For this assignment, you are going to do a thorough critique of a journal article based somewhere in the field of your future career in healthcare, or a personal interest of yours (i.e. a medical condition you or a loved one have, a medical treatment or issue that interests you, etc.). really take the time to pick an article that you think is well written and analyze every aspect of it- from the title, to the way they present their research question, to the way they wrote their methodology, to their use of subheadings, to how they wrap up the paper in a thoughtful conclusion. Look at their use of language- consider how they present the process they went through. Does everything make sense to you? How could the author(s) have done better? Read it a few times if you can. Be critical! Even great articles have weaknesses, and even boring/monotonous articles have good points. Remember, you are not critiquing the study, but the actual article itself. This is also not a place to just summarize the article or give your opinion about the topic. (P.S I will attach the article below)
Critiques must be one to two (1-2) full pages of writing, excluding cover page. The entire document must be in APA formatting. That means 12 point font, one inch margins, double spacing, page headers, references, and a standard APA cover page (see APA examples in module)- but you don’t need an author’s note or abstract. APA adherence will be part of your grade! Don’t use bullets or lists in your critique- it should be in paragraph form using full sentences. You don’t need any external resources for this aside from your article.
RESEARCH ARTICLE Open Access
Socio-economic inequalities in the multiple dimensions of access to healthcare: the case of South Africa Tanja Gordon1*, Frederik Booysen2 and Josue Mbonigaba3
Abstract
Background: The National Development Plan (NDP) strives that South Africa, by 2030, in pursuit of Universal Health Coverage (UHC) achieve a significant shift in the equity of health services provision. This paper provides a diagnosis of the extent of socio-economic inequalities in health and healthcare using an integrated conceptual framework.
Method: The 2012 South African National Health and Nutrition Examination Survey (SANHANES-1), a nationally representative study, collected data on a variety of questions related to health and healthcare. A range of concentration indices were calculated for health and healthcare outcomes that fit the various dimensions on the pathway of access. A decomposition analysis was employed to determine how downstream need and access barriers contribute to upstream inequality in healthcare utilisation.
Results: In terms of healthcare need, good and ill health are concentrated among the socio-economically advantaged and disadvantaged, respectively. The relatively wealthy perceived a greater desire for care than the relatively poor. However, postponement of care seeking and unmet need is concentrated among the socio-economically disadvantaged, as are difficulties with the affordability of healthcare. The socio-economic divide in the utilisation of public and private healthcare services remains stark. Those who are economically disadvantaged are less satisfied with healthcare services. Affordability and ability to pay are the main drivers of inequalities in healthcare utilisation.
Conclusion: In the South African health system, the socio-economically disadvantaged are discriminated against across the continuum of access. NHI offers a means to enhance ability to pay and to address affordability, while disparities between actual and perceived need warrants investment in health literacy outreach programmes.
Keywords: Access, Health inequality, Healthcare, Concentration index, Decomposition, South Africa
Background The United Nation’s Sustainable Development Goal (SDG) 3.8 strives towards the achievement of access to quality, effective, and affordable medical care for all and the assurance of universal coverage [1]. In addition, mandated in South Africa’s National Development Plan (NDP) is the goal to provide universal equitable, efficient and quality healthcare [2]. In light of these global and national policy prerogatives, socio-economic inequalities in access to healthcare remain high on the policy agenda.
