Health Cluster PiN Guidance · 08/05/2020 · Health Cluster PiN Guidance | August 5th, 2020 3 The...

GHC Guidance: People in Need Calculations

Introduction The following document seeks to provide general guidance on how to produce people in need figures for the Humanitarian Needs Overview (HNO). It has been written

specifically for the process that will be undertaken for the 2021 HNOs, in light of the restrictions to data collection that have arisen due to COVID-19. It is recognized that the data landscape will vary greatly from one location to another and for this reason, the

guidance remains broad and flexible to help encompass all potential situations. Even so, it is possible that some clusters may lack the resources required to utilize this guidance. In

those circumstances ad hoc support will be provided. In order to apply this guidance, it needs to be read in conjunction with the

accompanying Health PiN Calculator. While the included annexes do detail how the indicators are calculated for PiN numbers, it is still necessary to ensure you have the excel

workbook.

Background The (Humanitarian Needs Overview) HNO is designed to support the development of a shared understanding of the impact and evolution of a crisis and inform the Humanitarian

Response Plan (HRP). It is the responsibility of the cluster/sector to provide the required information on health needs to inform the intersectoral HNO. Clusters/sectors are required to contribute to the development of the HNO and must provide people in need (PiN)

figures, ideally disaggregated by gender and age group, as well as flagging the most vulnerable groups and factors associated with “critical problems related to physical and

mental wellbeing”. It is also necessary to quantify the severity of health needs. This information is used in the Humanitarian Response Plan (HRP) to outline the various sectoral

needs and provide an explanation of the various funding requests included in the response plan. The severity of needs is then factored into the intersectoral analysis and overall severity that is, in part, used to help donors identify where to allocate funding. For

this reason, it is extremely important to ensure the HNO is well informed.

In an ideal situation, a coordinated needs assessment would be conducted to help inform Health PiN and severity in conjunction with the available Public Health Information Services (PHIS), but funding (and access) may not always be available for large-scale

needs assessments. If a coordinated needs assessment is not possible, particularly in locations with limited active PHIS, it is necessary to either conduct a sector-wide joint

assessment (funding permitting) or to pool existing harmonized data from completed assessments to create a concerted picture of need across the crisis affected area. When

Health Cluster PiN Guidance | August 5th, 2020

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producing information for the HNO, it is necessary to clearly document where the information came from and the process used to calculate all provided figures.1

Severity When looking at needs it is necessary to understand ‘how bad’ the situation is. In this sense, need can be understood as a simple question, is someone/a household in need

or not? Severity further elaborates by helping to understand how badly in need a person/household/population is. Measurements of severity usually measure either degree or magnitude. Indicators using degree can often be measured at the

household/individual level and help to classify how severe a situation is, while measurements of magnitude (extent) are often determined at the area level helping to

classify how prevalent a situation is. For example, if someone is sick, their symptoms may indicate how severe their illness is, with different symptoms associated with different

degrees of an illness. While measuring the degree of severity is a very useful objective, health needs and health responses are determined at more macro levels and thus must rely on measuring magnitude. For example, severity as it relates to measles vaccinations

looks at the proportion of the population vaccinated and becomes progressively more severe as that proportion becomes lower (e.g. If at least 95% are vaccinated in urban

areas it is considered ok, while less than 85% vaccinated might be considered a severity level of 5). To provide another example, distance to health facilities could be looked at

both ways, it is possible to measure degree by determining different levels of time to reach the nearest health facility (e.g. less than an hour might be severity 2 while 2 hours might be severity 4), or it can be measured by the percent of people living less than an

hours walk from the nearest health facility (e.g. 80% or more living less than an hours walk away is severity level 1, while less than 60% is severity level 5).

People in Need (PiN) PiN refers to the quantification of the number of people presenting needs, disaggregated as appropriate.2 It provides the number of people in need of e.g. health assistance,

broken down by geographic region (the administrative level may vary by crisis but is often set around the District level) and often by category of service (e.g. Health PiN of

reproductive health services). Data are usually disaggregated by women, men, girls, boys as well as displacement status (refugee, IDP, returnee), population group and

people with disabilities.

Purpose The purpose of this document is to provide guidance to produce Health PiN numbers. It is hoped that through the provision of guidance these figures can become more standardized enabling a more comprehensive picture of health need globally. It is also

felt that guidance can help to clarify the process required for Health PiN calculation particularly as health needs do not generally follow the same process as many of the

other sectors and, as such, are not always simple to calculate.

1 The following two sources are useful for providing further details [PHIS Standards and TOOLKIT] &

[GHC Guide] 2 THE TEMPLATE FROM LAST YEAR IS BEING REVISED RIGHT NOW – WILL LINK TO THAT DOCUMENT

ONCE IT IS AVAILABLE.

https://www.who.int/health-cluster/resources/publications/PHIS-Toolkit/en/


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The IASC defines people in need as “those members

[of a population]:

• whose physical security, basic rights, dignity, living conditions or livelihoods are threatened or have been disrupted, AND

• whose current level of access to basic services, goods and social protection is inadequate to re-establish normal living conditions with their accustomed means in a timely manner without additional assistance.”3

The humanitarian community currently classifies six population groups (though these are usually disaggregated into additional groups that will be explained in detail further on).

The diagram above shows how PiN figures fit within the wider population group. Among the population affected by a given crises, a subset is identified as ‘In Need’. In the case

of the Health Cluster, this would mean people identified as being in need of some form of health assistance.

Population The first step for any needs identification is clearly outlining the total population. In some locations this is a straightforward process and merely requires access to agreed

population data. Yet, in many humanitarian locations this process is not so simple. Population figures are generally based on census data, after a census is completed (which is a very time consuming process), each year the population figures are updated

based on projections. Normally, a new census would be conducted after a certain interval, for example in the US, UK, Italy, Thailand and China it is run every 10 years.

However, in many humanitarian settings, population data are not so easy to come by,

and even when it can be found, it is often unreliable. There are some countries where cyclical census are not run. For example, Lebanon has not run an official census since 1932. Sometimes, there are large gaps between census, such as in Pakistan where there

was a 19 year gap between the most recent census (2017) and the one before it. In a few locations, even with more recent census data, the information may no longer be

valid. Where widespread displacement occurs, movements can alter the distribution of the population, and in situations where people flee across boarders, the overall

population figures can change. Beyond population movements, conflict, and natural disasters, can lead to loss of life that would not be accounted for in the standard projections. Ultimately, in order to determine who is in need, it is necessary to first

determine who is there.

