Skip to main content

Advertisement

SpringerLink
Log in

The association between healthcare resource allocation and health status: an empirical insight with visual analytics

  • Original Article
  • Published:
Journal of Public Health Aims and scope Submit manuscript

Abstract

Aim

Healthcare resource allocation varies worldwide. It is integral that countries identify optimal allocation methods to distribute healthcare resources to ensure and sustain good population heath. This study examines the association between healthcare resource allocation and perception of health status across countries.

Subject and methods

Data from OECD Health Statistics and OECD Health Care Resources is analyzed with visual analytics methodology.

Results

Findings show that the relationship between factors that influence resource allocation and health status differ based on the development status and geographic location of countries. In developing countries, there is a significant relationship between the number of hospitals and absence from work due to perceived poor health. Medical resource allocation is positively associated with health status perception in countries where the allocation is proportional to incidence of diseases. Among the various medical resources, medical personnel are the most important factor in both developing and developed countries in influencing the positive perceived health status of the population.

Conclusions

With more healthcare resources people’s life expectancy should increase, and overall mortality should decline. This study offers several implications for the future. Governments can take differential actions based on their citizens’ needs to improve their perceived health status. In general, there should be an emphasis on allocation of human medical resources, rather than medical equipment. Businesses should invest more in healthcare education to be able to implement and administer government health policies. Strategic investments in key healthcare resources can boost revenue and offer sufficient incentives for development of innovative medical technology.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

Availability of data and materials

The dataset analyzed during the current study is available from the corresponding author on reasonable request.

Abbreviations

OECD:

Organization for Economic Co-operation and Development

References

  • Ahlert M, Felder S, Vogt B (2012) Which patients do I treat? An experimental study with economics and physicians. Heal Econ Rev 2:1–11

    Article  Google Scholar 

  • Angelis A, Kanavos P, Montibeller G (2017) Resource allocation and priority setting in health care: a multi-criteria decision analysis problem of value? Global Policy 8:6–83

    Article  Google Scholar 

  • Angelis A, Kanavos P (2017) Multiple Criteria Decision Analysis (MCDA) for evaluating new medicines in Health Technology Assessment and beyond: The Advance Value Framework, Soc Sci Medicine 188:137–156, ISSN 0277-9536. https://doi.org/10.1016/j.socscimed.2017.06.024

  • Bangdiwala SI, Fonn S, Okoye O, Tollman S (2010) Workforce resources for health in developing countries. Public Health Rev 32(1):296–318

    Article  Google Scholar 

  • Beauchamp TL (2003) Methods and principles in biomedical ethics. J Med Ethics 29(5):269–274

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Benishek LE, Kachalia A, Daugherty Biddison L, Wu AW, Biddison LD (2020) Mitigating healthcare worker distress from scarce medical resource allocation during a public health crisis. Chest 158(6):2285–2287

    Article  CAS  PubMed  Google Scholar 

  • Bennett S, Chanfreau C (2005) Approaches to rationing antiretroviral treatment: ethical and equity implications. Bull World Health Organ 83:541–547

    PubMed  PubMed Central  Google Scholar 

  • Chaudhury N, Hammer J, Kremer M, Muralidharan K, Rogers FH (2006) Missing in action: teacher and health worker absence in developing countries. J Econ Perspect 20(1):91–116

    Article  PubMed  Google Scholar 

  • Chen C, Wang J, Yang C, Fan J (2016) Nurse practitioner job content and stress effects on anxiety and depressive symptoms, and self-perceived health status. J Nurs Manag 24:695–704

    Article  PubMed  Google Scholar 

  • Cloos P, Ndao EM, Aho J, Benoît M, Fillol A, Munoz-Bertrand M, et al (2020) The negative self-perceived health of migrants with precarious status in Montreal, Canada: A cross-sectional study. PLoS ONE 15(4):e0231327. https://doi.org/10.1371/journal.pone.0231327

  • Diderichsen F (2004) Resource Allocation for Health Equity : Issues and Methods. Health, Nutrition and Population (HNP) Discussion paper. World Bank, Washington, DC. © World Bank. https://openknowledge.worldbank.org/handle/10986/13619. License: CC BY 3.0 IGO

  • Dieleman JL, Haakenstad A (2015) The complexity of resource allocation for health. Lancet Glob Health 3:e8–e9

