Hospital costs attributable to obesity, diabetes, and hypertension in COVID-19 patients in South Africa

Abstract

Background:

The Coronavirus disease (COVID-19) pandemic has highlighted the inequities that exist in many countries worldwide. Access to health services and the cost of services due to the rise in the number of COVID-19 cases are some of the issues that countries have had to contend with. Those with pre-existing conditions such as hypertension, obesity and diabetes have had to bear the brunt of the COVID-19 crisis.

Objectives:

The aim of this study is to estimate the hospital costs attributable to obesity, diabetes and hypertension in COVID-19 patients in South Africa.

Design and Methods:

A prevalence-based disease-specific cost of illness approach was conducted to estimate the direct medical costs of hypertension, obesity and diabetes in COVID-19 patients. The population attributable fraction was computed and multiplied by the total treatment cost of COVID-19. A total of 78,464 hospital admissions were included based on data collected for Wave 1 (D614G variant) between June and August 2020.

Results:

The direct healthcare costs attributed to hypertension in COVID-19 admissions were estimated to be approximately US$2.7 million. The total costs of admissions attributed to obesity were estimated to be approximately US$1.2 million. The cost attributable to diabetes was estimated to be approximately US$1.7 million across the public sector wards.

Conclusion:

COVID-19 patients with an additional diagnosis of hypertension, obesity and diabetes have shown to exert a heavy financial burden on South Africa’s healthcare system. The study emphasizes the importance of investing in the prevention of non-communicable diseases (NCDs) as a key component of future pandemic planning and response strategies. Preventing underlying conditions such as NCDs can decrease costs and mortality, and help populations better withstand future pandemics.

Significance for public health

• This is the first study to quantify the direct healthcare costs of hospitalized COVID-19 patients attributable to hypertension, obesity and diabetes in South Africa.

• This study significantly contributes to the literature by estimating the share of healthcare cost of COVID-19 resulting from hypertension, obesity, and diabetes among COVID-19 patients utilizing public hospitals.

• The study emphasizes the importance of investing in the prevention of non-communicable diseases in light of the COVID-19 pandemic. The analysis shows that non-communicable disease investments should be a key component of future pandemic planning and response strategies.

Introduction

Since 2020, much of the world’s attention has been focused on the Coronavirus disease (COVID-19) and the massive health and economic devastation caused by the virus. ¹ COVID-19, however, is far from the only health concern confronting the world today. The pandemic has exposed a direct relationship between communicable and non-communicable diseases (NCDs). ² The infectious disease, COVID-19, puts people with NCDs at a higher risk of more severe disease and death. The pandemic has wreaked havoc on the preventive and treatment services of NCDs ² and has threatened to reverse multiple development gains in recent decades. ³ According to the World Health Organization (WHO), ² nearly 75% of countries reported delays in accessing NCD services as a result of the COVID-19 pandemic.

Worldwide, healthcare costs continue to rise rapidly, putting a strain on national budgets.^4,5 The economic burden of NCDs could undermine healthcare systems’ long-term viability by worsening the health of patients with chronic illnesses. NCDs have been shown to increase poor health outcomes in patients with COVID-19. ^{6 2} Despite the detrimental long-term effects of NCDs on people and economies, NCD prevention and control has received very little investment in many low-income countries (LIC’s) and low-and middle-income (LMICs). Globally, 85% of all NCD-related premature deaths occur in low- and middle-income countries. ² Aside from the human toll, these countries are the hardest hit by NCD-related economic losses. Premature deaths rob people of their most productive economic years, lowering human capital and production while raising costs associated with severe sickness, disability, and death, thus taking a heavy toll on healthcare spending and making it harder for countries to develop.

South Africa will be the discussed case study as it is a nation with significant health inequities and a steep burden of infectious and non-communicable disease. ⁷ NCDs account for 51% of all deaths in South Africa. ⁸ Based on the first 99 days of the COVID-19 pandemic in South Africa, NCDs such as hypertension and diabetes were the most common comorbidities among COVID-19 victims. ⁹ Individuals with pre-existing conditions such as obesity, hypertension and diabetes, and other NCDs bore the brunt of the COVID-19 crisis.

