Need-based projections

is, Model 1). regional data (n = 20) were available for only 11 years (from 2007 to 2018) for the number of physicians and nurses, categorized by nationality (Saudi or foreign), gender (male or female), and facility type (hospital or primary care setting). Furthermore, data were available only for health workers employed by the public MOH and private sectors; regional health worker data from nonMOH public sector employers, such as the Ministry of the National Guard and the Ministry of defense, were not available. despite these limitations, these data capture the largest portion (75 percent) of those employed in the public sector. The national proportion of non-MOH health workers out of all public sector health workers was projected (in lieu of disaggregated regional-level data for non-MOH public sector health workers) and added to the regional projections of health workers in the MOH and private sectors to obtain the total projected supply of health workers in Saudi Arabia from 2020 through 2030.

NEED-BASED PROJECTIONS

Approaches to defining the need-based criterion

Predicting the future need-based requirement for health workers rests on how need is defined. Several approaches for defining this requirement have been used in the past (Ansah et al. 2017; bruckner, Liu, and Scheffler 2016; Scheffler, cometto, et al. 2016; WHO 2006). These can be summarized by the six approaches described below.

Workforce-to-population ratio is the simplest approach to determining the number of health workers required to serve a given population. The ratio is often taken from a reference country or region with a slightly more developed health care sector for use as a benchmark. Alternatively, an international standard (for example, a standard from the World Health Organization [WHO]) can be adopted. However, this approach does not consider other factors, such as utilization, and it does not take into account any country-specific details.

Example: A country could adopt a target workforce-to-population ratio based on another country’s workforce density that it would like to reach.

Utilization-based approaches estimate future health care workforce requirements using the current level of services used by the population as a proxy for satisfied demand. This approach assumes that the current consumption of health care services reflects the desired level of consumption of a population that will seek out and have the ability to purchase health care in the current context of the health care system. in other words, setting current utilization as the need-based criteria assumes that the status quo is acceptable.

Example: A country could choose the existing utilization level (for example, 70 percent of the population are vaccinated against influenza) and set the corresponding level of health worker density needed to achieve this utilization level as the target; future health worker numbers would then be determined solely by population growth and, by definition, no shortages would currently exist.

A bottom-up need-based approach projects health workforce requirements based on the current estimated health care needs of a particular population (rather than of a different population) to deliver a specific level of health services. in essence, a fully deterministic model is built to delineate the number of full-time equivalent employees required to provide health services. Additional factors, such as burden of disease, can be taken into account and can determine

the specific illnesses that are a priority for the health care system to address. This approach requires epidemiological data on the prevalence of diseases and a determination of the specific number of health workers (or workdays) per patient in need (that is, staffing ratios) to identify the overall health workers required to deliver care.

Example: Countries could define a specific service (for example, having 100 percent of the population obtain a primary care checkup every year) and calculate the number of full-time equivalent health workers required to reach that service delivery target for the population.

A top-down need-based approach uses aggregate variation in service coverage data to identify an acceptable threshold of health worker density that relates to a desired service coverage level (assuming diminishing returns). This threshold is calculated from statistical models that assess the relation between the number of health workers needed and their attainment of key health system targets. Additional factors that influence need (for example, urbanization) may also be added. However, an external decision is still required to identify the desired level of population coverage for the service in question. Notably, the WHO adopted this approach in 2006 to identify a global threshold of 2.28 health workers per 1,000 population as the minimum desired coverage level for having 80 percent of live births attended by a skilled professional (WHO 2006).

At the country level, this approach could use either (1) cross-country data to identify a globally relevant threshold or (2) subnational data within Saudi Arabia to identify a threshold from the internal variation in service delivery coverage and health worker densities. Using cross-country data ensures that any threshold identified is placed within the global context of need, based on the strength of the empirical relationship observed across settings.

Using subnational data implicitly assumes that the variation within a country already encompasses the need-based criteria that could be applied to all of a country (for example, that there are a few areas that represent the desired needbased criteria that planners want the entire nation to achieve).

Example: Using subnational data on health workers and treatment for mental health illness as the health service delivery indicator, a regression analysis could identify the threshold of health workers needed to achieve 80 percent coverage for mental health screening and treatment.

The disability-adjusted life year (DALY) weighted approach is a variant of the top-down need-based approach that can identify a threshold of health worker density that relates to achieving multiple service delivery indicators. Again, using aggregate-level variation (for example, aggregated at the country or subnational level), regression analysis is used to empirically identify the health worker density associated with the desired level of coverage for each service delivery indicator, and the resulting health worker densities are then aggregated using dALy weights for each indicator to arrive at the specific health worker threshold for each geographic unit. Using one set of dALy weights for all units will generate one health worker density threshold, whereas using unit-specific dALy weights will generate unit-specific health worker density thresholds.

Example: Using subnational data on health workers and three indicators for service delivery (for example, treatment for mental health illness, influenza vaccination, and four or more antenatal checkups), separate regression analyses could identify the threshold of health workers needed to achieve 80 percent coverage for each indicator. The final need-based criterion is then a weighted sum (according to

each service’s associated burden of disease relative to DALYs) of the identified health worker densities for all three indicators.

