Person:

Siedner, Mark

Background: Malaria is often considered a cause of adult sepsis in malaria endemic areas. However, diagnostic limitations can make distinction between malaria and other infections challenging. Therefore, the objective of this study was to determine the relative contribution of malaria to adult sepsis in south-western Uganda. Methods: Adult patients with sepsis were enrolled at the Mbarara Regional Referral Hospital between February and May 2012. Sepsis was defined as infection plus ≥2 of the following: axillary temperature >37.5°C or <35.5°C, heart rate >90 or respiratory rate >20. Severe sepsis was defined as sepsis plus organ dysfunction (blood lactate >4 mmol/L, confusion, or a systolic blood pressure <90 mmHg). Sociodemographic, clinical and laboratory data, including malaria PCR and rapid diagnostic tests, as well as acid fast bacteria sputum smears and blood cultures were collected. Patients were followed until in-patient death or discharge. The primary outcome of interest was the cause of sepsis. Multivariable logistic regression was performed to assess predictors of mortality. Results: Enrollment included 216 participants who were 51% female with a median age of 32 years (IQR 27–43 years). Of these, 122 (56%) subjects were HIV-seropositive of whom 75 (66%) had a CD4+ T cell count <100 cells/μL. The prevalence of malaria was 4% (six with Plasmodium falciparum, two with Plasmodium vivax). Bacteraemia was identified in 41 (19%) patients. In-hospital mortality was 19% (n = 42). In multivariable regression analysis, Glasgow Coma Score <9 (IRR 4.81, 95% CI 1.80-12.8) and severe sepsis (IRR, 2.07, 95% CI 1.03-4.14), but no specific diagnoses were statistically associated with in-hospital mortality. Conclusion: Malaria was an uncommon cause of adult sepsis in a regional referral hospital in south-western Uganda. In this setting, a thorough evaluation for alternate causes of disease in patients presenting with sepsis is recommended.

Background: Accurate estimation of blood loss is central to prompt diagnosis and management of post-partum hemorrhage (PPH), which remains a leading cause of maternal mortality in low-resource countries. In such settings, blood loss is often estimated visually and subjectively by attending health workers, due to inconsistent availability of laboratory infrastructure. We evaluated the diagnostic accuracy of weighed blood loss (WBL) versus changes in peri-partum hemoglobin to detect PPH. Methods: Data from this analysis were collected as part of a randomized controlled trial comparing oxytocin with misoprostol for PPH (NCT01866241). Blood samples for complete blood count were drawn on admission and again prior to hospital discharge or before blood transfusion. During delivery, women were placed on drapes and had pre-weighed sanitary towels placed around their perineum. Blood was then drained into a calibrated container and the sanitary towels were added to estimate WBL, where each gram of blood was estimated as a milliliter. Sensitivity, specificity, negative and positive predictive values (PPVs) were calculated at various blood volume loss and time combinations, and we fit receiver-operator curves using blood loss at 1, 2, and 24 hours compared to a reference standard of haemoglobin decrease of >10%. Results: A total of 1,140 women were enrolled in the study, of whom 258 (22.6%) developed PPH, defined as a haemoglobin drop >10%, and 262 (23.0%) had WBL ≥500mL. WBL generally had a poor sensitivity for detection of PPH (<75% for most volume-time combinations). In contrast, the specificity of WBL was high with blood loss ≥ 500mL at 1h and ≥750mL at any time points excluding PPH in over 97% of women. As such, WBL has a high PPV (>85%) in high prevalence settings when WBL exceeds 750mL. Conclusion: WBL has poor sensitivity but high specificity compared to laboratory-based methods of PPH diagnosis. These characteristics correspond to a high PPV in areas with high PPH prevalence. Although WBL is not useful for excluding PPH, this low-cost, simple and reproducible method is promising as a reasonable method to identify significant PPH in such settings where quantifiable red cell indices are unavailable.

