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Establishing Webuye Health and Demographic Surveillance Site in Rural Western Kenya: Challenges and Lessons Learned

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Establishing Webuye Health and Demographic Surveillance Site in Rural
Western Kenya: Challenges and Lessons Learned
Chrispinus J Simiyu, BSc1*, Violet Naanyu, PhD1, Andrew A Obala, PhD1, David O Odhiambo,
MPH1, Paul Ayuo, MMed1, Dinah Chelagat, MSc1, Raymond Downing, MD1, Diana Menya,
MSc2, Emily Mwaliko, MMed1, Wendy P. Omeara, PhD2,3, Edwin O Were, MMed1, Marc
Twagirumukiza PhD4, Davy Vanden Broeck PhD4, Stanley Shitote, PhD5, Jan De Maeseneer,
PhD4 , Barasa O Khwa-Otsyula, MMed1
1. Moi University School of Medicine, Eldoret, Kenya; 2. Moi University School of Public Health, Eldoret, Kenya;
3. Duke University, Durham, USA; 4. Ghent University, Ghent, Belgium; 5. Moi University School of Engineering,
Eldoret, Kenya
*Corresponding Author: P.O. Box 4606, Eldoret – 30100, Kenya. E-mail: csimiyu@mu.ac.ke.
Telephone: +254 722 140 641 Fax: +254 53 2033041
Keywords: Webuye, Kenya, Health Demographic Surveillance
ABSTRACT
This paper describes the methodologies, challenges and lessons learned in establishing Webuye
Health and Demographic Surveillance System (HDSS) in Webuye Division of Bungoma County.
The Webuye HDSS was established in 2007 through a collaborative programme between Moi
University, (Eldoret, Kenya) and Ghent University (Ghent, Belgium.) through the Flemish
Interuniversity Council (VLIR), university cooperation for development (UOS) in Flanders
framework. The goal for establishing the HDSS was to provide reliable and comprehensive
demographic, health and economic data to inform health policy and planning at local and
national levels. The data were collected by households visit within the community twice a year,
using field interviewers from the local community. The participatory data collection methods
used enhanced locals’ interests to take part in data collection processes.
Challenges encountered include insufficient funding, refusals to participate by some household
members, modalities for coping with future anticipated community fatigue, responsibility to
protect both the University and community, threat by other programmes operating in the area and
staff retention.
Despite these challenges, the Webuye HDSS has been successfully established and maintained
for the last 4 years. To overcome the challenges establishing and running Webuye HDSS,
thorough explanation of the concept to both stakeholders and the community was found to be of
utmost importance.
BACKGROUND
Historically, the demographic surveillance system (DSS) is the process of monitoring births,
deaths, causes of deaths, and population dynamics data over time [9]. This approach has been
considered as the cornerstones of public health research, particularly in investigating and
tackling health disparities[Ref: ibidem], and nowadays the process have an added value of
collecting data on other determinants of health. This inclusive approach is operationally defined
as Health and Demographic Surveillance Systems (HDSS) which is a set of field and computing
operations designed to prospectively collect and analyse demographic and health related data of
well-defined populations in clearly defined geographic areas [7]. The HDSS sites play a critical
supplementary role of generating high-quality, longitudinal, population-based health and
demographic data and this fills the gap left by government registering systems. In many low and
middle income countries, such as those in sub-Saharan region, many people are born and die
before being formally registered in government systems [1-4]. Often demographic data that is
obtained comes from censuses or other transversal sample surveys in the community. However,
these surveys and censuses may be fraught with errors with some omissions of still births, peri-
natal deaths, and deaths from households that closed down. Moreover multiple reporting of
individuals and demographic events where the concepts of “household” and “family” are not
clearly differentiated; and the fact that these data quickly become out of date[1;2;4-6].
An international network of demographic surveillance systems (DSS) now operates mostly in
sub-Saharan Africa and Asia where thirty-eight DSS sites are coordinated by the International
Network for the Continuous Demographic Evaluation of Populations and Their Health in
developing countries (INDEPTH) [7-9;12]. Three of these sites are in Kenya [1].
