Implementing a “free” tuberculosis (TB) care policy under the integrated model in Jiangsu, China: practices and costs in the real world

Jia, Xinxin; Chen, Jiaying; Zhang, Siyuan; Dai, Bing; Long, Qian; Tang, Shenglan

doi:10.1186/s40249-016-0099-8

Cited by 67 publications

(90 citation statements)

References 19 publications

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“…A recent study suggested that urbanization, climate change, international trade and population movement were important factors that influenced this re-emergence of dengue in Guangzhou [5]. In order to improve early and rapid response to dengue outbreaks in Guangzhou, we combined dengue internet-based data (DBSI) with imported cases, temperature and rainfall to develop an early warning model.…”

Section: Discussionmentioning

confidence: 99%

Dengue Baidu Search Index data can improve the prediction of local dengue epidemic: A case study in Guangzhou, China

Liu

Zhu

et al. 2017

PLoS Negl Trop Dis

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BackgroundDengue fever (DF) in Guangzhou, Guangdong province in China is an important public health issue. The problem was highlighted in 2014 by a large, unprecedented outbreak. In order to respond in a more timely manner and hence better control such potential outbreaks in the future, this study develops an early warning model that integrates internet-based query data into traditional surveillance data.Methodology and principal findingsA Dengue Baidu Search Index (DBSI) was collected from the Baidu website for developing a predictive model of dengue fever in combination with meteorological and demographic factors. Generalized additive models (GAM) with or without DBSI were established. The generalized cross validation (GCV) score and deviance explained indexes, intraclass correlation coefficient (ICC) and root mean squared error (RMSE), were respectively applied to measure the fitness and the prediction capability of the models. Our results show that the DBSI with one-week lag has a positive linear relationship with the local DF occurrence, and the model with DBSI (ICC:0.94 and RMSE:59.86) has a better prediction capability than the model without DBSI (ICC:0.72 and RMSE:203.29).ConclusionsOur study suggests that a DSBI combined with traditional disease surveillance and meteorological data can improve the dengue early warning system in Guangzhou.

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Section: Discussionmentioning

confidence: 99%

Dengue Baidu Search Index data can improve the prediction of local dengue epidemic: A case study in Guangzhou, China

Liu

Zhu

et al. 2017

PLoS Negl Trop Dis

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“…Drawing attention to these processes is important, as are efforts to include them in the planning and policy cycle through, for example, scenario planning [23]. Another area where further work is needed is on the relationships between endemic zoonoses and food (in)security and human nutrition [10].…”

Section: Toward Solutions? Linking Better Research-policy-action In Amentioning

confidence: 99%

Engaging research with policy and action: what are the challenges of responding to zoonotic disease in Africa?

Bardosh

Cornwall-Scoones²,

Kalema‐Zikusoka³

et al. 2017

Phil. Trans. R. Soc. B

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Zoonotic diseases will maintain a high level of public policy attention in the coming decades. From the spectre of a global pandemic to anxieties over agricultural change, urbanization, social inequality and threats to natural ecosystems, effectively preparing and responding to endemic and emerging diseases will require technological, institutional and social innovation. Much current discussion emphasizes the need for a ‘One Health’ approach: bridging disciplines and sectors to tackle these complex dynamics. However, as attention has increased, so too has an appreciation of the practical challenges in linking multi-disciplinary, multi-sectoral research with policy, action and impact. In this commentary paper, we reflect on these issues with particular reference to the African sub-continent. We structure the themes of our analysis on the existing literature, expert opinion and 11 interviews with leading One Health scholars and practitioners, conducted at an international symposium in 2016. We highlight a variety of challenges in research and knowledge production, in the difficult terrain of implementation and outreach, and in the politicized nature of decision-making and priority setting. We then turn our attention to a number of strategies that might help reconfigure current pathways and accepted norms of practice. These include: (i) challenging scientific expertise; (ii) strengthening national multi-sectoral coordination; (iii) building on what works; and (iv) re-framing policy narratives. We argue that bridging the research-policy-action interface in Africa, and better connecting zoonoses, ecosystems and well-being in the twenty-first century, will ultimately require greater attention to the democratization of science and public policy.This article is part of the themed issue ‘One Health for a changing world: zoonoses, ecosystems and human well-being’.

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“…[6][7][8][9] Patient costs incurred during TB diagnosis and treatment, as well as seeking and receiving health care, could cause barriers to access and adherence that can affect treatment outcomes and increase the risk of transmission of disease. [10] Therefore, patient costs could hamper TB control, particularly for vulnerable populations such as immigrants (internal and international migrants). In China, people with TB-related symptoms can have free sputum smear tests and a primary treatment package if TB is confirmed.…”

Section: Introductionmentioning

confidence: 99%

“…However, there are many out-of-pocket health care costs associated with TB diagnosis and treatment. [10][11][12][13] In the recent past, internal migrants could not share the free TB policy in urban centers, and they were not allowed to use their health insurance in cities, only in the town or location where they were born.…”

Section: Introductionmentioning

confidence: 99%

Catastrophic costs of Tuberculosis care in a population with internal migrants in China

Jiang

Hong

et al. 2020

Preprint

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Background Although a free diagnosis and baseline treatment package was offered for tuberculosis (TB), hidden costs incurred by patients and their households could worsen their socio-economic and health status, particularly for migrants. We estimated the prevalence of catastrophic cost of TB patients and its associated factors in an urban population with internal migrants in China. Methods A cross-sectional survey was conducted to enroll culture-confirmed pulmonary TB patients in Songjiang district, Shanghai, between December 1, 2014, and December 31, 2015. Consenting participants completed a questionnaire, which collected direct and indirect costs before and after the diagnosis of TB. The catastrophic cost was defined as the annual expenses of TB care that exceeds 20% of total household income. We used logistic regression to identify factors associated with catastrophic costs. Results Overall, 248 drug-susceptible TB patients were enrolled, with 70% (174 of 248) of them being internal migrants. Migrant patients were significantly younger compared to resident patients. The total costs were 25,824 ($3,689) and 13,816 ($1,974) Chinese Yuan (RMB) for resident and internal migrant patients, respectively. The direct medical cost comprised about 70% of the total costs among both migrant and resident patients. Overall, 55% (132 of 248) of patients experienced high expenses ( > 10% of total household income), and 22% (55 of 248) experienced defined catastrophic costs. However, the reimbursement for TB care only reduced the prevalence of catastrophic costs to 20% (49 of 248). More than half of the internal migrants had no available health insurance (52%, 90 of 174). Hospitalizations, no available insurance, and older age contributed significantly to the occurrence of catastrophic costs. Conclusions. The catastrophic cost of TB service cannot be overlooked, despite the free policy. Migrants have difficulties benefiting from health insurance in urban cities. Interventions, including expanded medical financial assistance, are needed to secure universal TB care.

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Implementing a “free” tuberculosis (TB) care policy under the integrated model in Jiangsu, China: practices and costs in the real world

Cited by 67 publications

References 19 publications

Dengue Baidu Search Index data can improve the prediction of local dengue epidemic: A case study in Guangzhou, China

Dengue Baidu Search Index data can improve the prediction of local dengue epidemic: A case study in Guangzhou, China

Engaging research with policy and action: what are the challenges of responding to zoonotic disease in Africa?

Catastrophic costs of Tuberculosis care in a population with internal migrants in China

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