METHODS: A two-pronged strategy was used by evaluating data from peer-reviewed literature and official reports. A systematic search was conducted using a structured query in four databases (Web of Science, Scopus, Medline, and PubMed) to identify any reports of the occurrence of zoonotic TB. No language and time constraints were used during the search, but non-English language articles were later excluded. The official data were sourced from the World Organization for Animal Health's (WOAH) World Animal Health Information System (WAHIS) and WHO's global TB database.
RESULTS: The retrieved records from SEAR and WPR (n = 113) were screened for eligibility, and data about disease occurrence were extracted and tabulated. In SEAR, all of the five studies that conducted Mycobacterium speciation (5/6) in humans were from India, and the reported Mycobacterium species included M. tuberculosis, M. bovis, M. scrofulacium, M. kansasii, M. phlei, M. smegmatis and M. orygis. In WPR, Mycobacterium speciation investigations in humans were conducted in Australia (8), China (2), Japan (2), NewZealand (2) and Malaysia (1), and the reported Mycobacterium species included M. bovis, M. africanum and M. tuberculosis. Seven countries in WHO's SEAR have officially reported the occurrence of Mycobacterium bovis in their animals: Bangladesh, India, Indonesia, Myanmar, Nepal, Sri Lanka and Thailand. In WPR, the WAHIS information system includes reports of the identification of M. bovis from 11 countries - China, Fiji, Japan, Malaysia, Mongolia, New Zealand, the Philippines, the Republic of Korea, Singapore, Tonga and Viet Nam. In contrast, human zoonotic TB cases in the WHO database were only listed from Australia, Brunei Darussalam and Palau countries.
DISCUSSION: The available data suggests under-reporting of zoonotic TB in the regions. Efforts are required to strengthen zoonotic TB surveillance systems from both animal and human health sides to better understand the impact of zoonotic TB in order to take appropriate action to achieve the goal of ending the TB epidemic.
METHODS: As data on policy indicators were straightforward and fully available, we focused on studying 25 non-policy indicators: 23 GMFs and 2 PMIs. Gathering data availability of the target indicators was conducted among NCD surveillance experts from the six selected countries during May-June 2020. Our research team found information regarding whether the country had no data at all, was using WHO estimates, was providing 'expert judgement' for the data, or had actual data available for each target indicator. We triangulated their answers with several WHO data sources, including the WHO Health Observatory Database and various WHO Global Reports on health behaviours (tobacco, alcohol, diet, and physical activity) and NCDs. We calculated the percentages of the indicators that need improvement by both indicator category and country.
RESULTS: For all six studied countries, the health-service indicators, based on responses to the facility survey, are the most lacking in data availability (100% of this category's indicators), followed by the health-service indicators, based on the population survey responses (57%), the mortality and morbidity indicators (50%), the behavioural risk indicators (30%), and the biological risk indicators (7%). The countries that need to improve their NCD surveillance data availability the most are Cambodia (56% of all indicators) and Lao PDR (56%), followed by Malaysia (36%), Vietnam (36%), Myanmar (32%), and Thailand (28%).
CONCLUSION: Some of the non-policy GMF and PMI indicators lacked data among the six studied countries. To achieve the global NCDs targets, in the long run, the six countries should collect their own data for all indicators and begin to invest in and implement the facility survey and the population survey to track NCDs-related health services improvements once they have implemented the behavioural and biological Health Risks Population Survey in their countries.
MATERIALS AND METHODS: This descriptive study utilises a desk review approach and employs the WHO Data Quality Assurance (DQA) Tool to assess data quality of ASDK. The analysis involves measuring eight health indicators from ASDK and Survei Status Gizi Indonesia (SSGI) conducted in 2022. The assessment focuses on various dimensions of data quality, including completeness of variables, consistency over time, consistency between indicators, outliers and external consistency.
RESULTS: Current study shows that routine health data in Indonesia performs high-quality data in terms of completeness and internal consistency. The dimension of data completeness demonstrates high levels of variable completeness with most variables achieving 100% of the completeness.
CONCLUSION: Based on the analysis of eight routine health data variables using five dimensions of data quality namely completeness of variables, consistency over time, consistency between indicators, outliers. and external consistency. It shows that completeness and internal consistency of data in ASDK has demonstrated a high data quality.