article: Tungiasis is a parasitic disease caused by the sand flea Tunga penetrans also known as jigger flea. Communities living in precarious conditions in tropical and sub tropical countries bear the brunt of the infection. The main objective of this study was to determine the burden of Tungiasis in Vihiga County in Kenya. This was a cross-sectional study conducted in 21 villages in 3 Sub-locations in Vihiga County, western Kenya. A total of 437 participants, 5 years old and above were clinically examined for the presence of tungiasis after consenting to take part in the study. Diagnosis was made following standard methods. A semi- structured questionnaire was administered to assess socio-demographic factors, housing, presence and ownership of animals, knowledge and practice related to tungiasis. Data were analyzed using bivariate and multivariate regression analysis. GIS was used to map the geographic distribution of tungiasis in the area. The overall prevalence was found to be (21. 5%; 95% CI: 17. 7–25. 3%). The cases were analysed and visualized in a map form. Multivariate analysis suggested that the occurrence of tungiasis was associated with variables that indicated low economic status (like a monthly income of Ksh ≤ 1000 (adjusted odds ratio 27. 85; 95% CI: 4. 13–187. 59), earthen floor (0. 36; 0. 13–1. 024) and lack of toilet facilities (4. 27; 0. 82–22. 34), age of participant ≤14 (27. 414; 10. 02–74. 99), no regular use of closed footwear (1. 98; 0. 987–3. 97) and common resting place inside the house (1. 93; 0. 96–3. 89). Tungiasis is an important health problem in Vihiga County occasioned by the low economic status of the people affected. Factors that point to poverty contribute to the occurrence of tungiasis. These findings suggest a need to design control strategies for tungiasis that are cost effective and easily accessible.
  Tungiasis also referred to as jigger infestation is a neglected tropical disease due to the fact that it typically occurs in populations living in poverty, with poor housing, inadequate sanitation and in close contact with domestic animals akin to other neglected diseases. It is however not recognized officially by the world health organization (WHO) [1]. The disease affects communities living in precarious conditions trapping them in a vicious cycle of poverty and disease [2]. It is endemic in tropical and subtropical countries [3]. Identification of the exact areas affected is still a problem in most countries. The burden of tungiasis in the world is also unknown and has never been assessed [1]. In Kenya the few studies carried out on affected communities have documented a prevalence range of between 19 to 40% [4,5, 6,7]. During peak transmission, children and the elderly bear the brunt of the infection. In central Kenya, a prevalence of 57% in children between the ages of 5–12 years old has been recorded [5]. In endemic communities in Brazil and Nigeria prevalence as high as 60% has been reported in children as well [8,9]. Factors that have been reported to aggravate the problem include earthen and dusty floors, non regular use of closed footwear, common resting place outside the house and pigs on the compound [10]. Sleeping on the floor and crowded sleeping areas have also been cited [7]. Others are living with animals in the same house [11], occurrence of animals like dogs, cats, rats, cows, goats, chicken in the compound [12,13]and low education level [14]. Information on the spatial distribution of tungiasis is nonexistent save for a recent study which seeked to create web based GIS applications for the disease [15]. When the geographical distribution of neglected diseases is well understood, control measures are greatly improved [16]. For instance, due to limited resources in Sub Saharan Africa, health interventions at the national level in most countries target high transmission areas leaving out low or zero transmission areas [17]. For this to be possible, data on the geographical distribution of neglected diseases and the communities affected is important. Considerable progress on mapping of neglected diseases has been made [18,19]. However a lot still needs to be done for diseases like tungiaisis so as to understand where the communities affected live and the magnitude of the infection. For as long as objective and clear data on the geographical distribution and burden of tungiasis remains elusive, the communities affected continue to bear the brunt of the disease in silence. Areas that can be targeted to control or eliminate this ectoparasitosis are gaps that should be closed as well. This study describes the distribution, prevalence and potential risk factors of tungiasis in western Kenya.
