Transforming the health information system using mobile and geographic information technologies, Papua New Guinea.

In the context of declining economic growth, now exacerbated by the coronavirus disease 2019 pandemic, Papua New Guinea is increasing the efficiency of its health systems to overcome difficulties in reaching global health and development targets. Before 2015, the national health information system was fragmented, underfunded, of limited utility and accessed infrequently by health authorities. We built an electronic system that integrated mobile technologies and geographic information system data sets of every house, village and health facility in the country. We piloted the system in 184 health facilities across five provinces between 2015 and 2016. By the end of 2020, the system's mobile tablets were rolled out to 473 facilities in 13 provinces, while the online platform was available in health authorities of all 22 provinces, including church health services. Fractured data siloes of legacy health programmes have been integrated and a platform for civil registration systems established. We discuss how mobile technologies and geographic information systems have transformed health information systems in Papua New Guinea over the past 6 years by increasing the timeliness, completeness, quality, accessibility, flexibility, acceptability and utility of national health data. To achieve this transformation, we highlight the importance of considering the benefits of mobile tools and using rich geographic information systems data sets for health workers in primary care in addition to the needs of public health authorities.

Dans un contexte de déclin de la croissance économique, exacerbé par la pandémie de maladie à coronavirus, la Papouasie-Nouvelle-Guinée a décidé d'augmenter l'efficacité de ses systèmes sanitaires afin de surmonter les difficultés à atteindre les objectifs globaux en matière de santé et de développement. Avant 2015, le système d'information sanitaire national était fragmenté, sous-financé, peu utile et rarement consulté par les autorités sanitaires. Nous avons donc conçu un système électronique intégrant des technologies mobiles et des ensembles de données géographiques provenant de chaque ménage, de chaque village et de chaque établissement de soins du pays. Entre 2015 et 2016, nous avons piloté le système dans 184 établissements de soins répartis sur cinq provinces. Fin 2020, les tablettes mobiles du système ont été distribuées dans 473 établissements de 13 provinces, tandis que les autorités sanitaires des 22 provinces du pays, y compris les services sanitaires confessionnels, ont pu accéder à la plateforme en ligne. Les silos de données fragmentées des programmes de santé antérieurs y ont été incorporés et une plateforme destinée aux registres d'état civil a été créée. Le présent document se penche sur la manière dont les technologies d'information mobiles et géographiques ont transformé les systèmes d'information sanitaire en Papouasie-Nouvelle-Guinée ces six dernières années en améliorant la ponctualité, l'exhaustivité, la qualité, l'accessibilité, la flexibilité, la recevabilité et l'utilité des données nationales sur la santé. Pour réaliser cette transformation, il est à nos yeux essentiel de tenir compte des avantages que représentent les outils mobiles, et de tirer profit des vastes ensembles de données géographiques non seulement pour les travailleurs des soins de santé primaires, mais aussi pour les besoins des autorités de santé publique.

En el contexto de un crecimiento económico en declive, agravado ahora por la pandemia de la enfermedad por coronavirus, Papúa Nueva Guinea está aumentando la eficiencia de sus sistemas sanitarios para superar las dificultades para alcanzar los objetivos globales de salud y desarrollo. Antes de 2015, el sistema nacional de información sanitaria estaba fragmentado, carecía de fondos suficientes, su utilidad era limitada y las autoridades sanitarias accedían a él con poca frecuencia. Construimos un sistema electrónico que integraba tecnologías móviles y conjuntos de datos del sistema de información geográfica de cada casa, pueblo y centro de salud del país. Entre 2015 y 2016 pusimos a prueba el sistema en 184 centros de salud de cinco provincias. A finales de 2020, las tabletas móviles del sistema se implementaron en 473 centros de 13 provincias, mientras que la plataforma en línea estaba disponible en las autoridades sanitarias de las 22 provincias, incluidos los servicios de salud de las iglesias. Se han integrado los silos de datos fracturados de los programas sanitarios heredados y se ha establecido una plataforma para los sistemas de registro civil. Exponemos cómo las tecnologías móviles y los sistemas de información geográfica han transformado los sistemas de información sanitaria en Papúa Nueva Guinea en los últimos seis años, aumentando la puntualidad, la exhaustividad, la calidad, la accesibilidad, la flexibilidad, la aceptabilidad y la utilidad de los datos sanitarios nacionales. Para lograr esta transformación, destacamos la importancia de tener en cuenta los beneficios de las herramientas móviles y de utilizar conjuntos de datos ricos en sistemas de información geográfica para los trabajadores sanitarios de la atención primaria, además de las necesidades de las autoridades sanitarias públicas.

