Gut microbiota disturbance during helminth infection: can it affect cognition and behaviour of children?

Background: Bidirectional signalling between the brain and the gastrointestinal tract is regulated at neural, hormonal, and immunological levels. Recent studies have shown that helminth infections can alter the normal gut microbiota. Studies have also shown that the gut microbiota is instrumental in the normal development, maturation and function of the brain. The pathophysiological pathways by which helminth infections contribute to altered cognitive function remain poorly understood. Discussion: We put forward the hypothesis that gastrointestinal infections with parasitic worms, such as helminths, induce an imbalance of the gut-brain axis, which, in turn, can detrimentally manifest in brain development. Factors supporting this hypothesis are: 1) research focusing on intelligence and school performance in school-aged children has shown helminth infections to be associated with cognitive impairment, 2) disturbances in gut microbiota have been shown to be associated with important cognitive developmental effects, and 3) helminth infections have been shown to alter the gut microbiota structure. Evidence on the complex interactions between extrinsic (parasite) and intrinsic (host-derived) factors has been synthesised and discussed. Summary: While evidence in favour of the helminth-gut microbiota-central nervous system hypothesis is circumstantial, it would be unwise to rule it out as a possible mechanism by which gastrointestinal helminth infections induce childhood cognitive morbidity. Further empirical studies are necessary to test an indirect effect of helminth infections on the modulation of mood and behaviour through its effects on the gut microbiota

Mapping tuberculosis treatment outcomes in Ethiopia

Background: Tuberculosis (TB) is the leading cause of death from an infectious disease in Ethiopia, killing more than 30 thousand people every year. This study aimed to determine whether the rates of poor TB treatment outcome varied geographically across Ethiopia at district and zone levels and whether such variability was associated with socioeconomic, behavioural, health care access, or climatic conditions. Methods: A geospatial analysis was conducted using national TB data reported to the health management information system (HMIS), for the period 2015-2017. The prevalence of poor TB treatment outcomes was calculated by dividing the sum of treatment failure, death and loss to follow-up by the total number of TB patients. Binomial logistic regression models were computed and a spatial analysis was performed using a Bayesian framework. Estimates of parameters were generated using Markov chain Monte Carlo (MCMC) simulation. Geographic clustering was assessed using the Getis-Ord Gi* statistic, and global and local Moran's I statistics. Results: A total of 223,244 TB patients were reported from 722 districts in Ethiopia during the study period. Of these, 63,556 (28.5%) were cured, 139,633 (62.4%) completed treatment, 6716 (3.0%) died, 1459 (0.7%) had treatment failure, and 12,200 (5.5%) were lost to follow-up. The overall prevalence of a poor TB treatment outcome was 9.0% (range, 1-58%). Hot-spots and clustering of poor TB treatment outcomes were detected in districts near the international borders in Afar, Gambelia, and Somali regions and cold spots were detected in Oromia and Amhara regions. Spatial clustering of poor TB treatment outcomes was positively associated with the proportion of the population with low wealth index (OR: 1.01; 95%CI: 1.0, 1.01), the proportion of the population with poor knowledge about TB (OR: 1.02; 95%CI: 1.01, 1.03), and higher annual mean temperature per degree Celsius (OR: 1.15; 95% CI: 1.08, 1.21). Conclusions: This study showed significant spatial variation in poor TB treatment outcomes in Ethiopia that was related to underlying socioeconomic status, knowledge about TB, and climatic conditions. Clinical and public health interventions should be targeted in hot spot areas to reduce poor TB treatment outcomes and to achieve the national End-TB Strategy targets

Hospital effect on infections after four major surgeries: Outlier and volume-outcome analysis using all-inclusive state data

