Table of Contents

1. Executive Report for Senior Management……. 3
2. Technical Report on Data Analysis.. 5

Countries Selection.. 6

Exploratory Analysis. 6

Inferential analysis. 11

Final Country Selection……………. 14

• Technical Report on Multi-Attribute Value Analysis…. 16

The selection of the diseases…………….. 16

V.I.S.A Model……… 20

Stakeholder’s Results…………………………. 23

Final List…………….. 25

Sensitivity Analysis. 25

Recommendation.. 26

References…………….. 28

1.     Executive Report for Senior Management

The report is designed to target SAFH’s aims to select a country for SAFH funding, with main goal to reduce the disease burden of the country and reduce out-of-pocket healthcare expenditure. Part 2 strats with analysing the GDB and GHE Data set which representing data from 2000 to 2016. Members shortlisted the ten countries based on five chosen indicators that are: (1) Current Health Expenditure (CHE) per Capita in US$, (2) Compulsory Financing Arrangements (CFA) as % of Current Health Expenditure (CHE), (3) Population, (4) Out-of-pocket (OOPS) as % of Current Health Expenditure (CHE) and (5) External Health Expenditure (EXT) as % of Current Health Expenditure (CHE). Members choose the lowest six ranking countries that are Benin, Burkina Faso, Burundi, Guinea, Niger and Zambia, for further analysis.

Four regression models were developed on four models: (1) Model 1. GDP vs. CHE, (2) Model 2. DALYS per Capita Vs CHE (3) Model 3. EXT vs CHE and Model 4. EXT vs DALYS per Capita. From the four regression models it is found that Benin and Guinea found to have a good linear relationship in all.

Members choose the country for SAFH funding based on the lowest Out-of-pocket as % of Current Health Expenditure (CHE) and External Health Expenditure (EXT) as % of Current Health Expenditure (CHE) which is Niger.

Part 3 starts with the process of shortlisting the five diseases with major effect on Burkina Faso which has been selected because it had the highest average. GBD’s Global Burden of Disease Data was used as a basis for the review, analysis and shortlisting, were a ranking is done for each disease and the percentage of increase is calculated according to the number of deaths, incidents and DALYs for all ages and sexes.

This resulted in shortlisting the below diseases:

• Nutritional deficiencies
• Malaria
• Neonatal disorders
• Lower respiratory infections
• Diarrheal diseases

Subsequently, multi criteria decision making was used by CAUSE framework and value tree using V.I.S.A software to finalize a recommendation to SAFH for funding. A value tree was developed including criteria (GBD Data Set Measures) and sub criteria (age groups), which then a qualitative scoring were used thru calculating the mean/average from year 2000 to 2017 for each disease within each age group, the highest average were given excellent scoring and the lowest were given the worst scoring, the excellent indicates the disease has high impact on the age group, after completing the ranking, a weighting method was implemented in the value tree by considering the stakeholders main concern and interests, each was provided with different prioritization list of diseases, then the analyst has included the final recommended list for SAFH to fund.

In addition to that, a sensitivity analysis has been applied for all measures (Dalys, Deaths and Incidence) to identify the relation between each measures and the five diseases, in the analysis, it was noticed how in Deaths and Incidence the model was having high sensitivity, while DALYs is having low level of sensitivity.

The analysis was based on the data provided which has the following limitation:

1. An updated data should be provided for better recommendation for SAFH from year 2018 to 2019
2. GHE report was including data up to 2016, while GBD included data up to 2017
3. There were no data available for some of the diseases in GBD report
4. The interests of each stakeholders were guessed by the analyst as it was not provided

2. Technical Report on Data Analysis

The World health organisation has provided the Sub-Saharan African Fund for Health (SAFH) data set for the group analysis to shortlist a country from the ten suggested to consider funding healthcare programme. The purpose of the report is to explore the data provided to decide to select a country from the ten for SAFH funding. SAFH funding has main two goals that is to reduce the disease burden of the country and reduce out-of-pocket healthcare expenditure.

The first part of the report will show the evaluation of the ten countries and will aim at narrowing down the list of countries until a final worst-off country is selected.

This section will use two elements one is the technical analysis of the six countries and second is the linear regression.

