Background
Malaria remains an international public health challenge as there has been an increase in the number of estimated malaria cases; 5 million malaria cases from 2015 (211 million) to 2016 (216 million) [
1]. Worldwide,109 countries are now malaria-free, whereas malaria is still an endemic disease in about 99 countries [
2]. About 90 and 91% of malaria cases and deaths respectively, reported in 2016 occurred in the WHO Africa region with about 15 counties all in Sub-Saharan Africa (SSA) [
1]. The most prevalent malaria parasite in SSA is the
Plasmodium falciparum, accounting for 99% of malaria cases and most occurring in children under the age of five [
1]. In Cameroon, the epidemiological transmission of malaria is high (> 1 case per 1000 population) in about 71% (16.6 million people) and low (0–1 cases per 1000 population) in about 29% (6.8 million) in people of all sexes and age groups. Malaria prevalence in Cameroon is a major public health problem at both the regional (larger) and urban-rural (smaller) geographic scales, with an estimated 1.6 million confirmed cases reported in health facilities and 18,738 cases at the community level and 8000 (6000-10,000) estimated deaths in 2016 [
3]. Generally, malaria intervention policies and control strategies in both the regional and urban-rural scales in Cameroon, have been reported to focus on the use of insecticide treated bed-nets (ITNs), indoor residual spray (IRS),larval control, diagnostic testing, treatments, disease surveillance, and national campaigns [
3‐
6].
The WHO and the roll back malaria global action plan [
7] anticipate having a malaria-free world by 2030 through its set milestones and targets pillars with a major focus to ensure universal access to malaria prevention, diagnosis, and treatment. Malaria prevention strategies based on the use of ITNs and or IRS in Cameroon, has been a great method in the reduction of incident cases of the disease as about 13.6 million ITNs deliveries of the 80% ITNs deliveries in SSA, was made in Cameroon between 2014 and 2016 [
1].
Malaria risk maps and the applications of spatial malaria epidemiology in the fight against malaria in Africa has been limited. A review by Omumbo [
8], examining the most recent national malaria strategies, monitoring and evaluation plans, as well as the types of maps presented and how they have been used to define priorities for investments in malaria control in 47 countries in Africa, found that about 32% of the countries did not present malaria maps within their national malaria prevention strategies.
Small-area statistical analysis and spatial epidemiology have emerged to solve issues of where disease clusters and hotspots are located. Spatial epidemiology deals with the analysis and description of geographic health data with respect to demographic, environmental, behavioral, socioeconomic, genetic and other infectious agents or risk factors [
9]. A study by Elliot [
9] on the current approaches and future challenges of Spatial epidemiology reported that, recent advancements in data availability and analytical methods have created new openings for studies to improve on the local reporting of diseases at national or regional scale by observing changes in disease prevalence rates at a smaller scale. Although, they reported on the absence of a satisfying definition of the term small-area in studying the variations in disease incidence and mortality, [
10] suggested a working definition as a rough guide which we will apply in our study; any region containing fewer than about 20 cases of a disease can be considered a small area. For example, a disease with an annual incidence rate of about 5 per 100, 000 for a period of 5 years, a small area constitutes a population size of around 100, 000 or fewer in clusters of disease occurrence in a remote area or small village. They also identified four types of Spatial analysis at a small-area scale: disease mapping, geographic correlation studies, disease clusters, and surveillance. Some of the main techniques of spatial methods reviewed by Robertson [
11], used in emerging infectious disease research include; spatial autocorrelation, space-time interactions, hotspots and clusters.
The global spatial autocorrelation technique is used to characterize a full map in one quantitative value. This method measures the total joint counts of nearby regions, attributes or locations against a null hypothesis of no spatial autocorrelation [
11]. Moran’s I and Geary’s
c statistics are common methods of spatial autocorrelation. Positive spatial autocorrelation indicates the existence of clustered patterns of a disease, negative autocorrelation can suggest a dispersion in the transmission pattern or surveillance among given regions. Hotspot mapping and cluster detection are analyses executed through local spatial analysis methods. The basic technique is to calculate a test statistic for each location and then evaluate the distribution of these test statistics against a theoretical or random reference distribution. This technique is important in infectious diseases surveillance in that, it helps to identify geographic areas where and to what extent an observed spatial pattern of a disease is anticipated relative to a null hypothesis [
9,
11].
