Table 4.
Estimated annual reduction in severe malaria admissions among all-ages and among children under five years that could have been achieved with increases in malaria service point density to either 1 per 1,000 population or 1 per 750 population in the final 12 months of the study period (June 2019 to May 2020)*
| Province | # Districts with existing CCM scale-up (%) | Mean (min, max) population per malaria service point in a district, June 2019 to May 2020 | # Additional CHWs required to reach 1 malaria service point per 1000 | # Additional CHWs required to reach 1 malaria service point per 750 | All-age severe malaria admissions, June 2019 to May 2020 | U5 severe malaria admissions, June 2019 to May 2020 | ||||
|---|---|---|---|---|---|---|---|---|---|---|
| Observed (model prediction) | Estimated reduction with 1 malaria service point per 1000 | Estimated reduction with 1 malaria service point per 750 | Observed (model prediction) | Estimated reduction with 1 malaria service point per 1000 | Estimated reduction with 1 malaria service point per 750 | |||||
| Copperbelt | 4 (40%) | 4903 (601, 9245) | 1523 | 2207 | 10,510 (11,784) | 10.8% | 14.7% | 3340 (3313) | 12.9% | 17.5% |
| Eastern | 4 (30%) | 3244 (458, 6736) | 726 | 1025 | 6844 (5960) | 8.3% | 11.3% | 3743 (3291) | 10.4% | 14.1% |
| Luapula | 1 (8%) | 5719 (588, 12,721) | 916 | 1290 | 14,503 (16,340) | 10.5% | 14.2% | 9112 (12,369) | 10.7% | 14.3% |
| Muchinga | 3 (33%) | 6400 (1173, 11,249) | 684 | 1036 | 6205 (7074) | 12.8% | 17.9% | 3015 (3499) | 15.0% | 20.9% |
| Northern | 2 (17%) | 6700 (814, 9576) | 1228 | 1736 | 8875 (9243) | 13.0% | 17.6% | 4480 (4595) | 15.2% | 20.5% |
| North Western | 7 (64%) | 2648 (431, 7976) | 561 | 903 | 9702 (10,457) | 10.1% | 14.7% | 4174 (4319) | 11.5% | 16.6% |
| Western | 14 (88%) | 1238 (461, 5025) | 270 | 462 | 2679 (2686) | 2.0% | 3.3% | 1048 (1069) | 2.1% | 3.6% |
*Estimated % reductions in severe malaria admissions were produced by comparing the model prediction during June 2019 to May 2020 (using the observed district-month service point density) against model predictions for the two counterfactual scenarios of all districts achieving 1 malaria service point per 1000 population or 1 malaria service point per 750 people