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Preparing routine health information systems for immediate health responses to disasters.

ABSTRACT: During disaster times, we need specific information to rapidly plan a disaster response, especially in sudden-onset disasters. Due to the inadequate capacity of Routine Health Information Systems (RHIS), many developing countries face a lack of quality pre-disaster health-related data and efficient post-disaster data processes in the immediate aftermath of a disaster. Considering the significance of local capacity during the early stages of disaster response, RHIS at local, provincial/state and national levels need to be strengthened so that they provide relief personnel up-to-date information to plan, organize and monitor immediate relief activities. RHIS professionals should be aware of specific information needs in disaster response (according to the Sphere Project's Humanitarian Minimum Standards) and requirements in data processes to fulfil those information needs. Preparing RHIS for disasters can be guided by key RHIS-strengthening frameworks; and disaster preparedness must be incorporated into countries' RHIS. Mechanisms must be established in non-disaster times and maintained between RHIS and information systems of non-health sectors for exchanging disaster-related information and sharing technologies and cost.

SUBMITTER: Aung E

PROVIDER: S-EPMC3743306 | biostudies-literature | 2013 Aug

REPOSITORIES: biostudies-literature

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Preparing routine health information systems for immediate health responses to disasters.

Aung Eindra E Whittaker Maxine M

Health policy and planning 20120922 5

During disaster times, we need specific information to rapidly plan a disaster response, especially in sudden-onset disasters. Due to the inadequate capacity of Routine Health Information Systems (RHIS), many developing countries face a lack of quality pre-disaster health-related data and efficient post-disaster data processes in the immediate aftermath of a disaster. Considering the significance of local capacity during the early stages of disaster response, RHIS at local, provincial/state and ...[more]

PMID: 23002249

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Project description:BackgroundRoutine Health Information Systems data should be used in a systematic and institutionalised manner to support the making of plans, the monitoring of plans and in supportive supervision. To explore to what extent there is discussion about the linkage between planning, monitoring and supervision of sub-national programs using routine data we conducted a scoping review. The review question was: How are routine health information systems used in developing and monitoring health plans at district and facility level?MethodsFrom a search of Ovid Medline (all), EMBASE and Web of Science along with a review of grey literature and involving a number of key stakeholders in identifying any missing resources a total of over 2200 documents were reviewed and data from 13 documents were extracted.ResultsOverall, there are many descriptions of how to implement and strengthen systems, ways to assess and improve data availability and quality, tools to improve the data use context, training in data use and mechanisms to involve stakeholders and strengthen infrastructure. However, there are gaps in examples of routine health data being used in the development, monitoring and supervision of plans at district and facility level.ConclusionsThere appears to be no institutionalised obligation of planners to monitor plans, very little guidance on how to practically monitor programs and minimal discussion about how to use the routinely available data to supportively supervise the implementation of the plans. To overcome these shortcomings, we recommend that practical procedures to ensure linkage of existing district plans to regular monitoring of priority programs are institutionalised, that mechanisms for making managers institutionally accountable for monitoring and supervising these plans are put in place, and that practical guidelines for linking plans with routine health information system data and regular monitoring and supportive supervision are developed.

Project description:BackgroundReliable surveillance systems are essential for identifying disease outbreaks and allocating resources to ensure universal access to diagnostics and treatment for endemic diseases. Yet, most countries with high disease burdens rely entirely on facility-based passive surveillance systems, which miss the vast majority of cases in rural settings with low access to health care. This is especially true for malaria, for which the World Health Organization estimates that routine surveillance detects only 14% of global cases. The goal of this study was to develop a novel method to obtain accurate estimates of disease spatio-temporal incidence at very local scales from routine passive surveillance, less biased by populations' financial and geographic access to care.MethodsWe use a geographically explicit dataset with residences of the 73,022 malaria cases confirmed at health centers in the Ifanadiana District in Madagascar from 2014 to 2017. Malaria incidence was adjusted to account for underreporting due to stock-outs of rapid diagnostic tests and variable access to healthcare. A benchmark multiplier was combined with a health care utilization index obtained from statistical models of non-malaria patients. Variations to the multiplier and several strategies for pooling neighboring communities together were explored to allow for fine-tuning of the final estimates. Separate analyses were carried out for individuals of all ages and for children under five. Cross-validation criteria were developed based on overall incidence, trends in financial and geographical access to health care, and consistency with geographic distribution in a district-representative cohort. The most plausible sets of estimates were then identified based on these criteria.ResultsPassive surveillance was estimated to have missed about 4 in every 5 malaria cases among all individuals and 2 out of every 3 cases among children under five. Adjusted malaria estimates were less biased by differences in populations' financial and geographic access to care. Average adjusted monthly malaria incidence was nearly four times higher during the high transmission season than during the low transmission season. By gathering patient-level data and removing systematic biases in the dataset, the spatial resolution of passive malaria surveillance was improved over ten-fold. Geographic distribution in the adjusted dataset revealed high transmission clusters in low elevation areas in the northeast and southeast of the district that were stable across seasons and transmission years.ConclusionsUnderstanding local disease dynamics from routine passive surveillance data can be a key step towards achieving universal access to diagnostics and treatment. Methods presented here could be scaled-up thanks to the increasing availability of e-health disease surveillance platforms for malaria and other diseases across the developing world.

Project description:Background: Ethiopia is investing in the routine Health Management Information System. Improved routine data are needed for decision-making in the health sector. Objective: To analyse the quality of the routine Health Management Information System data and triangulate with other sources, such as the Demographic and Health Surveys. Methods: We analysed national Health Management Information System data on 19 indicators of maternal health, neonatal survival, immunization, child nutrition, malaria, and tuberculosis over the 2012-2018 time period. The analyses were conducted by 38 analysts from the Ministry of Health, Ethiopia, and two government agencies who participated in the Operational Research and Coaching for Analysts (ORCA) project between June 2018 and June 2020. Using a World Health Organization Data Quality Review toolkit, we assessed indicator definitions, completeness, internal consistency over time and between related indicators, and external consistency compared with other data sources. Results: Several services reported coverage of above 100%. For many indicators, denominators were based on poor-quality population data estimates. Data on individual vaccinations had relatively good internal consistency. In contrast, there was low external consistency for data on fully vaccinated children, with the routine Health Management Information System showing 89% coverage but the Demographic and Health Survey estimate at 39%. Maternal health indicators displayed increasing coverage over time. Indicators on child nutrition, malaria, and tuberculosis were less consistent. Data on neonatal mortality were incomplete and operationalised as mortality on day 0-6. Our comparisons with survey and population projections indicated that one in eight early neonatal deaths were reported in the routine Health Management Information System. Data quality varied between regions. Conclusions: The quality of routine data gathered in the health system needs further attention. We suggest regular triangulation with data from other sources. We recommend addressing the denominator issues, reducing the complexity of indicators, and aligning indicators to international definitions.

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Preparing routine health information systems for immediate health responses to disasters.

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Preparing routine health information systems for immediate health responses to disasters.

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