    Article  PubMed  Google Scholar 

  • Dieleman JL, Graves CM, Templin T, Johnson E, Baral R, Leach-Kemon K, Murray CJ (2014) Global health development assistance remained steady in 2013 but did not align with recipients’ disease burden. Health Aff 33(5):878–886

    Article  Google Scholar 

  • Dubois C-A, Mckee M (2006) Gross - national comparisons of human resources for health - what can we learn? Health Econ Policy Law 1:59–78

    Article  PubMed  Google Scholar 

  • Eddy DM (1991a) Clinical decision making: from theory to practice. The individual vs society. Is there a conflict. JAMA 265(11):1446–1450

    Article  CAS  PubMed  Google Scholar 

  • Eddy DM (1991b) Clinical decision making: from theory to practice. The individual vs society. Resolving the conflict. JAMA 265(18):2399–2406

    Article  CAS  PubMed  Google Scholar 

  • Emanuel EJ (2000) Justice and managed care. Four principles for the just allocation of healthcare resources. Hast Cent Rep 30(3):8–16

    Article  CAS  Google Scholar 

  • Fleck L (2001) Healthcare justice and rational democratic deliberation. Am J Bioeth 1(2):20–21

    Article  CAS  PubMed  Google Scholar 

  • Fox DM (2006) The determinants of policy for population health. Health Econ Policy Law 1:395–407

    Article  PubMed  Google Scholar 

  • Gandjour A, Lauterbach KW (2000) Allocating resources in health care. HEPAC 2:116–121

    Article  Google Scholar 

  • Ghosh B, Scott JE (2011) Antecedents and catalysts for developing a healthcare analytic capability. Commun Assoc Inf Syst 29(22):395–410

    Google Scholar 

  • Gil-Salmerón A, Valia-Cotanda E, Garces-Ferrer J (2018) The effect of perceived discrimination on the health status of immigrant population in Spain (Valencia). Int J Integr Care 18(S2):A345, pp. 1-8. https://doi.org/10.5334/ijic.s2345

    Article  Google Scholar 

  • Jamison DT, Breman JG, Measham AR, Alleyne G, Claeson M, Evans DB, Musgrove P (eds) (2006) Disease control priorities in developing countries (2nd edn). The World Bank, Washington, DC

  • Kaleta D, Polańska K, Dziankowska-Zaborszczyk E, Hanke W, Drygas W (2009) Factors influencing self-perception of health status. Cent Eur J Public Health 17(3):122–127

    Article  PubMed  Google Scholar 

  • Keim D, Andrienko G, Fekete J-D, Görg C, Kohlhammer J, Melançon G (2008) Visual analytics: definition, process, and challenges. Lect Notes Comput Sci 4950:154–176

    Article  Google Scholar 

  • Kephart G, Asada Y (2009)Need-based resource allocation: different need indicators, different results? BMC Health Serv Res 9(1):122

    Article  PubMed  PubMed Central  Google Scholar 

  • Klein R, Maybin J (2012) Thinking about rationing. The King's Fund, London. https://www.kingsfund.org.uk/sites/default/files/field/field_publication_file/Thinking-about-rationing-the-kings-fund-may-2012.pdf. Accessed on 1 May 2020

  • Kohlhammer J, Keim D, Pohl M, Santucci G, Andrienko G (2011) Solving problems with visual analytics. Procedia Comp Sci 7:117–120

    Article  Google Scholar 

  • Kong NY, Kim DH (2020) Factors influencing health care use by health insurance subscribers and medical aid beneficiaries: a study based on data from the Korea welfare panel study database. BMC Public Health 20:1133. https://doi.org/10.1186/s12889-020-09073-x

    Article  PubMed  PubMed Central  Google Scholar 

  • Kunst AE, Bos V, Lahelma E, Bartley M, Lissau I, Regidor E et al (2005) Trends in socioeconomic inequalities in self-assessed health in 10 European countries. Int J Epidemiol 34(2):295–305

    Article  PubMed  Google Scholar 

  • Li Q, Wei J, Jiang F, Zhou G, Jiang R, Chen M, Zhang X, Hu W (2020) Equity and efficiency in healthcare resource allocation in Jiangsu Province, China. Int J Equity Health 19:211. https://doi.org/10.1186/s12939-020-01320-2

    Article  PubMed  PubMed Central  Google Scholar 

  • Lin SH, Liao WC, Chen MY, Fan JY (2014) The impact of shift work on nurses’ job stress, sleep quality and self-perceived health status. J Nurs Manag 22(5):604–612