Hospital volume is known to have a direct impact on outcomes of major surgeries. However, it is unclear if the evidence applies specifically to surgical site infections. To determine if there are procedure-specific hospital outliers (with higher surgical site infection rates [SSIR]) for four major surgical procedures, and to examine if hospital volume is associated with SSIR in the context of outlier performance in New South Wales (NSW), Australia. Adults who underwent one of four surgical procedures (colorectal, joint replacement, spinal and cardiac procedures) at a NSW healthcare facility from 2002 through 2013 were included. The hospital volume for each of the four surgical procedures was categorised into tertiles (low, medium and high). Multivariable logistic regression models were built to estimate the expected SSIR for each procedure. The expected SSIR were used to compute an indirect standardised SSIR which was then plotted in funnel plots to identify hospital outliers. One hospital was identified to be an overall outlier (higher SSIR for 3 out of the 4 procedures performed in its facilities); whereas two hospitals were outliers for one specific procedure throughout the entire study period. Low-volume facilities performed the best for colorectal surgery and worst for joint replacement and cardiac surgery. One high-volume facility was an outlier for spinal surgery. Surgical site infections seem to be mostly a procedure-specific as opposed to a hospital-specific phenomenon in NSW. The association between hospital volume and SSIRs differs for different surgical procedures.ACAC is funded by an Australian National Health and Medical Research Council Senior Research Fellowship (#1058878)

Development of a risk score for prediction of poor treatment outcomes among patients with multidrug-resistant tuberculosis

Background Treatment outcomes among patients treated for multidrug-resistant tuberculosis (MDR-TB) are often sub-optimal. Therefore, the early prediction of poor treatment outcomes may be useful in patient care, especially for clinicians when they have the ability to make treatment decisions or offer counselling or additional support to patients. The aim of this study was to develop a simple clinical risk score to predict poor treatment outcomes in patients with MDR-TB, using routinely collected data from two large countries in geographically distinct regions. Methods We used MDR-TB data collected from Hunan Chest Hospital, China and Gondar University Hospital, Ethiopia. The data were divided into derivation (n = 343; 60%) and validation groups (n = 227; 40%). A poor treatment outcome was defined as treatment failure, lost to follow up or death. A risk score for poor treatment outcomes was derived using a Cox proportional hazard model in the derivation group. The model was then validated in the validation group. Results The overall rate of poor treatment outcome was 39.5% (n = 225); 37.9% (n = 86) in the derivation group and 40.5% (n = 139) in the validation group. Three variables were identified as predictors of poor treatment outcomes, and each was assigned a number of points proportional to its regression coefficient. These predictors and their points were: 1) history of taking second-line TB treatment (2 points), 2) resistance to any fluoroquinolones (3 points), and 3) smear did not convert from positive to negative at two months (4 points). We summed these points to calculate the risk score for each patient; three risk groups were defined: low risk (0 to 2 points), medium risk (3 to 5 points), and high risk (6 to 9 points). In the derivation group, poor treatment outcomes were reported for these three groups as 14%, 27%, and 71%, respectively. The area under the receiver operating characteristic curve for the point system in the derivation group was 0.69 (95% CI 0.60 to 0.77) and was similar to that in the validation group (0.67; 95% CI 0.56 to 0.78; p = 0.82). Conclusion History of second-line TB treatment, resistance to any fluoroquinolones, and smear non-conversion at two months can be used to estimate the risk of poor treatment outcome in patients with MDR-TB with a moderate degree of accuracy (AUROC = 0.69)

Sequelae of multidrug-resistant tuberculosis: protocol for a systematic review and meta-analysis

Introduction: The sequelae of multidrug-resistant tuberculosis (MDR-TB) are poorly understood and inconsistently reported. We will aim to assess the existing evidence for the clinical, psychological, social and economic sequelae of MDR-TB and to assess the health-related quality of life in patients with MDR-TB. Methods and analysis: We will perform a systematic review and meta-analysis of published studies reporting sequelae of MDR-TB. We will search PubMed, SCOPUS, ProQuest, Web of Science and PsychINFO databases up to 5 September 2017. MDR-TB sequelae will include any clinical, psychological, social and economic effects as well as health-related quality of life that occur after MDR-TB treatment or illness. Two researchers will screen the titles and abstracts of all citations identified in our search, extract data, and assess the scientific quality using standardised formats. Providing there is appropriate comparability in the studies, we will use a random-effects meta-analysis model to produce pooled estimates of MDR-TB sequelae from the included studies. We will stratify the analyses based on treatment regimen, comorbidities (such as HIV status and diabetes mellitus), previous TB treatment history and study setting. Ethics and dissemination: As this study will be based on published data, ethical approval is not required. The final report will be disseminated through publication in a peer-reviewed scientific journal and will also be presented at relevant conferences

Risk assessment of malaria in land border regions of China in the context of malaria elimination

