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Abstract
Objectives To explore factors that influenced the health resource allocation and utilisation before and after COVID-19, and subsequently offer sensible recommendations for advancing the scientific distribution of health resources.
Design A longitudinal survey using 2017–2020 data, which were collected for analysis.
Setting The study was conducted based on data collected from the Health Commission of Guangdong Province’s website.
Outcome measures Eight health resource indicators and four health resource utilisation indicators were included in the factor analysis. Four indices were calculated to measure the inequality in health resource allocation and utilisation. We analysed factors for the inequality indices using the recentred influence function index ordinary least squares decomposition method.
Results The health resource inequality indices peaked in 2020 (Gini coefficient (Gini): 0.578, Absolute Gini coefficient (AGini): 1.136, Concentration Index (CI): 0.417, Absolute CI (ACI): 0.821), whereas the health resource utilisation inequality indices declined year by year, thus reaching their lowest point in that same year. The majority of inequality indices in the annual change of health resource allocation were at their lowest in 2020 (Gini: −1.672, AGini: 0.046, CI: −0.189, ACI: 0.005), while the use of health resources declined dramatically, showing a negative growth trend. The inequality indices of health resource allocation and utilisation in 2020 were affected by a number of variables, including the COVID-19 level, (p<0.05), while the proportion of expenditure on public health was the most significant one.
Conclusions Guangdong Province’s health resource allocation and utilisation were still concentrated in economically developed regions from 2017 to 2020. The health resource allocation inequality indices increased, especially under COVID-19, but the health resource utilisation inequality indices decreased. Measures should be taken to adjust the health resource allocation scientifically, which will fulfil the changing needs and the use of resources more efficiently. One effective measure is reasonably increasing the proportion of expenditure on public health.
- COVID-19
- health economics
- health policy
- human resource management
- organisation of health services
- public health
Data availability statement
Data are available on reasonable request. The data sets analysed during the current study are available from the corresponding author on reasonable request.
This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
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- COVID-19
- health economics
- health policy
- human resource management
- organisation of health services
- public health
STRENGTHS AND LIMITATIONS OF THIS STUDY
This study has reference value for the formulation and improvement of policies about health resource allocation under public health emergency, because there were no research articles about the analysis on influencing factors of health resource allocation and utilisation, particularly in the COVID-19 crisis, much less about the research about the application of the Recentred Influence Function (RIF) analysis method to health resource allocation and utilisation.
Although the research did not look at resource distribution in terms of geography, geographical context has a direct influence on resource allocation.
Many other characteristic indicators which might affect health resource allocation were not taken into consideration and data was mainly confined from 2017 to 2020 in this study, as the data provided by government departments was very limited.
Although the RIF-Index-ordinary least squares decomposition method and other modern statistical analytic methods were used in this study, it was only a preliminary analysis of the relevant factors.
This study was a general evaluation of health resources or a preliminary exploration of the health resource allocation. Future studies can classify health resources scientifically and further explore the various health resource allocation and utilisation in the context of public health emergency.
Introduction
COVID-19, which emerged in 2019, is still rapidly spreading around the world and is classified as a category B infectious disease in China,1 which country has emphasised strengthening epidemic prevention and control in key periods, regions and populations to prevent a domestic rebound of the epidemic effectively.2 As of 8 May 2022, there had been 513 955 910 confirmed cases worldwide, with 6 249 700 deaths,3 and the China National Health Commission reported 219 625 confirmed COVID-19 cases in China, with 7144 in Guangdong Province accounting for 3.25% of the total.4 5
Equity in the allocation of health resources and public accessibility are important structural factors in evaluating the performance of health services, as well as an important foundation for regional health planning and implementation.6 As equity and equality are the primary policy goals of health resource allocation, increasing health equity is a top priority for numerous national governments and international organisations.7 Wagstaff et al,8 meanwhile, analysed 64 low-income and middle-income countries over the period 1990–2011 and found that socioeconomic inequalities in health status had increased in over half of the countries,8 and this problem also occurred in China in some regions, such as in Xi’an Shanxi province, China.9 There are no research on the analysis of factors that affect the health resource allocation and utilisation, particularly in the COVID-19 crisis. Even less is about the research on the use of the recentred influence function (RIF) method to study the health resources.
