The index is computed as a ratio of the health facilities with available and affordable medicines for primary health care over the total number of the surveyed health facilities:
For this indicator, the following variables are considered for a multidimensional understanding of the components of access to medicines:
- A core set of relevant essential medicines for primary healthcare
- Regional burden of disease
- Availability of a medicine
- Price of a medicine
- Treatment courses for each medicine (number of units per treatment & duration of treatment)
- National poverty line and lowest-paid unskilled government worker (LPGW) wage
- Proxy for quality of the core set of relevant essential medicines.
The index is measured for each facility separately. Then a proportion of facilities that have accessible medicines is computed. The following steps must be taken to compute the index at the facility level:
- Review and selection of the core basket of medicines for primary health care
- Estimate weights for the defined medicines based on regional burden of disease
- Measure the two dimensions of the access to medicine
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-
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- Availability
- Affordability
- Combine the two dimensions on availability and affordability (access to medicines)
- Apply weights to the medicine in the basket according to the regional prevalence of the diseases that are cured, treated, and controlled by these medicines
- Identify whether a facility has a core set of relevant essential medicines available and affordable
The next two steps are calculated at the country level across all the surveyed facilities:
- Calculate the indicator as the proportion of facilities with accessible medicines in the country
- Consideration of the quality of the accessible medicines in the country using a proxy
Below is a more detailed procedure of the index computation.
Step 1: Review and selection of the core basket of medicines for primary health care
For some of the disease categories captured by the proposed basket of medicines, a therapeutic category of medicine has been specified (e.g. statins, beta blockers, corticosteroids, etc.) and a specific medicine must be identified for monitoring. For example, beclomethasone is used to treat non-communicable respiratory disease and if it is not supplied in a particular country for some policy or market reason, an alternative corticosteroid inhaler must be included in the analysis. In other cases, more than one medicine should be included in the basket per disease category. This will require a preliminary review of the basket before starting the data collection process.
Step 2: Estimate weights for the defined medicines based on regional burden of disease
The following points must be considered when computing medicines’ weights:
- Equal weights are assigned to medicines that are used to treat, cure, and control the same disease(s) (e.g. gliclazide (or other sulfonylurea), metformin and insulin regular are assigned equal weights according to the diabetes disease burden).
- For a medicine indicated for multiple diseases, DALYs values for each disease are summed.
- For a medicine used for treating conditions for children (four medicines from the list) sum of DALYs is computed for males and females at the age between 0 and 14 years.
- For some of the medicines which cannot be assigned to a specific disease (e.g. paracetamol) the weight is computed as (where T is a total number of medicines in the surveyed basket) assuming equal use of the medicine relative to other medicines in the core list.
- For medicines not in the list but “suggested for monitoring” by the country, weight is computed as assuming a minor relevance of these medicines for this indicator and to avoid major issues in inter-country comparison.
To estimate the weight for each medicine, the following steps have to be undertaken:
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- Assign each medicine in the basket to one or several disease(s) that are treated/cured/controlled by that medicine (Annex 1 table 2)
- Assign to each disease the corresponding DALYs (if several diseases are treated with the same medicine, compute sum of these DALYs accordingly) [ ]
- Compute total sum of the DALYs per medicine [ ]
- Compute weight of each medicine as a proportion of the medicine specific DALYs to the total sum of DALYs in the basket [ ]:
As an example, the weights computed across regions for year 2015 are represented in Annex 2 table 2.1 and 2.2.
Step 3: Measure the two dimensions of access to medicine
Availability and affordability of medicines must be measured and transformed (when necessary) into the format of a binary variable.
- Availability is measured as a binary variable coded as “1” when the medicine is in the facility on the day of the survey and coded as “0” otherwise. This approach is currently used in the HAI/WHO methodology.
- Affordability is computed following these steps:
3.1 Compute daily price per dose of treatment for each medicine (price per DDD) in the selected basket of medicines
WHO treatment guidelines provide the needed information to compute DDD.
DDD of a medicine is defined using the following formula:
where:
- Units per treatments are tablets/vials or other forms that are needed for an individual with the average severity of the disease per one course of treatment of a duration of one month (365 days per year / 12 months per year = 30.42 days given 30 or 31 day per month), and
- Medicine prices are calculated per unit (per tablet/vial/other form) requiring adjustments for gram or milligram according to the potency.
This ratio varies between “0” and infinity and is measured in local currency units per day [LCU/d].
Information on the number of units per treatment is specified in Annex 3. The price per DDD can be measured in per day or per month.
3.2 Define National poverty line (NPL) and minimum wage of the LPGW for the analysed country
National poverty line (NLP): countries periodically recalculate and update their poverty lines based on new survey data and publish this information in their national reports on poverty. To adjust the latest available NPLs to the relevant year of analysis (when needed) information on the Consumer Price Index (CPI) in the analysed country has to be used to account for deflation/inflation.
