Blir du sykere hvis du får tenkt deg om? : på leting etter metodeeffekter i en undersøkelse om levekår

Artiklene i Samfunnsspeilet er tilgjengelige fra SSBs nettsider: http://www.ssb.no/ssp/Om et spørsmål er stilt på telefon eller ved besøk, vil av og til påvirke hvilke svar som blir gitt. Det samme gjelder om spørsmålet er stilt fra et sentralt telefonkorps eller av en intervjuer som ringer hjemmefra. Det viser seg at folk sier at de har flere sykdommer når en intervjuer er på besøk, enn de gjør når han ringer. Hvorfor er det sånn

Blir du sykere hvis du får tenkt deg om? : på leting etter metodeeffekter i en undersøkelse om levekår

Om et spørsmål er stilt på telefon eller ved besøk, vil av og til påvirke hvilke svar som blir gitt. Det samme gjelder om spørsmålet er stilt fra et sentralt telefonkorps eller av en intervjuer som ringer hjemmefra. Det viser seg at folk sier at de har flere sykdommer når en intervjuer er på besøk, enn de gjør når han ringer. Hvorfor er det sånn

The health management Information system in Malawi. Assessment of data quality and methods for improvement

Information on available resource- and service production for the health sector is crucial for any government in order to manage it well. Collecting information of high quality on the health sector has however proven to be a challenging task. This report is an assessment of data from the Health Management Information Systems (HMIS) in Malawi and suggests a way to improve data quality. The assessment is based on a “desk study” of the Malawian HMIS data. A statistical approach to improve data quality is presented, and it is used to identify possible data errors reported by the health facilities. Among other, the goal is to show a method for prioritizing the use of limited editing resources where they can improve quality the most. This quality assessment of the Malawian HMIS data has 4 objectives: 1. Identify areas for initial improvement of data quality 2. Present general guidelines for data editing 3. Present use of statistical methods to improve data quality 4. Recommend future activity This quality assessment is from 2014/2015 (desk study) and focuses on reported Malawian HMIS data for 2013, but for some analysis 2012 data is also included. Missing information is a severe threat to the Malawian HMIS data. A first step should be to reduce the amount of missing information. Further, it is also recommended to prioritize identifying health facilities that have errors influencing the end result at aggregated level. Improving quality by analysing already collected data to point out weaknesses in the data is an efficient approach. By doing this, a highly recommended process of working in a “circular” manner to improve quality will be established. The Health Information Systems Programme (HISP) is a global network emerging from the Department of Informatics at the University of Oslo. HISP has developed the District Health Information system (DHIS), a free open source software specialized for HMIS. Today HMIS data in Malawi is collected using DHIS2. The methods for editing suggested in this report can be implemented in DHIS2. The aim of the suggested approach for improving data quality is to: Avoid missing data from health facilities, correct major errors and improve the source

Sprikende tall på utvikling for FN-målene : hvordan måle FNs tusenårsmål

I 2000 ble medlemslandene i FN enige om åtte utviklingsmål som skulle brukes til å følge med på utviklingen i levekår på globalt nivå, de såkalte tusenårsmålene. Innen 2015 skal verden ha sett betydelige framskritt på en rekke områder som er viktige for mennesker som lever i utviklingsland. Men ofte viser statistikk som skal beskrive denne utviklingen ulike resultater. Statistikkbyråer i sju utviklingsland har samarbeidet med Statistisk sentralbyrå (SSB) her i Norge for å synliggjøre disse ulikhetene ved statistikken

Adjusting maternal mortality data for international comparisons. The case of vital registration systems

Register data on maternal deaths is adjusted in international reports to account for underreporting; however, there has been controversy around these adjustments. The objective of this article is to review the adjustment factors applied to maternal mortality register data. A literature review provided 72 studies on underreporting showing differences in the definition of maternal mortality. This has not previously been taken into account when calculating average adjustment factors. Our analysis showed that including psychiatric disease and maternal deaths occurring 42 days post-partum had significant effects on the adjustment factor. When using the strict WHO definition of maternal mortality, a median adjustment factor of 1.5 was calculated which is identical to the one used by the WHO. Guidelines on inclusion criteria for maternal deaths need to be clarified in order for figures to be internationally comparable

Adjusting maternal mortality data for international comparisons. The case of vital registration systems

Register data on maternal deaths is adjusted in international reports to account for underreporting; however, there has been controversy around these adjustments. The objective of this article is to review the adjustment factors applied to maternal mortality register data. A literature review provided 72 studies on underreporting showing differences in the definition of maternal mortality. This has not previously been taken into account when calculating average adjustment factors. Our analysis showed that including psychiatric disease and maternal deaths occurring 42 days post-partum had significant effects on the adjustment factor. When using the strict WHO definition of maternal mortality, a median adjustment factor of 1.5 was calculated which is identical to the one used by the WHO. Guidelines on inclusion criteria for maternal deaths need to be clarified in order for figures to be internationally comparable.publishedVersio