Smith [
12], conducted a systematic review of published reports of outbreak investigations worldwide to estimate the prevalence of infectious diseases using spatial methods such as dot maps, Moran’s I, rate maps, Gestis-Ord Gi* on different diseases; hepatitis, influenza, malaria, rabies and many others. Bhatt [
5], found that,
Plasmodium falciparum infection in endemic Africa has reduced and incidence of the clinical disease fell by 40% between 2000 and 2015; the authors used the Geographical Information Systems (GIS) applications. The GIS computer system can describe, analyze, and predict disease patterns using feature (cartographic) and attribute data. GIS has been used in many epidemiologic applications, including disease mapping, rate smoothing, cluster or hotspot analysis, and spatial modeling and have been reported and applied in small area units such as urban-rural and lower administrative scales [
9‐
13]. Dot maps and geographic profiling have been used both in the United Kingdom and Egypt as spatial methods to identify locations of sources of cholera and malaria infections respectively [
14]. The Moran’s I spatial method has also been used to identify cholera clusters in areas with lower coverage of latrines in a peri-urban area of Lusaka, Zambia and advise for effective drainage systems [
15]. Moreover, during the 2003 severe acute respiratory syndrome (SARS) outbreak in Hongkong, the Moran’s I technique was used to identify SARS cluster patterns at the community level [
16]. Findings from a study carried out in the small-area rural highlands of Western Kenya, identified significant spatial clusters of malaria in school children during an outbreak [
17]. The authors used household survey data and their analyses used the spatial scan statistic software.
Most studies focusing on malaria prevalence and incidence, or the use of ITNs / IRS, in Cameroon have applied the analytical statistics methods, tools evaluation, vector control and molecular techniques at both the higher and lower administrative levels [
4,
18,
19].
Understanding the distribution of malaria cases in Cameroon with the use of spatial statistical analysis approach, will help inform malaria control programs at a smaller scale. Thus, we aim to identify malaria clusters and hotspots in Cameroon at the urban-rural scale using the DHS Global Positioning System (GPS) data for households. Our objectives are to; i) use the spatial autocorrelation technique to analyze malaria spatial patterns in ArcGIS for desktop, ii) map the distribution of malaria cluster points, iii) identify urban-rural clusters with statistically significant hotspots of the disease, and iv) identify environmental factors associated with the distribution of malaria cases.
Discussion
The application of spatial analytical techniques focusing on malaria is not new. However, very limited studies have focused on smaller administrative levels [
17,
30,
31]. Given the z scores: 5.07 and 15.6 for the year 2000 and 2015 respectively, indicate there is a less than 1% likelihood for the observed clustered pattern to be due to chance (Fig.
2). The null hypothesis of complete randomness is rejected, and the presence of cluster patterns indicate neighboring locations have high malaria cases at a given urban-rural area. The z scores: 0.69 and 0.99 for the year 2005 and 2010 respectively, illustrate that the malaria pattern does not appear to be significantly different than random (Fig.
3), and the null hypothesis of complete randomness is accepted. This suggests that malaria cases are randomly spread across the urban-rural areas. Knowing the hotspot locations of areas with clustered malaria patterns can inform for national malaria prevention programs and surveillance.
The distribution of urban-rural malaria cases observed as graduated symbols in Figs.
4,
5,
6 and
7 call for prevention programs as some urban-rural areas in Yaoundé, Douala, Center, South West, North West, Littoral, West, and South region had high malaria cases, and low cases in Adamawa, East, North, and Far North region. Our finding is consistent with that reported by Gemperli [
28] where they found high malaria prevalence in the West and low prevalence in the North and Far North. Contrary to our study that focused on the distribution of malaria cases at a smaller scale, the author focused on malaria prevalence at a regional scale. Understanding the distribution of malaria cases and prevalence in these areas will advise for investments and prevention programs.
The hotspots analysis identified varying intensities of malaria hotspots in the urban-rural areas of the West, Southwest, Northwest, Douala, Yaoundé, Littoral, Central, and South regions (95% confidence) between 2000 and 2015(Figs.
4 and
7). In addition, there was a shift in the malaria hotspot location paradigm as some urban-rural areas in the East region recorded new incident malaria hotspots for 2015 which was not seen in the previous years. In a study
(32)
, which focused on the mapping of
Plasmodium falciparum mortality in Africa between 1990 to 2015, the authors reported that several malaria hotspots areas in Cameroon, Niger, Central Africa Republic and Ivory Coast, were associated with high mortality rate and low coverage of antimicrobial treatment(> 20 malaria deaths per 10,000) [
32]. This study did not locate in detail, the various regions or urban-rural areas in Cameroon with such hotspots. Our hotspots maps are an affirming tool at the regional and even urban-rural scale for malaria prevention programs. Furthermore, [
32] identified regions of Adamawa, North, and East of having high mortality (> 20 per 10,000) and low drug treatment < 10%. Our study reported these regions of having low malaria cases and no statically significant malaria hotspots except for the East region. However, our study focused on malaria cases and advise for continues preventive measures in the urban-rural areas or regions of low malaria cases and high mortality.
