Mapping the Prevalence of Measles Among Risk Population in Zamfara State, Nigeria.

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International Journal of Research and Innovation in Social Science (IJRISS) | Volume VI, Issue III, March 2022 | ISSN 2454–6186

Mapping the Prevalence of Measles Among Risk Population in Zamfara State, Nigeria.

Dr Abubakar Garba Fada

IJRISS Call for paper

Department of Geography, Faculty of Social Science, Usmanu Danfodiyo University, Sokoto, Nigeria

 

Abstract
Disease maps can be used to highlight populations at risk of a disease and its seasonal variability. Measles has been and is still being considered as one of the most infectious and perhaps the highest childhood mortality diseases world-wide. This paper examined the spatial risk of measles in Zamfara State, Nigeria with the objective highlighting areas of high risk and possible link with seasons and the available healthcare facilities. Measles prevalence and relative risk data of 2017 from the 14 LGAs of the state were used for the study. Relative risk rates were computed using ‘at risk’ population of ages 1-9 years as a proportion of the aggregated data. The maps were corroborated with seasons and distribution of healthcare facilities. While the raw data showed LGAs of highest risk located at the southernmost parts of the state, the reverse was the case with the relative risk rates. The later also highlighted relationships of relative risk rates with seasons and availability of healthcare facilities. It was concluded that the type of map drawn and the nature of data used could portray different virtualizations of disease pattern, and that both seasonality and availability of healthcare facilities play a role in the prevalence of measles in Zamfara State. It is recommended that, although the findings shed some light on the possible causal factors, more detailed studies should be carried out on these factors with a view to uncovering them for any informed actions to reduce the risk of the disease and its possible transmission chain, more especially with the current spate of banditry and forced migrations in the state.

Keywords: Prevalence, Prevalence Rates, ‘At risk’ population; Disease mapping; Seasonality

1.0 Introduction

Disease mapping is aimed at isolating and displaying the role of location as a risk factor; other abstract factors, such as social inequality, differing control programmes, physical barriers, can also help to explain particular patterns of disease, thus requiring more abstract mappings for their study. In other words, account needs to be taken of the distribution of the population at risk to a particular disease, or cause, before mapping its pattern (Barford and Dorling, 2014:3). However, small populations tend to give rise to the most extreme disease rates, even if the actual rates are similar across the area.
Mapping is usually for descriptive purposes; to identify patterns of geographical variation in diseases, to develop new ideas about the cause of disease and also contribute to verifying hypotheses concerning factors associated with the distribution of the disease (Brewer, 2006; Gatrell & Elliot, 2015; Pickle, 2002; Ricam and Salem, 2010). Displaying disparities and spatial patterns from maps leads inevitably to the elaboration of hypotheses about associated factors. One of the Chloropleth mappings is one of such methods where characteristics of the population, such as the standardized mortality ratios (SMRs) or relative risk rates (RRR) are shaded accordingly. The indicators (SMRs or RRR) can be used as expected-cases of the characteristics when computed. Because a large share of the population typically occupies a small share of the land, it places the characteristics of homogeneity of population at odds. The smaller the population size of a LGA, the more likely it will have an extreme rate, either high or low because of their inverse relationships. If the population of an area shaded is small, the rate of disease estimates shows a larger figure. A slight difference in the number of cases can therefore make a huge difference in the rates, a situation often regarded as the small number problem which can be minimized by using smoothing techniques. Thus, when the areas differ in population size, as is typically the case, the calculated rates of disease for those areas have different degrees of reliability. As such, chloropleth mapping is the commonest way of analyzing disease clustering. It is particularly useful for mapping disease as privacy and confidentiality concerns often dictate that such data are released as counts aggregated to some administrative unit. Additionally, census data such as population counts and age-sex structures are also collected at the same spatial scales, thereby enabling easy calculations of crude or population age-sex adjusted disease rates. Tiwari and Rushton (2005) have addressed the issuer of small numbers in disease mapping using the chloropleth method, but the discrete nature of the administrative boundaries is also not reflective of the spatially continuous nature of the disease risk being mapped.