DOI: https://doi.org/10.14710/jmki.7.1.2019.16-

Pengembangan Instrumen RDQA Untuk Surveilans Epidemiologi DBD Di Dinas Kesehatan Kota Tarakan

*Haikal Haikal  -  Universitas Diponegoro, Indonesia
Martini Martini  -  Universitas Diponegoro, Indonesia
Eko Sediyono scopus  -  Universitas Kristen Satya Wacana, Indonesia

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Abstract
In 2016 from 7 Puskesmas in Tarakan City, 4 (four) Puskesmas (58%) sent their reports above the 5th of each month. There is still a mismatch of the number of source data with the results of recapitulation such as the number of DHF cases in Tarakan DHO with the number of DHF cases in the Ministry of Health Republic of Indonesia SKDR as well as the incompatibility of the reporting format by recording surveillance staff. The purpose of this study was to develop a Routine Data Quality Assessment (RDQA) instrument for DHF Epidemiological Surveillance in Tarakan City Health Office.
The type of research used is Research and Development. The total subjects of the study were 9 people with details of 7 data management officers in the Puskesmas in the Tarakan DKK working area and 1 person in charge of the DHF Program in Tarakan DKK. The steps in this study, namely: (1) Potential and problems, (2) Data Collection (3) Product Design (4) Design Validation (5) Design Revision (6) Product Trial (7) Product Revision (8) Trial Use. The results of this study are that data quality assessment instruments have been developed according to RDQA and routine data quality assessments by the Ministry of Health with R & D research methods modified with eight indicators namely timeliness, data availability, data completeness, monitoring and evaluation unit capabilities, reporting indicators and guidelines, data collection and reporting format, data management process, linkages with the national reporting system and the use of data for decision making. The results of the assessment of the instruments developed, namely the aspect of content feasibility has an average value of 81%, the feasibility aspect of presentation is 78% and language assessment aspects 81%. Based on the results of the assessment of the three aspects assessed in the development of the RDQA instrument, a good conclusion is reached, but there are general recommendations given both by the Chair of the DHF Program in the DHO and the Epidemiological Surveillance Data Management Officer in DHO and Puskesmas, namely the use of modules and manuals RDQA instrument.

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Keywords: Surveilans Epidemiologi DBD; Routine Data Quality Assesment (RDQA).