The GDB and GHE Data set is used by the members representing data for 17 years including data from year 2000 to 2016. As a result, there will be a limitation for accuracy within the analyst will not be able to know the situation before 2000 or after 2016. During analysis it was found that some data were missing, according to the analyst, the missing data were excluded from assessment and comparison among countries. The missing data could help members in alter the decision making to countries that were excluded.

Members determined five indicators that are important to support SAFH funding to a country.

Technical Analysis Part 2

The data used in the below section is available in the Excel file ‘Indicators’ sheet

Countries Selection

The below table represent the overall ranking of the countries based on the 5 chosen indicators selected by the group members. Countries scoring the six poorest-off highlighted in grey were chosen by the group members for further analysis (Excel file ‘6 countries Selection’ sheet).

Exploratory Analysis

The Exploratory analysis data is available in the Excel file ‘Country Analysis’ sheet

1. Current Health Expenditure (CHE) per Capita in US$

The indicator represents the average amount spent by each country paid on the entire current healthcare for its people. The CHE includes the healthcare goods and services consumed by each country in each year and exclude the capital health expenditures such as buildings, machinery, stocks of vaccines for emergency or breakouts, as well as administration of the health systems. The CHE is used by the group members to compare countries internationally in standardised framework.

In 2016 it is observed that Zambia is the highest CHE from 2000 to 2016 that is for 17 years. Therefore, Zambia is less needy for the SAFH funding. In average Zambia has spent 33$ more than Burundi. In Standard deviation it is noticed that Zambia is the highest and higher than Niger by 10.89. Last, in the percentage change it is noticed that Zambia has the highest percentage change scoring 56% than other five countries.

• Compulsory Financing Arrangements (CFA) as % of Current Health Expenditure (CHE)

This indicator presents the amount spent by a country on healthcare through compulsory financing agreements such as governmental schemes and compulsory insurance.

It is noticed that Burkina Faso spent the highest in 2016 than Zambia that is 54. In average score Burkina is the highest than Niger by 26$. Burundi is the highest in standard deviation compared to Burkina Faso by

5.79 and Zambia by 3.64. In the percentage change it is noticed that Burkina Faso is the highest than Zambia and Burundi, Niger has the least percentage change. In the graph it is noticed that Burkina Faso contribute the most to healthcare through mandatory funding which can consider it as worst-off and consider it for SAFH funding.

• Population (in thousands)

The population indicator represents the number of individuals per year along with yearly changing individuals from births, deaths, and yearly migration numbers.

In the population graph it is noticed that Niger has the highest population number since 2003 for 14 years. Burundi is the lowest population numbers for 17 years. In the table it is observed that Niger is the highest scoring 20,673. It is agreed that the country with the highest population will possibly need the SAFH funding. Calculating the population led to determining the DALYs deaths (all ages, all genders, and all causes) per Capita to measure country’s disease problem aiding to early death of individuals.

The highest DALYs (deaths) in all ages, genders, causes per Capita since 2006 to 2016 is Burkina Faso. In this indicator is observed that Burkina Faso population is the second highest and the highest in death rates. As a result, group members choose Burkina Faso for SAFH funding in the population indicator.

• Out-of-pocket (OOPS) as % of Current Health Expenditure (CHE)

The OOPS indicator presents the percentage of the current healthcare expenditure representing the

country’s healthcare funding from households.

The chart shows that Guinea with the highest among the five other countries in 2005 to 2014. In last two years Niger is the highest score of 59. In the average Guinea is higher than Niger by 2$. It is reflected that Burundi, Guinea and Zambia similar high standard deviation that is 7. In the percentage change the lowest is Burundi result in 23 and the highest is Niger result in 57. From this information is agreed that Niger is the worst-off indicator.

• External Health Expenditure (EXT) as % of Current Health Expenditure (CHE)

The EXT indicator shows the amount that country spends on healthcare from external funds.

Niger has the lowest reliance on the external health expenditure for most of the year. On the other hand, Zambia has the highest reliance on the external health expenditure since 2008 to 2016. Determining the highest and lowest external health expenditure enables members to consider the worst-off country that is Niger and consider it for SAFH funding.