Globally, there are significant knowledge gaps about the health and economic costs of hypertension, obesity, and diabetes in COVID-19 patients. Whilst the effect of conditions on severe outcomes has been expressed, it is still not yet clear what the cost implications are in the context of the COVID-19 pandemic. Studies estimating the economic burden of diabetes mellitus in the African region have established that diabetes exerts a heavy economic burden on African countries.^10,11 A South Africa specific study aimed to estimate the direct medical costs associated with type 2 diabetes mellitus (T2DM) in the public health sector, showed that in 2018, the public sector costs of diagnosed T2DM patients were approximately ZAR 2.7 bn (US$ 165 million) and ZAR 21.8 bn (US$ 1 billion) if both diagnosed and undiagnosed patients are considered, thus also imposing a significant financial burden on the public healthcare system in South Africa. ¹⁰ In addition, a South African context-specific study quantified the health and economic burden of hypertension in the public healthcare system and concluded that hypertension also imposes a substantial economic and health burden on the nation. The direct healthcare costs associated with hypertension were estimated to be ZAR 10.1 bn (US$0.711 billion). ¹² Obesity and overweight have also been shown to impose a significant economic burden on healthcare costs.^13–15 A report that estimated the healthcare cost associated with the treatment of weight-related conditions from the perspective of the South African public sector quantified a total estimated cost of ZAR 33,194 million (US$ 2 million) for overweight and obesity in 2020. ¹³ Okunogbe et al. conducted a cost-of-illness study to quantify the economic impact of overweight and obesity across eight countries, including South Africa. ¹⁴ The main conclusion of this study was that there is indeed a substantial economic impact of obesity across the geographical contexts and the need for advocacy and awareness of the societal impact. ¹⁴

Even though prior research has estimated the healthcare cost of COVID-19, these studies have not quantified how much of the medical costs of COVID-19 are attributable to NCDs such as hypertension and diabetes. According to our knowledge, this is the first study to quantify the direct healthcare costs of hospitalized COVID-19 patients attributable to hypertension, obesity and diabetes in South Africa. This study significantly contributes to the literature by estimating the share of healthcare cost of COVID-19 resulting from hypertension, obesity, and diabetes among COVID-19 patients utilizing public hospitals. For our analysis, a prevalence-based cost-of-illness approach was adopted. ¹⁶

Methods

Data

This study used hospital surveillance data provided by South Africa’s National Institute for Communicable Diseases (NICD). ¹⁷ The NICD is a national public health organization that supports the government’s response to communicable disease threats by providing disease surveillance, specialized diagnostic services, outbreak response, public health research, and capacity building in the health sector. The NICD has established surveillance systems to collect and analyze hospital-based admissions data. These systems can use a combination of manual and electronic data collection methods to monitor and report on COVID-19 admissions in real-time. The hospital-based admissions data and summary analyses are provided by DATCOV, ¹⁷ which is used to support national modeling and reporting. Data on hospitalization was collected for Wave 1 (D614G variant) which lasted 76 days between June and August 2020. ¹⁸ A total of 78,564 hospital admissions were included (Table 1). Of the total admissions, 87% (n = 68,451) ended up in the general ward, while 6% ended up in the high-care ward (n = 4375) and 7% ended up in the ICU (n = 5738). ¹⁷ The direct healthcare costs were obtained from the Edoka et al. ¹⁹ costing study, which calculated the COVID-19 inpatient care costs per patient, per day in South Africa’s public hospitals in 2020.

Table 1.

Ward admission of COVID-19 patients with at least one comorbidity during Wave 1 (Source: NICD, 2022).

Ward types	Ward admission
General ward	68,451.00
High-care ward	4375.00
Intensive care unit (ICU)	5738.00
Total admission	78,564.00

By concentrating on the peak of the first COVID-19 wave, the authors aimed to understand the immediate economic impact, especially in healthcare, due to the rapid surge in COVID-19 cases. Given the limited resources and the availability of relevant data, this approach allowed the authors to give timely insights important to the crisis management and decision-making.

Data analysis

A prevalence-based disease-specific cost of illness approach was conducted to estimate the direct medical costs of hypertension, obesity and diabetes in COVID-19 patients. The general conceptual framework on the costs of the comorbidities was based on the OBCOST step-wise methodology ¹⁶ (Figure 1). The OBCOST (Obesity Costing) tool utilizes a top-down, 5-step prevalence-based method to answer the question: what current healthcare costs could have been avoided if the comorbidities mentioned above had been eradicated? The OBCOST methodology principles were tailored to the objectives and questions of this study. Figure 1 depicts the steps followed to calculate COVID-19 costs attributable to the three conditions. ¹⁷

Figure 1.

