Background
Considerable and diverse evidence demonstrates that health profiles differ between urban and rural areas across the globe, but especially in developing countries [
1‐
14]. Underlying these differences are diverse, context-specific factors involving the social and built environment that characterize and differentiate urban and rural spaces, and thereby affect health profiles. Studies often use dichotomous classifications of urban and rural to explain differences in disease prevalence or incidence, but such designations have been usually created for governmental administrative purposes [
15], and may be unrelated to topographical, infrastructural and economic patterns that might serve as important determinants of disease risk. Some studies have evaluated specific disease risk factors in relation to a derived urbanicity scale [
16], while others have created multi-country urbanicity metrics based on composite geographic, economic and political variables [
17]. Studies of health and urbanicity gradients have primarily considered chronic diseases [
18‐
20], but recent research has started to examine infectious outcomes as well [
21,
22]. As developing countries urbanize and potentially create new opportunities for infectious disease transmission and health [
23], developing and testing new measures of urbanicity becomes critical.
The need for a multifactorial, continuous "urbanicity" metric that can be generalized across different settings has been recognized [
22,
24], as it provides a more nuanced framework that captures the complexities of urban and rural environments and their impacts on health [
25]. Vlahov and Galea [
15] developed a theoretical framework for studying connections between urbanicity and health that included the social environment (spaces where people interact with one another through social and market exchanges), the physical environment (nature of the physical spaces where people live and work), and health and social services (access to medical care and interventions). Lourenco [
26] argued for recognizing a mosaic of rural and urban spaces, and for the need to consider a rural-urban continuum. Although the framework of a rural-urban continuum is not widely used in disease pattern studies, the features that contribute to a more health-relevant definition of urbanicity have been noted. Cuba, for example, understood the need to provide health care services in isolated and remote areas that were comparable in quality to those found in more affluent urban centres [
27]. This has resulted in improved health profiles in isolated areas. Similarly, rural areas in Kenya with access to telecommunications services (e.g. cell phones and mobile banking) have significantly better health profiles than other, nearby, equally rural areas that lack these services [
28]. Indeed, features associated with urbanicity may have more complex, indirect relationships to health. The building of improved transportation infrastructure in rural areas, for example, might increase the economic profile of rural communities, but at the same time lead to increased road accidents [
29‐
32]. Thus, a comprehensive, continuous measure of urbanicity should comprise relevant factors that differentiate rural and urban spaces in a manner that is associated with disease risks or health outcomes of interest. Some generic characteristics of such a metric are likely to be similar; however, details of what is relevant will vary with the ecology and epidemiology of each disease. Ultimately, such an urbanicity measure might help improve forecasting capacity and specificity to prevention or control.
Any robust measure of urbanicity would ideally allow for both within- and among-country comparisons. Studies with a scaled measure of urbanicity typically have relied on local survey data to create this construct [
33,
34]. While comprehensive, these studies lack generalizability and thus require local resources to collect the necessary data. A metric that uses readily available, spatially-referenced, Geographic Information System (GIS)-based data layers (e.g. census, environmental, economic) could be generalizable, while providing increased specificity for targeted interventions to high-risk regions.
Malaria is an example of a disease whose risks differ between rural and urban settings, and whose control strategies would benefit from a more nuanced understanding of which “rural” features might actually be protective, and which “urban” features could represent risk. Incidence of malaria is generally lower in urban compared to rural areas [
25,
35,
36]. Differences in malaria incidence between urban and rural spaces are partly explained by fewer opportunities for mosquito vector reproduction in urban areas and reduced blood feeding on humans [
37‐
39]. Urban habitats are considered generally less favourable for most competent
Anopheles species due to the paucity of suitable breeding sites [
40,
41]. In addition, urban house structures tend to restrict access of adult mosquitoes to humans at night, thus reducing transmission [
42]. However, despite historical reports indicating that malaria is a “rural” problem, this disease is considered to be an emerging threat in rapidly urbanizing areas of sub-Saharan Africa [
43,
44].
These complex relationships between malaria risk and urbanicity suggest that, in addition to malaria control programs that have promoted insecticide treated nets (ITNs), improved diagnostics and vector control, recent reductions in malaria incidence and mortality might be partly attributable to rapid urbanization and development [
45]. The lines between what were formerly considered rural and urban have become increasingly blurred; stronger connections between the city and country through human movement, increased “citylike” infrastructure in rural areas, and increasing population density in peri-urban areas have rendered a dichotomous classification obsolete [
21]. Factors that characterize very large urban settings are found within smaller urban and partly rural settings (e.g., improved housing, accessible and available health care, greater population density, as well as access to transportation and markets). Conversely, characteristics associated with rural settings, such as small-scale cropping, standing water, poor housing, and inadequate health services, can also be found in some urban areas. A more thorough understanding of how specific features of urbanization could influence
Plasmodium transmission across the urban-to-rural landscape gradient should improve the effectiveness and efficiency of targeted interventions.
To develop and apply a new urbanicity measure relevant to countrywide malaria risk in Malawi, the Lourenco [
26] and the Vlahov/Galea [
15] frameworks were integrated by creating a continuous, composite, scaled measure of urbanicity that comprises aspects of the social environment, the physical environment, as well as health and social services access. Using this composite urbanicity measure, the manner in which rural-like environments exist within, and proximate to, urban areas was analysed, as well as how urban-like contexts have emerged within otherwise rural settings. Malaria, a disease known to vary between urban and rural contexts, was evaluated for whether there are consistent and predictable patterns of risk along an urbanicity gradient.
