Assessment of integrated health of human, animal and environment: a holistic and stratified analysis based on global One Health index

doi:10.21203/rs.3.rs-1747712/v1

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Assessment of integrated health of human, animal and environment: a holistic and stratified analysis based on global One Health index

https://doi.org/10.21203/rs.3.rs-1747712/v1

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Background

One Health has been increasingly applied to health-related issues decision-making. As part of the global One Health index, the global One Health Intrinsic Drivers index (GOH-IDI) is a framework for evaluating the baseline conditions of human health, environmental health, and animal health. This study aim to provides a potential policy assessment tool for policy makers to help them better understand what can be enhanced in the future. This study is to assess the global performance in terms of GOH-IDI, analysis the difference under the world bank regional classification, and evauate the relationship between GOH-IDI and national economic levels.

Methods

The raw data among 146 countries worldwide were collected from authoritative databases and official reports in November 2021. The spatial and classificatory distribution of GOH-IDI were evaluated and identified by descriptive statistic analysis, data visualisations and shaping, Shapiro normality test and ridge map. The GOH-IDI was divided into five levels in the form of heat maps and visualised with regular heat maps and global maps. The differences under the world bank regional classification are analysed. The relationship between GOH-IDI and gross national income per capita was assessed using quadratic regression model. World Bank country classifications by income level are used to evaluate the differences in GOH-IDI performance between countries of various economic levels.

Results

We evaluated the performance of One Health Intrinsic Driver in 146 countries. The mean (SD) score of GOH-IDI is 52.58 (5.30). The mean (SD) values of different regions are North America (59.85, 2.34), Europe and Central Asia (56.60, 3.50), Middle East and North Africa (55.29, 2.62), East Asia and Pacific(52.72, 5.49), Latin America and The Caribbean (52.61,2.05), South Asia is (50.52,2.52) and Sub-Saharan Africa (46.21, 2.49). Of the 20 worst performing countries in GOH-IDI, 19 are from Sub-Saharan Africa. Gross national income per capita was moderately correlated with GOH-IDI(r = 0.744, P < 0.001). High-income countries (HICs) and other economic levels are statistically different from each other(P < 0.001). LICs have the best performance in some secondary indicators, including Non-communicable diseases and Mental Health and Health risks. Five indicators are not statistically different at each economic level, including Animal epidemic disease, Animal biodiversity, Air quality and climate change, Land resources and Environmental biodiversity.

Conclusions

The GOH-IDI is a crucial tool to evaluate the situation of One Health. There are inter-regional differences in GOH-IDI significantly at the worldwide level. The best performing region for GOH-IDI was North America and the worst was Sub-Saharan Africa.There is a positive correlation between the GOH-IDI and country economic status, with High-income countries (HICs) performing well in most indicators. GOH-IDI facilitates researchers' understanding of the multidimensional situation in each country and invests more attention in scientific questions that need to be addressed urgently.

One Health

Evaluation framework

Economic disparities

Global One Health index

The global pandemic of COVID-19 has increased awareness of the links and interactions between human health, animal health, and environmental health. To effectively address such complex health issues, many expert and academic groups have proposed “One health”, which means the concept of One Health is a way of studying and managing human health, animal health and environmental health in an entirety, which enhance the effectiveness of an element to improve well-being without compromising other elements[1,2].

Many countries and research institutions have different definitions of health and sustainability, deriving many indicator systems related to One Health but fundamentally different. There are currently a variety of datasets related to One Health, including Sustainable Development Goals (SDG), Gross Domestic Product (GDP), Human Development Index (HDI), Environmental Performance Index (EPI) and Global Burden of Disease[3–9]. The assessment tool for One Health performance has been established[10,11]. Global One Health Index (GOHI) consists of three parts: core drivers index, intrinsic drivers index, and external drivers index. However, the antecedent study describes the data holistically and does not discuss the intrinsic drivers index in detail[10]. Therefore, we conducted this study and accomplished data stratification and analysis using the socioeconomic factor Gini coefficient.

Global One Health Intrinsic Drivers index (GOH-IDI) focuses more on outcome metrics, including human health, animal health and Ecosystem Diversity, and environmental health[14]. Traditional indicator systems generally contain three components, including structure (stable components of the system), process (interventions on the system), and outcome (impact of the indicator on the system)[12,13]. A separate assessment of outcome indicators and intervention indicators is more conducive to researchers and policymakers understanding the implications of the data[1,8,15].