National poverty reports consistently provide information on the NPLs in local currency units but often refer to different recall periods from country to country (NPL can be measured per day, per month or per year). For consistency, NPL has to be adjusted to be measured per day [LCU/d].
The wage of the lowest paid unskilled government worker (LPGW): is estimated and published in the ILOSTAT database. For countries with the latest available data collected in a year different from the year of analysis, LPGW wage is actualised using the CPI conversion factor.
ILO provides information on the minimum LPGW wages in local currency units per month. LPGW wage has to be adjusted to be measured per day as well [LCU/d].
The NPL and LPGW wage can be measured in per day or per month.
3.3 Compute extra daily wages (EDW)
First, the LPGW wage is compared to the NPL and if it is lower, medicine is considered unaffordable. In this case, only medicines with a price equal to zero will be considered affordable.
Next, the affordability is measured via the number of extra daily wages (EDW) that are needed for the LPGW to pay for one-month course of treatment using the formula below. In particular, the number of extra daily wages can be computed using the following formula:
3.4 Transform EDW variable into a binary format
Following the definition, medicine is considered to be affordable when the sum of NPL and price of a daily dose of the treatment is equal to or less than the minimum daily wage of the LPGW:
Hence, the affordability of medicines is also measured as a binary variable that is coded as “1” when the medicine is affordable and “0” otherwise.
When the price of the medicine is 0, there is no need for the above-mentioned computations and the medicine is considered affordable (i.e. “1”). If all medicines in the country are provided free of charge, all medicines are directly marked as affordable and further computation of the index depends on the availability of these medicines.
Step 4: Combine the two dimensions on availability and affordability (access to medicines)
In this step, the two dimensions of access to medicines (availability and affordability) are combined into a multidimensional index.
The construction of a multidimensional index is based on the union identification approach proposed by S. Alkire and G. Robles.
The combination of the dimensions of medicines can be built in matrix form:
This matrix contains performance for n objects of analysis (specified in rows) in d dimensions (specified in columns). The performance of any object in all dimensions is represented by the d-dimensional vector for all . The performance in any dimension for all objects are represented by the n-dimensional vector for all . Overall, an index should be computed via two main steps: identification and aggregation. An example of how to combine the 2 dimensions can be found in Annex 4.
Step 5: Apply weights to the medicine in the basket according to the regional prevalence of the diseases that are cured/treated/controlled by these medicines
After identifying the access variable, medicines in the basket have to be weighted according to the prevalence of the disease(s) that these medicines are used to cure/treat/control using the weights identified in step 2 and provided in Annex 2, tables 2.1 and 2.2. This is performed by multiplying the access variable with the medicine weights:
Figure 1. Achievement matrix of weighted access to medicine
![](data:image/png;base64,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)
Step 6: Identify whether a facility has a core set of relevant essential medicines available and affordable
The following computations must be undertaken in this step:
6.1 Calculate proportion of medicines that are accessible (both available and affordable) in each facility
Because medicines are weighted, the proportion is computed as a weighted sum of medicines that are both available and affordable (accessible) in each facility using the following formula:
This variable is then transformed into a percentage and varies from 0 to 100.
The computed number of accessible medicines accounts for the importance of the analysed medicines in the country. In particular, if a medicine with a higher weight (for example hypertension) is not accessible, the index will be sensitive to this and will demonstrate the lack of access. On the contrary, if a medicine has a low weight (i.e. approaching zero, such as antimalarial medication in a non-endemic country) and is not accessible, the index will not be affected.
6.2 Mark facilities that have 80% or more of available and affordable medicines
The computed variable “access” is then transformed into the binary format identifying facilities that have the core basket of essential medicines available and affordable versus facilities that do not. A threshold of 80% is applied in order to transform the “access” variable into a binary format. In particular, at least 80% of all the medicines surveyed in a facility have to be both available and affordable. The transformation is made using the following formula:
This threshold is agreed upon and adopted by the WHO Global Action Plan on Non-Communicable Diseases and used as a reference in this proposed methodology.
Step 7: Calculate the indicator as the proportion of facilities with accessible medicines in the country
The proportion of facilities that have reached the 80% threshold is calculated out of the total number of surveyed facilities in a selected country using the following formula:
The computed indicator is a proportion that will then be converted into a percentage between 0-100%.
Step 8: Consideration of quality of the accessible medicines in the country using a proxy
The country level of medicine regulatory capacity assessed using the WHO NRA GBT is used as a proxy of the quality of the accessible medicines. The countries with a WHO Listed Authority (WLA corresponding to maturity level 3 and above) will be flagged to indicate the assured quality component.
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