Methodological approach for costing COVID-19 attributable to Hypertension, Obesity and Diabetes (HOD).

Derive the prevalence of the comorbidities and COVID-19 infection

The prevalence rate of diabetes, obesity and hypertension was computed based on the provided hospital admission data from the NICD. ¹⁷ The NICD provided us with the published aggregated data of admissions during wave 1 of the pandemic. The prevalence of each condition was computed based on total hospital admissions with COVID-19 (Table 3). Prevalence quantifies the proportion of people in a population who have the disease at a specific point in time. The equation is multiplied by 100 to depict the prevalence in percentages.

Table 3.

Prevalence, relative risk and population attributable fraction of hypertension, obesity, and diabetes in COVID-19 patients.

Conditions	No. of existing cases	Prevalence	RR	PAF
Hypertension	21,998.00	28.0%	1.08	2.19%
Obesity	2143.00	2.7%	1.37	1.01%
Diabetes	15,968.00	20.3%	1.07	1.40%

RR: relative risk; PAF: population attributable fraction.

The authors computated the prevalence of the conditions based on hospital admissions during wave 1 rather than the population-based prevalence data because the hospital admission data provided a more specific and direct representation of individuals who require medical care, especially during the initial wave of the COVID-19 pandemic when hospitalizations were most prevalent. Furthermore, by leveraging hospital admission data, the severity and acute management of COVID-19 cases were accounted for by distinguishing across the ward types, which might not be fully captured in population-based prevalence estimates. This approach ensured a more nuanced and context-specific prevalence of the comorbidities and COVID-19 infection allowing for a focused analysis on the subset of the population directly impacted by COVID-19, thus offering more accurate estimations of the healthcare costs associated with comorbidities.

Derive the relative risks for each comorbidity

The relative risk (RR) of contracting COVID-19 and associated severe complication given exposure to underlying conditions (obesity, diabetes and hypertension) was derived from a previously published study in the United Kingdom (UK) ²⁰ due to lack of South Africa specific data. The RR is a ratio of the probability of the event (acquiring COVID-19 infection) occurring in the exposed (hypertension, obesity, diabetes) group to the probability of the outcome occurring in a non-exposed (no hypertension, obesity and diabetes) group. The UK study focused on the risk of hospitalization with obesity as well as hypertension and diabetes. The data on COVID-19 hospitalization status were obtained from hospitals in England covering the period from 16 March 2020 up to 26 April 2020. ²⁰ The UK relative risk of the COVID-19 data can be transferred to a South African context during the same wave due to similarities in underlying demographics of the population, population factors as well as timing and public health response. The decision to utilize relative risks from a UK-based study for the South African context is supported by the global prevalence of risk factors such as hypertension, obesity and diabetes. The authors acknowledged contextual disparities, but contend that certain population factors, including the prevalence rates of these risk factors, may exhibit comparability between the two regions, justifying the extrapolation of RR estimates. The overlapping dates of data collection for DATCOV and the UK admissions further supported the adoption of the RR estimates.

Determine the proportion of COVID-19 cases/costs attributed to the co-morbidities

The Population Attributable Fraction (PAF) was employed to estimate how much of the hospitalized COVID-19 infections were attributable to the conditions. The basic idea of PAF is to estimate the proportion of outcomes in a given population that would theoretically not have occurred if none of the individuals had been exposed to the risk factor. ²¹ The PAF therefore indicates the proportion of the COVID-19 hospitalized admissions that is attributable to hypertension, obesity or diabetes. Equation (1), adapted from previous studies was used. ²² PAF was estimated for the Hypertension attributable fraction (HAF), Obesity attributable fraction (OAF) as well as the diabetes attributable fraction (DAF) in relation to COVID-19. The analysis was performed in Microsoft Office Excel (2016) as a worksheet-based model.

$$Population-attributablefraction=\frac{Pe\left(RR-1\right)}{Pe\left(RR-1\right)+1}$$ (1)

Equation (1) illustrates the PAF, where Pe is the prevalence of the exposure (hypertension, obesity or diabetes) and RR is the relative risk of disease (COVID-19) given exposure.

Estimation of the costs

The economic daily costs across the different wards per COVID-19 patient and the average length of hospital stay per ward was obtained from Edoka et al. ¹⁹ The economic costs were chosen instead of the financial costs as the daily economic costs per COVID-19 patient include a wide range of resources comprising of consumables, capital equipment, and human resources (see Supplemental Appendix A).

