Limits...
Estimating the capacity for ART provision in Tanzania with the use of data on staff productivity and patient losses.

Hanson S, Thorson A, Rosling H, Ortendahl C, Hanson C, Killewo J, Ekström AM - PLoS ONE (2009)

Bottom Line: A situation analysis including scrutiny of staff factors, such as available data on staff and patient factors including access to ART and patient losses, made us conclude that the lack of clinical staff is the main limiting factor for ART scale-up, assuming that sufficient drugs and supplies are provided by donors.A comparison of our scenario estimations and actual output 2006-2008 indicates that a simple user-friendly dynamic model can estimate the capacity for ART scale-up in resource-poor settings based on identification of a limiting staff factor and information on availability of this staff and patient losses.Thus, it is possible to set more achievable targets.

View Article: PubMed Central - PubMed

Affiliation: Division of International Health (IHCAR), Karolinska Institutet, Stockholm, Sweden. Stefan.Hanson@ki.se

ABSTRACT

Background: International targets for access to antiretroviral therapy (ART) have over-estimated the capacity of health systems in low-income countries in Sub-Saharan Africa. The WHO target for number on treatment by end 2005 for Tanzania was 10 times higher than actually achieved. The target of the national Care and Treatment Plan (CTP) was also not reached. We aimed at estimating the capacity for ART provision and created five scenarios for ART production given existing resource limitations.

Methods: A situation analysis including scrutiny of staff factors, such as available data on staff and patient factors including access to ART and patient losses, made us conclude that the lack of clinical staff is the main limiting factor for ART scale-up, assuming that sufficient drugs and supplies are provided by donors. We created a simple formula to estimate the number of patients on ART based on availability and productivity of clinical staff, time needed to initiate vs maintain a patient on ART and patient losses using five different scenarios with varying levels of these parameters.

Findings: Our scenario assuming medium productivity (40% higher than that observed in 2002) and medium loss of patients (20% in addition to 15% first-year mortality) coincides with the actual reported number of patients initiated on ART up to 2008, but is considerably below the national CTP target of 90% coverage for 2009, corresponding to 420,000 on ART and 710,000 life-years saved (LY's). Our analysis suggests that a coverage of 40% or 175,000 on treatment and 350,000 LY's saved is more achievable.

Conclusion: A comparison of our scenario estimations and actual output 2006-2008 indicates that a simple user-friendly dynamic model can estimate the capacity for ART scale-up in resource-poor settings based on identification of a limiting staff factor and information on availability of this staff and patient losses. Thus, it is possible to set more achievable targets.

Show MeSH
AIDS deaths.Estimated number of AIDS deaths without ART from 1999 to 2009 and with ART according to the five scenarios and to the CTP from 2004 to 2009.
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pone-0005294-g004: AIDS deaths.Estimated number of AIDS deaths without ART from 1999 to 2009 and with ART according to the five scenarios and to the CTP from 2004 to 2009.

Mentions: We also estimated the effect both of the CTP and the five scenarios on the number of projected AIDS deaths (figure 4). The number of new AIDS deaths were generated by entering the Estimation and Projection Package (EPP) data [32] from the sentinel surveillance sites in Tanzania of the latest surveillance round 2005/6 [10] into the Spectrum model [9]. The number of patients initiated on ART (new patients) according to the CTP and the four scenarios were then deducted from the projected new AIDS deaths without ART to calculate the effect of ART implementation. Life-years (LY) saved were defined as the sum of half of the patient-years saved for 1st year-patients, i.e. the average of six months saved per year, assuming an equal enrolment rate across the year, and patient-years saved for old patients during the same period.


Estimating the capacity for ART provision in Tanzania with the use of data on staff productivity and patient losses.

Hanson S, Thorson A, Rosling H, Ortendahl C, Hanson C, Killewo J, Ekström AM - PLoS ONE (2009)

AIDS deaths.Estimated number of AIDS deaths without ART from 1999 to 2009 and with ART according to the five scenarios and to the CTP from 2004 to 2009.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2667213&req=5

pone-0005294-g004: AIDS deaths.Estimated number of AIDS deaths without ART from 1999 to 2009 and with ART according to the five scenarios and to the CTP from 2004 to 2009.
Mentions: We also estimated the effect both of the CTP and the five scenarios on the number of projected AIDS deaths (figure 4). The number of new AIDS deaths were generated by entering the Estimation and Projection Package (EPP) data [32] from the sentinel surveillance sites in Tanzania of the latest surveillance round 2005/6 [10] into the Spectrum model [9]. The number of patients initiated on ART (new patients) according to the CTP and the four scenarios were then deducted from the projected new AIDS deaths without ART to calculate the effect of ART implementation. Life-years (LY) saved were defined as the sum of half of the patient-years saved for 1st year-patients, i.e. the average of six months saved per year, assuming an equal enrolment rate across the year, and patient-years saved for old patients during the same period.

Bottom Line: A situation analysis including scrutiny of staff factors, such as available data on staff and patient factors including access to ART and patient losses, made us conclude that the lack of clinical staff is the main limiting factor for ART scale-up, assuming that sufficient drugs and supplies are provided by donors.A comparison of our scenario estimations and actual output 2006-2008 indicates that a simple user-friendly dynamic model can estimate the capacity for ART scale-up in resource-poor settings based on identification of a limiting staff factor and information on availability of this staff and patient losses.Thus, it is possible to set more achievable targets.

View Article: PubMed Central - PubMed

Affiliation: Division of International Health (IHCAR), Karolinska Institutet, Stockholm, Sweden. Stefan.Hanson@ki.se

ABSTRACT

Background: International targets for access to antiretroviral therapy (ART) have over-estimated the capacity of health systems in low-income countries in Sub-Saharan Africa. The WHO target for number on treatment by end 2005 for Tanzania was 10 times higher than actually achieved. The target of the national Care and Treatment Plan (CTP) was also not reached. We aimed at estimating the capacity for ART provision and created five scenarios for ART production given existing resource limitations.

Methods: A situation analysis including scrutiny of staff factors, such as available data on staff and patient factors including access to ART and patient losses, made us conclude that the lack of clinical staff is the main limiting factor for ART scale-up, assuming that sufficient drugs and supplies are provided by donors. We created a simple formula to estimate the number of patients on ART based on availability and productivity of clinical staff, time needed to initiate vs maintain a patient on ART and patient losses using five different scenarios with varying levels of these parameters.

Findings: Our scenario assuming medium productivity (40% higher than that observed in 2002) and medium loss of patients (20% in addition to 15% first-year mortality) coincides with the actual reported number of patients initiated on ART up to 2008, but is considerably below the national CTP target of 90% coverage for 2009, corresponding to 420,000 on ART and 710,000 life-years saved (LY's). Our analysis suggests that a coverage of 40% or 175,000 on treatment and 350,000 LY's saved is more achievable.

Conclusion: A comparison of our scenario estimations and actual output 2006-2008 indicates that a simple user-friendly dynamic model can estimate the capacity for ART scale-up in resource-poor settings based on identification of a limiting staff factor and information on availability of this staff and patient losses. Thus, it is possible to set more achievable targets.

Show MeSH