Main Article Content
Surveillance, Population data, population health, behavioural , wellbeing and health modification
Introduction: The United Nations high-level meeting of the General Assembly on the Prevention and Control of Non-communicable Diseases passed a political declaration on Non-Communicable Diseases (NCD) prevention and control in 2011, emphasizing the great need for NCD surveillance including in Low-to-Middle-Income-Countries (LMICs).
Method: A review of literature was conducted and set for full text citations published in English dated 1 January, 2007 to 31 August 2019. MESH terms or key words were selected from the following groups of generic terms: the following words “Health surveillance systems” and “NCD monitoring and surveillance system”. The literatures were tabulated according to the authors, date that was published and which journal, the title of the study, the surveillance design and their recommendations. The 13 articles that were identified, only one was conducted in a developing country while the rest were conducted in high income countries.
Results: 60% of the NCD surveillace system reviewed use passive surveillance, 30% uses passive assisted sentinel surveillance and 10% use passive assited spatial surveillance. Based on countries surveillance system there was an equal distribution on involvement in policy development (33%), behavioural risk associated aggregates (33%) and intergrated health information System (33%).Through intense review, passive assisted sentinel surveillance was mostly practiced and the use of spatial surveillace in this context for interregional comparisons of specified diseases.
Conclusion: There was less evidence on surveillance in LMIC but the following surveillance systems were identified as essential for Fiji’s proposed NCD surveillance system. This study suggest that a probable surveillance system that can be adopted by Fiji is a passive assisted sentinel surveillance system enhanced with Spatial data. Further consultation and a feasibility study can be proposed as a way forward for this study findings.
2. Walker N, Bryce J, Black RE. Interpreting health statistics for policymaking: the Story behind the headlines. The Lancet. 2007;639(9565):956-63.
3. Mathers CD, Murray CJL, Ezzati M, Gakidou E, Salomon JA, Stein C. Population health metrics: crucial inputs to the development of evidence for health policy. Population Health Metrics. 2003;1:6.
4. Trans-fatty acid content in food. Copenhagen: The Ministry of Food, Agriculture and Fisheries of Denmark and the Danish Technical University, National Food Institute; 2014 (http://www.foedevarestyrelsen.dk/english/Food/Trans%20fatty%20acids/Pages/default.aspx)
5. Global action plan for the prevention and control of noncommunicable diseases, 2013-20. Geneva: WHO; 2013 (http://apps.who.int/iris/bitstream/10665/94384/1/9789241506236_eng.pdf)
6. The National FINRISK study. In: Terveyden ja Hyvinvoinnin Laitos [website]. Helsinki: National Institute for Health and Welfare; 2014 (http://www.thl.fi/fi/tutkimusjaasiantuntijatyo/vaestotutkimukset/finriski-tutkimus/the-national-finrisk-study)
7. Mapping salt-reduction initiatives in the WHO European Region. Copenhagen: WHO Regional Office for Europe; 2013 (http://www.euro.who.int/__data/assets/pdf_file/0009/186462/Mapping-salt-reductioninitiatives-in-the-WHO-European-Region.pdf).
8. Baldissera S, Campostrini S, Binkin N, Minardi V, Minelli G, Ferrante G, et al. Features and initial assessment of the Italian behavioral risk factor surveillance system (PASSI), 2007-2008. Prev Chronic Dis. 2011;8(1).
9. Diabetes: a national public health priority 2015-2020. Valletta: Ministry of Health of the Republic of Malta; 2014 (http://socialdialogue.gov.mt/en/Public_Consultations/MEHHEALTH/Documents/Diabetes%20%20A%20Public%20Health%20Priority%20final%20document.pdf).
10. Norwegian Institute of Public Health [website]. Public health profiles for municipalities and counties. Oslo: Norwegian Institute of Public Health; 2014 (http://www.fhi.no/artikler/?id=109337).
11. Norberg M et al. The Västerbotten Intervention Programme: background, design and implications. Global health Action. 2010;3:4643.
