was far higher amongst all children with MUAC <11.5 cm than amongst those who were simply aged under 6 months (Figure 2b and c) and the relationship between MUAC and mortality appeared to be less confounded by age than other nutritional indices. Weight‐for‐age performed as well as MUAC in predicting post‐discharge mortality in our study and previously was identified as a strong predictor of mortality in the community.32 Weight‐for‐age is not currently used to define acute malnutrition, but could be used as an alternative indicator. Height‐for‐age z‐score (stunting) also predicted post‐discharge mortality (Table S4), possibly reflecting chronic disadvantage and ill‐health.

Our findings concur with data from Bangladesh13 in suggesting that children who died after admission were younger and more severely malnourished. The proportions (7.3% and 11%) of severely malnourished children with severe pneumonia who died within 3 and 6 months after discharge were also very similar to published data from Gambia and Bangladesh.11, 13 Likewise, amongst children without malnutrition, our finding of ~1% who died post‐discharge also was very similar to studies in Gambia and Bangladesh,9, 11 suggesting generalisability when stratified by nutritional status, despite the potential limitations of our study outlined below.

We observed that post‐discharge mortality was not associated with hypoxia or other signs of acute severity, as previously has been reported from The Gambia.8 This is likely to be because children with severe signs more often died during the inpatient phase. However, in Uganda, in a study where severe pneumonia was not strictly defined according to WHO criteria, two features of severity, oxygen saturation and coma score were associated with post‐discharge mortality after adjustment for HIV and nutritional status.12

The strengths of this study were its large sample size; systematically collected data at admission to hospital, including prospective anthropometry and HIV status; and a long duration of follow‐up by systematic census with limited missing outcomes. An important limitation is that it includes only a subset of KHDSS‐resident children at a single site. Non‐KHDSS resident children had a higher prevalence of predictive co‐morbidities, a lower level of access and a higher inpatient case fatality ratio that suggest that their post‐discharge mortality is likely to be higher than that of KHDSS resident children. Other sites in lower middle income countries (LMICs) may also differ in relation to factors including immunization, access to health care services, and the prevalence of HIV, and facilities for its treatment. We were not able to assess the impact of tuberculosis (TB) contact or antiretroviral therapy or co‐trimoxazole prophylaxis in post‐discharge mortality amongst HIV‐infected children. Our investigation of variables potentially associated with post‐discharge mortality was guided by prior reports, but could have resulted in over‐fitting.

An inherent limitation is that the clinical syndrome of severe pneumonia is sensitive rather than specific for radiologically defined pneumonia and death. It captures other respiratory tract infections such as bronchiolitis or tuberculosis,33 as well as respiratory distress due to sepsis or malaria. However, it reflects the diagnostic criteria typically used in similar settings. It is also possible that some children with severe pneumonia were excluded from our study if they did not exhibit the usual clinical signs of pneumonia, which may occur more frequently amongst malnourished children.34

Further research is needed to examine the mechanisms of post‐discharge mortality, including the roles of ability to access health care, maternal mental and physical health, HIV care, undetected TB and the extent to which nosocomial acquisition of resistant pathogens may contribute to the mortality observed.

Conclusions

In rural Kenya, the majority of inpatient and post‐discharge child deaths were associated with severe pneumonia, often in children with underlying co‐morbidities. In this study and in prior studies, where assessed, anthropometric markers of malnutrition were consistently the principal driver of mortality. Post‐discharge mortality is under‐recognised within treatment guidelines and practice. Risk stratification and a better understanding of the mechanisms underlying post‐discharge mortality, especially for undernourished children, are needed to reduce mortality after treatment for pneumonia.

Supporting information

Table S1. Patient profile at admission by KHDSS residence.

Table S2. Univariable analysis of factors associated with post‐discharge mortality.

Table S3. Multivariable analysis of factors associated with 1‐year post‐discharge mortality.

Table S4. Association between nutritional status and post‐discharge mortality.

Acknowledgements

The authors wish to thank the patients and staff of the paediatric wards of Kilifi County Hospital, the KHDSS and laboratory staff for their contributions to this study. We thank the Wellcome Trust for core and personal funding. We thank the Pneumonia Etiology Research for Child Health (PERCH) study for supporting additional training in clinical signs from 2010 to 2013, funded by The Bill & Melinda Gates Foundation to the International Vaccine Access Center, Department of International Health, Johns Hopkins Bloomberg School of Public Health. JAB and MMN are supported by the CHAIN Network, funded by the Bill & Melinda Gates Foundation. The article is published with the permission of the Director of the Kenya Medical Research Institute. This work was supported by the Wellcome Trust (grant numbers 098532, 092654, 084633 and 083579), additional training and standardisation of recognition of clinical signs was supported by the Bill & Melinda Gates Foundation (PERCH grant number 48968) as part of the PERCH study of the aetiology of pneumonia between 2010 and 2013. JAB and MMN are supported by the Bill & Melinda Gates Foundation as part of the CHAIN study of childhood acute illness and nutrition (CHAIN grant number OPP1131320). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors declare that they have no competing interests.

