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

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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

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

Modelling Tuberculosis in Ethiopia: Spatiotemporal Transmission Dynamics and Effects of Public Health Interventions

© 2018 Debebe Shaweno AdewoTuberculosis (TB) is now the world’s leading infectious killer with an estimated 10million cases and 1.6 million deaths in 2016. A small number of countries bear the majority of the burden of disease, with two-thirds of cases occurring in only seven countries. TB transmission occurs in both households and the local community, leading to focal disease hotspots which perpetuate TB spread within and across community groups. Integrating spatial analysis with mathematical transmission dynamic models can help in evaluating the role of these hotspots in the spread of TB and understanding the potential impact of geographically targeted interventions. The first part of this thesis evaluates whether TB exhibits spatial heterogeneity in rural and remote regions of Ethiopia using data from all TB patients treated in a remote administrative region of the country. This study demonstrated considerable spatial heterogeneity in TB distribution in this resource-limited setting. However, most of these heterogeneities were accounted for by health facility availability, implying differential case detection between areas with better and poorer access to health care. Thus, this Chapter cautions that spatial analysis of TB and the identification of geographical hotspots using programmatic data alone can be misleading, as it may be strongly influenced by undetected cases, which is in turn dependent on local programmatic performance. Building on the findings outlined above, Chapter three presents a systematic review of methods used in published spatial analyses of TB. From this review, this Chapter elucidates limitations in the current approaches to spatial analysis of TB. Of particular importance is the consistent failure to account for unreported or undetected cases, despite notification data being used in 95 percent of the reviewed studies. The Chapter also describes methodological flaws to many of the studies, in particular the use of conventional regression analysis to draw spatial conclusions. In addition, most spatial analyses of TB distribution used residential information to define the location of patients, which potentially understates the importance of other community settings, despite more than 80% of all transmission events occurring outside households. The study in Chapter 4 proposes a method to address the limitations outlined in the previous chapters – particularly the lack of methods to account for undetected cases. The model estimates both incidence and case detection rates simultaneously across space and time, providing a useful platform for regularly tracking spatial patterns and temporal trends. In addition, this technique is general and can be applied to any disease in any setting. Applied to the Ethiopian setting, this model identifies previously unrecognized areas of high TB burden in locations with no available health care facilities. With the aim of quantifying the role of TB hotspots in community transmission as well as evaluating the potential impact of targeting spatial hotspots, Chapter 5 utilises incidence data generated by the novel method described above to identify spatial TB hotspots. At this point, the thesis constructs spatially structured mathematical models and quantifies the extent to which these hotspots account for the spatial spread of TB. Findings from this work suggest that TB transmission in the same study region in rural Ethiopia is localised and the role of spatial hotspots in the spatial spread is limited, although their impact is considerable in adjacent locations due to very high relative incidence in the hotspot compared to the other regions. Finally, Chapter 6 uses the same model introduced in Chapter 5 to evaluate the impact of various TB intervention strategies before concluding the thesis. Overall, this thesis advances current approaches to spatial analysis and provides a means to account for the problem of undetected cases. It also provides a platform to estimate both incidence and case detection rate simultaneously, and hence could provide as an alternative approach to the spatial interpretation of TB epidemiology. This is of particular importance to high endemic settings, where considerable number of TB cases are missed, and case notification is biased to areas with better access to health care. Importantly, the study also concludes that the impact of spatial TB hotspots on the spatial spread of disease in remote regions of Ethiopia is limited and transmission is predominantly locally driven. Hence, interventions strategies that are spatially targeted may not achieve anticipated outcomes, although the overall effect of these interventions remains considerable due to extremely high incidence in the hotspot regions

Additional file 1: of A novel Bayesian geospatial method for estimating tuberculosis incidence reveals many missed TB cases in Ethiopia

Model code. (DOCX 14 kb

Utilization of institutional delivery service at Wukro and Butajera districts in the Northern and South Central Ethiopia

BACKGROUND: Ethiopia has one of the highest maternal mortality in the world. Institutional delivery is the key intervention in reducing maternal mortality and complications. However, the uptake of the service has remained low and the factors which contribute to this low uptake appear to vary widely. Our study aims to determine the magnitude and identify factors affecting delivery at health institution in two districts in Ethiopia. METHODS: A community based cross sectional household survey was conducted from January to February 2012 in 12 randomly selected villages of Wukro and Butajera districts in the northern and south central parts of Ethiopia, respectively. Data were collected using a pretested questionnaire from 4949 women who delivered in the two years preceding the survey. RESULTS: One in four women delivered the index child at a health facility. Among women who delivered at health facility, 16.1% deliveries were in government hospitals and 7.8% were in health centers. The factors that significantly affected institutional delivery in this study were district in which the women lived (AOR: 2.21, 95% CI: 1.28, 3.82), women age at interview (AOR: 1.96, 95% CI: 1.05, 3.62), women's education (AOR: 3.53, 95% CI: 1.22, 10.20), wealth status (AOR: 16.82, 95% CI: 7.96, 35.54), women's occupation (AOR: 1.50, 95% CI: 1.01, 2.24), antenatal care (4+) use (AOR: 1.77, 95% CI: 1.42, 2.20), and number of pregnancies (AOR: 0.25, 95% CI: 0.18,0.35). We found that women who were autonomous in decision making about place of delivery were less likely to deliver in health facility (AOR: 0.38, 95% CI: 0.23,0.63). CONCLUSIONS: Institutional delivery is still low in the Ethiopia. The most important factors that determine use of institutional delivery appear to be women education and household economic status.Women's autonomy in decision making on place of delivery did not improve health facility delivery in our study population.Actions targeting the disadvantaged, improving quality of services and service availability in the area are likely to significantly increase institutional delivery

Mental health disorders, social stressors, and health-related quality of life in patients with multidrug-resistant tuberculosis: a systematic review and meta-analysis

Background: Mental health disorders, social stress, and poor health-related quality of life are commonly reported among people with tuberculosis (TB). We conducted a systematic review and meta-analysis to quantify mental health disorders, social stressors, and health-related quality of life in patients with multidrug-resistant tuberculosis (MDR-TB). Methods: We searched PubMed, SCOPUS, ProQuest, Web of Science, and PsycINFO databases for studies that reported data on mental health disorders, social stressors, and health-related quality of life among MDR-TB patients. Hand-searching the reference lists of included studies was also performed. Studies were selected according to pre-defined selection criteria and data were extracted by two authors. Pooled prevalence and weighted mean difference estimates were performed using random-effects meta-analysis. Heterogeneity was explored using meta-regression, and subgroup analyses were performed. Results: We included a total of 40 studies that were conducted in 20 countries. Depression, anxiety, and psychosis were the most common mental health disorders reported in the studies. The overall pooled prevalence was 25% (95% confidence interval (CI): 14, 39) for depression, 24% (95% CI: 2, 57) for anxiety, and 10% (95% CI: 7, 14) for psychosis. There was substantial heterogeneity in the estimates. The stratified analysis showed that the prevalence of psychosis was 4% (95% CI: 0, 22) before MDR-TB treatment commencement, and 9% (95% CI: 5, 13) after MDR-TB treatment commencement. The most common social stressors reported were stigma, discrimination, isolation, and a lack of social support. Health-related quality of life was significantly lower among MDR-TB patients when compared to drug-susceptible TB patients (Q = 9.88, p = 0.01, I-2 = 80%). Conclusions: This review found that mental health and social functioning are compromised in a significant proportion of MDR-TB patients, a finding confirmed by the poor health-related quality of life reported. Thus, there is a substantial need for integrating mental health services, social protection and social support into the clinical and programmatic management of MDR-TB