    Article  PubMed  Google Scholar 

  • Maia MJ, Moniz AB (2014) Equity in access to MRI equipment. In: Michalek T, Hebáková L, Hennen L, Scherz C, Nierling L, Hahn J (eds) Technology assessment and policy areas of great transitions, 1st PACITA project conference, Technologické centrum. AV ČR, Praha, pp 307–313

    Google Scholar 

  • Malinauskiene V, Leisyte P, Romualdas M, Kirtiklyte K (2011) Associations between self-rated health and psychosocial conditions, lifestyle factors and health resources among hospital nurses in Lithuania. J Adv Nurs 67(11):2383–2393

    Article  PubMed  Google Scholar 

  • Martin S, Rice N, Smith PC (2008) Does health care spending improve health outcomes? Evidence from English programme budgeting data. J Health Econ 27:826–842

    Article  PubMed  Google Scholar 

  • Martin S, Rice N, Smith PC (2012) Comparing costs and outcomes across programmes of health care. Health Econ 21(3):316–337

    Article  PubMed  Google Scholar 

  • McArthur JW (2013) Own the goals: what the millennium development goals have accomplished. Foreign Aff 92:152

    Google Scholar 

  • Mitton C, Donaldson C (2004) Health care priority setting: principles, practice and challenges. Cost Eff Res Alloc 2(1):3

    Article  Google Scholar 

  • Mohsenpour SR, Arab M, Razavi SHE, Sari AA (2017) Exploring the challenges of the Iranian parliament about passing laws for resource allocation in healthcare: a qualitative study. Electron Physician 9(10):5418–5426

    Article  PubMed  PubMed Central  Google Scholar 

  • Mukamel DB, Zwanziger J, Bamezai A (2002) Hospital completion resource allocation and quality of care. BMC Health Serv Res 2:1–9

    Article  Google Scholar 

  • Murray CJ, Vos T, Lozano R, Naghavi M, Flaxman AD, Michaud C et al (2012)Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990–2010: a systematic analysis for the global burden of disease study 2010. Lancet 380(9859):2197–2223

    Article  PubMed  Google Scholar 

  • Obermann K, Chanturidze T, Glazinski B, Schuetz DK, Steinhauer H (2018) The shaded side of the UHC cube: a systematic review of human resources for health management and administration in social health protection schemes. Heal Econ Rev 8:1–7

    Google Scholar 

  • Olson LM, Tang SFS, Newacheck PW (2005) Children in the United States with discontinuous health insurance coverage. N Engl J Med 353:382–391

    Article  CAS  PubMed  Google Scholar 

  • Osterdal LP (2005) Axioms for health care resource allocation. J Health Econ 24:679–702

    Article  PubMed  Google Scholar 

  • Otterson T, Evans DB, Mossialos E, Rottingen J-A(2017) Global Health financing towards 2030 and beyond. Health Econ Policy Law 12:105–111

    Article  Google Scholar 

  • Pontone GM, Williams JR, Anderson KE et al (2011) Anxiety and self-perceived health status in Parkinson’s disease. Parkinsonism Relat Disord 17(4):249–254

    Article  PubMed  PubMed Central  Google Scholar 

  • Raghupathi V, Raghupathi W (2013) An overview of health analytics. J Health Med Inform 14(3):132. https://doi.org/10.4172/2157-7420.1000132

    Article  Google Scholar 

  • Raghupathi V, Raghupathi W (2020) The influence of education on health: an empirical assessment of OECD countries for the period 1995–2015. Arch Public Health 78(20). https://doi.org/10.1186/s13690-020-00402-5

  • Raghupathi V, Ren J, Raghupathi W (2021) Understanding the nature and dimensions of litigation crowdfunding: a visual analytics approach. PLoS One 16(4):e0250522. https://doi.org/10.1371/journal.pone.0250522

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Rawls J (1999) A theory of justice. Belknap Press of Harvard University. http://www.hup.harvard.edu/catalog.php?isbn=9780674000780. Accessed on 1 May 2020

  • Resch S, Ryckman T, Hecht R (2015) Funding AIDS programmes in the era of shared responsibility: an analysis of domestic funding in 12 low-income and middle-income countries. Lancet Glob Health 3(1):e52–61

  • Ritchwood TD, Bishu KG, Egede LE (2017) Trends in healthcare expenditure among people living with HIV/AIDS in the United States: evidence from 10 years of nationally representative data. Int J Equity Health 16(1):188. https://doi.org/10.1186/s12939-017-0683-y