The inpatient costs were estimated across three levels of inpatient care, including general wards, high care wards, and intensive care units (ICUs). Three analyses were conducted as displayed in Table 2. One analysis assumes that all cases are admitted in the general ward, the second analysis is based on those admitted in the high-care ward and the other is solely based on the intensive care unit. In this analysis one intensive care unit was computed by aggregating all three ICU categories as follows: non-invasive ventilation (NIV), continuous positive airway pressure ventilation (CPAP), as well as invasive mechanical ventilation (IMV). Resource inputs included all equipment and items that are non-durable and cannot be reused (needs regular replacement), such as diagnostic tests, therapeutic agents (antibiotics), respiratory support and accessories (oxygen face masks) and hygiene infection and control (liquid hand soap, hand rub). Capital equipment included all resources with a useful life of more than 1 year. This included beds, bed linen, ventilators, medical devices, and respiratory support accessories. Human resources included the clinical and non-clinical staff responsible for the care of COVID-19 patients, such as support staff, medical staff, nurses, and allied health professionals. ¹⁹ The average length of hospital stay was also obtained from the Edoka et al., ¹⁹ which was based on the South African COVID-19 Hospital Sentinel Surveillance database (2020). ¹⁹

Table 2.

Daily economic costs and the average length of hospital stay per ward among COVID-19 patients.

Ward type & respiratory support	Average length of hospital stay	Economic daily costs (USD)
General wards	8	$ 128.46
High-care wards	7	$ 277.80
NIV (ICU)	7	$ 828.99
CPAP (ICU)	7	$ 811.11
IMV (ICU)	16	$ 797.89

Source: Edoka et al. ¹⁹

NIV: non-invasive ventilation; CPAP: continuous positive airway pressure ventilation; IMV: invasive mechanical ventilation.

Total healthcare costs of COVID-19 patients with underlying NCDs (hypertension, obesity, diabetes)

In order to obtain the hospital costs attributed to hypertension, obesity and diabetes in COVID-19 patients, the individual hospital costs for each ward given the average length of hospital stay (THCLoS) was computed by multiplying the average length of stay in each ward by the average daily costs of COVID-19 patients. Secondly, the total hospital costs were obtained by multiplying the total costs per ward by the total ward admissions. Lastly, the inpatient healthcare costs attributed to hypertension, obesity and diabetes in COVID-19 hospitalized patients were computed across the different wards by multiplying the total hospital costs (THC) of COVID-19 (THC-COVID-19) by the corresponding population attributable fraction (PAF) of each comorbidity.

Sensitivity analysis

For sensitivity analysis we varied the model parameters (i.e. prevalence and costs) simultaneously. The number of people with the condition and the cost per day were varied by ±20% (relative changes) of their baseline values. The choice of ±20% is based on previous costing study in South Africa. ¹⁰

Results

Prevalence rate, relative risks, and the population-attributable fractions

Table 3 shows the results of the estimated prevalence, relative risk and population attributable fractions. Twenty-eight percent (n = 21,998) of patients diagnosed with COVID-19 had hypertension while 2.7% (n = 2143) had obesity. Diabetes was recorded in 20.3% (n = 15,968) of the COVID-19 admissions. PAF estimates indicated that about 2.19% of all COVID-19 admissions were attributable to hypertension, 1.01% attributable to obesity and 1.40% attributable to diabetes.

Direct medical cost attributable to hypertension, obesity, diabetes in COVID-19 admissions

Table 4 shows the total hospital costs given the average length of stay for a COVID-19 admissions across the wards. The total cost of admission for a COVID-19 patient in a general ward was equivalent to US$ 1027.68; while high-care and ICU were US$ 1944.60 and US$ 7978.90 respectively. The total healthcare costs of all COVID-19 patients in the general ward was US$ 70 million, whereas the total cost for the high-care ward was approximately US$ 8 million. Lastly, the total cost for the ICU was approximately US$ 46 million. The total hospital costs of all COVID-19 admissions across all the wards was approximately US$ 125 million.

Table 4.

The hospital costs of COVID-19 admissions across the different wards.

