Demographical and population dynamics impact on public health of cutaneous Leishmaniasis in Al-Madinah Almonawra, Saudi Arabia.

BACKGROUND: A cross-sectional study conducted in Al-Madinah Almonawra in KSA held during the period from March 2014-March 2015. AIM OF THE STUDY: This study aimed at analyzing the effect of population dynamics on the current situation of cutaneous leishmaniasis in Al-Madinah Almonawarah area, a holy city that attracts millions of muslims annually. METHODOLOGY: Epidemiological and clinical data were collected by especial questionnaire and laboratory specimens were collected using skin scraping and needle aspiration. Staining and direct microscopy were done. LST was conducted. Analysis was done using SPSS program. RESULTS: The study included 164 patients, all of them were men. Saudi nationality comprised around 20% of the study group, the majority were Egyptians, 26.2%, followed by Pakistani, 21.3%. CONCLUSION: The presence of non-Saudi nationality as foreign workers, immigrants and refugees has worsened the current situation of the disease.

Representing a large region with few sites: The Quality Index approach for field studies.

Many environmental studies require the characterization of a large geographical region using a range of representative sites amenable to intensive study. A systematic approach to selecting study areas can help ensure that an adequate range of the variables of interest is captured. We present a novel method of selecting study sites representing a larger region, in which the region is divided into subregions, which are characterized with relevant independent variables, and displayed in mathematical variable space. Potential study sites are also displayed this way, and selected to cover the range in variables present in the region. The coverage of sites is assessed with the Quality Index, which compares the range and standard deviation of variables among the sites to that of the larger region, and prioritizes sites that are well-distributed (i.e. not clumped) in variable space. We illustrate the method with a case study examining relationships between agricultural land use, physiography and stream phosphorus (P) export, in which we selected several variables representing agricultural P inputs and landscape susceptibility to P loss. A geographic area of 110,000km2 was represented with 11 study sites with good coverage of four variables representing agricultural P inputs and transport mechanisms taken from commonly-available geospatial datasets. We use a genetic algorithm to select 11 sites with the highest possible QI and compare these, post-hoc, to our sites. This approach reduces subjectivity in site selection, considers practical constraints and easily allows for site reselection if necessary. This site selection approach can easily be adapted to different landscapes and study goals, as we provide an algorithm and computer code to reproduce our approach elsewhere.