Burden of Disease Estimates for 2011 and the effects of the by hedongchenchen

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									Table 7 Burden of disease (in DALYS) associated with the 2011/12 “resource based” EHP2 in
Malawi for each of the chosen interventions


 Burden of Disease Estimates for 2011 and the
 potential effects of the Essential Health Package on
 Malawi’s health burden

Introduction
An evaluation of the essential health package (EHP) using WHO Burden of Disease (BOD)
methods suggests significant health gains were made by the first EHP associated with the first
SWAp [1]. An economic appraisal of the second EHP associated with the Health Sector
Strategic Programme (HSSP) is a useful tool to help make priorities and choose best buy
interventions. This appraisal can use various approaches. One approach is to build on previous
work based on Malawi’s Burden of Disease as measured by Disability Adjusted Life Years
(DALYs). Various assumptions and adjustments are required because of the shortage of vital
statistical data. This paper describes the methods used in updating the BOD estimates and how
they have been applied to the proposed EHP (EHP2).

Methods
2011 Burden of Disease
The estimates of the BOD are the foundation of later analysis. The 2002 WHO estimates for
Malawi were assessed in 2004 and found to be robust [2]. These estimates were updated in 2008
and used in the evaluation of the EHP [1]. WHO is in the process of updating their estimates,
using funding from the Gates Foundation, but these are not yet available. The 2002 estimates
have been used to update the Malawi estimates for this study. The following approach was used:
1. Age specific mortality rates were calculated from the 2008 census providing best estimates of
   current infant, child and adult mortality (including females of reproductive age) using
   National Statistics Office (NSO) published life tables created using INDEPTH
   methodology. Actual deaths for 2011 were estimated using the specific death rates and 2011
   population figures.
2. The population figures were calculated using 2008 census data populating the SPECTRUM
   Demproj module. By this approach the population has been calculated to expand from
   13.1m in 2008 to 14.3m in 2011, which is very close to the recent NSO population estimates
   published in November 2010.
3. Incidence rates of the 159 conditions that make up the BOD model have been assumed to
   remain as in 2008 except for:-
       a. HIV/AIDS where incidences of HIV and AIDS have been taken from the 2010
           SPECTRUM projections used by the MOH HIV Unit.
       b. Hypertension and diabetes disability levels increased to reflect results of STEP study
           for disability but not mortality (because the natural history of these conditions is
           unknown in Malawi).
4. Important diseases causing a heavy burden such as malaria have been assessed using recent
   survey data and found to remain similar to those used in 2008.
Potential Burden averted by the EHP
The EHP2 updated model containing the new interventions such as mental health has been used
to calculate the incidence and burden (in DALYS) of preventable or treatable conditions chosen
in the EHP.
Table 7 Burden of disease (in DALYS) associated with the 2011/12 “resource based” EHP2 in
Malawi for each of the chosen interventions
Assumptions used in the cost model for this exercise
The MOH cost model used for the first EHP has been adapted by the Ministry of Health
(MOH) for EHP2. The costs have been revised. Staff numbers have been revised to
accommodate recent revisions of the staff establishment. A number of assumptions have been
used to derive the activity estimated from the model under different funding scenarios. They
are:-
   1. The model has been calibrated using 2009/10 activity based on HMIS data and 2010/11
      estimated costs. The model over-predicts costs by some 25%. This is due to drug and
      staff costs being less than predicted by the model because of staff absences and drug
      stock-outs. The effect is that activity over-predicts beneficial effect by some 20%. The
      model has been re-calibrated to take this into account.
   2. The core scenario used is based on the MOH resource based estimate of budgets in
      2011/12 and 2015/6. It is assumed that 33% of the MOH budget in 2011/2 are used to
      fund non-EHP activity and 66% to fund EHP activity. This reduces to 25% in 2015/6
      as more services become part of the EHP as part of the HSSP. An ideal scenario has
      also been modelled to estimate activity and effect on the burden of disease using the
      MOH “ideal” resource assumptions provided in the HSSP.
   3. Two other scenarios have been modelled. The first assumes a 30% reduction in pool
      funding (equivalent to DFID not supporting the HSSP in any way). Pool funded activity
      is reduced by 30% and earmarked funded activity by 10%. The second assumes no
      DFID pool funding and only discrete funding of projects. In this scenario earmarked
      funded activity is reduced by 5%, pooled funded activity by 12.5% and reproductive
      health activity increased by 20% (assuming no stock out of drugs etc.).
   4. The size of the burden of disease alleviated has been evaluated by assuming one DALY
      alleviated is equivalent to the Gross Domestic Product of Malawi (based on IMF
      projections).
Prevention interventions
Various assumptions were used to estimate the burden of disease that has already been
prevented by prevention interventions such as immunisations as to stop these would have the
effect of increasing these diseases in future. To gauge what would happen if immunisations were
reduced, the Sub-Saharan incidence rates of vaccine preventable diseases have been used to
calculate the effect of a suboptimal immunisation programme, adjusted by the estimated levels of
disease pre-immunisation era.
Clinical treatments
For those interventions involving clinical treatment 2009/10 HMIS data have been used, being
the most recent year of currently available data. Treatments have been adjusted in two ways; by
a factor for treatment effectiveness (as an example, antibiotics work in 84% of times for
pneumonia in children); and by a factor measuring the affected population (as an example, 50%
of adults and 70% of children registered in HMIS as malaria are not, so only half or less of the
number treated will benefit from antimalarial drugs). Treatment effectiveness factors are taken
from recent authoritative sources and referenced in the table.
Summation of benefits of the EHP
The burden of disease calculated in DALYS for each intervention for 2011 (and succeeding
years) can be summed to provide an overall estimate of burden averted by the programme. As
the costs are also contained in the EHP model it will be possible to measure the cost
effectiveness of each and all interventions combined at the levels of activity agreed once funding
is known.
Table 7 Burden of disease (in DALYS) associated with the 2011/12 “resource based” EHP2 in
Malawi for each of the chosen interventions

