Health shocks, medical insurance and household vulnerability: Evidence from South Africa.

BACKGROUND: South Africa has a dual system of healthcare model differentiated across socio-economic lines. While on the one hand there exists high quality private facilities that is expensive and accessible to the minority, on the other is the free but stretched and over-crowded public healthcare that the rest of the population relies on. Accessing private facilities requires private medical insurance or requires coping strategies that can lead to household vulnerability.
OBJECTIVE: The objective of this study is to analyse the relationship between health shocks and household vulnerability in the South African context of high poverty and low medical insurance penetration rate. DATA: The study employs data from waves three to five of South Africa's nationally representative National Income Dynamics Study (NIDS) conducted between the period 2012-2017 in approximately two-year intervals.
METHODS: Using food expenditure shock as an indicator for vulnerability, the study utilises a range of econometric techniques from panel logit regression to quasi-experimental design based difference in difference regressions to assess the association between health shocks, medical insurance and household vulnerability.
FINDINGS: The main finding of the study is that a significant proportion of households in the upper income quintile utilise private healthcare even when not covered by private medical insurance. This preference for private over public health facilities make them vulnerable to health shocks as they cope by sacrificing food consumption to incur additional health expenditure. In contrast, lower income households that are unable to access the high-cost private healthcare tend to rely on the strained public healthcare system. They are not able to use their constrained food expenditure as a coping strategy for private or out-of-pocket medical expenses because their food consumption is already at a bare minimum.
CONCLUSION: The results confirm that access to quality healthcare is a privilege in South Africa, available only to the minority of the population. The study paints a grim picture of household vulnerability in South Africa and underlines the need for a National Health Insurance that would enable universal access to quality healthcare in the country.

Introduction

The third goal of the United Nations’ 2030 agenda for sustainable development aims at ensuring that all people have access to quality health services, while goals one and two are aimed at eliminating poverty and hunger respectively. These three goals are interlinked as poor and uninsured households are vulnerable to health shocks which pushes them deeper into poverty [1, 2]. The twofold mechanisms of the increased burden of out-of-pocket health spending [3, 4]; and decreased labour supply in terms of both hours and labour participation rate link health shocks to household vulnerability [5, 6], forcing households to adopt coping mechanisms like reducing non-medical, non-food consumption as well as food consumption [7-11]. This implies that insurance, which is intended to cover individuals against health shocks, is important in preventing extreme economic outcomes [12].

South African healthcare system currently has a dual model of high-quality private facilities that is accessible to a minority and the stretched, over-crowded public facilities that the majority of population have to rely on. Public healthcare facilities are free to all residents, but residents have the option to purchase private insurance in order to be treated at private facilities. The Parliamentary Monitoring Group [13] reported after an inspection of public health facilities that “Common to all facilities were challenges regarding patient safety being compromised, good pharmacy practice not being adhered to, waste mismanagement, lack of cleanliness, as well as poor maintenance of grounds and equipment”. Other studies comparing public and private facilities [14, 15] highlight the major gap between private and public healthcare in South Africa. Public healthcare has many disadvantages such as long wait times, poor quality of care, rushed appointments, old facilities, and poor disease control and prevention practices [14]. Private healthcare on the other hand is expensive [14, 15], but provide quality facilities and care. Peltzer & Phaswana-Mafuya [14] indicates that healthcare responsiveness perception was higher in private than in public inpatient and outpatient healthcare facilities among older South Africans.

The General Household Survey (GHS) reported that only 16.9 percent of individuals in South Africa were covered by private medical insurance in 2017 [16]. This, together with the persistent headcount poverty rates of above 50 percent [17], implies that a large proportion of South Africans have to rely on the inadequate public facilities or have to put in place coping strategies to access private healthcare making them vulnerable to a sudden health shock. This solidifies the need for government intervention in the provision of national health insurance in the country [17]. South Africa remains in the planning phase of a National Health Insurance (NHI) that will ensure that all South Africans have access to health services [18]. However, there remains several delays in rolling out the proposed NHI scheme, especially with garnering sufficient public funding [19]. In addition, the successful implementation of the NHI requires a transformation to legislation that will enable the establishment of NHI Fund which will be used to fund and sustain the scheme [19].

Against this backdrop, understanding the association between health shocks, household vulnerability and medical insurance will provide context and urgency to the NHI discourse in South Africa. This study, which is the first of its kind in South Africa, contributes to the global literature on the household effects of health shocks by using methodological innovations in terms of defining vulnerability risk and estimation strategies within a quasi-experimental difference-in-difference framework. Studies undertaken in different country contexts show the negative effects of health shocks on income, food expenditure and non-food expenditure [9, 20–22]. Contrary to this, some studies [23, 24] indicate increased food expenditure due to preferences for special food to assist with recovery from illness. Further, the two-way causal relationship between food and health often arising from medical poverty trap also needs to be emphasised. A medical poverty trap refers to the self-perpetuating state where the poor are at greater risk of ill health and the ill health in turn increases the likelihood of becoming poor through out of pocket costs for public and private health services [25-27]. Given the two-way causal relationship, it needs to be emphasised that this study is focussed on understanding the association between health shock and food expenditure shock, and does not make claims of causal effect.

The studies cited above assess the association between health shock and food expenditure but none of them consider the relationship between health shock and food expenditure shock. This study introduces a new measurement variable of vulnerability with respect to food expenditure shocks. Food consumption per capita is considered to be a preferred measure of absolute poverty for developing countries [28] also evidenced by Statistics South Africa which includes a measure of poverty using the food poverty per person [17]. Therefore, this research focuses on significant (more than one standard deviation) declines in food expenditure per person as a food expenditure shock, which represents household vulnerability. This food expenditure shock is a binary variable indicating whether the household experienced a significant decrease in per capita real food expenditure. The study uses three consecutive waves of the National Income Dynamics Study (NIDS) panel data to investigate this relationship. The investigation utilizes waves three to five, conducted in approximately two year intervals between 2012 and 2017. The independent variable (health shock) used in this study is the significant (more than one standard deviation) decline in body mass index (BMI) of non-obese individuals.

Analytical framework

Existing literature has used different combinations of variables as measures of health shocks such as; a) death of a working age adult household member due to illness [11, 21, 22, 29, 30]; b) self-reported serious illnesses that prevented a household member from being able to work [5, 8, 11, 31] and c) ability to perform daily living activities [7, 32].

However, there are limitations to the NIDS data regarding the above suggested health shock measures. Serious illness as a health shock is defined as any illness that prevented a household member from doing normal activities, serious illness can include any health problems, for example, disability, disease, injury or any other chronic diseases [5, 11, 33]. The NIDS questionnaire changed from wave 4 and the question that asks whether there was a “serious illness or injury of a household member” was discontinued.

The study therefore uses a significant decrease in body mass index (BMI) of non-obese household heads as a health shock measure following Wagstaff [20]. James et al. [34] states that BMI is a recognized and reliable measure of the current nutritional status of adults. Rather than using low BMI scores (representing underweight individuals, implying they were ill from the outset), the health shock variable is defined as a binary variable taking the value one if the BMI score reduced by more than one standard deviation in each period, and zero otherwise. By excluding the obese individuals, the study accounts for the high proportion of obesity in South Africa resulting from very factors including diabetes and also unhealthy diet [35]. The association of both diabetes and food habits with socio-economic status is debatable [36, 37]. A reduction in BMI of obese individuals is not undesirable and hence the study chooses to exclude it from the definition of health shock.