We must reconsider how to allocate health resources more rationally as epidemic prevention and control have become the normalisation in China, necessitating a large number of health resources as a strong backstop. The purpose of this study was to investigate the factors that influence health resource allocation and utilisation, as well as the impact of COVID-19 level on this subject in Guangdong Province, and to provide several sensible recommendations for optimal resource allocation.
Methods
Patient and public involvement
Patients/the public were not involved in the recruitment and conduct of the study, nor were they asked to assess the burden of the intervention and required time to participate in the research, so no research question(s) and outcome measures were developed and informed by their priorities, experience and preferences. Indirect participants in this study were residents of 21 prefecture-level cities in Guangdong Province who participated in a government-organised census, and the results of our study may indicate how decision-makers should improve the health resource allocation in the context of COVID-19 to benefit the residents in their daily use of health resources. Thus, this study used secondary data from yearbooks (2015–2020) in Guangdong province of China and did not require patient or public involvement directly.
Data source
We obtained relevant documents, such as Guangdong Province’s 2015–2020 Health Statistical Yearbook, which was published by the Health Commission of Guangdong Province and included some health resource serial statistics data, as well as other demographic and economic indicators* (*We can use the search function in the official website of the Health Commission of Guangdong Province to find the related links of the health statistics yearbook for each year). Of Guangdong province health statistics yearbook from 2015 to 2020, year of the corresponding link according to the arrangement, in turn, is: http://www.gdhealth.net.cn/ebook/2015tjnj/mobile/index.html; http://www.gdhealth.net.cn/ebook/2016tongjinianjian/mobile/index.html%23p=2; http://www.gdhealth.net.cn/ebook/2017tongjinianjian/mobile/index.html%23p=2; http://www.gdhealth.net.cn/ebook/2018nianjian/mobile/index.html; http://www.gdhealth.net.cn/ebook/2019nianjian/mobile/index.html; http://www.gdhealth.net.cn/ebook/2020tongjinianjian/mobile/index.html%23p=4. The above documents do not need to go through the process of applying for data disclosure, it is an open database.). Besides, the official website of the Health Commission of Guangdong Province was used to extract the number of confirmed COVID-19 cases in 21 prefecture-level cities as of April 2022,5 and the COVID-19 level was divided into 21 levels based on the same group distance (125 cases). Python V.3, SPSS V.24.0, Stata MP V.14 and GraphPad Prism V.8 were used for data extraction, factor analysis and non-parametric test, RIF regression and statistical graph drawing, respectively.
Data analysis
First, the factor analysis, which is a method to reflect most information of the original data with fewer factors by combining most indicators through dimensionality reduction,10 11 was performed on health resource data from 2015 to 2020, including eight health resource indicators and four health resource utilisation indicators (see figure 1). The comprehensive scores were obtained from 2017 to 2020, which comprised the health resource scores (HRS), the health resource utilisation scores (HRUS), the annual change (between the adjacent years from 2017 to 2020) in the comprehensive score of health resources (HRSC) and the annual change (from 2017 to 2020) in the comprehensive score of health resource utilisation (HRUSC).
Violin plot of factors on health resource allocation and utilisation in Guangdong Province from 2017 to 2020.
Second, we calculated four types of inequality indices in each year (from 2017 to 2020) for the HRS, HRUS, HRSC and HRUSC, then we tested whether the differences between adjacent years were statistically significant. Inequality indices are reasonably assumed to be non-negative when they are used in the measurement of resources or wealth. However, inequality indices were not only used in this study in the evaluation of resource with non-negative values, but were also used in indicators with negative values which might be the annual change indicators in this study. When the indicator is negative and its overall mean value is also negative, the inequality indices such as the Gini will also be negative, while its absolute value may be greater than 1, which also reflects the inequality measure of the indice to a certain extent. Therefore, the application of the inequality indices in this study is not limited to the ideal non-negative hypothesis, which is the further innovative application of the inequality indices.