The framework and weights of the GOH-IDI have been calculated in previous studies by our team, combining the current background of the times, hoping to establish the right value orientation and let more and more people pay attention to animal health and environmental health. The previous study accomplished by the same key members used grounded theory (GT) method and fuzzy analytical hierarchy process (FAHP) to construct the indicators and calculate the weights of GOH-IDI[14]. This study described Global distribution of GOH-IDI, analyzed correlation between GOHIDI scores and social economic factors, and made policy recommendations in the perspective of global realities and typical case discussions.

Data collection and resources

The raw data collection and calculation was constructed in five steps, including framework formulation, indicator selection, database building, weight determination and GOHI scores calculation[11,16]. The data relating to the GOH-IDI project is stored on GitHub. GOH-IDI's database consists of 13 open source and reliable databases (Table 1)[11,16].

Table 1

Main database
Dimensions	Database
Human Health	SGD Dashboard WHO IHME-GBD
Animal Health and Ecosystem Diversity	Global Animal Disease Information System (EMPRES-I) OIE-WAHIS (OIE World Animal Health Information System) Environmental Performance Index Our World in Data
Environmental Health	World Bank State of Global Air Global Climate Risk Index Environmental Performance Index Our World in Data SGD Dashboard

GOH-IDI framework overview

Our team have established a scientific standard to evaluate the intrinsic drivers and a scientific standard to measure the development level in different regions for One Health[14]. The specific procedure for the construction and score calculation of the GOH-IDI framework has published and is organised in Additional file 1 and Additional file 2 [11,14,16]. The indictor scheme for GOH-IDI composes of 3 first-level indicators, 15 second-level indicators, 61 third-level indicators. Additional file 1 contains the detailed indicators and weights of GOH-IDI[14].

Statistical analysis

Version control of all data in this project is hosted on Github Desktop 2.9.11. Data and algorithms are open sources (https://github.com/DayuGuo/OHI-IDI-Animal-Environmental). Data were analysed using R studio 2021.09.1, R version 4.1.2 (https://www.r-project.org/). R Packages used in the analysis include tidyverse, ggrepel, ggplot2, cowplot, ggstatsplot, palmerpenguins, readr, and esquisse.

Methods of analysis and evaluation of data include descriptive statistics, data visualisations and data shaping. The visualization of the spatial distribution was done with ArcGIS 10.5. and Affinity Photo. The heat map shows the relationship between different countries and indicators, completed with R and excel. Spatial distribution and the heat map were divided into five groups by score ranking with the same criteria. Data correlation analysis was performed using quadratic regression, data from the World Bank database(https://data.worldbank.org/indicator). Pearson's correlation coefficient was used to evaluate the correlation between the data[17]. GOH-IDI data used in this paper are normally distributed, so means and standard deviations (SD) are used, and GraphPad Prism 9.3.1 is used for this part of the graph. The mean-SD histogram was done with GraphPad Prism 9.3.1, ordinary one-way ANOVA tests were conducted on some of the data. The country with the highest score in each world bank income level was selected for case studies. The data analysis of the case studies was done through Excel.

Global distribution of GOH-IDI scores

A total of 146 countries worldwide were included in this study, of which 19 were East Asia and Pacific, 47 were Europe and Central Asia, 18 were Latin America and The Caribbean, 16 were Middle East and North Africa, 16 Middle East and North Africa, 2 for North America, 7 for South Asia, and 37 for Sub-Saharan Africa (see Additional file 2 for details). The mean (standard deviation, SD) score of GOH-IDI is 52.58 (5.30), the lowest score is 41.63 and the highest score is 63.56 (Fig. 1). The mean (SD) score of Human Health is 52.43 (7.63); Animal Health is 54.10 (6.22); Environmental Health is 51.24 (5.54).

The Shapiro normality test proved that the GOH-IDI score profile fits a normal distribution, as do human health and environmental health. The animal health section has many qualitative indicators unsuitable for normal distribution analysis(Fig. 2).

The regional difference of GOH-IDI shows in Fig. 3. 100% of North America countries are ranked 1–30. 65% of Sub-Saharan Africa(SSA) countries were ranked 121–146. The highest-scoring country in SSA is Senegal (50.8), and the lowest score is Chad (41.63). The highest-scoring country in North America is Canada (61.51), and the lowest is the United States of America (58.19). The highest-scoring country in South Asia is Sri Lanka (54.24), and the lowest is India (47.73). The highest-scoring country in the Middle East and North Africa is the United Arab Emirates (59.28), and the lowest is Iraq (50.27). In Latin America and The Caribbean, the country with the highest score is Chile (55.93), and the lowest score is the Dominican Republic (48.9). The highest-scoring country in East Asia and Pacific is Singapore (62.72), and the lowest is Laos (43.87). Among Europe and Central Asia, the highest-scoring country is Norway (63.56), and the lowest is North Macedonia (47.92)(Figure 3).

