Instilling Good Knowledge, Attitude and Practices among the Indigenous People of Malaysia Concerning Dog Associated Zoonotic Infections

rashid, a.; Fong, L. S.; Megat Abd Rani, P. A.; Kader Maideen, S. F.; Shafie, I. N. F.; Ahmad, N. I.; Mustaffa Kamal, F.; Ajat, M.; Omar, S.

doi:10.1101/381350

bioRxiv · 10.1101/381350

Instilling Good Knowledge, Attitude and Practices among the Indigenous People of Malaysia Concerning Dog Associated Zoonotic Infections

rashid, a.·Fong, L. S.·Megat Abd Rani, P. A.·Kader Maideen, S. F.·Shafie, I. N. F.·Ahmad, N. I.·Mustaffa Kamal, F.·Ajat, M.·Omar, S.

View original source ↗Export metadata ↓

Abstract

BackgroundThe Jahai, a subethnic of the indigenous people of peninsular Malaysia, have commonly used dogs for hunting but have started to move away from traditional hunter-gatherer lifestyle, leaving dogs which were commonly used for hunting to wander around the villages and to multiply in numbers.\n\nObjectiveThe objective of this study was to instil good knowledge, attitude and practices of the Jahai community concerning dog associated zoonotic infections using One Health concept.\n\nMethodsThis non-experimental pre and post-test intervention study was conducted among Jahai villagers aged 12 years and above living in a village located in the Belum forest in Malaysia. Interventions included health education and promotion using discussions, posters, slide presentations, comics and video clips with relevant content. In addition the children of the village were taught correct hand washing techniques and dog associated zoonotic infections.\n\nResultsIn general most aspects of knowledge, attitude and practice improved post intervention. The knowledge on risk of infections transmitted from pet dogs (X2=4.293, p= 0.038) and the practice of washing hands before eating (X2=14.984, p <0.001) improved significantly. The increase in the mean scores of the participants knowledge (t=-9.875, p=<0.001) and attitude (t= -4.100, p=<0.001) post intervention was statistically significant.\n\nConclusionThis study showed the effectiveness of a multidisciplinary team using One Health concept to successfully improve knowledge, attitude and practices related to dog associated infections. A sustained and committed health education and promotion interventions involving the community and school children in promoting heath should be custom made for indigenous communities, and sanitation and hygienic practices reinforced at every opportunity.\n\nAuthors SummaryThe indigenous people of peninsular Malaysia are a marginalized group; they are socio economically deprived and have low levels of education. One such group is the Jahai, who commonly used dogs for hunting, but have recently started to move away from traditional hunter-gatherer lifestyle, resulting in the dogs to multiply in numbers and roam as strays in the village. The community is now at risk of dog associated zoonotic infections. Studies have shown that health education and promotion can improve knowledge, attitude and practices of dog associated infections. However most of the studies were done among dog owners and in communities with fairly good education levels. A holistic approach using One Health concept was used to instil good knowledge, attitude and practices of the Jahai community concerning dog associated zoonotic infections. This non-experimental pre and post-test intervention study was conducted among Jahai villagers aged 12 years and above living in a village located in the Belum forest in Malaysia. The findings of this study showed the effectiveness of a multidisciplinary team using One Health concept to successfully improve knowledge, attitude and practices related to dog associated zoonotic infections.

Explore related subjects

epidemiology Preprint

Keep this discovery

BibTeX RIS

rashid, a., Fong, L. S., Megat Abd Rani, P. A., Kader Maideen, S. F., Shafie, I. N. F., Ahmad, N. I., Mustaffa Kamal, F., Ajat, M., Omar, S.. 2018-07-31. Instilling Good Knowledge, Attitude and Practices among the Indigenous People of Malaysia Concerning Dog Associated Zoonotic Infections. https://doi.org/10.1101/381350

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2018

Translating surveillance data into incidence estimates

Monitoring a population for a disease requires the hosts to be sampled and tested for the pathogen. This results in sampling series from which to estimate the disease incidence, i.e. the proportion of hosts infected. Existing estimation methods assume that disease incidence is not changing between monitoring rounds, resulting in underestimation of the disease incidence. In this paper we develop an incidence estimation model accounting for epidemic growth with monitoring rounds sampling varying incidence. We also show how to accommodate the asymptomatic period characteristic to most diseases. For practical use, we produce an approximation of the model, which is subsequently shown accurate for relevant epidemic and sampling parameters. Both the approximation and the full model are applied to stochastic spatial simulations of epidemics. The results prove their consistency for a very wide range of situations.

epidemiology↗

bioRxiv2018

The Swiss Primary Ciliary Dyskinesia registry: objectives, methods and first results

Primary Ciliary Dyskinesia (PCD) is a rare hereditary, multi-organ disease caused by defects in ciliary structure and function. It results in a wide range of clinical manifestations, most commonly in the upper and lower airways. Central data collection in national and international registries is essential to studying the epidemiology of rare diseases and filling in gaps in knowledge of diseases such as PCD. For this reason, the Swiss Primary Ciliary Dyskinesia Registry (CH-PCD) was founded in 2013 as a collaborative project between epidemiologists and adult and paediatric pulmonologists.\n\nThe registry records patients of any age, suffering from PCD, who are treated and resident in Switzerland. It collects information from patients identified through physicians, diagnostic facilities, and patient organisations. The registry dataset contains data on diagnostic evaluations, lung function, microbiology and imaging, symptoms, treatments, and hospitalizations.\n\nBy May 2018, CH-PCD has contacted 566 physicians of different specialties and identified 134 patients with PCD. At present this number represents an overall 1 in 63,000 prevalence of people diagnosed with PCD in Switzerland. Prevalence differs by age and region; it is highest in children and adults younger than 30 years, and in Espace Mittelland. The median age of patients in the registry is 25 years (range 5-73), and 49 patients have a definite PCD diagnosis based on recent international guidelines. Data from CH-PCD are contributed to international collaborative studies and the registry facilitates patient identification for nested studies.\n\nCH-PCD has proven to be a valuable research tool that already has highlighted weaknesses in PCD clinical practice in Switzerland. Development of centralised diagnostic and management centres and adherence to international guidelines are needed to improve diagnosis and management--particularly for adult PCD patients.

epidemiology↗

bioRxiv2018

Perfect Counterfactuals for Epidemic Simulations

Simulation studies are often used to predict the expected impact of control measures in infectious disease outbreaks. Typically, two independent sets of simulations are conducted, one with the intervetnion, and one without, and epidemic sizes (or some related metric) are compared to estimate the effect of the intervention. Since it is possible that controlled epidemics are larger than uncontrolled ones if there is substantial stochastic variation between epidemics, uncertainty intervals from this approach can include a negative effect even for an effective intervention. To more precisely estimate the number of cases an intervention will prevent within a single epidemic, here we develop a single world approach to matching simulations of controlled epidemics to their exact uncontrolled counterfac-tual. Our method borrows concepts from percolation approaches prune out possible epidemic histories and create potential epidemic graph that can be realized to create perfectly matched controlled and uncontrolled epidemics. We present an implementation of this method for a common class of compartmental models, and its application in a simple SIR model. Results illustrate how, at the cost of some computation time, this method substantially narrows confidence intervals and avoids non-sensical inferences.

epidemiology↗