BACKGROUND:Cross-border malaria transmission poses a challenge for countries to achieve and maintain malaria elimination. Because of a dramatic increase of cross-border population movement between China and 14 neighbouring countries, the malaria epidemic risk in China's land border regions needs to be understood.METHODS: In this study, individual case-based epidemiological data on malaria in the 136 counties of China with international land borders, from 2011 to 2014, were extracted from the National Infectious Disease Information System. The Plasmodium species, seasonality, spatiotemporal distribution and changing features of imported and indigenous cases were analysed using descriptive spatial and temporal methods.RESULTS:A total of 1948 malaria cases were reported, with 1406 (72.2%) imported cases and 542 (27.8%) indigenous cases. Plasmodium vivax is the predominant species, with 1536 malaria cases occurrence (78.9%), following by Plasmodium falciparum (361 cases, 18.5%), and the others (51 cases, 2.6%). The magnitude and geographic distribution of malaria in land border counties shrunk sharply during the elimination period. Imported malaria cases were with a peak of 546 cases in 2011, decreasing yearly in the following years. The number of counties with imported cases decreased from 28 counties in 2011 to 26 counties in 2014. Indigenous malaria cases presented a markedly decreasing trend, with 319 indigenous cases in 2011 reducing to only 33 indigenous cases in 2014. The number of counties with indigenous cases reduced from 26 counties in 2011 to 10 counties in 2014. However, several bordering counties of Yunnan province adjacent to Myanmar reported indigenous malaria cases in the four consecutive years from 2011 to 2014.CONCLUSIONS:The scale and extent of malaria occurrence in the international land border counties of China decreased dramatically during the elimination period. However, several high-risk counties, especially along the China-Myanmar border, still face a persistent risk of malaria introduction and transmission. The study emphasizes the importance and urgency of cross-border cooperation between neighbouring countries to jointly face malaria threats to elimination goals

Geographical outcome disparities in infection occurrence after colorectal surgery: An analysis of 58,096 colorectal surgical procedures

Background Despite improved surgical practices and in-hospital surveillance systems, surgical site infections remain a major public health problem worldwide and often require readmission to hospital. The aim was to apply an advance and innovative spatial analysis approach to identify spatial pattern and clustering (hotspots) of surgical site infection rate (CSIR), and quantifying disparities across communities. Methods We used the Admitted Patient Data Collection for patients aged 18 years and over who underwent colorectal surgery in a public hospital between 2002 and 2013 in the Australian State of New South Wales (NSW). The colorectal surgical infection rate (CSIR) was computed. We assessed geographical variation and clustering in CSIR patterning to demonstrate spatial pattern and clustering across communities in NSW, Australia. Results There were 58,096 colorectal surgical procedures conducted in NSW from 2002 to 2013. The overall occurrence of CSIR was 9.64% (95%CI 9.40-9.88%). We found significant clusters of both high and low CSIR in outer regional and remote areas of NSW. Conclusion Use of advanced spatial analyses allows identification of hotspots/clusters of adverse events that can help policy makers and clinicians better understand national patterns and initiate research to address disparities/geographical variation, and clustering of adverse events after surgery. 1 2017 IJS Publishing Group LtdScopu

Methods used in the spatial analysis of tuberculosis epidemiology: a systematic review

Background: Tuberculosis (TB) transmission often occurs within a household or community, leading to heterogeneous spatial patterns. However, apparent spatial clustering of TB could reflect ongoing transmission or co-location of risk factors and can vary considerably depending on the type of data available, the analysis methods employed and the dynamics of the underlying population. Thus, we aimed to review methodological approaches used in the spatial analysis of TB burden. Methods: We conducted a systematic literature search of spatial studies of TB published in English using Medline, Embase, PsycInfo, Scopus and Web of Science databases with no date restriction from inception to 15 February 2017. The protocol for this systematic review was prospectively registered with PROSPERO (CRD42016036655). Results: We identified 168 eligible studies with spatial methods used to describe the spatial distribution (n = 154), spatial clusters (n = 73), predictors of spatial patterns (n = 64), the role of congregate settings (n = 3) and the household (n = 2) on TB transmission. Molecular techniques combined with geospatial methods were used by 25 studies to compare the role of transmission to reactivation as a driver of TB spatial distribution, finding that geospatial hotspots are not necessarily areas of recent transmission. Almost all studies used notification data for spatial analysis (161 of 168), although none accounted for undetected cases. The most common data visualisation technique was notification rate mapping, and the use of smoothing techniques was uncommon. Spatial clusters were identified using a range of methods, with the most commonly employed being Kulldorff's spatial scan statistic followed by local Moran's I and Getis and Ord's local Gi(d) tests. In the 11 papers that compared two such methods using a single dataset, the clustering patterns identified were often inconsistent. Classical regression models that did not account for spatial dependence were commonly used to predict spatial TB risk. In all included studies, TB showed a heterogeneous spatial pattern at each geographic resolution level examined. Conclusions: A range of spatial analysis methodologies has been employed in divergent contexts, with all studies demonstrating significant heterogeneity in spatial TB distribution. Future studies are needed to define the optimal method for each context and should account for unreported cases when using notification data where possible. Future studies combining genotypic and geospatial techniques with epidemiologically linked cases have the potential to provide further insights and improve TB control

