Effectiveness of malaria control in eritrea 1996 to 2003
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Effectiveness of malaria control in Eritrea, 1996 to 2003. Patricia M Graves June 6, 2008 IRI. Eritrea. Eritrea – malaria prevalence survey 2002. Sintasath et al, 2005: 176 villages, 2,779 HH, 12,937 people. Overall prevalence 2.2%.

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Effectiveness of malaria control in eritrea 1996 to 2003 l.jpg

Effectiveness of malaria control in Eritrea, 1996 to 2003

Patricia M Graves

June 6, 2008

IRI



Eritrea malaria prevalence survey 2002 l.jpg
Eritrea – malaria prevalence survey 2002

Sintasath et al, 2005: 176 villages, 2,779 HH, 12,937 people. Overall prevalence 2.2%


1a eritrea malaria situation reported clinical malaria cases by age group eritrea 1996 to 2003 l.jpg
1a: Eritrea – malaria situationReported clinical malaria cases by age-group, Eritrea 1996 to 2003






Routine surveillance data l.jpg
Routine surveillance data

  • Can assist in:

    • Monitoring trends

    • Clarifying and measuring seasonality

    • Prioritizing areas for intervention

    • Defining and quantifying epidemics

    • Evaluating control measures


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Source of cases dataset

  • 325 health facilities (1 to 9 per subzone)

  • Excluded:

    • National referral hospitals

    • Specialty clinics (ophthalmic and MCH)

    • Non-functioning facilities (no reports)

    • Private doctors

    • Worksite clinics

  • Remaining: 243 health facilities representing all subzones.



Focus on reported outpatient clinical malaria cases l.jpg
Focus on reported outpatient clinical malaria cases

  • Few facilities had diagnostics at start of period

  • Diagnostic capacity increased during study

  • Too few deaths and inpatients

  • Seasonal patterns clearly seen in clinical malaria cases

  • Inconsistency in lab forms for Pf/Pv.



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Reported clinical malaria cases

Incidence / 1000 / yr, 1998


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Reported clinical malaria cases

Incidence / 1000 / yr, 2000


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Reported clinical malaria cases

Incidence / 1000 / yr, 2002


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Data sources used to analyze effectiveness of interventions

  • Datasets for 96 months (1998 to 2003), 58 subzones (districts)

  • Outpatient clinical malaria cases by month from new NHMIS (restricted here from 325 to 242 health facilities).

  • Satellite-derived rainfall (CPC CMAP 0407) and NDVI (version e from USGS/ADDS), averaged over subzone.

  • Amounts of interventions applied (IRS, ITNs, larval control)


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Intervention data collected by subzone and month

  • Residual spraying (amount of chemical, people covered)

  • Number of impregnated nets issued and reimpregnated

  • Number of larval habitats eliminated

  • Chemical larviciding (number of sites, amount of chemical)

  • Treatments given by village health agents






Analysis l.jpg
Analysis

  • Outcome variable: number of clinical cases by subzone and month.

  • Cross-sectional multivariate time-series regression/ Poisson regression

  • First tested for ‘endogeneity’ (i.e. control being done in response to climate or increased case numbers) – no consistent pattern.

  • Independent variables expressed as ‘anomalies’ (deviations from subzone/calendar month means) to adjust for seasonality

  • Subzone fixed effect variables

  • Depreciation/cumulation of insecticides and nets

  • Climate variables as aggregated lags (Rain 2-3 months; NDVI 0-1 month.


Positive relationships between climate variables and malaria cases anseba and gash barka l.jpg
Positive relationships between climate variables and malariacases (Anseba and Gash Barka)

* p<0.05

*** p<0.001


Negative relationships between intervention variables and malaria cases l.jpg
Negative relationships between intervention variables and malaria cases

* p<0.05 *** p<0.001


Eritrea analysis conclusions l.jpg
Eritrea analysis conclusions

  • Reduction in cases in Eritrea from 1998 to 2003 was not solely due to climate shifts.

  • Both IRS (with DDT or malathion) in one zone, and ITNs in two zones, were independently associated with reduction in cases.

  • There was evidence of effectiveness of larval control in one zone.

  • Better monitoring of interventions, especially larval control, is needed.

  • Routine malaria surveillance data (despite known drawbacks) is useful for evaluating the effectiveness of control measures, as long as climate variation is taken into account.



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