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DHS vs. Service Data
 

DHS vs. Service Data

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    DHS vs. Service Data DHS vs. Service Data Presentation Transcript

    • DHS versus Service Data
    • Outline
      • HIV prevalence in Sub-Saharan Africa
      • Caesarean Sections
      • Estimates of Prenatal Care, Institutional Births, C-Sections and Maternal Deaths in Peru
      • HIV prevalence in Sub-Saharan Africa
    • HIV Prevalence Estimates
      • “ Comparison of HIV prevalence estimates from antenatal care surveillance and population-based surveys in sub-Saharan Africa”, by LS Montana, V Mishra and R Hong
        • Sex Transm Infect 2008; 84(Suppl I): i78-i84
      • Compares HIV prevalence estimates from Demographic and Health Surveys (DHS) and AIDS Indictor Surveys (AIS) with those of antenatal care (ANC) sentinel surveillance surveys in Ethiopia, Malawi, Tanzania, Uganda
    • HIV Prevalence Estimate Comparison: Methods
      • Uses GIS methods to map ANC surveillance sites and DHS/AIS survey clusters within 15 km of ANC site
      • Compares DHS/AIS HIV prevalence estimates for women and men
        • Nationally
        • For clusters within 15 km
        • For women, comparisons within current pregnancy status, experience of a recent childbirth, and received ANC for the last birth
    • HIV Prevalence Estimate Comparison: Data
      • DHS/AIS clusters within 15 km of sentinel sites expected to represent catchment areas
      • Sentinel sites georeferenced to nearest town or villages
      • DHS/AIS clusters georeferenced at cluster center
      HIV Prevalence Estimate Comparison: Locating
    • Sentinal Sites and Survey Clusters in Tanzania
      • National estimates
        • Women 15-49: lower from household surveys in 4 of 5 countries
          • Ethiopia: DHS estimate 1/3 of ANC
          • Uganda: DHS 7.5% vs ANC 6.0%
      • 15 km radius (catchment) estimates
        • Women 15-49: higher in DHS/AIS for catchment área than all women in DHS/AIS – Urban bias
        • DHS/AIS estimates for women same or higher than ANC (4 countries) and narrowed gap for Ethiopia
        • Male prevalence (from DHS/AIS) in catchment areas much lower than ANC female prevalence
        • ANC higher for women 15-24 and lower for women 35-49
      HIV Prevalence Estimate Comparison: Results
    • HIV Prevalence Estimate Comparison: Results
      • ANC surveillance surveys tend to overestimate HIV prevalence compared to prevalence among women in the general population in DHS/AIS surveys.
      • However, the ANC and DHS/AIS estimates are similar when restricted to women and men, or to women only, residing in catchment areas of ANC sites.
      • Patterns by age and urban/rural residence suggest possible bias in the ANC estimates.
      HIV Prevalence Estimate Comparison: Conclusions
      • Caesarean Sections
    • Caesarian Section Estimates
      • “ Reliability of data on caesarean sections in developing countries” by C K Stanton, D. Dubourg, V De Brouwere, M Pujades and C. Ronsmans
        • Bulletin of the World Health Organization June 2005; 83(6):449-455
      • Examines the reliability of reported rates of caesarean sections from developing countries from Demographic and Health Surveys (DHS) with those of Unmet Obstetric Need Network (UON) studies of clinic records in Benin, Burkina Faso, Haiti, Mali, Morocco, and Niger
      • Population-based rates for caesarean section obtained from two sources: DHS and health facility-based records (UNO) with estimates of live births
      • Compared for six developing countries 1989 to 1999
      • DHS data: interviews with women 15-49 who had a birth in 3 or 5 preceding years
      • UNO numerator data: all major surgical obstetric interventions performed within a specific administrative region (all public and private health facilities with surgical capacity)
      • UNO denominator data: population projections from last census together with DHS data on fertility rates
      Caesarian Section Estimate Comparison: Methods
      • Rate definitions:
        • DHS
          • A) Includes all reported caesareans (multiple births reported as multiple), excludes missing data from numerator, no stillbirth data
          • B) Multiple births counted once for numerator, excludes missing data from both numerator and denominator, excludes caesarean births in home, health post, health center or dispensary)
        • UON
          • Numerator: Caesareans that ended in live births (including laparotomies and hysterectomies if infant delivered during procedure)