Research finds that over one billion people in low- and middle-income countries (LMIC) are unable to afford healthcare and that the poor within these countries benefit least from healthcare utilisation [3, 4]. In the case of South Africa, the socio-economically disadvantaged are more likely to experience poor health status, disabil- ity, the simultaneous occurrence of more than one con- dition/disease (multi-morbidity) and are less likely to use inpatient care [5–7]. The South African health system is two-tiered with the least advantaged heavily dependent on the under-resourced public sector, while the wealthy (many of whom have private medical insurance) use the private sector [8–15]. Since 1996, user fees were waived for all seeking primary public healthcare, although eligi- bility for free care at public sector hospitals is subject to
© The Author(s). 2020 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
* Correspondence: tanjagordon@gmail.com 1Research Impact Assessment programme (RIA), Human Sciences Research Council (HSRC), HSRC Building 134 Pretorius Street, Pretoria 0002, South Africa Full list of author information is available at the end of the article
Gordon et al. BMC Public Health (2020) 20:289 https://doi.org/10.1186/s12889-020-8368-7
http://crossmark.crossref.org/dialog/?doi=10.1186/s12889-020-8368-7&domain=pdf
http://creativecommons.org/licenses/by/4.0/
http://creativecommons.org/publicdomain/zero/1.0/
mailto:tanjagordon@gmail.com
a means-test [16, 17]. In order to access a private health- care facility one has to pay out-of-pocket (OOP) or be covered by health insurance (even then the patient may incur a co-payment). In 2015/16, private healthcare ex- penditure was 4.4% and OOP expenditure 0.06% of GDP, whereas public healthcare expenditure amounted to 4.1% of GDP and is funded from general tax [8, 17]. Although each health sector makes an almost equal con- tribution to GDP, the public sector services approxi- mately 84% of the population while the private sector services a mere 16% [8, 9]. South African studies on health inequalities, however,
with the exception of Harris et al. [18], are rather unidi- mensional in nature, generally focusing only on a limited number of outcomes rather than a wide variety of di- mensions of access to healthcare. Studies tend to look at single dimensions on the pathway of access, for example, healthcare outcomes such as multi-morbidity and dis- ability [6], life-style diseases [19, 20], child [21, 22] and maternal health [23, 24], and healthcare utilisation [7]. Current research, therefore, is limited in that it fails to examine the full spectrum of the dimensions of access. Another important point to note is that inequality in ac- cess, where it has been analysed comprehensively [18], has only been measured descriptively, whereas this study adopts a more standard method and makes use of the concentration index and employs a decomposition ana- lysis to determine the main contributors to inequality in healthcare utilisation. As the country embarks on the implementation of National Health Insurance (NHI) [8], advancing the understanding of inequalities in access to healthcare and tracking these inequalities remains a priority. The one purpose, therefore, of this study is to describe
socio-economic inequalities in South Africans’ access to healthcare using a standardised indicator of inequality applied to an integrated conceptual framework. The other purpose is to determine how upstream need and access barriers contribute to downstream inequality in healthcare utilisation in the private and public sectors with the aid of a decomposition analysis.
Conceptual framework Elsewhere, access has been defined as availability (the lo- cation of the healthcare facility and the ability of the in- dividual to access the facility), affordability (direct/ indirect costs of healthcare utilisation and the ability of the individual to meet these costs); and acceptability (the point at which the service from the provider meets the expectation of the patient) [25]. This study however, uses the even more detailed framework adopted by Lev- esque et al. [26] to conceptualise the various dimensions of access to healthcare (Fig. 1). These authors define ac- cess as ‘realised utilisation’. More intrinsically, access comprises the perception of an individual’s need for
care, healthcare seeking, healthcare reaching and the utilisation of healthcare and its consequences. The path- way is influenced by individual and community-level health system supply-side factors: 1) approachability; 2) acceptability; 3) availability and accommodation; 4) af- fordability and; 5) appropriateness as well as demand- side factors: 1) ability to perceive; 2) ability to seek; 3) ability to reach; 4) ability to pay and; 5) ability to engage. Given the broad dynamics of this definition, this study uses proxies that best fit the applicable stages or dimensions of access and selected demand- and supply-side factors.
