Regional disparities in the distribution of Sudan’s health resources

Ismail, Mohamed

doi:10.26719/emhj.20.056

Cited by 18 publications

(13 citation statements)

References 18 publications

(23 reference statements)

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“…People who have tested for cholesterol and/or received health care advice are more likely to use health services and, consequently, may reduce the odds of undiagnosed HTN [ 16 ]. Rural residents in Sudan may have poorer access to health care services than urban residents, hindering them from screening of blood pressure [ 30 ]. Contrary to several previous studies [ 5 , 7 , 13 - 15 ], we did not find an association between socioeconomic status (education) with undiagnosed HTN.…”

Section: Discussionmentioning

confidence: 99%

Undiagnosed hypertension in Sudan: results of the cross-sectional national STEPS survey in 2016

Pengpid¹,

Peltzer²

2022

Pan Afr Med J

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Introduction the rate of hypertension has been increasing in Africa. The study aimed to estimate the prevalence and associated factors of undiagnosed hypertension (HTN) among adults in Sudan. Methods cross-sectional data were analyzed from 7,226 persons (18-69 years, median 37 years, interquartile range 27-49 years, 4557 were females) who participated in the 2016 Sudan STEPS survey, had complete blood pressure measurement and non-pregnant, and responded to a questionnaire, physical measures, and biomedical tests. Logistic regression was used to determine the predictors of undiagnosed HTN. Results the prevalence of undiagnosed HTN was 26.2% (n=2057) (79.2% of total HTN), diagnosed HTN 6.9% (n=690) and total HTN 33.1% (n=2747). In multivariable analysis older age (50-69 years) (adjusted risk ratio-aRR: 2.49, 95% CI: 2.02-3.09; p<0.001), obesity (aRR: 2.51, 95% CI: 1.97-3.21; p<0.001), diabetes (aRR: 1.59, 95% CI: 1.17-2.16; p=0.002) and elevated total cholesterol (aRR: 1.48, 95% CI: 1.19-1.84; p<0.001) were positively associated and health care advice (aRR: 0.79, 95% CI: 0.64-0.98; p=0.036) was negatively associated with undiagnosed HTN versus no HTN. Male sex (adjusted odds ratio-aOR: 2.22, 95% CI: 1.63-3.01; p<0.001) was positively associated, and older age (50-69 years) (aOR: 0.31, 95% CI: 0.21-0.46; p<0.001), married (aOR: 0.45, 95% CI: 0.33-0.62; p<0.001), urban residence (aOR: 0.70, 95% CI: 0.51-0.96; p=0.022), health care advice (aOR: 0.32, 95% CI: 0.22-0.45; p<0.001), ever cholesterol measured (aOR: 0.43, 95% CI: 0.27-0.67; p<0.001), overweight (aOR: 0.63, 95% CI: 0.47-0.85; p=0.003) and heart attack or stroke (aOR: 0.31, 95% CI: 0.17-0.55; p<0.001) were negatively associated with undiagnosed HTN versus diagnosed HTN. Conclusion one in four adults in Sudan had undiagnosed HTN (eight in ten of total HTN) and several associated factors that can help guide interventions were identified.

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Section: Discussionmentioning

confidence: 99%

Undiagnosed hypertension in Sudan: results of the cross-sectional national STEPS survey in 2016

Pengpid¹,

Peltzer²

2022

Pan Afr Med J

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“…The World Health Organization points out that equity in health services means that members of society should have demand-oriented access to health services, rather than depending on factors such as ethnicity, social status, income level, and religious beliefs [ 3 ]. However, inequitable allocation of health resources is currently a global problem, especially in developing countries [ 4 ]. In the 2030 Agenda for Sustainable Development, the United Nations has clearly identified "ensuring universal access to health and health care services and achieving universal health coverage" as the main goal.…”

Section: Introductionmentioning

confidence: 99%

Regional disparities and influencing factors of high quality medical resources distribution in China