          • Denominator:
          • C) projecting regional women 15-49 from census and applying DHS region-specific general fertility rate
          • D) projecting total regional population and applying DHS region-specific crude birth rates
          • E) projecting total regional population and applying national census based crude birth rates
      Caesarian Section Estimate Comparison: Methods
    • Caesarian Section Estimate Comparison: Results
      • Sensitivity analysis:
        • Between DHS A & B definitions
          • No difference for 11 of 31 regions
          • For 20 remaining regions definition B is lower than A: due to exclusion for improbable location (only 1 with a significant difference)
          • Negligible effect of double counting and exclusion of missing data
        • Between UON C, D & E definitions
          • No difference in 9 regions between C & D
          • In 17 regions difference of 0.1 between C & D with D higher than C
          • No difference in 12 of 28 regions between D & E
          • In 9 regions difference of 0.1 between D & E
          • In 7 regions difference up to 0.6
          • Def D always higher than E where different
    • Caesarian Section Estimate Comparison: Results Caesarian section rates by definition 0.3 0.3 0.3 0.5 0.6 Niger 1.0 1.1 1.0 1.6 2.0 Morocco 0.6 0.6 0.5 0.9 1.1 Mali Not available 1.0 0.7 0.5 0.6 Haiti (3 depts) 0.4 0.4 0.4 1.1 1.1 Burkina Faso 1.5 1.7 1.6 1.3 1.9 Benin (3 depts) UON ‘E’ UON ‘D’ UON ‘C’ DHS ‘B’ DHS ‘A’ Country
    • Caesarian Section Estimate Comparison: Results
      • Comparison DHS – UON
        • Compared DHS ‘B’ to UON ‘C’
        • In 23 of 31 regions, DHS higher than health facility based rate
        • Regression with DHS as dependent and UON as independent:
          • Intercept = 0.363
          • Slope = 1.05 (0.7 – 1.15)
          • Large differences in 8 regions: DHS 3 to 9 times health-facility based rate
    • Caesarian Section Estimate Comparison: Conclusions
      • DHS surveys tend to produce higher caesarean section rates than data collected from health facilities
      • For global and national monitoring, precision of DHS rates in countries where the number of caesareans is very low is sufficient to reflect seriously inadequate provision of services
      • Care is required in interpretation of trends due to imprecision of indicators
      • DHS rate insufficiently precise to evaluate regional programmes
    • Caesarian Section Estimate Comparison: Results
      • Comparing Ministry of Health (MINSA)
      • service statistics
      • with
      • Peru Continuous Demographic and Health Survey 2004-2008 (DHS)
    • Peru MINSA-DHS Comparison: Purpose
      • Compare maternal health statistics from published and unpublished Peru Ministry of Health data with those of the Peru Continuous DHS
        • Medical prenatal care
        • Institutional births
        • Caesarean section deliveries
        • Maternal deaths
    • Peru MINSA-DHS Comparison: Methods
      • Compare latest complete available data (2007)
      • Convert MINSA counts to percentages
      • Estimate DHS percentages and rates to total counts
        • Estimate births using DHS general fertility rate for 0-2 years prior to 2007 survey and 2007 Peru Population Census counts of women 15 to 44, nationally and by department (state)
    • Peru: Ministry of Health HIS and DHS, 2007: Results
    •  
    • Institutional Births, Peru DHS 2007
    •  
    • Estimation of C-sections
    • Percentage of C-Sections in Health Facilities, 2008
    • Estimation of maternal deaths
    • Maternal Mortality Ratio, 2008, using HIS distribution and level from Peru DHS 2004-08 (centered on 2003)
    • Peru HIS-DHS: Conclusions
      • In a country with fairly good data, HIS and DHS are comparable
      • HIS data only cover public facilities
      • Outcomes that differ by public or private facility, such as C-sections, are understated in HIS data
        • The greater the coverage of private medical facilities, the greater the undercoverage
      • Maternal mortality deaths are severely under-reported in HIS data
    • Overall conclusions
      • For the maternal health indicators, examined DHS data appear to provide greater coverage than HIS data
      • It should be possible to use HIS data for year-to-year trends once calibrated (adjusted) using results of good household surveys
      • Household surveys provide additional information for policies and programs that is not available from HIS data