Ward type	Total costs per patient	Total hospital cost
General wards	$1027.68	$70,345,723.68
High-care wards	$1944.60	$8,507,625.00
ICU	$7978.90	$45,782,928.20
Grand total		$124,636,276.88

In total, across all the wards, the estimated healthcare cost of COVID-19 admissions attributable to hypertension was US$ 2.7 million (Table 5), while obesity incurred approximately US$ 1.2 million to the healthcare system. Likewise, the healthcare costs of COVID-19 attributable to diabetes was US$ 1.7 million. Comparing across the hospital wards, general wards incurred, US$ 1.5 million for COVID-19 patients due to hypertension. A total of approximately US$ 708 thousand was attributable to obesity in COVID-19 admissions in the general ward.

Table 5.

Costs attributed to hypertension, obesity and diabetes in COVID-19 patients.

Conditions	General ward	High-care ward	ICU	Total
Hypertension	$1,541,226.35	$186,396.20	$1,003,072.42	$2,730,694.97
Obesity	$708,420.86	$85,676.55	$461,059.74	$1,255,157.15
Diabetes	$ 984,822.37	$119,104.60	$640,949.43	$1,744,876.41
Grand total				$5,730,728.52

The inpatient cost of COVID-19 admissions with diabetes resulted in an additional cost of approximately US$ 984 thousand to the South African healthcare system. In the high-care ward, US$ 186 thousand was attributable to hypertension in those admitted to hospital because of COVID-19. Around US$ 85 thousand was attributable to obesity in COVID-19 patients in the high-care ward, while the cost attributable to diabetes was equivalent to US$ 119 thousand. In the ICU, approximately US$ 1 million was attributable to hypertension, US$ 461 thousand to obesity and US$ 640 thousand was attributable to diabetes.

Sensitivity analysis

Table 6 displays the results from the sensitivity analyses. We utilized uncertainty intervals with 20% lower and upper bound, to show the effect of the uncertainty of the parameters. The overall cost attributable to hypertension in COVID-19 patients in South Africa was estimated to be between US$1.4 million and US$4.7 million. The total cost attributable to obesity was between US$638 thousand and US$2 million across all the wards. While the total cost attributable to diabetes was estimated to be between US$897 thousand and US$3 million.

Table 6.

Costs attributed to hypertension, obesity and diabetes in COVID-19 patients (March’20–August’20).

Conditions	Total	Lower estimate	Upper estimate
Hypertension	$2,730,695	$1,404,269	$4,698,055
Obesity	$1,255,157	$638,884	$2,147,634
Diabetes	$1,744,876	$897,686	$3,012,736
	$5,730,729	$2,940,839	$9,858,425

Discussion

The aim of this study was to to estimate the direct hospital costs attributable to obesity, diabetes and hypertension in COVID-19 patients in South Africa. We analyzed the impact of COVID-19 from June to August 2020. This time frame included South Africa’s first lockdown, which imposed the toughest restrictions, and the first wave, which peaked in July 2020. ²³

Our study estimated the healthcare cost of treating 78,564 COVID-19 patients with an underlying health condition in public hospitals in South Africa. The analysis shows that a significant part of the cost of treating COVID-19 patients were due to hypertension, obesity and diabetes. Because NCDs increase the risk of infection, hospitalization and death from COVID-19, ²⁴ COVID-19 admissions were exacerbated by NCDs. The results are expected since hypertension and diabetes are the most common comorbidities associated with severe illness or death in COVID-19 patients. ²⁵ It has also been established that obese people have a higher risk of admissions to general hospital or ICU, higher need for ventilator support, and have increased mortality from influenza ²⁶ to which similar findings have been reported for COVID-19 and obesity. ²⁷ In addition, Hamer et al. ²⁰ conducted a large-scale general population study in England using data from a community-dwelling sample with prospective linkage to a COVID-19 hospitalization registry. It was established that obesity is a risk factor for COVID-19 hospitalization. There was an increase in risk even with minor weight gain. One of the mechanisms could be impaired glucose and lipid metabolism. ²⁰ In South Africa, findings from virtual cohort study in the Western Cape Province show that people living with diabetes are more susceptible to COVID-19 hospitalization and even death. ²⁸ This demonstrates another way that NCDs impose a huge financial burden on the health system. Improving people’s health and reducing the burden of NCDs are critical components of mitigating the effects of any health crisis. People with NCDs were more vulnerable to complications and more likely to die from COVID-19 during the pandemic’s acute phase, and they also experienced disruptions in ongoing care. ²⁹

To our knowledge this is the first study quantifying the direct healthcare costs of hospitalized COVID-19 patients attributable to hypertension, obesity and diabetes in South Africa; therefore, the comparison with previous studies is limited. Two European costing studies determined to what extent the higher risk for those with diabetes and obesity may affect the cost of COVID-19 secondary care in Europe during the first wave of the pandemic from January to June 2020. The studies have established that obesity and diabetes are associated with excess costs in COVID-19 patients.^30,31 Furthermore, COVID-19 pandemic was exacerbated by diabetes, obesity and overweight, leading to more severe outcomes for people and higher secondary care costs across Europe.