Webuye Health and Demographic Surveillance System was established in 2007 by the
collaborative research programme between Moi University (Kenya) and Ghent University
(Belgium) through the Flemish Interuniversity Council (VLIR), university cooperation for
development (UOS) in Flanders framework. It is a component of the Health Science Project
within this programme, and is run by staff from the schools of Medicine (MUSOM) and Public
Health (MUSPH) of Moi University. The establishment of the site was a natural progression of
the Moi University’s Community Based Education and Service (COBES) programme, which
emphasizes teaching of students as well as provision of service in the community[13]. The main
objective of the site is to provide reliable and comprehensive demographic, health and economic
data to inform health policy and planning at local and national levels as well as being a
community ‘classroom’ for teaching, medical practice and research. The expertise support to set
up the Webuye HDSS was received from CDC Kisumu-HDSS and from the INDEPTH-Network.
PROJECT SITE
Webuye HDSS is located in Webuye Division of Bungoma County, approximately 380km west
of Nairobi. The County borders the Republic of Uganda to the West and lies between latitude 0
25.3’ and 0 53.2’ north and longitude 34 21.4’ and 35 04’ East. It covers a land area of 3032 km2
or a quarter of the former western province [14].
The County is mostly inhabited by people of the Luhya ethnic group. The population of the
county is estimated at 1.37 million according to the 2009 population and housing census report
[15]. It is evenly distributed with an average population density of 453 persons per square km.
There are heavier population concentrations in the main urban centres and around major
factories. These include Pan African Paper Mills in Webuye, Nzoia Sugar Company, Bungoma
Town, Kimilili, Sirisia, Malakisi Tobacco Leaf Centre, Chwele and Tongaren. Urban population
is about 30 per cent of the total.
The main economic activity is small scale with maize, sunflower, sugarcane, coffee, tobacco,
potatoes, beans, sorghum and millet being some of the main crops as well as cattle and chicken
raring. Of the total labour force of about 565,000, 52 percent are engaged in agricultural
production which provides 60 percent all household incomes; 19 percent wage employment and
13 percent urban self employment [14].
Typical characteristics of the population include high unemployment, low participation of locals
in commercial enterprises, low agricultural productivity, child labour due to high school dropout
rate, high dependency ratio, high population growth and a high youth/adult ratio. Most
households are poor with 61% of the population living below poverty line and generally social
amenities like water and electricity are not readily available to the majority [14;15].
Webuye Division has 5 administrative locations with a total population of 230,252 persons living
in area of 269.1 km2 in 2009 . Webuye HDSS covers four administrative locations with total area
of 130km2[14;15]. Figure 1 is a map showing the six sub-locations within these four locations.
FUNDING
The Webuye HDSS is mainly funded by the collaborative programme between Moi University
and VLIR- UOS [16] The vision of the programme is to improve the socio-economic welfare of
western Kenya through human capacity building, development of innovative research and
extension strategies, review and development of curricula and working with stakeholders to
address and resolve problems identified in the community[16]. Other sources of funding for
Webuye HDSS come from the nested research studies carried out at the site.
SITE ADMINISTRATION
The site operates under the general direction of the Scientific Committee of Moi University-
VLIR-OUS Health Sciences Project which is a multi-disciplinary team. The team guides the
site’s research agenda and reviews new and on-going projects. It reports to University
Management through the Moi University_VLIR UOS Steering Committee and works closely
with the Community Advisory Board, which is the stakeholder taking care of the interests of the
community.
There is a Site Manager who is responsible for the day to day running of the site with an office
located at the Webuye District Hospital. The team under him includes the Data Manager, Data
Quality Checkers (2), Data Entry Clerks (5), Field Supervisors (5), Community Interviewers
(32), and a Secretary. Studies conducted within the Webuye HDSS use the reference laboratory
at the Moi University School of Medicine in Eldoret.
METHODS
Ethical Considerations
This project received study approval no FAN:IREC000653 of the joint Institutional Review and
Ethical Committee (IREC) of Moi University and Moi Teaching and Referral Hospital (MTRH)
Community Sensitization and Mobilization
In the initial stages, meetings were held with the stakeholders, who included government
officials, church leaders, local leaders and Webuye District Hospital administration, to explain
the concept of the HDSS and how it will be conducted. Questions were raised on a variety of
issues including how the project would benefit the community and the possibility of securing
employment in the project. After these issues were addressed, the stakeholders and the
community accepted the project. Following community acceptance, additional educational and
sensitization activities commenced. Several barazas [17] (local community meetings), were
conducted in each sub-location within the 4 locations to allow community members to ask
questions about the proposed site. The barazas were usually attended by the Chiefs (Locational
administrator), Assistant Chiefs, bakasa (village elders) and the villagers.