When population data are considered unreliable, or is simply not available, it is important to identify alternate sources. In the past few decades humanitarians have turned to remote sensing options to help identify population figures. One such source of this data

3 IASC, Humanitarian Population Figures, 2016


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is the LandScan Global Population Database4, which uses algorithms that look at a variety of factors, including lights at night, to determine how many people are in a given

area. These data are updated annually. Population figures derived from high-resolution population distribution data can help to provide additional sources of information when

the available data are considered questionable.

Where displaced populations are concerned, IOM and/or UNHCR data on population movements can be used to assist in updating figures. In some locations this process may be intersectoral with organizations coming together to agree on how to factor in

displacement.

Where there is no agreed mechanism for identifying overall population figures, the Health Cluster will need to agree with partners on which figures to use.

Health Affected Once there is an agreed population dataset, it is necessary to determine which population groups fall into the ‘Health Affected’ category. The Health Affected

population are those whom the health cluster identifies as people who should be covered by the services offered through cluster partners. This may sometimes differ from the wider ‘affected population’. For example, it may be determined that the health

cluster is only supporting displaced populations while host communities may also be identified as ‘affected’ by the humanitarian crisis. Identifying who should be reached is

the first step to narrowing down Health PiN figures.

Health Needs One of the most important factors in humanitarian settings is understanding what

affected populations’ “accustomed means” actually are. While Sphere has produced minimum standards for humanitarian response, there is always a dilemma between what

falls under humanitarian responsibility and what falls under the responsibility of development actors. The clearest distinction is generally understanding the situation before the crisis as the baseline, and the goal of humanitarian actors being to arrive back

at said baseline. Meaning, if the situation did not meet the minimum standards before the crisis, arguably it does not need to afterwards.

Health needs can be difficult to quantify as they are not defined purely on an individual’s

health status but rather must take into consideration the ability of health systems to respond to needs. It is also necessary to consider any additional factors that may exacerbate standard health requirements, such as outbreaks, famine, conflict etc. In this

sense, there are two major considerations for identifying health needs, and thus the number of people in need of health assistance.

4 Though the LandScan data are one of the more well known population databases, it does come

with its own set of drawbacks. Some of the more commonly noted ones are that it is not entirely

transparent about the methodology used to calculate populations and factor in displacement. Please note, updated LandScan data needs to be purchased, and it is often not current enough

for factoring in recent displacement figures.


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i. Health Service Requirements - How many people can be covered by the existing

service capacity at a minimum (and therefore, how many people cannot be

covered – who are then classified as ‘in need’), and;

ii. Compounding Factors - What factors exist that may increase the number of

individuals the health system is required to service and thus increase the number of individuals

In some of the longer-standing clusters, additional groups have been identified (e.g.

Health Seeking Behaviours, Health Outcomes, Health Accessibility, and Impact on Populations Health and Morbidity). The diagram below shows how those groups can fit into the broader two categories outlined above.

In future years it is likely that Health PiN will be calculated through a wider range of sub-topics. However, due to COVID-19 and restrictions on primary data collection, it was

decided to limit the classification of indicators into sub-topic in order to ensure some clusters do not end up with large gaps for entire topics.

Health Service Requirements In the case of humanitarian settings, the entire ‘Health Affected’ Population should be taken as the starting point. It is the objective of the health sector to ensure all affected

people have access to the health services they require. In this sense, we end up with a

‘Health Onion’ to identify the health needs of a population. This ‘onion’ (shown on the left)

has three circles, with the middle circle representing the current capacity of health services to cover a minimum set of

requirements outlined in the core health indicators. The list of indicators that are

factored into ‘standard service capacity’ can be found below, but include such factors as

number of community health workers per 500 people in rural and hard-to-reach locations, percentage of population that can access

primary healthcare within one hour’s walk from dwellings, number of skilled birth

attendants per 10,000 people etc.

Affected Population

Health Service Requirements Compounding Factors

+ Health Service

Coverage

Health Resource and Service

Availability

Health Coping Strategies

Health Seeking

Behaviours

Health Outcomes

Impact on Populations Health and Morbidity

Health Accessibility


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The exact thresholds used for these indicators may vary by location. The ones suggested

in the core indicator list are based on minimum standards, but, as noted above, there may be situations where the pre-crisis conditions were below the minimum standards. In

those situations, the thresholds may be altered to be in line with the pre-crisis baseline.

Compounding Factors Standard service delivery looks at the minimum requirements for providing healthcare to

a population, but these requirements may not be sufficient in areas where there are added stressors on the health of the affected population. In outbreaks, it may be

necessary to have more beds available to treat increased numbers of sick patients. In famines, there is an expectation of higher numbers of people with severe acute

malnutrition with complications that would need treatment. During conflict, attacks can lead to greater numbers of injuries while psychosocial trauma may also increase. To ensure the affected population has access to the services they require it is necessary to

calculate the additional needs these factors may place on health facilities. In light of these factors, the service capacity must be adjusted as the diagram above indicates.

Thus:

Health PiN Calculation The Health PiN calculator provides the first step in determining Health PiN requirements. It is broken into two sections that look at Health Service Requirements and then Compounding Factors. It follows the Global Health Cluster Core Indicator list using

available thresholds. Each cluster may add indicators they deem relevant to their context, but it is important to ensure thresholds are added as well. As Health PiN is not

calculated based on specific individual data, it is not currently possible to determine who needs what (and where needs converge), for this reason, the Health PiN calculator

assumes overlaping needs. To accurately determine how different needs relate, it would be necessary to have household or individual level data that could tell us which households need what (and how many different needs a single household/individual

may have). As we are not able to collect all data from a single source, and have to rely on area-level, and facilities-based, data for most indicators, we must then assume that

needs are overlapping. For each administrative area, the calculator will take the highest ‘in need’ figure

available, accounting for all included indicators. What this means is that in areas where 50% of the population is not covered by availability of ICU beds and 20% of the

population is not covered by skilled birth attendants, the higher need (i.e. 50%) would be used. This decision assumes that lack of sufficient service then overlaps with any

extenuating circumstances (e.g. high rates of children not vaccinated for measles) which may not always be accurate and is likely to underestimate need. For this reason, the Health PiN calculator can only be considered a first step towards the identification of

Health PiN numbers and expert discussion must be undertaken before any figures can be finalized.