The results of this study as well as the European reports highlight the importance of emphasizing NCD prevention and treatment, as well as the need to pay close attention to avoiding COVID-19 infection among those who have already been diagnosed. The prevalence of NCDs continues to rise year after year, necessitating targeted public health immunization measures to better protect people with NCDs against COVID-19 and other respiratory viruses. ³² Individuals with NCDs are at a greater risk of severe COVID-19 illness and mortality, thus controlling these NCDs is crucial. ³³ To avoid excess death from the quadruple burden of diseases and limit increases in their incidence during and after the COVID-19 pandemic, the continuity of health promotion, disease prevention, and treatment services must be assured. ³⁴ It is crucial to comprehend the hospital costs of COVID-19 patients with NCDs in order to assess the economic impact of the COVID-19 pandemic on healthcare, to provide key data for risk mitigation and response planning, and to advance understanding of the economic assessment of global health emergencies. ³⁵ Now more than ever, efficient and equitable resource distribution is vital. This will require addressing these underlying health conditions effectively, both in terms of prevention and management, to reduce the overall burden on healthcare systems and optimize resource allocation. By prioritizing strategies to prevent and manage these NCDs, healthcare systems can potentially reduce the economic strain and improve patient outcomes.

Limitations

The biggest constraint in conducting this costing study was limited data. Individual patient level data was not available to estimate the relative risks for hypertension, diabetes and obesity in COVID-19 admissions. Current studies have shown that patients with hypertension, diabetes and obesity are more susceptible to severe COVID-19 complications. ¹⁷ Additionally, the absence of locally derived relative risk estimates for South Africa necessitated the use of data from the UK-based study. The decision was based on the recognition of shared global prevalence of risk factors and comparable population characteristics between countries. While leveraging evidence from diverse settings offers valuable insights, caution should be exercised in generalizing findings to the South African context. Future research should aim to validate these findings using locally derived data to enhance the robustness and validity of the analyses.

The use of attributable fractions also has its own limitations as one assumes only one exposure. For any given disease, there can be several exposures and therefore the approach may overestimate attributable costs. For instance, a patient may be simultaneously suffering hypertension, diabetes and obesity. Another limitation was the collection of obesity data which was not routinely collected and was based on a subjective assessment of the attending health-care worker, instead of the use of reliable tools such as body mass index (BMI). Therefore many patients were unaccounted for which may result in underestimation of costs.

Our analysis also focused on direct costs of managing patients. The costs exclude the indirect costs such as productivity losses from absenteeism, presenteeism and premature mortality, and direct non-medical costs, including travel costs for inpatient and outpatient visits for persons with the underlying condition.

Future studies

Future studies estimating the cost of these diseases among patients with COVID-19 and other infectious diseases should consider utilizing individual-level data. Therefore, patient information from hospitals (administrative data) should be used in future studies to calculate the cost of hypertension, obesity and diabetes. This will reveal the precise amount of resources and clinical methods employed in the treatment or management of the specified comorbidities. Additionally, disaggregating the medical costs of COVID-19 hospitalizations by NCDs across demographics (such as across age, gender and income) will be useful. Lastly, future research should use South African data to assess the relative risk of these diseases.

Conclusion

In an uncertain future, one thing is certain; NCDs will continue to represent a substantial danger to global health unless action is taken. This study demonstrates that diabetes and hypertension which are fueled by obesity impose significant costs on South Africa’s healthcare system, which the nation can ill-afford. In the absence of strong action to prevent the aforementioned NCDs, the prevalence of NCDs in South Africa will continue to accelerate.

The results of this study highlighted the importance of NCD investments as a key component of future pandemic planning and response strategies. The recovery from COVID-19 allows the globe to rebuild, strengthen health security, and preserve the health of those who need it the most. NCDs prevention is a critical component of this response and will help vulnerable populations better withstand future pandemics. Preventing underlying conditions could decrease cost substantially and even mortality.