What defines a sex? Although we tend to think there are only two - males and females - there are many different ways to mix and match the attributes of sexes

From a human perspective, sexes seem a relatively simple thing to get one's head around—there are females, and there are males. But our perspective seems biased and narrow when applied to life as a whole, says evolutionary biologist Laurence Hurst of the University of Bath, United Kingdom.“If you were a single-celled alga sitting in a pond, you wouldn't see the world as splitting into males and females.”In fact, different species have evolved a bewildering number of ways to mix and match the attributes of sexes. Some do not have males and females, but have adaptations that mean each individual performs a specific role during sex. There are other species of which every member is sexually equivalent, but individuals nevertheless divide into groups for the purposes of mating. And in some species, individuals make both eggs and sperm ( Box 1 ). This biological diversity has produced a semantic muddle among biologists—everyone who thinks about the evolution of sexes seems to have a slightly different take on what a sex is. “The literature is highly confusing—we need to clarify our terminology,” comments Rolf Hoekstra, a geneticist at the University of Waageningen in the Netherlands.As things stand, there are three main aspects to the definition of a sex: who you are, who you can mate with, and who your parents are. The third part of this trinity—parental number—shows the least variation in nature. No known organism needs more than one mother and one father. But even this assumption is now starting to break down at the level of biological systems. In a recently discovered hybrid system within the harvester ant genus  Pogonomyrmex , queens must mate with two types of males to produce both reproductive individuals and workers ( Figure 3 ). These ants are the first species known which truly has more than two sexes—with colonies effectively having three parents— argues Joel Parker of the University of Lausanne, Switzerland.Parker's ideas might reactivate evolutionary biologists' interest in sexes, which has lain somewhat dormant since the 1990s. It could also provide a new route to experiments— something often lacking in the field. Not everyone agrees that it makes sense to define the ants' genetic quirks as new sexes. Each ant is still only a mix the genes from no more than two parents, after all. But Parker believes that our current ideas about mating systems may not be adequate to describe the ingenuity of evolution. “Until you see a three-sex system, you don't know what it'll look like,” he says.Little and Large To address whether these ants have more than two sexes, we first need to look at other candidates for sexes, their numbers in different species, and how these systems evolved. One thing biologists do agree on is that males and females count as different sexes. And they also agree that the main difference between the two is gamete size: males make lots of small gametes—sperm in animals, pollen in plants—and females produce a few big eggs. But researchers also think that before males and females evolved, sex occurred between organisms with equal-sized gametes, a state called isogamy. Evolutionarily speaking, an isogamous species faces two pressures. Individuals can make more smaller gametes, thus increasing their potential number of offspring, or they can make fewer bigger gametes, thus giving their offspring a better start in life by providing them with more resources. Theoretical analyses suggest that this pressure is particularly great if being big carries large benefits, making isogamy unstable. The original identical gametes will evolve towards the opposite ends of the size spectrum. In many species, however, one size of gamete still fits all. The organisms that have hung on to isogamy are found among the less complex branches of life, such as fungi, algae, and protozoa. This might be because large gametes, yielding well-funded zygotes, are likely to be more strongly selected if the resulting offspring needs to grow into a large and complex organism. The benefits of large gametes in simple and unicellular organisms are not so obvious. Some support for this hypothesis comes from the algae belonging to the group Volvocales. The variation in gamete size within each species matches its degree of complexity. For example, the unicellular species  Chlamydomonas rheinhardtii  is isogamous, while  Volvox rouseletti , which lives in balls of up to 50,000 cells, has large and small gametes ( Figure 4 ). Figure 4 Four Different Species of Volvocales Algae (A)  Gonium pectorale , (B)  Eudorina elegans , (C)  Pleodorina californica , and (D)  Volvox carteri . These are unicellular organisms that live in colonies and have both large and small gametes. Photo courtesy of Aurora M. Nedelcu, from the Volvocales Information Project ( http:\\www.unbf.ca\vip\index.htm) .
Little and Large
To address whether these ants have more than two sexes, we first need to look at other candidates for sexes, their numbers in different species, and how these systems evolved. One thing biologists do agree on is that males and females count as different sexes. And they also agree that the main difference between the two is gamete size: males make lots of small gametes—sperm in animals, pollen in plants—and females produce a few big eggs. But researchers also think that before males and females evolved, sex occurred between organisms with equal-sized gametes, a state called isogamy.Evolutionarily speaking, an isogamous species faces two pressures. Individuals can make more smaller gametes, thus increasing their potential number of offspring, or they can make fewer bigger gametes, thus giving their offspring a better start in life by providing them with more resources. Theoretical analyses suggest that this pressure is particularly great if being big carries large benefits, making isogamy unstable. The original identical gametes will evolve towards the opposite ends of the size spectrum.In many species, however, one size of gamete still fits all. The organisms that have hung on to isogamy are found among the less complex branches of life, such as fungi, algae, and protozoa. This might be because large gametes, yielding well-funded zygotes, are likely to be more strongly selected if the resulting offspring needs to grow into a large and complex organism. The benefits of large gametes in simple and unicellular organisms are not so obvious. Some support for this hypothesis comes from the algae belonging to the group Volvocales. The variation in gamete size within each species matches its degree of complexity. For example, the unicellular species  Chlamydomonas rheinhardtii  is isogamous, while  Volvox rouseletti , which lives in balls of up to 50,000 cells, has large and small gametes ( Figure 4 ).The Opposite of Sexes? The question of sexes, and their number, is complex in isogamous species. Such species still typically comprise different groups for mating purposes. They have genes that allow them to mate with everyone except those belonging to the same “mating type” (this is presumably to avoid inbreeding and to produce offspring that are genetically diverse to cope with environmental change or biological enemies). Species with mating types, rather than males and females, aren't limited to two interbreeding groups: the ciliate protozoan  Tetrahymena thermophila  has seven, and the mushroom  Schizophyllum commune  has more than 28,000, for example. Some biologists call these mating types sexes; others think that, in the absence of traits other than sexual compatibility or the lack thereof, it makes more sense to view species with many mating types as having no sexes, rather than lots. Yet most isogamous species have only two mating types. This seems perverse—it excludes half the population as potential mates without gaining the benefits of specialization in sexual biology. With William Hamilton, Hurst came up an explanation for this apparent inefficiency. Two-group mating systems, they proposed, evolved as a way for genes in the nucleus to police the DNA in organelles. Cellular structures with their own genomes, such as mitochondria and chloroplasts, can divide more rapidly than the cells that house them. If the inheritance of organelles was biparental, selfish mutations in their DNA could spread rapidly, Hurst and Hamilton showed. A nuclear gene that enforces uniparental inheritance of organelles, along with a label that allows such cells to recognize each other so that their nuclear genes can share the benefits of cytoplasmic policing, should be favored. The mating biology of isogamous species offers considerable support for this idea. The aforementioned  C. rheinhardtii , for example, comes in two mating types called plus and minus. When the two fuse, the plastid of the minus cell is detroyed. Most isogamous species that fuse cells have a similar mechanism. Male-killer parasites such as  Wolbachia , a parasite of arthropods, show the selection pressure that intracellular passengers can exert (see also the primer by Wernegreen in the March issue of  PLoS Biology ). And cellfusion experiments hint that biparental inheritance of organelles does indeed cause problems, says Hurst. “Hybrids are often rubbish, but they can be better if a drug is administered that inhibits the mitochondria of one cell line.” The species that have lots of mating types, such as ciliate protozoa, exchange nuclear DNA, but not cytoplasm, and hence not intracellular organelles. Since individuals are freed from the need to police their organelles or keep out parasites, selection favors the widest assortment of possible mates, and thus the evolution of a large number of mating types so that one's own type—which one can't mate with—is a small subset of the population. It is possible to imagine species with cytoplasmic policing likewise having many mating types, but such a situation would be much more prone to break down and be invaded by selfish agents than one with two clearly defined types, which is what we usually see in nature. Some have argued that cytoplasmic policing might also be a selective force for different-sized gametes. Sperm could be small so that they do not import mitochondria into the egg. More than a decade after he devised it, Hurst's is still the leading hypothesis explaining the number of mating types in a species. But experimental evidence remains frustratingly elusive. “I wouldn't say I was entirely satisfied,” says Hurst. “We've got all these ideas, and they turn out to be quite hard to test—there's no simple thing one can do on a single species.” There are species where the uniparental inheritance of organelles is not so strictly enforced, says Hoekstra, such as yeasts and plants. “It's not easy to see if selection [on organelles] is strong enough,” he says.
The Opposite of Sexes?
