Meningitis Project Annual Report

Submitted to XXXXX, google.org

October 2011

From Rajul Pandya, University Corporation for Atmospheric Research (UCAR)Representing the work of Abudulai Adams-Forgor, Patricia Akweongo (NHRC), and Abraham Hodgson (Navrongo Health Research Centre – NHRC); Vanja Dukic (University of Colorado); Katie Dickinson, Mary Hayden, and Tom Hopson (National Center for Atmospheric Research – NCAR); Benjamin Lamptey (Regional Maritime University, Ghana); Roberto Mera and Fred Semazzi (North Carolina State University – NCSU); Jennie Rice (Pacific Northwest National Laboratory); Madeleine Thomson and Sylwia Trazka (International Research Institute for Climate and Society - IRI); Tom Yoksas (UCAR/Unidata); Raj Pandya (UCAR)

12 Months from Agreement Date (October 2009)In addition to the activities reported against this milestone in previous reports, we have made additional progress in the last year.

12.1: Local Partnerships and Formal Agreements Established

a) Expanded partnership in AfricaAs documented in the previous reports, we have subcontracts with Navrongo Health Research Centre (NHRC) to collaboratively perform the socio-economic and knowledge, attitudes and practices survey outlined in Milestones 24.1, 32.1, and 36.1. In addition, Dr. Patricia Akweongo is now affiliated with the University of Ghana and Dr. Abraham Hodgson has become Director of Research for Ghana Health Service, which as allowed informal connections to both of those resources.

b) Progress on work with Regional Maritime UniversityAlthough we have a subcontract with Dr. Benjamin Lamptey at the Royal Maritime University (RMU), his work on the project has been hampered by poor internet connectivity, difficulty setting-up the 4-node workstation necessary to perform necessary model simulations, and inability to locate a graduate student to participate in the research. In August of 2011, Lamptey spent two months in residence at NCAR focused on the training necessary to make the computer modeling operational. In addition, his contribution to the project was redesigned to be less-dependent on bandwidth – he will now focus on the reanalysis of the weather during the 2010 meningitis season which will be used to evaluate the benefit of improved prediction (as described in deliverable 36.1). If time permits, he will extend his analysis to look at the likely impact of climate on meningitis by simulating a future years weather projection (and probability of meningitis) based on IPCC projections, as well as participate in a new initiative to investigate the

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impacts of assimiliating a new measurements of atmospheric vertically-integrated water vapor (COSMIC program) into numerical weather prediction models to improve the latter’s forecasting skill. Lamptey has also hired a Master’s student.

12.2 Existing Meningitis and weather data located, archived and documentedNo new historical data has been added to the project since the previous written report (Oct 2010).

12.3 Demonstrated Weather-Meningitis Links In addition to the work previously documented, we have additional evidence for a robust weather-meningitis link, and have preliminary evidence suggesting a link between air-quality and meningitis, described below.

a) The role of weather assessed through population-level modeling of meningitis spread Dr. Tom Hopson has collaborated with Dr. Vanja Dukic to model the transmission of meningitis using a differential equation-based epidemiological compartmental model of a known disease and physical insight into meningitis transmission. Using the data described in 12.2, they performed a statistical analysis to test the model for correlation with meteorological variables. They showed that relative humidity, vapor pressure, and northeasterly wind all show positive correlation with current and future cases of meningitis, as compared to simple historical persistence. Their SCIS (susceptible-colonized-infected-susceptible) model is based on a continuous Methicillin-resistant Staphylococcus aureus (MRSA) model, but simplified and tested based on the following assumptions: The number of cases of disease is small compared to overall population District population is constant, Carriage (the number of people who harbor the bacteria but don’t have the disease) is proportional to population, The proximity to neighboring districts with cases of meningitis influences the chances of having a case The same mechanisms determine transmission and infection across the wholebreadth of the meningitis belt The disease cycle is less than two weeks Weather in the centroid of the district is representative of district-wide weather

In the figures below we show some of the results of this study. In the left panel of Figure 1 we show the World Health Organization districts across part of the meningitis belt, with the meningitis epidemic likelihood dependence on relative humidity shown in the rightmost panel. In Figure 2 below, we show how a one-week ahead average relative humidity (RH) ensemble mean forecast over the meningitis belt in Spring of 2010 (top panel) translates into a 3-week ahead meningitis forecast across the belt (bottom panel).

