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Background The mortality impact of pulse oximetry use during infant and childhood pneumonia management at the primary healthcare level in low-income countries is unknown. We sought to determine mortality outcomes of infants and children diagnosed and referred using clinical guidelines with or without pulse oximetry in Malawi. Methods and findings We conducted a data linkage study of prospective health facility and community case and mortality data. We matched prospectively collected community health worker (CHW) and health centre (HC) outpatient data to prospectively collected hospital and community-based mortality surveillance outcome data, including episodes followed up to and deaths within 30 days of pneumonia diagnosis amongst children 0–59 months old. All data were collected in Lilongwe and Mchinji districts, Malawi, from January 2012 to June 2014. We determined differences in mortality rates using <90% and <93% oxygen saturation (SpO2) thresholds and World Health Organization (WHO) and Malawi clinical guidelines for referral. We used unadjusted and adjusted (for age, sex, respiratory rate, and, in analyses of HC data only, Weight for Age Z-score [WAZ]) regression to account for interaction between SpO2 threshold (pulse oximetry) and clinical guidelines, clustering by child, and CHW or HC catchment area. We matched CHW and HC outpatient data to hospital inpatient records to explore roles of pulse oximetry and clinical guidelines on hospital attendance after referral. From 7,358 CHW and 6,546 HC pneumonia episodes, we linked 417 CHW and 695 HC pneumonia episodes to 30-day mortality outcomes: 16 (3.8%) CHW and 13 (1.9%) HC patients died. SpO2 thresholds of <90% and <93% identified 1 (6%) of the 16 CHW deaths that were unidentified by integrated community case management (iCCM) WHO referral protocol and 3 (23%) and 4 (31%) of the 13 HC deaths, respectively, that were unidentified by the integrated management of childhood illness (IMCI) WHO protocol. Malawi IMCI referral protocol, which differs from WHO protocol at the HC level and includes chest indrawing, identified all but one of these deaths. SpO2 < 90% predicted death independently of WHO danger signs compared with SpO2 ≥ 90%: HC Risk Ratio (RR), 9.37 (95% CI: 2.17–40.4, p = 0.003); CHW RR, 6.85 (1.15–40.9, p = 0.035). SpO2 < 93% was also predictive versus SpO2 ≥ 93% at HC level: RR, 6.68 (1.52–29.4, p = 0.012). Hospital referrals and outpatient episodes with referral decision indications were associated with mortality. A substantial proportion of those referred were not found admitted in the inpatients within 7 days of referral advice. All 12 deaths in 73 hospitalised children occurred within 24 hours of arrival in the hospital, which highlights delay in appropriate care seeking. The main limitation of our study was our ability to only match 6% of CHW episodes and 11% of HC episodes to mortality outcome data. Conclusions Pulse oximetry identified fatal pneumonia episodes at HCs in Malawi that would otherwise have been missed by WHO referral guidelines alone. Our findings suggest that pulse oximetry could be beneficial in supplementing clinical signs to identify children with pneumonia at high risk of mortality in the outpatient setting in health centres for referral to a hospital for appropriate management.
Our objectives were to determine whether pulse oximetry at outpatient CHW and HC primary care levels identifies infant and child pneumonia patients for referral to hospital independently of clinical signs included in the Malawi and WHO guidelines for CHW and HC patients (Table 1) and the fatality outcomes at 30 days postdiagnosis. To determine fatality, we linked CHW and HC outpatient pneumonia data sets of 0- to 59-month–olds in Lilongwe and Mchinji districts, Malawi (Fig 1) from 1st Jan 2012 to 30th June 2014 [6, 13] to hospital [13] data for the same time period. We also linked the outpatient data to community-surveillance mortality data available for the Mchinji district only [14]. We developed and then followed our prespecified analysis plan (S1 Appendix) as far as we were able given the limitations of the final matched data set. We added the sensitivity and specificity analyses at the request of the statistical reviewer. This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (S1 Checklist). CHW, community health worker. In the Malawi healthcare system, children are intended to access care at either village clinics or HCs. At the village clinic level, if the child is found to be referral eligible, then the child is expected to be referred by the CHW to either the HC or hospital. At the HC level, children meeting referral criteria are referred to hospital. Children were aged 0–59 months with a clinical pneumonia diagnosis according to routine data prospectively collected by 38 CHWs and providers from 18 HCs in rural Lilongwe and Mchinji districts, Malawi (Table 1) [6]. Healthcare providers underwent a 1-day training in pulse oximetry, medical record keeping, and the definition of pneumonia at the start of the study period; they had continued support through monthly mentorship visits. SpO2 measurements were taken using the Lifebox device (Acare Technology, Xinzhuang, Taiwan, China), with a universal adult clip probe applied to the child’s big toe if less than 2 years of age or below 10 kg. Otherwise, for older or heavier children, providers were instructed to use either the big toe or an appropriately sized finger. A paediatric probe was not available during this time period. CHW and HC workers were trained and retrained in the use of pulse oximetry by a paediatric pulmonologist (EDM) as described by McCollum and colleagues [6]. Providers were trained to record measurements that demonstrated consistent plethysmography waveforms along with a stable, nondrifting SpO2 and age-appropriate pulse rate. Given this work was conducted within a routine clinical context, providers were not required to repeat measurements meeting these quality criteria. This study showed moderate agreement in sequentially obtained SpO2 readings between EDM and each cadre of health workers [6]. Matching of the CHW and HC data sets with the hospital and population mortality surveillance outcome data was done using the following parameters: child name, caregiver or