The question of sexes, and their number, is complex in isogamous species. Such species still typically comprise different groups for mating purposes. They have genes that allow them to mate with everyone except those belonging to the same “mating type” (this is presumably to avoid inbreeding and to produce offspring that are genetically diverse to cope with environmental change or biological enemies). Species with mating types, rather than males and females, aren't limited to two interbreeding groups: the ciliate protozoan  Tetrahymena thermophila  has seven, and the mushroom  Schizophyllum commune  has more than 28,000, for example. Some biologists call these mating types sexes; others think that, in the absence of traits other than sexual compatibility or the lack thereof, it makes more sense to view species with many mating types as having no sexes, rather than lots.Yet most isogamous species have only two mating types. This seems perverse—it excludes half the population as potential mates without gaining the benefits of specialization in sexual biology. With William Hamilton, Hurst came up an explanation for this apparent inefficiency.Two-group mating systems, they proposed, evolved as a way for genes in the nucleus to police the DNA in organelles. Cellular structures with their own genomes, such as mitochondria and chloroplasts, can divide more rapidly than the cells that house them. If the inheritance of organelles was biparental, selfish mutations in their DNA could spread rapidly, Hurst and Hamilton showed. A nuclear gene that enforces uniparental inheritance of organelles, along with a label that allows such cells to recognize each other so that their nuclear genes can share the benefits of cytoplasmic policing, should be favored.The mating biology of isogamous species offers considerable support for this idea. The aforementioned  C. rheinhardtii , for example, comes in two mating types called plus and minus. When the two fuse, the plastid of the minus cell is detroyed. Most isogamous species that fuse cells have a similar mechanism. Male-killer parasites such as  Wolbachia , a parasite of arthropods, show the selection pressure that intracellular passengers can exert (see also the primer by Wernegreen in the March issue of  PLoS Biology ). And cellfusion experiments hint that biparental inheritance of organelles does indeed cause problems, says Hurst. “Hybrids are often rubbish, but they can be better if a drug is administered that inhibits the mitochondria of one cell line.”PLoS BiologyThe species that have lots of mating types, such as ciliate protozoa, exchange nuclear DNA, but not cytoplasm, and hence not intracellular organelles. Since individuals are freed from the need to police their organelles or keep out parasites, selection favors the widest assortment of possible mates, and thus the evolution of a large number of mating types so that one's own type—which one can't mate with—is a small subset of the population. It is possible to imagine species with cytoplasmic policing likewise having many mating types, but such a situation would be much more prone to break down and be invaded by selfish agents than one with two clearly defined types, which is what we usually see in nature. Some have argued that cytoplasmic policing might also be a selective force for different-sized gametes. Sperm could be small so that they do not import mitochondria into the egg.More than a decade after he devised it, Hurst's is still the leading hypothesis explaining the number of mating types in a species. But experimental evidence remains frustratingly elusive. “I wouldn't say I was entirely satisfied,” says Hurst. “We've got all these ideas, and they turn out to be quite hard to test—there's no simple thing one can do on a single species.” There are species where the uniparental inheritance of organelles is not so strictly enforced, says Hoekstra, such as yeasts and plants. “It's not easy to see if selection [on organelles] is strong enough,” he says.Three's Company Yet even in a species such as  S. commune , with its thousands of mating types, each sexual encounter involves only two cells. Nor are we likely to find a species that defies this pattern. The technical difficulties of combining more than two sets of genetic information into one individual, and of parceling out that information during meiosis, must be vast, says Brian Charlesworth of the University of Edinburgh. “We've reached the point of two cells fusing, and stuck with that; two cells are probably just as good as three,” he says. The ant colonies that Parker suggests have three parents are a hybrid of the species  Pogonomyrmex rugosus  and  P. barbatus . The hybrids have not yet been classed as a new species, but they are well established across the southwestern United States, and there is no evidence of contemporary gene flow between hybrids and their parent species. Each ant has one parent if it is male, because male ants are produced from unfertilized eggs, or two if it is female. But each sex also comes in two genetic strains. If a queen mates with a male of her own strain, her offspring will be queens, and if she mates with a male from the other strain, the sperm will give rise to workers. So, for a colony to function fully it—and the queens it produces, because workers raise queens—must have two fathers and one mother. And if any one group were to disappear, the population as a whole would go extinct—unlike fungal mating types, where it's easy to imagine that the species would carry on if a few disappeared. “If you lose any one, the whole thing collapses,” says Parker. “It's really different from any other system.” So, Parker argues,  Pogonomyrmex  has four sexes: the males and females of each strain. The idea is particularly potent if one views a social insect colony as a “superorganism,” with the workers equivalent to the cells of a body. It's as if a female mates with one male to produce her offspring's somatic cells, and another to produce its germ cells. The ants form chaotic mating swarms, so most queens have no problem mating multiply and getting sperm from males of both strains, although one would expect that males would strongly favor mating with females of their own strain. It's not known how the system originated. Separating the worker and reproductive castes by genetics—other social insects do this by environment, that is, by rearing workers and reproductives differently—may allow selection to operate more efficiently on each lineage, and the workers may benefit from hybrid vigor: field researchers report them as being highly aggressive. In an echo of Hurst's hypothesis, the system also mixes mitochondrial and nuclear genes differently in queens and workers. Some evolutionary biologists, such as Charlesworth, do not consider  Pogonomyrmex ' s  mating types sexes, arguing that to define sexes in yet another way only confuses the picture further. “[The ants] are an interesting system, but I wasn't persuaded by Parker's interpretation,” Charlesworth says. “I'm not a fan of the idea that it's useful to use the word ‘sex’ to describe compatibility between mating types—it muddies the waters.” Others are more positive towards Parker's interpretation: “It deserves to be taken seriously,” says evolutionary biologist Eörs Szathmáry of the Collegium Budapest in Hungary. “He's thrown a stone in the water—now we need to see what kinds of ripples it makes. You can't falsify a definition in the way you can a hypothesis; what determines their fate is whether people find them useful or not.” Species in which some individuals give up their reproductive opportunities to form part of a breeding group, such as slime molds, might have a system similar to that of the ants, Parker believes. “There may be hidden mating incompatibilities,” he says. “Now [that] people know to look, we're going to start seeing more of these systems.”
Three's Company
Yet even in a species such as  S. commune , with its thousands of mating types, each sexual encounter involves only two cells. Nor are we likely to find a species that defies this pattern. The technical difficulties of combining more than two sets of genetic information into one individual, and of parceling out that information during meiosis, must be vast, says Brian Charlesworth of the University of Edinburgh. “We've reached the point of two cells fusing, and stuck with that; two cells are probably just as good as three,” he says.The ant colonies that Parker suggests have three parents are a hybrid of the species  Pogonomyrmex rugosus  and  P. barbatus . The hybrids have not yet been classed as a new species, but they are well established across the southwestern United States, and there is no evidence of contemporary gene flow between hybrids and their parent species.Each ant has one parent if it is male, because male ants are produced from unfertilized eggs, or two if it is female. But each sex also comes in two genetic strains. If a queen mates with a male of her own strain, her offspring will be queens, and if she mates with a male from the other strain, the sperm will give rise to workers. So, for a colony to function fully it—and the queens it produces, because workers raise queens—must have two fathers and one mother. And if any one group were to disappear, the population as a whole would go extinct—unlike fungal mating types, where it's easy to imagine that the species would carry on if a few disappeared. “If you lose any one, the whole thing collapses,” says Parker. “It's really different from any other system.”So, Parker argues,  Pogonomyrmex  has four sexes: the males and females of each strain. The idea is particularly potent if one views a social insect colony as a “superorganism,” with the workers equivalent to the cells of a body. It's as if a female mates with one male to produce her offspring's somatic cells, and another to produce its germ cells. The ants form chaotic mating swarms, so most queens have no problem mating multiply and getting sperm from males of both strains, although one would expect that males would strongly favor mating with females of their own strain.It's not known how the system originated. Separating the worker and reproductive castes by genetics—other social insects do this by environment, that is, by rearing workers and reproductives differently—may allow selection to operate more efficiently on each lineage, and the workers may benefit from hybrid vigor: field researchers report them as being highly aggressive. In an echo of Hurst's hypothesis, the system also mixes mitochondrial and nuclear genes differently in queens and workers.Some evolutionary biologists, such as Charlesworth, do not consider  Pogonomyrmex ' s  mating types sexes, arguing that to define sexes in yet another way only confuses the picture further. “[The ants] are an interesting system, but I wasn't persuaded by Parker's interpretation,” Charlesworth says. “I'm not a fan of the idea that it's useful to use the word ‘sex’ to describe compatibility between mating types—it muddies the waters.” Others are more positive towards Parker's interpretation: “It deserves to be taken seriously,” says evolutionary biologist Eörs Szathmáry of the Collegium Budapest in Hungary. “He's thrown a stone in the water—now we need to see what kinds of ripples it makes. You can't falsify a definition in the way you can a hypothesis; what determines their fate is whether people find them useful or not.”sSpecies in which some individuals give up their reproductive opportunities to form part of a breeding group, such as slime molds, might have a system similar to that of the ants, Parker believes. “There may be hidden mating incompatibilities,” he says. “Now [that] people know to look, we're going to start seeing more of these systems.”Figure 1 Two Individuals of  Pseudobiceros bedfordi  About to Have a Sperm Battle Species of the flatworm genus  Pseudobiceros  are hermaphroditic and have two penises that are used to inject sperm into the partner.  P. bedfordi  is exceptional in that it applies sperm onto the partner's skin rather than injecting it. Photo courtesy of Nico Michiels. Figure 2 Scars of Sex (A) Streaks of sperm (St) received after a mating interaction in the hermaphroditic flatworm,  Pseudobiceros bedfordi . (B) Received sperm appears to “burn” holes (H) in the receiver. Some (unknown) component of the ejaculate dissolves the skin tissue. Sc, scar tissue. (C) Exceptional case where an individual received a large amount of sperm somewhere in the middle of the body, resulting in a large hole (asterisk). The the body subsequently tore in two. Individuals like these are occasionally found in the field and can regenerate much of their body. Photo courtesy of Nico Michiels. Box 1. The Best of Both Worlds? One option for dividing up the sexes is “both”—hermaphroditism. This might seem like an ideal solution—everyone becomes a potential partner, and everyone can bear offspring. In practice, however, hermaphroditism is uncommon among multicellular animals. The reasons are similar to those explaining why evolution favors unequal-sized gametes—once sexes have evolved, it's better to commit all one's resources to one role or the other, rather than try and be a jack-of-all-trades. After all, there are many good uses for mating resources other than simply producing eggs or sperm. An animal could defend a territory or provide parental care, for example. Hermaphroditism, however, is useful if one's sexual options are severely limited. In particular, it can be favored when encounters with potential mates are extremely rare. It makes no sense for an animal to invest heavily in the biological equipment of maleness, say, if it will have almost no opportunities to use it: better to hedge your bets. Animals with low or unpredictable population densities and those that are immobile, have poor senses, or lack long-distance signalling are often hermaphroditic. These include sponges, worms—whether flat, nematode, or annelid—and many molluscs (and, of course, plants, the majority of which are hermaphroditic). Most hermaphrodites still need to find at least one mate in their lifetimes: the cost of inbreeding prevents self fertilization from becoming common. Hermaphroditic animals have some weird sexual adaptations.  Helix aspersa  snails shoot calcareous love darts into one another. And when the marine flatworm  Pseudobiceros bedfordi  mates, each worm has two penises, which they fence with in a battle to smear one another with sperm without being fertilized themselves in the process ( Figures 1  and  2 ). Such oddities result when the mating opportunities of a hermaphroditic species increase, and specialization starts to become more favorable, says evolutionary biologist Nico Michiels, of the University of Muenster in Germany. In a species with two separate sexes, males and females often have different ideas about whether a mating is a good idea—males tend to be keener, females tend to be choosier. The result can be an evolutionary arms race, with each sex evolving adaptations that help them get their way. By exercising mate choice, each sex has some influence on the types of adaptations that evolve—anything too outlandish is unlikely to be favored. This counterbalance is not present in hermaphrodites, however. Rather than having one half of a species resist a particular mating strategy, the whole species is just as likely to adopt it. “Hermaphrodites run into awkward and bizarre mating conflicts,” says Michiels. Michiels believes that hermaphroditism was the ancestral state for animals, and thinks that we might be able to find the relics of this past in contemporary species with separate sexes. To test these ideas, he is searching for groups containing closely related hermaphroditic and bisexual species. Such taxa are very rare, however.

