It was hypothesized that as the distance from a water body increases, the frequency of meningitis cases would also increase, and that this relationship would be due to decreasing RH. To test this hypothesis, a two-step process was used. First, the correlation between distance from the reservoir and the frequency of meningitis cases was determined. Next, to see if RH was a link between distance and meningitis cases, the correlation between the distance from the reservoir and RH was determined.

To analyze the correlation between RH and meningitis cases, a Geographic Information System (GIS) was employed to map cases in the region of the Navrongo Health Research Centre in northern Ghana during the dry season of 2010. Buffer zones were created every 1 kilometer radiating from the Tono Dam, a large body of water, with the largest buffer zone being 7km. By determining the number of cases in each buffer zone, and by normalizing this number by the population in each buffer zone, the ratio of cases per 100,000 in a year was determined at various distances from the reservoir.

To test the assumption that RH and the distance from a water body are negatively correlated, the relationship between RH and the distance from the Tono Dam was determined. First, the distance from each RH data logger to the reservoir was measured. Next, for each data logger, the average RH over the driest period (11/28/2010 to 2/28/2011) was calculated and plotted against distance (Figures 6 and 7).

Results were statistically analyzed using the R Project (www.r-project.org).

The African “meningitis belt,” an area of sub-Saharan Africa which stretches from Ethiopia to Senegal, is characterized by frequent epidemics of meningitis, a disease involving the swelling of protective tissue surrounding the brain and spinal cord. The disease, which can result in severe brain damage and death, is often caused by a bacteria, Neisseria meningitidis. In the meningitis belt, serogroup A meningococci accounts for 80 to 85 percent of cases. Epidemics of the disease occur in multi-year cycles which include prominent increases in the rate of disease during the dusty dry season and decreases at the onset of the wet season, when the rate of the disease approaches zero (WHO 2010).

and on a belt-wide scale, it hopes to provide relevant daily to weekly weather and epidemic forecasts. These forecasts would inform health partners of areas most susceptible to the disease, allowing them to be more effective in their containment, vaccination, and preparation measures.

One aspect of weather which appears to be most closely linked to meningitis outbreaks is relative humidity (RH). Contraction of meningococcal meningitis tends to be most

Nevertheless, understanding the correlation between RH and meningitis cases has the possibility of offering more relevant forecasts. Here we explore the relationship between meningitis and RH by examining the connection between case load and proximity to a large water body within the domain of the Navrongo Health Research Centre, located in the Kassena/Nankana District of northern Ghana (Figure 2).

The Relationship Between Meningitis and Relative Humidity in Northern GhanaKristen McCormack1, T. Hopson2, M. Hayden2, M. Dalaba3, J. Boehnert2, K. Dickinson2, R. Pandya4

1: Boulder High School, Pomona College, 2: RAL/NCAR, 3: Navrongo Health and Research Centre, 4: UCAR

Background

Analysis shows a positive correlation between the distance away from the reservoir and the number of meningitis cases per 100,000 in a year (Figure 6). While the results are not highly statistically significant (26% chance that the correlation could have been derived from a strictly random process), they indicate that as a population moves 1km from a reservoir, the number of meningitis cases will increase by 20.739 cases per 100,000. Even if this result were highly statistically significant it would be important to note that possible other factors such as lifestyle, proximity to other bodies of water, etc. must be taken into account in the application of a model.

If the frequency of meningitis cases is dependent on RH, then a formula directly relating meningitis to RH may be mathematically created from the formulas shown in Figures 6 and 7 by replacing distance with RH. The resulting formula is as follows:

Formula 1:

This work was performed under the auspices of the High School Internship and Research Opportunities (HIRO) Program.

This work was partially supported by a generous grant from the Google.org foundation.

Griffin, D.W. (2007) Atmospheric movement of microorganisms in clouds of desert dust and implications for human health. Clin. Microbiol. Rev. 20: 459-477.Molesworth, A.M., Thomson, M.C., Connor, S.J., Cresswell, M.P., Morse, A.P., Shears, P. et al. (2002) Where is the meningitis belt? Defining an area at risk of epidemic meningitis in Africa. Transactions of the Royal Society of Tropical Medicine and Hygiene 96: 242-249.Moore, P.S. (1992) Meningococcal meningitis in sub-Saharan Africa: a model for the epidemic process. Clin. Infect. Dis. 2: 515–525.World Health Organization (2010): Meningitis fact sheet N 141. Revised. February 2010. Online at http://www.who.int/mediacare/ factsheets/fs141/en/index.html. Accessed 20 July 2011.