(1) Gini: also known as the relative Gini coefficient, the calculation formula is as follows:
(1)12
Where 
represents the absolute value of the income difference between any pair of samples, n is the sample size and μ is the mean income.
(2) AGini: it is the most original GINI coefficient, the calculation formula is as follows:
(2)13
(3) CI is an important index recommended by the World Bank to evaluate the fairness of health services among regions with different economic development levels. It can quantitatively evaluate the fairness of health resource allocation related to economic levels.14 The value range is −1 to 1, in which 0 means completely equal. Using the formula proposed by Kakwani et al, it can be directly calculated with individual-level data, the formula is as follows:
(3)15
where 
is the health resource indicator score of individual i, μ is the average health resource indicator score of individual i, 
is the relative ranking of individual i according to GDP, 
is the variance of 
, β is CI, 
is the error term.
(4) ACI: The CI mentioned above reflects all the circumstances of the whole. Therefore, if the size of various groups changes, even if the average of their health resources does not change, the CI will change. However, the limitation of CI is that if the number of health resources per study individual is doubled, the value of CI will not change, but the difference can be measured by ACI. ACI measures the relative indice of CI according to the average value of the indice studied. If the indice studied by each individual changed, ACI will reflect it.16 Therefore, ACI can be used to measure the status and distribution of health resources.
Third, RIF regression modelling was performed based on the aforementioned results for the HRS, HRUS, HRSC and HRUSC to explore the influential factors with data from 2017 to 2020. COVID-19 data and the information on city classification which we divided the 21 prefecture-level cities into five levels (first-tier, second-tier, third-tier, fourth-tier and fifth-tier cities) were included.17 The name RIF stands for ‘recentralisation influence function’, which is used to measure the influence of a small change in the sample on the statistic, while the unconditional expectation of RIF is the corresponding statistic itself. RIF is taken as the explained variable and OLS (ordinary least squares) regression is conducted to obtain the RIF-I-OLS estimation model:
(4)18
Take the unconditional expectation on the left and right sides of this equation. Because the unconditional expectation of RIF is the corresponding statistic, we can obtain the following:
(5)18 the meaning of 
is that when other conditions remain unchanged and the mean value of the kth influencing factor 
in the population increases by one unit ν as the statistic of y will improve 
.
Results
General conditions of the 21 prefecture-level cities in Guangdong from 2017 to 2020
More than 24 indicators, such as maternal mortality, neonatal mortality, natural growth rate, number of hospitals, number of beds, number of medical technicians, number of visits, number of discharges and so on, were extracted. Indicator distributions were depicted by violin plots in figures 1 and 2, which showed how the majority of indicators in 2020 changed dramatically when compared with three preceding years.
Violin plot and percentage accumulation bar chart of various indicators related to health resource allocation and utilisation in Guangdong Province from 2017 to 2020.
Factor analysis and non-parametric test of the indicators about the health resource allocation and utilisation in Guangdong from 2017 to 2020
KMO and Bartley sphericity tests were conducted for the health resource indicators and health resource utilisation indicators. The KMO of health resource indicators and health resource utilisation indicators were 0.688 and 0.537, respectively, both greater than the threshold value of 0.500, and the p values of the Bartley sphericity test were less than 0.0001, indicating that the two categories of indicators were suitable for factor analysis. We calculated the comprehensive scores and annual change scores (HRS, HRUS, HRSC and HRUSC) by weighing and evaluating the rotated eigenvalues and cumulative contribution rate. Consult the online supplemental tables 1 and 2 presented in appendix for more information (see the online supplemental additional file).
Supplemental material
The number of health institutions and health personnel were calibrated prior to the factor analysis of health resource indicators. The hospital grade coefficient and health technology component coefficient were calculated based on the hospital grade, the composition of educational background and the professional title of health technicians in each prefecture-level city, while the number of health institutions and health personnel were multiplied by the coefficient to obtain the calibrated indicators, which were then factored. Consequently, HRS and HRSC not only reflect the quantity of health resources, but also the quality level of health resources.