Discussion
The analyses of this study applied different methods that created a more informative and relevant geosocial classification of malaria risk along an "urban-to-rural" continuum, indicate that there is a graded relationship with urbanicity. Indeed, the results suggest the value of creating a more complex, nuanced characterization of urbanicity when assessing Plasmodium transmission risk than what a classical binary classification offers. There are a number of special features in the results that are worth noting.
The analyses found that while
Plasmodium parasitaemia status was strongly associated with distance to lakes, this was not so with proximity to rivers. Lakefronts may provide breeding opportunities for Anopheles, particularly
Anopheles funestus [
63,
64]. In addition, lake water is generally more seasonally stable in volume than that of rivers. However, the water layers used for this study did not include information on river width, depth or degree of water flow. A more comprehensive map of waterways for Malawi could yield more detailed results. Different river widths create environments that are more or less amenable to
Anopheles species-specific reproduction, and might include conditions that are similar to lake and other still water bodies.
Other community-level factors such as greater distances to health services and roads were also found to be important risk factors (Table
3). Appreciating these community contexts, and thereby identifying specific areas at higher potential risk, will be important to developing more focused prevention. This process will lead to a better understanding of the causal mechanisms underlying risk and may unveil the manner in which closer proximity to certain features of more urbanized environments mitigates against
Plasmodium infection in children.
In addition to the community-based urbanicity metric, various household-level factors, such as greater material wealth (a measure of SES) and the use of ITNs, were also found to be associated with reduced malaria risk in children. The analysis suggests that higher SES, greater ITN use, and living in more urbanized contexts each acted independently to decrease malaria risk. Thereby, the important household-level causal pathways appear to be more than house construction and condition, as is often posited. Considerably more work is needed to untangle the relationships between household- and community-level factors, and their relationships with malaria. The issue of urban–rural classification, a community-level construct, will become ever more salient as sub-Saharan African (SSA) countries continue to develop, and urbanization continues to expand.
This study and others have demonstrated the limitations of using a politically defined urban–rural construct. Within a politically defined urban or rural environment, there exists heterogeneity with respect to geographic, economic and socio-political risk factors. In general, the urban designations in use are not consistently defined across the countries of SSA. As this study in Malawi has shown, where fewer areas are classified as “urban”, people might be considered residents of rural areas even though their communities share indicators of urban settings. The reverse is also true. As other researchers have noted, independent of population density or remoteness, some “rural” communities experience better standards of living, increased economic opportunities, and city-like services such as health care and schools [
15]. Inversely, small-area environmental conditions (family gardens, lowland springs, river edges) in urban settings might create highly suitable breeding sites for
Anopheles vectors of malaria, much like what is typically thought of as rural.
The limitations of using a dichotomous definition of urbanicity have practical implications. When providing anti-malaria interventions, some “rural” communities may have areas with low disease risk, yet receive extensive prevention support, leading to a waste of resources that could be better targeted elsewhere. Similarly, rural-like areas near large and dense human settlements may be overlooked during anti-malaria interventions. Uneven geographic distribution of malaria risk and inadequate knowledge of the locations of transmission foci have been recognized as challenges to the spatial targeting of malaria control [
54] in settings of heterogeneous transmission across geographic locations [
65]. Results from the present study confirm that the transmission profile throughout a holoendemic country like Malawi varies considerably over space. These same analyses offer measurable and easily obtained markers that could potentially be used to aid in efficient targeting of anti-malaria interventions.
Another important aspect of the urban–rural continuum involves the level of connectivity between more rural and more developed contexts. In Malawi, however, ease of transport between areas along the developmental gradient is challenging. One study showed that people in extremely rural areas must rely on animal-drawn carts to receive basic medical services and must spend money to reach better secondary and tertiary care facilities located in larger cities and towns [
66]. Transportation expenses have been shown to be a major barrier to receiving prompt care for serious health conditions in other African contexts [
67]. Travel distance has been shown to be an obstacle to obtaining HIV care in Malawi [
68]. While the present research used a simple measure of access to roads and health facilities as a proxy for spatial access to areas that provide economic opportunities and government services, future efforts might employ more detailed measures that take into account different types of transport infrastructure or travel times.
To further refine the use of a continuous urbanicity metric, at least three major areas need further consideration. First, although this study created a malaria-relevant urbanicity measure based on data that are readily available for any developing country, the lack of more specific markers of social and ecological contexts and economic development hampered the analysis. Data on the locations of schools or markets, for example, might have helped to make the results more focally accurate. Given the experiences and knowledge of the authors in Malawi, however, this analysis suggests that the composite measure is a reasonable representation of the gradient of urbanicity in Malawi. Second, associations with river and lake locations may not to be generalizable for cross-country comparisons. With some exceptions, colonial powers tended to establish SSA cities far from swamps and fresh water bodies. Residents of rural areas, on the other hand, often live near surface water. Even within cities, wealthier areas tend to be located at higher elevations and away from areas where water usually collects. These SSA generalities stand in contrast to European contexts which tended to favor locations amenable to trade by water routes. Third, the cross-sectional malaria data that were used may ignore important temporal effects of seasonal malaria. Seasonal transmission of
Plasmodium has been demonstrated in Malawi in other studies [
69,
70], although transmission does occur throughout the year. Data used in the present study were collected to assess infection prevalence toward the end of peak transmission, but seasonal patterns of transmission may also differ by urbanicity and developmental context. Regardless, seasonal variation cannot confound the relationship between urbanicity and parasitaemia, because the urbanicity measure does not vary in time in this analysis. It is recognized, however, that the data were collected in a single year and may not be representative of other years.
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