Similar to health shocks, there are different possible measurements for household vulnerability. Hoddinott et al. [38] define vulnerability as the likelihood that an individual will have a level of welfare below a given benchmark at a given time in the future. They further discuss three methods which are used to estimate vulnerability, a) Vulnerability as Low Expected Utility (VEU), b) Vulnerability as Expected Poverty (VEP) and, c) Vulnerability as Uninsured Exposure to Risk (VER) [38]. Ligon et al. [39] define VEU as the difference between the utility derived from a certain level of certainty-equivalent consumption at and above which the household would not be considered vulnerable. Chaudhuri et al. [40] define VEP as the probability that expected consumption expenditure of a household will fall into poverty in the future. Hoddinott et al. and Ligon et al. [38, 39] define VER as a method that assesses welfare loss in the absence of effective risk management tools. The VER approach allows an ex-post evaluation of the scope of the negative shock causing a loss of well-being using panel data. The current study is based on the VER measure of vulnerability, adapted from the general VER model [38] in Eq 1 as follows:

Which denotes that a change in log consumption per capita of household h, in period t is a function of health shocks S(i), and X, a vector of household or household head characteristics. This also means that when the coefficient λ is equal to zero, it implies immunity from health shocks.

Methods

Broadly, two approaches—a three period panel data and a quasi-experimental data design—are used for analysis. The quasi-experimental data design based difference-in-difference method which uses a non-randomised assignment to treatment or control group in order to estimate the impact of drop in BMI to the sample. In this study, a quasi-experimental sample was used whereby outcomes between a treatment and control groups were compared for two pre-treatments and one post-treatment periods. Model I (Panel Logit)

The following method will use the first dataset, which allows the health shock to be measured as a dummy variable in the three investigation periods based on changes in BMI from the previous period. The panel logit model using binary dependent variable framework is as follows:

Where:

Individual and time identifiers are subscripted as i and t respectively,

y represents the food expenditure shock dummy variable taking value 1 if there was a significant decrease in food expenditure and 0 otherwise,

X is a vector of control variables and,

ε is a non-zero residual variable containing both conditional errors and time invariant unobservables

Due to the absence of an appropriate counterfactual, the estimator is limited, which may bias results in the presence of self-selection [41]. In addition, given the panel data structure and potential two-way causal relationship between food shocks and health shocks, there is the likelihood that control variables and the treatment variable are correlated with time invariant unobservables captured in the composite error. Some of these limitations are accounted for in a difference-in-difference (DID) estimation. The first difference which is usually called the ‘naïve’ estimator can either compare the treated individuals pre and post treatment or compare treated and untreated individuals post treatment. The problem with differencing only once is that the estimator yield biased estimate of the treatment and differencing does not account for the limitations mentioned initially. Therefore, application of double differencing estimator compares treatment and control groups in terms of outcome changes over time relative to the outcomes observed for a pre-intervention baseline in order to isolate the treatment effect [42].

1.1. Model II (DID)

Using the quasi-experimental data design (where waves 3 and 4 are pre-treatment, and wave 5 is the post-treatment period), the difference-in-difference (DID) model can be used to estimate the effect that the health shock has on the treated (i.e. households experiencing health shocks) in the post-treatment period. DID is usually applied to continuous dependent variable, however, in this case, the dependent variable is a binary variable (with value 1 for households that experienced food expenditure shock and, 0 for the rest of the households). Therefore, a non-linear DID model is applicable in this study. Karaca-Mandic et al. [43] suggest that a non-linear DID model should have the conditional probability that the dependent variable equal to 1 be expressed as a function of the usual DID function with continuous dependent variable. The non-linear DID model is represented as follows:

While for a linear DID a positive and significant β12 indicates the vulnerability of households to health shocks, the treatment effect in non-linear DID is the difference between the cross differences for each outcome category [44]. Hence the probabilities associated with the marginal cross-difference, used to ascertain the treatment effect, are presented along with the DID results.

The DID estimation is based on the parallel trend assumption which is tested following Autor’s [45] methodology. The results presented in S2 Table in the supplementary information, shows insignificant interactions in period 1 implying that the assumption of the parallel trend is satisfied in our dataset. Given that the assignment mechanism between the treatment and control groups is not random, the estimator suffers from the potential for self-selection based on unobservables [45]. This necessitates that measures be taken to account for potential structural differences between the two cohorts. However, there exists a dimensionality problem, that is; it is not clear how each characteristic should be weighted when matching characteristics between treatment group and control group. Rosenbaum and Rubin [46] suggest the use of propensity score matching (PSM) to solve the problem of multi-dimensionality which is undertaken in Model III.

1.2. Model III (PSM-DID)

The PSM technique involves a construction of an artificial control group by identifying an untreated observation that has the most similar observable characteristic for every treated observation. A stepwise logistic regression to select variables was estimated, as proposed by Rosenbaum and Rubin [46]. This resulted in significantly related variables in the final model. The matching algorithm employed in this study uses the nearest neighbour matching algorithm which is also known as ‘traditional pairwise matching’. The common support, or overlap condition, was tested and found to be satisfactory (Fig 1).

Fig 1

Common Support for entire sample.

The final propensity score matching drops the observation that are off-support. The propensity scores were obtained from Stata command psmatch2 which were utilised in the estimation of the following PSM-DID model. However, it is worth noting that PSM reduces but does not eliminate the bias generated by unobservable confounding factors.

The conditional independence assumption (CIA) assumes that the spread of the propensity scores is identical for those receiving treatment and not receiving treatment because all factors that generate dependence is the same. However, the CIA is always assumed in most cases because it cannot be tested in practice since it involves unobserved potential outcomes. S3 Table shows that the entire samples’ matched propensity scores result in a reduction in selection bias.

Despite attempts to minimise endogeneity, the concern of reverse causality remains. Added to this, due to data limitations, using an instrumental variable approach to reducing endogeneity was difficult to implement. Thus the results of the empirical analysis are best interpreted as association rather than causation.

Data

The data used in the investigation is extracted from the National Income Dynamics Study (NIDS) of South Africa. Supervised by the Southern Africa Labour Development Research Unit (SALDRU) [47], the NIDS dataset is a national representative panel study and currently has five waves with approximately 2 year intervals. The panel dataset has approximately 7 300 households and 28 000 individuals captured in the database. To ensure continued national representation of the population, the NIDS dataset contains panel weights to account for systematic attrition and non-responses.

The study uses two structures of data derived from NIDS to suit the different estimation strategies. First, a three period panel dataset of non-obese household heads is constructed using waves 3 to 5. Shocks in health and food expenditures for wave 3 is estimated based on changes from wave 2. Therefore, although only waves 3–5 are included in the analysis, wave 2 is utilised for key variable construction. The second dataset is a quasi-experimental dataset using only waves 3 to 5, where treatment is defined as the non-obese household heads suffering from a health shock between period 4 and 5. The sample is restricted to those not suffering from health shocks in waves 3 and 4. As such, waves 3 and 4 are constructed as pre-treatment periods and wave 5 as the post-treatment period. Detailed definition of variables is provided in S1 Table in the supplementary file.

Households experiencing health and food expenditure shocks calculated in the panel dataset is summarised in Table 1.

Table 1

Percentage experiencing health shocks and food expenditure shocks.

Variables	Wave 3	Wave 4	Wave 5
Health shock	1,96	3,50	2,73
Food Expenditure Shock	6,89	9,67	10,70
Number of Households	1 684	1 686	1 683

Source: Authors’ calculation from weighted NIDS data

The quasi-experimental dataset (used for DID) restricted the sample such that health shocks which represent a significant reduction in the BMI are observed only between the 4th and 5th waves, and divided the sample between treatment and control groups. The treatment group consists of non-obese household heads who experienced an over 2.9-point decline in BMI in wave 5.

Further, various households and household head characteristics are included as control variables. The household characteristics include location, household size and household income; while household head characteristics include age, sex, education, medical aid coverage and employment status. Table 2 contains the average weighted means and standard errors for all variables used in the quasi-experimental dataset. Table 2 illustrates observable differences in variables between treatment and control groups for the three periods. The following has been divided between those who experienced health shocks, the ‘Treated’ group, and those who did not experience a health shock, the ‘Control’ group. The dataset contains 4 812 observations.

Table 2

Summary statistics of DID sample.

