Integration of point-of-care screening for type 2 diabetes mellitus and hypertension with COVID-19 rapid antigen screening in Johannesburg, South Africa

Alana T Brennan; Beatrice Vetter; Mohammed Majam; Vanessa T Msolomba; Francois Venter; Sergio Carmona; Kekeletso Kao; Adena Gordon; Gesine Meyer-Rath

doi:10.1371/journal.pone.0287794

. 2023 Jul 7;18(7):e0287794. doi: 10.1371/journal.pone.0287794

Integration of point-of-care screening for type 2 diabetes mellitus and hypertension with COVID-19 rapid antigen screening in Johannesburg, South Africa

Alana T Brennan ^1,^2,^3,^*, Beatrice Vetter ⁴, Mohammed Majam ⁵, Vanessa T Msolomba ⁵, Francois Venter ⁵, Sergio Carmona ⁴, Kekeletso Kao ⁴, Adena Gordon ¹, Gesine Meyer-Rath ^1,^2,⁶

Editor: Mobolanle Balogun⁷

PMCID: PMC10328308 PMID: 37418394

Abstract

Aims

We sought to evaluate the yield and linkage-to-care for diabetes and hypertension screening alongside a study assessing the use of rapid antigen tests for COVID-19 in taxi ranks in Johannesburg, South Africa.

Methods

Participants were recruited from Germiston taxi rank. We recorded results of blood glucose (BG), blood pressure (BP), waist circumference, smoking status, height, and weight. Participants who had elevated BG (fasting ≥7.0; random ≥11.1mmol/L) and/or BP (diastolic ≥90 and systolic ≥140mmHg) were referred to their clinic and phoned to confirm linkage.

Results

1169 participants were enrolled and screened for elevated BG and elevated BP. Combining participants with a previous diagnosis of diabetes (n = 23, 2.0%; 95% CI:1.3–2.9%) and those that had an elevated BG measurement (n = 60, 5.2%; 95% CI:4.1–6.6%) at study enrollment, we estimated an overall indicative prevalence of diabetes of 7.1% (95% CI:5.7–8.7%). When combining those with known hypertension at study enrollment (n = 124, 10.6%; 95% CI:8.9–12.5%) and those with elevated BP (n = 202; 17.3%; 95% CI:15.2–19.5%), we get an overall prevalence of hypertension of 27.9% (95% CI:25.4–30.1%). Only 30.0% of those with elevated BG and 16.3% of those with elevated BP linked-to-care.

Conclusion

By opportunistically leveraging existing COVID-19 screening in South Africa to screen for diabetes and hypertension, 22% of participants received a potential new diagnosis. We had poor linkage-to-care following screening. Future research should evaluate options for improving linkage-to-care, and evaluate the large-scale feasibility of this simple screening tool.

Introduction

Prior to the COVID-19 pandemic, South Africa had entered a health transition in which mortality rates from non-communicable chronic diseases (NCDs) started to surpass mortality rates from infectious diseases [1], with type 2 diabetes mellitus (diabetes) having an estimated adult prevalence of 11% and hypertension at 30% [2, 3]. Challenges like suboptimal screening, diagnosis and management of diabetes and hypertension coupled with poor access to diagnostic tests remains frequent in clinical practice in low- and middle-income countries (LMICs) [4, 5]. As such, improved access to diagnostic tests is an integral component of optimal management of diabetes and hypertension and is necessary for reducing NCD-related morbidity and mortality.

Although possibly transitory in nature, COVID-19 derailed many gains in LMICs in screening, diagnosis and management of NCDs. According to a recent World Health Organization survey of 155 countries, 95% reported employees working in NCD care being reassigned to support COVID-19 efforts, while more than 50% of countries reported postponing public health screening programs for diabetes and hypertension [6]. This impact is of significant concern given that people living with NCDs, specifically diabetes and hypertension, are in turn at higher risk of severe COVID-19-related illness and death [7]. Maintaining screening, diagnosis, quality care and treatment of people with these conditions is critical.

In South Africa, screening for diabetes and hypertension is predominantly done in a primary health care (PHC) setting. The government provides algorithms for health personnel to provide care in a stepwise fashion for both conditions [8]. However, South Africa’s PHC clinics have been facing an unmanageable workload for the decades [9], so adequate screening, diagnosis and care and treatment for people living with diabetes and/or hypertension in this setting is difficult.