12. Vienna Declaration on Nutrition and Noncommunicable Diseases in the Context of Health 2020. Copenhagen: WHO Regional Office for Europe; 2013 http://www.euro.who.int/en/mediacentre/events/events/2013/07/vienna-conference-on-nutrition-
13. STEPS: A framework for surveillance. The WHO STEPwise approach to Surveillance of
Non-communicable diseases (STEPS). Geneva: WHO; 2003 (WHO/NMH/CCS/03.01; http://www.who.int/ncd_surveillance/en/steps_framework_dec03.pdf).
14. The first national study on the prevalence of non-communicable disease risk factors in the Republic of Moldova. The UN in Moldova Magazine. 2014;2(59):29-31 (file:///C:/Users/Anna/Downloads/rev_ONU_259_25-07-2014_pentru_WEB%20(1).pdf).
15. Alwan A, MacLean D, Riley LM et al. Monitoring and surveillance of chronic non-communicable diseases: progress and capacity and high-burden countries. Lancet. 2010;376:1861-8
16. Understanding parental perceptions of immunisation in real-time. Jakarta: United Nations Global Pulse, 2014 (http://www.unglobalpulse.org/immunisation-parent-perceptions)
17. Chunara R, Bouton L, Ayers JW, Brownstein JS. Assessing the online social environment for surveillance of obesity prevalence. PLoS ONE. 2013;8(4) (http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0061373).
18. Kosinski M, Stillwell D, Graepel T. Private traits and attributes predict digital records of human behavior. PNAS. 2013;110(15):5802-5 (http://www.pnas.org/content/110/15/5802.full) .
19. FoodSwitch. In: The George Institute for Global Health Australia [website]. Sidney: The George Institute for Global Health Australia; 2013 (http://www.georgeinstitute.org.au/projects/foodswitch).
20. Karpati T, Cohen-Stavi CJ, Leibowitz M, Hoshen M, Feldman BS, Balicer RD. Towards a subsiding diabetes epidemic: trends from a large population-based study in Israel. Popul Health Metr.2014;12(1):32.
21. Coordinating technical group of the behavioural risk factor system (PASSI). PASSI (Progressi Delle Aziende Sanitarie per la Salute in Italia), an Italian behavioural risk factor system: results 2007. Rome (IT): Istituto Superiore di Sanità; 2009. http://www.iss.it/binary/publ/cont/07-30.1195128446.pdf. Accessed August 11, 2010.
22. Behavioral Risk Factor Surveillance System: questionnaires. Atlanta (GA): Centers for Disease Control and Prevention. http://www.cdc.gov/brfss/questionnaires/questionnaires.htm. Accessed February 4, 2010.
23. Nelson DE, Holtzman D, Waller M, Leutzinger C, Condon K. Objectives and design of the Behavioral Risk Factor Surveillance System. Proceedings of the Survey Research Methods Section, American Statistical Association; 1998. https://www.amstat.org/sections/SRMS/Proceedings/papers/1998_032.pdf. Accessed February 4, 2010.
24. Gentry EM, Kalsbeek WD, Hogelin GC, Jones JT, Gaines KL, Forman MR, et al. The behavioral risk factor surveys: II. Design, methods, and estimates from combined state data. Am J Prev Med 1985;1(6):9-14.
25. Remington PL, Smith MY, Williamson DF, Anda RF, Gentry EM, Hogelin GC. Design, characteristics, and usefulness of state-based behavioral risk factor surveillance: 1981-87. Public Health Rep 1988;103(4):366-75.
26. Alwan A, Maclean DR, Riley LM, d’Espaignet ET, Mathers CD, et al. (2010) Monitoring and surveillance of chronic non-communicable diseases: progress and capacity in high-burden countries.Lancet 376: 1861–1868.
27. Parkin DM, Bray F, Ferlay J, Jemal A (2014) Cancer in Africa 2012. Cancer Epidemiol Biomarkers Prev.
28. Mathers CD, Loncar D (2006) Projections of global mortality and burden of disease from 2002 to 2030.PLoS Med 3: e442.
29. Beaglehole R, Bonita R, Horton R, Adams C, Alleyne G, et al. (2011) Priority actions for the non-communicable disease crisis. Lancet 377: 1438–1447.
30. Peck R, Mghamba J, Vanobberghen F, Kavishe B, Rugarabamu V, et al. (2014) Preparedness of Tanzanian health facilities for outpatient primary care of hypertension and diabetes: a cross-sectional survey. Lancet Glob Health 2: e285–e292.