Variable		N		Mean		Std. Error
	Sample	Treated	Control	Treated	Control	Treated	Control
Household characteristics
Health Shock	Whole	4812		0.026		0.157
Food Shock		123	4 689	0.082	0.088	0.276	0.283
Income		123	4 689	4019,62	3927,71	4383,36	8016,45
Food Expenditure		123	4 689	608,88	521,69	651,25	605,85
Household Size		123	4 689	3.86	3.75	2,55	2.67
Urban		123	4 689	0,5905	0,6897	0,4938	0,4627
Household head characteristics
Medical Aid Coverage		123	4 689	0,0764	0,1598	0,2668	0,3664
Female		123	4 689	0,2856	0,5239	0,4535	0,4995
Married		123	4 689	0,3794	0,1393	0,4872	0,3463
Age		123	4689	54.64	50.08	14.2	14.9
Education		123	4 689	6.6	8.48	4.49	58050
Pensioner		123	4 689	0,2257	0,1367	0,4197	0,3436
Employed		123	4 689	0,4238	0,5560	0,4962	0,4969

Source: Authors’ calculation from weighted NIDS data

On average, 2.56 percent of households experienced a health shock in wave 5. There is no significant difference in the proportion of sample that experienced health shocks by their medical insurance status. Evidence from literature is mixed in terms of the effect of medical insurance on health status, with a recent review [48] indicating that of the 12 studies: nine studies found a positive effect, one study reported a negative effect, and two studies reported no effect. Therefore the indication that medical insurance does not make a substantial impact on health shock is not contradictory to existing evidence.

Food shocks captures whether the household experienced a significant decrease in real total food expenditure or not. Food shocks were experienced by 8.20 and 8.80 percentages of treated and control group households respectively. Household income per capita and food expenditure per capita have large deviations and is indicative of the high level of inequality in the country. From above, on average only 7.64 percent of those belonging to the treatment group are covered by a medical aid while 16 percent of those in the control group are covered by medical aid. The average age of the household head is 50 years old. The low average levels of education attachment, especially among the treated group, is also evident. Similarly, lower percentage of household heads are employed among the treated as compared to the control group. This shows that health shocks can significantly reduce labour supply; as highlighted by [5, 8, 49, 50]. This indicates that the mechanism of the health shock transmission to food shocks therefore could be from foregone labour, as well as out-of-pocket medical expenditure.

Table 3 combines the household and individual characteristics per quintile. The total monthly medical expenditure for higher income households on average is approximately four times higher than that of lower income households. Possible explanation for this is that higher income households (even in the absence of medical insurance) prefer private healthcare which are generally more expensive than the public healthcare facilities. This is supported by the statistic that 48% of the quintile 2 individuals that accessed private healthcare, did not have medical insurance. On average, 1.47 percent of lower income household (quintile 1) heads are covered by medical insurance while 30.15 percent of higher income household (quintile 2) heads are covered by medical insurance. One of the limitation of NIDS data however is that details regarding the medical insurance scheme is not provided. We therefore have to treat this as a binary variable.On average, approximately 2 percent of the households experienced a health shock.

Table 3

Summary statistics of household and individual characteristics (average weighted, Quintile).

Variable	N		Mean		Std. Error		Min		Max
Sample	Quintile 1	Quintile 2	Quintile 1	Quintile 2	Quintile 1	Quintile 2	Quintile 1	Quintile 2	Quintile 1	Quintile 2
Total Medical Expenditure	3 094	1 718	23,34	101,38	107,54	775,50	0,00	0,00	3067,49	10224,95
HH Food Expenditure	3 094	1 718	251,18	796,38	235,85	730,09	0,00	0,00	4089,98	8179,96
HH Income	3 094	1 718	747,19	7116,90	387,86	10307,57	51,12	1549,03	1547,38	152777,80
Urban	3 094	1 718	0,5740	0,8012	0,4946	0,3992	0	0	1	1
Household Size	3 094	1 718	4,1252	1,8466	2,6993	1,4807	1	1	24	22
Age	3 094	1 718	48,3844	44,2585	13,9490	12,6682	17	17	91	90
Head Education attainment	3 094	1 717	7,6603	13,0562	4,7068	5,5409	0	0	24	24
Employed	3 094	1 718	0,3336	0,7729	0,4716	0,4191	0	0	1	1
Food Expenditure Shock	3 070	1 701	0,1116	0,0470	0,3149	0,2116	0	0	1	1
Health Shock	3 094	1 718	0,0190	0,0243	0,1365	0,1541	0	0	1	1
Medical Aid Coverage	3 094	1 718	0,0147	0,3015	0,1204	0,4590	0	0	1	1
Pension Recipient	3 094	1 718	0,2182	0,0590	0,4131	0,2356	0	0	1	1

Source: Authors’ calculation from weighted NIDS data

The average household size is higher for the quintile 1 (lower per capita household income sample) households compared to the higher income households. The higher income household heads have, on average, higher years of education attainment. The education attainment is a cardinal variable denoting the years of education obtained. Therefore, it is not surprising that, on average, there is a higher percentage of employed heads of households in higher income quintile than those in lower income households. The eligibility for old age pension grant is income below R156 240 per annum for married and R78 120 per annum for single individuals [51]. Thus, it is not surprising that on average, only 6 percent of household heads in higher income quintile while 22 percent of lower income households receive the old age government grant. The average household income per capita of quintile 1 households is (R747.19). In contrast, the higher income households have on average monthly income per capita that is almost 3 times more than monthly old age grant.

Results

Given the binary nature of the dependent variable (food shock) that measures vulnerability, various non-linear panel data models are estimated for the entire sample and sub-samples based on household per capita income. The parallel trend assumption is satisfied for the use of DID regression (S2 Table). Further the propensity score matching tests satisfy the assumptions for PSM-DID and are presented in S3 Table. Results from the panel logit, DID and PSM-DID are presented in Tables 4, 5 and 6 respectively. The results show remarkable consistency with respect to the strong positive and significant association between health shocks and food shocks among quintile 2 households (higher per capita household income sample) and lack of association between health shocks and food shocks among the quintile 1 (lower per capita household income sample) sample. The results strongly suggest that higher income households respond to health shocks by reallocating household spending towards the sick household member and allocating away from overall household food expenditure [20]. This reallocation of household resources is also visible in higher income households that access private healthcare that involves out-of-pocket expenditure which they try to manage by sacrificing food consumption. This is in line with several studies [9, 11, 22, 23, 52] who also found that the health shocks decrease food consumption by 1.80%, 17.30%, 4.80%, 15.30%, 18.80% and 4.20% respectively. While studies [23, 24] have indicated increased food expenditure incurred for special food for the patient, this study shows a reduction in the overall household per capita food expenditure following a health shock.

Table 4

Panel logit model outputs—Model I.

	Whole sample	Quintile 1	Quintile 2
Health shock	0.2977	-0.2141	1.1417*
	(0.3812)	(0.4983)	(0.6860)
Medical Aid coverage	0.0602	-1.3793	0.5298
	(0.2838)	(0.8456)	(0.3344)
Female	-0.0611	-0.2816	0.1554
	(0.1892)	(0.2441)	(0.3102)
Married	-0.3558*	-0.2903	-0.5241
	(0.1968)	(0.2389)	(0.3981)
Age
30–49	-0.0065	0.1725	-0.3172
	(0.2903)	(0.3969)	(0.4387)
50–64	-0.4262	-0.3328	-0.4841
	(0.3260)	(0.4397)	(0.5140)
65–91	-0.1104	-0.1137	0.5655
	(0.4026)	(0.5306)	(0.6997)
Education Attainment
Primary	-0.0950	-0.1574	-0.0072
	(0.2498)	(0.2819)	(0.6410)
Secondary	-0.7806***	-0.8000**	-1.2086*
	(0.2843)	(0.3364)	(0.6558)
Certificate	-0.6652*	-0.3515	-1.2101*
	(0.3650)	(0.4753)	(0.7175)
Undergraduate	-0.0930	0.7380	-0.5511
	(0.4018)	(0.7427)	(0.7062)
Postgraduate	-0.3338	-	-0.3062
	(0.7391)		(0.9579)
Household size
4–6	0.7463***	0.7176***	0.5237
	(0.1720)	(0.2156)	(0.3562)
7–9	0.9801***	0.9020***	1.1033
	(0.2416)	(0.2811)	(0.7600)
10–24	1.2936***	1.1886***	2.0339
	(0.3625)	(0.4028)	(1.5962)
Pension recipient	-0.5299**	-0.4015	-1.5749**
	(0.2567)	(0.3009)	(0.6772)
Employed	-0.2855*	-0.4396**	0.4477
	(0.1697)	(0.2119)	(0.3916)
Urban	0.1444	0.1296	0.1932
	(0.2398)	(0.3045)	(0.4099)
Constant	-3.0914***	-3.0589***	-3.8602***
	(0.4507)	(0.5629)	(1.0711)
Observations	5053	3248	1 801

*** Significant at 1% level

** significant at the 5% level

* Significant at the 10* level

Table 5

Difference in differences model output—Model II.

