Using Systems Dynamics for Capturing the Multicausality of Factors Affecting Health System Capacity in Latin America while Responding to the COVID-19 Pandemic
Abstract
:1. Introduction
2. Materials and Methods
2.1. System Dynamics (SD)–Group Model Building (GMB)
2.2. Validation Phase
3. Results
3.1. GMB Models
3.1.1. Bolivia
3.1.2. Nicaragua
3.1.3. Uruguay
3.1.4. Main Outcomes
3.2. EFA: Comparative Analysis and Validation
3.2.1. The Correlations among Factors
3.2.2. Mediating Effects
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Indicator | Bolivia | Nicaragua | Uruguay | Latin America and Caribbean | Source |
---|---|---|---|---|---|
Income-classification | Lower middle income | Lower middle income | High income | - | World Bank (data.worldbank.org) |
GDP, PPP (current international $)-2020 | 97,672,053 | 36,899,427 | 79,183,811 | 10,350,590 | World Bank (data.worldbank.org) |
GDP per capita, PPP (current international $)-2020 | 8367 | 5570 | 22,795 | 15,868 | World Bank (data.worldbank.org) |
Informal economy-2020 | 73% | 76% | 23% | 50% | International Labor organization |
Total population | 11,513,101 | 6,545,502 | 3,461,731 | 652,276,325 | Populationpyramid.net |
Urban population (% of total population)-2020 | 70 | 59 | 96 | 81 | World Bank (data.worldbank.org) |
Total population density (people per Km2 of land area)-2018 | 10 | 54 | 20 | 32 | World Bank (data.worldbank.org) |
Range of density in the major cities (habitants/ Km2) | 1810–4464 | 1186–4000 | 2194–6726 | - | Wikipedia |
Domestic general government health expenditure (% of current health expenditure)-2018 | 71% | 60% | 73% | 51% | World Bank (data.worldbank.org) |
Life expectancy at birth, total (years) | 72 | 74 | 78 | 76 | World Bank (data.worldbank.org) |
Poverty headcount ratio at $1.90 a day (2011 PPP) (% of population)-2019 † | 3.2% | 3.4% | 0.1% | 3.7% | World Bank (data.worldbank.org) |
Appendix B
Bolivia N = 7 | Nicaragua N = 7 | Uruguay N = 6 | |
---|---|---|---|
Place of work | |||
Department of Health Services | 1 | --- | 2 |
Public health facility | 3 | 6 | 4 |
Health insurance facility | 3 | --- | --- |
University | 2 | 1 | 1 |
NGO-dedicated to health | --- | 1 | --- |
Positions at work | |||
Family doctor | 1 | ||
Intensive care | 1 | ||
Nurse | 1 | 1 | 1 |
Medical doctor | 3 | 4 | 2 |
University docent | 2 | 1 | 1 |
HIV-unit coordinator at SEDES, Province level | 1 | ||
Physiotherapeutic | 1 | ||
Community building & researcher | 1 | ||
Psychologist | 1 | ||
Emergency doctor | 1 | ||
Health coordinator at ASSE, national level | 2 | ||
Advanced degree | |||
health professional (doctor, nurse, psychologist) | 7 | 7 | 6 |
Clinical medicine experience | |||
minimum 10 years | yes | yes | yes |
Experience in epidemiology | |||
work in primary care, or knowledge | yes | yes | yes |
Outbreak response experience | |||
currently or in the past | yes | yes | yes |
Health system capacity building experience | yes | yes | yes |
Work experience in Latin American countries | yes | yes | yes |
Appendix C
Appendix D
BOLIVIA | Mean | SD | NICARAGUA | Mean | SD | URUGUAY | Mean | SD |
---|---|---|---|---|---|---|---|---|
Construct and Item Description | Factors and Variable Description | Factors and Variable Description | ||||||
Health system capacity | Health system capacity | Health system capacity | ||||||
Non-Risky working environment | 4.00 | 1.34 | Non-threatened health personnel | 4.49 | 1.03 | Decongestion | 4.46 | 0.83 |
Access to health care | 3.76 | 1.41 | Availability of COVID-19 training | 4.09 | 1.28 | Use of technology (HS) | 4.15 | 1.05 |
Transparent statistics | 4.54 | 1.09 | Private and public collaboration | 4.46 | 0.89 | |||
Continuous information | 4.32 | 1.09 | ||||||