Methods Data Data analysis was conducted using the nationally repre- sentative 2012 South African National Health and Nutri- tion Examination Survey (SANHANES-1). The objective of the survey was to examine the current health and nu- trition status of South Africans in relation to non- communicable disease (NCD) prevalence and their asso- ciated risk factors. For the purpose of the survey, 500 Enumerator Areas (EA’s) representative of the demo- graphic profile of South Africa were identified from the 2007 HSRC Master Sample of 1000 EAs selected from the 2001 population census. Thereafter, 20 visiting points were randomly selected from each EA totalling a sample of 10,000 visiting points (VPs). Of the 10,000 households (VPs) sampled, 8168 were valid households of which 6307 (77.2%) were successfully interviewed. From the total number of valid households who con- sented to participate in the study, 27,580 individuals aged 15 years and older were eligible for interview. Over- all, 92.6% of all qualified individuals completed the indi- vidual interview. The SANHANES-1 survey received ethical clearance from the Research Ethics Committee (REC) of the Human Science Research Council (HSRC) (REC 6/16/11/11) [27].
Health and healthcare outcomes Table 1 below maps out the variables selected to repre- sent each dimension of access to healthcare based on the study’s conceptual framework (see Fig. 1).
Wealth index To investigate the socio-economic gradient in each of the health and healthcare outcomes in the access path- way, a wealth index and corresponding wealth quintiles were constructed by applying Multiple Correspondence Analysis (MCA) to the household survey data. Use was made of a total of 16 variables, including housing type, water and sanitation services, and ownership of 13 household assets. The percentage inertia explained by the first dimension is approximately 90%. The wealth index was used as it is considered a more reliable
Gordon et al. BMC Public Health (2020) 20:289 Page 2 of 13
measure of socio-economic status (SES) in developing countries as compared to income [28].
The concentration index The concentration curve plots the cumulative propor- tion of the population by SES, beginning with the least advantaged and ending with the most advantaged, against the cumulative proportion of health or ill health. The line of equality or the diagonal signifies the absence of inequality. If the curve lies above the line, ill health falls on the least advantaged in the population, and if it lies below, the more advantaged. The further the curve lies from the diagonal the greater the degree of inequal- ity. The concentration index is defined as twice the area between the curve and the line of equality. It takes on a positive value when it lies below the line of equality and a negative value when it lies above. A positive value can be interpreted as the concentration of health among the relatively wealthy and a negative value among the rela- tively poor. The minimum value the index can take is − 1 and the maximum value is + 1. Should the index be equal to zero (or not statistically significantly different from zero), no inequality exists [29–31]. According to the literature, the standardised concen-
tration index is suitable for variables with a ratio scale, the equation of which is as follows:
C ¼ 2 μ
cov h; rð Þ ð1Þ
where C is the standardised concentration index, h is the healthcare variable, μ is the mean of the healthcare vari- able, and r is the ith- ranked individual in the socio- economic distribution from the relatively poorest to the richest [28, 29, 31, 32]. Bounded variables, on the other hand, complicate the
measurement of inequality. Given that bounded variables can take the form of attainments or short falls the mir- ror property that requires absolute values of health I(h) and ill health I(1 − h) to be equal with different signs, is not satisfied with the standardised concentration index [32]. In this regard, one common practice concerning variables with a limit is the use of the Erregyer corrected concentration index. The index is desirable as it satisfies properties required for bounded variables [33]. The equation for the Erregyer index is as follows:
CCI ¼ 4μ b−a
�C ð2Þ
where CCI is the corrected concentration index, μ is the mean of the attained healthcare, b and a the maximum and minimum values, respectively, and C the standar- dised concentration index [32–34].