	Whole sample	Quintile 1	Quintile 2
Post	0.4467**	0.6489***	-0.0034
	(0.1797)	(0.2000)	(0.3598)
Treat	-1.5646***	-1.1333*	-2.7073**
	(0.5522)	(0.5988)	(1.2104)
Treat*Post	2.3725***	1.3821	5.7218***
	(0.8638)	(0.9582)	(1.6597)
Medical Aid coverage	-0.4324	-1.8649*	0.1521
	(0.3151)	(1.0191)	(0.4207)
Female	-0.3529**	-0.6428***	0.1123
	(0.1786)	(0.2009)	(0.3070)
Married	0.1271	0.3771	-0.7280
	(0.2350)	(0.2590)	(0.5114)
Age 30–49	0.0535	0.1389	-0.3026
	(0.3383)	(0.4514)	(0.4852)
Age 50–64	-0.1950	-0.2339	-0.1898
	(0.3848)	(0.4951)	(0.6246)
Age 65–91	0.4317	0.1172	1.3573
	(0.5817)	(0.5855)	(0.9484)
Education: Primary	0.4556*	0.4894*	0.1119
	(0.2557)	(0.2723)	(0.8645)
Education: Secondary	-0.1041	-0.1329	-0.2871
	(0.3054)	(0.3476)	(0.7648)
Education: Certificate	-0.2325	0.1413	-0.5652
	(0.4082)	(0.4645)	(0.8959)
Education: University	0.4743	1.1142	0.3199
	(0.4234)	(0.7448)	(0.8083)
Household size 4–6	0.6904***	0.3840*	0.7931*
	(0.1910)	(0.2211)	(0.4239)
Household size 7–9	0.6020**	0.2663	1.0036
	(0.2396)	(0.2582)	(0.6124)
Household size 10–24	0.8836***	0.5037	2.7845**
	(0.3133)	(0.3395)	(1.1118)
Pension recipient	-0.6926*	-0.4501	-0.8109
	(0.3762)	(0.3139)	(0.8391)
Employed	-0.2983	-0.2326	0.8722*
	(0.2011)	(0.2290)	(0.5026)
Urban	0.1345	0.1702	0.3577
	(0.1688)	(0.1922)	(0.3161)
Constant	-2.3943***	-2.0273***	-3.7639***
	(0.4143)	(0.5053)	(1.0350)
Observations	4771	3067	1701
Cross difference (P-value)	0.0693	0.2272	0.0319

*** Significant at 1% level

** significant at the 5% level

* Significant at the 10* level

Table 6

PSM-DID results—Model III (propensity score weighted).

	whole sample	Quintile 1	Quintile 2
Post	0.3262***	0.0630	1.3604**
	(0.1191)	(0.1488)	(0.5706)
Treat	-0.2585	-0.5202	-13.3631***
	(0.5047)	(0.5430)	(0.6153)
Treat*Post	0.3089	-1.0599	17.1028***
	(0.7156)	(1.1705)	(1.2857)
Medical Aid coverage	-0.4790**	-1.0962	-0.3836
	(0.2175)	(1.0160)	(0.5286)
Female	0.3007**	-0.2629	0.8439**
	(0.1299)	(0.1643)	(0.4294)
Married	-0.0083	0.0391	-0.4356
	(0.1704)	(0.2030)	(0.5216)
Age : 30–49	-0.1317	-0.1542	-0.0672
	(0.2079)	(0.3046)	(0.7032)
Age : 50–64	-0.3690	-0.3635	-1.3979*
	(0.2308)	(0.3333)	(0.7671)
Age : 65–91	-0.3405	-0.1187	-4.1163***
	(0.2848)	(0.3960)	(1.2383)
Education: Primary	0.1152	0.1670	-0.5827
	(0.1718)	(0.1884)	(0.8306)
Education: Secondary	-0.3041	-0.0475	-1.1580
	(0.1972)	(0.2271)	(0.9666)
Education: Certificate	-0.1611	0.0700	-2.2624**
	(0.2649)	(0.3524)	(0.8786)
Education: University	0.2542	0.2708	-0.7904
	(0.2862)	(0.8044)	(0.8221)
Household size 4–6	0.7147***	0.5060***	0.4346
	(0.1319)	(0.1661)	(0.4997)
Household size 7–9	0.7716***	0.6369***	-1.0352
	(0.1777)	(0.2009)	(1.0420)
Household size 10–24	0.6637**	0.4996*	3.1548**
	(0.2637)	(0.2939)	(1.3187)
Pension recipient	-0.2098	-0.2105	1.0420
	(0.1895)	(0.2304)	(1.5078)
Employed	-0.3673***	-0.2649	0.3902
	(0.1412)	(0.1716)	(0.9611)
Urban	0.1581	0.2168	0.2089
	(0.1240)	(0.1440)	(0.5505)
Constant	-2.0684***	-2.1681***	-2.1403
	(0.2761)	(0.3803)	(1.5954)
Observations	4,771	2,678	1,122
Cross differences (P-value)	0.6681	0.3142	0.0000

*** Significant at 1% level

** significant at the 5% level

* Significant at the 10% level

Our results contradict a study conducted by Gertler et al. [7], which concluded that households with higher income seem to be better insured against negative effects of illness shocks. Our study shows that even relatively higher income households are vulnerable to health shocks given their preference to access better quality private sector healthcare.

The absence of association between health shocks and food shocks among the poorer households shows that they do not have room to further bring down food expenditure to access healthcare that requires out-of-pocket expenditure. Based on this explanation, it appears that the poor forego quality healthcare, as they cannot trade-off their already constrained food consumption further for medical expenses. Literature has also provided various other reasoning for this. Islam & Maitra [53] highlight the role access to microcredit, as a means for consumption smoothing in the presence of health shocks. Mohanan [54] argue that society, particularly from poorer subpopulations, usually intervene when the ability of a household’s food consumption smoothing is limited by a shock. The community intervention can be in the form of neighbours and relatives of the household. This is further highlighted by Udry [55], Townsend [56] and Morduch [57] who also confirmed that informal coping mechanisms (borrowing from landowners, relatives’ transfers or other support network) are very popular in developing countries because of the absence or malfunctions of formal credit and insurance markets. In the South African context both sets of reasoning are likely to hold. Poorer households access healthcare only in the free public sector which is severely constrained. The NIDS data at hand substantiates this with 84% of lower income household dependent on public healthcare compared to under 44 percent of quintile 2 households. Other non-medical expenses related to accessing healthcare like transport and foregone labour may be compensated by social intervention, but studies on this is limited in the South African context.

The negative association between medical insurance and household vulnerability is brought out at 5 percent significance level for the whole sample. Furthermore, the results show that larger sized households and households with female heads were more vulnerable to food shocks. These results are in line with the other studies that conclude that increase in household size tends to increase vulnerability to poverty in sub-Saharan countries [2, 58]. Education and employment, on the other hand, are seen to be strong protection against household vulnerability on lines of Dhanara [59]. This is consistent with the findings based on Nigeria data that concluded that in general households with heads who have no schooling are vulnerable [58].

