Implementing a new tuberculosis surveillance system in Zhejiang, Jilin and Ningxia: improvements, challenges and implications for China's National Health Information System

BACKGROUND

China is still faced with the public health challenge of tuberculosis (TB), and a robust surveillance system is critical for developing evidence-based TB control policies. The Tuberculosis Information Management System (TBIMS), an independent system launched in 2005, has encountered several challenges in meeting the current needs of TB control. The Chinese government also planned to establish the National Health Information System (NHIS) aggregating data in different areas. The China National Health Commission-Gates TB Project Phase III launched a new TB surveillance system to address these challenges and also as a pilot for the countrywide implementation of the NHIS. This commentary highlights the improvements and challenges in implementing the new TB system and also discusses the implications for the roll-out of the NHIS.

MAIN TEXT

The new TB surveillance system piloted in each prefecture of the project provinces was designed based on the local information system under the unified principle of organizing patient information under a unique ID and realizing the function of data exchange. Upon mid-2019, the data exchange successful rate reached almost 100%, and the system showed good performance in data completeness. Major improvements of the new system included achieving automatic data extraction instead of manual entry, assisting clinical service provision, and the augmented statistical functions. The major challenges in the implementation and scale-up of the new system were the licensing issue and the diversities of infrastructures that hinder the promotion of the new system at a low cost. This pilot also accumulated experiences for the roll-out of the NHIS regarding the technical solutions of reforming current information systems as well as effective training approaches for the developers and users of the new system.

CONCLUSIONS

The successful implementation of the new TB surveillance system in the three TB designated medical institutions demonstrated how the diverse infrastructures of the information system could be reformed to achieve the functions of automatic data extraction and data exchange and better cater to the needs of healthcare workers. This pilot also accumulated rich experiences and lessons learnt for developing technical solutions and personnel training for the scale-up of the NHIS.

A spatial hierarchical model for integrating and bias-correcting data from passive and active disease surveillance systems

Disease surveillance data are important for monitoring disease burden and occurrence, and for informing a wide range of efforts to improve population health. Surveillance for infectious diseases may be conducted passively, relying on reports from healthcare facilities, or actively, involving surveys of the population at risk. Passive surveillance typically provides wide spatial coverage, but is subject to biases arising from differences in care-seeking behavior, diagnostic practices, and under-reporting. Active surveillance minimizes these biases, but is typically constrained to small areas and subpopulations due to resource limitations. Methods based on linkage of individual records between passive and active surveillance datasets provide a means to estimate and correct for the biases of each system, leveraging the size and coverage of passive surveillance and the quality of data in active surveillance. We develop a spatial Bayesian hierarchical model for bias-correcting data from both systems to yield an improved estimate of disease measures after adjusting for under-ascertainment. We apply the framework to data from a passive and an active surveillance system for pulmonary tuberculosis (PTB) in Sichuan, China, and estimate the average sensitivity of the active surveillance system at 70% (95% credible interval: 62%, 78%), and the passive system at 30% (95% CI: 24%, 35%). Passive surveillance sensitivity exhibited considerable spatial variability, and was positively associated with a site's gross domestic product per capita. Bias-corrected estimates of county-level PTB prevalence in the province in 2010 identified regions in the southeast with the highest PTB burden, yielding different geographic priorities than previous reports.

Using decision fusion methods to improve outbreak detection in disease surveillance

BACKGROUND

When outbreak detection algorithms (ODAs) are considered individually, the task of outbreak detection can be seen as a classification problem and the ODA as a sensor providing a binary decision (outbreak yes or no) for each day of surveillance. When they are considered jointly (in cases where several ODAs analyze the same surveillance signal), the outbreak detection problem should be treated as a decision fusion (DF) problem of multiple sensors.

METHODS

This study evaluated the benefit for a decisions support system of using DF methods (fusing multiple ODA decisions) compared to using a single method of outbreak detection. For each day, we merged the decisions of six ODAs using 5 DF methods (two voting methods, logistic regression, CART and Bayesian network - BN). Classical metrics of accuracy, prediction and timelines were used during the evaluation steps.

RESULTS

In our results, we observed the greatest gain (77%) in positive predictive value compared to the best ODA if we used DF methods with a learning step (BN, logistic regression, and CART).

CONCLUSIONS

To identify disease outbreaks in systems using several ODAs to analyze surveillance data, we recommend using a DF method based on a Bayesian network. This method is at least equivalent to the best of the algorithms considered, regardless of the situation faced by the system. For those less familiar with this kind of technique, we propose that logistic regression be used when a training dataset is available.

Dealing with uncertainty when using a surveillance system

INTRODUCTION

Epidemiologists manage outbreak identification and confirmation by means of a "situation diagnosis", which involves validating (or invalidating) an alarm (signal identified as abnormal) as an alert (a real, characterized outbreak) and proposing the first countermeasures. This work investigates how uncertainty is materialized during this stage, and how experts develop strategies to address this uncertainty with the help of an early warning system.

METHODS

We built an experiment using a simulation platform with a scenario involving both a natural and an intentional outbreak. Observations of expert activities were recorded and formalised using a specific task analysis method. These formatted data were then categorized by applying RAWFS (Reduction- Assumption - Weighing - Forestalling- Suppression) heuristics.

RESULTS

We quantified uncertainty and the mechanisms involved. During the situation diagnosis, two sorts of uncertainty were characterized: practice-imposed uncertainty and situation-imposed uncertainty. We did not find either weighing pros and cons or suppression strategies in this area of expertise, but highlight the predominance of coping strategies that relied on reduction (66,4%) and assumption-based reasoning. We observed a predominance of the phone (89%) to cope with uncertainty and among electronic tools, the surveillance system plays a major role (69% of cases) and is mainly used in reduction strategies. We detail tools and systems used to support experts in their coping strategy.

CONCLUSION

We confirmed that a surveillance system must include different features that provide relevant information to help users reduce uncertainty and thus support their decision making. In that perspective, the flow diagram and proposal presented in this study can help prioritize the necessary changes to surveillance system design.

Infectious diseases in children and adolescents in the Republic of Korea; Past & recent status

Compared to the past decades, in recent decades, environmental and hygienic conditions in the Republic of Korea have improved along with socioeconomic developments, and the incidence of most infectious diseases, especially vaccine-preventable diseases, has greatly decreased due to active immunization with the developed level of health care. However, the incidence of some diseases has been increasing, and new diseases have been emerging. To cope with such changes actively, the government put the "Law for Control and Prevention of Infectious Diseases" into effect; this law was entirely revised on December 30, 2010. In this report, I review the past and recent status of infectious diseases in the Republic of Korea, following the introduction of this law, on the basis of data in the "National Notifiable Disease Surveillance System", which had been accumulated between the years 1960 and 2010.