The Word Bank regional classification has statistically significant differences in the performance of GOH-IDI across the seven regions. The mean (SD) values of GOH-IDI scores by region are (High to low): North America is 59.85(2.34); Europe and Central Asia is 56.60(3.50); Middle East and North Africa is 55.29(2.62); East Asia and Pacific is 52.72(5.49); Latin America and The Caribbean is 52.61(2.05); South Asia is 50.52(2.52); SSA is 46.21(2.49). SSA mean scores are statistically different from all six other regions, more significant in the Human Health section. The mean (SD) of SSA in Human Health is 43.30(4.33) (Fig. 4).

Relationship between GOH-IDI scores and economic factors

Countries in different geographical regions have different economic profiles. Its economic level determines a country's resources dedicated to governance. Analysing the current situation of each country in conjunction with the economic level and GOH-IDI performance can lead to more effective strategies. The World Bank assigns the world's economies to four income groups based on gross national income per capita(GNI per capita), including High income(HICs), Upper middle income(UMICs), Lower middle income(LMICs) and Low income(LICs) (see Additional file 2 for details). In the correlation analysis section, GNI per capita was moderately correlated with GOH-IDI by Pearson's correlation coefficients(R² = 0.654, r = 0.744, P < 0.001)(Fig. 5).

The World Bank income group is distinguished by four colors (see Additional file 2 for details). Correlation analysis GOH-IDI scores and GNI per capita, Atlas method(current US$), y = -4E-09x² + 0.0005x + 48.223 R² = 0.654, r = 0.744, P < 0.001.

The mean (SD) values of GOH-IDI scores by World Bank income groups are (High to low): HICs are 57.98(3.26); UMICs are 52.32(2.85); LMICs are 49.70(3.44); LICs are 45.66(2.40). All four data sets were statistically different (Fig. 6). The LICs-HICs, LICs-UMICs, LMICs-HICs were statistically significant differences in the human health, animal health and environmental health score profiles(Fig. 6). In GOH-IDI, the highest country of each different World Bank group is Norway(63.56), Jordan(56.5), Uzbekistan(55.37) and Niger(50.39). In GOH-IDI-human health, the highest country among each different World Bank group is Singapore(70.3), Turkey(59.07), Uzbekistan(59.07) and Rwanda(50.37). In GOH-IDI-animal health, the highest country of each different World Bank group is Hungary(66.14), Fiji(62.8), Uzbekistan(63.44) and Sierra Leone(57.72). In GOH-IDI-environmental health, the highest country of each different World Bank group is Switzerland(64.64), Colombia(56.99), Iran(56.52) and Malawi(50.21).

Statistical differences between economic conditions are indicated in the graph by asterisks, P values less than 0.001 are summarised with three asterisks, and P values less than 0.0001 are summarised with four asterisks. The critical indicator is equivalent to the three-level indicator in this indicator system. As shown in Fig. 7, most of the indicators are statistically different under the World Bank classification. Five key indicators are not statistically different under the four World Bank groups, including B.1 Animal epidemic disease, B.4 Animal biodiversity, C.1 Air quality and climate change, C.2 Land resources and C.5 Environmental Biodiversity. Most of the critical indicators had the highest scores for HICs, while A.3 Non-communicable diseases and Mental Health and A.6 Health risk had the lowest scores for HICs and the highest scores for LICs(Fig. 7). Nigeria performs best in Non-communicable Diseases and Mental Health(85.73). Madagascar performs best in Health risk(99.35).

This study analyses the performance of GOH-IDI from multiple organisations from a multidisciplinary perspective to provide the first objective assessment of 146 countries worldwide on the three dimensions of One Health performance. The primary indicators of human health, environmental health and animal health are equally weighted, reflecting the One Health philosophy's emphasis on animal health and ecology. This study is based on an authoritative database and optimised by expert consultation and scientific algorithms.

The significant difference between the maximum and minimum values in human health may be linked to the current global economic inequalities. Countries such as Norway, Singapore, Switzerland, Iceland, and Australia have the best performance at the indicator level, indicating that these countries have a better overall governance level and more abundant essential ecological land resources. Namibia, Liberia, the Central African Republic, Cote d'Ivoire, Chad, and other countries have poor performance, and there is still much room for improvement. These countries have something in common, all belong to SSA and have poor objective local conditions(Fig. 3). A combination of many negative factors led to this situation, including geopolitical and geographic resource limitations. In Animal Nutritional, North America scored high, probably due to its advanced intensive production model. In addition, we notice that the air quality in the Middle East & North Africa and South Asia is slightly worse than in the other regions.

