1. Comparison of Degree of Concordance between 2 Systems of Dengue Reporting in Thailand, 2015
Pantila Taweewigyakarn(1)
Darin Areechokchai(2)
Yongjua Laosirithavorn(2)
Evelyn O. Talbott(1)
(1) Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh
(2)Bureau of Epidemiology, Department of Disease Control, Thailand

2. Introduction: Dengue Dengue : An infection caused by Dengue Virus
Mode of transmission : Vector-borne (Aedes Aegypti mosquito)
Symptoms : Wide range
Asymptomatic Infection
Mild Symptoms (Dengue Fever) : Fever, headache, nausea, vomiting
Moderate – Severe symptoms (Dengue Hemorrhagic Fever, Dengue Shock Syndrome) : Abnormal bleeding, Plasma Leakage Symptoms, Shock, Hepatitis, Renal Failure
Death
Burden of the disease :
Worldwide: 3.2 million cases reported in 2015(1) f (approx / 100,000 pop)
Thailand : 144,952 cases reported ( / 100,000 population)
(1) WHO. Dengue and Severe Dengue.

3. Study Question
Bhatt, estimated burden of Dengue in 2010
Worldwide: 96 million people should have clinical symptoms while WHO showed only 2.2 million cases reported
Thailand : 1.37 million people with clinical symptoms , while the surveillance system captured 116,947 reported cases
To capture all of symptomatic dengue cases is impossible.
At least the surveillance should detect the cases visiting hospitals
And reflect the correct distribution in terms of Time, Place, Person in order to indicate an appropriate response
Thus, Is the surveillance system data concordant with the real situation?
Bhatt S. et al. The global distribution and burden of dengue. Nature. 2013 Apr 25;496(7446):504-7

4. Communicable Disease Surveillance system in Thailand (R506) – Electronic Based
A data collection process is done manually by hospital epidemiologists.
The process might vary in each hospital.
Regional office of Disease Prevention and Control
Government Hospitals
1 mo
Bureau of Epidemiology
(National level)
1-2 wk
District
Health office
Provincial
Health office
1-2 wk
1 mo
Private Hospitals

5. The Electronic National Health Record (ENHR)
The system collects every single visit of patients and automatically sends the data to the database at provincial health office
Health Data Center
Bureau of Policies and Strategies,
Ministry of Public Health
Government hospitals
Auto Sync
The data collected in 43 different folders, depend on types of variables e.g. demographic data, diagnosis, lab study, medications, etc.
Private hospitals
Data Center at
Provincial Health office
Auto Sync
1 mo
Primary Care Unit
(Subdistrict Level)
Auto Sync

6. Difference between R506 and ENHR
Aspects
R506
ENHR
Objective/ Utilization
Surveillance data
Routinely used for surveillance purpose
Health information of all Thai population; from Birth to Death
Not routinely used
Used for research, annual summary for some certain diseases e.g. chronic disease,
Data collection
Only notifiable communicable diseases
Only variables that are relevant for disease prevention and control
Hospital epidemiologists are supposed to collect the cases that meet the definition to be reported
All diseases or purposes of hospital visit
Most of information of patients e.g. demographic data, diagnosis code, laboratory results, prescription history admission information, cost of services
The data is electronically sent from the hospitals to the provincial data center => closed to “the real world data”
Number of folder
A single folder contains relevant variables
43 folders: PERSON, ADDRESS, DIAGNOSIS_OPD, CHRONIC, …. Etc.
Size of data
- Small
Big data

7. Methods : Data Preparation
Electronic National Health Records – using MySQL
6 folders selected : DIAGNOSIS_OPD(outpatient), DIAGNOSIS_IPD(inpatient), DEATH, PERSON, ADDRESS, HOME
Selected Dengue cases from
DIAGNOSIS_OPD : variable diagnosis code contains A90 or A91 (ICD-10)
DIAGNOSIS_IPD : variable diagnosis code contains A90 or A91 (ICD-10)
DEATH: 6 variables of disease/condition contribute to death contains A90 or A91(ICD-10)
Join the cases from 3 folder together (Union command)
Removal duplication of records using ID
Keep the record of the day of last diagnosis, regardless of patients type (OPD/IPD)
If the day of diagnosis is the same but the diagnosis is different between OPD/IPD, keep IPD diagnosis
Join the non-duplicated dataset to the folder PERSON, ADDRESS, HOME (Left join Command)
Selected variables : ID, Sex, Birthdate, Diagnosis code, Date of Diagnosis, Death status (Yes/No), province

8. Methods : Data Preparation
Data from Communicable disease surveillance system (R506)
Single folder
De-identified data
Selected variables : ID, Sex, Age in year, Disease code, Date of Visit, Death status (Yes/No), province

9. Methods: Data Analysis
Time Period : Jan 1 – Dec 31, 2015
Inclusion criteria
All records were included
Exclusion Criteria
When analyze each variables, the missing record would be excluded
We study the distribution of the cases in term of
sex,
age group,
race,
months of diagnosis,
address (province level).

10. Results
Figure1. Number of Patients Diagnosed with Dengue recorded in the ENHR and the R506 surveillance databases by Area

11. Figure2 Number of Patients diagnosed Dengue by Disease severity
Results
 Figure3. Number of Patients diagnosed Dengue by Sex

12. Results
Figure4 Number of Patients diagnosed Dengue Syndrome by Age Group

13. Results
Figure7. Number of Patients diagnosed Dengue by Month of diagnosis

14. Figure8. Number of dengue cases by month and region (Bangkok excluded)
Results

15. Discussion
Similar distribution between data from the surveillance system and ENHR
sex, age group, race, disease severity, and type of treatment
Different distribution
Geographical data : missing data in ADDRESS and HOME folder in ENHR
Time of diagnosis : missing data in ENHR (an effect of fiscal year activities)
The Surveillance system (R506) captured approximately 30% less than the cases recorded in ENHR
Human-dependent process in the surveillance system
The patients in NEHR might have the diagnosis changed later (The information in NEHR might be overestimated)
Surveillance system detected more dead cases than ENHR
Physicians incorrectly reported a cause of death
Huge missing data problem found in ENHR in ADDRESS, HOME folders
Less problem seen in other folder such as DIAGNOSIS, PERSON and DEATH

16. Conclusion
The data from R506 represent the similar pattern of distribution of dengue to the data from ENHR in terms of sex, age, race, age groups at national level
The distribution of dengue cases in terms of time of diagnosis and places were not be able to be compared due to poor data quality of the ENHR.
ENHR requires improvement of data quality so that the system is able to an effective data source for health study and research.
Acknowledgement
We would like to express our appreciation to the center of epidemiological informatics (CEI), Bureau of Epidemiology, Ministry of Public Health, Thailand for a great cooperation.

17. References
World Health Orgainization(2016). Dengue and Sever Dengue. Retrieved from
Rojanapithayakorn W.(1998). Dengue Hemorrhagic Fever in Thailand. Dengue Bulletin
Hammon, WM. (1973). Dengue Hemorrhagic Fever – Do we know its cause?. The American Journal of Tropical Medicine and Hygiene. 22(1).83-91
Bhatt S. (2013). The global distribution and burden of dengue.Nature.496(7446):504-7
Limpakanjanarat K.,Thiraratkul A.,Ungchusak K.(n.d.) Three decades of DHF Surveillance in Thailand, Retrieved from
Bureau of Epidemiology.(n.d.). Concept of Surveillance system adjustment. Retreived from