Measurement of health equity as a driver for impacting policies

Inequality in the distribution of resources in the health sector before and after the Health Transformation Plan in Qazvin, Iran

BACKGROUND

The equitable distribution of healthcare resources represents a paramount objective in the realm of global health systems. Thus, the present study sought to assess the fairness in the allocation of health resources at Qazvin University of Medical Sciences (QUMS), both prior to and subsequent to the implementation of the Health Transformation Plan (HTP) using the Gini coefficient and the Hirschman-Herfindahl index (HHI).

METHODS

This descriptive-analytical study aimed to investigate the distribution of healthcare resources among general practitioners (GPs), specialists, and subspecialists employed at QUMS between 2011 and 2017. Demographic data pertaining to the cities were obtained from the statistical yearbooks of the Statistical Center of Iran, while information regarding the healthcare workforce was extracted from QUMS records. The analysis utilized two key measures, namely the Gini coefficient and the HHI, to assess the fairness of resource distribution. Data analysis was performed using Microsoft Excel 2016 and the Stata statistical software.

RESULTS

The highest number of GPs, specialists, and subspecialists was observed in 2014, 2017, and 2017, respectively, while the lowest number was recorded in 2016, 2011, and 2015, respectively. From 2011 to 2017, the Gini coefficient for GPs ranged between 0.61 and 0.63. Among specialists, the lowest Gini coefficient value was observed in 2015 (0.57), while the highest was recorded in 2017 (0.60). The Gini coefficient for subspecialists remained constant at 0.52 from 2011 to 2017. The HHI revealed a high concentration of GPs in the cities of Qazvin province. Although the disparity gradually decreased in the specialties of eye, ear, nose, and throat, and pediatrics, the concentration still persists in Qazvin. In general surgery, the index value is low, indicating some level of inequality. In anesthesia and neurology, the index value decreased after the HTP and reached 5700; however, achieving equality (below 1000) still requires further efforts. No significant change in the index was observed after the HTP in specialties such as neurosurgery, rehabilitation, and nuclear medicine. Subspecialists also exhibited a concentration in the city of Qazvin.

CONCLUSION

Based on the analysis of the Gini and HHI, it is evident that the distribution of GPs has not undergone significant changes following the implementation of the HTP. The Gini coefficient, which ranges from 0.4 to 0.6, indicates a high to complete level of inequality in the distribution of specialists and subspecialists. Moreover, the HHI exceeds 1000, reflecting a concentration of resources in specific areas. As a result, the HTP has not yet achieved its goal of ensuring a fair distribution of human resources. To address this issue, it is recommended to redesign distribution policies, including the allocation of physical health resources, such as specialized hospitals, beds, and medical equipment. Additionally, increasing student admissions in specialized and subspecialized fields and implementing tariff incentives can contribute to a more equitable distribution of resources. By aligning distribution policies with the principle of fairness, the healthcare system can better address the issue of resource distribution.

Context-led capacity building in time of crisis: fostering non-communicable diseases (NCD) research skills in the Mediterranean Middle East and North Africa

Background: This paper examines one EC-funded multinational project (RESCAP-MED), with a focus on research capacity building (RCB) concerning non-communicable diseases (NCDs) in the Mediterranean Middle East and North Africa. By the project’s end (2015), the entire region was engulfed in crisis.

Objective: Designed before this crisis developed in 2011, the primary purpose of RESCAP-MED was to foster methodological skills needed to conduct multi-disciplinary research on NCDs and their social determinants. RESCAP-MED also sought to consolidate regional networks for future collaboration, and to boost existing regional policy engagement in the region on the NCD challenge. This analysis examines the scope and sustainability of RCB conducted in a context of intensifying political turmoil.

Methods: RESCAP-MED linked two sets of activities. The first was a framework for training early- and mid-career researchers through discipline-based and writing workshops, plus short fellowships for sustained mentoring. The second integrated public-facing activities designed to raise the profile of the NCD burden in the region, and its implications for policymakers at national level. Key to this were two conferences to showcase regional research on NCDs, and the development of an e-learning resource (NETPH).

Results: Seven discipline-based workshops (with 113 participants) and 6 workshops to develop writing skills (84 participants) were held, with 18 fellowship visits. The 2 symposia in Istanbul and Beirut attracted 280 participants. Yet the developing political crisis tagged each activity with a series of logistical challenges, none of which was initially envisaged. The immediacy of the crisis inevitably deflected from policy attention to the challenges of NCDs.

Conclusions: This programme to strengthen research capacity for one priority area of global public health took place as a narrow window of political opportunity was closing. The key lessons concern issues of sustainability and the paramount importance of responsively shaping a context-driven RCB.

Measuring Geographic and Wealth Inequalities in Health Distribution as Tools for Identifying Priority Health Inequalities and the Underprivileged Populations

Background

Currently, there is no consensus on standard measure that can be routinely part of the health information systems to alert countries to inequalities in health and identify the priority health inequality conditions.

Objectives

To identify the health inequality measures relevant for assessing geographic and wealth inequalities; use the measures as a demonstration to what can happen in practice to recognize the geographic and wealth-related priority health inequalities within a country; and identify the geographic and wealth underprivileged populations.

Method

Egypt data were used as demonstration. Pearson coefficient of correlation was calculated to compare the various geographic and wealth health inequality measures. T test was used to identify significant correlations. The relevant inequality measures were used to rank geographic and wealth health inequalities and identify the underprivileged populations.

Results

The wealth inequalities in health measured by the concentration index provide a familiar and perform adequately in identifying economic inequalities in health. However, the geographic health inequalities identified by the index of dissimilarity appear to provide a more comprehensive profile of health inequalities within a country.

Conclusion

There is a need for a feasible inequality measure in the health information systems. A country’s geographic health distribution measured by the index of dissimilarity appears to provide a feasible first-step alarm to inform and guide the uptake of equity-sensitive policies.

Distribution of hospital beds in Tehran Province based on Gini coefficient and Lorenz curve from 2010 to 2012

INTRODUCTION

Fair distribution of hospital beds across various regions is a controversial subject. Resource allocation in health systems rarely has focused on those who need it most and, in addition, is often influenced by political interests. The study assesses the distribution of hospital beds in different regions in Tehran, Iran, during 2010-2012.

METHODS

This cross-sectional study was conducted in all regions of Tehran (22 regions) during 2010 to 2012. All hospital beds in these regions are included in the study. Data regarding populations of each region were obtained from the Statistics Center of Iran. According to the data, the total number of beds (N.B) and population (P) in 2010 (N.B=19075, P= 7585000), 2011 (N.B=21632, P= 9860500), and 2012 (N.B=21808, P=12818650). The instrument was a form, including the name of the hospital, the district in which the hospital was located, the number of staffed beds, the name of each region, and its population. Data analysis was performed using DASP software version 2.3.

RESULTS

The results demonstrate that the Gini coefficient of distributed beds in 22 regions of Tehran was 0.46 in all three years and specifically calculated 0.4666 in 2010, 0.4658 in 2011 and 0.4652 in 2012. The Gini coefficient of beds in 22 regions of Tehran is not fair in comparison with the population of each region during the years 2010 to 2012.

CONCLUSION

The results demonstrate that the distribution of beds in regions in Tehran is not fair in relation to the population of each region-and some regions had no hospitals. Therefore, it is essential for policymakers to frequently monitor this issue and investigate the fair distribution of hospital beds.