Theoretical background and research question

Health expenditure is widely recognized as a key determinant of population health because it reflects the financial resources available for prevention, diagnosis, treatment, rehabilitation, and public health interventions. Cross-country evidence suggests that countries with higher levels of health spending generally achieve better health outcomes, although the magnitude of these gains varies substantially across health systems and levels of development (Ochalek et al., 2015).

To measure population health, this study uses age-standardized Disability-Adjusted Life Years (DALYS). DALLYS combine years of life lost due to premature mortality and years lived with disability into a single indicator of disease burden, allowing meaningful comparisons across countries with different demographic structures. The age-standardized DALYS rate is therefore an appropriate measure of overall population health performance in cross-country analyses.

However, financial resources alone may not be sufficient to improve health outcomes. Health systems also depend on the availability of qualified health professionals capable of transforming financial investments into effective healthcare services. The World Health Organization (WHO) identifies physician, nurse, and midwife density as key indicators of health system capacity and progress toward Universal Health Coverage (WHO, 2016).

Empirical evidence supports the importance of the health workforce for population health outcomes. Using cross-national data, Castillo-Laborde et al. (2011) found a statistically significant negative relationship between health worker density, particularly physician density, and DALYs, indicating that countries with more health professionals tend to experience lower disease burden.

More recent global studies have reinforced these findings. Haakenstad et al. (2022) demonstrated a strong relationship between health workforce availability and Universal Health Coverage across 204 countries and territories, highlighting physician and nurse density as critical components of health system performance. Their analysis estimated that achieving high levels of effective coverage requires substantial densities of physicians, nurses, and midwives. Similarly, Ahmat et al. (2022) reported that health workforce density is a key prerequisite for achieving Universal Health Coverage and improving health system effectiveness.

Building on this literature, the present study investigates whether Health Expenditure Per Capita is associated with Age-Standardized DALLYS Rates across countries and whether this association varies according to Physician and Nurse And Midwife Density. The underlying assumption is that financial investments in health may generate greater health gains when sufficient health workforce capacity exists to translate resources into effective healthcare delivery.

Study design

Cross-sectional ecological study using secondary country-level data.

Sample and data management

Data were obtained from two international secondary data sources: the Global Burden of Disease (GBD) 2023 database and the World Health Organization (WHO) Global Health Observatory (GHO). The GBD database provided Age-Standardized DALYs Rates for all countries, while the WHO database provided information on Health Expenditure Per Capita, Physician Density, and Nurse and Midwife Density.

For each dataset, observations were restricted to the year 2023, when the most recent DALY data were collected. Relevant variables were extracted and standardized using a common country identifier. Country names were harmonized across datasets to ensure compatibility during the merging process.

The four datasets were then merged using country as the common key. An inner join procedure was applied, retaining only countries with complete information available for all study variables. The resulting analytical dataset included 106 countries.

Depedency Model

The dependent variable is the Age-Standardized DALYs Rate. DALYs combine years of life lost due to premature mortality and years lived with disability into a single measure of disease burden. Lower DALYs rates indicate better population health outcomes.

The primary independent variable is Health Expenditure Per Capita (US$). Physician Density and Nurse And Midwife Density, measured as the number of professionals per 10,000 population, were included as moderating variables for this relationship.

             Physician Density
                   │
                   ▼

Health Expenditure ───────► DALYs Rate

          Nurse & Midwife Density
                   │
                   ▼

Health Expenditure ───────► DALYs Rate Nurse & Midwife Density

Analysis

First, Pearson correlation analysis was conducted to examine the bivariate relationships between Age-Standardized DALYs Rates, Health Expenditure Per Capita, Physician Density, and Nurse and Midwife Density. Correlation analysis provides an initial assessment of the direction and strength of associations between variables and helps identify potential relationships prior to multivariable modeling.

Second, a multivariate linear regression approach was used to explore the relationship between all variables. Hierarchical or multilevel modeling was not applied because the dataset contains one observation per country and does not include nested observations. Therefore, standard multivariable linear regression with interaction terms was considered more appropriate.