Discussion and conclusion

This study investigated the relationship between household heads’ health shocks and household vulnerability as measured by food expenditure shock. Following Wagstaff [22] the study defined health shock as a decline in BMI of more than one standard deviation among the non-obese. The use of BMI as a measure of health shock has limitations as BMI can decrease due to other measures for example, ageing, change in lifestyle, seasonality. The study estimated Panel Logit models, DID models and, PSM with DID models to address selection bias issues.

The results of the three models are consistent and show that higher income households are most likely to experience food expenditure shocks in the presence of health shocks. This is due to households responding to health shocks by reallocating the households’ funds away from food expenditure and towards the ill member (22). Our results do not show similar association within the lower income households (quintile 1) sample. This can be partially explained by the difference in healthcare access between income quintiles using the NIDS survey question “where did the last (medical) consultation take place?”. While 84% of responses within quintile 1 indicated public health facility, 56% of responses within quintile 2 indicated private health facility. The figures are even starker for the top 20% of households, where 86% indicated private health facility as the place of their last medical consultation. Of the quintile 2 households that accessed private healthcare, over 48% did not have medical insurance. This explains the need for resource allocation away from food and the resulting food shock experienced among quintile 2 households.

This is therefore indicative that higher income households prefer to access private healthcare even at the cost of food consumption. However, the poor cannot use food expenditure as a coping mechanism to access private healthcare, as food consumption is already low. How the poor cope in South Africa in the event of a health shock in terms of non-medical expenses such as transport to public clinics or public hospitals needs to be studied further. The informal coping mechanisms as suggested by Udry [53], Townsend [54, 55] and Morduch [12] have not been explored in the South African context and provides avenues for future research. It appears from our research that the poor forego quality healthcare that requires additional expenditure because they are not able to employ diversion of food resources as it is already at a bare minimum. Conversely, higher income households show a definite preference for private healthcare and are prepared to incur additional expenditure even at the cost of food expenditure.

Furthermore, large-sized household and female headed households show greater vulnerability to food shocks. The presence of at least one employed household member also mitigates the health shock effect on food consumption. Overall, the study shows that the vulnerability of South African population to health shocks in the absence of universal medical insurance is precarious and costly to those most unable to afford private health insurance. Given that private healthcare is only available to a minority of the population, the vast majority of the populations is vulnerable because of a lack of healthcare insurance. This underlines and motivates the need for a National Health that would enable universal access to health care in the country.

Description of variables.

(DOCX)

Click here for additional data file.

Parallel trend assumption tests.

(DOCX)

Click here for additional data file.

PS-matching T-test results–logit.

(DOCX)

Click here for additional data file.

30 Sep 2019

PONE-D-19-21685

Health Shocks, Medical Insurance and Household Vulnerability: Evidence from South Africa

PLOS ONE

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Please respond to each of the reviewers' comments in turn taking particular note to address comments concerning: households' preference for public vs private healthcare; explain in more detail out-of-pocket expenditure and public-private mix in the context of the South African Health Care System and its funding; consider further explanation of use of reduced BMI in non-obese people as measure of health shock and also the pitfalls of food consumption estimates; and clearly elaborate on the difference-in-difference analysis performed (as per Reviewer 2's comments); and noting the important differences in the analyses by three different models.

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Reviewer #1: This paper analyses the relationship between health shocks in heads of households and household vulnerability in South Africa, using food expenditure as an indicator for vulnerability. The paper defined ‘health shock’ as a decline in BMI of more than one standard deviation in a non-obese head of household. The paper is well structured and easy to read.

Comments:

1) The authors assume that quality care can only be achieved in a private setting. An explanation for this is needed, as well as references.

2) On page 3, the authors state that 80% of South Africans are likely to be vulnerable to a sudden health shock. It is not clear how this percentage was calculated. Is this represented by the 100%- 16.9%= 83.1% of individuals not covered by medical aid in 2017? If yes, it should be made clearer.

3) BMI decline in non-obese individuals was defined as a health shock. However, this is a measure sensitive to other factors as well. BMI can also change as a result of ageing, change in lifestyle, seasonality (was the data collected during the same period of the year?), etc. It might be worth exploring this in a sensitivity analysis or recognising it as a limitation in the conclusion/discussion section.

4) Make sure all tables are referenced in text.

5) Last paragraph, page 11- it is not very clear if participants covered by medical aid experienced health shocks. If the answer is yes, it might be worth adding a few comments explaining possible reasons for this.

6) The authors comment throughout the paper that lower income households are not reducing their food consumption as a result of a health shock. However, there might be other explanations for this, such as consumption of basic food from own production, that is unaffected by illness. To account for this, it might be worth dividing food consumption into food purchased and food produced. Looking at the questionnaire for Wave 5 I think this is possible.

Reviewer #2: Reviewer: Nicola Foster

Manuscript number: PONE-D-19-21685

Title: Health shocks, medical insurance and household vulnerability: evidence from South Africa

Thank you for the opportunity to review the manuscript Health shocks, medical insurance and household vulnerability: evidence from South Africa.

It is really great to see the innovative ideas presented in this paper, and the questions identified are highly relevant for South Africa (and globally) for debates related to conceptualising Universal Health Coverage. The comments presented are aimed at strengthening the analysis presented here, and more broadly the conclusions drawn from the work. Comments are structured following the sections of the paper.

Overarching

• Currently, the manuscript reads as if written for an economics journal, and while this is not a problem, I would recommend that in order to make the paper more accessible to a wider audience, there is a need to use a style that is similar in structure to biomedical papers typically published in PLoS ONE. For each of the sections of the manuscript, I offer suggestions that may assist in improving this.

• In terms of the structure of the manuscript, perhaps you could consider amending headings to for example “measuring health shocks and household vulnerability” to “analytical framework”, “econometric strategy” perhaps rather as “methods”. Section 5. Data could possibly be subdivided to have a section “main results” and “robustness checks”. “6. Conclusion” could be renamed as “Discussion and Conclusion” as there is a decent proportion of section 6 that is in fact a detailed discussion of the results of the study.

Abstract

• Suggest rewriting the abstract to follow a format with introduction, methods, findings and conclusions. Currently, the paragraph opens with the objective of the study which feels out of place. It would improve the reading if you started with the global background to the study, for example the current debates around Universal Health Coverage (UHC).

• From the rest of the paper, I am not convinced that households are in fact showing a preference for private healthcare – is it possible that this is rather what they have access to? While all taxpayers (including through VAT) contribute to the financing of the public health sector in South Africa, those who are in formal employment are often enrolled into private health insurance by their employers.

• Please substantiate statement “the results confirm that access to quality healthcare is a privilege in South Africa”. It would be helpful if you could expand on how the quality of healthcare was conceptualised in this study and how implemented in this analysis?

Introduction

• Suggest adding a section that explicitly describes the characteristics of the South African health system. With an emphasis on the arrangement of and funding of health services between the public and private health system.

• If preferences for services (and how this intersects with affordability) is a thread that you would like to further explore in your work, then I suggest that this needs to be unpacked in more detail.

• It would be helpful to explicitly discuss out-of-pocket payments as one of the funding mechanisms for healthcare in South Africa – and how the burden of out of pocket payments affects people from different socio-economic status groups who are accessing health services from the public – and private health sectors. Perhaps you can also further expand on the funding for private health care services, which will include private health insurance, but also out of pocket payments? And perhaps a bit more on the range of benefit packages offered by various private health insurance schemes. For example, would households experience the same burden of out-of-pocket payment irrespective of the insurance scheme that they belong to? This would set up the introduction to your study better.