Results
All results are found in a folder of spreadsheets available on the COM/Community Health
National Research website at
http://www.medcol.mw/commhealth/publications/national%20research/national_research.htm
There are excel files for all 159 conditions listed, and estimates of incidence, prevalence, deaths
and DALYs are available. Summary files are also available for DALYs, deaths, life expectancy,
healthy life expectancy (HALES) and risk factors, by age and sex group. Spreadsheets calculating
the DALYS averted by the EHP scenarios are also available for download.
Deaths and life expectancy
Deaths are less than in 2002 and 2008 (the last time the BOD spreadsheets were updated) and
reflect the crude mortality rates found in the 2008 census (Table 1). HIV/AIDS remains the
leading cause of death in both sexes.
Table 1- Leading causes of death in Malawi in 2011




Table 2 – Life expectancy and Healthy Life Expectancy (HALE) in Malawi in 2011




The life expectancy estimates are similar and slightly longer than the census 2008 estimates of 48
in males and 51 years in females (Table 2).
Burden of disease in DALYS
There has been a reduction in DALYS since previous estimates despite the increase in
population (Table 3). This may partly be due to the last EHP. HIV/AIDS remains the leading
cause, followed by lower respiratory infections.
Table 7 Burden of disease (in DALYS) associated with the 2011/12 “resource based” EHP2 in
Malawi for each of the chosen interventions
Table 3 – Leading causes of Disease Burden in Malawi in 2011 – all ages




The leading cause of disease burden in children remains lower respiratory disease, followed by
malaria and diarrhoeal diseases (Table 4).
Table 4 – Leading causes of disease burden in Malawi in 2011 – children (0 – 15 years of age)




Risk factors
The leading risk factor remains unsafe sex, followed by under-nutrition.
Table 5 – Leading causes of disease burden (DALYs) due to selected risk factors in Malawi in
2011




The burden of disease alleviated by the EHP2
Estimates of disease burden averted and prevented by three scenarios and a baseline using 2010
data are found in Table 6. Estimates of the two components of the burden of disease (measured
in DALYS) years of life lost (YLL) and years lived with disability (YDL) are also shown. The
cost of each package is used to calculate the cost-effectiveness ratio for each scenario.
All scenarios for either year are cost-effective. Using the common yardstick of a cost-effective
ratio less than the country’s GDP Malawi would find a ratio of a whole health package below
Table 7 Burden of disease (in DALYS) associated with the 2011/12 “resource based” EHP2 in
Malawi for each of the chosen interventions
$350, the current GDP for Malawi, good value for money. The 2011/12 ratios are higher than
the baseline for 2010 due mainly to higher levels of staff and their salaries and due to additional
interventions, such as mental illness services, which are not all as cost-effective as those in the
first EHP.
Table 6 – The burden of disease associated with the EHP2 based on actual activity for 2011
and various scenarios for 2011/12 and 2015/6




Clearly the choice of intervention and the level of coverage will affect the cost effectiveness of
the package. The value of each intervention in terms of DALYs averted has been calculated for
2011/2 using the resource based scenario (Error! Reference source not found.Table 7).

References
1. Bowie C, Mwase T (2011) Assessing the use of an essential health package in a sector wide
   approach in Malawi. Health Res Policy Syst 9: 4. doi:10.1186/1478-4505-9-4

2. Bowie C (2006) The burden of disease in Malawi. Malawi Medical Journal 18: 103-110.
Table 7 Burden of disease (in DALYS) associated with the 2011/12 “resource based” EHP2 in
Malawi for each of the chosen interventions

								
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