Spatial Frailty Survival Model for Multidrug-Resistant Tuberculosis Mortality in Amhara Region, Ethiopia

Woya, Ashenafi Abate; Tekile, Abay Kassa; Basha, Garoma Wakjira

doi:10.1155/2019/8742363

Cited by 7 publications

(17 citation statements)

References 12 publications

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“…The proportion of mortality among all patients treated for MDR/ RR-TB was reported by 12 studies (Alene et al, 2017;Woldeyohannes et al, 2019;Meressa et al, 2015;Getachew et al, 2013;Girum et al, 2017;Shimbre et al, 2020;Gebre et al, 2020;Fantaw et al, 2018;Molalign and Wencheko, 2015;Woya et al, 2019;Abebe et al, 2020;Baye et al, 2018). Data were extracted from these studies by defining mortality as any death after the start of anti-TB treatment for MDR/RR-TB due to any cause in the TICs in Ethiopia.…”

Section: The Pooled Incidence Of Mortalitymentioning

confidence: 92%

“…Then, 347 articles were screened by title and abstract. The full-text screening was conducted for 58 articles, and 17 articles that fulfilled the eligibility criteria were included in the final analysis (Ketema et al, 2019;Molie et al, 2019;Alene et al, 2017;Limenih and Workie, 2019;Woldeyohannes et al, 2019;Tola et al, 2019;Meressa et al, 2015;Getachew et al, 2013;Girum et al, 2017;Shimbre et al, 2020;Gebre et al, 2020;Fantaw et al, 2018;Molalign and Wencheko, 2015;Woya et al, 2019;Abebe et al, 2020;Baye et al, 2018;Tulu and Kahissay, 2014) (Table 1 and Figure 1). All of the studies were hospital-based retrospective cohort studies conducted among patients with DR-TB treated under TICs found in different parts of the country.…”

Section: Study Characteristicsmentioning

confidence: 99%

“…Additionally, the clinical profiles (comorbidity types, clinical complication types, anatomical site of infection, diagnostic methods, drug resistance profiles, HIV serostatus, treatment outcomes and definition of anemia) of DR-TB patients per individual study were assessed and we summarized this in detail in Table 2. Among all 17 studies, 12 (Alene et al, 2017;Woldeyohannes et al, 2019;Meressa et al, 2015;Getachew et al, 2013;Girum et al, 2017;Shimbre et al, 2020;Gebre et al, 2020;Fantaw et al, 2018;Molalign and Wencheko, 2015;Woya et al, 2019;Abebe et al, 2020;Baye et al, 2018) and 15 (Ketema et al, 2019;Molie et al, 2019;Alene et al, 2017;Limenih and Workie, 2019;Woldeyohannes et al, 2019;Meressa et al, 2015;Getachew et al, 2013;Girum et al, 2017;Shimbre et al, 2020;Gebre et al, 2020;Fantaw et al, 2018;Molalign and Wencheko, 2015;Woya et al, 2019;Abebe et al, 2020;Baye et al, 2018) studies explored mortality and poor treatment outcomes, respectively. The incidence density rate of poor treatment outcome and mortality using the time to an event (poor treatment outcome or mortality) were explored by 12 (Ketema et al, 2019;Alene et al, 2017;Limenih and Workie, 2019;Woldeyohannes et al, 2019;Getachew et al, 2013;Girum et al, 2017;Shimbre et al, 2020;Gebre et al, 2020;Fantaw et al, 2018;<...>…”

Section: Study Characteristicsmentioning

confidence: 99%

“…Among all studies, 15 (Ketema et al, 2019;Molie et al, 2019;Alene et al, 2017;Limenih and Workie, 2019;Woldeyohannes et al, 2019;Meressa et al, 2015;Getachew et al, 2013;Girum et al, 2017;Shimbre et al, 2020;Gebre et al, 2020;Fantaw et al, 2018;Molalign and Wencheko, 2015;Woya et al, 2019;Abebe et al, 2020;Baye et al, 2018) and 12 (Ketema et al, 2019;Alene et al, 2017;Limenih and Workie, 2019;Woldeyohannes et al, 2019;Getachew et al, 2013;Girum et al, 2017;Shimbre et al, 2020;Gebre et al, 2020;Fantaw et al, 2018;Molalign and Wencheko, 2015;Woya et al, 2019;Abebe et al, 2020) studies reported proportion and incidence rate of poor treatment outcome among patients treated for MDR/RR-TB under TICs in Ethiopia respectively. The largest sample size (n = 751) was from 14 TICs found in Ethiopia by Molie et al (2019), while the smallest sample size was from Boru Meda hospital in a study done by Baye et al (2018) (n = 90).…”

Section: The Pooled Incidence Of Poor Treatment Outcomementioning

confidence: 99%

“…However, as high as 35.56% of poor treatment outcomes were reported by Baye et al (2018). Twelve studies (Ketema et al, 2019;Alene et al, 2017;Limenih and Workie, 2019;Woldeyohannes et al, 2019;Getachew et al, 2013;Girum et al, 2017;Shimbre et al, 2020;Gebre et al, 2020;Fantaw et al, 2018;Molalign and Wencheko, 2015;Woya et al, 2019;Abebe et al, 2020) reported time to poor treatment outcomes using different person time scales. We changed all scales to per 1000 PMs scale.…”

Section: The Pooled Incidence Of Poor Treatment Outcomementioning

confidence: 99%

See 4 more Smart Citations

Poor treatment outcome and its predictors among drug-resistant tuberculosis patients in Ethiopia: A systematic review and meta-analysis