This study shows that GOH-IDI is statistically correlated with economic performance (GNI per capita). HICs countries are more probably to have higher scores in Human Health. LICs countries performed poorly in Reproductive, Maternal, Newborn and Child Health. However, HICs countries performed worse in terms of Health Risk and Non-communicate Disease and Health risk. LICS countries performed worse in Hazardous Chemicals, Reproductive, maternal, newborn and child health and Animal nutritional status. Those indicator is dependent on national policies and complete industrialisation systems. Five indicators are not statistically different between regions at each economic level, including Animal epidemic disease, Animal biodiversity, Air quality and climate change, Land resources and Environmental biodiversity. Economic development may not be effective in improving the performance of these indicators. At this stage, economic development may not be effective in enhancing the performance of these indicators, or these indicators are not the focus of national policy at the moment. These indicators may require global policy guidance and concerted efforts to improve climate change and air quality.

Countries in the same World Bank income groups have similar economic and development statuses. Countries with high GOH-IDI scores in each group could function as a model. However, it is difficult to see the characteristics of these countries from the data only, and an in-depth understanding of their governance systems is needed. Based on the three-level data, the strengths and weaknesses of each country could be analysed.

(1) Niger has the best GOH-IDI performance in LICs. Niger's great performance may be attributed to its three well-performing second-level indicators, including Non-communicable Diseases and Mental Health, Health Risk, and Land Resources(Fig. 1). Niger focuses on health policies at the national level[18], has a pilot study based on One Health and values the positive effect of systematic research on policy development[19,20]. The specificity of Niger's performance in GOH-IDI deserves more research analysis and discussion. (2) Uzbekistan has the best GOH-IDI performance in LMICs. Uzbekistan's overall performance in Human Health and Animal Health was great, especially for the three second-level indicators, including Infectious Diseases, Animal Welfare, Relevant Regulations and Policy Support, and Animal Nutritional Status(Fig. 1). Since 1991, Uzbekistan has implemented several major healthcare innovations to improve the efficiency of healthcare services, including healthcare availability, governance and financing, and to ensure equitable access to healthcare resources[21]. (3) Jordan has the best GOH-IDI performance in UMICs. Jordan's overall performance was more balanced, with five well-performing second-level, including Infectious Diseases, Non-communicable Diseases and Mental Health, Universal Health Coverage and Health Systems, Animal Biodiversity, and Animal Nutritional Status. Jordan has conducted a lot of research and practice on several components of the One Health philosophy, such as antibiotic resistance, human-animal diseases and climate warming[22–24]. (4)Norway has the best GOH-IDI performance in HICs. Norway performs well in most indicators, with only three that need improvement, including Non-communicable Diseases and Mental Health, Health Risk, and Land Resources(Fig. 1). Norway attaches importance to applying and promoting the One Health concept and economic development[25,26]. Norway values cross-disciplinary development and introduces the latest technology in practice to aid in the effectiveness of interventions[27].

Policy recommendations:

After analysing and assessing the global performance of GOH-IDI and its relationship with regional economic development level, the following recommendations might be considered for policy-making in this field.

(1) International organisations are needed to take the lead in establishing a global governance paradigm and changing the governance strategies of countries around the globe. Some indicators do not differ significantly between income groups globally(Fig. 7). These indicators are challenging to improve effectiveness through direct economic inputs, and the efforts of individual countries are limited.

(2) Increase international and regional cooperation to achieve a win-win situation. Regional differences are apparent, with countries such as Niger, Uzbekistan, Jordan and Norway being among the top performers in each income group (Fig. 5). The governance paradigms of these countries are valuable for their peers to learn from. In a time of globalisation, complementing each other's strengths, strengthening regional multilateral cooperation, and engaging in more international cooperation will be a paradigm for improving the One Health index in the future. As countries and international organisations around the world join One Health-related initiatives and actions, the gaps between countries and regions will narrow and further improve together.

(3) Global animal-related databases need increased diversity and data reliability. There are still relatively few animal-related databases(Table 1). Strengthening basic testing facilities and teams is necessary, and reliable data sources combined with professional analysis teams to generate realistic policy recommendations.

(4) Strengthening the multilateral consensus and framework for action related to global climate change is necessary. Global climate change is still a big problem. The countries that emit the most greenhouse gases are not necessarily the most affected ones. Climate change has the most significant impact on tropical regions, especially in SSA. Many of the less developed regions in the tropics suffer from extreme climate impacts while needing to fight poverty-induced malnutrition and numerous socioeconomic problems.