Recruitment and Training
The site manager and the data manager were competitively recruited through an open, nation-
wide advertisement. The candidates for these positions were required to have a minimum
academic qualification of bachelors’ degree in a relevant field. The CI's were also competitively
recruited and employed on a 3-month contract twice a year. They were required to have
completed at least four years of high school and must be residents of the surveillance area. Field
supervisors were selected from among the team of community interviewers.
Before the commencement of the baseline survey, the field teams received classroom and field-
based training. This was to ensure proper understanding of the concept of HDSS and the accurate
completion of questionnaires and utilization of all the relevant tools used in the field in
accordance with INDEPTH recommendations [18]. The training modules included an overview
on the HDSS operations, use of Personal Digital Assistants (PDAs), use of Geographical
Positioning Systems (GPSs) units, community entry techniques, questionnaire administration and
standardization. At the end of the training, each participant was required to take and pass a
Human Subjects Protection (HSP) test on social and behavioral research ethics as required by
IREC. A pilot study was carried out in an adjacent area before the baseline survey. Refresher
trainings are subsequently conducted prior to each update cycle. Additional trainings are
conducted for all the other additional studies.
Mapping the Webuye HDSS
The entire Webuye HDSS was mapped during the baseline census (Fig. 1). The mapping team
visited each homestead in the HDSS and took the geo-coordinates. Mapping was performed
using a differential Global Positioning System (GPS) [19]. The digital coordinates of family
compounds and other sentinel sites such as markets, schools, health facilities, churches, and
water sources were taken using the GPS units (Trimble Navigation, Limited, Sunnyvale, CA).
The coordinates captured from each compound were used to create a digital map used to identify
these compounds. They then assigned each household a unique code and painted this code on the
door.
Data Collection
Prior to data collection at baseline, verbal informed consent was sought from the head of the
household. The area and its sub-units and households mapping was carried out through a GIS-
based approach. The baseline census, which took 4 months, was carried out in November 2008
using paper questionnaires only. Subsequently data collection is carried out twice a year with
each cycle lasting 3 months.
A household is defined as a group of people who regularly eat from the same “pot” regardless of
whether they live or sleep in the same homestead. [Ref] A resident is defined as an individual
who has lived in the Webuye HDSS surveillance area continuously for a period of at least four
calendar months prior to the interview date [7].
Information collected includes an assigned household code; name and assigned code of
household head; number of inhabitable rooms; name and code of sub location and the GPS
coordinates for each homestead. Other information collected included: demographic information
for each household member; household drinking water type and source; household possessions
including domestic animals, land size and use; type of fuel used for cooking; lighting source;
land ownership; tenure status of the dwelling place; waste disposal methods and source of
finance for the household members.
Data Management
A quality control system was put in place at every stage of data collection to ensure data quality.
In this system, completed data collection questionnaires are first checked in the field by the field
supervisors for completeness after which the questionnaires are sent to the field office where
they are reviewed by data quality checkers for completeness, logic and consistency. The
incorrectly filled questionnaires are returned to the respective CIs for correction. The correctly
filled questionnaires are then passed on for data entry into the database. After data entry,
questionnaires are checked again through automated internal consistency checks and those found
to be incomplete are again sent back to the CIs for verification and correction.
The data management system is modeled on the Household Registration System [20; 21]. This
model ensures accuracy and consistency of the database specifically for longitudinal follow-ups
of individuals over a long period of time. All data are stored in a Mysql structured database
(Mysqlab Inc, 2011).
Data Use
Data collected are first analyzed and the results shared with stakeholders including Ministries of
Health and the community. It will be published for general consumption by researchers.
CURRENT STATUS
Webuye HDSS has been in operation for four years. It has carried out six update cycles since the
baseline census. It has registered a total population of 77,000 people in 13,333 households and
9,784 compounds within the area. Figure 2 is a map showing the distribution of the compounds
within the six administrative sub locations. The information collected include longitudinal
follow-up data on the births, deaths, morbidity, socio-economic status, pregnancies,
immunizations, parental survival, water, sanitation and health seeking.