The models are estimated sequentially. Model 1 includes only Health Expenditure Per Capita to assess the crude association with DALYs. Model 2 adds Physician Density and Nurse And Midwife Density to examine whether workforce capacity is independently associated with DALYs. Model 3 includes interaction terms between Health Expenditure and workforce density to test whether the association between expenditure and DALYs varies according to workforce availability.

Because Health Expenditure Per Capita is highly skewed across countries, its logarithmic transformation was used to reduce the influence of extreme values and better reflect diminishing returns to health spending.

Sample description

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Table 1. Distribution Of Countries By WHO Region
WHO Region N Percent
Africa 23 21.7
Americas 15 14.2
Eastern Mediterranean 9 8.5
Europe 39 36.8
South-East Asia 6 5.7
Western Pacific 14 13.2
Table 2. Characteristics Of The 106 Countries Included In The Analysis
Variable Mean SD Median Min Max
Age-Standardized DALYs Rate 33062.4 13719.2 29042.8 15703.3 82432.2
Health Expenditure Per Capita (US$) 1580.3 2193.9 542.2 17.4 11784.0
Doctors Per 10,000 Population 23.9 17.7 23.8 0.4 67.1
Nurses And Midwives Per 10,000 Population 52.2 40.2 41.3 1.7 192.0

Correlation analysis

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Table 3. Pearson Correlation Matrix
DALYS Health Expenditure Physicians Nurses & Midwives
DALYS 1.00 -0.56 -0.77 -0.64
Health Expenditure -0.56 1.00 0.63 0.86
Physicians -0.77 0.63 1.00 0.71
Nurses & Midwives -0.64 0.86 0.71 1.00

Multivariable regression analysis

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Table 4. Multivariable Linear Regression Results
Model 1 Model 2 Model 3
+ p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001
Log Health Expenditure Per Capita -6800.976*** -5215.453*** -6825.819***
[-7745.058, -5856.895] [-7198.036, -3232.871] [-8883.764, -4767.874]
Physicians Per 10,000 Population -242.766** -1279.739**
[-395.955, -89.578] [-2045.187, -514.291]
Nurses And Midwives Per 10,000 Population 30.109 76.387
[-33.558, 93.777] [-225.789, 378.562]
Log Health Expenditure × Physicians 153.382**
[48.030, 258.733]
Log Health Expenditure × Nurses And Midwives -10.767
[-49.509, 27.975]
Num.Obs. 106 106 106
R2 0.662 0.693 0.736
R2 Adj. 0.659 0.684 0.723

Discussion

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Limitations

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References

Ahmat, A., Okoroafor, S. C., Kazanga, I., Asamani, J. A., Millogo, J. J. S., Illou, M. M. A., … & Campbell, J. (2022). Estimating the threshold of health workforce densities towards universal health coverage in Africa. BMJ Global Health, 7(4), e008310.

Castillo-Laborde, C., Matthys, F., & Brouwer, W. (2011). Human resources for health and burden of disease: An econometric approach. Human Resources for Health, 9(4), 1–9.

GBD 2019 Human Resources for Health Collaborators. (2022). Measuring the availability of human resources for health and its relationship to universal health coverage for 204 countries and territories from 1990 to 2019: A systematic analysis for the Global Burden of Disease Study 2019. The Lancet, 399(10341), 2129–2154.

Ochalek, J., Lomas, J., & Claxton, K. (2015). Cost per DALY averted thresholds for low- and middle-income countries: Evidence from cross-country data. Centre for Health Economics Research Paper 122. University of York.

World Health Organization. (2016). Global strategy on human resources for health: Workforce 2030. Geneva, Switzerland: WHO.

A small correction: in APA 7, the outcome should consistently be written as DALYs (Disability-Adjusted Life Years), not DALLYS. I know your dataset and variable names use “DALLYS”, but in the manuscript and references I strongly recommend using the standard scientific term DALYs.