Alemu

Bitew

Worku

2020

International Journal of Infectious Diseases

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To assess poor treatment outcomes and their predictors among drug-resistant tuberculosis patients treated in Ethiopia. Methods: Data were searched from both electronic databases and other sources. From the whole search, 404 articles were reviewed and 17 articles that fulfilled the inclusion criteria were included in the analysis. Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist was followed and Joanna Briggs Institute Critical Appraisal checklist was used for assessing the quality. Risk of bias was assessed using forest plot and Egger's regression test. Data were analyzed using STATA version 15 and Review Manager Software version 5.3. Results: The overall pooled proportion of poor treatment outcome and mortality was 17.86% and 15.13% respectively. The incidence density rate of poor treatment outcome and mortality was 10.41/1000 person-months and 9.28/1000 person-months respectively. Survival status and successful treatment outcomes were 76.97% and 63.82% respectively. HIV positivity, non-HIV comorbidities, clinical complications, extrapulmonary involvement, undernutrition, anemia, treatment delay, lower body weight, and older age were the predictors of poor treatment outcome. Conclusion:Better survival and treatment success rates were noted in Ethiopia as compared to the global average. The majority of the poor treatment outcomes occurred within the intensive phase. Early initiation of anti-tuberculosis treatment would be important for successful treatment outcomes.

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Section: The Pooled Incidence Of Mortalitymentioning

confidence: 92%

Section: Study Characteristicsmentioning

confidence: 99%

Section: Study Characteristicsmentioning

confidence: 99%

Section: The Pooled Incidence Of Poor Treatment Outcomementioning

confidence: 99%

Section: The Pooled Incidence Of Poor Treatment Outcomementioning

confidence: 99%

See 3 more Smart Citations

Poor treatment outcome and its predictors among drug-resistant tuberculosis patients in Ethiopia: A systematic review and meta-analysis

Alemu

Bitew

Worku

2020

International Journal of Infectious Diseases

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Parametric Bayesian Modelling of Tuberculosis Mortality determinants and Facility level heterogeneity effect using Gamma and Gaussian shared frailty techniques

Fwemba

Ngah

Rangoanana

et al. 2022

Preprint

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Background: In a normal regression analysis for determinants of TB outcomes, assumptions that the sample is homogenous is made. This model does not account for the overall effect of unobserved or unmeasured covariates. This study aims to quantify the amount of heterogeneity that exists at community level, and to ascertain the determinants of TB mortality across all the catchment areas in Lesotho. Methods: This was a retrospective record review of patients on TB treatment registered between January 2015 to December 2020 at 12 health care facilities in the district of Butha Buthe, Lesotho. Data collected from patient medical and statistical analysis was performed using R and INLA statistical software. Descriptive statistics were presented using frequency tables. Differences between binary outcomes were analysed using Person's X2 test. Mixed effect model with five Bayesian regression models of varying distributions were used to assess heterogeneity at facility level. Kaplan-Meier curves were used to demonstrate time-to-death events. Results: The total number of patients included in the analysis were 1729 of which 70% were males. And half of them were employed (54.2%). Being over 60 years (HR: 0.02, Cl: 0.01-0.04) and having a community health worker as a treatment contact person (HR: 0.36, Cl: 0.19-0.71) decreased the risk of dying. Miners had 1.73 times increased risk of dying (HR: 1.73, Cl: 1.07-2.78). The frailty variance was observed to be very minimal (<0.001), but significant indicating heterogeneity between catchment areas. Although similar hazard ratios and confidence intervals of covariates are seen between Gamma and Gaussian frailty log-logistic models, the credibility intervals for the Gamma model are consistently narrower. Conclusion: The results from both Gamma and Gaussian demonstrate that heterogeneity affected significance of the determinants for TB mortality. The results showed community level to significantly affect the risk of dying indicating differences between catchment areas.

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Determinants associated with areas with higher tuberculosis mortality rates: an ecological study

Lima

Rocha

Araújo

et al. 2019

Tropical Med Int Health

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Objective To characterise tuberculosis deaths in a region of northeast Brazil during the period from 2006 to 2017 and to identify determinants associated with areas with higher tuberculosis mortality rates. Methods Ecological descriptive study of deaths from tuberculosis with multivariate mapping and logistic regression, carried out from 2006 to 2017 in the 75 municipalities of Sergipe, Brazil. The focus of the analysis was the mean mortality rate from tuberculosis, dichotomised according to the median. The independent variables were selected based on the conceptual model of the social determinants of health. Results Mortality due to tuberculosis in Sergipe, Brazil, was most prevalent among males, mixed‐race people, and people over 40 years old and with a low level of education. Multivariate logistic regression identified the mean incidence rate for tuberculosis (aOR: 1.06), the proportion of HIV testing (aOR: 7.10), people without primary education and with informal occupation (aOR: 1.26) and people living in urban households without waste collection service (aOR: 0.10) as determinants associated to municipalities with higher tuberculosis mortality rates, with area under the ROC curve of 84% (P‐value 0.000). Mapping revealed evident spatial variability. Conclusions The tuberculosis epidemic in Brazil is determined by access to health services, especially the provision of HIV testing among those diagnosed with tuberculosis, accelerated urbanisation with large pockets of poverty and unsanitary housing conditions, corroborating global trends.

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Spatial Frailty Survival Model for Multidrug-Resistant Tuberculosis Mortality in Amhara Region, Ethiopia

Cited by 7 publications

References 12 publications

Poor treatment outcome and its predictors among drug-resistant tuberculosis patients in Ethiopia: A systematic review and meta-analysis

Poor treatment outcome and its predictors among drug-resistant tuberculosis patients in Ethiopia: A systematic review and meta-analysis

Parametric Bayesian Modelling of Tuberculosis Mortality determinants and Facility level heterogeneity effect using Gamma and Gaussian shared frailty techniques

Determinants associated with areas with higher tuberculosis mortality rates: an ecological study

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