In addition to the regular update of the demographic-events data, there have been nested studies
carried out within the Webuye HDSS. These include: Prevalence of Intestinal worms in children
under 5 years of age; Prevalence of malaria in children under 5 years of age; Type and level of
disabilities among the residents; Causes and treatment of jiggers from the infested households;
Assessment of cardiovascular risk factors among the residents; Assessment of the quality of
water; Survey on the availability, accessibility and affordability of antimalaria medicine in retail
outlets; and a survey of injuries in children below 18 years of age.
CHALLENGES
Challenges encountered up to this point have been few. These include inadequate funding,
refusal by some individuals to participate, loss of good workers to other employers who offer
better terms. One has to be aware all the time of the enormous responsibility to protect the
community and university image. The other major challenge likely to occur later is community
fatigue.
To address the challenge of protecting the image of the University and that of the community,
Webuye HDSS established a community advisory board with the responsibility of advising the
management on issues of community interest. Regular interactive meetings with the community
for feedback and sharing with them about their concerns. The HDSS has developed a strict field
standard operating procedures in line with the university’s policy and ethical standards in
research. This ensures that the field staff do not contravene both the University and community
norms that may raise conflict.
To address the challenge of community fatigue, Webuye HDSS intends to conduct studies to
understand the causes of fatigue and how to deal with it. The HDSS has also been providing
incentives to the community such as employing people from the community and conducting
annual free health days in partnership with the faculty, staff and students of Moi University. The
HDSS also engages the community to participate in the decision-making process within the
through the Community Advisory Board. Prior to the launch of each cycle, the HDSS conducts a
launch. During these launches, the HDSS keeps the community engaged by providing feedback
on the studies already conducted and respond to the questions that are raised by the community.
Inadequate funding is being addressed by continuously developing more nested research studies
to attract more funding to sustain the HDSS. Fund raising through the donor community and
local partners is also being exploited to find strategic partners in implementing some community
health intervention projects.
Risk of losing staff to other programmes working within the surveillance area has been addressed
through development of staff incentives that motivate the staff. Some of these incentives include
continuously providing in-house training that sharpens the skills of the staff. Where possible, the
HDSS collaborates with the other programmes to find areas of synergy between them to ensure
resource sharing and reduce competition.
The HDSS addresses the challenge of refusals by some villagers to participate in the project
through continued sensitization and education on the importance of participating the Webuye
HDSS activities, and how this contributes to the development of interventions that benefit the
nation as a whole. This has proven successful thus far as the coverage has increased since the
baseline census.
LESSONS LEARNED
In the 4 years that the Webuye HDSS has been in operation, many lessons have been learned. and
can serve to when setting up an HDSS in other area in developing countries First, collaboration
with colleagues from complementary institutions, both public and private is crucial. Developing
strong institutional relationships provides a good opportunity for cordial relationships and
existence. Noteworthy, leadership in any of the collaborating institutions is transitory in nature.
However, this should not affect the fundamental relationship that exists. A strong institutional
partnership and a stable research team with strong interpersonal relationships remain crucial to
achieve the longitudinal studies goals. Running a HDSS requires a multidisciplinary approach,
and the scientific team should comprise experts in the field of data management, data analysis,
community outreach and Information and Communication Technology).
Second, HDSS in general plays a significant role in filling the gaps in the Health Management
Information Systems (HMIS) nationally [1]. It is therefore imperative that the management of the
HDSS sites take up their role in the national health information system agenda. Fostering good
inter-site relationship, and between individual sites and government can greatly improve
information flow in the health sector, thereby enhancing speed and accuracy in decision-making
at the various levels. This relationship should be of symbiotic nature such that HDSS sites collect
accurate and timely information and share it with government. Government on the other hand
should support HDSS activities by providing the needed infrastructure upon which the HDSS
sites operate and lobby on behalf of the HDSS sites for funding to support the HDSS activities
[22].
