1. Omondi Robert Sadia University of Nairobi
Assessment of Community Based Disease Surveillance for Suspected Malaria Cases in Kisumu East
Omondi Robert Sadia
University of Nairobi

2. Background
Globally , an estimated 219 million cases of malaria occur annually
Locally , in Kisumu East district , confirmed cases of malaria were reported in 2013 (DHIS)
Malaria also accounts for 29.3 % of all hospital attendance in Kisumu East
Yet , only a tenth are detected and reported through national surveillance systems
Need for strengthening of malaria surveillance systems
Determine burden of malaria

3. Description of Community Based Disease Surveillance
The Community Based Disease Surveillance system (CBDS) is a SYNDROME-BASED SURVEILLANCE SYSTEM
Community health workers (CHWs) perform weekly visits to households within their community units
Using simplified case definitions , they assess household members for symptoms of certain conditions , document the cases seen , follow-up on patient attendance to hospital for facility care.
It was introduced as a pilot program in 2011 and is still in pilot stage

4. Flow Chart of Community Based Malaria Surveillance

5. CBDS Data Collection Form

6. Study Objectives General Objective
To assess the performance of the malaria Community Based Disease Surveillance (CBDS)
Specific Objectives
To describe the performance of the community based surveillance system from 2012 to 2014
To describe trends of suspected malaria cases
To outline the challenges and limitations of the system and provide recommendations

7. Methods
Conducted a retrospective data review, key informant interviews, direct observation
Data was abstracted from the CBDS data collection forms
Variables
No of malaria cases
Households visited
Household coverage rate
Malaria case reporting rates
Case Definition
Suspected malaria case was defined as a person of any age presenting with fever without a rash

8. Methods The data was analysed for trends in reporting patterns
Malaria incidence and prevalence were estimated from the data using reported suspected malaria cases and the enrolled population of Kisumu East
The enrolled population was estimated as by the number of enrolled households (12000) and the average household size in kisumu east (4.3)
Data analysis was performed on Microsoft Excel

9. Results 1/2
A total of 162 Community Health Workers from 12 community units were transmitting surveillance data from the community
This number varied through the study period
The results showed a fluctuating pattern in reported suspected malaria cases with peaks occurring in the months of May to June
Suspected Malaria cases were however marginally higher in members of the population older than 5 years than in children under 5 (n=8372 ,n=7935)

10. Results 2/2
The household reporting rate while stable was quite low ,with an average of 1838 (15.32 %)
Informant interviews with the community health workers revealed that the areas of coverage were large hence their low household reporting rates
Direct observation of the data collection process also showed this
Suspected malaria prevalence , O 2012 to O 2014 was % ( cases , n = , E = 2.3 % ,p = 1.96 %
Hospital attendance rate was 67.3 %

12. Conclusions
The results are consistent with current research on Malaria transmission rate seasonality (" Meghna Desai et all 3)and prevalence rates (Kenya malaria indicator survey 2010)
Hospital attendance rate is not satisfactory considering CHWs refer patients. However it mirrors findings in KMIS
However , the data is fraught with limitations such as: low household coverage rates , sensitivity to reporting rates which make conclusions problematic

13. Recommendations
CHWs should be motivated and their number increased so as to increase the household coverage rate
CHU r.r should be stabilised through village surveillance centres
The CBDS system should be integrated with other measures such as community case management , so as to increase its utility as a public health intervention
A KAP survey should be undertaken to establish why hospital attendance rate is not complete even after referral by the CHWs.
ITN distribution
IR spraying
-Guided by the peaks in malaria cases

14. References
Disease Surveillance for Malaria Control : Operational Manual." WHO. N.p.'n.d Web. 03 Dec 2014.
R.M .Montanari. "Three case definitions of malaria and their Effect on diagnosis, treatment and surveillance in Cox' Bazaar District, Bangladesh," Policy and Practice 79.7 (2001) : Print.
Meghna Desai et al "Age specific Malaria Mortality rates in KEMRI/CDC Health , Demographic Surveillance system in western Kenya " PLOS . N.p.n.d Web
Guofa Zhou et al . " Modest Additive effects of integrated vector control measures on malaria prevalence and transmission in western Kenya " Malaria journal
" Kenya Malaria Indicator survey 2010"

15. Acknowledgements
The research for this study was financially supported by the Field Epidemiology and Laboratory Training Program (FELTP Kenya)
Guidance and assistance in data collection by John Kwambai ,David Arunga and Phillip Ogutu from Kisumu East district health system
Helpful review input was also received from Waqo Boru , Jane Githuku , Mark Obonyo