On the HRS and HRUS from 2017 to 2020 and the HRSC and HRUSC from 2018 to 2020 which were attained by factor analysis, the Friedman test and pin-pair comparison were used. It showed that there were statistically significant differences in the distribution of the HRS, HRUS, HRSC and HRUSC from 2017 to 2020 (p<0.05), while the HRS, HRUS and HRUSC in 2020 were statistically significant different compared with 2019. Furthermore, in 2020, the data characteristics of the HRUS (median:0.753 (IQR:0.506) and HRUSC (−0.247 (0.356)) revealed the health resource utilisation decreased obviously and had a negative growth trend compared with previous years. See figure 3 and table 1 for further details.
Friedman test results of various comprehensive indicators in 21 prefecture-level cities of Guangdong Province from 2017 to 2020
Violin plot of comprehensive health indicators in Guangdong Province from 2017 to 2020.
Inequality indices of HRS, HRUS, HRSC and HRUSC in Guangdong from 2017 to 2020
Notably, there was prorich resource inequality in Guangdong, and all inequality indices of HRS and HRUS changed over time. In 2018, all inequality indices of the HRS were at the lowest point in 4 years (Gini: 0.492, AGini: 0.727, CI: 0.338, ACI: 0.500), while reaching the highest point in 4 years in 2020 (GINI: 0.578, AGINI: 1.136, CI: 0.417, ACI: 0.821). The HRUS’s inequality indices, with the exception of CI (0.241), reached their highest point in 4 years in 2018 (GINI. 0.424, AGINI: 0.613, ACI: 0.348), while all inequality indices were the lowest in 4 years in 2020 (GINI: 0.309, AGINI: 0.288, CI: 0.134, ACI: 0.125).
If the HRSC or HRUSC is negative, it indicates that the HRS or HRUS is lower than it was in the previous year; if positive, it is higher. The inequality indices of the HRSC showed that the changes of health resources were mainly concentrated in the regions with rapid economic growth, but compared with 2018 and 2019, this situation was significantly alleviated or even the changes of health resources were concentrated in some regions with slow economic growth according to the lowest inequality indices in 2020 (GINI: −1.672, AGINI: 0.046, CI: −0.189). The results of the HRUSC revealed that the changes of health -resource utilisation were still concentrated in regions with rapid economic growth. For more information, see table 2.
Inequality indices of comprehensive indicators in 21 prefecture-level cities of Guangdong province from 2017 to 2020
Influencial factors of the inequality indices of HRS, HRUS, HRSC and HRUSC in Guangdong from 2017 to 2020
Two multicollinearity indicators were eliminated from the breakdown, leaving 16 out of 18 indicators gathered. We conducted analysis on these 16 factors that may affect the four inequality indices by using the RIF-I-OLS decomposition method. The results showed that the per capita GDP, population density and natural growth rate hindered the HRSC’s inequality indices from 2019 to 2020, but that the information about whether 2020 and COVID-19 level (for more information, see online supplemental table 3 in the additional file) increased the HRSC’s inequality indices. The proportion of expenditure on public health, resident population, per capita GDP and natural growth rate all worsened the HRUSC’s inequality indices from 2019 to 2020, while both whether 2020 and the COVID-19-level indicators became constrained variables. However, none of the 16 indicators made an impact on the HRUSC and HRSC inequality indices from 2018 to 2019. Further details may be found in tables 3 and 4 and online supplemental table 4.
The RIF-I-OLS analytical results of influencing factors of the HRS and HRUS’s inequality indices from 2018 to 2020 (β-value)
The RIF-I-OLS analytical results of influencing factors of the HRSC and HRUSC’s inequality indices from 2018 to 2020 (β-value)
The coefficients of ‘the proportion of public health expenditure’ in the HRUSC’s inequality indices models with statistical significance were Gini model 1 (39.047), CI model 3 (48.253) and ACI model 4 (−14.640), while its absolute values were the highest among all factor indicators’ coefficients with statistical significance. It means that about HRUSC, the Gini increases by 39.047, the CI increases by 48.253, while the ACI decreases by 14.640, if the proportion of expenditure on public health increases by one unit (1%) in 21 prefecture-level cities.
