Efforts to reduce the disparity between permanent residents and temporary migrants: Stop TB experiences in Shanghai, China

Lü, Hui; Jing, Chen; Wang, Wei; Wu, Laiwa; Shen, Xin; Yuan, Zhigang; Yan, Fei

doi:10.1111/tmi.12512

Cited by 13 publications

(19 citation statements)

References 13 publications

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“…Socially marginalized, high-risk subgroups commonly experience access barriers to TB care that result in poor treatment outcomes [14]. Economic migrants, defined as non-permanent residents in search of economic opportunity [15,16], constitute a key affected population. These migrants may face systemic barriers to housing, education, financing and healthcare (Table 1) [17,18].…”

Section: (Continued From Previous Page)mentioning

confidence: 99%

Tuberculosis among economic migrants: a cross-sectional study of the risk of poor treatment outcomes and impact of a treatment adherence intervention among temporary residents in an urban district in Ho Chi Minh City, Viet Nam

Vo¹,

Codlin²,

Forse³

et al. 2020

BMC Infect Dis

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Background: Tuberculosis (TB) remains a major cause of avoidable deaths. Economic migrants represent a vulnerable population due to their exposure to medical and social risk factors. These factors expose them to higher risks for TB incidence and poor treatment outcomes. Methods: This cross-sectional study evaluated WHO-defined TB treatment outcomes among economic migrants in an urban district of Ho Chi Minh City, Viet Nam. We measured the association of a patient's government-defined residency status with treatment success and loss to follow-up categories at baseline and performed a comparative interrupted time series (ITS) analysis to assess the impact of community-based adherence support on treatment outcomes. Key measures of interest of the ITS were the differences in step change (β 6) and post-intervention trend (β 7). Results: Short-term, inter-province migrants experienced lower treatment success (aRR = 0.95 [95% CI: 0.92-0.99], p = 0.010) and higher loss to follow-up (aOR = 1.98 [95% CI: 1.44-2.72], p < 0.001) than permanent residents. Intraprovince migrants were similarly more likely to be lost to follow-up (aOR = 1.86 [95% CI: 1.03-3.36], p = 0.041). There was evidence that patients > 55 years of age (aRR = 0.93 [95% CI: 0.89-0.96], p < 0.001), relapse patients (aRR = 0.89 [95% CI: 0.84-0.94], p < 0.001), and retreatment patients (aRR = 0.62 [95% CI: 0.52-0.75], p < 0.001) had lower treatment success rates. TB/HIV co-infection was also associated with lower treatment success (aRR = 0.77 [95% CI: 0.73-0.82], p < 0.001) and higher loss to follow-up (aOR = 2.18 [95% CI: 1.55-3.06], p < 0.001). The provision of treatment adherence support increased treatment success (IRR(β 6) = 1.07 [95% CI: 1.00, 1.15], p = 0.041) and reduced loss to follow-up (IRR(β 6) = 0.17 [95% CI: 0.04, 0.69], p = 0.013) in the intervention districts. Loss to follow-up continued to decline throughout the post-implementation period (IRR(β 7) = 0.90 [95% CI: 0.83, 0.98], p = 0.019).

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Section: (Continued From Previous Page)mentioning

confidence: 99%

Tuberculosis among economic migrants: a cross-sectional study of the risk of poor treatment outcomes and impact of a treatment adherence intervention among temporary residents in an urban district in Ho Chi Minh City, Viet Nam

Vo¹,

Codlin²,

Forse³

et al. 2020

BMC Infect Dis

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“…3,4 Heng County is a key area in the Guangxi Beibu Gulf Economic Zone, with a large transient population that promotes the spread of tuberculosis. 5,6 Currently, the prevalence of human immunodeficiency virus (HIV)-tuberculosis coinfections represent a serious new challenge to the public health system. In 2011, about 1.1 million (13%) of the 8.7 million people infected with tuberculosis worldwide were HIVpositive.…”

Section: Introductionmentioning

confidence: 99%

A New Hybrid Model Using an Autoregressive Integrated Moving Average and a Generalized Regression Neural Network for the Incidence of Tuberculosis in Heng County, China

Wei

Jiang

Gao

et al. 2017

The American Journal of Tropical Medicine and Hygiene

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It is a daunting task to eradicate tuberculosis completely in Heng County due to a large transient population, human immunodeficiency virus/tuberculosis coinfection, and latent infection. Thus, a high-precision forecasting model can be used for the prevention and control of tuberculosis. In this study, four models including a basic autoregressive integrated moving average (ARIMA) model, a traditional ARIMA-generalized regression neural network (GRNN) model, a basic GRNN model, and a new ARIMA-GRNN hybrid model were used to fit and predict the incidence of tuberculosis. Parameters including mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE) were used to evaluate and compare the performance of these models for fitting historical and prospective data. The new ARIMA-GRNN model had superior fit relative to both the traditional ARIMA-GRNN model and basic ARIMA model when applied to historical data and when used as a predictive model for forecasting incidence during the subsequent 6 months. Our results suggest that the new ARIMA-GRNN model may be more suitable for forecasting the tuberculosis incidence in Heng County than traditional models.