Third, a cordial HDSS-community relationship is vital. Survival and success of the HDSS sites
largely depends on this. Employing community Interviewers from the community improves the
relationship with the community. This helps the HDSS to understand and respect the
communities’ cultures and related traditions. Designing interventions that contravene local
traditions or conducting studies in a manner that is offending to the local population can
antagonize the cordial relationship between the two sides. Indeed, HDSS sites have a duty to
maintain this relationship and to continually provide feedback to the community on the outcomes
of the interventions carried out. The feedback mechanisms need to be elaborate and exhaustive
and must include all the stakeholders, starting from the district to the household level. It is also
important to ensure that the stakeholders from the community are adequately informed prior to
conducting any field activity.
Fourth, an understanding of all the parties (stakeholders, funders and the community)
concerning the activities to be carried out and use as well as sharing of data is extremely
important as it avoids conflict that could arise at a future date [9].
Fifth, it is important to keep the staff well trained and motivated at all times. Field activities are
particularly exhaustive both physically and psychologically. The accuracy and reliability of the
data received from the field is, to a large extent, dependent on the interviewing skills and morale
of the field worker. Within a limited resource constraints context and insufficiency of funding,
like the situation faced, other kind of incentives can help to keep all staff motivated. This has to
be planned ahead of the HDSS establishment.
Finally, running a HDSS site is expensive and sensitive to time. Proper advance planning is
important to avoid staggering the planned activities and thereby ensuring accurate measurement
of the variables of interest.
CONCLUSIONS
Implementing a HDSS site provides several challenges, however there are enormous benefits of
a HDSS especially the generation of timely and representative data from the community that not
only supplements health facility generated data, but also facilitates formulation of local health
interventions, which are community friendly. Nonetheless, whatever the challenges and obstacles
encountered, we learned that these can be overcome if the concept of the HDSS was explained
and was acceptable to the stakeholders and the community. By sharing our challenges in setting
up the Webuye HDSS, we hope these experiences and the techniques used in solving them will
inform others who wish to start HDSS in other parts of the sub-Saharan Africa to address local
health issues.
ACKNOWLEDGEMENTS
We thank the Moi University-VLIR UOS collaborative programme for providing funds to run
this project. The CDC Kisumu HDSS is acknowledged for assisting with the logistics to start the
Webuye HDSS. The Provincial Administration and other stakeholders are acknowledged for
allowing us to access the community, while the community is thanked for accepting to
participate in the project. We also wish to thank the Deans of Schools of Medicine and Public
Health for granting permission to setup the Webuye HDSS site and compile this report.
AUTHOR CONTRIBUTIONS:
All the authors participated in the design of the project. CJS, VN, AAO, RD and BOK drafted the
manuscript. PA, JDM, WPO, MT, DVB, DM, EM, DC, DOO, EOW participated in the review of
the manuscript.
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FIGURE 1: MAP SHOWING THE WEBUYE HDSS ENUMERATION AREA
FIGURE 2. MAP SHOWING THE DISTRIBUTION OF HOUSEHOLDS IN WEBUYE HDSS
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... The building of geospatial databases can help facilitate future geospatial analysis studies by providing coordinates for key locations and variables of interest. Kenya's demographic surveillance system undertakes a growing number of regularly administered surveys [29] that incorporate GIS data. The data collection efforts within the WHDSS serve as a role model to other regions looking to expand their own databases and opportunities for geospatial research. ...
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... 24 The Webuye Health and Demographic Surveillance System run by Moi University is located in Bungoma County in the western region of Kenya. 25 Multiple cardiovascular risk factors have been identified in this area. 26 Table 1 outlines the study site characteristics. ...
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Objective: To estimate the direct and indirect costs of diabetes mellitus care at five public health facilities in Kenya. Methods: We conducted a cross-sectional study in two counties where diabetes patients aged 18 years and above were interviewed. Data on care-seeking costs were obtained from 163 patients seeking diabetes care at five public facilities using the cost-of-illness approach. Medicines and user charges were classified as direct health care costs while expenses on transport, food, and accommodation were classified as direct non-health care costs. Productivity losses due to diabetes were classified as indirect costs. We computed annual direct and indirect costs borne by these patients. Results: More than half (57.7%) of sampled patients had hypertension comorbidity. Overall, the mean annual direct patient cost was KES 53 907 (95% CI, 43 625.4-64 188.6) (US$ 528.5 [95% CI, 427.7-629.3]). Medicines accounted for 52.4%, transport 22.6%, user charges 17.5%, and food 7.5% of total direct costs. Overall mean annual indirect cost was KES 23 174 (95% CI, 20 910-25 438.8) (US$ 227.2 [95% CI, 205-249.4]). Patients reporting hypertension comorbidity incurred higher costs compared with diabetes-only patients. The incidence of catastrophic costs was 63.1% (95% CI, 55.7-70.7) and increased to 75.4% (95% CI, 68.3-82.1) when transport costs were included. Conclusion: There are substantial direct and indirect costs borne by diabetic patients in seeking care from public facilities in Kenya. High incidence of catastrophic costs suggests diabetes services are unaffordable to majority of diabetic patients and illustrate the urgent need to improve financial risk protection to ensure access to care.