Health information system strengthening and malaria elimination in Papua New Guinea.

BACKGROUND: The objective of the study was to describe an m-health initiative to strengthen malaria surveillance in a 184-health facility, multi-province, project aimed at strengthening the National Health Information System (NHIS) in a country with fragmented malaria surveillance, striving towards enhanced control, pre-elimination. METHODS: A remote-loading mobile application and secure online platform for health professionals was created to interface with the new system (eNHIS). A case-based malaria testing register was developed and integrated geo-coded households, villages and health facilities. A malaria programme management dashboard was created, with village-level malaria mapping tools, and statistical algorithms to identify malaria outbreaks. RESULTS: Since its inception in 2015, 160,750 malaria testing records, including village of residence, have been reported to the eNHIS. These case-based, geo-coded malaria data are 100% complete, with a median data entry delay of 9 days from the date of testing. The system maps malaria to the village level in near real-time as well as the availability of treatment and diagnostics to health facility level. Data aggregation, analysis, outbreak detection, and reporting are automated. CONCLUSIONS: The study demonstrates that using mobile technologies and GIS in the capture and reporting of NHIS data in Papua New Guinea provides timely, high quality, geo-coded, case-based malaria data required for malaria elimination. The health systems strengthening approach of integrating malaria information management into the eNHIS optimizes sustainability and provides enormous flexibility to cater for future malaria programme needs.

Impact of logging on aboveground biomass stocks in lowland rain forest, Papua New Guinea.

Greenhouse-gas emissions resulting from logging are poorly quantified across the tropics. There is a need for robust measurement of rain forest biomass and the impacts of logging from which carbon losses can be reliably estimated at regional and global scales. We used a modified Bitterlich plotless technique to measure aboveground live biomass at six unlogged and six logged rain forest areas (coupes) across two approximately 3000-ha regions at the Makapa concession in lowland Papua New Guinea. "Reduced-impact logging" is practiced at Makapa. We found the mean unlogged aboveground biomass in the two regions to be 192.96 +/- 4.44 Mg/ha and 252.92 +/- 7.00 Mg/ha (mean +/- SE), which was reduced by logging to 146.92 +/- 4.58 Mg/ha and 158.84 +/- 4.16, respectively. Killed biomass was not a fixed proportion, but varied with unlogged biomass, with 24% killed in the lower-biomass region, and 37% in the higher-biomass region. Across the two regions logging resulted in a mean aboveground carbon loss of 35 +/- 2.8 Mg/ha. The plotless technique proved efficient at estimating mean aboveground biomass and logging damage. We conclude that substantial bias is likely to occur within biomass estimates derived from single unreplicated plots.

Estimating rainforest biomass stocks and carbon loss from deforestation and degradation in Papua New Guinea 1972-2002: Best estimates, uncertainties and research needs.

Reduction of carbon emissions from tropical deforestation and forest degradation is being considered a cost-effective way of mitigating the impacts of global warming. If such reductions are to be implemented, accurate and repeatable measurements of forest cover change and biomass will be required. In Papua New Guinea (PNG), which has one of the world's largest remaining areas of tropical forest, we used the best available data to estimate rainforest carbon stocks, and emissions from deforestation and degradation. We collated all available PNG field measurements which could be used to estimate carbon stocks in logged and unlogged forest. We extrapolated these plot-level estimates across the forested landscape using high-resolution forest mapping. We found the best estimate of forest carbon stocks contained in logged and unlogged forest in 2002 to be 4770 Mt (+/-13%). Our best estimate of gross forest carbon released through deforestation and degradation between 1972 and 2002 was 1178 Mt (+/-18%). By applying a long-term forest change model, we estimated that the carbon loss resulting from deforestation and degradation in 2001 was 53 Mt (+/-18%), rising from 24 Mt (+/-15%) in 1972. Forty-one percent of 2001 emissions resulted from logging, rising from 21% in 1972. Reducing emissions from logging is therefore a priority for PNG. The large uncertainty in our estimates of carbon stocks and fluxes is primarily due to the dearth of field measurements in both logged and unlogged forest, and the lack of PNG logging damage studies. Research priorities for PNG to increase the accuracy of forest carbon stock assessments are the collection of field measurements in unlogged forest and more spatially explicit logging damage studies.