Health PiN = Affected Population – Affected Population covered by Adjusted Service Capacity


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8,000 in Need (DTC3

Vaccinations)

20,000 in Need (available health

facilities)

PiN is the

Max.

(20,000)

Vs

.

PiN is the

Sum

(28,000)

8,000 in

Need (DTC3 Vaccination

s)

20,000 in Need (available health

facilities)

Scenario A Scenario B

In Scenario A, needs are shown as overlapping, assuming that the 8,000 people in need of vaccination support are also living in areas without functioning health facilities. Scenario B assumes the opposite, that those in need of vaccination support are not also living in areas without functioning health facilities. In reality, the situation is likely to be a mix of the two. Unfortunately, as we are not in a position (at this time) to determine exactly how needs converge at the household / individual level, the maximum approach is the best assumption as we can say with some confidence that we know at least 20,000 people are in need. The work of the expert judgement group is to help adjust the numbers if they feel some needs are not overlapping, and thus more people are in need. Using this fictional example, available 3/4 W data may indicate that a vaccination campaign was recently carried out across much of the administrative area, but missed two communities of roughly equal size. The available data from HeRAMS shows that one of the two communities has a functioning health facility nearby while the other does not, it can be assumed that half of the population in need of vaccination support do not overlap with those in need of access to primary health facilities. Thus the expert judgement group may revise the suggested PiN number up from 20,000 to 24,000 and note that the decision was based on available 3/4 W data.

EXAMPLE:

A note on displaced populations In camp situations it is likely that many (or all) of the health services available are being

provided by partners rather than the state. In these situations, those services should not be included in the service capacity as funding will be required to ensure they can be

maintained. As the objective of Health PiN is to provide an overview of needs to inform the Humanitarian Response Plan (and thus funding requests), it is important to ensure any

services that need to be maintained are counted as ‘in need’. In this sense, if health services in camps are provided solely by partners, the entire camp population would be deemed ‘in need’. In many situations, camp or spontaneous settlement populations may

be considered in need of health assistance regardless of weather or not services are provided by state or non-state actors. It is important to note if displaced populations are

considered ‘in need’ of health assistance, and if so, to include their entire population figure.

COVID-19 While, at present, there is a Global Humanitarian Response Plan that covers the COVID-19 specific needs, that response plan will expire at the end of 2020 when COVID needs

should be absorbed into country specific Humanitarian Response Plans (HRPs). For this reason, the HNO’s for 2021 will need to factor in COVID-19 needs.


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At present we are recommending COVID-19 be considered in two ways, the first is in Health PiN and the second is in scenario projections. To calculate Health PiN, the Adaptt

tool is recommended. Adaptt will provide an estimated number of beds required for scenarios based on four different attack rates: Very Low, Low, Medium, and High. For

Health PiN we request that the ‘Low’ attack rate be used with the bed requirement for critical cases comprising the Health PiN requirement for COVID for that location. As it is

possible to edit the population figures used in Adaptt, the population can be adjusted if the crises affected population does not cover the whole state (e.g. Cox’s Bazar). For the scenario projections, we recommend that scenario descriptions be provided for both

Medium and High attack scenarios as well. The results of the Adaptt tool using the low attack rate should then be input into the Health PiN calculator in the Compounding

factors sheet.

Severity Calculation Much like PiN, severity is calculated using Health Service Requirements and

Compounding Factors, however this time greater weight is placed on compounding factors. The indicators included under Compounding Factors tend to be more severe as

they focus on the present situation rather than the capacity of health services to meet their day-to-day requirements. For this reason, they play a greater role in determining severity. It is important to note that in many cases, the highest PiN figures will arise from

the Health Services Requirement grouping of indicators as services are required for entire population groups and gaps can often be large. As severity is weighted more towards

the indicators classified as compounding factors, it is very possible to have a large discrepancy between the calculated severity figure and the calculated PiN figure. For

example, it is possible that an area will be classified as a severity level 2 but have more than 80% identified as in need. These discrepancies should be discussed by the expert judgement group and, if no alterations are made, a brief rationale for the discrepancy

should be provided (e.g. There are very few medical facilities in this area which is a situation that needs to be addressed, however, at present there are no pressing health

concerns requiring immediate attention).

Severity Thresholds

For the 2021 HNO process, the severity thresholds are designed in-line with the JIAF thresholds that will be used for intersectoral severity. As the JIAF only uses severity levels 3

and above to determine PiN, the current (core) health indicators only provide 4 scales instead of 5, with the provided scale for severity levels 1 and 2 being the same5. The main

threshold is the one provided for severity level 3. Not all indicators have thresholds provided. Those without the requisite levels of thresholds

will not be used for determining severity6. In the case of any indicators added at the cluster level, it will be important to ensure severity thresholds are included so indicators

may help for identifying severity. This is particularly true of indicators measuring incident rates and/or case fatality ratios for key diseases in those locations.

5 There is one exception to this rule, the indicator measuring disabilities based on The Washington

Group Questions has five different levels of severity, however it is measured at the household level 6 It is not always necessary to provide thresholds for all severity levels, but it is recommended to at

least provide three.

https://www.euro.who.int/en/health-topics/Health-systems/pages/strengthening-the-health-system-response-to-covid-19/surge-planning-tools/adaptt-surge-planning-support-tool

https://www.euro.who.int/en/health-topics/Health-systems/pages/strengthening-the-health-system-response-to-covid-19/surge-planning-tools/adaptt-surge-planning-support-tool


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How the Thresholds Have Been Designed

Severity thresholds of indicators are usually classified in one of two ways, measuring the

degree of an outcome vs. the magnitude of the situation. The degree of severity is generally measured at the individual or household level. For example, the Washington

Group Questions classify disabilities at the individual level from “no difficulties” at severity level 1 to “at least four domains are ‘cannot do all’7” at severity level 5. Symptoms of

diseases are often classified in this way as well with certain symptoms being associated with a more severe prognosis. However, as health needs are not determined at the individual or household level, outside of disabilities, degree of severity is not an overly

useful method for health clusters. Thus, we must rely on magnitude. When classifying severity by magnitude, two factors are considered, the first is binary – e.g. who has or has

not received a given vaccination, and the second is the percent of the population that classifies in that group (e.g. 70% of the population has received the measles vaccine). Severity thresholds are then designed around that proportion, e.g. 95% and above is not

considered ‘in-need’ and thus is below a severity level 38. Less than 95% is at least a level 3. Degrees of severity have then been broken down into equal groupings. In this case,

5%. So, below 95% is a 3, below 90% is a 4 and below 85% is a 5.