... It is not to say that HDSS has succeeded equally everywhere ; there are systems that have failed to establish as per the expectations. [16,17] The underlying reasons for this discrepant situation are diverse. A priori realization made us to include Fortunately few funders have started to agree to provide some support for running the core HDSS in INDEPTH projects . ...
... The WHDSS is in the Bungoma district of western Kenya (Fig. 1). The study area is home to approximately 80,000 persons and has a primary economy of subsistence farming and that provided by a local sugarprocessing factory [32]. The area is holo-endemic for malaria, with over 40% of a random sample of the people living in the Western Province having Plasmodium falciparum parasites in their peripheral blood [33]. ...
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Background Most patients with malaria seek treatment first in retail drug shops. Myriad studies have examined retailer behaviours and characteristics to understand the determinants to these behaviours. Geospatial methods are helpful in discovering if geographic location plays a role in the relationship between determinants and outcomes. This study aimed to discover if spatial autocorrelation exists in the relationship between determinants and retailer behaviours, and to provide specific geographic locations and target behaviours for tailoring future interventions. Methods Retailer behaviours and characteristics captured from a survey deployed to medicine retailers in the Webuye Demographic and Health Surveillance Site were analysed using geographic weighted regression to create prediction models for three separate outcomes: recommending the first-line anti-malarial therapy to adults, recommending the first-line anti-malarial therapy to children, and selling that therapy more than other anti-malarials. The estimated regression coefficients for each determinant, as well as the pseudo R2 values for each final model, were then mapped to assess spatial variability and local areas of best model fit. ResultsThe relationships explored were found to be non-stationary, indicating that spatial heterogeneity exist in the data. The association between having a pharmacy-related health training and recommending the first-line anti-malarial treatment to adults was strongest around the peri-urban centre: comparing those with training in pharmacy to those without training (OR = 5.75, p = 0.021). The association between knowing the first-line anti-malarial and recommending it to children was strongest in the north of the study area compared to those who did not know the MOH-recommended anti-malarial (OR = 2.34, p = 0.070). This is also the area with the strongest association between attending a malaria workshop and selling the MOH-recommended anti-malarial more than other anti-malarials, compared to retailers who did not attend a workshop (OR = 2.38, p = 0.055). Conclusion Evidence suggests that spatial heterogeneity exists in these data, indicating that the relationship between determinants and behaviours varies across space. This is valuable information for intervention design, allowing efforts to focus on those factors that have the strongest relationship with their targeted behaviour within that geographic space, increasing programme efficiency and cost-effectiveness.
... The WHDSS is in the Bungoma district of western Kenya (Fig. 1). The study area is home to approximately 80,000 persons and has a primary economy of subsistence farming and that provided by a local sugarprocessing factory [32]. The area is holo-endemic for malaria, with over 40% of a random sample of the people living in the Western Province having Plasmodium falciparum parasites in their peripheral blood [33]. ...