On page 3:

o In the figures related to medical aid coverage quoted, please specify enrolment in health insurance by type of insurance.

o Please provide a reference for the poverty rates quoted, and a definition for how poverty was defined here.

o Would be helpful to update the current developments related to the National Health Insurance in South Africa, especially the White Paper. Could you please reference the statement that the delays are related to sufficient public funding?

o Could you expand on your literature review related to vulnerability and food expenditure? In particular, could you unpack the direction of the relationships that have been explored in the literature you reviewed? One review to perhaps consider/ that may assist in identifying additional literature sources is Russell. 2004. The economic burden of illness for households in developing countries: a review of studies focusing on malaria, tuberculosis and HIV. In his review, he found that some of the studies relying on primary data collection, showed that household expenditure on food may increase when ill – but I think the question as to whether this may mean that there are intra-household shifts in food expenditure is less clear.

o The last paragraph before section 2, starting with “Given the binary nature of the dependent variable…”, would probably best fit in your main results section.

o On the definition of health shocks, I agree with the use of reductions in BMI as a measure, following the work of Wagstaff in Vietnam, however it would be helpful to expand a bit on your explanation of this measure. Could you say something about how relevant the work from Vietnam is likely to be to South Africa? Is this measure transferable and relevant, especially given the mixed evidence related to association and trend in South Africa between poverty and type II diabetes which is associated with higher BMIs? For example, Mutyambizi et al. 2019. Lifestyle and socio-economic inequalities in diabetes prevalence in South Africa: a decomposition analysis – find some evidence of an association between diabetes and higher SES in South Africa. However, Ataguba in an analysis of panel data, found a reduction in this trend over time. Is the focus on non-obese household heads, an attempt to control for the association with Diabetes, if so, please specify?

On page 6:

o Under econometric strategy, could you please explain here in which ways your study used a quasi-experimental data design?

On page 7:

o Given, the audience, it would be helpful to rewrite the sentence beginning with “Furthermore, given the panel data structure…” in simpler language. Especially, given that reverse causality could be used to wither mean that the direction of the cause and effect relationship is here the opposite of what one would normally consider, OR that there is a two-way causal relationship. I think that you mean the latter, but it would be helpful to be clear.

o With your difference-in-difference explanation, could you first succinctly explain what the first difference being assessed is; and then the second. An additional explanation would again assist readers without an economics background to understand your study better. Ideally this explanation should come before the equation (number 3) on page 7. Could you add a sentence to explain why you are using a non-linear DiD specifically with your question in mind?

On Page 10:

o Here, it would be worth reminding the reader that a health shock is defined as a significant reduction in BMI.

On Page 11:

o Table 2 would read better if you presented the comparison between Treated and Control in columns.

o Please be consistent in reporting the number of decimal points.

o Would suggest defining food shocks and how measured in the text here.

o In the table/ below the table, it would be helpful to repeat the definition of who the treated and control groups are again, i.e. those with a health shock vs those not.

Page 12:

o Remind the reader what a quintile one household is in the text. While it seems redundant, it really assists the readability.

o The reporting of changes between the groups could be improved by being consistent in reporting the decimal positions.

o Could you be specific about the threshold or the SA government pension grant (plus add a reference). How does that relate to the mean household income for each of your quintiles?

Page 13:

o In Table 3, again, it might be helpful to put the comparators (i.e. quintile 1 and quintile 2) on the horizontal axis; with the sample characteristics reported on the vertical axis.

o In your labelling, for each of the variables, it would be helpful if you could also specify the time period. So, for example, total medical expenditure per month? Per annum? In addition, please make sure that you report to the same number of decimal points.

o Page 13 (Table 3), could you please also discuss the finding that richer households has much greater total medical expenditure?

o Is “Head education attainment” a categorical variable? Or number of years of schooling? Please, clarify in the text.

o It would be helpful to also explain how medical aid coverage is defined i.e. does this include all patients who are accessing any form of health insurance whether it is full coverage or hospital only?

Tables 5, 6 and 7:

o For the reader, it would really improve the ability to see what is going on in the tables if you would add what the comparators are that are being reported.

Page 14:

o It would be helpful to substantiate from your results, why you conclude that “higher income households respond to health shocks by reallocating household spending towards the sick household member…”

o You may need to expand on how your results show that “higher income households have a preference to access better quality private sector healthcare”.

o Could your finding that poorer households do not have a reduction in food expenditure in the face of health shocks not also be explained by the protective effect of fee-free public health services in South Africa?

o Agree that this affect might be mediated by informal community support systems, and donations, but could it also be affected by labour behaviour. In the absence of out of pocket costs for healthcare, we would suspect that the largest cost contributor to health shocks would be loss of income. However, perhaps in South Africa (and especially in those informally employed) it might be that even significant reductions in BMI would not lead to a loss of income.

Page 19:

o The discussion/ conclusion section is well explained and points well argued.

o It would be helpful if you could add a section explaining the difference in the results between the three models used. Why was this important, what are the remaining limitations of the analysis and how does this affect your results?

**********

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Reviewer #1: Yes: Laura Rosu

Reviewer #2: Yes: Nicola Foster

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Submitted filename: Foster_review_2019_0925.docx

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18 Nov 2019

The authors wish to thank the reviewers for their insightful comments and the opportunity to revise the manuscript. The authors have made every effort to comply with the comments received. Specific responses are provided below each comment.

Response TO:

Reviewer: Nicola Foster

Manuscript number: PONE-D-19-21685

Title: Health shocks, medical insurance and household vulnerability: evidence from South Africa

Thank you for the opportunity to review the manuscript Health shocks, medical insurance and household vulnerability: evidence from South Africa.

It is really great to see the innovative ideas presented in this paper, and the questions identified are highly relevant for South Africa (and globally) for debates related to conceptualising Universal Health Coverage. The comments presented are aimed at strengthening the analysis presented here, and more broadly the conclusions drawn from the work. Comments are structured following the sections of the paper.

Overarching

• Currently, the manuscript reads as if written for an economics journal, and while this is not a problem, I would recommend that in order to make the paper more accessible to a wider audience, there is a need to use a style that is similar in structure to biomedical papers typically published in PLoS ONE. For each of the sections of the manuscript, I offer suggestions that may assist in improving this.

• In terms of the structure of the manuscript, perhaps you could consider amending headings to for example “measuring health shocks and household vulnerability” to “analytical framework”, “econometric strategy” perhaps rather as “methods”. Section 5. Data could possibly be subdivided to have a section “main results” and “robustness checks”. “6. Conclusion” could be renamed as “Discussion and Conclusion” as there is a decent proportion of section 6 that is in fact a detailed discussion of the results of the study.

� Changed headings in accordance to the comments.

Abstract

• Suggest rewriting the abstract to follow a format with introduction, methods, findings and conclusions. Currently, the paragraph opens with the objective of the study which feels out of place. It would improve the reading if you started with the global background to the study, for example the current debates around Universal Health Coverage (UHC).

� Restructured the abstract accordingly.

� From the rest of the paper, I am not convinced that households are in fact showing a preference for private healthcare – is it possible that this is rather what they have access to? While all taxpayers (including through VAT) contribute to the financing of the public health sector in South Africa, those who are in formal employment are often enrolled into private health insurance by their employers.

� Public healthcare is freely available all residents in South Africa. Those with private health insurance have the option of choosing public health facility over private facility with no additional cost implication. The NIDS data however indicates that only 4% of private insurance holders consulted public health facility (this is explained by the limited nature of some insurance packages ). On the contrary, over 22% of non-insurance holders were willing to incur costs to access private facility. This can be construed as a preference for private health facility over public health facility.

• Please substantiate statement “the results confirm that access to quality healthcare is a privilege in South Africa”. It would be helpful if you could expand on how the quality of healthcare was conceptualised in this study and how implemented in this analysis?

� References have been added that indicate higher satisfaction level for private health users as compared to public health users.

Introduction

• Suggest adding a section that explicitly describes the characteristics of the South African health system. With an emphasis on the arrangement of and funding of health services between the public and private health system.