Please note, the only threshold that has been widely agreed is the one provided under severity level 3. At present we have recommended thresholds using even groupings for severity levels 4 and 5, but these will need to be re-examined and adjusted based on

lessons learned.

Universal Severity

The premise behind universal severity is that all indicators are classified using the same

scale. At present, all clusters are classifying each of their indicators (or indicators that can be classified) using the same 5 point scale. Meaning that all indicators should have thresholds provided for each level. Universal severity, on the other hand, would provide

a common definition for each severity level and indicators would only have thresholds provided where suitable. For example, if a level 5 equates to imminent death, then most

indicators would not have a threshold for level 5.

Critical Indicators

In lieu of universal severity, critical indicators are being used. Indicators that measure the most severe situations (e.g. those that, in a universal severity model, would have

thresholds for level 5) are flagged as being ‘critical’. The severity level of these indicators can then override the severity of others. Using an individual level example, think of a

person with extremely severe malnutrition. If they are in such a severe state that death is imminent, it does not matter if all other indicators are positive, they still should be measured as a severity level 5.

Critical indicators that measure magnitude are quite a bit more difficult to classify.

COVID-19 can provide a useful example. The first measure that should be determined is where the situation becomes a problem. So, for COVID-19 this would mean identifying

the point where the incidence rate is likely to exceed the capacity of medical services. Thankfully, there are a number of tools available which can help to project resource

7 For further detail on The Washington Group questions please reference their guidance

8 In rural communities it would be 90%

http://www.washingtongroup-disability.com/wp-content/uploads/2016/12/WG-Document-4-The-Washington-Group-Short-Set-on-Functioning-Question-Specifications.pdf


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requirements for COVID-19 and these can help set the thresholds. Using a fictional example, lets say the projected bed requirement for a medium attack rate (using

Adaptt) is 215 ICU beds, and you know there are a total of 250 in-patient beds in the area. Considering you require 215 ICU beds for COVID-19 and this leaves only 35 beds for

every other medical issue that might arise, you know you will have a bed shortage at the medium attack rate (set at an incident rate of 20%).9 At the local level you will then need

to agree where to set the different thresholds. You know, once the COVID-19 requirements start taking up too many of the available resources care for other needs will suffer. At a certain point this resource strain will start to impact critical care and could

result in increased mortality. At a severity level 5, people will be dying because the resources are not available to keep them alive. As these lines are strongly based around

the available resources in-country, they need to be locally defined. Even at the local level they may prove difficult to set as the situation may vary from one location the next.

Defining critical indicators will be one of the most important roles for the expert judgement group, and setting those thresholds may prove one of their most difficult tasks.

To do this, they must ensure that these indicators are highly severe, and that the thresholds created at the highest levels (e.g. severity 5) equate to ‘imminent death’.

For example, it may be decided that in camps situations where living conditions are crowded, lower incidence rates may equate to higher thresholds as the R0 would be

higher.

Calculating Severity

Once thresholds have been agreed for the various severity levels, and critical indicators

have been identified, severity can be calculated. Using the same ‘Score Card’ used for PiN (scrolling down on the worksheet), the severity calculations will be shown. They are determined by comparing the results of all the indicators included in the severity

calculations to the various available thresholds. A severity level is then provided for each indicator.

Of the core indicators included, 10 have severity thresholds provided, 5 classified as

Health Service Requirements and 5 as Compounding Factors. Their results will be averaged for each grouping. They are then combined with the Health Service Requirements providing 40% of the weight and Compounding Factors providing 60% of

the weight. Compounding Factors has increased weighting as these are the indicators that generally measure humanitarian circumstances.

Any critical indicators that are included will then be used to over-ride the calculated severity. An over-ride would equate to increasing the score only. Thus, the weighted

severity score will remain the same if the critical indicators are a lower severity level, but if they are a higher level, they will over-ride the weighted score and provide the basis for

severity.

For example, if you have the following indicators:

9 Other medical resources can also be factored in as the tools available help to project many

different resource requirements

https://euro.sharefile.com/share/view/s6cee0ef49a44b42a/fo62fb4f-ba90-4a9a-a0e7-7c98dea86b9a


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Indicator Severity Weight Calculation Score Average Health Service

Requirements Severity 3 0.4 1.2

3.6 Average Compounding

Factors Severity 4 0.6 2.4

Ebola Severity (Critical) 2 3

Plague Severity (Critical) 3

Using the example above, the severity for each of the two groups is multiplied by the

weight then the two results are added together to provide a weighted score. For critical indicators, the score is the maximum severity for the provided critical indicators. The final

severity level is then calculated using an ‘if’ function. If the critical severity is higher than the weighted severity, the overall severity should be the critical figure. If the critical severity is lower, then the overall severity would reflect the weighted severity score (in this

case 3.6).

Expert Discussion Once the Health PiN calculator has been used, the results should be shared with a pre-identified panel of ‘experts’. Defining ‘experts’ can be problematic in humanitarian

settings. Often the identified individuals are just the ones who were available to attend the meeting. In this particular case, it is recommended that the following profiles be

included in the panel that will provide expert judgement on Health PiN. • Knowledge of analysis and a firm understanding of the Health PiN calculator and

the formulas behind it

• Local knowledge on the areas being evaluated – this can be individuals who have spent significant amounts of time working on the ground, conducting research, or

who are from that area • Knowledge of the datasets being used to inform the analysis, particularly anyone

who worked on the assessments in question In humanitarian terms, these would be field staff who are well informed about the various

areas, M&E or assessment staff, IMOs and/or analysts. In some cases, there may be staff who are known to be knowledgeable that should be sought out – regardless of their

current title. The objective is to find a panel of individuals who are knowledgeable on the subject matter and who the wider cluster recognizes as being knowledgeable. To ensure

a productive discussion, it is recommended that the group be kept relatively small (approximately 10 people).

Once the panel is identified, it is recommended that a workshop be arranged for them to meet and run through the various indicators and information available for each

administrative area. These ‘scorecards’ – the output of the Health PiN calculator - then need to be considered alongside additional indicators that may not have specific thresholds (e.g. percentage of identified SSA incidents verified).