    Article  PubMed  PubMed Central  Google Scholar 

  • Rural Information Hub (RIH) (2019) Demographic changes and aging population. https://www.ruralhealthinfo.org/toolkits/aging/1/demographics. Accessed on 1 May 2020

  • Sallasky E, Gursky EA (2006) The case for transforming governmental public health. Health Aff 25(4):1017–1028

    Article  Google Scholar 

  • Savel TG, Foldy S (2012) The role of public health informatics in enhancing public health surveillance. Morb Mortal Wkly Rep 61(3):20–24

    Google Scholar 

  • Šplíchalová A, Tomášková H, Šlachtová H (2003) Risks of different self-approach to health in an industrial city population. Cent Eur J Public Health 11(3):142–148

    PubMed  Google Scholar 

  • Tao YK, Henry K, Zou Q, Zhong X (2014) Methods for measuring horizontal in health resource allocation: a comparative study. Heal Econ Rev 4:1–10

    Google Scholar 

  • Thomas JJ, Cook KA (2006) A visual analytics agenda. IEEE Comp Graph Appl 26:10–13

    Article  Google Scholar 

  • Vingilis ER, Wade TJ, Seeley JS, Predictors of Adolescent Self-Rated Health (2002) Analysis of the National Population Health Survey. Can J Public Health 93(3):193–197

    Article  PubMed  PubMed Central  Google Scholar 

  • Wada K, Higuchi Y, Smith DR (2015) Socioeconomic status and self-reported health among middle-aged Japanese men: results from a nationwide longitudinal study. BMJ Open 5:e008178

    Article  PubMed  PubMed Central  Google Scholar 

  • Wagstaff A, Van Doorslaer E (1993) Equity in the finance and delivery of health care: an Int perspective. J Epidemiol Community Health 47(4):338–339

  • Wall AE, Pruett T, Stock P, Testa G (2020) Coronavirus disease 2019: utilizing an ethical framework for rationing absolutely scarce health-care resources in transplant allocation decisions. Am J Transplant 20(9):2332–2336

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Wang F, Wang J-D, Huang Y-X(2016) Health expenditures spend for prevention economic performance and social welfare. Heal Econ Rev 6:1–10

    Google Scholar 

  • Wróblewska W (2005) The male-female differences in health: the role of the health-related behaviours. In: Szamotulska K (ed) Polish population review. GUS, Warsaw, p 27

Download references

Author information

Author notes

  1. Viju Raghupathi and Wullianallur Raghupathi contributed equally to this work.

Authors and Affiliations

  1. Koppelman School of Business, Brooklyn College of the City University of New York, 2900 Bedford Ave, Brooklyn, NY, 11210, USA

    Viju Raghupathi

  2. Gabelli School of Business, Fordham University, 140 W. 62nd Street, New York, NY, 10023, USA

    Wullianallur Raghupathi

Authors
  1. Viju Raghupathi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wullianallur Raghupathi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Viju Raghupathi.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix 1 Descriptive statistics

Appendix 1 Descriptive statistics

Descriptive statistics

     
 

#

Min

Max

Mean

Std. Dev.

Year

543

2000

2016

2007.97

4.44

Females at age 40

543

40.50

53.80

48.11

2.16

Females at age 60

543

21.80

34.10

28.55

1.95

Females at age 65

543

17.50

29.20

23.80

1.85

Females at age 80

543

7.00

14.70

10.87

1.30

Females at birth

543

80.00

93.40

87.83

2.21

Males at age 40

543

18.10

44.13

32.55

4.59

Males at age 60

543

8.70

22.70

16.49

2.83

Males at age 65

543

7.60

18.70

13.29

2.23

Males at age 80

543

4.10

8.50

6.01

0.85

Males at birth

543

51.80

78.90

69.95

5.36

Hospital beds

543

29,161.76

1,839,205.16

177,310.09

337,038.83

Bad/very bad health, total aged 15+

543

1.41

27.21

10.59

5.36

Fair (not good, not bad) health, total aged 15+

543

6.90

51.80

23.00

8.70

Good/very good health, total aged 15+

543

28.49

189.82

129.99

30.82

Good/very good health, total aged 15+, High education (ISCED 5–8)

543

38.40

102.06

79.68

11.27

Good/very good health, total aged 15+, Income quintile 1 (lowest)

543

18.60

94.39

58.66

14.70

Good/very good health, total aged 15+, Income quintile 5 (highest)

