Abstract
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.
Highlights
The health care system in Papua New Guinea is fragile, and surveillance systems infrequently meet international standards
Sensitivity Using National Health Information System (NHIS) as reference, we found that MOPBASSS was more sensitive at detecting measles cases than the Hospital Based Active Surveillance (HBAS) (95% vs. 26%) (Table 2)
The low number of notifications for the condition “prolonged fever” in MOPBASSS compared with a similar syndrome reported in the NHIS indicate the sensitivity for detection of this syndrome may be low
Summary
Health System Papua New Guinea’s population is unevenly distributed among 4 regions; almost 40% of the population lives in the highlands region. Validity—the accuracy of the system to detect outbreaks, measured by comparing reports across systems, including laboratory surveillance data. Data quality—the completeness of information recorded in the online database as reported by stakeholders compared with data in the paperbased collection tool. Public Health Event Detection MOPBASSS data were extracted from the online database, and analyses were performed to describe outbreak detection and user experience. Comparisons were made between MOPBASSS and the NHIS, HBAS, and measles laboratory databases and included the average reporting delay (in days), the completeness of reporting, and the number of measles cases (a frequently reported syndrome common to all 4 systems). Data collection included information on training, the online database, case investigation and diagnosis, reporting using mobile phones, and surveillance guidelines.
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