Step 1: Identify Health Affected Population In some circumstances the population identified as ‘health affected’ may not be as clear

as in others. It is important to ensure all decisions are recorded and explained so the methodology can be well understood by partners. As noted above, this population may


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not be the same as the wider ‘crisis affected population’, and likely is a subset of the total population. As Health PiN is used for the development of the HNO and thus informs the

HRP, it is important to ensure the affected population falls within the boundaries of the population covered by the HRP. In this sense, the number of health affected should not

exceed the population affected. As all information will be calculated for that group of individuals, it will be important to ensure all data are available at that level.

Step 2: Reliability Data reliability is always a concern. With rapidly evolving situations, it is important to ensure data are as up-to-date as possible, sufficiently representative, and collected using

a transparent and well-defined methodology. However, in humanitarian situations, it is often necessary to rely on data that are not considered highly reliable. At this stage we

do not have a set methodology for quantifying reliability, rather, it is recommended that during expert judgement, a reliability score should be set for each data source and that score should be noted for the record. Each data source should be graded on a score of

1-5, where 5 = Reliable and 1 = not useable. Any provided score should be explained (e.g. insufficient sample size, high non-response rate, sufficient data but out of date, etc.)

Where datasets are scored below 5, their results should be considered by the expert

judgement group and the following questions should be asked: a) Is the data so unreliable that it should be excluded? b) Are the results lower than they should be?

c) Are the results higher than they should be?

If required, the resulting Health PiN for unreliable data can be altered, however it is important to record the reasons that decision was taken and what information was used

to draw the conclusion reached.

Step 3: Review Thresholds The thresholds provided are based on minimum standards, many of which have been

taken from Sphere. However, these may not always be the best thresholds to use. Generally speaking, humanitarian assistance is provided after a crisis to ‘put things back’

while development assistance deals with longer-term, socio-economic issues. If the situation in a given context was below minimum standard prior to a disaster, it would then not be the obligation of humanitarians to meet our set minimum standard, but rather

ensure that things are at the standard they were prior to the hazard that led to the crisis.

For example, in the fictional Kingdom of Arnor over 70% of the population lives more than an hour’s walk from a health facility. Arnor experiences a large earthquake and the entire

country is affected. Of their 500 health facilities, 30 are directly damaged and 20,000 people have been displaced. It is the responsibility of humanitarian actors to repair the damaged facilities and ensure people have access to healthcare in those areas, as well

as ensuring the 20,000 displaced have access to sufficient healthcare. However, it is not the responsibility of those humanitarians to build more health facilities and provide

healthcare to the point of meeting the minimum standard. In essence, if the nearest health facility is 3 hours away before a disaster, it is not the responsibility of humanitarians to ensure it is closer after the disaster. This particular topic can be quite contentious –

particularly when there are areas of high need that may not be related to the crisis being


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responded to. While the purpose of this guidance is not to tell you how to decide which people to consider ‘health affected’ within the context of a given crisis, it may sometimes

be necessary to alter thresholds so they meet a pre-crisis baseline. In this case, if only 30% of people lived within an hour’s journey of a health facility before the crisis, the threshold

for the percentage of the population living within an hour’s walk from the nearest health facility could be revised to 30%.

In many situations, baselines will not be available. For those circumstances we recommend using the thresholds provided as a starting point but allowing for adjustment

based on discussion.

Please note, that while it is often the objective to return people to the situation they were in before the crisis, when their previous circumstance far exceeds the minimum standards we do not recommend revising them upwards.

When reviewing the thresholds to be used for assessing severity, it is also necessary for the

expert judgement group to identify critical indicators (if any have been used). Please ensure any indicator identified as ‘critical’ meet the definition where a severity level 5

would equate to imminent death.

Step 4: Review the ‘scorecards’ and finalize Health PiN Using the figures produced by the Health PiN calculator, this group will then need to

agree on a final Health PiN number of each location. It will be very important to consider additional factors like accessibility (e.g. hard-to-read or remote areas where they may

not have any access to health services would then have their entire population in need). For each location, the additional ‘expert judgement’ indicators will need to be discussed

and factored in using the following questions: a) Does this information suggest that more people are in need than the Max Health

PiN?

i. If yes, what should the Health PiN be adjusted to? b) Does this information suggest that less people are in need than the Max Health

PiN? i. If yes, what should the Health PiN be adjusted to?

Step 5: Severity Severity ‘scorecards’ can be found below the PiN scorecard, and they also provide a breakdown for each indicator. The calculated severity should be examined against the

individual breakdown of indicators, and the following questions should help guide the discussion:

a) Are there any outlier indicators? (indicators that are particularly high that may suggest the situation is more severe than the calculated severity suggests)

b) Does the severity level make sense in light of the finalized PiN? (as a guide, the

following thresholds can be used: Severity 1 approx. 15% in need, Severity 2 approx. 35% in need, Severity 3 approx. 55% in need, Severity 4 approx. 70% in

need, Severity 5 approx. 90% in need)10

10 These proportions have been derived from those used at present in some cluster locations. They

should be re-examined against the final calculations across all clusters for the 2021 HNO.


14

c) Are there any additional data sources that suggest a different severity level for this area? (Even if they do not provide the threshold requirements)

d) Is this data sufficiently reliable or is there more recent anecdotal or qualitative information available that should be factored in?

Note on Reporting Against Health PiN As mentioned before, Health PiN is not as straightforward as determining the health status of an individual or household. In order to ensure sufficient capacity to respond to health

needs, it is necessary to project standard need requirements and plan on meeting them. This helps to provide a Health PiN figure, but can be complicated when it comes to

measuring the response. As the services provided by health partners generally do not involve distributions to household or individual like many of the other sectors, the way

they are reported is also different. Response indicators will vary from the indicators used to calculate Health PiN and may include measures to determine the types of medical procedures provided, treatment courses, number of consultations etc.

When it comes to measuring the COVID-19 response, indicators can be extremely difficult

to identify. In some scenarios it has been suggested to use district level mortality rates, however, even with wide intervention, if cases are still increasing, it is likely the mortality

rates will also rise so it is entirely possible that successful interventions will not result in an overall decrease as they would be reducing the rates that might have occurred but did not.

