Abstract:
Objectives Maximising the impact of community-based
programmes requires understanding how supply of,
and demand for, the intervention interact at the point of
delivery.
Design Post-hoc analysis from a large- scale community
health worker (CHW) study designed to increase the
uptake of malaria diagnostic testing.
Setting Respondents were identified during a household
survey in western Kenya between July 2016 and April
2017.
Participants Household members with fever in the last
4 weeks were interviewed at 12 and 18 months post-
implementation. We collected monthly testing data from
244 participating CHWs and conducted semistructured
interviews with a random sample of 70 CHWs.
Primary and secondary outcome measures The
primary outcome measure was diagnostic testing before
treatment for a recent fever. The secondary outcomes
were receiving a test from a CHW and tests done per
month by each CHW.
Results 55% (n=948 of 1738) reported having a malaria
diagnostic test for their recent illness, of which 38.4% were
tested by a CHW. Being aware of a local CHW (adjusted
OR=1.50, 95% CI: 1.10 to 2.04) and belonging to the
wealthiest households (vs least wealthy) were associated
with higher testing (adjusted OR=1.53, 95% CI: 1.14 to 2.06).
Wealthier households were less likely to receive their test
from a CHW compared with poorer households (adjusted
OR=0.32, 95% CI: 0.17 to 0.62). Confidence in artemether–
lumefantrine to cure malaria (adjusted OR=2.75, 95% CI:
1.54 to 4.92) and perceived accuracy of a malaria rapid
diagnostic test (adjusted OR=2.43, 95% CI: 1.12 to 5.27)
were positively associated with testing by a CHW. Specific
CHW attributes were associated with performing a higher
monthly number of tests including formal employment,
serving more than 50 households (vs <50) and serving areas
with a higher test positivity. On demand side, confidence of
the respondent in a test performed by a CHW was strongly
associated with seeking a test from a CHW.Conclusion Scale- up of community-based malaria testing
is feasible and effective in increasing uptake among the
poorest households. To maximise impact, it is important to
recognise factors that may restrict delivery and demand
for such services.