Box 1. The Best of Both Worlds?
One option for dividing up the sexes is “both”—hermaphroditism. This might seem like an ideal solution—everyone becomes a potential partner, and everyone can bear offspring. In practice, however, hermaphroditism is uncommon among multicellular animals. The reasons are similar to those explaining why evolution favors unequal-sized gametes—once sexes have evolved, it's better to commit all one's resources to one role or the other, rather than try and be a jack-of-all-trades. After all, there are many good uses for mating resources other than simply producing eggs or sperm. An animal could defend a territory or provide parental care, for example.Hermaphroditism, however, is useful if one's sexual options are severely limited. In particular, it can be favored when encounters with potential mates are extremely rare. It makes no sense for an animal to invest heavily in the biological equipment of maleness, say, if it will have almost no opportunities to use it: better to hedge your bets. Animals with low or unpredictable population densities and those that are immobile, have poor senses, or lack long-distance signalling are often hermaphroditic. These include sponges, worms—whether flat, nematode, or annelid—and many molluscs (and, of course, plants, the majority of which are hermaphroditic). Most hermaphrodites still need to find at least one mate in their lifetimes: the cost of inbreeding prevents self fertilization from becoming common.Hermaphroditic animals have some weird sexual adaptations.  Helix aspersa  snails shoot calcareous love darts into one another. And when the marine flatworm  Pseudobiceros bedfordi  mates, each worm has two penises, which they fence with in a battle to smear one another with sperm without being fertilized themselves in the process ( Figures 1  and  2 ).Such oddities result when the mating opportunities of a hermaphroditic species increase, and specialization starts to become more favorable, says evolutionary biologist Nico Michiels, of the University of Muenster in Germany. In a species with two separate sexes, males and females often have different ideas about whether a mating is a good idea—males tend to be keener, females tend to be choosier. The result can be an evolutionary arms race, with each sex evolving adaptations that help them get their way. By exercising mate choice, each sex has some influence on the types of adaptations that evolve—anything too outlandish is unlikely to be favored. This counterbalance is not present in hermaphrodites, however. Rather than having one half of a species resist a particular mating strategy, the whole species is just as likely to adopt it. “Hermaphrodites run into awkward and bizarre mating conflicts,” says Michiels.Michiels believes that hermaphroditism was the ancestral state for animals, and thinks that we might be able to find the relics of this past in contemporary species with separate sexes. To test these ideas, he is searching for groups containing closely related hermaphroditic and bisexual species. Such taxa are very rare, however.


Background In 1998, the World Health Organization recognized Buruli ulcer (BU), a human skin disease caused by  Mycobacterium ulcerans  (MU), as the third most prevalent mycobacterial disease. In Ghana, there have been more than 2000 reported cases in the last ten years; outbreaks have occurred in at least 90 of its 110 administrative districts. In one of the worst affected districts, Amansie West, there are arsenic-enriched surface environments resulting from the oxidation of arsenic-bearing minerals, occurring naturally in mineral deposits.
Background
In 1998, the World Health Organization recognized Buruli ulcer (BU), a human skin disease caused by  Mycobacterium ulcerans  (MU), as the third most prevalent mycobacterial disease. In Ghana, there have been more than 2000 reported cases in the last ten years; outbreaks have occurred in at least 90 of its 110 administrative districts. In one of the worst affected districts, Amansie West, there are arsenic-enriched surface environments resulting from the oxidation of arsenic-bearing minerals, occurring naturally in mineral deposits.Mycobacterium ulceransResults Proximity analysis, carried out to determine spatial relationships between BU-affected areas and arsenic-enriched farmlands and arsenic-enriched drainage channels in the Amansie West District, showed that mean BU prevalence in settlements along arsenic-enriched drainages and within arsenic-enriched farmlands is greater than elsewhere. Furthermore, mean BU prevalence is greater along arsenic-enriched drainages than within arsenic-enriched farmlands.
Results
Proximity analysis, carried out to determine spatial relationships between BU-affected areas and arsenic-enriched farmlands and arsenic-enriched drainage channels in the Amansie West District, showed that mean BU prevalence in settlements along arsenic-enriched drainages and within arsenic-enriched farmlands is greater than elsewhere. Furthermore, mean BU prevalence is greater along arsenic-enriched drainages than within arsenic-enriched farmlands.Conclusion The results suggest that arsenic in the environment may play a contributory role in MU infection.
Conclusion
The results suggest that arsenic in the environment may play a contributory role in MU infection.

Background Buruli ulcer (BU) is a skin disease, which usually begins as a painless nodule or papule and may progress to massive skin ulceration. If untreated BU may lead to extensive soft tissue destruction, with inflammation extending to deep fascia. The parts of the body most affected are the extremities. Subsequent complications may include contractural deformities. The main form of treatment is wide excisional surgery, including amputation of limbs, which requires prolonged hospitalization and is thus a significant burden on hospital resources and budgets. In recent years, there has been increased incidence of BU in West Africa (including Benin, Burkina Faso, Cote d'Ivoire, Ghana, Guinea, Liberia and Togo), Mexico, French Guyana, Papua New Guinea and Australia. The disease seems to affect mostly impoverished inhabitants in remote and rural areas; children are the most vulnerable, accounting for about 70% of the cases [ 1 ]. The World Health Organization (WHO) has recognized BU as the third most prevalent mycobacterial disease after tuberculosis and leprosy and has called for urgent action to control it [ 2 ]. The causative agent of BU is  Mycobacterium ulcerans  (MU), which was first described in Bainsdale, Australia, in 1948 [ 3 ]. From the medical point of view, MU is among the group of mycobacteria that are potentially pathogenic in humans and animals under special circumstances [ 4 ]. It is suggested MU enters through a small break or trauma in the skin because it is not known to penetrate through intact or healthy skin [ 5 , 6 ]. Portaels et al. [ 7 ] have suggested that insects may be involved in the transmission of the disease because insects found in the roots of trees tested positive with the mycobacterium. Marsolliers et al. [ 8 ] found through an experimental study that the bite of MU-infected waterborne insects transmitted infection to mice. In terms of human infection, however, the reservoir of MU and the mode of transmission of BU are still unclear [ 4 , 9 - 12 ]. Epidemiological data suggest that environmental factors such as climate, soil, geology, geochemistry, etc. may indirectly influence or contribute to MU infection [ 4 ]. In addition, the frequencies of some diseases caused by mycobacteria indicate that species are distributed geographically [ 13 ]. For example, MU has been observed mainly in the tropics and [ 4 ] especially in anthropogenically-polluted areas [ 14 ]. Since MU is known to be present in nature although its reservoir is not known and since the epidemiology of BU is still unclear, there is a need to have a better understanding of environmental, ecological, and behavioural factors that predispose to infection. Spatial analysis potentially contributes important information leading to the understanding of the epidemiology and etiology of BU. The main objective of this paper is to explore relationships between some spatial environmental factors and the prevalence of BU.
Background
Buruli ulcer (BU) is a skin disease, which usually begins as a painless nodule or papule and may progress to massive skin ulceration. If untreated BU may lead to extensive soft tissue destruction, with inflammation extending to deep fascia. The parts of the body most affected are the extremities. Subsequent complications may include contractural deformities. The main form of treatment is wide excisional surgery, including amputation of limbs, which requires prolonged hospitalization and is thus a significant burden on hospital resources and budgets.In recent years, there has been increased incidence of BU in West Africa (including Benin, Burkina Faso, Cote d'Ivoire, Ghana, Guinea, Liberia and Togo), Mexico, French Guyana, Papua New Guinea and Australia. The disease seems to affect mostly impoverished inhabitants in remote and rural areas; children are the most vulnerable, accounting for about 70% of the cases [ 1 ]. The World Health Organization (WHO) has recognized BU as the third most prevalent mycobacterial disease after tuberculosis and leprosy and has called for urgent action to control it [ 2 ].The causative agent of BU is  Mycobacterium ulcerans  (MU), which was first described in Bainsdale, Australia, in 1948 [ 3 ]. From the medical point of view, MU is among the group of mycobacteria that are potentially pathogenic in humans and animals under special circumstances [ 4 ]. It is suggested MU enters through a small break or trauma in the skin because it is not known to penetrate through intact or healthy skin [ 5 , 6 ]. Portaels et al. [ 7 ] have suggested that insects may be involved in the transmission of the disease because insects found in the roots of trees tested positive with the mycobacterium. Marsolliers et al. [ 8 ] found through an experimental study that the bite of MU-infected waterborne insects transmitted infection to mice. In terms of human infection, however, the reservoir of MU and the mode of transmission of BU are still unclear [ 4 , 9 - 12 ].Mycobacterium ulceransEpidemiological data suggest that environmental factors such as climate, soil, geology, geochemistry, etc. may indirectly influence or contribute to MU infection [ 4 ]. In addition, the frequencies of some diseases caused by mycobacteria indicate that species are distributed geographically [ 13 ]. For example, MU has been observed mainly in the tropics and [ 4 ] especially in anthropogenically-polluted areas [ 14 ].Since MU is known to be present in nature although its reservoir is not known and since the epidemiology of BU is still unclear, there is a need to have a better understanding of environmental, ecological, and behavioural factors that predispose to infection. Spatial analysis potentially contributes important information leading to the understanding of the epidemiology and etiology of BU. The main objective of this paper is to explore relationships between some spatial environmental factors and the prevalence of BU.Results For the buffers tested,  P  values range from 0.09 to 0.46. The buffers with the highest  P  values (i.e., 0.09) are 100 m for drainage channels and 400 m for farmlands. With these buffers 24 of the 61 settlements (i.e., 39%) fall within 100 m of arsenic-enriched drainage channels (Figure  4 ) and 41 of the 61 settlements (i.e., 67%) fall within 400 m of As-enriched farmlands (Figure  5 ). The mean BU prevalence within the drainage buffer is 0.7% whereas the mean BU prevalence inside the farmland buffer is 0.6%. Thus the naturally smaller number of settlements within the drainage buffer (i.e., 24) has a slightly higher BU prevalence than the relatively larger number of settlements within the farmland buffer (i.e., 41). Figure 4 Map of As-enriched farmlands.  Distances to As-enriched farmlands (red; with > 15 ppm As in stream sediments) and locations of villages with BU cases. Figure 5 Map of As-enriched streams.  Distances to As-enriched streams (black; with > 15 ppm As in stream sediments) and locations of villages with BU cases.