Average variance extracted (AVE) | 0.78 | Average variance extracted (AVE) | 0.52 | Average variance extracted (AVE) | 0.61 | |||
Composite reliability (CR) | 0.87 | Composite reliability (CR) | 0.75 | Composite reliability (CR) | 0.76 | |||
Public administration-Preparedness | Public administration | Public Administration-risk | ||||||
Planning and coordination | 3.95 | 1.30 | Scientific collaboration | 4.14 | 1.27 | Non Self-medication | 2.17 | 1.34 |
Preparedness | 4.03 | 1.25 | Availability COVID-19 data | 4.08 | 1.42 | Motivated personnel | 2.02 | 1.24 |
Interinstitutional collaboration | 4.01 | 1.23 | Level of care | 3.98 | 1.42 | Missing border control | 2.63 | 1.31 |
Pandemic pressure | 3.47 | 1.31 | Knowledge exchange and experience | 4.11 | 1.43 | |||
Preparedness management | 4.09 | 1.24 | ||||||
Comprehensive epidemiology | 4.19 | 1.09 | ||||||
Protocols | 4.11 | 1.05 | ||||||
Average variance extracted (AVE) | 0.81 | Average variance extracted (AVE) | 0.44 | |||||
Composite reliability (CR) | 0.95 | Composite reliability (CR) | 0.70 | |||||
Preparedness | Preparedness | |||||||
Decentralized measures | 4.19 | 1.39 | Capacity increase | 4.28 | 0.86 | |||
Usage of biosecurity material | 4.24 | 1.32 | Health personnel trust in the management | 4.28 | 0.84 | |||
Social media to communicate | 4.49 | 0.90 | Home brigades | 4.48 | 0.75 | |||
Non-politicized environment | 4.24 | 1.03 | Protocols by science | 4.38 | 0.68 | |||
Interinstitutional collaboration | 4.25 | 0.98 | ||||||
Trust of society | 4.22 | 0.89 | ||||||
Trust of health personnel | 4.39 | 0.86 | ||||||
Average variance extracted (AVE) | 0.51 | Average variance extracted (AVE) | 0.34 | Average variance extracted (AVE) | 0.56 | |||
Composite reliability (CR) | 0.87 | Composite reliability (CR) | 0.66 | Composite reliability (CR) | 0.90 | |||
Information | Information | Information | ||||||
Rapid case tracing | 3.84 | 1.35 | Health system informing about for treatments | 4.22 | 0.94 | Society trust in the management | 4.40 | 0.90 |
Information | 3.88 | 1.29 | Health system informing about for medicaments | 3.83 | 1.33 | Home brigades | 4.31 | 1.00 |
Official information by the government | 4.19 | 1.22 | Strengthening of first level | 4.55 | 0.71 | |||
Comprehensive epidemiology | 4.45 | 0.73 | ||||||
Information for demand planning | 4.31 | 0.85 | ||||||
Average variance extracted (AVE) | 0.34 | Average variance extracted (AVE) | 0.54 | Average variance extracted (AVE) | 0.55 | |||
Composite reliability (CR) | 0.50 | Composite reliability (CR) | 0.77 | Composite reliability (CR) | 0.85 | |||
Collective Self-efficacy | Collective Self-efficacy | not applicable | ||||||
Knowledge on deaths and cases | 3.40 | 1.28 | Availability of biosecurity material | 4.13 | 1.20 | |||
Knowledge on hospital space | 3.16 | 1.37 | Transparent epidemiological data | 4.19 | 1.28 | |||
Difficulty to stay at home (informal economy) | 3.41 | 1.33 | ||||||
Self-diagnosis and medication | 3.70 | 1.37 | ||||||
Informal economy as a limitation | 3.57 | 1.26 | ||||||
Average variance extracted (AVE) | 0.39 | Average variance extracted (AVE) | 0.75 | |||||
Composite reliability (CR) | 0.75 | Composite reliability (CR) | 0.86 |
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Systemic Feedback Mechanisms | Main Factors | Description of the Factor |
---|---|---|
Health system capacity | Public administration | Refers to resources and the capacity to collaborate between the State, the civil society, and the market (scientific community, universities, private and public health system) to stop the spread of COVID-19 while addressing the economic needs of the country [14,35] |