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Medicine retailers are at the forefront of malaria treatment and control in this region because most people seek treatment for febrile illness first in retail drug shops. This work endeavored to provide context to the determinants to medicine retailer behavior, and discover whether under or unutilized methods exist that could help us understand their behaviors, in order to improve malaria case management in Kenya.^ To do this, both qualitative and quantitative methods were employed. Six focus groups were conducted, and a quantitative survey administered, to those medicine retailers working in drug shops that are within or accessible to those living within the Webuye Demographic Surveillance Site (WHDSS). The focus group discussions were examined using content analysis to discover the perceptions medicine retailers had of their roles. The survey data was analyzed for spatial clusters using SaTScan, and for spatial heterogeneity using geographic weighted regression.^ Within the qualitative data, evidence for role ambiguity and role conflict was found among medicine retailer perceptions of their role. Retailers conveyed perceptions of their role as physicians, medicine dispensers, businesspersons, and caretakers. Retailers’ perceptions of how customers viewed their roles included those who offer diagnostic testing, medicine dispensers, and drug sellers interested in a profit. The existence of multiple roles results is evidence for role ambiguity as it clouds the expectations for medicine retailer performance. The contradiction between roles, when fulfillment of two sets of expectations is impossible, is evidence for role conflict.^ The cluster analysis of survey data found several statistically significant spatial clusters, indicating that medicine retailer behaviors have geographic variation. Variables with statistically significant clusters included having training in pharmacy, recommending the appropriate antimalarial medication to adults, and selling that medication more than other antimalarials. The results of the geographic weighted regression yielded statistically significant associations between determinants and outcome behaviors that vary across space, indicating that spatial heterogeneity exist in the data. These determinants to medicine retailer behavior included having a health-related training, knowing the MOH-recommended firstline antimalarial therapy, and attending a malaria workshop.^ This work discovered that medicine retailers operate within a multifaceted context, involving influence from their training, their customers, regulatory agencies, and their collegial neighbors. This effort also underscores the benefits of using quantitative and qualitative approaches to provide a richer context than using one method alone.
... The largest paper factory in Africa and chemical processors are located within Webuye Town [15]. The HDSS is described in detail elsewhere [16]. Malaria burden in Bungoma East is high, due to the suitable climate and elevation, coupled with low coverage of insecticide-treated bed nets [17,18]. ...
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Households in sub-Saharan Africa are highly reliant on the retail sector for obtaining treatment for malaria fevers and other illnesses. As donors and governments seek to promote the use of artemisinin combination therapy in malaria-endemic areas through subsidized anti-malarials offered in the retail sector, understanding the stocking and pricing decisions of retail outlets is vital. A survey of all medicine retailers serving Bungoma East District in western Kenya was conducted three months after the launch of the AMFm subsidy in Kenya. The survey obtained information on each anti-malarial in stock: brand name, price, sales volume, outlet characteristics and GPS co-ordinates. These data were matched to household-level data from the Webuye Health and Demographic Surveillance System, from which population density and fever prevalence near each shop were determined. Regression analysis was used to identify the factors associated with retailers’ likelihood of stocking subsidized artemether lumefantrine (AL) and the association between price and sales for AL, quinine and sulphadoxine-pyrimethamine (SP). Ninety-seven retail outlets in the study area were surveyed; 11% of outlets stocked subsidized AL. Size of the outlet and having a pharmacist on staff were associated with greater likelihood of stocking subsidized AL. In the multivariable model, total volume of anti-malarial sales was associated with greater likelihood of stocking subsidized AL and competition was important; likelihood of stocking subsidized AL was considerably higher if the nearest neighbour stocked subsidized AL. Price was a significant predictor of sales volume for all three types of anti-malarials but the relationship varied, with the largest price sensitivity found for SP drugs. The results suggest that helping small outlets overcome the constraints to stocking subsidized AL should be a priority. Competition between retailers and prices can play an important role in greater adoption of AL.
... The Webuye HDSS area comprises six sublocations in Webuye Division of Bungoma County, Kenya. The study site is exhaustively described elsewhere [16]. ...
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Background. The intestinal parasitic infections (IPIs) are globally endemic, and they constitute the greatest cause of illness and disease worldwide. Transmission of IPIs occurs as a result of inadequate sanitation, inaccessibility to potable water, and poor living conditions. Objectives. To determine a baseline prevalence of IPIs among children of five years and below at Webuye Health and Demographic Surveillance (HDSS) area in western Kenya. Methods. Cross-sectional survey was used to collect data. Direct saline and formal-ether-sedimentation techniques were used to process the specimens. Descriptive and inferential statistics such as Chi-square statistics were used to analyze the data. Results. A prevalence of 52.3% (417/797) was obtained with the male child slightly more infected than the female (53.5% versus 51%), but this was not significant (χ (2) = 0.482, P > 0.05). Giardia lamblia and Entamoeba histolytica were the most common pathogenic IPIs with a prevalence of 26.1% (208/797) and 11.2% (89/797), respectively. Soil-transmitted helminths (STHs) were less common with a prevalence of 4.8% (38/797), 3.8% (30/797), and 0.13% (1/797) for Ascaris lumbricoides, hookworms, and Trichuris trichiura, respectively. Conclusions. Giardia lamblia and E. histolytica were the most prevalent pathogenic intestinal protozoa, while STHs were less common. Community-based health promotion techniques are recommended for controlling these parasites.