Added

• If preferences for services (and how this intersects with affordability) is a thread that you would like to further explore in your work, then I suggest that this needs to be unpacked in more detail

Added

• It would be helpful to explicitly discuss out-of-pocket payments as one of the funding mechanisms for healthcare in South Africa – and how the burden of out of pocket payments affects people from different socio-economic status groups who are accessing health services from the public – and private health sectors. Perhaps you can also further expand on the funding for private health care services, which will include private health insurance, but also out of pocket payments? And perhaps a bit more on the range of benefit packages offered by various private health insurance schemes. For example, would households experience the same burden of out-of-pocket payment irrespective of the insurance scheme that they belong to? This would set up the introduction to your study better.

o This is an important matter as all insurance schemes do not offer the same coverage. The NIDS data however do not offer the details on medical insurance coverage and this is cited in the limitation of the study.

On page 3:

o In the figures related to medical aid coverage quoted, please specify enrolment in health insurance by type of insurance.

o As indicated above, the NIDS data does not give details on the type of insurance

o Please provide a reference for the poverty rates quoted, and a definition for how poverty was defined here.

� Added.

o Would be helpful to update the current developments related to the National Health Insurance in South Africa, especially the White Paper. Could you please reference the statement that the delays are related to sufficient public funding?

� Referenced and added some updates.

o Could you expand on your literature review related to vulnerability and food expenditure? In particular, could you unpack the direction of the relationships that have been explored in the literature you reviewed? One review to perhaps consider/ that may assist in identifying additional literature sources is Russell. 2004. The economic burden of illness for households in developing countries: a review of studies focusing on malaria, tuberculosis and HIV. In his review, he found that some of the studies relying on primary data collection, showed that household expenditure on food may increase when ill – but I think the question as to whether this may mean that there are intra-household shifts in food expenditure is less clear.

� Included discussion on increased food expenditure due to “special food” expenditure to assist with quick recovery of patent. Also introduced discussions on “medical poverty trap”.

o The last paragraph before section 2, starting with “Given the binary nature of the dependent variable…”, would probably best fit in your main results section.

� Moved accordingly.

o On the definition of health shocks, I agree with the use of reductions in BMI as a measure, following the work of Wagstaff in Vietnam, however it would be helpful to expand a bit on your explanation of this measure. Could you say something about how relevant the work from Vietnam is likely to be to South Africa? Is this measure transferable and relevant, especially given the mixed evidence related to association and trend in South Africa between poverty and type II diabetes which is associated with higher BMIs? For example, Mutyambizi et al. 2019. Lifestyle and socio-economic inequalities in diabetes prevalence in South Africa: a decomposition analysis – find some evidence of an association between diabetes and higher SES in South Africa. However, Ataguba in an analysis of panel data, found a reduction in this trend over time. Is the focus on non-obese household heads, an attempt to control for the association with Diabetes, if so, please specify?

� By excluding the obese individuals we have been able to account for the high proportion of obesity in South Africa resulting from various factors including diabetes and also unhealthy diet. The association of both diabetes and food habits with SES is debatable. This is further explained in the study for clarity.

On page 6:

o Under econometric strategy, could you please explain here in which ways your study used a quasi-experimental data design?

� Explained under Methods section.

On page 7:

o Given, the audience, it would be helpful to rewrite the sentence beginning with “Furthermore, given the panel data structure…” in simpler language. Especially, given that reverse causality could be used to wither mean that the direction of the cause and effect relationship is here the opposite of what one would normally consider, OR that there is a two-way causal relationship. I think that you mean the latter, but it would be helpful to be clear.

� Attempted to rephrase.

o With your difference-in-difference explanation, could you first succinctly explain what the first difference being assessed is; and then the second. An additional explanation would again assist readers without an economics background to understand your study better. Ideally this explanation should come before the equation (number 3) on page 7. Could you add a sentence to explain why you are using a non-linear DiD specifically with your question in mind?

� Explained when introducing DID and added the sentence.

On Page 10:

o Here, it would be worth reminding the reader that a health shock is defined as a significant reduction in BMI.

� Reminded the reader what health shock is.

On Page 11:

o Table 2 would read better if you presented the comparison between Treated and Control in columns.

� Done

o Please be consistent in reporting the number of decimal points.

� The within reporting are kept them at 4 decimals, however, for money values it made sense to keep them at 2 decimals. However, in body decimals are restricted to 2 decimals.

o Would suggest defining food shocks and how measured in the text here.

� Done

o In the table/ below the table, it would be helpful to repeat the definition of who the treated and control groups are again, i.e. those with a health shock vs those not.

� Defined it before the table.

Page 12:

o Remind the reader what a quintile one household is in the text. While it seems redundant, it really assists the readability.

� Included the explanations

o The reporting of changes between the groups could be improved by being consistent in reporting the decimal positions.

� Within table is kept at 4 decimal places except for monetary values.

o Could you be specific about the threshold or the SA government pension grant (plus add a reference). How does that relate to the mean household income for each of your quintiles?

� Added reference and related it to the household income.

Page 13:

o In Table 3, again, it might be helpful to put the comparators (i.e. quintile 1 and quintile 2) on the horizontal axis; with the sample characteristics reported on the vertical axis.

� The comparators are on the horizontal.

o In your labelling, for each of the variables, it would be helpful if you could also specify the time period. So, for example, total medical expenditure per month? Per annum? In addition, please make sure that you report to the same number of decimal points.

� The expenditure values are for the “last 30 days” of the survey period. Mentioned that in text.

o Page 13 (Table 3), could you please also discuss the finding that richer households has much greater total medical expenditure?

� Included possible explanation for this.

o Is “Head education attainment” a categorical variable? Or number of years of schooling? Please, clarify in the text.

� In regression analysis education attainment is a Dummy variable for each level (no education, primary, secnoday, matriculation, technical, university). In summary tables “Head educational attainment” is in years of education (cardinal variable). Details Included in variable definition in appendix.

o It would be helpful to also explain how medical aid coverage is defined i.e. does this include all patients who are accessing any form of health insurance whether it is full coverage or hospital only?

� NIDS data (manual and surveys) does not clarify the kind of coverage this is. Whether it partial or full coverage. This is a limitation and can explain why a small proportion of medical insurance holders access public health facilities.

Tables 5, 6 and 7:

o For the reader, it would really improve the ability to see what is going on in the tables if you would add what the comparators are that are being reported.

� Now Table 4,5,6. The comparators are added, and to try to make it clearer, titles have been adjusted to show which models each table corresponds to. And removed the numbering within tables.

Page 14:

o It would be helpful to substantiate from your results, why you conclude that “higher income households respond to health shocks by reallocating household spending towards the sick household member…”

� cited additional literature to explain this

o You may need to expand on how your results show that “higher income households have a preference to access better quality private sector healthcare”.

o Could your finding that poorer households do not have a reduction in food expenditure in the face of health shocks not also be explained by the protective effect of fee-free public health services in South Africa?

� Yes, public health facilities are free in South Africa to all residents irrespective of Income criteria. However 14% of quintile 1 individuals without medical insurance seek private healthcare, 37% percent of quintile 2 individuals with medical insurance are accessing healthcare in private facility. In other words, 48% of quintile 2 individuals who access private health facilities, do not have medical insurance. This is indicative of preference for private over public healthcare.

medical aid public private other

No % 84 13.93 2

q1 Yes % 43 56.76 0

All % 84 14.64 2

No % 62 37.28 1

q2 Yes % 7 92.39 1

All % 45 54.23 1

o Agree that this affect might be mediated by informal community support systems, and donations, but could it also be affected by labour behaviour. In the absence of out of pocket costs for healthcare, we would suspect that the largest cost contributor to health shocks would be loss of income. However, perhaps in South Africa (and especially in those informally employed) it might be that even significant reductions in BMI would not lead to a loss of income.

It is possible, but not certain, that health shock leads to a loss of income which in turn could result in food shock. This was controlled in the covariates using the dummy variable for employed individuals.

Page 19:

o The discussion/ conclusion section is well explained and points well argued.

Response TO:

Reviewer #1: Laura Rosu

Reviewer #1: This paper analyses the relationship between health shocks in heads of households and household vulnerability in South Africa, using food expenditure as an indicator for vulnerability. The paper defined ‘health shock’ as a decline in BMI of more than one standard deviation in a non-obese head of household. The paper is well structured and easy to read.