Results
For the buffers tested,  P  values range from 0.09 to 0.46. The buffers with the highest  P  values (i.e., 0.09) are 100 m for drainage channels and 400 m for farmlands. With these buffers 24 of the 61 settlements (i.e., 39%) fall within 100 m of arsenic-enriched drainage channels (Figure  4 ) and 41 of the 61 settlements (i.e., 67%) fall within 400 m of As-enriched farmlands (Figure  5 ). The mean BU prevalence within the drainage buffer is 0.7% whereas the mean BU prevalence inside the farmland buffer is 0.6%. Thus the naturally smaller number of settlements within the drainage buffer (i.e., 24) has a slightly higher BU prevalence than the relatively larger number of settlements within the farmland buffer (i.e., 41).PPDiscussion Siting of rural settlements in the study area is based primarily on proximity to and availability of water for drinking and other domestic purposes. Consequently, many settlements are located within the optimum buffer distance of 100 m from drainage channels. Where water is abstracted from drainage channels enriched in arsenic, chronic ingestion of arsenic-enriched water through drinking and cooking is likely. This renders the inhabitants susceptible to several kinds of diseases [ 15 , 16 ] including BU. Amofah et al. [ 17 ] studied 90 BU patients and found that 52 used surface water as the source of their drinking water. The result of the statistical analysis corroborates this observation in that BU prevalence is highest where the inhabitants have ready access to domestic water supplies from arsenic-enriched surface drainage. Subsistence farmlands, especially those that depend partially on irrigation, tend to be located along stream floodplains. Soils in these floodplains have a high cation exchange capacity [ 18 ] so that, where streams carry high concentration of arsenic, there is accumulation of arsenic in the soils of the floodplains. These high concentrations of arsenic are in part taken up by the foodcrops grown there [ 19 - 21 ]. The results of the statistical analysis suggest that a high proportion of settlements with high BU prevalence exploit such floodplain farmlands enriched in arsenic. Through consumption of arsenic-enriched drinking water and arsenic-enriched foodcrops, inhabitants in some settlements in the Amansie West District are prone to chronic ingestion of higher-than-average (but sub-toxic) levels of arsenic. Arsenic interacts with and inhibits several enzymes in the body [ 15 ] leading to several multisystemic non-cancer effects [ 16 ], which could predispose to defect the immune system [ 22 ]. Subjects exposed to high levels of arsenic concentrations were known to have impaired immune response [ 23 ]. Immunosuppression due to arsenic has been found to defect antigen processing of splenic macrophages with consequent defective mechanism of helper T-cells [ 24 , 25 ]. Down-regulation of the immune system is known to be a risk factor for the development of BU [ 26 , 27 ]. Several studies [e.g., [ 28 - 31 ]] have reported of impaired resistance to viral/bacterial infection via arsenic ingestion.
Discussion
Siting of rural settlements in the study area is based primarily on proximity to and availability of water for drinking and other domestic purposes. Consequently, many settlements are located within the optimum buffer distance of 100 m from drainage channels. Where water is abstracted from drainage channels enriched in arsenic, chronic ingestion of arsenic-enriched water through drinking and cooking is likely. This renders the inhabitants susceptible to several kinds of diseases [ 15 , 16 ] including BU. Amofah et al. [ 17 ] studied 90 BU patients and found that 52 used surface water as the source of their drinking water. The result of the statistical analysis corroborates this observation in that BU prevalence is highest where the inhabitants have ready access to domestic water supplies from arsenic-enriched surface drainage.Subsistence farmlands, especially those that depend partially on irrigation, tend to be located along stream floodplains. Soils in these floodplains have a high cation exchange capacity [ 18 ] so that, where streams carry high concentration of arsenic, there is accumulation of arsenic in the soils of the floodplains. These high concentrations of arsenic are in part taken up by the foodcrops grown there [ 19 - 21 ]. The results of the statistical analysis suggest that a high proportion of settlements with high BU prevalence exploit such floodplain farmlands enriched in arsenic.Through consumption of arsenic-enriched drinking water and arsenic-enriched foodcrops, inhabitants in some settlements in the Amansie West District are prone to chronic ingestion of higher-than-average (but sub-toxic) levels of arsenic. Arsenic interacts with and inhibits several enzymes in the body [ 15 ] leading to several multisystemic non-cancer effects [ 16 ], which could predispose to defect the immune system [ 22 ]. Subjects exposed to high levels of arsenic concentrations were known to have impaired immune response [ 23 ]. Immunosuppression due to arsenic has been found to defect antigen processing of splenic macrophages with consequent defective mechanism of helper T-cells [ 24 , 25 ]. Down-regulation of the immune system is known to be a risk factor for the development of BU [ 26 , 27 ]. Several studies [e.g., [ 28 - 31 ]] have reported of impaired resistance to viral/bacterial infection via arsenic ingestion.Conclusions The results of this study reveal spatial dependency of BU prevalence upon proximity to drainage channels and farmlands containing > 15 ppm arsenic. Proximity implies chronic exposure to and/or ingestion of elevated concentrations of arsenic, which influences susceptibility to infection.
Conclusions
The results of this study reveal spatial dependency of BU prevalence upon proximity to drainage channels and farmlands containing > 15 ppm arsenic. Proximity implies chronic exposure to and/or ingestion of elevated concentrations of arsenic, which influences susceptibility to infection.Methods Research hypotheses It has been consistently theorized that BU is acquired when MU enters the body through a skin rupture [ 32 , 27 ]. However, several people who were affected by the disease do not recall having any break or trauma in their skin prior to being infected [ 33 ]. A possible alternative is entry through non-ruptured but unusually unhealthy or thin skin. Several dermatological diseases (e.g., Bowen's disease, hyperkeratosis, hyperpigmentation) are related to arsenic ingestion and exposure [ 34 ]. Bioaccumulation of arsenic in the fatty tissues of the skin [ 35 ], due to its high lipid solubility [ 36 , 37 ], may provide a favourable environment for MU in the skin because arsenic is known to help microorganisms grow [ 38 ]. It can be hypothesized, therefore, that (a) arsenic induces MU adhesion to human tissues and (b) arsenic influences the ability of MU to establish BU. In a case study, Amofah et al. [ 17 ] reported that about 44% of the BU patients were farmers whilst about 54% were school children. In Ghana many children help their parents on farms. Not only do farmers and children come in contact with natural drainage areas on their journeys to and from their farmlands, but also the farms are located near water bodies or drainage systems for obvious irrigation purposes [ 39 ]. If farmlands and surface drainage channels are contributory factors to BU, farmlands and surface drainage channels enriched in arsenic may contribute to still higher prevalence of BU. Research methodology Spatial analysis of data provides opportunities for epidemiologists to study associations between environmental factors and spatial distribution of diseases [ 40 ]. A geographic information system (GIS) is capable of analyzing and integrating large quantities of geographically distributed data as well as linking geographic data to non-geographic data to generate information useful in further scientific (or medical) research and in decision-making. In this study, topographic map data, stream sediment geochemical data for arsenic, ASTER satellite imagery and locations of settlements with BU cases were the basic data inputs into the GIS. Spatial data processing was carried out (a) to delineate arsenic-enriched catchment basins based on arsenic concentrations in stream sediment samples, (b) to delineate farmlands from ASTER satellite imagery and determine arsenic-enriched farmlands based on catchment basin data and (c) to extract drainage channels from the topographic map and determine arsenic-enriched drainage channels based on arsenic-enriched catchment basins. Proximity analysis was undertaken to determine spatial relationships between BU-affected areas and the arsenic-enriched areas determined from the data inputs. The study area History of BU in Ghana The study area is in Ghana, where the first case of BU was reported in 1971 and, between 1991 and 1997, more than 2000 cases have been reported [ 41 ]. The disease has affected all of the ten regions and at least 90 of the 110 districts in Ghana [ 42 ]. The Ashanti Region is the worst affected, accounting for about 60% of all reported cases, of which the greatest percentage is in the Amansie West District (Figure  1 ). Figure 1 The study area.  Amansie West District, Ghana, showing the study area (box) and villages with BU cases (black dots). Location of study area The Amansie West District lies between latitudes 6°N and 6°45'N and longitudes 1°30'W and 2°15'W. It covers an area of about 1,136 km 2 . The district capital, Manso Nkwanta, is about 40 km south of Kumasi. The district is drained by the Offin and Oda rivers. Vegetation in the district is composed mainly of secondary forests, thicket, forb regrowth (i.e., soft-stemmed leafy herbs, mostly the weeds, which appear on farms and have to be cut regularly) and swamp vegetation. Vegetation thrives in ferric fluvisols, which are the major soil types in the district. These soils have been developed through yearly rainfall ranging from 125 to 200 cm with temperatures of 22 to 30°C. The landscape of the district varies from gentle to broken. The district is underlain by Lower Proterozoic Birimian and, to a lesser extent, Tarkwaian rocks. Throughout Ghana, Birimian rocks of West Africa are mainly volcanic greenstones with intervening sedimentary rocks and granitoid intrusions, in places containing deposits composed of pyrite, arsenopyrite, minor chalcopyrite, sphalerite, galena, native gold and secondary hematite [ 43 ]. The district has about 310 settlements (though not all these settlements are mapped) with a population in 2000 of 108,726. There are approximately equal percentages of males and females (49% and 51%, respectively), of whom 70% are farmers and 22% are engaged in legal and 'galamsey' (or illegal) mining. The study area is the east-central part of the Amansie West District (covered by a single topographic map sheet, 0602C1), with an area of about 623 km 2 , comprising 61 settlements and including the Bilpraw goldmine (formerly a treasure mine of the Ashanti Kings). The BU cases per settlement range from 1–29. Materials The following are the sources of spatial data input to the GIS. • Incidence of BU per settlement in 1999, obtained from Korle-BU Teaching Hospital, Accra, Ghana. • Settlement population estimates for 2000, projected by the Ministry of local government and rural development. • Topographic map (Sheet 0602C1, 1974, at a scale of 1: 50,000), a single sheet covering the study area, obtained from the Survey Department, Accra, Ghana. • Location map (at scale of 1: 62,500) of stream sediment samples collected in part of the Amansie West District in 1992 and list of arsenic