  This study was conducted in Vihiga County in Western Kenya which has 5 constituencies namely Sabatia, Vihiga, Emuhaya, Luanda and Hamisi. It is one of the most densely populated rural areas in Kenya. It has a population of 554,622 with 45% of these being 14 year olds and below. A large percentage of the population (79. 4%) stay in homes with earthen/mud floors [20]. The poverty levels are high (41%) and the GDP per capita income was reported as US $ 1,103 in 2013 [21]. The area has two rainy seasons. Long rainy season in April, May and June and the short rains in September, October and November. This was a community based, cross- sectional study that took place between February and March 2016, a dry season when prevalence of Tungiasis is known to be at its peak. The study was carried out in 21 villages in 3 sub locations namely, Hamuyundi, Viyalo and Evojo which were randomly selected from Sabatia constituency, Vihiga County, Kenya (Fig 1). A listing of all households in the 3 sub-locations was carried out and after proportionately allocating number required per sub-location, selection was done using systematic sampling method with a random start. In each sampled household, one eligible participant was sampled at random. The sample size was determined using the Daniel, 1999 formula which uses prevalence as the P value [22]. The prevalence of tungiasis ranges between 15% to 60% and therefore a proportion of 50% was used to draw the sample size [23; 24]. The calculated sample size was 385 plus 20% to account for non-response or missing values [24]. Thus total number of participants was adjusted to 460. The inclusion criteria for the study population was all residents in the randomly selected households aged 5 years and above, who were willing and who had resided in the County in the last 24 months. Residents below the age of 5 years in the randomly selected households, individuals who declined or their minors to participate in the study, and none residents were excluded from the study. A total of 437 participants consented and were enrolled in the study. Their hands and feet were clinically examined for the presence of vital, egg-producing, involuting, or dead fleas once consent was obtained. The following findings were diagnostic for tungiasis: an itching red-brownish spot with a diameter of 1–3 mm; a circular lesion presenting as a white patch with a diameter of 4–10 mm with a central black dot; a black crust surrounded by necrotic tissue; and partially or totally removed fleas leaving a crater like sore in the skin [25]. A pre-tested semi-structured questionnaire was administered to the head of the household or the oldest adult in the family, in English, swahili or luhya, the local dialect, upon consenting. This was done by researchers with the help of two trained community health workers. The community health workers were trained for 2 days on the questionnaire, consenting process and what the whole study entailed by the research staff. The information collected consisted of four categories. (1) Socio-demographic factors such as sex, age, education. (2) Housing and associated factors such as type of construction of the house, type of floor inside house, sanitary conditions, presence of electricity and waste disposal. (3) Ownership and presence of domestic animals. (4) Knowledge and practices related to Tungiasis such as knowledge on transmission, regular use of footwear, common resting place, preventive measures and treatment. A household was revisited incase a family member was absent. Details about the location of the participant’s homes were mapped using a hand held Garmin eTrex 10 global positioning system (Garmin international inc. , Olathe, Kansas). The waypoints data representing household location and elevation information, were collected using the World Geodetic Survey (WGS 1984) datum using the UTM projection system zone 36 north. Data was entered into excel database and after cleaning, transferred to SPSS version 21. 0 for data analysis. Data analysis involved descriptive statistics (means, standard deviations, medians and frequency distributions, etc.) as well as computation of prevalence rate ratios (PRR). Variables that were associated with tungiasis following bivariate analysis level at p<0. 25, were entered into a Binary logistic regression model using backward elimination method and adjusted odds ratios calculated. Multicollinearity among explanatory variables was tested using Spearman rank correlation coefficient (ordinal variables) and chi-square test (nominal variables). All variables that remained in the model are presented. GIS analysis was carried out using Arc GIS 10. 5 from Esri. The basemap, from the topographical map of the area at a scale of 1: 50,000, was projected to the Universal Transverse Mercator (UTM) projection system and used to digitize layers i. e. roads, rivers, towns. The GPS coordinates associated with Tungiasis cases were plotted on to the GIS software, projected to UTM system and converted to point shapefiles representing the distribution of cases. The tungiasis case concentration map was produced by interpolating the number of cases recorded per site using the inverse distance weighting (IDW) algorithm tool found in ArcGIS Spatial Analyst tool—a spatial autocorrelation method that interpolates unknown values/cases based on values/cases from nearby known locations [26]. This method assumes the existence of a spatial pattern and similarity for cases occurring at close proximity. A semivariogram was used to assess spatial autocorrelation. The number of cases represented the magnitude in the input point feature layer. The output was a floating point interpolated raster surface in form of a heat map showing the clustering/concentration of the cases per village in the study area. Approval for this study was sought from the Kenya Medical Research Institute (KEMRI) Scientific and Ethics Review Unit (SERU). Approval number KEMRI/SERU/CTMDR/015/3116. Informed written consent was obtained from all participants including children, where the guardian provided an informed consent on their behalf. All data analyzed were coded and identity of participants kept confidential. All participants were treated for tungiasis according to the National Policy Guidelines on Prevention and Control of Jigger Infestations in Kenya [27]. This was by (removing the embedded flea with a sterile needle and disinfection of the skin lesion) or bathing the affected area in 0. 05% potassium permanganate for 10 minutes. A nurse who was part of the team also vaccinated the participants against tetanus. Severe cases were referred to the health center by Community Health Extension Workers from the study area.