This study reported on the use of ITNs and IRS as one of the most effective preventive strategy for malaria control in Cameroon and though an effective method, Fig.
1 demonstrates low (< 50%) coverage of ITNs and or the use of IRS in all the regions. More campaigns and universal distribution of free ITNs that was initiated in Cameroon in 2011 [
4] should be focused in urban-rural areas of regions with very low ITNS/ IRS usage. A study on the
Mapping of Plasmodium falciparum mortality in Africa between 1990 and 2015 estimated that areas with high mortality rates(10–20 per 10,000) were associated with low coverage of ITNs (30–50%) for most regions in Cameroon, Nigeria, Angola and parts of Congo, Central African Republic, Guinea and Equatorial Guinea [
32]. Furthermore, an observational study that assesed ITNs possesion and their protective effects on malaria infection in semi-urban and rural communities in the South West region of Cameroon, found that ITNs ownership was lower in rural settings compared to semi-urban settings [
4]. This also calls for malaria prevention and control campaigns such as those on ITNs distributions in urban-rural areas and particularly hotspots locations.
The population density map (Fig.
8) at the urban-rural areas showed that malaria cases and hotspots locations were higher in regions of higher population density and lower in regions of lower population density. This corroborated with the findings of Kabaria [
33] who reported the relationship between human population densities and malaria infection risk in children aged < 5 in Africa using the DHS data. They identified the correlation between high malaria risk prevalence in urban areas and argues for the decrease in transmission in rural areas due to urbanization. Yaoundé (Central region) and Douala (littoral region) are the capital and economic capital of Cameroon respectively and are full of more human activities than the other regions. We could not evidently support the reasons for the association between high malaria cases and high population densities and call for more research at a smaller scale in the future.
The Pearson’s coefficient, r (Table
3) shows a positive association with some environmental factors such as rainfall, vegetation, and nightlights. Again, this is not a new finding as a similar report on malaria prevalence on climatic factors have been demonstrated in Cameroon, where the authors derived spatial distribution maps for malaria transmission under different climatic and intervention scenarios. Their predictive study showed that temperature and rainfall were associated with malaria transmission [
34]. The association between malaria cases and rainfall (
p < 0.001 and
r = 0.25) examined in 2015 for example, highlights the necessity for malaria surveillance and response systems during the rainy seasons in Cameroon since standing water provides breeding grounds for anopheles mosquitos responsible for transmission of the parasites. In the northern part of the country, the rainy seasons are from May to September (little rainfall) and from March to August (major rainfall) in the southern part. Moreover, the nightlights composite (p < 0.001 and
r = 0.44) in 2015 which indicates the number of human activities at night shows that cities in Cameroon such as Douala and Yaoundé with the highest population densities have more night time activities due to increasing urbanization. The government should carry out more malaria preventive measures and campaigns in the urban-rural areas of these regions. Vegetation Indices are spectral shift of two or more bands designed to heighten the contribution of vegetation properties and allow reliable spatial and temporal inter-comparisons of terrestrial photosynthetic activity and leaf canopy structural changes [
35]. Vegetations near human settlements increase the population of malaria vectors and thus transmission of malaria. Kar [
36] in their study; a review of malaria transmission dynamics in forest ecosystem illustrated that forests serve as beds for malaria transmission as they provide favorable conditions such as vegetation cover, temperature, rainfall, and humidity for malaria transmission. In Cameroon, most rural settlements and villages are located within forest areas and prevention campaigns should be extended to such areas with malaria clusters and hotspots. Our study has the following limitations; i) The malaria prevalence clusters and hotspots at the various urban-rural areas, could be misinforming as the GPS clusters data for these areas were displaced for confidentiality, though the clusters were maintained within the DHS administrative unit. ii) our study did not use socio-demographic factors that could find the association between malaria prevalence and social determinants of health and some related environmental data were missing, iii) The DHS project samples collection are subjected to bias due to disparities in the different urban-rural settings and various forms of bias such as the interviewee response bias. iv) the correlation analyses may be confounded by other factors and spatial techniques such as the geographically weighted regression may be considered to analyze the association between environmental variables and malaria distribution. We did not apply this technique because of missing GPS urban-rural data points in some of the malaria years.
The strength of this study includes; the application of spatial statistics and the use of ArcGIS in malaria research at a smaller geographic scale for public health interventions, the design of this study demonstrated the importance of using spatial data in DHS research. Also, our study, unlike others will provide a new insight to the prevention of malaria in Cameroon at the small-area scale and the techniques used can be applied to other disease phenomena.