concentrations determined in these samples, obtained from the Geological Survey Department, Accra, Ghana. • Boundary map (at scale of 1: 250,000, surveyed in 1991) of the district, obtained from the Amansie West District Administration. • ASTER imagery (level 1B) acquired on 15/01/2002, obtained from the US Geological Survey. • Landuse/landcover map of Ghana (traced on Landsat TM data of 1998 and published in the same year), obtained from the Remote Sensing Application Unit (RSAU), University of Ghana, Legon. The GIS operations were carried out in three principal steps: (1) spatial data capture; (2) generation of spatial factor maps; and (c) spatial data analysis. The GIS operations were carried out using ILWIS (Integrated Land and Water Information Systems), a GIS software package developed by the International Institute for Geo-information Science and Earth Observation (ITC) in the Netherlands. Spatial data capture The different analog maps were scanned then georeferenced (by defining the x and y coordinates of the corner points of the maps) into a UTM coordinate system. From the scanned map, spatial data were captured by screen digitizing. From the topographic map, rivers, streams and gullies were digitised as line segments as were elevation contours. The boundaries of the district were digitised as line segments and then polygonized. The locations of centres of 61 settlements (identifiable on the topographic map) were digitised as points and the BU incidence in 1999 was recorded as spatial attribute of each settlement. From the stream sediment sample location map, the locations of the samples were digitised as points and the arsenic concentrations (in ppm) were recorded as a spatial attribute of each sample. The ASTER imagery was also georeferenced to the same coordinate system using eight reference points (tie points), which were selected in the image and which could be identified in the topographic map. Using an affine transformation, a root mean square error (RMSE) of 0.58 pixel was obtained in georeferencing the ASTER imagery. For each of the settlements with incidence of BU the percentage prevalence of BU was calculated. Prevalence expresses cases of a disease in terms of the proportion of the population afflicted at a specified time [ 44 ]. It is expressed here as the number of BU cases in a settlement in 1999 divided by the estimated population in 2000 multiplied by 100 to yield a percentage. Spatial factor maps The spatial factor maps generated from the stream sediment geochemistry data for use in the spatial analysis were: (a) map of arsenic-enriched catchment basins; (b) map of arsenic-enriched farmlands; (c) map of arsenic-enriched drainage channels. Arsenic-enriched catchment basins The stream sediment geochemical data for arsenic were initially analysed statistically to determine a threshold value that divides the data into background (normal) classes and anomalous (abnormally high) classes of arsenic concentrations. The data are lognormally distributed and, after removing obvious outliers in the data, a geometric mean of 8.9 ppm As and standard deviation of 2.8 ppm As were obtained. The threshold value was therefore set at 15 ppm As (i.e., approximately the mean plus two standard deviations). The spatial distribution of arsenic was then mapped through the generation of a catchment basin anomaly map in which a sample catchment basin is assigned the geochemical attribute of the corresponding sample [ 45 , 46 ]. Generation of sample catchment basins involved the following steps (using ILWIS): • creation of a raster digital elevation model (DEM) through interpolation of elevation contours; • generation of raster map of drainage lines; and • calculation of sample catchment basin boundaries via an iterative calculation procedure involving the DEM and the raster map of drainage lines. The catchment basin map of arsenic concentrations was then classified into a binary map showing arsenic-normal areas (with ≤ 15 ppm As) and arsenic-enriched areas (with > 15 ppm As) as shown in Figure  2 . About 24% of the study area is occupied by arsenic-enriched catchment basins. Figure 2 Binary map of the catchment basin.  Binary map (of the catchment basin) showing arsenic-normal and arsenic-enriched areas. Arsenic-enriched farmlands A supervised classification of ASTER imagery was carried out to distinguish between the major landcover/landuse classes known in the area. These landcover classes are (a) forest areas, (b) residential areas or settlements (bare of vegetation), and (c) farmlands. Using the available landuse/landcover map and topographic map as references, training pixels of known landuse/landcover classes were selected using a colour composite of ASTER bands 2, 3 and 4. These three bands gave the highest optimal index factor (OIF), which indicates the combination of three spectral bands that provide optimum information about landcover [ 47 ]. The box classifier [ 48 ] was chosen for the image classification. The classified image (Figure  3 ), which was also validated in the field, has an overall accuracy of at least 91% with reference to the landcover/landuse map. The classified image indicates that about 91% of the area is farmland. Figure 3 Landcover/landuse map.  Landcover/landuse map based on supervised classification of ASTER data. To determine arsenic-enriched farmlands, a Boolean AND operation was performed by crossing the catchment basin anomaly map and the classified landcover/landuse image. About 21% of the total area of farmlands in the classified image is arsenic-enriched. Arsenic-enriched portions of the drainage systems A Boolean AND operation was performed by crossing the catchment basin anomaly map and the raster map of drainage lines. About 22% of the total length of drainage lines is indicated to be arsenic-enriched. Spatial data analysis The inhabitants of a settlement earn their livelihoods by exploiting the resources of the surrounding land. This land influences their exposure to infections and to environmental factors that dispose to infections. Proximity analysis was therefore used to determine spatial relationships between BU prevalence per settlement and (i) arsenic-enriched farmlands and (ii) arsenic-enriched portions of the drainage system. The proximity analysis was carried in two principal steps. First, maps of distances from arsenic-enriched farmlands and arsenic-enriched portions of the drainage system were generated. Second, the point map of BU prevalence per settlement was overlaid on (or crossed with) each of these maps. A buffer is a zone of specified distance around a selected map feature. A GIS creates buffer zones around selected map features such as arsenic-enriched farmlands and arsenic-enriched portions of drainage systems. Around each of these, buffers were set at intervals of 100 m up to 1000 m. Each buffer zone map was crossed with BU prevalence data of settlement to determine how many of these fall within and outside of the buffer. At each increasing interval of 100 m, a test of the significance of the difference of the mean BU prevalence within the buffer and outside of the buffer is made using the t-statistic: where  ,   are the sample means,   is the pooled sample variance,  n i  and  n j  are the sample sizes from population  i  and  j . Using  t ij  and degrees of freedom given by  n i  +  n j -2, a  t  distribution look-up table provides the probability,  P  that the means are significantly different.
Methods
Research hypotheses It has been consistently theorized that BU is acquired when MU enters the body through a skin rupture [ 32 , 27 ]. However, several people who were affected by the disease do not recall having any break or trauma in their skin prior to being infected [ 33 ]. A possible alternative is entry through non-ruptured but unusually unhealthy or thin skin. Several dermatological diseases (e.g., Bowen's disease, hyperkeratosis, hyperpigmentation) are related to arsenic ingestion and exposure [ 34 ]. Bioaccumulation of arsenic in the fatty tissues of the skin [ 35 ], due to its high lipid solubility [ 36 , 37 ], may provide a favourable environment for MU in the skin because arsenic is known to help microorganisms grow [ 38 ]. It can be hypothesized, therefore, that (a) arsenic induces MU adhesion to human tissues and (b) arsenic influences the ability of MU to establish BU. In a case study, Amofah et al. [ 17 ] reported that about 44% of the BU patients were farmers whilst about 54% were school children. In Ghana many children help their parents on farms. Not only do farmers and children come in contact with natural drainage areas on their journeys to and from their farmlands, but also the farms are located near water bodies or drainage systems for obvious irrigation purposes [ 39 ]. If farmlands and surface drainage channels are contributory factors to BU, farmlands and surface drainage channels enriched in arsenic may contribute to still higher prevalence of BU.
Research hypotheses
It has been consistently theorized that BU is acquired when MU enters the body through a skin rupture [ 32 , 27 ]. However, several people who were affected by the disease do not recall having any break or trauma in their skin prior to being infected [ 33 ]. A possible alternative is entry through non-ruptured but unusually unhealthy or thin skin.Several dermatological diseases (e.g., Bowen's disease, hyperkeratosis, hyperpigmentation) are related to arsenic ingestion and exposure [ 34 ]. Bioaccumulation of arsenic in the fatty tissues of the skin [ 35 ], due to its high lipid solubility [ 36 , 37 ], may provide a favourable environment for MU in the skin because arsenic is known to help microorganisms grow [ 38 ]. It can be hypothesized, therefore, that (a) arsenic induces MU adhesion to human tissues and (b) arsenic influences the ability of MU to establish BU.In a case study, Amofah et al. [ 17 ] reported that about 44% of the BU patients were farmers whilst about 54% were school children. In Ghana many children help their parents on farms. Not only do farmers and children come in contact with natural drainage areas on their journeys to and from their farmlands, but also the farms are located near water bodies or drainage systems for obvious irrigation purposes [ 39 ]. If farmlands and surface drainage channels are contributory factors to BU, farmlands and surface drainage channels enriched in arsenic may contribute to still higher prevalence of BU.Research methodology Spatial analysis of data provides opportunities for epidemiologists to study associations between environmental factors and spatial distribution of diseases [ 40 ]. A geographic information system (GIS) is capable of analyzing and integrating large quantities of geographically distributed data as well as linking geographic data to non-geographic data to generate information useful in further scientific (or medical) research and in decision-making. In this study, topographic map data, stream sediment geochemical data for arsenic, ASTER satellite imagery and locations of settlements with BU cases were the basic data inputs into the GIS. Spatial data processing was carried out (a) to delineate arsenic-enriched catchment basins based on arsenic concentrations in stream sediment samples, (b) to delineate farmlands from ASTER satellite imagery and determine arsenic-enriched farmlands based on catchment basin data and (c) to extract drainage channels from the topographic map and determine arsenic-enriched drainage channels based on arsenic-enriched catchment basins. Proximity analysis was undertaken to determine spatial relationships between BU-affected areas and the arsenic-enriched areas determined from the data inputs.