Lei

Cao

Wang

et al. 2023

Int J Equity Health

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Background With the gradual increase of residents’ income and the continuous improvement of medical security system, people’s demand for pursuing higher quality and better medical and health services has been released. However, so far little research has been published on China's high quality medical resources (HQMR). This study aims to understand the spatiotemporal variation trend of HQMR from 2006 to 2020, analyze regional disparity of HQMR in 2020, and further explore the main factors influencing the distribution of HQMR in China. Methods The study selected Class III level A hospitals (the highest level medical institutions in China) to represent HQMR. Descriptive statistical methods were used to address the changes in the distribution of HQMR from 2006 to 2020. Lorentz curve, Gini coefficient (G), Theil index (T) and High-quality health resource density index (HHRDI) were used to calculate the degree of inequity. The geographical detector method was used to reveal the key factors influencing the distribution of HQMR. Results The total amount of HQMR in China had increased year by year, from 647 Class III level A hospitals in 2006 to 1580 in 2020. In 2020, G for HQMR by population was 0.166, while by geographic area was 0.614. T was consistent with the results for G, and intra-regional contribution rates were higher than inter-regional contribution rates. HHRDI showed that Beijing, Shanghai, and Tianjin had the highest allocated amounts of HQMR. The results of the geographical detector showed that total health costs, government health expenditure, size of resident populations, GDP, number of medical colleges had a significant impact on the spatial distribution of HQMR and the q values were 0.813, 0.781, 0.719, 0.661, 0.492 respectively. There was an interaction between the influencing factors. Conclusions China's total HQMR is growing rapidly but is relatively inadequate. The distribution of HQMR by population is better than by geography, and the distribution by geography is less equitable. Population size and geographical area both need to be taken into account when formulating policies, rather than simply increasing the number of HQMR.

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“…This paper, in line with Shinjo [ 96 ] and Paramita [ 97 ], finds that interregional differences in medical resources in China, Japan, and Indonesia are small, and the Gini indices of hospitals, doctors, and beds are generally less than 0.4, indicating the achievement of spatially balanced development. In contrast, other scholars found that the intercontinental distribution of health resources is extremely unequal in Ethiopia, Mongolia, Iran, and Sudan [ 98 ]. For example, Woldemichael [ 99 ], Chavehpour [ 100 ], and Rezaei [ 101 ] found that the Gini index of medical resources in Ethiopia and Iran is generally greater than 0.4, and even up to 0.75; further, Erdenee [ 102 ] found that the Gini indices of doctors and beds in Mongolia were 0.74 and 0.69, respectively.…”

Section: Discussionmentioning

confidence: 95%

Geographical Pattern Evolution of Health Resources in China: Spatio-Temporal Dynamics and Spatial Mismatch

et al. 2022

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(1) Background: The rational allocation of limited medical resources is the premise of safeguarding the public health. Especially since the outbreak of COVID-19, the evolution dynamics and spatial mismatch of medical resources have been a focal and frontier issue in academic discussions. (2) Methods: Based on the competitive state model and spatial mismatch index, this paper uses GIS and Geodetector spatial analysis methods and three typical indicators of hospitals, doctors, and beds to conduct an empirical study on the evolutionary characteristics and degree of mismatch in the geographic pattern of health resources in China from 2010 to 2020 (the data are from official publications issued by the National Bureau of statistics in China), in two dimensions of resource supply (economic carrying capacity) and demand (potential demand or need of residents). (3) Results: The spatial pattern of health resources at the provincial level in China has been firmly established for a long time, and the children and elderly population, health care government investment, and service industry added value are the key factors influencing the geographical distribution of health resources. The interaction between the different influence factors is dominated by bifactor enhancement, and about 30–40% of the factor pairs are in a nonlinear enhancement relationship. Hospital, doctor, and bed evolution trends and the magnitude and speed of their changes vary widely in spatial differentiation, but all are characterized by a high level of geographic agglomeration, heterogeneity, and gradient. Dynamic matching is the mainstream of development, while the geographical distribution of negative and positive mismatch shows strong spatial agglomeration and weak spatial autocorrelation. The cold and hot spots with evolution trend and space mismatch are highly clustered, shaping a center-periphery or gradient-varying spatial structure. (4) Conclusions: Despite the variability in the results of the analyses by different dimensions and indicators, the mismatch of health resources in China should not be ignored. According to the mismatch types and change trend, and following the geographic differentiation and spatial agglomeration patterns, this paper constructs a policy design framework of “regionalized governance-classified management”, in line with the concept of spatial adaptation and spatial justice, in order to provide a decision making basis for the government to optimize the allocation of health resources and carry out health spatial planning.

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Regional disparities in the distribution of Sudan’s health resources

Cited by 18 publications

References 18 publications

Undiagnosed hypertension in Sudan: results of the cross-sectional national STEPS survey in 2016

Undiagnosed hypertension in Sudan: results of the cross-sectional national STEPS survey in 2016

Regional disparities and influencing factors of high quality medical resources distribution in China

Geographical Pattern Evolution of Health Resources in China: Spatio-Temporal Dynamics and Spatial Mismatch

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