... The Webuye HDSS area comprises six sublocations in Webuye Division of Bungoma County, Kenya. The study site is exhaustively described elsewhere [16]. ...
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Background. The intestinal parasitic infections (IPIs) are globally endemic, and they constitute the greatest cause of illness and disease worldwide. Transmission of IPIs occurs as a result of inadequate sanitation, inaccessibility to potable water, and poor living conditions. Objectives. To determine a baseline prevalence of IPIs among children of five years and below atWebuye Health and Demographic Surveillance (HDSS) area in western Kenya. Methods. Cross-sectional survey was used to collect data. Direct saline and formal-ether-sedimentation techniques were used to process the specimens. Descriptive and inferential statistics such as Chi-square statistics were used to analyze the data. Results. A prevalence of 52.3% (417/797) was obtained with the male child slightly more infected than the female (53.5% versus 51%), but this was not significant (𝜒2 = 0.482, 𝑃 > 0.05). Giardia lamblia and Entamoeba histolyticawere themost common pathogenic IPIs with a prevalence of 26.1% (208/797) and 11.2% (89/797), respectively. Soil-transmitted helminths (STHs) were less common with a prevalence of 4.8% (38/797), 3.8% (30/797), and 0.13% (1/797) for Ascaris lumbricoides, hookworms, and Trichuris trichiura, respectively. Conclusions. Giardia lamblia and E. histolytica were the most prevalent pathogenic intestinal protozoa, while STHs were less common. Community-based health promotion techniques are recommended for controlling these parasites.
... The Bungoma East District is located approximately 380 kilometers west of Nairobi, the capital of Kenya. The WHDSS is home to approximately 70,000 residents, and is described in detail elsewhere [14]. The primary economy is subsistence farming, with roughly 1,500 residents employed by a local sugar-processing factory, and others growing sugar cane to sell to the factory. ...
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Malaria is a major cause of morbidity and mortality in Kenya, where it is the fifth leading cause of death in both children and adults. Effectively managing malaria is dependent upon appropriate treatment. In Kenya, between 17 to 83 percent of febrile individuals first seek treatment for febrile illness over the counter from medicine retailers. Understanding medicine retailer knowledge and behaviour in treating suspected malaria and dispensing anti-malarials is crucial. To investigate medicine retailer knowledge about anti-malarials and their dispensing practices, a survey was conducted of all retail drug outlets that sell anti-malarial medications and serve residents of the Webuye Health and Demographic Surveillance Site in the Bungoma East District of western Kenya. Most of the medicine retailers surveyed (65%) were able to identify artemether-lumefantrine (AL) as the Kenyan Ministry of Health recommended first-line anti-malarial therapy for uncomplicated malaria. Retailers who correctly identified this treatment were also more likely to recommend AL to adult and paediatric customers. However, the proportion of medicine retailers who recommend the correct treatment is disappointingly low. Only 48% would recommend AL to adults, and 37% would recommend it to children. It was discovered that customer demand has an influence on retailer behaviour. Retailer training and education were found to be correlated with anti-malarial drug knowledge, which in turn is correlated with dispensing practices. Medicine retailer behaviour, including patient referral practice and dispensing practices, are also correlated with knowledge of the first-line anti-malarial medication. The Kenya Ministry of Health guidelines were found to influence retailer drug stocking and dispensing behaviours. Most medicine retailers could identify the recommended first-line treatment for uncomplicated malaria, but the percentage that could not is still too high. Furthermore, knowing the MOH recommended anti-malarial medication does not always ensure it is recommended or dispensed to customers. Retailer training and education are both areas that could be improved. Considering the influence that patient demand has on retailer behaviour, future interventions focusing on community education may positively influence appropriate dispensing of anti-malarials.
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