References

  • 1. Walker CL, Rudan I, Liu L, Nair H, Theodoratou E, Bhutta ZA, et al Global burden of childhood pneumonia and diarrhoea. Lancet 2013; 381:1405–1416. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2. Bryce J, Boschi‐Pinto C, Shibuya K, Black RE, Group WHOCHER . WHO estimates of the causes of death in children. Lancet 2005; 365:1147–1152. [DOI] [PubMed] [Google Scholar]
  • 3. Webb C, Ngama M, Ngatia A, Shebbe M, Morpeth S, Mwarumba S, et al Treatment failure among Kenyan children with severe pneumonia–a cohort study. The Pediatric Infectious Disease Journal 2012; 31:e152–e157. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4. McNally LM, Jeena PM, Gajee K, Thula SA, Sturm AW, Cassol S, et al Effect of age, polymicrobial disease, and maternal HIV status on treatment response and cause of severe pneumonia in South African children: a prospective descriptive study. Lancet 2007; 369:1440–1451. [DOI] [PubMed] [Google Scholar]
  • 5. Asghar R, Banajeh S, Egas J, Hibberd P, Iqbal I, Katep‐Bwalya M, et al Chloramphenicol versus ampicillin plus gentamicin for community acquired very severe pneumonia among children aged 2‐59 months in low resource settings: multicentre randomised controlled trial (SPEAR study). BMJ 2008; 336:80–84. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6. Jeena P, Thea DM, MacLeod WB, Chisaka N, Fox MP, Coovadia HM, et al Failure of standard antimicrobial therapy in children aged 3‐59 months with mild or asymptomatic HIV infection and severe pneumonia. Bulletin of the World Health Organization 2006; 84:269–275. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7. Wiens MO, Pawluk S, Kissoon N, Kumbakumba E, Ansermino JM, Singer J, et al Pediatric post‐discharge mortality in resource poor countries: a systematic review. PLoS ONE 2013; 8:e66698. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8. West TE, Goetghebuer T, Milligan P, Mulholland EK, Weber MW. Long‐term morbidity and mortality following hypoxaemic lower respiratory tract infection in Gambian children. Bulletin of the World Health Organization 1999; 77:144–148. [PMC free article] [PubMed] [Google Scholar]
  • 9. Ashraf H, Alam NH, Chisti MJ, Salam MA, Ahmed T, Gyr N. Observational follow‐up study following two cohorts of children with severe pneumonia after discharge from day care clinic/hospital in Dhaka, Bangladesh. BMJ Open 2012;2:e000961. doi: 10.1136/bmjopen‐2012‐000961. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10. Villamor E, Misegades L, Fataki MR, Mbise RL, Fawzi WW. Child mortality in relation to HIV infection, nutritional status, and socio‐economic background. International Journal of Epidemiology 2005; 34:61–68. [DOI] [PubMed] [Google Scholar]
  • 11. Chhibber AV, Hill PC, Jafali J, Jasseh M, Hossain MI, Ndiaye M, et al Child mortality after discharge from a health facility following suspected pneumonia, meningitis or septicaemia in rural Gambia: a cohort study. PLoS ONE 2015; 10:e0137095. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12. Wiens MO, Kumbakumba E, Larson CP, Ansermino JM, Singer J, Kissoon N, et al Postdischarge mortality in children with acute infectious diseases: derivation of postdischarge mortality prediction models. BMJ Open 2015;11:5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13. Chisti MJ, Graham SM, Duke T, Ahmed T, Faruque AS, Ashraf H, et al Post‐discharge mortality in children with severe malnutrition and pneumonia in Bangladesh. PLoS ONE 2014; 9:e107663. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14. WHO . Pocket Book of Hospital Care for Children: Guidelines for the Management of Common Illnesses with Limited Resources. Geneva, Switzerland: World Health Organization; Dept. of Child and Adolescent Health and Development, 2005. [Google Scholar]
  • 15. Moisi JC, Kabuka J, Mitingi D, Levine OS, Scott JA. Spatial and socio‐demographic predictors of time‐to‐immunization in a rural area in Kenya: Is equity attainable? Vaccine 2010; 28:5725–5730. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16. Ayieko P, Griffiths UK, Ndiritu M, Moisi J, Mugoya IK, Kamau T, et al Assessment of health benefits and cost‐effectiveness of 10‐valent and 13‐valent pneumococcal conjugate vaccination in Kenyan children. PLoS ONE 2013; 8:e67324. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17. Scott JA, Bauni E, Moisi JC, Ojal J, Gatakaa H, Nyundo C, et al Profile: the Kilifi Health and Demographic Surveillance System (KHDSS). International Journal of Epidemiology 2012; 41:650–657. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18. Berkley JA, Lowe BS, Mwangi I, Williams T, Bauni E, Mwarumba S, et al Bacteremia among children admitted to a rural hospital in Kenya. The New England Journal of Medicine 2005; 352:39–47. [DOI] [PubMed] [Google Scholar]