The diagram below is a fictional example to illustrate this:

In this graph, the dark blue line represents the current

situation, where there is a mortality rate of 3.2. The yellow line represents the situation without partner intervention, reaching a mortality of 5.1, the bright

blue dotted line represents the mortality with intervention which is 4.1. If the baseline is taken as 3.2

the intervention would appear to have been unsuccessful as the mortality still increased, it just did

not increase as much as it would have without intervention. The problem is that we are

then trying to measure something that did not happen. Quantifying a negative such as this can be very difficult. One option to consider is to monitor against projections as

opposed to baselines, but projections are estimates so should not be considered concrete evidence.

Health PiN Indicators Most of the following indicators are taken from the GHC Core Indicator List, those in red

are from other clusters lists. There is an annex of this document that provides more detail on each of these indicators with comments on how they should be collected. They should not be considered exhaustive and will require supplementation with indicators more

specific to the context. Some indicators we recommend you consider are: • Cost of healthcare as a means of accessing healthcare

• Number of elderly as a proportion of the population • Number of children under 5 years old as proportion of the population

Days

De

ath

s

Mortality: 3.2

Mortality: 5.1

Mortality: 4.1


15

• Number of attacks on healthcare facilities • Access to sufficient electricity

The following questions are provided as possible suggestions for inclusion into MSNA’s if

they are taking place. If an MSNA is going forward the Global Health Cluster IM Team can provide support and guidance on useful questions to include11:

• Has there been an unusual number of deaths in the last 4 months? [time period can be adjusted]

• Is there anything that prevents you from accessing health services?

• Are there any people/groups who have a harder time accessing health services? • What sort of obstacles do people/groups face when trying to access health

services? • Are there any problems with access to medication? (not available, high cost, low

quality, etc.)

• What do people in your household do if someone gets sick? (traditional healers vs. healthcare facilities, pharmacies, etc.)

• Are there healthcare workers working in your community? • What are your top 3 concerns about you and your family’s health right now?

• Are there many people in your community who are currently so upset to be unable to conduct usual daily activities (for example getting out of bed, ability to work, to take care of family…)?

• Have you ever heard of COVID-19? • If yes, what can you do to stop you and your household getting sick? (use

contextualized answer options – e.g. this disease does not affect our ethnic group, take malaria medication, etc. along with recognized methods for reducing the

spread. These answer options should not be read out, the question should be asked and the answer options that best describe what the individual says should be selected).

Prior to commencing with Health PiN calculation, it will be important for partners to agree

on which indicators to add and where the data will be sourced from. Please note, many of these indicators may not be available, so use the information you have on hand or are

able to procure.

Health Service Requirements

Indicator Threshold

Average urban population per functioning health facility 1 per 10,000

Average rural population per functioning health facility 1 per 250,000 Number of inpatient beds per 10,000 people >= 18

Number of community health workers per 500 people in rural and hard-to-reach locations

Locally determined

Percentage of population that can access primary healthcare within one hour’s walk from dwellings

>= 80%

Percentage of healthcare facilities that deliver essential

package of health services >= 80%

11 These indicators would all fit under ‘Compounding Factors’ and could help to inform severity,

but need to be combined with information on service availability in order to determine Health PiN


16

Proportion of healthcare facilities with a trained IPC health worker

100%

Proportion of healthcare facilities where the main source of water is an improved source, located on premises, from

which water is available

100%

Number of health facilities with Basic Emergency Obstetric

Care per 500,000 population >= 4 BEmOC

Number of health facilities with Comprehensive Emergency Obstetric Care per 500,000 population

>= 1 CEmOC

Number of skilled birth attendant personnel per 10,000 people

>= 23

Compounding Factors

Indicator Threshold

Coverage of DTC3 (DPT3 / PENTA3) in <1 year old > 90%

Under 5 Mortality <= 2 per 10,000 per day

Crude Mortality <= 1 per 10,000 per

day Number of cases or incidence rates for selected diseases

relevant to the local context (cholera, measles, acute meningitis, others)

Locally determined

Percentage of children aged 6 months to 15 years who have received measles vaccination

> 95% in high density locations and > 90% in rural areas

Severe acute malnutrition (SAM) in children 6 to 59 months Taken as a whole figure

Percent of Households having access to an improved water

source

All without access considered at risk of

requiring medical support

Percent of the population identified as having disabilities (in line with the Washington Group Questions)

All considered in

need of medical support

Pregnant and lactating women as a percentage of total population

All considered in need of medical support

Displaced population

Need to clarify if partners are

providing primary healthcare. If so, total

population is taken (Where Integrated Phase Classification is in place) Proportion of the population identified as IPC Phase 5 and Phase 4

Total population

Expert Judgement Indicators with no thresholds available

Indicator Threshold


17

Percent of health facilities providing clinical management of rape (EC, PEP and STI treatment)

Number of days essential medicines are not available in a one-month period Percentages of medical facilities, social service facilities and community programs

who have staff trained to identify mental disorders and to support people with mental health and psychosocial problems

Percentage of identified SSA incidents verified

Health PiN Calculation Process: Checklist

1) Create Expert Judgement Group

Put together a small working group for the HNO Health PiN

calculations comprised of representatives from partners with experience in analysis and/or extensive local

knowledge for the geographic region covered by the HNO.

2) Identify Indicators

Go through the list of indicators and determine which you

will include. Please be sure to factor in which indicators you

will have information for and how recent that information is.

Add indicators that you feel are relevant to your specific

context

Ensure all indicators listed in Health Service Requirements

and Compounding Factors have thresholds applied. If major changes are required reach out to the GHC so the

calculator can be adjusted to your specific requirements

3) Run the Calculator

Input available data at the smallest administrative level

required for reporting. Run the calculator and share the results with the Expert

Judgement Group

4) Run Workshop

Recommend planning a workshop for the Expert

Judgement Group to go through the results of the

calculator and consider the various indicators listed under ‘Expert Judgement’. This group must agree on a final

Health PiN figure for each required administrative area The expert judgement group must also agree on severity for

each required administrative area It is the responsibility of cluster coordination to ensure all

rationalizations for each decision taken are recorded. A

comments space is provided for each indicator and beside each administrative area for these decisions to be


18

documented. Once completed the workbook should be saved to provide a record of the methodology used.