543

34.50

102.60

78.28

12.21

Good/very good health, total aged 15+, Low education (ISCED 0–2)

543

14.95

87.30

55.10

13.23

Good/very good health, total aged 15+, Medium education (ISCED 3, 4)

543

32.20

103.00

72.18

13.21

Good/very good health, total aged 15–24

543

45.50

112.70

89.34

8.82

Good/very good health, total aged 25–44

543

32.20

111.60

81.01

11.12

Good/very good health, total aged 45–64

543

15.21

96.79

60.48

17.08

Good/very good health, total aged 65+

543

22.43

88.30

39.37

20.80

Certain conditions originating in the perinatal period

543

15,542.67

38,837.40

2718.35

6982.86

Certain infectious and parasitic diseases

543

33,325.87

141,337.20

10,904.21

23,414.27

Complications of pregnancy, childbirth, and the puerperium

543

567.31

1336.70

72.07

238.66

Congenital malformations and chromosomal abnormalities

543

2811.15

21,395.90

2251.48

4541.72

Diseases of the blood and blood-forming organs

543

168.79

21,396.10

2034.65

3745.68

Diseases of the circulatory system

543

456,107.01

1,885,254.70

192,577.06

311,860.65

Diseases of the digestive system

543

199.50

214,946.73

24,925.09

37,400.85

Diseases of the genitourinary system

543

859.44

132,455.90

12,278.76

23,030.59

Diseases of the musculoskeletal system and connective tissue

543

641.74

29,009.20

3057.99

5163.39

Diseases of the nervous system

543

4793.00

331,357.80

20,119.95

46,550.37

Diseases of the respiratory system

543

46,673.32

522,647.70

52,087.60

97,908.31

Diseases of the skin and subcutaneous tissue

543

736.38

8922.50

967.83

1568.05

Endocrine, nutritional, and metabolic diseases

543

282.03

246,692.06

22,167.65

44,707.85

External causes of mortality

543

211.90

404,976.00

35,694.34

64,803.33

Malignant neoplasms

543

1464.57

1,184,586.70

139,441.14

222,773.90

Neoplasms

543

54,067.44

1,216,751.60

145,132.53

229,107.25

Symptoms, signs, ill-defined causes

543

23,712.90

186,250.00

17,036.52

26,083.03

All causes of death

543

627,557.82

5,257,809.20

544,431.65

901,221.76

Compensated absence from work due to illness

543

25.61

30.37

11.86

5.39

Self-reported absence from work due to illness

543

11.73

21.10

7.05

2.63

# of psychiatrists

543

5552.25

45,961.74

4575.23

8170.39

# of physicians

543

1476.86

820,251.00

91,586.48

141,759.22

# of pharmacists

543

2742.50

295,620.92

30,329.61

56,478.56

# of nurses

543

8301.09

22,108,726.56

1,343,730.45

3,288,910.41

# of midwives

543

16,840.63

53,508.48

7207.68

11,722.67

For-profit, privately owned hospitals

543

612.66

9385.71

398.43

753.62

General hospitals

543

1414.00

8179.69

831.43

1544.66

# of hospitals

543

1182.77

9259.09

1197.96

1903.85

Non-profit, privately owned hospitals

543

422.80

4265.90

303.54

676.20

Publicly owned hospitals

543

264.34

2422.29

419.16

442.80

# of dentists

543

45,130.83

195,700.61

26,142.84

38,109.69

# of pediatricians

543

996.75

81,209.15

5659.56

12,579.70

Computed tomography scanners, total

543

260.24

13,818.87

886.67

2220.26

Digital subtraction angiography units, total

543

474.86

3315.37

183.65

337.31

Gamma cameras, total

543

213.61

15,951.02

343.07

1464.17

Lithotripters, total

543

65.09

1115.03

81.87

173.12

MRI units, total

543

238.72

12,554.00

581.77

1659.77

Mammographs, total

543

272.59

15,200.22

1065.83

2626.92

PET scanners, total

543

394.32

1650.13

64.18

201.92

Radiation therapy equipment, total

543

544.96

3927.47

161.28

386.18

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Raghupathi, V., Raghupathi, W. The association between healthcare resource allocation and health status: an empirical insight with visual analytics. J Public Health (Berl.) 31, 1035–1057 (2023). https://doi.org/10.1007/s10389-021-01651-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10389-021-01651-6

Keywords

Search

Navigation