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  1. Kementerian Kesehatan RI. Penilaian Mandiri Kualitas Data Rutin (PMKDR) Sistem Informasi Kesehatan. Jakarta: Kemenkes RI; 2013
  2. Saeed KMI, Bano R and Asghar RJ. Evaluation of the national tuberculosis surveillance system in Afghanistan. Eastern Mediterranean Health Journal. Feb 2013; vol 19;no.2;200-207
  3. Matheson AI, et al. Implementation and Expansion of An Electronic Medical Record for HIV Care and Treatment in Haiti: An Assessment of System Use and The Impact of Large-Scale Disruptions. International Journal of Medical Informatics. Apr 2012; vol 81;no.4;244-256
  4. Ayub M and Petter N. Reporting Practices And Data Quality In Health Information Systems In Developing Countries: An Exploratory Case Study In Kenya. Journal of Health Informatics in Developing Countries. Dec 2016; vol 10;no.1;114-126
  5. Muthee V, et al. The Impact of Routine Data Quality Assessments on Electronic Medical Record Data Quality in Kenya. PLOS ONE. Apr 2018; vol 13;no.4
  6. Joseph AF, et al. Conducting a Surveillance Data Quality Audit in Grand Bassa County, Liberia. The Pan African Medical Journal. Mei 2017; vol 32; no.10
  7. Petter S and Fruhling A. Evaluating The Success of An Emergency Response Medical Information System. International Journal of Medical Informatics. Jul 2011; vol 80;no.7;480-489
  8. Chen H, Yu P and Wang N. Do we have the reliable data? An exploration of data quality for AIDS information system in China. Stud. Health Technol. Inform. Aug 2012, vol 192;1042–1042
  9. World Health Organization. Manual on Use of Routine Data Quality (RDQA) tool for TB Monitoring. Geneva: WHO; 2011
  10. Nalianya JM and Stephen WL. Monitoring and Evaluation Systems and Performance of Non-Governmental Based Maternal Health Projects in Bungoma South Sub-County, Kenya. European Scientific Journal. Aug 2017;vol 13;no.23;11-38
  11. Antoinette AB, et al. Quality of routine facility data for monitoring priority maternal and newborn indicators in DHIS2: A case study from Gombe State, Nigeria. PLOS ONE. Jan 2019; vol 14;no.1
  12. Ahanhanzo YG, Ouendo EM, Kpozehouen A, Leveque A, Makoutode M and Wilmet MD. Data Quality Assessment in The Routine Health Information System: an Application of The Lot Quality Assurance Sampling in Benin. Health Policy and Planning. Jul 2014; vol 30; no.7; 837-843
  13. Sarah G, et al. An Assessment Of Routine Primary Care Health Information System Data Quality In Sofala Province, Mozambique. Population Health Metrics. Jan 2012; vol 9;no.12
  14. Maramraj KK, et al. Evaluation Of Dengue Surveillance System, Telangana State, India. International Journal of Infectious Diseases. Aug 2018;vol 73;263-267
  15. PPeraturan Menteri Kesehatan No 45 Tahun 2014. Penyelenggaraan Surveilans Kesehatan. Jakarta: Kemenkes RI; 2015
  16. Rahel A, et al. A Comparison of Electronic Medical Record Data to Paper Records in Antiretroviral Therapy Clinic in Ethiopia: What is affecting the Quality of the Data?. Online Journal of Public Health Informatics. Sep 2018;vol 10;no.2
  17. eDepartemen Kesehatan RI. Petunjuk Tekhnis Pengamatan Penyakit Demam Berdarah Dengue. Jakarta: Depkes RI; 1999
  18. Sugiyono. Metode Penelitian Kuantitatif, Kualitatif dan R&D. Bandung: Alvabeta; 2015
  19. Lippeveld, T., Sauerborn, R., & Bodart, C. Design and implementation of health information systems Design and implementation of health information systems. Geneva: WHO; 2000
  20. Singh, Gajinder P, Jordan T and Michael PR. Improving Data for Decision-Making: Leveraging Data Quality Audits in Haryana, India. Delhi: USAID India; 2014
  21. Petter N. Reporting Practices and Data Quality in Health Information Systems in Developing Countries: An Exploratory Case Study in Kenya. Journal of Health Informatics in Developing Countries. Dec 2016; vol 10; no.1; 114-126
  22. Colton, David dan Robert W. Covert. Designing and Constructing Instruments for Social Research and Evaluation. San Francisco: Jossey-Basse; 2007
  23. Chen H, Hailey D, Wang N and Yu P. a Review of Data Quality Assessment Methods for Public Health Information Systems. International Journal of Environmental Research and Public Health. May 2014; vol 11; no.5; 5170–520
  24. AbouZahr C and Boerma T. Health Information Systems: The Foundations of Public Health. Bulletin of the World Health Organization. Jan 2005; vol 83; no.8; 578–583
  25. Bowen S, Erickson T, Martens PJ and Crockett S. more than “Using Research”: The Real Challenges in Promoting Evidence-Informed Decision-Making. Healthcare Policy. May 2009; vol 4; no.3; 87–102
  26. Ndabarora E, Chipps JA and Uys L. Systematic Review of Health Data Quality Management and Best Practices at Community and District Levels in LMIC. Information Development. Dec 2013; vol 30; no.2; 103–120
  27. Jørn B, Arthur H and Sundeep S. Improving Quality and Use of Data. Bull World Health Organ. Jan 2012; vol 90; no.1; 379–384

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