Research methodology
Spatial analysis of data provides opportunities for epidemiologists to study associations between environmental factors and spatial distribution of diseases [ 40 ]. A geographic information system (GIS) is capable of analyzing and integrating large quantities of geographically distributed data as well as linking geographic data to non-geographic data to generate information useful in further scientific (or medical) research and in decision-making.In this study, topographic map data, stream sediment geochemical data for arsenic, ASTER satellite imagery and locations of settlements with BU cases were the basic data inputs into the GIS. Spatial data processing was carried out (a) to delineate arsenic-enriched catchment basins based on arsenic concentrations in stream sediment samples, (b) to delineate farmlands from ASTER satellite imagery and determine arsenic-enriched farmlands based on catchment basin data and (c) to extract drainage channels from the topographic map and determine arsenic-enriched drainage channels based on arsenic-enriched catchment basins. Proximity analysis was undertaken to determine spatial relationships between BU-affected areas and the arsenic-enriched areas determined from the data inputs.The study area History of BU in Ghana The study area is in Ghana, where the first case of BU was reported in 1971 and, between 1991 and 1997, more than 2000 cases have been reported [ 41 ]. The disease has affected all of the ten regions and at least 90 of the 110 districts in Ghana [ 42 ]. The Ashanti Region is the worst affected, accounting for about 60% of all reported cases, of which the greatest percentage is in the Amansie West District (Figure  1 ). Figure 1 The study area.  Amansie West District, Ghana, showing the study area (box) and villages with BU cases (black dots). Location of study area The Amansie West District lies between latitudes 6°N and 6°45'N and longitudes 1°30'W and 2°15'W. It covers an area of about 1,136 km 2 . The district capital, Manso Nkwanta, is about 40 km south of Kumasi. The district is drained by the Offin and Oda rivers. Vegetation in the district is composed mainly of secondary forests, thicket, forb regrowth (i.e., soft-stemmed leafy herbs, mostly the weeds, which appear on farms and have to be cut regularly) and swamp vegetation. Vegetation thrives in ferric fluvisols, which are the major soil types in the district. These soils have been developed through yearly rainfall ranging from 125 to 200 cm with temperatures of 22 to 30°C. The landscape of the district varies from gentle to broken. The district is underlain by Lower Proterozoic Birimian and, to a lesser extent, Tarkwaian rocks. Throughout Ghana, Birimian rocks of West Africa are mainly volcanic greenstones with intervening sedimentary rocks and granitoid intrusions, in places containing deposits composed of pyrite, arsenopyrite, minor chalcopyrite, sphalerite, galena, native gold and secondary hematite [ 43 ]. The district has about 310 settlements (though not all these settlements are mapped) with a population in 2000 of 108,726. There are approximately equal percentages of males and females (49% and 51%, respectively), of whom 70% are farmers and 22% are engaged in legal and 'galamsey' (or illegal) mining. The study area is the east-central part of the Amansie West District (covered by a single topographic map sheet, 0602C1), with an area of about 623 km 2 , comprising 61 settlements and including the Bilpraw goldmine (formerly a treasure mine of the Ashanti Kings). The BU cases per settlement range from 1–29. Materials The following are the sources of spatial data input to the GIS. • Incidence of BU per settlement in 1999, obtained from Korle-BU Teaching Hospital, Accra, Ghana. • Settlement population estimates for 2000, projected by the Ministry of local government and rural development. • Topographic map (Sheet 0602C1, 1974, at a scale of 1: 50,000), a single sheet covering the study area, obtained from the Survey Department, Accra, Ghana. • Location map (at scale of 1: 62,500) of stream sediment samples collected in part of the Amansie West District in 1992 and list of arsenic concentrations determined in these samples, obtained from the Geological Survey Department, Accra, Ghana. • Boundary map (at scale of 1: 250,000, surveyed in 1991) of the district, obtained from the Amansie West District Administration. • ASTER imagery (level 1B) acquired on 15/01/2002, obtained from the US Geological Survey. • Landuse/landcover map of Ghana (traced on Landsat TM data of 1998 and published in the same year), obtained from the Remote Sensing Application Unit (RSAU), University of Ghana, Legon. The GIS operations were carried out in three principal steps: (1) spatial data capture; (2) generation of spatial factor maps; and (c) spatial data analysis. The GIS operations were carried out using ILWIS (Integrated Land and Water Information Systems), a GIS software package developed by the International Institute for Geo-information Science and Earth Observation (ITC) in the Netherlands. Spatial data capture The different analog maps were scanned then georeferenced (by defining the x and y coordinates of the corner points of the maps) into a UTM coordinate system. From the scanned map, spatial data were captured by screen digitizing. From the topographic map, rivers, streams and gullies were digitised as line segments as were elevation contours. The boundaries of the district were digitised as line segments and then polygonized. The locations of centres of 61 settlements (identifiable on the topographic map) were digitised as points and the BU incidence in 1999 was recorded as spatial attribute of each settlement. From the stream sediment sample location map, the locations of the samples were digitised as points and the arsenic concentrations (in ppm) were recorded as a spatial attribute of each sample. The ASTER imagery was also georeferenced to the same coordinate system using eight reference points (tie points), which were selected in the image and which could be identified in the topographic map. Using an affine transformation, a root mean square error (RMSE) of 0.58 pixel was obtained in georeferencing the ASTER imagery. For each of the settlements with incidence of BU the percentage prevalence of BU was calculated. Prevalence expresses cases of a disease in terms of the proportion of the population afflicted at a specified time [ 44 ]. It is expressed here as the number of BU cases in a settlement in 1999 divided by the estimated population in 2000 multiplied by 100 to yield a percentage. Spatial factor maps The spatial factor maps generated from the stream sediment geochemistry data for use in the spatial analysis were: (a) map of arsenic-enriched catchment basins; (b) map of arsenic-enriched farmlands; (c) map of arsenic-enriched drainage channels. Arsenic-enriched catchment basins The stream sediment geochemical data for arsenic were initially analysed statistically to determine a threshold value that divides the data into background (normal) classes and anomalous (abnormally high) classes of arsenic concentrations. The data are lognormally distributed and, after removing obvious outliers in the data, a geometric mean of 8.9 ppm As and standard deviation of 2.8 ppm As were obtained. The threshold value was therefore set at 15 ppm As (i.e., approximately the mean plus two standard deviations). The spatial distribution of arsenic was then mapped through the generation of a catchment basin anomaly map in which a sample catchment basin is assigned the geochemical attribute of the corresponding sample [ 45 , 46 ]. Generation of sample catchment basins involved the following steps (using ILWIS): • creation of a raster digital elevation model (DEM) through interpolation of elevation contours; • generation of raster map of drainage lines; and • calculation of sample catchment basin boundaries via an iterative calculation procedure involving the DEM and the raster map of drainage lines. The catchment basin map of arsenic concentrations was then classified into a binary map showing arsenic-normal areas (with ≤ 15 ppm As) and arsenic-enriched areas (with > 15 ppm As) as shown in Figure  2 . About 24% of the study area is occupied by arsenic-enriched catchment basins. Figure 2 Binary map of the catchment basin.  Binary map (of the catchment basin) showing arsenic-normal and arsenic-enriched areas. Arsenic-enriched farmlands A supervised classification of ASTER imagery was carried out to distinguish between the major landcover/landuse classes known in the area. These landcover classes are (a) forest areas, (b) residential areas or settlements (bare of vegetation), and (c) farmlands. Using the available landuse/landcover map and topographic map as references, training pixels of known landuse/landcover classes were selected using a colour composite of ASTER bands 2, 3 and 4. These three bands gave the highest optimal index factor (OIF), which indicates the combination of three spectral bands that provide optimum information about landcover [ 47 ]. The box classifier [ 48 ] was chosen for the image classification. The classified image (Figure  3 ), which was also validated in the field, has an overall accuracy of at least 91% with reference to the landcover/landuse map. The classified image indicates that about 91% of the area is farmland. Figure 3 Landcover/landuse map.  Landcover/landuse map based on supervised classification of ASTER data. To determine arsenic-enriched farmlands, a Boolean AND operation was performed by crossing the catchment basin anomaly map and the classified landcover/landuse image. About 21% of the total area of farmlands in the classified image is arsenic-enriched. Arsenic-enriched portions of the drainage systems A Boolean AND operation was performed by crossing the catchment basin anomaly map and the raster map of drainage lines. About 22% of the total length of drainage lines is indicated to be arsenic-enriched. Spatial data analysis The inhabitants of a settlement earn their livelihoods by exploiting the resources of the surrounding land. This land influences their exposure to infections and to environmental factors that dispose to infections. Proximity analysis was therefore used to determine spatial relationships between BU prevalence per settlement and (i) arsenic-enriched farmlands and (ii) arsenic-enriched portions of the drainage system. The proximity analysis was carried in two principal steps. First, maps of distances from arsenic-enriched farmlands and arsenic-enriched portions of the drainage system were generated. Second, the point map of BU prevalence per settlement was overlaid on (or crossed with) each of these maps. A buffer is a zone of specified distance around a selected map feature. A GIS creates buffer zones around selected map features such as arsenic-enriched farmlands and arsenic-enriched portions of drainage systems. Around each of these, buffers were set at intervals of 100 m up to 1000 m. Each buffer zone map was crossed with BU prevalence data of settlement to determine how many of these fall within and outside of the buffer. At each increasing interval of 100 m, a test of the significance of the difference of the mean BU prevalence within the buffer and outside of the buffer is made using the t-statistic: where  ,   are the sample means,   is the pooled sample variance,  n i  and  n j  are the sample sizes from population  i  and  j . Using  t ij  and degrees of freedom given by  n i  +  n j -2, a  t  distribution look-up table provides the probability,  P  that the means are significantly different.