Decomposition analysis A decomposition analysis was conducted to determine how upstream factors such as health status, need and ac- cess barriers contribute to downstream socio-economic inequality in healthcare utilisation. Following Wagstaff
Fig. 1 Dimensions of access to healthcare: a conceptual framework
Gordon et al. BMC Public Health (2020) 20:289 Page 3 of 13
Table 1 Health and healthcare outcomes, by access dimension
Access dimension Outcome Survey question
Healthcare need:
Self-reported health (SRH) Binary: Very good and good 1, 0 otherwise
In general how would you rate your health today? [AQ]
World Health Organisation Disability Schedule (WHODASscore)
Continuous In the last 30 days, how much difficulty did you have in …? (12 questions) [AQ]
Kessler Psychological Distress Scale (K10)
Binary: Psychological distressed 1, 0 otherwise
The following questions concern how you have been feeling over the past 30 days. (10 questions) [AQ]
Post-Traumatic Stress Disorder (PTSD)
Binary: PTSD 1, 0 otherwise In the past week, how much trouble have you had with the following symptoms? (17 questions) [AQ]
Perceived healthcare need:
Needed care Binary: Needed care 1, 0 otherwise When was the last time you needed health care (from a doctor or hospital)? [AQ]
Healthcare seeking:
Household healthcare postponed Binary: Household healthcare postponed 1, 0 otherwise
In the last 12 months, have you put off or postponed getting the healthcare you need? [VPQ]
Availability:
Household distance to a healthcare facility
Binary: 0–10 Km away from a healthcare facility 1, 0 otherwise
How far do you live from the nearest health clinic or hospital? [VPQ]
Healthcare reaching:
Unmet need Binary: Unmet need 1, 0 otherwise
The last time you needed health care, did you get health care? [AQ]
Affordability:
Household difficulty affording cost of care
Binary: Yes 1, 0 otherwise In the past 12 months, have you had difficulty affording the cost of necessary medical care? [VPQ]
Household difficulty affording prescription medicine
Binary: Yes 1, 0 otherwise In the past 12 months, have you had difficulty affording the cost of prescription medication? [VPQ]
Ability to pay:
Household private medical insurance
Binary: In my own name/ through a family member 1, 0 otherwise
Do you have private medical aid/ health insurance either in your own name or through another family member? [VPQ]
Healthcare utilisation:
Household private care Binary: Private 1, 0 otherwise Where do you usually get your healthcare from? [VPQ]
Household public care Binary: Public 1, 0 otherwise
Individual private care Binary: Private doctor/hospital/ clinic in the last year 1, 0 otherwise
When was the last time that you received health care from a private doctor/hospital/clinic? [AQ]
Individual public care Binary: Public doctor/hospital in the last year 1, 0 otherwise
When was the last time that you received health care from a public doctor/hospital/clinic? [AQ]
Overall individual care Binary: Individual private or public care in the last year 1, 0 otherwise
Healthcare consequences:
Healthcare service satisfaction Binary: Very satisfied and satisfied, 0 otherwise
In general, how satisfied were you with how the health care services were run in your area? [AQ]
Healthcare service provider satisfaction
Binary: Very satisfied and satisfied, 0 otherwise
How would you rate the way health was provided in your area? [AQ]
AQ adult individual questionnaire, VPQ visiting point household questionnaire
Gordon et al. BMC Public Health (2020) 20:289 Page 4 of 13
[35], Eq. 3 depicts the linear relationship between the health variable (utilisation) and its determinants:
hi ¼ β0 XK
k¼1 βkxik þ εi ð3Þ
where hi is the healthcare variable of interest, xik the set of demographic and socio-economic contributing factors, and εi the error term. Like the concentration indices, the decomposition technique used for the standard concentra- tion index (C) (not shown here) [35–37] is modified to suit the corrected concentration index (CCI) as follow:
CCI hð Þ ¼ 4 XK
k¼1 βkxkC xkð Þ þ GCε
” # ð4Þ
The decomposed CCI is the summed product of the degree of responsiveness, i.e. the elasticity ðβk�xkÞ to health changes and the degree of socio-economic in- equality C(xk) in that determinant, plus the generalised concentration index of the error term (GCε), all multi- plied by 4. All things being equal, a positive contribution (x% > 0) by a factor would decrease socio-economic in- equality. Alternatively, a negative contribution (x%
The post appeared first on Versed Writers.