Mapping the Probability of Schistosomiasis and Associated Uncertainty, West Africa

We aimed to map the probability of Schistosoma haematobium infection being >50%, a threshold for annual mass praziquantel distribution. Parasitologic surveys were conducted in Burkina Faso, Mali, and Niger, 2004–2006, and predictions were made by using Bayesian geostatistical models. Clusters with >50% probability of having >50% prevalence were delineated in each country

Comparison of the validity of smear and culture conversion as a prognostic marker of treatment outcome in patients with multidrug-resistant tuberculosis

Background The World Health Organization (WHO) has conditionally recommended the use of sputum smear microscopy and culture examination for the monitoring of multidrug-resistant tuberculosis (MDR-TB) treatment. We aimed to assess and compare the validity of smear and culture conversion at different time points during treatment for MDR-TB, as a prognostic marker for end-of-treatment outcomes. Methods We undertook a retrospective observational cohort study using data obtained from Hunan Chest Hospital, China and Gondar University Hospital, Ethiopia. The sensitivity and specificity of culture and sputum smear conversion for predicting treatment outcomes were analysed using a random-effects generalized linear mixed model. Results A total of 429 bacteriologically confirmed MDR-TB patients with a culture and smear positive result were included. Overall, 345 (80%) patients had a successful treatment outcome, and 84 (20%) patients had poor treatment outcomes. The sensitivity of smear and culture conversion to predict a successful treatment outcome were: 77.9% and 68.9% at 2 months after starting treatment (difference between tests, p = 0.007); 95.9% and 92.7% at 4 months (p = 0.06); 97.4% and 96.2% at 6 months (p = 0.386); and 99.4% and 98.9% at 12 months (p = 0.412), respectively. The specificity of smear and culture non-conversion to predict a poor treatment outcome were: 41.6% and 60.7% at 2 months (p = 0.012); 23.8% and 48.8% at 4 months (p<0.001); and 20.2% and 42.8% at 6 months (p<0.001); and 15.4% and 32.1% (p<0.001) at 12 months, respectively. The sensitivity of culture and smear conversion increased as the month of conversion increased but at the cost of decreased specificity. The optimum time points after conversion to provide the best prognostic marker of a successful treatment outcome were between two and four months after treatment commencement for smear, and between four and six months for culture. The common optimum time point for smear and culture conversion was four months. At this time point, culture conversion (AUROC curve = 0.71) was significantly better than smear conversion (AUROC curve = 0.6) in predicting successful treatment outcomes (p < 0.001). However, the validity of smear conversion (AUROC curve = 0.7) was equivalent to culture conversion (AUROC curve = 0.71) in predicting treatment outcomes when demographic and clinical factors were included in the model. The positive and negative predictive values for smear conversion were: 57.3% and 65.7% at two months, 55.7% and 85.4% at four months, and 55.0% and 88.6% at six months; and for culture conversions it was: 63.7% and 66.2% at two months, 64.4% and 87.1% at four months, and 62.7% and 91.9% at six months, respectively. Conclusions The validity of smear conversion is significantly lower than culture conversion in predicting MDR-TB treatment outcomes. We support the WHO recommendation of using both smear and culture examination rather than smear alone for the monitoring of MDR-TB patients for a better prediction of successful treatment outcomes. The optimum time points to predict a future successful treatment outcome were between two and four months after treatment commencement for sputum smear conversion and between four and six months for culture conversion. The common optimum times for culture and smear conversion together was four months