The study area
History of BU in Ghana The study area is in Ghana, where the first case of BU was reported in 1971 and, between 1991 and 1997, more than 2000 cases have been reported [ 41 ]. The disease has affected all of the ten regions and at least 90 of the 110 districts in Ghana [ 42 ]. The Ashanti Region is the worst affected, accounting for about 60% of all reported cases, of which the greatest percentage is in the Amansie West District (Figure  1 ). Figure 1 The study area.  Amansie West District, Ghana, showing the study area (box) and villages with BU cases (black dots).
History of BU in Ghana
The study area is in Ghana, where the first case of BU was reported in 1971 and, between 1991 and 1997, more than 2000 cases have been reported [ 41 ]. The disease has affected all of the ten regions and at least 90 of the 110 districts in Ghana [ 42 ]. The Ashanti Region is the worst affected, accounting for about 60% of all reported cases, of which the greatest percentage is in the Amansie West District (Figure  1 ).Location of study area The Amansie West District lies between latitudes 6°N and 6°45'N and longitudes 1°30'W and 2°15'W. It covers an area of about 1,136 km 2 . The district capital, Manso Nkwanta, is about 40 km south of Kumasi. The district is drained by the Offin and Oda rivers. Vegetation in the district is composed mainly of secondary forests, thicket, forb regrowth (i.e., soft-stemmed leafy herbs, mostly the weeds, which appear on farms and have to be cut regularly) and swamp vegetation. Vegetation thrives in ferric fluvisols, which are the major soil types in the district. These soils have been developed through yearly rainfall ranging from 125 to 200 cm with temperatures of 22 to 30°C. The landscape of the district varies from gentle to broken. The district is underlain by Lower Proterozoic Birimian and, to a lesser extent, Tarkwaian rocks. Throughout Ghana, Birimian rocks of West Africa are mainly volcanic greenstones with intervening sedimentary rocks and granitoid intrusions, in places containing deposits composed of pyrite, arsenopyrite, minor chalcopyrite, sphalerite, galena, native gold and secondary hematite [ 43 ]. The district has about 310 settlements (though not all these settlements are mapped) with a population in 2000 of 108,726. There are approximately equal percentages of males and females (49% and 51%, respectively), of whom 70% are farmers and 22% are engaged in legal and 'galamsey' (or illegal) mining. The study area is the east-central part of the Amansie West District (covered by a single topographic map sheet, 0602C1), with an area of about 623 km 2 , comprising 61 settlements and including the Bilpraw goldmine (formerly a treasure mine of the Ashanti Kings). The BU cases per settlement range from 1–29.
Location of study area
The Amansie West District lies between latitudes 6°N and 6°45'N and longitudes 1°30'W and 2°15'W. It covers an area of about 1,136 km 2 . The district capital, Manso Nkwanta, is about 40 km south of Kumasi. The district is drained by the Offin and Oda rivers. Vegetation in the district is composed mainly of secondary forests, thicket, forb regrowth (i.e., soft-stemmed leafy herbs, mostly the weeds, which appear on farms and have to be cut regularly) and swamp vegetation. Vegetation thrives in ferric fluvisols, which are the major soil types in the district. These soils have been developed through yearly rainfall ranging from 125 to 200 cm with temperatures of 22 to 30°C. The landscape of the district varies from gentle to broken.The district is underlain by Lower Proterozoic Birimian and, to a lesser extent, Tarkwaian rocks. Throughout Ghana, Birimian rocks of West Africa are mainly volcanic greenstones with intervening sedimentary rocks and granitoid intrusions, in places containing deposits composed of pyrite, arsenopyrite, minor chalcopyrite, sphalerite, galena, native gold and secondary hematite [ 43 ].The district has about 310 settlements (though not all these settlements are mapped) with a population in 2000 of 108,726. There are approximately equal percentages of males and females (49% and 51%, respectively), of whom 70% are farmers and 22% are engaged in legal and 'galamsey' (or illegal) mining.The study area is the east-central part of the Amansie West District (covered by a single topographic map sheet, 0602C1), with an area of about 623 km 2 , comprising 61 settlements and including the Bilpraw goldmine (formerly a treasure mine of the Ashanti Kings). The BU cases per settlement range from 1–29.Materials The following are the sources of spatial data input to the GIS. • Incidence of BU per settlement in 1999, obtained from Korle-BU Teaching Hospital, Accra, Ghana. • Settlement population estimates for 2000, projected by the Ministry of local government and rural development. • Topographic map (Sheet 0602C1, 1974, at a scale of 1: 50,000), a single sheet covering the study area, obtained from the Survey Department, Accra, Ghana. • Location map (at scale of 1: 62,500) of stream sediment samples collected in part of the Amansie West District in 1992 and list of arsenic concentrations determined in these samples, obtained from the Geological Survey Department, Accra, Ghana. • Boundary map (at scale of 1: 250,000, surveyed in 1991) of the district, obtained from the Amansie West District Administration. • ASTER imagery (level 1B) acquired on 15/01/2002, obtained from the US Geological Survey. • Landuse/landcover map of Ghana (traced on Landsat TM data of 1998 and published in the same year), obtained from the Remote Sensing Application Unit (RSAU), University of Ghana, Legon. The GIS operations were carried out in three principal steps: (1) spatial data capture; (2) generation of spatial factor maps; and (c) spatial data analysis. The GIS operations were carried out using ILWIS (Integrated Land and Water Information Systems), a GIS software package developed by the International Institute for Geo-information Science and Earth Observation (ITC) in the Netherlands.
Materials
The following are the sources of spatial data input to the GIS.• Incidence of BU per settlement in 1999, obtained from Korle-BU Teaching Hospital, Accra, Ghana.• Settlement population estimates for 2000, projected by the Ministry of local government and rural development.• Topographic map (Sheet 0602C1, 1974, at a scale of 1: 50,000), a single sheet covering the study area, obtained from the Survey Department, Accra, Ghana.• Location map (at scale of 1: 62,500) of stream sediment samples collected in part of the Amansie West District in 1992 and list of arsenic concentrations determined in these samples, obtained from the Geological Survey Department, Accra, Ghana.• Boundary map (at scale of 1: 250,000, surveyed in 1991) of the district, obtained from the Amansie West District Administration.• ASTER imagery (level 1B) acquired on 15/01/2002, obtained from the US Geological Survey.• Landuse/landcover map of Ghana (traced on Landsat TM data of 1998 and published in the same year), obtained from the Remote Sensing Application Unit (RSAU), University of Ghana, Legon.The GIS operations were carried out in three principal steps: (1) spatial data capture; (2) generation of spatial factor maps; and (c) spatial data analysis. The GIS operations were carried out using ILWIS (Integrated Land and Water Information Systems), a GIS software package developed by the International Institute for Geo-information Science and Earth Observation (ITC) in the Netherlands.Spatial data capture The different analog maps were scanned then georeferenced (by defining the x and y coordinates of the corner points of the maps) into a UTM coordinate system. From the scanned map, spatial data were captured by screen digitizing. From the topographic map, rivers, streams and gullies were digitised as line segments as were elevation contours. The boundaries of the district were digitised as line segments and then polygonized. The locations of centres of 61 settlements (identifiable on the topographic map) were digitised as points and the BU incidence in 1999 was recorded as spatial attribute of each settlement. From the stream sediment sample location map, the locations of the samples were digitised as points and the arsenic concentrations (in ppm) were recorded as a spatial attribute of each sample. The ASTER imagery was also georeferenced to the same coordinate system using eight reference points (tie points), which were selected in the image and which could be identified in the topographic map. Using an affine transformation, a root mean square error (RMSE) of 0.58 pixel was obtained in georeferencing the ASTER imagery. For each of the settlements with incidence of BU the percentage prevalence of BU was calculated. Prevalence expresses cases of a disease in terms of the proportion of the population afflicted at a specified time [ 44 ]. It is expressed here as the number of BU cases in a settlement in 1999 divided by the estimated population in 2000 multiplied by 100 to yield a percentage.
Spatial data capture
The different analog maps were scanned then georeferenced (by defining the x and y coordinates of the corner points of the maps) into a UTM coordinate system. From the scanned map, spatial data were captured by screen digitizing. From the topographic map, rivers, streams and gullies were digitised as line segments as were elevation contours. The boundaries of the district were digitised as line segments and then polygonized. The locations of centres of 61 settlements (identifiable on the topographic map) were digitised as points and the BU incidence in 1999 was recorded as spatial attribute of each settlement.From the stream sediment sample location map, the locations of the samples were digitised as points and the arsenic concentrations (in ppm) were recorded as a spatial attribute of each sample. The ASTER imagery was also georeferenced to the same coordinate system using eight reference points (tie points), which were selected in the image and which could be identified in the topographic map. Using an affine transformation, a root mean square error (RMSE) of 0.58 pixel was obtained in georeferencing the ASTER imagery.For each of the settlements with incidence of BU the percentage prevalence of BU was calculated. Prevalence expresses cases of a disease in terms of the proportion of the population afflicted at a specified time [ 44 ]. It is expressed here as the number of BU cases in a settlement in 1999 divided by the estimated population in 2000 multiplied by 100 to yield a percentage.Spatial factor maps The spatial factor maps generated from the stream sediment geochemistry data for use in the spatial analysis were: (a) map of arsenic-enriched catchment basins; (b) map of arsenic-enriched farmlands; (c) map of arsenic-enriched drainage channels. Arsenic-enriched catchment basins The stream sediment geochemical data for arsenic were initially analysed statistically to determine a threshold value that divides the data into background (normal) classes and anomalous (abnormally high) classes of arsenic concentrations. The data are lognormally distributed and, after removing obvious outliers in the data, a geometric mean of 8.9 ppm As and standard deviation of 2.8 ppm As were obtained. The threshold value was therefore set at 15 ppm As (i.e., approximately the mean plus two standard deviations). The spatial distribution of arsenic was then mapped through the generation of a catchment basin anomaly map in which a sample catchment basin is assigned the geochemical attribute of the corresponding sample [ 45 , 46 ]. Generation of sample catchment basins involved the following steps (using ILWIS): • creation of a raster digital elevation model (DEM) through interpolation of elevation contours; • generation of raster map of drainage lines; and • calculation of sample catchment basin boundaries via an iterative calculation procedure involving the DEM and the raster map of drainage lines. The catchment basin map of arsenic concentrations was then classified into a binary map showing arsenic-normal areas (with ≤ 15 ppm As) and arsenic-enriched areas (with > 15 ppm As) as shown in Figure  2 . About 24% of the study area is occupied by arsenic-enriched catchment basins. Figure 2 Binary map of the catchment basin.  Binary map (of the catchment basin) showing arsenic-normal and arsenic-enriched areas. Arsenic-enriched farmlands A supervised classification of ASTER imagery was carried out to distinguish between the major landcover/landuse classes known in the area. These landcover classes are (a) forest areas, (b) residential areas or settlements (bare of vegetation), and (c) farmlands. Using the available landuse/landcover map and topographic map as references, training pixels of known landuse/landcover classes were selected using a colour composite of ASTER bands 2, 3 and 4. These three bands gave the highest optimal index factor (OIF), which indicates the combination of three spectral bands that provide optimum information about landcover [ 47 ]. The box classifier [ 48 ] was chosen for the image classification. The classified image (Figure  3 ), which was also validated in the field, has an overall accuracy of at least 91% with reference to the landcover/landuse map. The classified image indicates that about 91% of the area is farmland. Figure 3 Landcover/landuse map.  Landcover/landuse map based on supervised classification of ASTER data. To determine arsenic-enriched farmlands, a Boolean AND operation was performed by crossing the catchment basin anomaly map and the classified landcover/landuse image. About 21% of the total area of farmlands in the classified image is arsenic-enriched. Arsenic-enriched portions of the drainage systems A Boolean AND operation was performed by crossing the catchment basin anomaly map and the raster map of drainage lines. About 22% of the total length of drainage lines is indicated to be arsenic-enriched.