  Visual investigation of the distribution of cases did not reveal any obvious spatial pattern in terms of the changes in elevation. There is similar distribution in both the low and high altitudes (Fig 2). The spatial dependence of tungiasis cases was further tested using a semi-variogram in ArcGIS (version 10. 5) Spatial Analyst tool. A lag size of 0. 0074196 was used on the semi-variogram to test autocorrelation in tungiasis concentration. The semivariogram revealed no spatial autocorrelation which could indicate that tungiasis prevalence in the study area might be random. The soil type in the three sub locations did not vary, it was mainly observed to be sandy clay (Fig 3). Among the households selected in the three sub-locations, the villages with the highest concentration of cases were Chambiti and Selelwe villages (Fig 4). Five of the villages Wamage, Kivagala B, Walduga B, Kivagala A and Galoni had no jigger infestation. In the bivariate analysis, the age group ≤14, school going, self-treatment, occasional or not using closed footwear were shown to be significantly associated with tungiasis (Table 2). Others were being illiterate or low literacy level, bathing without soap, common resting place inside the house, having rats in the compound, lack of electricity and lack of a toilet. When it came to knowledge and practice associated with tungiasis, 45% of the participants interviewed had no knowledge on its transmission. In the multivariate analysis, the occurrence of tungiasis was significantly associated with variables that indicated low economic status (like monthly income <1000 adjusted OR 27. 85 (4. 13–187. 59) and age (≤14 years) of participant adjusted OR 27. 414 (10. 02–74. 99) (Table 3). Others retained in the model though not significant were earthen floor, toilet availability, occasional or not using closed footwear and common resting place inside the house.
  Tungiasis is an important health problem in Vihiga County occasioned by the low economic status of the people affected. Factors that point to poverty contribute to the occurrence of tungiasis in Vihiga County. These findings suggest a need to design control strategies for tungiasis that are cost effective and easily accessible by the communities affected.
summary: Tungiasis even though curable, continues to cause a lot of morbidity and in some cases death to millions of people living in tropical and subtropical countries. The global burden of tungiaisis is not known unlike for most diseases which attract a lot of attention and funding. There are also gaps on the exact geographical distribution of this disease and the communities affected, hence it is largely ignored and neglected by the scientific community, the health sector, pharmaceutical industries and policy makers. As a consequence, there are no universally accepted methods to treat and control tungiasis. The study attempted to close this gap by combining geographic information system and questionnaire data to visualize the burden and risk factors of tungiasis in Vihiga County, Kenya. The researchers found that tungiasis is an important health burden in Vihiga County, Kenya and that its occurrence was associated with factors that point to poverty. These findings suggest a need to design control strategies for tungiasis that are cost effective and easily accessible by the communities affected.
keywords: invertebrates
  medicine and health sciences
  education
  population dynamics
  sociology
  geographical locations
  social sciences
  animals
  geoinformatics
  health care
  age groups
  fleas
  medical risk factors
  population biology
  africa
  public and occupational health
  computer and information sciences
  geography
  epidemiology
  schools
  insects
  arthropoda
  people and places
  socioeconomic aspects of health
  kenya
  eukaryota
  earth sciences
  biology and life sciences
  population groupings
  geographic information systems
  organisms
  geographic distribution
title: Spatial distribution, prevalence and potential risk factors of Tungiasis in Vihiga County, Kenya