Your SlideShare is downloading. ×
0
Analysis of cross-country changes in health services
Analysis of cross-country changes in health services
Analysis of cross-country changes in health services
Analysis of cross-country changes in health services
Analysis of cross-country changes in health services
Analysis of cross-country changes in health services
Analysis of cross-country changes in health services
Analysis of cross-country changes in health services
Analysis of cross-country changes in health services
Analysis of cross-country changes in health services
Analysis of cross-country changes in health services
Analysis of cross-country changes in health services
Analysis of cross-country changes in health services
Analysis of cross-country changes in health services
Analysis of cross-country changes in health services
Analysis of cross-country changes in health services
Analysis of cross-country changes in health services
Analysis of cross-country changes in health services
Analysis of cross-country changes in health services
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

Analysis of cross-country changes in health services

1,136

Published on

This presentation was given in a session at the Global Symposium on Health Systems Research which was organised by the Future Health Systems Consortium. The author is Toru Matsubayashi from Johns …

This presentation was given in a session at the Global Symposium on Health Systems Research which was organised by the Future Health Systems Consortium. The author is Toru Matsubayashi from Johns Hopkins Bloomberg School of Public Health

Published in: Health & Medicine, Sports
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
1,136
On Slideshare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
5
Comments
0
Likes
0
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide
  • Increasing focus has been placed on categorical programs such as disease-specific and intervention-specific global health initiatives. International targets have been set for key health services as part of MDGs, including DPT coverage, SBA, DOT TB case detection and successful treatment. These targets, however, were not necessarily set in view of country-based plans, experiences, expectations, or feasibility. We wondered about what have been country-specific experiences. We also asked “is there any way to illustrate feasible benchmarks based upon previous experiences?”.SBA: 80% for 2005, 85% for 2010, and 90% for 2015
  • ObservedThis graph does not give us detailed ideas about how different countries have been faring.
  • By using random coefficient models, we can examine each country’s experience visually. Equatorial Guinea: 77% in 1990, 33% in 2008Gabon: 78% to 38%
  • Ugly, but that’s because SBA indicators are not comprehensively available in many countries. However, by using a multilevel model and coming up with empiric bayes estimates, we can estimate the trend more accurately.
  • Equatorial Guinea (steepest curve)Bhutan (2nd steepest curve)
  • In light of the experiences from the last 20 years, less than two thirds of LMICs are projected to reach the universal targets for these priority health services in 2015.
  • A series of country context variables were examinedCountry contexts seems to be strongly correlated with health services. The strong correlations between health services and impact variables such as U5MR are as expected. Other country characteristics such as governance and literacy seem to be rather consistently associated with changes in health services.
  • The steeply increasing curve is for Iraq
  • Transcript

    • 1. 1 Analysis of cross-country changes in health services Toru Matsubayashi, MD MSc Johns Hopkins Bloomberg School of Public Health
    • 2. Background for the study  MDGs and categorical programs 1.International targets for priority health services • 90% for DPT, 90% for SBA, 70% for TB detection, 85% for TB cure • Feasible benchmarks? 2.Associations between different health services indicators 3.Roles of country characteristics 2
    • 3. Objectives of the study  By using longitudinal and multilevel analyses, 1. To illustrate cross-country trends in priority health services 2. To estimate the levels of achievable changes in priority health services 3. To explore country characteristics affecting changes in priority health services 3
    • 4. Methods for the study 1. National panel data from publicly available sources were constructed 2. Health service variables in three distinct areas were examined 3. Variables indicative of country characteristics explored 4. Multilevel models were fitted for the analyses 4
    • 5. Trends of DPT3 Immunization 5
    • 6. Country-specific estimates by random coefficient model, DPT3 coverage 6
    • 7. Trends of SBA 7
    • 8. Country-specific estimates by random coefficient model, SBA coverage 8
    • 9. Projected to reach the 2015 target  SBA: 92/159 countries (57%)  DPT: 94/154 countries (61%)  Measles: 92/154 countries (59%)  DOT Case Detection: 92/165 (56%)  DOT Successful Treatment: 99/162 (61%) 9
    • 10. Country characteristics and health services 10 DPT3 SBA TB Dx TB Tx Governance 0.51* 0.47* 0.27* 0.04 U5MR -0.71* -0.74* -0.24* -0.30* Fertility -0.64* -0.72* -0.18* -0.27* Population -0.02 -0.06 -0.15* 0.12* Literacy rate 0.61* 0.79* 0.22* 0.24* GNI per capita 0.41* 0.56* 0.11* 0.11* Health Expenditure 0.36* 0.50* 0.18* 0.06 ODA 0.05* 0.13* 0.17* 0.16* Pairwise correlation coefficients * p<0.05
    • 11. Key findings  Significant heterogeneity among different countries  Individual country differences more important than international averages or universal target  Less than two thirds of LMICs projected to reach the universal targets  Country context variables such as governance playing a major role in country-specific experiences 11
    • 12. Application of the method  Longitudinal data set provides rich sources of analyses  Different countries have different starting point and inherent differences: multilevel analyses are useful to incorporate such factors  Applicable to the analysis of health services at national, regional, and facility levels 12
    • 13. The End 13
    • 14. SECTION BREAK SLIDE Text here 14
    • 15. Trends of DOT TB detection 15
    • 16. Trends of DOT TB detection (model based) 16
    • 17. Trends of DOT TB successful treatment 17
    • 18. Trends of DOT TB successful treatment (model based) 18
    • 19. U5MR Model Based Estimates 19

    ×