Spatial factor maps
The spatial factor maps generated from the stream sediment geochemistry data for use in the spatial analysis were: (a) map of arsenic-enriched catchment basins; (b) map of arsenic-enriched farmlands; (c) map of arsenic-enriched drainage channels.Arsenic-enriched catchment basins The stream sediment geochemical data for arsenic were initially analysed statistically to determine a threshold value that divides the data into background (normal) classes and anomalous (abnormally high) classes of arsenic concentrations. The data are lognormally distributed and, after removing obvious outliers in the data, a geometric mean of 8.9 ppm As and standard deviation of 2.8 ppm As were obtained. The threshold value was therefore set at 15 ppm As (i.e., approximately the mean plus two standard deviations). The spatial distribution of arsenic was then mapped through the generation of a catchment basin anomaly map in which a sample catchment basin is assigned the geochemical attribute of the corresponding sample [ 45 , 46 ]. Generation of sample catchment basins involved the following steps (using ILWIS): • creation of a raster digital elevation model (DEM) through interpolation of elevation contours; • generation of raster map of drainage lines; and • calculation of sample catchment basin boundaries via an iterative calculation procedure involving the DEM and the raster map of drainage lines. The catchment basin map of arsenic concentrations was then classified into a binary map showing arsenic-normal areas (with ≤ 15 ppm As) and arsenic-enriched areas (with > 15 ppm As) as shown in Figure  2 . About 24% of the study area is occupied by arsenic-enriched catchment basins. Figure 2 Binary map of the catchment basin.  Binary map (of the catchment basin) showing arsenic-normal and arsenic-enriched areas.
Arsenic-enriched catchment basins
The stream sediment geochemical data for arsenic were initially analysed statistically to determine a threshold value that divides the data into background (normal) classes and anomalous (abnormally high) classes of arsenic concentrations. The data are lognormally distributed and, after removing obvious outliers in the data, a geometric mean of 8.9 ppm As and standard deviation of 2.8 ppm As were obtained. The threshold value was therefore set at 15 ppm As (i.e., approximately the mean plus two standard deviations). The spatial distribution of arsenic was then mapped through the generation of a catchment basin anomaly map in which a sample catchment basin is assigned the geochemical attribute of the corresponding sample [ 45 , 46 ]. Generation of sample catchment basins involved the following steps (using ILWIS):• creation of a raster digital elevation model (DEM) through interpolation of elevation contours;• generation of raster map of drainage lines; and• calculation of sample catchment basin boundaries via an iterative calculation procedure involving the DEM and the raster map of drainage lines.The catchment basin map of arsenic concentrations was then classified into a binary map showing arsenic-normal areas (with ≤ 15 ppm As) and arsenic-enriched areas (with > 15 ppm As) as shown in Figure  2 . About 24% of the study area is occupied by arsenic-enriched catchment basins.Arsenic-enriched farmlands A supervised classification of ASTER imagery was carried out to distinguish between the major landcover/landuse classes known in the area. These landcover classes are (a) forest areas, (b) residential areas or settlements (bare of vegetation), and (c) farmlands. Using the available landuse/landcover map and topographic map as references, training pixels of known landuse/landcover classes were selected using a colour composite of ASTER bands 2, 3 and 4. These three bands gave the highest optimal index factor (OIF), which indicates the combination of three spectral bands that provide optimum information about landcover [ 47 ]. The box classifier [ 48 ] was chosen for the image classification. The classified image (Figure  3 ), which was also validated in the field, has an overall accuracy of at least 91% with reference to the landcover/landuse map. The classified image indicates that about 91% of the area is farmland. Figure 3 Landcover/landuse map.  Landcover/landuse map based on supervised classification of ASTER data. To determine arsenic-enriched farmlands, a Boolean AND operation was performed by crossing the catchment basin anomaly map and the classified landcover/landuse image. About 21% of the total area of farmlands in the classified image is arsenic-enriched.
Arsenic-enriched farmlands
A supervised classification of ASTER imagery was carried out to distinguish between the major landcover/landuse classes known in the area. These landcover classes are (a) forest areas, (b) residential areas or settlements (bare of vegetation), and (c) farmlands. Using the available landuse/landcover map and topographic map as references, training pixels of known landuse/landcover classes were selected using a colour composite of ASTER bands 2, 3 and 4. These three bands gave the highest optimal index factor (OIF), which indicates the combination of three spectral bands that provide optimum information about landcover [ 47 ]. The box classifier [ 48 ] was chosen for the image classification. The classified image (Figure  3 ), which was also validated in the field, has an overall accuracy of at least 91% with reference to the landcover/landuse map. The classified image indicates that about 91% of the area is farmland.To determine arsenic-enriched farmlands, a Boolean AND operation was performed by crossing the catchment basin anomaly map and the classified landcover/landuse image. About 21% of the total area of farmlands in the classified image is arsenic-enriched.Arsenic-enriched portions of the drainage systems A Boolean AND operation was performed by crossing the catchment basin anomaly map and the raster map of drainage lines. About 22% of the total length of drainage lines is indicated to be arsenic-enriched.
Arsenic-enriched portions of the drainage systems
A Boolean AND operation was performed by crossing the catchment basin anomaly map and the raster map of drainage lines. About 22% of the total length of drainage lines is indicated to be arsenic-enriched.Spatial data analysis The inhabitants of a settlement earn their livelihoods by exploiting the resources of the surrounding land. This land influences their exposure to infections and to environmental factors that dispose to infections. Proximity analysis was therefore used to determine spatial relationships between BU prevalence per settlement and (i) arsenic-enriched farmlands and (ii) arsenic-enriched portions of the drainage system. The proximity analysis was carried in two principal steps. First, maps of distances from arsenic-enriched farmlands and arsenic-enriched portions of the drainage system were generated. Second, the point map of BU prevalence per settlement was overlaid on (or crossed with) each of these maps. A buffer is a zone of specified distance around a selected map feature. A GIS creates buffer zones around selected map features such as arsenic-enriched farmlands and arsenic-enriched portions of drainage systems. Around each of these, buffers were set at intervals of 100 m up to 1000 m. Each buffer zone map was crossed with BU prevalence data of settlement to determine how many of these fall within and outside of the buffer. At each increasing interval of 100 m, a test of the significance of the difference of the mean BU prevalence within the buffer and outside of the buffer is made using the t-statistic: where  ,   are the sample means,   is the pooled sample variance,  n i  and  n j  are the sample sizes from population  i  and  j . Using  t ij  and degrees of freedom given by  n i  +  n j -2, a  t  distribution look-up table provides the probability,  P  that the means are significantly different.
Spatial data analysis
The inhabitants of a settlement earn their livelihoods by exploiting the resources of the surrounding land. This land influences their exposure to infections and to environmental factors that dispose to infections. Proximity analysis was therefore used to determine spatial relationships between BU prevalence per settlement and (i) arsenic-enriched farmlands and (ii) arsenic-enriched portions of the drainage system. The proximity analysis was carried in two principal steps. First, maps of distances from arsenic-enriched farmlands and arsenic-enriched portions of the drainage system were generated. Second, the point map of BU prevalence per settlement was overlaid on (or crossed with) each of these maps.A buffer is a zone of specified distance around a selected map feature. A GIS creates buffer zones around selected map features such as arsenic-enriched farmlands and arsenic-enriched portions of drainage systems. Around each of these, buffers were set at intervals of 100 m up to 1000 m. Each buffer zone map was crossed with BU prevalence data of settlement to determine how many of these fall within and outside of the buffer.At each increasing interval of 100 m, a test of the significance of the difference of the mean BU prevalence within the buffer and outside of the buffer is made using the t-statistic:where  ,   are the sample means,   is the pooled sample variance,  n i  and  n j  are the sample sizes from population  i  and  j . Using  t ij  and degrees of freedom given by  n i  +  n j -2, a  t  distribution look-up table provides the probability,  P  that the means are significantly different.n iin jjijt ijijn iin jjtPAuthors' contributions AAD carried out the research and drafted the manuscript. EJMC guided parts of the research and both EJMC and MH reviewed the manuscript.
Authors' contributions
AAD carried out the research and drafted the manuscript. EJMC guided parts of the research and both EJMC and MH reviewed the manuscript.