Considered for SAFH funding-

1. Current Health Expenditure (CHE) per Capita in US$ – Zambia
2. Compulsory Financing Arrangements (CFA) as % of Current Health Expenditure (CHE)- Burkina Faso
3. Population (in thousands)- Burkina Faso
4. Out-of-pocket (OOPS) as % of Current Health Expenditure (CHE) – Niger
5. External Health Expenditure (EXT) as % of Current Health Expenditure (CHE) – Niger

Inferential analysis

This section will explain the relationships and the countries that are most affected by any changes in funding. Accordingly, the country that is the best choice for funding will be selected.

Model 1. GDP vs. CHE

Independent Variable: Gross Domestic Product in million current US$ Dependent Variable: Current Health Expenditure (CHE) per Capita in US$

All the countries can be considered as having a strong positive correlation between the Gross Product and the CHE per capita. This comes naturally since the income stream of the country increases and so does the expenditure on the Healthcare. As can be noticed from the above that the country with the strongest linear relationship is Burkina Faso, and the lowest is Burundi. Below is the line fit plot for Burkina Faso, as shown from the chart below that its positively correlated.

Moreover, the r square that is the percentage variance in the dependant the is explained by the independent variable. The strongest r square in Burkina Faso and the weakest in Guinea.

Model 2. DALYS per Capita Vs CHE

Independent Variable: Current Health Expenditure (CHE) per Capita in US$ Dependent Variable: DALYs per Capita (all ages, all genders, all causes)

Almost all countries have a strong negative relationship between the Current Health expenditure and the DALYS. This means that the more the country spends the number of lost years also decreases in turn, this signals a good health care as the expenditure increases. The strongest negative correlation is Burkina Faso and the least is Guinea. The below line fit plot graph shows the negative correlation of Burkina Faso.

Model 3. EXT vs CHE

Independent Variable: External Health Expenditure (EXT) as % of Current Health Expenditure (CHE) Dependent Variable: Out-of-pocket (OOPS) as % of Current Health Expenditure (CHE)

The only two countries with strong negative correlation are Benin and Burkina Faso. This simply means that countries that receive more foreign funding can reduce the healthcare pressure on its people. This is important for SAFH since they need to predict the OOPS if they achieve a certain amount of funding. Benin had the strongest negative relationship as shown below. Zambia had the weakest.

Model 4. EXT vs DALYS per Capita

Independent Variable: External Health Expenditure (EXT) as % of Current Health Expenditure (CHE) Dependent Variable: DALYS per Capita (all ages, all genders, all causes)

All countries showed a negative relationship except Guinea. Nigeria had the strongest negative relationship and it portrayed in the line fit plot below. This indicator is important for SAFH because it indicates these countries that are able to reduce the disease burden if they received extra financing, which means that SAFH can increase the funding to these countries like Niger and actually make a significate impact. The lowest country is Burkina Faso.

Niger Model 4 EXT vs DALYS 0.14 0.12 0.1 0.08 0.06 0.04 0.02 0 0 2 4 6 8 10 12 14 16 External Health Expenditure (EXT) as % of Current Health Expenditure (CHE) DALYs per Capita Predicted DALYs per Capita Final Country Selection

The table below concludes the final result of the regression analysis, all the relationships are portrayed below, the positive tick mark means that there is a good linear relationship while the cross shows that the two variables don’t have any relationship or it’s a weak relationship.

The table shows that only Benin show significantly strong positive results in all models, which means that SAFH can use the regression analyses to decide whether their funding will have an impact in these indicators chosen. Whilst Guinea, Nigeria and Zambia scored positively in three out of the four models.

However, the country to be selected is Niger since it scored the highest in Out-of-pocket (OOPS) as % of Current Health Expenditure (CHE) and External Health Expenditure (EXT) as % of Current Health Expenditure (CHE) in the last two years and also increased population in the past five years as evident in the below charts. SAFH should consider financing Niger since it’s the most country with the financing need and the regression model will assist SAFH in predicting the overall impact in these indicators.

3. Technical Report on Multi-Attribute Value Analysis

The data used for this part is shown in excel file Part3.

The selection of the diseases:

The section will be focusing on selecting the most impactful diseases in Burkina Faso since this country has scored the highest overall in initial analysis.