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b) The role of weather assessed through generalize additive modeling of meningitis incidence To examine how robust the relationship is between relative humidity and water vapor with meningitis incidence, Dr. Mary Hayden and Dukic developed a statistical model – a generalized additive model – to robustly examine the effect of weather variables on meningitis incidence over time. This model adjusts for time-varying confounding processes that co-vary with the weather variables of interest (in particular, relative humidity) and which also may impact meningitis spread. Regular seasonal human behavior patterns fall in this category (e.g. harvest-worker migration.) Their analysis on 11 years of Navrongo incidence revealed substantial stability in the relationship of meningitis to several weather variables, under various modeling assumptions, and a wide range of degree of confounding adjustment. Temperature and relative humidity were among the variables most persistently related to meningitis incidence, in addition to the amount of CO in the air due to burning biomass (fires).

12.4 Ensemble derived forecasts for meningitis management developed and verifiedThis deliverable is complete, as documented in previous reports. Since then, Dr. Hopson Thomas Hopson has negotiated the use of more accurate forecast products from the World Meteorological Organization (instead of using 2-day delayed ensemble forecasts, he is able to use the forecasts as soon as they are available) and is evaluating the improvement in forecast this enables.

12.5 Protocol and technology for health-weather communication definedThis was meant to be a logistical and technical infrastructure that would collect, quality control and make available health and weather related data so that a decision support system could be reliably operated. Unlike meteorological data, which are readily and currently and available due to long-standing international agreements and processes, health data has proved difficult to locate and incorporate in any ongoing way. Even our MERIT colleagues at the World Health Organization don’t always have up-to-date or comprehensive health data. Given that, we’ve decided to build our DSS using a numerical model that relates case trends to weather variables without requiring initial case data. However, for those users willing to enter their case data, we will be able to provide a more accurate forecast and ask them to contribute that data to an archive for general use. In this way, we hope to incentivize the contribution of health data to our DSS and to our partner efforts.

24 Months from Agreement Date (October 2010)

24.1 & 24.2 Decision support system for Meningitis management developed, including forecast products.This milestone is pending…e pending milestone, concerning the deployment and dissemination of a decision support system (DSS), is the one requiringthat needs the most work is the deployment and dissemination of a decision support system. Theis

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system is being developed toshould be easy to use and provide actionable information to health-decision makers, while also allowing access to underlying data and models. In order for this to happen, the decision support systemDSS will is being designed to include underlying epidemiological data (Section 12.2) and meteorological forecasts (Section 12.4), analysis and visualization tools, and the a mathematical models (Section 12.3) to predict future meningitis outbreak risk thresholdsevents from current climate conditions and data, as well as time-evolving estimated meningitis outbreak end-of-season dates, critical for optimal vaccine allocation decision-making. Our best-case scenariogoal is to work with an African partner to build this decision support system in Africa. As a precursor to this we , but at the same time are developing a working prototype we are willing to start in Colorado as a proof of concept here in based at UCAR, while elicitingsoliciting feedback from the World Health Organization..

After three years in the Google.org project, we’ve built all the necessary precursors. Unidata has the visualization, analysis and data management tools that can

support the DSS. Our team has documented the relationship between meningitis and several

meteorological variables, with an especially strong signal on humidity. Further, this relationship can be used to predict future cases of meningitis

We’ve developed the technical and logistical infrastructure necessary to forecast those relevant meteorological variables and built relationships with relevant forecast centers to access those data in a regular way.

This is the deliverable that we will focus most on in the coming year. Hopson has worked with Mr. Tom Yoksas and others to create a precursor very pilot website that provides the forecasts for the current season based on weather variables and projects the end of the season. (Please see http://www.cbp.ucar.edu/activities/google_project/index.php). Another DSS feature under development is web functionality forthat will allow local users to incorporate insert their local health information, which iswill then be passed to a more site-specific epidemiological model and current climatological conditions, producingto produce a more refined local forecast of epidemiological conditions. This functionality will also allow us to record (with permission of the user) these local health data into a data base, which itself will be accessible to other regional and international stakeholders in real-time. Based on this rough prototype, we intend to engage with stakeholders in the region and internationally to continue to build and refine a usable decision support system over the upcoming year.

Within a few weeks, we intend to submit to Google a detailed plan and budget that would reprogram the remaining funds to this deliverable.

24.3 Surveillance protocol arrangements set up and new health data collectedExisting surveillance in the K-N district included the routine identification and laboratory confirmation of new cases of meningitis, at the rate of a hundred or so new cases per season. The quality of the surveillance has been steady through the

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last 10 years, and there was little need to extend the current protocol in our study area, Navrongo.