Additionally, the coefficient with statistical significance of ‘COVID-19 level’ in the HRSC’s inequality indices models was Gini model 1 (0.140), and it in the HRUSC’s inequality indices models were AGini model 2 (0.096), CI model 3 (0.176) and ACI model 4 (−0.057). It means that about HTUSC, the AGINI and CI will increase by 0.096 and 0.176, while the Gini of HRSC will increase by 0.140, when the COVID-19 level of 21 prefecture-level cities increases by one unit (125 patients), but the ACI of the HRUSC would decrease by 0.057.
Discussion
This study described the inequality about health resources in Guangdong Province from 2017 to 2020 using four inequality indices, as well as analysed 16 factors which may affect the inequality indices by using the RIF-I-OLS decomposition method, noting that the outbreak of COVID-19 does make an impact on the equity and efficiency of health resource allocation and utilisation. However, we can achieve more rational health resource allocation and more efficient resource utilisation by intervening on controllable factors identified in this study.
First, the study observed significant differences in health resource allocation, utilisation and annual change in utilisation in 2020 compared with 2019, while the health resource utilisation clearly decreased under COVID-19. Some possible explanations for this may be that the primary and single medical-seeking pattern in modern life is that people passively became used to see doctors face to face or be hospitalised in medical organisations, but some health workers have to leave their jobs and be redeployed to help with problems caused by public health emergencies, while people are restricted from going to public places. Health resource decision-makers need to develop scientific management strategies for COVID-19 trends and innovate the pattern, in which people can use health resources more conveniently. COVID-19 is one of the many public health emergencies in the history of human development that changed the way society routinely operates, following the rise of digital and networked change.19 Its impact on health resources has inspired numerous organisations or individuals to innovate technologies that enrich the way people use health resources, optimise residents’ health management, and link digitalisation and networking closely with changes in public health emergency. Decision-makers can rationally use these emerging technologies to manage health resources, such as building health management networks and standardising the ‘medical+internet’ model, to allocate health resources scientifically and dynamically, as well as achieve efficient use of health resources by people.
Second, the study found that the per capita GDP, population density, natural growth rate, whether 2020, COVID-19 level, proportion of expenditure on public health and resident population would influence health resource allocation and utilisation, thus resulting in a change in inequality indices, particularly from 2019 to 2020. The core problem of health resource allocation is how to match the regulating function of health resource market with the macrocontrol of health resources by the state. This is also a classic problem about resource allocation in the history of economics.20 The analysis of ‘the proportion of expenditure on public health’ in this study indirectly revealed how the government conducted macrocontrol of health resource allocation. The allocation of health resources under COVID-19, which is solely based on the regulating function of the health resource market, will inevitably exacerbate the inequality of health resources between regions, thus causing health resources to be concentrated in wealthy groups or wealthy areas and are unable to be used efficiently by more people with the same demand but limited economic conditions, especially in countries or regions with a wide disparity between the rich and poor. Further government intervention and direct government regulation are required to promote the equitable operation of health resource markets and improve the efficient use of health resources by the general public, particularly in the context of a massive public health emergency, such as increasing the proportion of expenditure on public health to increase convenient public health equipment or cultivate the community-level medical workers’ ability of public health prevention further.
Third, the study discovered that, the health resource utilisation inequality indices and the majority of inequality indices of annual change in health resource allocation were the lowest in 2020, with the former clearly decreasing compared with 2019, but the health resource allocation inequality indices were the highest. The indices reveal that although the annual change in health resource allocation in 2020 was the most equitable, the health resources remained concentrated in regions with rapid economic growth, while the people’s use of health resources decreased substantially, reflecting the negative impact of public health emergencies. We need to establish a sound public health prevention system to prevent the large-scale outbreak of public health emergencies. The COVID-19 outbreak has compelled decision-makers to mobilise health resources urgently to address the health issues it has caused, which has impacted people’s normal use of resources on a daily basis and exacerbated the unequal distribution of health resources in local areas. Furthermore, handling a widespread public health outbreak is more expensive than to prevent it. According to statistics, a worldwide influenza epidemic, would reduce global wealth by an estimated US$3 trillion.21 The COVID-19 could have contributed to around 17 million deaths. The hit to the global economy could reach US$12.5 trillion by 2024.22 Global pandemic prevention is estimated to cost US$10.5 billion each year—a sizeable sum, but a fraction of the cost of not being prepared.23 Several studies have shown that the stage of globalisation has increased the threat of infectious disease outbreaks,24 demonstrating that more outbreaks of infectious disease are inevitable. However, preventing many of them from turning into public health emergencies in the context of increasing globalisation is possible. Consequently, we need a flexible public health emergency warning and response system to prepare for public health emergencies, while the government should accelerate the pace of establishing a healthy public health governance and prevention system, training a number of stronger, higher quality and permanent public health teams, as well as investing in public health infrastructure to guard the health of the general public and deal with the long-term test of public health emergencies. Such work will reduce the frequency of public health emergencies at most and ensure the rational operation of health resource allocation and efficient use of health resources.