It would be challenging to convince the stakeholders on which diseases the funds will be invested in, SAFH wants to have a justifiable and systematic analysis to shortlist 5 diseases where the funds will be focused on. a multi-attribute value analysis was used as well as V.I.S.A software to support the evaluation, shortlisting of the diseases as well as to establish the basis of the final recommendation.

The analysis starts by viewing and analysing the GBD data to shortlist the diseases based on incidences, deaths, and DALYs related to those diseases for all ages and sexes. Accordingly, the ranking was done, shown on the blow figures, where the diseases have been identified with the highest mean under each category as well as the highest percentage increase for each disease.

This analysis was conducted to identify the diseases that show up the most in both the highest mean and highest percentage increase analysis as it will interpret its strong effect on incidences, deaths, and DALYs , which only showed “Nutritional Deficiencies” repeated in both evaluations. Other diseases like Malaria, Lower respiratory infections, and Diarrheal are repeated in diseases with the highest means for all causes but not shown at all in the diseases with the highest percentage increase, oppositely diseases like Dengue, Prostate cancer and Breast cancer repeated in all causes with the highest percentage increase but did not shown at all in the diseases with the highest mean as presented in the below bar charts.

With only one disease “Nutritional Deficiencies” replicated in both analyses conducted above it was shortlisted into the list of priority diseases. It was then required to work on further evaluation to shortlist the other four diseases. The decision of shortlisting the remaining diseases was based on the calculation of the proportions of each disease signified out of the total mean for the period as shown in the below charts:

1. Criteria

In all these charts, diseases with a high proportion of incidences, deaths, and DALYs are Malaria, Neonatal disorders, Lower respiratory infections, and Diarrheal Diseases. Diseases like Cardiovascular Dengue, Prostate cancer, Breast cancer, and others had low or unavailable proportions and therefore was not shown in the charts.

Centred by all the above analysis we have shortlisted the below diseases that SAFH should focus on as they have the highest impact on the country.

Other alternatives can be applied to find the most harmful disease, such as multi criteria decision making, which is a tool used for complex decision making, this tool will assist in shortlisting and selecting the most logical option from the five diseases identified above.

For MCDA, CAUSE framework has been used for creating the value tree using V.I.S.A software to provide the most suitable and appropriate decision.

GBD data set has three measuring criteria for Burkina Faso, DALYs (Disability-Adjusted Life Years), deaths and Incidence. These criteria have been used to create the decision tree, since it’s the main measure, followed by five age groups which has been used as sub criteria (below 5, Between 5-14, between 15-49, between 50-69 and above 70)

• Alternative

Disease were shortlisted to five most impacted diseases on Burkina Faso based on measures given in the report, then a ranking will be done using the value tree.

• Uncertainties

Uncertainty can consider factors which could be affecting MCDA, for instance the final decision and analysis can vary between the analyst and the stakeholders, in addition the main impacts on the diseases growth are not provided in neither GBD nor GHE, for example, studies shown that Malaria is having seasonal growth trend. (J, 2016) which can be an important cause to consider while implementing MCDA.

• Stakeholders

Including parties who have interests in the problem and decision making, and are involved formally in the decision-making process, the analyst should consider their interests and perspectives and investigate the criteria that might be interested to them.

Stakeholders involved in the decision making are:

• Ministry of Health
  • Other Governmental Branches
  • Advocacy groups
  • Donors of SAFH

• External / Environment

There are many factors to be considered for decision making, for instance the country has suffered from problems such as droughts and revolutions, which affected their economy and quality of healthcare given to the citizens. (Anon., 2018), SAFH should consider these alarming conditions of the country when providing the funds.

V.I.S.A Model

The value tree above was development based on 3 measures (Dalys, Deaths and incidence), in order to run the analyses, MCDA should be in line with SAFH objective which is to identify the most impactful disease in the country. Also, considering various views related to reduction of disease and financial burden and whether the fund should be allocated to the disease that has the most impact on population. Then, age groups provided by the GBD data set were added as a sub criterion.