Comments:

1) The authors assume that quality care can only be achieved in a private setting. An explanation for this is needed, as well as references.

� Included the following: The Parliamentary Monitoring Group in 2016 reported after an inspection of 10% of public health faclites that “Common to all facilities were challenges regarding patient safety being compromised, good pharmacy practice (GPP) not being adhered to, waste mismanagement, lack of cleanliness, as well as poor maintenance of grounds and equipment” (1). Other studies comparing public and private facilities (2) highlight the major gap between private and public healthcare in South Africa. Public healthcare has many disadvantages such as long wait times, poor quality of care, rushed apppointments, old facilities, and poor disease control and prevention practices. Private healthcare on the other hand is expensive, but has short wait times, quality care, better facilities, adequate resources available, appointments are not rushed, and proper disease control and prevention practices are utilized. Another study among older South Africans

(3) indicates that healthcare responsiveness perception was higher in private than in public inpatient and outpatient healthcare facilities.

1) PMG (2016) Public Health Facilities audit results: Office of Health Standards Compliance (OHSC) briefing, Parliamentary Monitoring Group, South Africa. https://pmg.org.za/committee-meeting/22233/

2) Young M ( 2016) Private vs. Public Healthcare in South Africa, Honours Theses Paper 2741, Western Michigan University. https://scholarworks.wmich.edu/cgi/viewcontent.cgi?article=3752&context=honors_theses

3) Karl Peltzer & Nancy Phaswana-Mafuya (2012) Patient experiences and health system responsiveness among older adults in South Africa, Global Health Action, 5:1, DOI: 10.3402/gha.v5i0.18545

2) On page 3, the authors state that 80% of South Africans are likely to be vulnerable to a sudden health shock. It is not clear how this percentage was calculated. Is this represented by the 100%- 16.9%= 83.1% of individuals not covered by medical aid in 2017? If yes, it should be made clearer.

� Yes, made clearer.

3) BMI decline in non-obese individuals was defined as a health shock. However, this is a measure sensitive to other factors as well. BMI can also change as a result of ageing, change in lifestyle, seasonality (was the data collected during the same period of the year?), etc. It might be worth exploring this in a sensitivity analysis or recognising it as a limitation in the conclusion/discussion section.

� Included it as a limitation in the discussion section.

4) Make sure all tables are referenced in text.

� Tables are referenced in text.

5) Last paragraph, page 11- it is not very clear if participants covered by medical aid experienced health shocks. If the answer is yes, it might be worth adding a few comments explaining possible reasons for this.

health

shock medical aid dummy

dummy 0 (%) 1 (%) Total

0 1,305 455 1,760

(97.75) (97.85) 97.78

1 30 10 40

(2.25) (2.15) 2.22

Total 1,335 465 1,800

(100) (100) 100

� Included the following: There is no significant difference in the proportion of sample that experienced health shocks by their medical insurance status. Evidence in literature is mixed in terms of the effect of medical insurance on health status, with a recent review (1) indicating that of the 12 studies: nine studies found a positive effect, one study reported a negative effect, and two studies reported no effect. Therefore the indication that medical insurance does not make a substantial impact on health shock is not contradictory from existing evidence.

(1) Erlangga D, Suhrcke M, Bloor K, Ali S (2019) The impact of public health insurance on health care utilisation, financial protection and health status in low- and middle-income countries: A systematic review. PLoS ONE 14(8): e0219731. https://doi.org/10.1371/journal.pone.0219731

6) The authors comment throughout the paper that lower income households are not reducing their food consumption as a result of a health shock. However, there might be other explanations for this, such as consumption of basic food from own production, that is unaffected by illness. To account for this, it might be worth dividing food consumption into food purchased and food produced. Looking at the questionnaire for Wave 5 I think this is possible.

� We introduced an additional binary variable among the covariates to control for food production by the household. The coefficient of the new variable is insignificant and does not change the results of our key variables. This is not surprising because just over 5% of the sample reported some element of home production (either agricultural or animal related produce) of food. The results are reported in table below but we prefer to use the original model keeping in mind the principle of parsimony in model specification.

(All sample) (Quintile 1) (Quintile 2)

VARIABLES food shock food shock food shock

postperiod 0.449** 0.655*** -0.0234

(0.180) (0.198) (0.365)

treatmentA -1.552*** -1.117* -2.714**

(0.550) (0.598) (1.249)

treatpost 2.345*** 1.348 5.786***

(0.861) (0.959) (1.713)

w5_h_fdprd -0.0272 0.349 -0.489

(food production) (0.215) (0.406) (0.631)

medicaid -0.402 -1.851* 0.155

(0.320) (1.021) (0.424)

female -0.352** -0.638*** 0.113

(0.179) (0.200) (0.308)

married 0.124 0.382 -0.787

(0.234) (0.259) (0.503)

ageb 0.0569 0.137 -0.281

(0.339) (0.452) (0.490)

agec -0.190 -0.237 -0.172

(0.385) (0.495) (0.621)

aged 0.439 0.126 1.368

(0.582) (0.587) (0.938)

educb 0.464* 0.481* 0.118

(0.258) (0.274) (0.868)

educc -0.0891 -0.124 -0.315

(0.309) (0.349) (0.767)

educd -0.212 0.141 -0.576

(0.414) (0.472) (0.898)

educe 0.483 1.091 0.300

(0.426) (0.751) (0.813)

educf -1.176 -1.406

(0.836) (1.151)

hhsb 0.689*** 0.391* 0.810*

(0.191) (0.221) (0.424)

hhsc 0.608** 0.278 1.015*

(0.240) (0.259) (0.614)

hhsd 0.886*** 0.518 2.853**

(0.316) (0.343) (1.124)

pensionrecp -0.696* -0.462 -0.795

(0.377) (0.317) (0.825)

employment -0.296 -0.232 0.894*

(0.203) (0.231) (0.505)

urban 0.234 0.271 0.311

(0.205) (0.265) (0.352)

toileth -0.171 -0.186 0.101

(0.216) (0.270) (0.411)

waterp 0.0885 0.123 -0.0936

(0.265) (0.270) (0.608)

Constant -2.405*** -2.793*** -2.779

(0.586) (0.953) (1.777)

Observations 4,771 3,067 1,701

Submitted filename: Response to Reviewers.docx

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7 Jan 2020

Health Shocks, Medical Insurance and Household Vulnerability: Evidence from South Africa

PONE-D-19-21685R1

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Additional Editor Comments (optional):

The reviewers and I have accepted your manuscript for publication in PLOS One and would like to take the opportunity to congratulate you on your important work. The only comment by one of the reviewers that should be addressed during the production stages is: "Please provide a reference for the statement that private health care in South Africa is of good quality. "Private healthcare on the other hand is expensive, but provide quality facilities and care." Quality is a highly subjective description. Given the measuring yard used for public health facilities in that same paragraph, and the statement is contrasting the two - has a similar (independent) audit been conducted on private health care facilities? If so, it would provide balance to that assessment if such work could be quoted and referenced in the text. Alternatively, would suggest rephrasing the statement.

We look forward to seeing your research in print.

Yours sincerely,

Tom Wingfield

Reviewers' comments:

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Reviewer #1: The authors have nicely addressed all comments, so I have no further comments.

Reviewer #2: Please provide a reference for the statement that private health care in South Africa is of good quality. "Private healthcare on the other hand is expensive, but provide quality facilities and care."

Quality is a highly subjective description. Given the measuring yard used for public health facilities in that same paragraph, and the statement is contrasting the two - has a similar (independent) audit been conducted on private health care facilities? If so, it would provide balance to that assessment if such work could be quoted and referenced in the text. Alternatively, would suggest rephrasing the statement.

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Reviewer #1: Yes: Laura Rosu

Reviewer #2: Yes: Nicola Foster

21 Jan 2020

PONE-D-19-21685R1

Health Shocks, Medical Insurance and Household Vulnerability: Evidence from South Africa

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