Getting suitable data range of data sources surveys: sampling strategies, questionnaires reporting systems: forms, outputs sentinel sites: clinics, programmes,
Post on 08-Jan-2018
222 Views
Preview:
DESCRIPTION
Transcript
Getting suitable data
• range of data sources• surveys: sampling strategies, questionnaires• reporting systems: forms, outputs• sentinel sites: clinics, programmes, survey clusters
Ctown4.ppt
Data Sources in Relation to Characteristics of Required Information.
Source/Characteristics
Administrative Householdsurveys
(vary by size,q’aire, etc)
Rapid Assessment
(RAP etc)Ad hoc(e.g. clinics)
Sentinel site School(census)
Timeliness(delay approx.)
1-3 months 1 month 6-12 months 3-6 months Variable
Associations/causality
(+) + + +++ ++
Fineness ofgeographicaltargeting
Medium(e.g. district)
Medium(e.g. district)
High(e.g. village)
Low (e.g.province) to
medium
Low
Externalvalidity
Low Medium Medium High Low
Comprehens-iveness ofvariablescovered
Low Low Low Medium High
Data quality Low High Medium High High
Source: ICN documents, FAO/WHO, 1992
Ad hoc(e.g. clinics)
Surveys• sampling strategy – general (see next slide)
o multi-stage, PPS, known probability of individual selectiono small-scale: 30*30 (or 33*6, or …); segmentation vs spin-a-bottle
• match earlier surveyso sample same populationo match age-bandso keep same measures/questions (don’t change questionnaire lightly)o match seasons
DHS anthropometric questionnaire module
Source: DHS Kenya report 1994
DHS feeding practices questionnaire module
Source: DHS Kenya report 1994
Example of reference standardsSource: WHO ‘Measuring Change in Nutritional Status’ (1983)
FSAU nutritionquestion-naire module (1)
Source: FSAU
FSAU nutritionquestion-naire module(2)
Source: FSAU
UNICEF modelquestion-naire module
Source: UNICEF MICS Manual (1995)
UNICEF modelquestion-naire module
Source: UNICEF MICS Manual (1995)
Reporting systems (clinics, programmes)
• need to be useful at all levels• provide information on trends not levels• use all, or select by convenience• stepwise aggregation (district, province …)• preferably should have validation surveys/capacity
Sentinel sites (clinics, programmes)• same principles plus:• select sites usually for early change• focus on good data quality, training, data flow, supervision• use as signal of change in that area (but note not representative, by design)• capacity to follow up, validate, important
Sentinel sites – survey• resample same clusters at regular intervals• issue of if same hhds/kids, replacement, etc• otherwise much the same as for sentinel clinics etc• see examples of Zimbabwe, ALRMP-Kenya, Namibia plan.
Reporting form
Source: FSAU
Reporting form. Source: EOS, Ethiopia
FSAUIntegratedPhase Classification(IPC).
Source: FSAU, Tech Manual V 1, Table 1, May 2006
FSAUIPC:General Interpretation
Source: FSAU, Tech Manual V 1, Table 2, May 2006
FSAUIPC:Wasting
Source: FSAU, Tech Manual V 1, Table 4, May 2006
FSAUIPC:Responseframework
Source: FSAU, Tech Manual V 1, Table 16, May 2006
Equivalent wasting level
Uganda 10%Somalia 15%Ethiopia 20%Kenya & Sudan pastoralists 25%
Eh?
1.1 Malnutrition by areaWasting was highest in Mudzi (9%). A verification exercise using clinic data was done for Mudzi and there was an indication of sharp increase in malnutrition in January 2005. Results from the 2005 vulnerability assessments done in May 2005 revealed that Mudzi district was among the districts that were food insecure. Comparison with data collected in November 2004 shows that wasting rates are higher in all the 10 sites. This is an indication of worsening of nutritional situation as it is expected that nutrition should improve during this time (March) as people start eating food from their agricultural produce.
Comparison of wasting rates, Nov 2004 and Mar 2005
0123456789
10
% c
hild
ren
w asting Mar 2005 w asting Nov 2004
Source:Zimbabwe
Pilot Food and Nutrition Sentinel Site Surveillance Report
March 2005
Food and Nutrition Council in collaboration with Epidemiology Dept, Nutrition Unit, Ministry of Health and
Child Welfare
Region 2002 2003 2004 2005 2006Turkana Kaleng, Kibish, Lapur, Lokitaung
11%(9-13.3)
27.6%(23.8-29.8)
34.4%(31.3-37.4)
22.1%(18.5-26.2)
24%(20-27.9)
Kakuma, Oropoi, Lokichoggio
11.4%(9.4-13.7)
18.9%(15.8-21)
23.3%(20.7-26.2)
19.2%(15.8-23.1)
26.6%(22.4-30.7)
Kalanuk, Katilu 12.7%(10.6-15.1)
24%(21.2-27.1)
20.1%(17.6-22.9)
21.3%(18.8-24.1)
1.2%(17.3-25.1)
Loima, Turkwell 11.8%(9.8-14.4)
22.4%(19.7-25.3)
23.3%(20.7-26.2)
21.4%(18.8-24.1)
23.6%(19.6-27.7)
Lokichar, Lokori 19.4%(16.9-22.2)
32.8%(30-35.7)
25.5%(22.9-28.3)
25.9%(21.7-30)
Central, Kerio, Kalokol
21.3%(18.7-24.2)
37.3%(34.3-40.3)
25%(22.3-27.8)
26.6%(21.7-30)
Isiolo Merti, Sericho
15.6%(13.5-18)
28.5%(25.6-31.6)
Kina, Garbatulla, Oldoniyro and Central
13.2%(11.2-15.6)
Kwale 5.8%(4.48-7.28)
5.9%(4.6-7.4)
West Pokot 10.9%(9.1-13.1)
Makeuni 2.3%(1.4-3.7)
4%(2.8-5.4)
Taita Taveta Wundanvi Mwambi
3% (1.7-4.1)
Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov DecPastoralistsAgro-Pastoralists
= Hunger Season
= Post Rains/Harvest
= Moderate
Source: Small scale survey dataset SEMmrge10_21B.sav
Results of area-level surveys, Kenya
Source: Small scale survey dataset SEMmrge10_21B.sav
Source: CHANIS Report, Oct 2006
Source: CHANIS Report, Oct 2006
Source: FSAU Nutrition Update September 2006
IPC Survey Results, Sool Plateau, Somalia June 2006
Sentinel site surveillance results, Bakool, SomaliaAugust 2006
Source: FSAU Nutrition Update September 2006
Source: Small scale survey dataset SEMmrge10_21B.sav
Program data
top related