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. 2022 Mar 24;17(3):e0265352.
doi: 10.1371/journal.pone.0265352. eCollection 2022.

Impact of the malaria comprehensive case management programme in Odisha, India

Madan M Pradhan  1 , Sreya Pradhan  1 , Ambarish Dutta  2   3 , Naman K Shah  4 , Neena Valecha  5 , Pyare L Joshi  6 , Khageshwar Pradhan  7 , Penny Grewal Daumerie  8 , Jaya Banerji  8 , Stephan Duparc  8 , Kamini Mendis  9 , Surya K Sharma  5 , Shiva Murugasampillay  10 , Anupkumar R Anvikar  5
Affiliations

Impact of the malaria comprehensive case management programme in Odisha, India

Madan M Pradhan et al. PLoS One. .

Abstract

Background: The Comprehensive Case Management Project (CCMP), was a collaborative implementation research initiative to strengthen malaria early detection and complete treatment in Odisha State, India.

Methods: A two-arm quasi-experimental design was deployed across four districts in Odisha, representing a range of malaria endemicity: Bolangir (low), Dhenkanal (moderate), Angul (high), and Kandhamal (hyper). In each district, a control block received routine malaria control measures, whereas a CCMP block received a range of interventions to intensify surveillance, diagnosis, and case management. Impact was evaluated by difference-in-difference (DID) analysis and interrupted time-series (ITS) analysis of monthly blood examination rate (MBER) and monthly parasite index (MPI) over three phases: phase 1 pre-CCMP (2009-2012) phase 2 CCMP intervention (2013-2015), and phase 3 post-CCMP (2016-2017).

Results: During CCMP implementation, adjusting for control blocks, DID and ITS analysis indicated a 25% increase in MBER and a 96% increase in MPI, followed by a -47% decline in MPI post-CCMP, though MBER was maintained. Level changes in MPI between phases 1 and 2 were most marked in Dhenkanal and Angul with increases of 976% and 287%, respectively, but declines in Bolangir (-57%) and Kandhamal (-22%). Between phase 2 and phase 3, despite the MBER remaining relatively constant, substantial decreases in MPI were observed in Dhenkanal (-78%), and Angul (-59%), with a more modest decline in Bolangir (-13%), and an increase in Kandhamal (14%).

Conclusions: Overall, CCMP improved malaria early detection and treatment through the enhancement of the existing network of malaria services which positively impacted case incidence in three districts. In Kandhamal, which is hyperendemic, the impact was not evident. However, in Dhenkanal and Angul, areas of moderate-to-high malaria endemicity, CCMP interventions precipitated a dramatic increase in case detection and a subsequent decline in malaria incidence, particularly in previously difficult-to-reach communities.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Study design and location of CCMP and intervention blocks.
API, annual parasite index = total number of positive slides for parasite in a year x1000 / total population; CCMP, Comprehensive Case Management Project; MBER, monthly blood examination rate = total number of slides examined in a month x1000 / total population; MPI, monthly parasite index = total number of positive slides for parasite in a month x1000 / total population.
Fig 2
Fig 2. Difference-in-difference (DID) estimates for MBER and MPI in CCMP intervention blocks.
Data are shown as CCMP intervention block unadjusted values and adjusted for contemporaneous changes in the control blocks; a value of 1 indicates no change, less than 1 is a decrease and more than 1 represents an increase. CCMP, Comprehensive Case Management Project; MBER, monthly blood examination rate = total number of slides examined in a month x1000 / total population; MPI, monthly parasite index = total number of positive slides for parasite in a month x1000 / total population.
Fig 3
Fig 3. Interrupted time-series analysis of changes in malaria indices for pooled data.
MBER, monthly blood examination rate = total number of slides examined in a month x1000 / total population; MPI, monthly parasite index = total number of positive slides for parasite in a month x1000 / total population.
Fig 4
Fig 4. Interrupted time-series analysis of changes in malaria indices for Bolangir.
MBER, monthly blood examination rate = total number of slides examined in a month x1000 / total population; MPI, monthly parasite index = total number of positive slides for parasite in a month x1000 / total population.
Fig 5
Fig 5. Interrupted time-series analysis of changes in malaria indices for Dhenkanal.
MBER, monthly blood examination rate = total number of slides examined in a month x1000/total population; MPI, monthly parasite index = total number of positive slides for parasite in a month x1000 / total population.
Fig 6
Fig 6. Interrupted time-series analysis of changes in malaria indices for Angul.
MBER, monthly blood examination rate = total number of slides examined in a month x1000 / total population; MPI, monthly parasite index = total number of positive slides for parasite in a month x1000 / total population.
Fig 7
Fig 7. Interrupted time-series analysis of changes in malaria indices for Kandhamal.
MBER, monthly blood examination rate = total number of slides examined in a month x1000 / total population; MPI, monthly parasite index = total number of positive slides for parasite in a month x1000 / total population.
Fig 8
Fig 8. Framework for scale up of access to malaria diagnosis and treatment.
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