19

Annex A GHC Core Indicators incorporated into the Health PiN Calculator

Health Service Requirements Indicators

GH

_4

Average population per functioning health facility

(HF), by type of HF and by administrative unit

Baseline,

Outcome

HeRAMS or

similar service EPI

microplanning data from vaccine or NID

campaign

One healthcare

facility per 10,000 people; and

One district or rural hospital per 250,000

people

Comments: This is a proxy indicator of geographical accessibility and of equity in

availability of health facilities across different administrative units within the crisis areas Please note, this will not adequately measure healthcare coverage in all settings

and should be used in conjunction with other indicators. If an existing current population dataset is available, then HERAMS is preferable for

broad statements like national or sub-national (but not community) coverage. When planning at LOCAL levels then other methods should be used. It is important to assess the quality of the data available and make a note of said

quality where the indicator is reported.

GC

_2

Number of inpatient beds

per 10,000 people

Baseline /

Output HeRAMS, RHA >= 18

Comments: Indicator for the availability of hospital beds across crisis areas and

proxy indicator of equity in the allocation of resources.

GC

_3

Number of community health

workers per 500 people in rural and hard-to-reach locations

Baseline / Output

HeRAMS or similar service,

Cluster Partner Reporting

(3/4W)

1 - 2 (depending

on the identified constraints)

Comments: Community programming with CHWs (including volunteers) increases

access to hard-to-reach populations, including marginalised or stigmatised

populations. If there are geographical constraints / acceptability issues in diverse communities, one CHW may only be able to serve 300 people rather than 500.

Depending on circumstance, this indicator may only relate to rural areas (which would need to be clearly defined).

GC

_4

Percentage of population that can access primary healthcare within one hour’s

walk from dwellings

Baseline / Outcome

Survey >= 80%

GC

_5

Percentage of healthcare

facilities that deliver essential package of health services

Baseline /

Output

HeRAMS or

similar service >= 80%

Comments: The ‘essential package of health services’ must be defined to use this

indicator. Use the minimum package of services that has been agreed at country level.


20

IP_1

Proportion of healthcare facilities with a trained IPC

health worker

Baseline /

Output

HeRAMS /

similar service 1

Comments: Proxy indicator for infection, prevention and control (IPC)

IP_2

Proportion of health care facilities where the main source of water is an

improved source, located on premises, from which water is

available

Baseline /

Output

HeRAMS /

similar service 1

Comments: Proxy indicator for infection, prevention and control (IPC). Could be

considered an output indicator if running water is being installed as part of a

project. This indicator should be coordinated with the WASH Cluster.

SR

_1

Number of HF with Basic Emergency Obstetric Care/

500,000 population, by administrative unit

Input, proxy HeRAMS /

similar service

>= 4 BEmOC/500

000

Comments: Proxy indicators for the physical availability and geographical

accessibility of emergency obstetric services and their distribution across districts in the affected areas. An unbalance between the availability of BEmOC and CEmOC

(with too few BEmOC) is often observed. Recommend coordinating with UNFPA regarding figures

SR

_2

Number of HF with

Comprehensive Emergency Obstetric Care/500,000

population, by administrative unit

Input HeRAMS /

similar service

>= 1 CEmOC/500

000

Comments: Proxy indicators for the physical availability and geographical

accessibility of emergency obstetric services and their distribution across districts in the affected areas. An unbalance between the availability of BEmOC and CEmOC

(with too few BEmOC) is often observed. Recommend coordinating with UNFPA regarding figures

SR

_3

Number of skilled birth

attendant personnel (doctors, nurses, certified midwives) per 10,000 people

Baseline / Output

HeRAMS >= 23

Comments: SPHERE standard.

Compounding Factors Indicators

CH

_1

Coverage of DTC3 (DPT3 / PENTA3) in < 1 year old, by administrative unit

Outcome HIS, Survey > 90%

Comments: This data may be available through projections, but if so, the quality of

the data should be checked and noted.


21

CH

_2

Proportion of under 5 year-olds confirmed malaria cases

who received antimalarial treatment after diagnosis

(clinical and lab)

Output

EWARs or other

surveillance system

Comments: Only for malaria endemic areas.

Though data may not always be available on treatment received (rather than

number of confirmed cases), it is advised that treatment data be collected where possible in order to provide an adequate output measure.

Research has suggested that treatment coverage is not suitable for household level collection. For further information, please see:

https://malariajournal.biomedcentral.com/articles/10.1186/s12936-018-2636-3

GH

_3

% of the population identified

as having disabilities (in line with the Washington Group

Questions)

Baseline HH or Individual level survey

Thresholds must be defined according

to the local context and the

nature of the crisis. Measure trends

Comments: The Washington Group Questions should be used for collecting this data

if the data are not already available from more detailed (reliable and recent) surveys.

It may be necessary to add some questions in certain contexts. (e.g. If there is concern about disproportionally high percentages of people living with disability

because of the crisis, it may be necessary to ask when the difficulty started.) For further information please look at: https://reliefweb.int/report/world/disability-data-collection-summary-review-use-washington-group-questions-development

Please note, it is important to ensure enumerators are properly trained and questions are CAREFULLY translated.

Expert Judgement Indicators

GB

_2

% of health facilities providing

clinical management of rape (EC, PEP and STI treatment disaggregate by which of the

three services are being provided)

Baseline / Output

HeRAMS or similar service

1

Comments: Comprehensive post-rape care -for this indicator- is defined as offering

EC, PEP and STI treatment. This indicator is a more comprehensive measure of clinical management of rape

(CMR).

GC

_1

Number of days essential medicines are not available in a one-month period

Baseline / Output

IRA, RHA,

Surveys, Broader survey of health

facilities

<= 4 days out of 30


22

Comments: SPHERE standard. Essential medicines include drugs, vaccines and

blood products. Should be based on the national essential medicines list. The

indicator will need a list of 'essential medicines' for each context. ANY of those being unavailable for a day counts as 1 day. Expired medicines should not be

counted

MH

_1

Percentages of medical facilities, social services

facilities and community programs who have staff

trained to identify mental disorders and to support

people with mental health and psychosocial problems

Baseline /

Output

Comments: MHPSS programs are likely to vary a lot from one country to the next,

not just in terms of content, but also in terms of delivery methods. It is important to make note of the local context, so it may be considered if results ever need to be

aggregated at higher levels. Inclusion of social services facilities and community programs is according to the size of the area under measurement, as it can sometimes be feasible and

sometimes not. If not feasible (i.e. no idea about the denominator), it is better to focus on health facilities only, recognising that real coverage is likely

underestimated (i.e. we are missing all services provided through other providers).