The ultimate goal of rational health resource allocation is to improve equity and efficiency so that they can serve the general population and improve overall health.25 When studying the allocation of health resources, we should consider not only social productivity, but also demographic characteristics. However, many studies focused only on geospatial aggregation distribution, without integrating socioeconomic and demographic factors such as mortality rates, hospital-grade, health technician’s education and title.26–28 Some studies used limited inequality indices to study the equality of health resources, which were difficult to reflect the overall distribution changes when the data were multiplied,29–31 and no research on the use of the RIF method exists to study the health resources. Based on the influential factors confirmed by this study, the health resource allocation programme can be improved more scientifically and accurately, providing valuable references for other countries or regions with similar socioeconomic, geographical and demographic compositions. Yet, the mentioned factors in this study are not exhaustive, while the study on the impact of COVID-19 is insufficiently in-depth, with the most significant reason being that officially published data is extremely limited. For example, neither complete mortality indicators from chronic diseases divided according to the age composition of the population, nor the corresponding environmental change indicators every year are available, where the influence of population characteristics or environmental factors on the health resource allocation and utilisation remains to be explored. The data on the COVID-19 are still being updated, while the supplement of these data will further serve to support the study’s central thesis, as well as explore in further detail how much or how quickly a major public health emergency will affect health resource allocation.
Conclusion
The use of health resources was higher in regions with higher economic development than in regions with lower economic development, but it tended to be consistent in each region year after year, particularly under COVID-19. The COVID-19 pandemic increased the degree of inequality in health resource allocation, while simultaneously decreasing the inequality of annual change in health resource utilisation. This indicates that the health resource allocation in Guangdong Province has improved over the last 4 years, but the impact of COVID-19 on the health resource allocation and utilisation should not be overlooked. To achieve more rational health resource allocation and promote more efficient health resource utilisation by people in the context of public health emergencies, decision-makers must adjust health resource allocation flexibly in response to the development trend of public health emergencies by using emerging technologies. This will prevent the waste of health resources and allow us to deal with health issues arising from public health emergencies more effectively. We need to increase the various ways by which people access health services and to standardise the development and management of online medical treatment, where they have more convenient options for fulfilling their fundamental demands for health resources in the event of public health emergencies. We must actively prevent public health emergencies, such as establishing a sound public health prevention system. Future studies that integrate the indicators used in this study with more indicators of population characteristics must be conducted in deeply exploring health resource allocation under COVID-19.
Data availability statement
Data are available on reasonable request. The data sets analysed during the current study are available from the corresponding author on reasonable request.
Ethics statements
Patient consent for publication
Supplementary materials
Supplementary Data
This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.
Footnotes
Contributors The first author QW was involved in the study concept and design, data acquisition, data analysis, interpretation of data and drafting of the manuscript. LW and XL were involved in the acquisition of data, interpretation of data and critical revision of the manuscript. WW and YX were involved in the critical revision of the manuscript. JX was involved in the critical revision of the manuscript and overall study supervision. All authors had access to and verified the data and had final responsibility for the decision to submit for publication. QW and JX are the guarantors of the work.
Funding Our work was supported by the National Natural Science Foundation of China (project no.71673126), Key Project of Guangdong Institute of Health Economics, China (project no.2022-WJZD03).
Competing interests None declared.
Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.
Provenance and peer review Not commissioned; externally peer reviewed.
Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.