Monitoring data quality in syndromic surveillance: learnings from a resource limited setting

Modelling the Effectiveness of Epidemic Control Measures in Preventing the Transmission of COVID-19 in Malaysia

Malaysia is currently facing an outbreak of COVID-19. We aim to present the first study in Malaysia to report the reproduction numbers and develop a mathematical model forecasting COVID-19 transmission by including isolation, quarantine, and movement control measures. We utilized a susceptible, exposed, infectious, and recovered (SEIR) model by incorporating isolation, quarantine, and movement control order (MCO) taken in Malaysia. The simulations were fitted into the Malaysian COVID-19 active case numbers, allowing approximation of parameters consisting of probability of transmission per contact (β), average number of contacts per day per case (ζ), and proportion of close-contact traced per day (q). The effective reproduction number (Rt) was also determined through this model. Our model calibration estimated that (β), (ζ), and (q) were 0.052, 25 persons, and 0.23, respectively. The (Rt) was estimated to be 1.68. MCO measures reduce the peak number of active COVID-19 cases by 99.1% and reduce (ζ) from 25 (pre-MCO) to 7 (during MCO). The flattening of the epidemic curve was also observed with the implementation of these control measures. We conclude that isolation, quarantine, and MCO measures are essential to break the transmission of COVID-19 in Malaysia.

Reducing defects in the datasets of clinical research studies: conformance with data quality metrics

Background

A dataset is indispensable to answer the research questions of clinical research studies. Inaccurate data lead to ambiguous results, and the removal of errors results in increased cost. The aim of this Quality Improvement Project (QIP) was to improve the Data Quality (DQ) by enhancing conformance and minimizing data entry errors.

Methods

This is a QIP which was conducted in the Department of Biostatistics using historical datasets submitted for statistical data analysis from the department’s knowledge base system. Forty-five datasets received for statistical data analysis, were included at baseline. A 12-item checklist based on six DQ domains (i) completeness (ii) uniqueness (iii) timeliness (iv) accuracy (v) validity and (vi) consistency was developed to assess the DQ. The checklist was comprised of 12 items; missing values, un-coded values, miscoded values, embedded values, implausible values, unformatted values, missing codebook, inconsistencies with the codebook, inaccurate format, unanalyzable data structure, missing outcome variables, and missing analytic variables. The outcome was the number of defects per dataset. Quality improvement DMAIC (Define, Measure, Analyze, Improve, Control) framework and sigma improvement tools were used. Pre-Post design was implemented using mode of interventions. Pre-Post change in defects (zero, one, two or more defects) was compared by using chi-square test.

Results

At baseline, out of forty-five datasets; six (13.3%) datasets had zero defects, eight (17.8%) had one defect, and 31(69%) had ≥2 defects. The association between the nature of data capture (single vs. multiple data points) and defective data was statistically significant (p = 0.008). Twenty-one datasets were received during post-intervention for statistical data analysis. Seventeen (81%) had zero defects, two (9.5%) had one defect, and two (9.5%) had two or more defects. The proportion of datasets with zero defects had increased from 13.3 to 81%, whereas the proportion of datasets with two or more defects had decreased from 69 to 9.5% (p = < 0.001).

Conclusion

Clinical research study teams often have limited knowledge of data structuring. Given the need for good quality data, we recommend training programs, consultation with data experts prior to data structuring and use of electronic data capturing methods.

Data quality shortcomings with the US HIV/AIDS surveillance system

This study investigates some of the data quality challenges facing the HIV surveillance system in the United States. Using the content analysis method, Center for Disease Control annual HIV surveillance reports (1982-2014) are systematically reviewed and evaluated against relevant data quality metrics from previous literature. Center for Disease Control HIV surveillance system has made several key achievements in the last decade. However, there are several outstanding challenges that need to be addressed. The data are unrepresentative, incomplete, inaccurate, and lacks the required granularity limiting its usage. These shortcomings weaken the country's ability to track, report, and respond to the new HIV epidemiological trends. Furthermore, the problems deter the country from properly identifying and targeting the key subpopulations that need the highest resources by virtue of being at the highest risk of HIV infection. Several recommendations are suggested to address these issues.