SS_1

Percentage of identified SSA

incidents verified Process SSA


23

ANNEX B Health PiN Calculator Description of Calculations

Indicator Description

Number of functioning urban health facilities

Calculated by determining the number of people that can be reached with the number of health facilities in place ([Health Facilities] x 10,000), then subtract that from the total population to see if there are any people left over. These people are classified as 'in need'.

Number of functioning rural health facilities

Calculated the same way as above, but for rural areas the figure used is 250,000 instead of 10,000. So ([Health Facilities] x 250,000) subtracted from the total rural population.

Number of functioning health facilities

This option has been included for areas where health facilities are not identified as urban or rural. To use this indicator, it is important to determine generic thresholds per xx of the population as the set minimum standards are wildly different (per 10,000 vs. per 250,000)

Number of inpatient beds

Bed requirements are calculated as the number of beds divided by 18, and then multiplied by 10,000. This figure is then subtracted from the total population to determine if there is any deficit.

Number of community health workers in rural and

hard-to-reach locations

This calculation is set to the lowest threshold of 1. It can be revised. It calculates by multiplying the number of community health workers in rural and hard-to-reach locations by 500, then subtracting that number from the total population in those areas to determine if there is a deficit.

Percent of population that can access primary healthcare within one hour's walk from dwellings

To calculate 'in need' the objective is to reach the minimum. In this case, if the proportion who are within an hour's walk are less than 80%, the difference between the actual number and 80% is multiplied by the population to calculate those in need (meaning if 75% are within an hour’s walk, 5% of the population would be considered 'in need' in order to reach 80%)

Number of health facilities

that deliver essential package of health services

The default divider being used for health facilities is 10,000. This makes 2 assumptions: 1) the hospital lacking essential health services is in an urban area 2) there is no overlap in hospital coverage If either of these assumptions are inaccurate these

formulas will need to be adapted. They currently take the result, subtract that from the total number of health facilities, then any facilities not providing the required services (outside of the threshold) are multiplied by 10,000 to determine the number of people missed.

Number of health facilities with a trained health worker

Number of health facilities where the main source of water is an improved source, located on premises, from which water is available


24

Number of health facilities with basic Emergency

Obstetric Care

Takes the total population and subtracts the covered population by dividing the result (number of BEmOC) by 4, then multiplying by 500,000. Any that are not covered are considered the deficit and thus in need.

Number of health facilities with comprehensive

emergency obstetric care

Takes the total population and subtracts the covered population by multiplying the number of CEmOC by 500,000. Any that are not covered are considered the deficit and thus in need.

Number of skilled birth attendant personnel

Calculates the number of people covered by dividing the result by 23, then multiplying that by 10,000. This is then subtracted from the total population

Percent of children <1 who

have received DTC3 (DPT3/PENTA3)

We apply this calculation to the entire population, to err on the side of caution; as it is likely that, where there is low coverage in children, there is also low coverage amongst adults

Percent of children who have received the measles vaccine (Rural)

We apply this calculation to the entire population, to err on the side of caution; as it is likely that, where there is low coverage in children, there is also low coverage amongst adults

Percent of children who have received the measles vaccine (Urban)

We apply this calculation to the entire population, to err on the side of caution; as it is likely that, where there is low

coverage in children, there is also low coverage amongst adults

SAM Total Number

Percent of households that have access to an improved water source

All those not covered are considered a deficit

Percent of the population identified as having

disabilities

All are considered in need

Pregnant and lactating women as a percent of the total population

All are considered in need

Displaced Population Unless otherwise determined, all are considered in need

Number of people in IPC Phase 5

This presumes that all of the population within the high IPC phases are also considered health affected - thus even if the health affected population is smaller than the population affected that is being used for IPC, the total figure they calculate as IPC 4 or 5 are counted. This figure is capped at the total health affected population, so

cannot be higher than the number of health affected)

Number of people in IPC

Phase 4


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ANNEX C Severity Thresholds

Severity

Indicator

Critical

Indicators 1 2 3 4 5

He

alth

Se

rvic

e R

eq

uire

me

nts

Number of inpatient beds No >= 18 >= 18 >= 12 >= 6 <6

Number of community health workers in rural and hard-to-reach locations

No 1 1 0 0 0

Percent of population that can access

primary healthcare within one hour's walk from dwellings

No >=80% >=80% >= 70% >= 60% < 60%

Number of health facilities with basic Emergency Obstetric Care

No >=4 >=4 >= 3 >= 2 < 2

Number of skilled birth attendant personnel No >= 23 >= 23 >= 20 >= 17 >= 14

Co

mp

ou

nd

ing

Fa

cto

rs

Percent of children <1 who have received DTC3 (DPT3/PENTA3)

No > 90% > 90% >= 85% >= 75% < 75%

Percent of children who have received the measles vaccine (Rural)

No >= 90% >= 90% >=85% >=75% <75%

Percent of children who have received the measles vaccine (Urban)

No >= 95% >= 95% >=90% >=85% <85%

Prevalence of Global Acute Malnutrition

(GAM) based on weight for height Z-score (WHZ)<-2 and/or bilateral pitting oedema

among children 0-59 months (if no data, use 6-59 months)

No <5% <10% <15% <30% >=30%


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Severity

Indicator

Critical

Indicators 1 2 3 4 5

Co

mp

ou

nd

ing

Fa

cto

rs

Prevalence of Global Acute Malnutrition

(GAM) based on Mid-Upper Arm Circumference (MUAC) <125mm and/or bilateral pitting oedema among children 6-59 months

No <5% <10% <15% >=15% >=15%

Percent of households that have access to an improved water source

No >80% <=80% <= 60% <= 40% <= 20%

Percent of the population identified as having disabilities

No

All domains are no

difficulties

No domain is

a lot of difficulties or cannot do at all, 1, 2, or 3 domains are some difficulties

No domain is cannot

do at all, 1, 2, or 3

domains are a lot

of difficulties

OR no domain is

a lot of difficulties or cannot do at all; at least 4 domains are some difficulties

No domain is cannot

do at all, 1, 2, or 3

domains are a lot

of difficulties

OR no domain is

a lot of difficulties or cannot do at all; at least 4 domains are some difficulties

At least 4

domains are

cannot do all

IPC Phase Yes 1 2 3 4 5

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