Preparedness | According to the World Health Organization (2019), preparedness refers to a framework to manage multisectoral disaster risk management, and all-hazards emergency preparation and response, including for epidemics, health systems strengthening and community-centered primary health care [36]. In our view, it refers to a set of actions which are aimed to decrease pandemic outcomes, such as protocols, biosecurity measures, rapid tracing, use of epidemiologic information and communication. | |
Collective efficacy | Self-efficacy means the responsibility of an individual to behave careful or be effective in a pandemic. By collective efficacy we refer to a group’s shared perception that together they can stay healthy during the COVID-19 [37], it supports actions that could help avoid being infected by COVID-19. | |
Information & Misinformation | The official information refers to messages by the government or formal sources to support the management of the pandemic (timely and sufficiently detailed). Misinformation fills the lack of detailed information or knowledge that according to the Mills, et al. (2020) this is done through: (1) distrust of science or selective use of expert authority; (2) distrust in pharmaceutical companies and government; (3) straightforward explanations; (4) use of emotion; and, (5) information bubbles [38] |
Description | Health System Capacity | Public Administration | Preparedness | Information | Collective Self-Efficacy |
---|---|---|---|---|---|
Literature supporting the direct relation to health system capacity | [4,5,9,39] | [9,14,35,40,41,42,43,44] | [6,7,35,45] | [38,39,46,47,48] | [14,41,49,50] |
BOLIVIA | |||||
Cronbach’s alpha for the whole survey | 0.82 | ||||
Kaiser-Meyer-Olkin | 0.76, p < 001 | ||||
Cronbach’s alpha for every construct | 0.87 | 0.87 | 0.67 | 0.74 | |
Factor Loadings | 0.87–0.89 | 0.46–0.84 | not applicable | 0.43–0.70 | 0.39–0.81 |
NICARAGUA | |||||
Cronbach’s alpha for the whole survey | 0.84 | ||||
Kaiser-Meyer-Olkin | 0.68, p < 001 | ||||
Cronbach’s alpha for every construct | 0.78 | 0.96 | 0.68 | 0.78 | 0.89 |
Factor Loadings | 0.86–0.93 | 0.49–0.85 | 0.43–0.75 | 0.56–0.82 | 0.84–0.90 |
URUGUAY | |||||
Cronbach’s alpha for the whole survey | 0.95 | ||||
Kaiser-Meyer-Olkin | 0.76, p < 001 | ||||
Cronbach’s alpha for every construct | 0.82 | 0.69 | 0.95 | 0.92 | |
Factor Loadings | 0.45–0.69 | 0.52–0.77 | 0.62–0.90 | 0.53–0.86 | not applicable |
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Share and Cite
Cordova-Pozo, K.L.; Korzilius, H.P.L.M.; Rouwette, E.A.J.A.; Píriz, G.; Herrera-Gutierrez, R.; Cordova-Pozo, G.; Orozco, M. Using Systems Dynamics for Capturing the Multicausality of Factors Affecting Health System Capacity in Latin America while Responding to the COVID-19 Pandemic. Int. J. Environ. Res. Public Health 2021, 18, 10002. https://doi.org/10.3390/ijerph181910002
Cordova-Pozo KL, Korzilius HPLM, Rouwette EAJA, Píriz G, Herrera-Gutierrez R, Cordova-Pozo G, Orozco M. Using Systems Dynamics for Capturing the Multicausality of Factors Affecting Health System Capacity in Latin America while Responding to the COVID-19 Pandemic. International Journal of Environmental Research and Public Health. 2021; 18(19):10002. https://doi.org/10.3390/ijerph181910002
Chicago/Turabian StyleCordova-Pozo, Kathya Lorena, Hubert P. L. M. Korzilius, Etiënne A. J. A. Rouwette, Gabriela Píriz, Rolando Herrera-Gutierrez, Graciela Cordova-Pozo, and Miguel Orozco. 2021. "Using Systems Dynamics for Capturing the Multicausality of Factors Affecting Health System Capacity in Latin America while Responding to the COVID-19 Pandemic" International Journal of Environmental Research and Public Health 18, no. 19: 10002. https://doi.org/10.3390/ijerph181910002