parent name, age at known date or date of birth, address, and PCV13 vaccination dates, using a probabilistic algorithm (S2 Appendix, pp. 1–3). We compared outpatient pneumonia episodes successfully matched to 30-day fatality data to those remaining unmatched to assess the representativeness of the matched sample. We constructed sets of 6 mutually exclusive and complete groupings of episodes according to whether they met SpO2 thresholds for referral or failed attempted SpO2 measurement (defined as no stable reading after 5 minutes of measurement [6]), clinical referral criteria, both, or neither (Table 2). aHypoxaemic cases and deaths identified with pulse oximetry that would not have been identified using clinical guidelines alone. bCases and deaths identified by failure of attempted pulse oximetry that would not have been identified using clinical guidelines alone. Abbreviations: CHW, community health worker; HC, health centre; SpO2, oxygen saturation; WHO, World Health Organization. We described the distribution of deaths in the matched data set and crude differences in fatality for each of the 6 groupings in the SpO2 and clinical guidelines exposure sets (Table 2). We determined the independent associations of SpO2 and danger sign exposures on fatality, using generalised linear models (GLMs) of the binomially distributed binary outcome, with a log link (Eq 1); these are analogous to logistic regression but produce Risk Ratios (RRs), which are easier to interpret than the odds ratios produced by logistic regression [20]. We ran GLMs for the matched CHW and HC data separately, i.e., for each of the 6 exposure sets. The base-case unadjusted model using <90% SpO2 and Malawi guidelines clinical referral criteria thresholds (Model M90, see Table 3), is where μi is the probability of death for individual i, Y1 is the outcome, death, for individual i, and the exponent of β1 is the modelled parameter of interest: the relative risk of death when SpO2 is measured at <90% (X1_1 = 1) compared to when it is measured at ≥90% (X1_1 = 0). Children whose SpO2 reading failed are separately categorised (X1_1 = 2), not shown for simplicity), controlling for presence of Malawi guidelines clinical signs (X2_1). We constructed separate models for SpO2 < 93% (X1_2) (Model M93) and, for HC data for which WHO guidelines are different from Malawi guidelines, for WHO (X2_2) guidelines (Models W90 and W93; see Table 3). × = interaction term. Please note that we know these models are correctly specified because they predict the observed mortality rates for each category shown in Table 2. (empty) = no deaths in this group, so coefficient was not possible to estimate. aSee Table 2, CHW data, left orange panel, n = 3 and 0 deaths in group ‘SpO2 < 90% but Malawi clinically eligible’ and n = 5 and 0 deaths in group ‘failed SpO2 measurement but Malawi clinically eligible’. bSee Table 2, CHW data, right yellow panel, n = 5 and 0 deaths in group ‘failed SpO2 measurement but Malawi clinically eligible’. cSee Table 2, HC data, top left orange panel and top right yellow panel, n = 34 and 0 deaths in group ‘failed SpO2 measurement but Malawi clinically eligible’ and n = 15 (SpO2 < 90%) or n = 39 (SpO2 < 93%) and 0 deaths in group ‘SpO2 < 90% (<93%) only and not Malawi clinically eligible’. dSee Table 2, HC data, bottom left green panel and bottom right blue panel, n = 20 and 0 deaths in group ‘failed SpO2 measurement but WHO clinically eligible’. Abbreviations: CHW, community health worker; GLM, generalised linear model; HC, health centre; ref, reference (baseline) category; RR, risk ratio; SpO2, oxygen saturation; WHO, World Health Organization. We adjusted for confounding by age, sex, respiratory rate, and, in analyses of HC data only, Weight for Age Z-score (WAZ). Missing data prevented us from including maternal age, education, marital status, and wealth quintile as potential confounders. Too few deaths in each exposure group prevented assessment of effect modification by age group, sex, or CHW or HC level. Although we adjusted for clustering of illness episodes by child and CHW and HC catchment area, these models were unstable and not presented. Our unadjusted models were robust to clustering, with similar headline results following 100 iterations for models that did not converge. The extent of missing data on outcomes due to the majority of outpatient episodes remaining unmatched to 30-day postdiagnosis survival (mortality) outcomes (Table 1) meant that multiple imputation of the missing outcome data was not feasible. The small numbers of deaths and episodes with low SpO2 also precluded our planned regression discontinuity analyses of the effect of changing the SpO2 threshold on mortality and referral outcomes. Separately to the fatality outcome, we determined the association between our SpO2/clinical sign exposures and hospital referral as the outcome (Y2) using the same logistic regression Eq (1) except substituting the fatality outcome (Y1) with Y2. Because not all severely ill children referred to hospital actually arrive, as a sensitivity analysis, we repeated this analysis with referral decision from the outpatient exposure data set regardless of actual hospital arrival as the outcome (Y3). We calculated the sensitivity, specificity, and diagnostic odds ratio (DOR, with 95%CI) [21] of clinical and SpO2 eligibility on the mortality outcome and compared it to the sensitivity, specificity, and DOR of clinical eligibility only for SpO2 eligibility thresholds of <90% and <93% SpO2 (WHO and Malawi eligibilities) at CHW and HC levels. We included SpO2 failed measurements as well as SpO2 below threshold as SpO2 eligible in these analyses, given failed SpO2 measurements are associated with mortality. Because data were deidentified and analysed anonymously, no authorisation or waiver of authorisation by patients for the release of individually identifiable protected health information was required. This study is a data linkage study, and the data it links together are from studies approved by the ethics boards of University College London (protocol 2006/002), the Malawi National Health Sciences Research Committee (protocols 941 and 837), and the London School of Hygiene & Tropical Medicine (protocol 6047), as detailed in the published research articles from the original studies [6, 9, 13–16].