  • 19. Berkley JA, Maitland K, Mwangi I, Ngetsa C, Mwarumba S, Lowe BS, et al Use of clinical syndromes to target antibiotic prescribing in seriously ill children in malaria endemic area: observational study. BMJ 2005; 330:995. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20. Ministry of Health GoK . Guidelines for Antiretroviral drug therapy in Kenya. http://www.who.int/hiv/pub/guidelines/kenya_art.pdf.[last accessed 7 March 2016].
  • 21. Nokes DJ, Ngama M, Bett A, Abwao J, Munywoki P, English M, et al Incidence and severity of respiratory syncytial virus pneumonia in rural Kenyan children identified through hospital surveillance. Clinical Infectious Diseases 2009; 49:1341–1349. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22. Berkley JA, Munywoki P, Ngama M, Kazungu S, Abwao J, Bett A, et al Viral etiology of severe pneumonia among Kenyan infants and children. JAMA 2010; 303:2051–2057. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23. Crowe S, Seal A, Grijalva‐Eternod C, Kerac M. Effect of nutrition survey ‘cleaning criteria’ on estimates of malnutrition prevalence and disease burden: secondary data analysis. PeerJ 2014; 2:e380. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24. Moisi JC, Gatakaa H, Berkley JA, Maitland K, Mturi N, Newton CR, et al Excess child mortality after discharge from hospital in Kilifi, Kenya: a retrospective cohort analysis. Bulletin of the World Health Organization 2011;89:725–732, 32A. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25. Myatt M, Khara T, Collins S. A review of methods to detect cases of severely malnourished children in the community for their admission into community‐based therapeutic care programs. Food and Nutrition Bulletin 2006; 27(3 Suppl):S7–S23. [DOI] [PubMed] [Google Scholar]
  • 26. UNICEF Wa . WHO Child Growth Standards and the Identification of Severe Acute Malnutrition in Infants and Children. A Joint Statement by the World Health Organization and the United Nations Children's Fund. Geneva, Switzerland: World Health Organization and UNICEF, 2009. http://apps.who.int/iris/bitstream/10665/44129/1/9789241598163_eng.pdf [last accessed 14 March 2016]. [PubMed] [Google Scholar]
  • 27. Mwangome MK, Fegan G, Prentice AM, Berkley JA. Are diagnostic criteria for acute malnutrition affected by hydration status in hospitalized children? A repeated measures study Nutrition Journal 2011; 10:92. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28. Greenland S, Drescher K. Maximum likelihood estimation of the attributable fraction from logistic models. Biometrics 1993; 49:865–872. [PubMed] [Google Scholar]
  • 29. Berkley JA, Ngari M, Thitiri J, Mwalekwa L, Timbwa M, Hamid F, et al Daily co‐trimoxazole prophylaxis to prevent mortality in children with complicated severe acute malnutrition: a multicentre, double‐blind, randomised placebo‐controlled trial. The Lancet Global Health 2016; 4:e464–e473. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30. Nair H, Simoes EA, Rudan I, Gessner BD, Azziz‐Baumgartner E, Zhang JS, et al Global and regional burden of hospital admissions for severe acute lower respiratory infections in young children in 2010: a systematic analysis. Lancet 2013; 381:1380–1390. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31. Enarson PM, Gie RP, Mwansambo CC, Maganga ER, Lombard CJ, Enarson DA, et al Reducing deaths from severe pneumonia in children in Malawi by improving delivery of pneumonia case management. PLoS ONE 2014; 9:e102955. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32. Kielmann AA, McCord C. Weight‐for‐age as an index of risk of death in children. Lancet 1978; 1:1247–1250. [DOI] [PubMed] [Google Scholar]
  • 33. Puumalainen T, Quiambao B, Abucejo‐Ladesma E, Lupisan S, Heiskanen‐Kosma T, Ruutu P, et al Clinical case review: a method to improve identification of true clinical and radiographic pneumonia in children meeting the World Health Organization definition for pneumonia. BMC Infectious Diseases 2008; 8:95. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34. Falade AG, Tschappeler H, Greenwood BM, Mulholland EK. Use of simple clinical signs to predict pneumonia in young Gambian children: the influence of malnutrition. Bulletion of the World Health Organization 1995; 73:299–304. [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Table S1. Patient profile at admission by KHDSS residence.

Table S2. Univariable analysis of factors associated with post‐discharge mortality.

Table S3. Multivariable analysis of factors associated with 1‐year post‐discharge mortality.

Table S4. Association between nutritional status and post‐discharge mortality.


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