(5) Combining the One Health concept to develop cross-disciplinary research and practice for efficient and universal policy development. Single discipline and departmental efforts is no longer sufficient to address the current complex international health challenges. The One Health concept guides a multidimensional view of problems. One Health research is directed at three main levels at this stage, including research, implementation, and governance. Areas worthy of future enhancement include drug resistance traceability, food safety, and climate change. Technical tools worthy of research include early warning systems, One Health strategic policy research, biosafety protection, One Health communication and mass behaviour, big data analytics, artificial intelligence, and cloud computing technology applications.

In the process of this study, we noticed that many environmental health and animal health datasets do not have time series and lack the possibility of multidimensional assessment. However, according to our observation, the attention to data is increasing in all world regions. In addition, there are more than 250 countries and regions in the world, and only 146 were included in this study. We will update the existing dataset and integrate a multilingual dataset in a future release.

The GOH-IDI is an analysis of human health, animal health and environmental health data to assess global performance on each indicator, which helps countries understand their situation, improve global One Health action, and promote balanced development of humans, animals, plants, and ecosystems. The best performing region for GOH-IDI was North America and the worst was Sub-Saharan Africa. There is a positive correlation between the GOH-IDI and country economic status, with High-income countries (HICs) performing well in most indicators. From the perspective of policymakers, they need to find role models with sustainable development strategies that are similar to their national context to learn from. This project allows them to do so, and case studies were conducted in four typical countries - Niger, Uzbekistan, Jordan and Norway. If this project is sustained, information on the time dimension can be collected. The development of theory is an iterative process. The One Health evaluation index system proposed in this project is only an initial research exploration, which needs to be continuously verified, revised and improved by subsequent studies. For the public health field, this project can facilitate researchers' understanding of the multidimensional situation in each country, and invest more attention in scientific questions that need to be addressed urgently.

COVID-19

Coronavirus Disease 2019

EAP

East Asia and Pacific

ECA

Europe and Central Asia

EPI

Environmental Performance Index

FAO

Food and Agriculture Organization

FAHP

Fuzzy Analytical Hierarchy Process

GOHI

Global One Health Index

GOH-IDI

Global One Health Intrinsic Driver Index

GBD

Global Burden of Disease

HIC

high income-country

IHME

Institute for Health Metrics and Evaluation

LAC

Latin America and The Caribbean

LIC

low-income country

LMIC

lower middle-income country

MENA

Middle East and North Africa

North America

OIE

World Organization for Animal Health

South Asia

SDGs

Sustainable Development Goals

SSA

Sub-Saharan Africa

UMIC

upper middle-income country.

WHO

World Health Organization

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and materials

The full study protocol and the datasets are available, following manuscript publication, upon request from the corresponding author (Shizhu Li, [email protected]).

Competing interests

Xiaonong Zhou is an Editor-in-Chief of infectious Diseases of Poverty. Jin Chen is an editor of Infectious Diseases of Poverty. The authors have no other competing interests to disclose.

Funding

This work was supported by the National Key Research and Development Program of China (No. 2021YFC2300800, 2021YFC2300804); National Natural Science Foundation of China (No.32161143036).

Authors' contributions

ZG, FJ, JC, and SL conceived and designed the manuscript. ZG, JF, JX, and LA collected and analysed the data, and wrote the manuscript. JL, XZ, CC, JX, SX, and XZ revised the paper. Supervision: JC and SL. ZG, FJ, JC and SL finalized the manuscript. All authors read and approved the final manuscript.

Author details

¹National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), NHC Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Shanghai 200025, China.

²School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.

Acknowledgements

We specially give thanks to experts from World Health Organization, Food and Agriculture Organization of the United Nation, The World Bank, and universities or institutions, involved in the interview for comments and suggestion.

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Additionalfile1.xlsx
Additional file 1: Full Scheme for the indicators and weights of GOH-IDI
Additionalfile2.csv
Additional file 2: GOH-IDI complete data and regional classification

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Assessment of integrated health of human, animal and environment: a holistic and stratified analysis based on global One Health index

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Abstract

Background

Methods

Results

Conclusions

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Background

Methods

Data collection and resources

GOH-IDI framework overview

Statistical analysis

Results

Global distribution of GOH-IDI scores

Relationship between GOH-IDI scores and economic factors

Discussion

Policy recommendations:

Conclusions

Abbreviations

Declarations

References

Supplementary Files

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