Kristen McCormackEmail: kristen234@gmail.comPhone: (303)847-5367

While limited research has been conducted in the Sahel of Africa on meningococcal meningitis, the disease has been studied relatively little compared with other African diseases like malaria, or other strains of meningitis that affect Western countries.

Working with data from people has inherent complications, as variables can not be entirely controlled, and socioeconomic, geographic, and individual circumstances affect the relationship between cases and distance. In addition, data collection is particularly difficult in many parts of the developing world because of limited resources; this is true in the meningitis belt of Africa where epidemiological and climatological data are often insufficient to draw significant conclusions.

Considering the case data used from Navrongo, it would be more helpful to have case data from multiple years or from multiple areas so that trends in the data could be seen more easily. The use of the Navrongo data was limited also by other bodies of water which may have affected results. Information on whether cases were from rural or urban areas would also have been helpful, as well as a more detailed land use map, if applicable, since there was not great enough variability in land use to consider it a factor.

When working with RH data, some limitations were created because there were not enough loggers recording uncorrupted outdoor data for analysis to be based solely on one type of data. While indoor data were somewhat comparable, results would be more accurate if only one type of data were used. Furthermore, it would have been ideal if the RH data had included the entire dry season (often defined as December to June).

Our results indicate that theoretically, if a population were to move 1km closer to a large body of water, there would be a decrease in meningitis cases of about 20.7 cases per 100,000 (Figure 6). Formula 1 shows that as relative humidity increases by 1%, meningitis cases decrease by 21.2 per 100,000. It is important to note that further work with a larger data set is necessary to obtain a result with greater statistical significance.

Methods

Results

Acknowledgements

References

Contact Information

Figure 1: The African Meningitis Belt (Moore, 1992)

common in the dry, dusty conditions of the dry season, when RH is low. Infection, rather than transmission, is seasonally based, as demonstrated by the rising ratio of disease to carriage during the dry season (Moore, 1992). This supports a hypothesized explanation of the seasonal trend which suggests that dry, dusty conditions irritate the protective linings of the respiratory system, allowing the bacteria to penetrate into deep tissue and infect the individual. The reasons for seasonal trends are not proven (Griffin, 2007; Moore, 1992).

In 2009, Google.org, the philanthropic arm of Google, funded a project involving the University Corporation for Atmospheric Research and global and regional health partners including the World Health Organization. The three year project integrates epidemiological, meteorological, and social aspects of meningitis outbreaks. By studying the relationship between weather and health in both the northernmost part of Ghana

Data

The epidemiological data used in this study was collected during the 2010-2011 dry season by the Navrongo Health and Research Centre from within its domain. It includes location information about 222 cases of meningitis from the region. Regional land use data were used from the European Union’s Global Land Cover 2000 product. We also used population raster data from the LandScan 2008TM High Resolution global Population Data Set. Weather information came from 22 data loggers placed across the district which recorded RH at an hourly rate from November 28th, 2010 to February 28th, 2011.

Figure 6: Meningitis Cases/100,000 v. Distance Figure 7: RH v. Distance

Discussion/Conclusion

The correlation between RH and distance is statistically significant, with a 2% chance that the correlation could be derived from a strictly random process. The results show a negative correlation between RH and the distance from the reservoir, with a decrease in RH of 0.9774 percent for a 1km increase of distance from the reservoir.

It is necessary to note that both of the above analyses were done using a small data set, and further work with a larger data set would be necessary to confirm results.

Figure 2: The Navrongo Region of Ghana (nationsonline.org)

Figure 5: ArcGIS image of 1km buffer zones radiating out from the Tono Dam.

Figure 4: ArcGIS image of cases (red), data loggers (yellow) and the Tono Dam (blue)

Figure 3: ArcGIS image of the Navrongo region of Ghana

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