The composite index method further builds on the dALy-weighted top-down approach by first developing a composite index score that reflects the achievement of desired service coverage levels across many health indicators, weighted by each health condition’s share of the global burden of disease (that is, relative dALy weights). The composite index score is then regressed against the density of health workers to describe the relationship between health worker availability and level of service delivery coverage; the final threshold of desired service coverage, however, still requires policy-maker input or some other external decision rule. This method was recently developed by the WHO for identifying the threshold of 4.5 health workers per 1,000 population as the level of health worker availability needed to achieve the median score (25 percent) for 12 Sustainable development Goal tracer indicators (Scheffler et al. 2018; Scheffler, cometto, et al. 2016).

Example: With subnational data on health workers and three indicators for service delivery (for example, treatment for mental health illness, influenza vaccination, and four or more antenatal checkups), each subnational unit can be assigned a score (from 0 to 3). One point is given for each indicator for which coverage of 80 percent or greater of the population is achieved, and the points across indicators are summed, weighted by each indicator’s contribution to the burden of disease within a country (in relation to DALYs). A regression analysis of the composite score and health worker densities across subnational units can then be used to trace this curvilinear relationship. A decision rule can be applied to identify the composite score that best reflects public health planning goals.

Each method for determining the need-based criterion has advantages and particular considerations, as summarized in table 2.3. The next subsections further explain the methods for approaches 3–6.

Building bottom-up need

This approach does not involve any statistical methods, but rather builds a deterministic model of how services can be delivered at the desired level of population coverage. it begins with the priority health conditions that policy makers want to address through the health system, which may reflect policy makers’ anticipation of the future burden of disease. For each of these health conditions, population-based estimates of the prevalence of the conditions are needed to understand the size of the target population in need for each condition. The relevant health service delivery model (including appropriate staffing ratios) can then be applied to the expected volume of inpatient and outpatient services at the level of service coverage that planners desire.

The service delivery model can differ across health conditions and can be informed by the current health system structure and policies, by international standards, or by other sources that recommend a particular staffing ratio or skills mix. Factors that should be considered in defining service delivery models include the proportion of cases needing care on either an outpatient or inpatient basis, the number of expected visits per person, the need for a hospital bed, and staffing full-time equivalents for different occupation groups. Health service delivery needs are calculated for each health condition and then summed across conditions to obtain an aggregate estimate. This process ultimately yields a target count of health workers of different types.

TABLE 2.3 Approaches to identifying the need-based criterion

APPROACH ADVANTAGES

1. Workforce-topopulation ratio • Simple and straightforward • Requires only identifying a reference context • No empirical data needed

2. Utilization • Requires only data on current utilization and health workers

3. Bottom-up need • Is context-specific and builds on the services and delivery system of the context under study • Can incorporate multiple service delivery goals

CONSIDERATIONS

• Does not consider actual utilization or service coverage aims • Must assume the reference context is sufficiently similar (for example, health system, burden of disease) • Must assume that current utilization is the desired level for the population and, hence, current workforce-to-population ratios are adequate • Requires data on the prevalence of priority health conditions, treatment coverage targets, and service delivery models for each condition

4. Top-down need • Is based on empirical analysis of data across contexts (for example, countries, subnational units) • Only one service delivery indicator is needed • Can additionally account for modifying factors that influence service delivery

5. DALY weighted • Generates a health worker density threshold for multiple service delivery indicators • Can incorporate differential burdens of disease

6. Composite index • Can incorporate multiple service delivery indicators

• Can incorporate differential burdens of disease • Uses only one regression model to identify the health worker density threshold • The final threshold of desired service coverage still requires an external decision rule • Requires data on cause-specific DALYs • The composite score is not easily interpretable

Source: Based on SHC and RAS 2020, table 3. Note: DALY = disability-adjusted life year. • Requires data on health workers and at least one service delivery indicator • Requires an external decision to identify the desired population coverage level (for example, 80 percent) for the service indicator

• Requires future values of modifying factors to identify a future health worker density need level • Must estimate separate regressions for each service delivery indicator • Requires data on cause-specific DALYs • Requires an external decision to identify the desired population coverage level for each service included

because the user can specify the degree of complexity for this model, different service delivery models that incorporate urban-rural differentials or health worker composition (for example, foreign versus national, or by profession) can be taken into account. However, this approach implicitly assumes that the service delivery models used represent the ideal skills mix and staffing ratios of the future.

Regression analysis for top-down approaches

Approaches 4–6 require statistical analyses that all build on a basic regression methodology. This subsection describes this general regression method to illustrate how it can be used to define a need-based requirement threshold. it is important to note, however, that the statistical approach requires data for as many units as possible. Past research shows that, at a minimum, about 50 observations are needed to generate stable regression estimates, regardless of the unit of analysis chosen (for example, national, subnational).

Model specification Using cross-sectional data for subnational units g, a regression model can be used to estimate a curvilinear relationship between service delivery and health worker density: