| Literature DB >> 16635265 |
Peter M Nyarango1, Tewolde Gebremeskel, Goitom Mebrahtu, Jacob Mufunda, Usman Abdulmumini, Andom Ogbamariam, Andrew Kosia, Andemariam Gebremichael, Disanayike Gunawardena, Yohannes Ghebrat, Yahannes Okbaldet.
Abstract
BACKGROUND: Malaria is a huge public health problem in Africa that is responsible for more than one million deaths annually. In line with the Roll Back Malaria initiative and the Abuja Declaration, Eritrea and other African countries have intensified their fight against malaria. This study examines the impact of Eritrea's Roll Back Malaria Programme: 2000-2004 and the effects and possible interactions between the public health interventions in use.Entities:
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Year: 2006 PMID: 16635265 PMCID: PMC1501031 DOI: 10.1186/1475-2875-5-33
Source DB: PubMed Journal: Malar J ISSN: 1475-2875 Impact factor: 2.979
Expected sample sizes and samples size achieved per zoba
| Anseba | 380 | 71 | 72 | 64 |
| Debub | 380 | 48 | 72 | 61 |
| Gash-Barka | 380 | 42 | 72 | 55 |
| Northern Red Sea | 380 | 35 | 72 | 58 |
Figure 1Malaria morbidity and case fatality rates 1999–2004.
Trends in malaria incidence rate (IR) and case fatality rate (CFR) by zoba
| Year | Anseba | Debub | G. Barka | Maekel | Northern Red Sea | Southern Red Sea | ||||||
| IR | CFR | IR | CFR | IR | CFR | IR | CFR | IR | CFR | IR | CFR | |
| 1999 | 1938.7 | 0.3 | 2198.0 | 0.2 | 7696.5 | 0.2 | 461.6 | 0.1 | 2270.5 | 0.3 | 940.7 | 1.3 |
| 2000 | 830.1 | 0.4 | 1223.2 | 0.2 | 4462.1 | 0.2 | 190.8 | 0.4 | 1717.9 | 0.2 | 1059.8 | 0.9 |
| 2001 | 686.4 | 0.7 | 1196.2 | 0.2 | 4636.6 | 0.2 | 360.2 | 0.2 | 2523.4 | 0.2 | 1082.2 | 0.4 |
| 2002 | 327.9 | 0.4 | 754.3 | 0.2 | 2546.8 | 0.4 | 272.4 | 0.1 | 1053.8 | 0.1 | 1018.5 | 0.1 |
| 2003 | 332.7 | 0.7 | 1031.8 | 0.3 | 2691.3 | 0.2 | 253.7 | 0.3 | 820.9 | 0.1 | 825.6 | 0.2 |
| 2004 | 57.0 | 0 | 503.2 | 0.1 | 1318.0 | 0.1 | 124.5 | 0.3 | 328.1 | 0.4 | 490.0 | 0 |
Malaria control activities in all zobas of Eritrea, 2000–2004
| Number of houses sprayed | 39,838 | 76,754 | 60,433 | 97,069 | 92,107 |
| Malathion used (kg) | 2,399 | 7,904 | 5,555 | 21,890 | 30,388 |
| DDT used (kg) | 4,045 | 8,362 | 8,500 | 17,423 | 13,103 |
| Population protected by IRS | 117,017 | 202,652 | 159,551 | 227,675 | 259,420 |
| Breeding sites filled (pools) | 15,988 | 23,810 | 25,355 | 22615 | 27494 |
| Breeding sites treated (pools) | 11,691 | 7,690 | 12,547 | 67,684 | 33,442 |
| Abate (Temephos) used (litres) | 14.9 | 18.5 | 145.0 | 90.5 | 80.2 |
| Population participating in treating and filling breeding site | 54,219 | 72,824 | 51,666 | 48,948 | 111,494 |
| ITNs distributed | 127,863 | 67,708 | 276,038 | 187,709 | 214,752 |
Figure 2Distribution of ITNs and trends in malaria incidence rate.
Number of personnel trained in case management
| Year | Community Health Agents | Health workers | Laboratory Technicians | Rural drug Vendors | Military health personnel | Community members |
| 2000 | 936 | 370 | 15 | 0 | 593 | 1121 |
| 2001 | 1419 | 497 | 0 | 0 | 314 | 673 |
| 2002 | 1077 | 274 | 15 | 66 | 0 | 666 |
| 2003 | 1382 | 160 | 41 | 0 | 0 | 1176 |
| 2004 | 1446 | 80 | 62 | 37 | 0 | 689 |
Figure 3Average annual rainfall (mm) in Eritrea 1999–2004.
Figure 4Proportion of children, adults and pregnant women sleeping under ITN.
Availability of ITNs in households by zoba
| Anseba | 386 | 98% | 97% | 96% | 94% |
| Debub | 380 | 84% | 82% | 75% | 69% |
| Gash-Barka | 377 | 77% | 75% | 68% | 67% |
| Northern Red Sea | 380 | 64% | 62% | 52% | 18% |
Proportion of persons sleeping under Net/ITN in previous night by zoba and age
| Anseba | 79.8% | 77.6% | 72.3% | 71.0% | 73.6% | 72.1% | |||
| Debub | 56.3% | 51.6% | 40.3% | 35.2% | 42.8% | 37.8% | |||
| Gash-Barka | 57.2% | 51.3% | 43.9% | 39.8% | 45.8% | 41.5% | |||
| Northern Red Sea | 41.6% | 15.0% | 27.0% | 7.9% | 29.6% | 9.2% | |||
Malaria prevention indicators for pregnant women by zoba
| Anti-malarial | ||||||||||
| Any Net | ITN | ITN in last 6 months | At least 1 visit | 1–2 visits | 3–6 visits | Used | Correct dose | Other illness | ||
| Anseba | 64 | 81.3% | 81.3% | 76.6% | 78.1% | 65.6% | 12.5% | 3.1% | 3.1% | 23.4% |
| Debub | 61 | 59.0% | 59.0% | 59.0% | 73.8% | 41.0% | 32.8% | 0.0% | 0.0% | 19.7% |
| Gash-Barka | 55 | 60.0% | 56.4% | 56.4% | 83.6% | 63.6% | 20.0% | 18.2% | 14.5% | 54.5% |
| Northern Red Sea | 58 | 29.3% | 13.8% | 6.9% | 77.6% | 58.6% | 19.0% | 0.0% | 0.0% | 31.0% |
Figure 5Proportion of households participating in ecological management by zoba.
Time series analysis (ARIMA) of cases and deaths, against malaria control interventions
| Univariate analysis | ||
| β coefficient | Probability | |
| ITNs (number distributed) | -0.125 | 0.005 |
| Number of ITNs retreated | -0.016 | 0.02 |
| IRS (kg of DDT & Malathion) | -2.352 | 0.05 |
| Malathion (kg) | -3.270 | 0.05 |
| CHAs trained (number) | -55.483 | 0.6 |
| Rainfall | 275.95 | 0.4 |
| Population protected | -0.728 | 0.11 |
| β coefficient | Probability | |
| IRS (kg of DDT & Malathion) | -0.002 | 0.08 |
| Health workers trained | 0.226 | 0.03 |
| Abate (litres) | -0.468 | 0.3 |
| Multivariate | ||
| β coefficient | Probability | |
| ITNs | -0.1663 | 0.13 |
| IRS | 0.832 | 0.45 |
| β coefficient | Probability | |
| ITNs | -0.00011 | 0.6 |
| IRS | 0.0006 | 0.9 |