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“…44 As prior evidence has shown, the use of incentives and subsidies, and support for side effect management can also have a significant positive impact on TB treatment outcomes in urban migrants. 16,45 Even though our comparative ITS analysis on the subpopulation of temporary residents failed to detect a significant change after implementation of the intervention possibly due to data sparsity, it may be reasonable to extend our findings to economic migrants given their high proportion in the general population (~38%) and TB patient cohort (23%) of Go Vap. The disparity in these two proportions also reinforces the WHO recommendation of systematic screening among migrants, as studies have shown that intensified case finding along with patient education, advocacy and robust referral mechanisms can have a positive impact on detection within this vulnerable group.…”

Section: Discussionmentioning

confidence: 74%

Tuberculosis among economic migrants: A cross‐sectional study of the risk of poor treatment outcomes and impact of a treatment adherence intervention among temporary residents in an urban district in Ho Chi Minh City, Viet Nam

Codlin

Forse

et al. 2020

Preprint

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Background: Tuberculosis (TB) remains a major cause of avoidable deaths. Economic migrants represent a vulnerable population due to their exposure to medical and social risk factors. These factors expose them to higher risks for TB incidence and poor treatment outcomes. Methods: This cross-sectional study evaluated WHO-defined TB treatment outcomes among economic migrants in an urban district of Ho Chi Minh City, Viet Nam. We measured the association of a patient’s government-defined residency status with treatment success and loss to follow-up categories at baseline and performed a comparative interrupted time series (ITS) analysis to assess the impact of community-based adherence support on treatment outcomes. Key measures of interest of the ITS were the differences in step change (β6) and post-intervention trend (β7). Results: Short-term, inter-province migrants experienced lower treatment success (aRR=0.95 [95% CI: 0.92-0.99], p=0.010) and higher loss to follow-up (aOR=1.98 [95% CI: 1.44-2.72], p<0.001) than permanent residents. Intra-province migrants were similarly more likely to be lost to follow-up (aOR=1.86 [95% CI: 1.03-3.36], p=0.041). There was evidence that patients >55 years of age (aRR=0.93 [95% CI: 0.89-0.96], p<0.001), relapse patients (aRR=0.89 [95% CI: 0.84-0.94], p<0.001), and retreatment patients (aRR=0.62 [95% CI: 0.52-0.75], p<0.001) had lower treatment success rates. TB/HIV co-infection was also associated with lower treatment success (aRR=0.77 [95% CI: 0.73-0.82], p<0.001) and higher loss to follow-up (aOR=2.18 [95% CI: 1.55-3.06], p<0.001). The provision of treatment adherence support increased treatment success (IRR(β6)=1.07 [95% CI: 1.00, 1.15], p=0.041) and reduced loss to follow-up (IRR(β6)=0.17 [95% CI: 0.04, 0.69], p=0.013) in the intervention districts. Loss to follow-up continued to decline throughout the post-implementation period (IRR(β7)=0.90 [95% CI: 0.83, 0.98], p=0.019). Conclusions: Economic migrants, particularly those crossing provincial borders, have higher risk of poor treatment outcomes and should be prioritized for tailored adherence support. In light of accelerating urbanization in many regions of Asia, implementation trials are needed to inform evidence-based design of strategies for this vulnerable population.

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Efforts to reduce the disparity between permanent residents and temporary migrants: Stop TB experiences in Shanghai, China

Cited by 13 publications

References 13 publications

Tuberculosis among economic migrants: a cross-sectional study of the risk of poor treatment outcomes and impact of a treatment adherence intervention among temporary residents in an urban district in Ho Chi Minh City, Viet Nam

Tuberculosis among economic migrants: a cross-sectional study of the risk of poor treatment outcomes and impact of a treatment adherence intervention among temporary residents in an urban district in Ho Chi Minh City, Viet Nam

A New Hybrid Model Using an Autoregressive Integrated Moving Average and a Generalized Regression Neural Network for the Incidence of Tuberculosis in Heng County, China

Tuberculosis among economic migrants: A cross‐sectional study of the risk of poor treatment outcomes and impact of a treatment adherence intervention among temporary residents in an urban district in Ho Chi Minh City, Viet Nam

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