Background: The Ebola virus disease (EVD) epidemic has threatened access to basic health services through facility closures, resource diversion, and decreased demand due to community fear and distrust. While modeling studies have attempted to estimate the impact of these disruptions, no studies have yet utilized population-based survey data. Methods and Findings: We conducted a two-stage, cluster-sample household survey in Rivercess County, Liberia, in March–April 2015, which included a maternal and reproductive health module. We constructed a retrospective cohort of births beginning 4 y before the first day of survey administration (beginning March 24, 2011). We then fit logistic regression models to estimate associations between our primary outcome, facility-based delivery (FBD), and time period, defined as the pre-EVD period (March 24, 2011–June 14, 2014) or EVD period (June 15, 2014–April 13, 2015). We fit both univariable and multivariable models, adjusted for known predictors of facility delivery, accounting for clustering using linearized standard errors. To strengthen causal inference, we also conducted stratified analyses to assess changes in FBD by whether respondents believed that health facility attendance was an EVD risk factor. A total of 1,298 women from 941 households completed the survey. Median age at the time of survey was 29 y, and over 80% had a primary education or less. There were 686 births reported in the pre-EVD period and 212 in the EVD period. The unadjusted odds ratio of facility-based delivery in the EVD period was 0.66 (95% confidence interval [CI] 0.48–0.90, p-value = 0.010). Adjustment for potential confounders did not change the observed association, either in the principal model (adjusted odds ratio [AOR] = 0.70, 95%CI 0.50–0.98, p = 0.037) or a fully adjusted model (AOR = 0.69, 95%CI 0.50–0.97, p = 0.033). The association was robust in sensitivity analyses. The reduction in FBD during the EVD period was observed among those reporting a belief that health facilities are or may be a source of Ebola transmission (AOR = 0.59, 95%CI 0.36–0.97, p = 0.038), but not those without such a belief (AOR = 0.90, 95%CI 0.59–1.37, p = 0.612). Limitations include the possibility of FBD secular trends coincident with the EVD period, recall errors, and social desirability bias. Conclusions: We detected a 30% decreased odds of FBD after the start of EVD in a rural Liberian county with relatively few cases. Because health facilities never closed in Rivercess County, this estimate may under-approximate the effect seen in the most heavily affected areas. These are the first population-based survey data to show collateral disruptions to facility-based delivery caused by the West African EVD epidemic, and they reinforce the need to consider the full spectrum of implications caused by public health emergencies.

Background: The test positivity rate (TPR), defined as the number of laboratory-confirmed malaria tests per 100 suspected cases examined, is widely used by malaria surveillance programs as one of several key indicators of temporal trends in malaria incidence. However, there have been few studies using empiric data to examine the quantitative nature of this relationship. Methods: To characterize the relationship between the test positivity rate and the incidence of malaria, we fit regression models using the confirmed malaria case rate as the outcome of interest and TPR as the predictor of interest. We varied the relationship between the two by alternating linear and polynomial terms for TPR, and compared the goodness of fit of each model. Results: A total of 7,668 encounters for malaria diagnostic testing were recorded over the study period within a catchment area of 25,617 persons. The semi-annual TPR ranged from 4.5% to 59% and the case rates ranged from 0.5 to 560 per 1,000 persons. The best fitting model was an exponential growth model (R2 = 0.80, AIC = 637). At low transmission levels (TPR<10%), the correlation between TPR and CMCR was poor, with large reductions in the TPR, for example from 10% to 1%, was associated with a minimal change in the CMCR (3.9 to 1.7 cases per 1,000 persons). At higher transmission levels, the exponential relationship made relatively small changes in TPR suggestive of sizeable change in estimated malaria incidence, suggesting that TPR remains a valuable surveillance indicator in such settings. Conclusions: The TPR and the confirmed malaria case rate have a non-linear relationship, which is likely to have important implications for malaria surveillance programs, especially at the extremes of transmission.