title: Analysis of malaria surveillance data in Ethiopia: what can be learned from the Integrated Disease Surveillance and Response System?
abstract: BACKGROUND
  Routine malaria surveillance data is useful for assessing incidence and trends over time, and in stratification for targeting of malaria control. The reporting completeness and potential bias of such data needs assessment.
  
  
  METHODS
  Data on 17 malaria indicators were extracted from the Integrated Disease Surveillance and Response System database for July 2004 to June 2009 (Ethiopian calendar reporting years 1997 to 2001). Reporting units were standardized over time with 2007 census populations. The data were analysed to show reporting completeness, variation in risk by reporting unit, and incidence trends for malaria indicators.
  
  
  RESULTS
  Reporting completeness, estimated as product of unit-month and health facility reporting, was over 80% until 2009, when it fell to 56% during a period of reorganization in the Ministry of Health. Nationally the average estimated annual incidence of reported total malaria for the calendar years 2005 to 2008 was 23.4 per 1000 persons, and of confirmed malaria was 7.6 per 1,000, with no clear decline in out-patient cases over the time period. Reported malaria in-patient admissions and deaths (averaging 6.4 per 10,000 and 2.3 per 100,000 per year respectively) declined threefold between 2005 and 2009, as did admissions and deaths reported as malaria with severe anaemia. Only 8 of 86 reporting units had average annual estimated incidence of confirmed malaria above 20 per 1,000 persons, while 26 units were consistently below five reported cases per 1,000 persons per year.
  
  
  CONCLUSIONS
  The Integrated Disease Surveillance and Response System functioned well over the time period mid 2004 to the end of 2008. The data suggest that the scale up of interventions has had considerable impact on malaria in-patient cases and mortality, as reported from health centres and hospitals. These trends must be regarded as relative (over space and time) rather than absolute. The data can be used to stratify areas for improved targeting of control efforts to steadily reduce incidence. They also provide a baseline of incidence estimates against which to gauge future progress towards elimination. Inclusion of climate information over this time period and extension of the dataset to more years is needed to clarify the impact of control measures compared to natural cycles on malaria.
authors.affiliation: Public Health Emergency Management, Ethiopian Health and NutritionResearch Institute, Addis Ababa, Ethiopia.
authors.forename: Daddi
authors.initials: D
authors.lastname: Jima
authors.forename: Milliyon
authors.initials: M
authors.lastname: Wondabeku
authors.forename: Abebe
authors.initials: A
authors.lastname: Alemu
authors.forename: Admas
authors.initials: A
authors.lastname: Teferra
authors.forename: Nuraini
authors.initials: N
authors.lastname: Awel
authors.forename: Wakgari
authors.initials: W
authors.lastname: Deressa
authors.forename: Adamu
authors.initials: A
authors.lastname: Adissie
authors.forename: Zerihun
authors.initials: Z
authors.lastname: Tadesse
authors.forename: Teshome
authors.initials: T
authors.lastname: Gebre
authors.forename: Aryc W
authors.initials: AW
authors.lastname: Mosher
authors.forename: Frank O
authors.initials: FO
authors.lastname: Richards
authors.forename: Patricia M
authors.initials: PM
authors.lastname: Graves
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002648:Child; D002675:Child, Preschool; D062665:Epidemiological Monitoring; D005002:Ethiopia / Q000453:epidemiology; D005260:Female; D006302:Health Services Research; D006801:Humans; D015994:Incidence; D007223:Infant; D007231:Infant, Newborn; D008288:Malaria / Q000175:diagnosis / Q000188:drug therapy / Q000453:epidemiology* / Q000517:prevention & control; D008297:Male; D008875:Middle Aged; D055815:Young Adult