The Role of Clinical Virology Laboratory and the Clinical Virology Laboratorian in Ensuring Effective Surveillance for Influenza and Other Respiratory Viruses: Points to Consider and Pitfalls to Avoid

Influenza and respiratory viruses have a global impact on public health. Clinical virology laboratories and laboratorians play an important role in not only the diagnosis but also the surveillance of these pathogens. Surveillance for influenza and other respiratory pathogens is important, as it informs public health decision making in terms of influenza vaccine and antiviral effectiveness, informs clinicians and public health practitioners about the pathogenicity of specific viral strains, guides clinical practice, and supports laboratory panning activities. Key background issues include the following: the fact that the laboratory is only one of several data providers to a surveillance system, the biologic nature of influenza and respiratory viruses and the laboratory needs to keep up to date on the diagnosis of these agents, the need for laboratorians to be involved in case definition development, the impact of push and pull data flow models on laboratory resources, and the fact that laboratories may be asked to provide more than just test results to surveillance programs. This review also identifies some key issues or questions that arise during the pre-analytic, analytic, and post-analytic phases that could impact on the ability of the laboratory to link to surveillance programs. Finally, issues surrounding virus characterization programs and how they link to surveillance programs are identified and discussed.

Collecting syndromic surveillance data by mobile phone in rural India: implementation and feasibility

Background

Infectious disease surveillance has long been a challenge for countries like India, where 75% of the health care services are private and consist of both formal and informal health care providers. Infectious disease surveillance data are regularly collected from governmental and qualified private facilities, but not from the informal sector. This study describes a mobile-based syndromic surveillance system and its application in a resource-limited setting, collecting data on patients’ symptoms from formal and informal health care providers.

Design

The study includes three formal and six informal health care providers from two districts of Madhya Pradesh, India. Data collectors were posted in the clinics during the providers’ working hours and entered patient information and infectious disease symptoms on the mobile-based syndromic surveillance system.

Results

Information on 20,424 patients was collected in the mobile-based surveillance system. The five most common (overlapping) symptoms were fever (48%), cough (38%), body ache (38%), headache (37%), and runny nose (22%). During the same time period, the government's disease surveillance program reported around 22,000 fever cases in one district as a whole. Our data – from a very small fraction of all health care providers – thus highlight an enormous underreporting in the official surveillance data, which we estimate here to capture less than 1% of the fever cases. Additionally, we found that patients from more than 600 villages visited the nine providers included in our study.

Conclusions

The study demonstrated that a mobile-based system can be used for disease surveillance from formal and informal providers in resource-limited settings. People who have not used smartphones or even computers previously can, in a short timeframe, be trained to fill out surveillance forms and submit them from the device. Technology, including network connections, works sufficiently for disease surveillance applications in rural parts of India. The data collected may be used to better understand the health-seeking behaviour of those visiting informal providers, as they do not report through any official channels. We also show that the underreporting to the government can be enormous.

Mobile phone-based syndromic surveillance system, Papua New Guinea

The health care system in Papua New Guinea is fragile, and surveillance systems infrequently meet international standards. To strengthen outbreak identification, health authorities piloted a mobile phone-based syndromic surveillance system and used established frameworks to evaluate whether the system was meeting objectives. Stakeholder experience was investigated by using standardized questionnaires and focus groups. Nine sites reported data that included 7 outbreaks and 92 cases of acute watery diarrhea. The new system was more timely (2.4 vs. 84 days), complete (70% vs. 40%), and sensitive (95% vs. 26%) than existing systems. The system was simple, stable, useful, and acceptable; however, feedback and subnational involvement were weak. A simple syndromic surveillance system implemented in a fragile state enabled more timely, complete, and sensitive data reporting for disease risk assessment. Feedback and provincial involvement require improvement. Use of mobile phone technology might improve the timeliness and efficiency of public health surveillance.

A review of data quality assessment methods for public health information systems

High quality data and effective data quality assessment are required for accurately evaluating the impact of public health interventions and measuring public health outcomes. Data, data use, and data collection process, as the three dimensions of data quality, all need to be assessed for overall data quality assessment. We reviewed current data quality assessment methods. The relevant study was identified in major databases and well-known institutional websites. We found the dimension of data was most frequently assessed. Completeness, accuracy, and timeliness were the three most-used attributes among a total of 49 attributes of data quality. The major quantitative assessment methods were descriptive surveys and data audits, whereas the common qualitative assessment methods were interview and documentation review. The limitations of the reviewed studies included inattentiveness to data use and data collection process, inconsistency in the definition of attributes of data quality, failure to address data users’ concerns and a lack of systematic procedures in data quality assessment. This review study is limited by the coverage of the databases and the breadth of public health information systems. Further research could develop consistent data quality definitions and attributes. More research efforts should be given to assess the quality of data use and the quality of data collection process.