While developing the value tree model, a qualitative scoring has been found more appropriate for this model, the scoring starts from 0 (minimum disease impact) to 100 (maximum disease impact), the below table will explain further:

For every age group, the average from 2000 to 2017 were calculated for each criteria measure, then the sum of the average were calculated to detect the proportion percentage from the age group , afterwards, the scoring were allocated, for example, 100 has been scored to the highest percentage proportion, as it shown in the below alternative window extracted from V.I.S.A, the qualitative scoring was applied based on the average calculation.

V.I.S.A Alternative Window

Example: Dalys – Age Under 5

The below chart extracted from V.I.S.A, shows that Malaria has maximum disease impact on the country. However, further investigation and exploration will be conducted to verify the result.

A score profile across tree was generated to ensure that all the sub criteria were scored, and initially see the trend in the impact of the five diseases over different age groups. From the profile Diarrheal diseases can be identified as a dominative alternative which dominating Malaria disease in DALY’s, but not necessarily on the remaining criteria.

Based on SAFH objective, which is to decrease the disease burden, the highest weight were given to DALY’s since it’s the most important in calculating health gaps against health expectancies (Organization, 2020), focusing on the age group (15-49), because they contains of different generations in the age groups. In

addition to their capabilities to work and produce to enhance the country’s economy. The remaining weight then distributed to deaths and then incidence.

As it can be seen from the below thermometer and across tree profile, Malaria Disease scored the highest impactful disease, which affected mostly DALY’s and has downward curve in Deaths and incidence, the second impactful disease is Diarrheal related diseases, which relatively has less impact on DALYs and death, and high number of incidence.     Third ranking is Neonatal

disorders, despite having high numbers in DALYs and Incidence, it has the highest number of deaths. The Fourth disease is Lower Respiratory Infections which has similar numbers across all the measures. Finally, the least impactful disease is Nutritional Deficiencies which has reported lowest number of deaths.

V.I.S.A Thermometer and Criteria Profile

Stakeholder’s Results

In this section, the weighting of the value tree will be distributed based on the stakeholder’s main concerns and interests.

• Ministry of Health

From the above weighting, the weighting has been distributed equally among the age groups, since MOH is responsible for all age’s health and would expect the analyst to have similar weighting over all criteria. For that reason, considering deaths prevention is highest priorities by MOH, this measure has been given highest score, followed by Incidence and then DALYs.

The fund is recommended to be allocated to the diseases which has the greatest number of deaths, as per the below ranking:

1. Neonatal Disorders
2. Malaria
3. Lower respiratory Infections
4. Diarrheal Diseases
5. Nutritional Deficiencies

• Other Governmental Branches

The main concerns of the government are reducing diseases infectious and decrease number of deaths as much as possible. For that reason, an equal weight has been given to DALYs and Deaths, while incidence has been given less weight. As for the age group, the government would prefer focusing on population with age group from 5-14 and 15-49, because any disease at age 5-14 can be contained, 15-49 is also important age group as they contribute in economy. As for the remaining age groups, they were given equal less weights.

From the above, a recommendation can be made as per the government criteria set by focusing on diseases which has impact on DALYs and Deaths. The below ranking of the diseases is an alternative prioritization as per the government’s interests. In this case Malaria has been prioritized as it has high level of DALYs and death rates.

1. Malaria
2. Neonatal Disorders
3. Lower respiratory Infections
4. Diarrheal Diseases
5. Nutritional Deficiencies

• Advocacy groups

Advocacy groups main concern is to be in line with the government interest, in addition, caring more about prevention of the disease itself. For that reason, more weight has been given to DALYs followed by deaths and then incidence.

• Donors of SAFH

Moreover, more weights have been given to age groups below 5, 5-14 and 15-49, since it easier to diagnose and provide treatment age groups between 0-49, the weighting below using the value tree is reflecting that.

Malaria scored the highest impactful diseases related to number of DALYs, and Nutritional Deficiencies scored the lowest.

1. Malaria
2. Diarrheal Diseases
3. Lower respiratory Infections
4. Neonatal Disorders
5. Nutritional Deficiencies

Since Donors are the main resource of funding the SAFH, they are mostly concerned with number of Incidence, followed by Deaths and then DALYs, donors main concerns is to focus on diseases which require long-term treatment, which eventually causing major hospital bills. Once incidence and deaths number are being reduce, this will reflect on decreasing number of DALYs.