Dukic has used this opportunity to develop IP surveillance tools that could be used for wider Ghana, and possibly extended to the entire meningitis Belt, with the help of WHO. IP Surveillance is based on Google Insights for Search, and preliminary results reveal high degree of correlation between Navrongo reported incidence and several meningitis-related search terms. Predictive validation is currently ongoing.

In November 2011, Dr. Rajul Pandya and Hodgson will attend the next meeting of the MERIT steering committee, and one of the topics of discussion will be the sharing of data among project participants and public health stakeholders and decision makers. We hope to emerge with a protocol, or set of protocols, that will allow us to outline next steps in refining the decision-support tool.

24.4 Draft economic evaluation completeAs outlined in previous reports, this milestone is completed.

32 Months from Agreement Date (August 2010)

32.1 Baseline data collected to inform economic evaluationThis milestone is completed. We have performed Cost-of-Illness surveys on 74 households in the K-N district who have experienced meningitis. Ms. Jennie Rice and Dr. Jeff Lazo drafted the instrument; the household level survey was refined, pre-tested and conducted by Akweongo, Hayden and the team at the Navrongo Health Research Centre.

32.2 An extensible weather and Meningitis data-archive and a distribution protocol for integrating health and weather data completePlease see Milestone 12.5. As stated there, the regular collection and sharing of health data has proven to be beyond the scale of our ability or connections, and even challenges our WMO partners. As such, our archive will be launched with only the historical data we already have permission to use, but it will incentivize the collection of additional data via its link to the DSS.

36 Months from Agreement Date (October 2011)

36.1 Preliminary economic evaluation of the decision support systemAs mentioned in 36.1, quantitative Cost of Illness (COI) interviews were conducted with 74 cases and the cost of illness was computed from patients’ answers to questions about direct medical costs, direct non-medical costs and productivity lost due to meningitis. Analysis by Maxwell Dalaba and Timothy Akwine, supervised by Patricia Akweongo, suggest that the average household cost of treating meningitis case was GH₵232 (US$156) per case, which is higher than the average income of farmers GH₵130 (US$87) in the district. Much of the total cost of meningitis was from productivity lost (60%) and the average number of days lost due to meningitis was 29 days. In addition, the average cost when meningitis sequalae (such as

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hearing, neurological and vision problems) occurred was GH₵ 1,257 (US$843) per case.

Additional costs are born by the healthcare infrastructure, and we are working with Adams-Forgor, who has left NHRC to serve as the director of Navrongo’s War Memorial Hospital (the only hospital in the K-N district), to obtain an estimate of these costs.

To determine the potential benefit of the DSS, we plan to retrospectively forecast a past meningitis season, apply the DSS to that season, and compare the results of those decisions to the results of decisions actually made. Lamptey is performing the necessary forecasts for that analysis. We will also estimate the cost of maintaining the DSS, and extrapolate the impact of the DSS in K-N to estimate the impact of the DSS across the entire belt.

36.1 Full report, for non-technical audiences. A full report is pending the completion of the project. A very brief overview of the project, intended for non-technical audiences, is available athttp://prezi.com/6qf0djihwl0r/weather-forecasts-and-applications-in-africa-challenges-value-and-communication/

In addition, some material about the project intended for a public audience UCAR’s Press Release http://www2.ucar.edu/news/899/health-and-weather-ucar-weather-forecasts-aim-reduce-african-meningitis-epidemicsA story on Voice of Americahttp://www.voanews.com/learningenglish/home/a-23-2008-11-24-voa1-83139962.htmlAn article from the Daily Camerahttp://www.voanews.com/learningenglish/home/a-23-2008-11-24-voa1-83139962.html

36.1 Demonstrated dissemination in Ghana and throughout western and eastern Africa of strategies and lessons The UCAR team has participated in several MERIT meetings, in Ethiopia and

Niger, which have introduced the project to public health services in both locations. MERIT also provides a forum to engage with the World Health Organization and the Health-Climate Foundation, which have reach across Africa.

Lamptey is a member of the World Meteorological Organization’s Thorpex-Africa steering committee, and has ensured that the google.org-funded project is included in their guiding documents as an example of the application of meteorological data. This document is the product of deliberations that include representatives of every African Weather Service, and

The UCAR team has presented the project quite widely in the international geoscience community including several presentations at international geoscience conferences.

This work has been and will be presented at the meeting of the Annual Meeting of the American Society of Tropical Medicine and Hygiene, which, in spite of its name, is the premier international meeting for the field of tropical medicine.

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Additionally, the work will be presented at the Annual Meeting of the American Meteorological Society in early 2012.