Donors main age group concerns are from 0-14, because this age group can be better in responding to treatments.

The recommended list for the donors is as below, Diarrheal Diseases has scored the highest related to number of incidences.

1. Diarrheal Diseases
   1. Nutritional Deficiencies
   1. Malaria
   1. Lower respiratory Infections
   1. Neonatal Disorders

Final List

The below table will contain the final ranking of Diseases based on the stakeholder’s preferences and main

concerns.

Based on the table above, the below ranking of the most impactful diseases were prioritized by the stakeholder’s preference and analyst recommendation, Malaria has been ranked the first because on its recurrence by three parties, Diarrheal Diseases has ranked the second, Neonatal Disorders is the third ranking, Lower respiratory infections in the fourth and finally, Nutritional deficiencies with the majority of the fifth prioritization ranked the last. SAFH should focus the fund on this prioritization for Burkina Faso.

1. Malaria
2. Diarrheal Diseases
3. Lower Respiratory Infections
4. Neonatal Disorders
5. Nutritional Deficiencies

Sensitivity Analysis

The sensitivity Analysis has been applied on all three measures (DALYs, Deaths and Incidences) against the 5 diseases.

1. DALYs

As it can be seen from the above graph, the current weights related to DALYs has been set on 0.5, the priorities of the diseases seem no to be changed if the weights is less or more, Malaria and Diarrheal Diseases have moderately steep slopes and when Malaria increases, Diarrheal Diseases decreases followed by Lower Respiratory infections and Nutritional deficiencies with the same moderately level of sensitivity, while Neonatal disorders is steeper from the remaining diseases, this indicates that this diseases is less sensitive comparing to the others.

• Deaths

The weight set related to Deaths and its effects on diseases were at approximately 0.1, which indicates that

the prioritization of the diseases will not change, unless it moves on the right side (more than 0,1), this will change some of the prioritization (Malaria will become higher than Diarrheal Diseases), Neonatal Disorders and lower respiratory infections has a positive slopes in comparison with Malaria, The graph indicates that the sensitivity is high when its response to the weight of deaths.

• Incidences

For this graphs, the weight is set at 0.4, which means that regardless the prioritization will remain the same unless it reaches 0.9, Nutritional deficiencies will overtake Malaria, this indicated that if incidence valued less then it will be changes in the prioritization, the overall model shows that the sensitivity level is high when its response to the weight of incidence.

Recommendation

Based on the SAFH main goals, which they are:

1. Reduce Disease Burden
2. Reduce Financial Burden
3. Addressing the most impactful disease and vulnerability
4. Fund the most impactful diseases

The analyst is recommending funding the most impactful diseases in Burkina Faso which ranked the first and second from (Malaria and Diarrheal Diseases), focusing on funding these diseases will improve generally the heath conditions and reduces numbers of DALYs, Deaths and Incidence, as well as reducing disease and financial burden, the full list diseases prioritized are:

1. Malaria
2. Diarrheal Diseases

• Lower Respiratory Infections
• Neonatal Disorders
• Nutritional Deficiencies

The 5 diseases were narrowed from the GBD data set based on their effect on number increase of three measures given, DALYS, deaths and incidences, and after utilize MCDA and taking into consideration stakeholders’ main interests, the diseases list were ranked and prioritized.

The analyst is recommending the final decision to not depend solely on the data set provided, but also should include data for year 2018-2019 as it was not included in the data set, also, some diseases have no data which affected the shortlisting of the diseases and was found unreliable in final shortlisting.

References

Anon., 2018. BBC. [Online]

Available at: https://www.bbc.com/news/world-africa-13072774 [Accessed 11 August 2020].

J, M., 2016. NCBI. [Online]

Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4836195/#:~:text=Malaria%20incidence%20showed%2 0a%20clear,1000)%20but%20never%20reached%20zero.

[Accessed 11 August 2020].

Organization, W. H., 2020. World Health Organization. [Online]

Available at: https://www.who.int/quantifying_ehimpacts/publications/en/9241546204chap3.pdf?ua=1 [Accessed 11 August 2020].

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