Additional Accomplishments, not part of the original scope or work

A.1 A survey of Knowledge, Attitudes, and PracticesKnowledge, attitudes and practices (KAP) regarding meningitis transmission are poorly understood and likely to show considerable variation across the meningitis belt. With encouragement from MERIT, NHRC and UCAR decided to document KAP as part of our cost-of-illness survey. Akweongo, Hodgson, and Hayden designed the survey and NHRC staff, led by Mr. Maxwell Dalaba, conducted quantitative interviews with 74 cases (people who had contracted meningitis between 2008 and the present) and 148 controls (age, gender and location matched group made up of people who had not had meningitis after 2008). The preliminary results showed: High knowledge about stiffness of waist or neck as a symptom of meningitis by

both cases and controls, but cases were more likely to mention other, real early symptoms (high body temperature, vomiting, severe headache, loss of the appetite) than controls. Both cases and controls recognized meningitis as a serious disease requiring immediate treatment. Together, these findings suggest that education about early symptoms might lead to earlier health-care seeking behavior and subsequent treatment for meningitis with improved health outcomes.

No significant differences between the cases and controls with regards to perception of the causes of meningitis: heat was the most common cause mentioned by both cases and controls. Additionally, seventy percent of both cases and controls mentioned hot and dry periods as the time of the year meningitis is most severe.

Seasonal Migration can confer protective benefit. Many rural men travel south during the dry season to seek farm-related work. Because these men leave the meningitis region and often do not return until planting in the north begins with the start of the rainy season, they are protected from meningitis. However, these same men are at greater risk if they need to return to northern Ghana during the meningitis season (for funerals, for example) as they will have missed the vaccination campaign.

More wealthy individuals often miss the vaccination campaigns, which are aimed at poorer, rural areas. This fact, combined with increased mobility, meant that many wealthier individuals showed increased risk of meningitis compared to less wealthy peers.

A.1 An analysis of cook stove emissions As part of the KAP/COI survey monitoring in February of 2011, our team was

able to leverage funds from other sources to allow us test air quality at several homes in K-N district, both with and without improved clean cook stoves. This

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effort, led by UCAR scientist Christine Weidinmyer and collaborators at the University of Colorado, suggests that air-quality in the region is poor in the dry season, and that the use of clean cook stoves can decrease exposure to indoor smoke resulting from traditional cooking practices. This preliminary work is the basis for a collaborative proposal with Hodgson, Akweongo and others that will explore the link between air quality and respiratory illness and the potential impact of scaled-up clean cook stove use.

A.2 A characterization of the variation in relative humidity across West Africa during the dry season

As part of the effort to determine the variability of relative humidity, given its impact on meningitis, Hopson and Warner collaborated with Dr. Mark Seefeldt on a study to examine the variation of relative humidity across West Africa during the dry season evaluated using the NASA MERRA dataset and the method of self-organizing maps. The patterns in relative humidity were analyzed in terms of frequency of each pattern as well as the sequencing from one pattern to the next. The variations in relative humidity are characterized sub-annually for individual years from 1979 to 2009 as well as decadally over the entire 30-year duration of dry seasons in West Africa. The progression from relatively moist patterns to relatively dry patterns and back to the moist patterns over the course of the dry season corresponds to the northward and then southward migration of the intertropical convergence zone. The results indicate distinctly different frequency and sequencing of relative humidity patterns from year to year. The year-to-year changes in relative humidity patterns are gradual. There is some indication of a larger, possibly decadal, pattern to the year-to-year changes in the variation of relative humidity over the course of the dry season.

A.3 An analysis of proximity to water bodies and the impact on meningitis casesAs part of the effort to examine the impacts of relative humidity meningitis, Hopson and Hayden supervised Kristen McCormack, a local high school student through UCAR’s HIRO program, to investigate the relationship between proximity to water bodies and suppression of meningitis cases. The study was carried using meningitis case data from the Navrango Health and Research Centre in proximity to Tono Dam, located in the Kassena/Nankana District of northern Ghana. Results are shown immediately below where we see a possible linear relationship between distance from water and increased meningitis vulnerability.

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Figure 6: Meningitis Cases/100,000 v. Distance

Figure 1: World Health Organization districts across part of the meningitis belt shown in the left-most panel, with the meningitis epidemic (here, as defined by 10 cases/100,000) likelihood dependence on relative humidity shown in the rightmost panel.

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Figure 2: Top panel shows the one-week ahead average relative humidity (RH) ensemble mean forecast over the meningitis belt, initialized on March 27, 2011. The bottom panel shows how this RH forecast converts to a 3-week ahead meningitis forecast across the belt, represented as probability of exceeding 10 cases/100,000 reported over that week.

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