The large proportion of the population that has elevated blood glucose and/or blood pressure that are either unaware of their condition or are aware and uncontrolled [10] are partly a results of a poorly managed PHC system. As such, simple and effective community-based routine screening outside of the PHC setting for NCDs can help strengthen local healthcare systems, ease the burden on providers, increase access to healthcare, and improve patient outcomes, by diagnosing individuals quickly and earlier, shortening the time to seek treatment for these conditions [11, 12]. Community-level screening for tuberculosis and HIV has been integral components of standard testing algorithms in sub-Saharan Africa since 2004, but there continues to be a lack of use of screening for NCDs [13, 14].

COVID-19 community screening programs provide temporary opportunities where individuals are already gathered to seek medical care. The objective of our study was therefore to evaluate the feasibility, yield, and linkage-to-care of rapid screening for diabetes and hypertension alongside the existing COVID-19 digital health supported antigen rapid diagnostic test (Ag-RDT) field study [15].

Methods

This prospective cohort study was carried out from 3 August-29 September 2021, embedded into an operational study investigating the use of Ag-RDTs for COVID-19 in busy public transport hubs (taxi ranks) in Johannesburg, South Africa [15]. Participants for our study were recruited from the Germiston taxi rank. After participants were screened for study eligibility, consented, enrolled and had completed the COVID-19 risk score screening, study staff conducted a random glucose test (via glucose meter), and measured blood pressure (taken while the individual was seated via a once-off measurement with a manual blood pressure device with cuff), waist circumference in centimeters (via measuring tape), height in centimeters, and weight in kilograms (via scales and a height measuring rod). All study relevant measures were recorded before nasopharyngeal swabs for COVID-19 testing were administered, eliminating the role these swabs could have had on elevating blood pressure. Fasting was defined as participants who had nothing but water in the previous 8 hours before study enrollment (n = 286, or 24.5%). Participants who had elevated blood glucose (random glucose ≥11.1 mmol/L; fasting glucose ≥7.0 mmol/L [8, 16]) and/or blood pressure (diastolic ≥90 mmHg; systolic ≥140 mmHg [8, 17]) were provided with a written referral indicating the results of blood glucose and blood pressure screening and were encouraged to link to their local PHC for confirmation and management of their diabetes and/or hypertension. A formal laboratory test (glycated hemoglobin A1c (HbA1c), fasting, or random blood glucose) is required to confirm diagnosis, while repeat testing is required to confirm a hypertension diagnosis [8]. Participants referred to their local PHC were contacted once a week for three weeks to confirm if they had visited their PHC for the necessary follow-up.

Informed consent

The principles if informed consent in the current edition of the Declaration of Helsinki were implemented before any protocol-specified procedures or interventions were carried out. The consent form described the purpose of the study, the procedures to be followed, and the risks and benefits of participation. Potential participants had the opportunity to have any questions answered before and after completing the screening questionnaire or the informed consent form. Participants signed the informed consent form electronically (paper based informed consent forms were used as back-ups when the electronic system was down), before any project procedures were performed. For this study we explained the informed consent in the participants preferred language but informed the participant the electronic participant declaration would be in English. Participants were able to request a copy of this to keep digitally for their own records. Participants were told that they have the right to withdraw from the study at any time. If a participant withdrew from the study, their research data was removed from all platforms and was not included in the analysis.

Ethics statement

Approval for analysis of the de-identified cohort was granted by Boston University’s Institutional Review Board (Protocol No. H-42030), Human Research Ethics Committee of the University of the Witwatersrand (Protocol No. M210411).

Study population

Inclusion and exclusion criteria for our pilot study followed the same criteria for the COVID-19 digital health supported Ag-RDT field study [15]. As such, all commuters, vendors, and drivers at the Germiston taxi rank who were ≥18 years old and literate were invited to participate. Participants had to have a mobile phone capable of receiving unstructured supplementary service data, SMS, or WhatsApp messaging. The study excluded 1) participants who refused consent or were unable to provide informed consent; 2) any participants with contraindications to nasopharyngeal sample collection for the COVID-19 Ag-RDT; 3) vulnerable populations as deemed inappropriate for the study by study personnel; 4) personnel directly involved in the conduct of the study; 5) participants at risk of failing to comply with the provisions of the protocol as to cause harm to self or seriously interfere with the validity of the study results; and 6) participants who had a confirmed positive COVID-19 diagnosis up to 3 months prior.

Outcomes

Our primary outcomes of interest were as follows:

number of participants with elevated blood glucose level (i.e., ≥11.1 mmol/L if measurement is random; ≥7.0 mmol/L if measurement is equivalent to fasting)
number of participants with elevated blood pressure (i.e. diastolic ≥90 mmHg; systolic ≥140 mmHg)
number of participants classified as pre-obese (body mass index (BMI) 25.0–29.9 kg/m²), obese (BMI 30.0–39.9 kg/m²) and severely obese (BMI > = 40 kg/m²)
number of male participants with a waist circumference >90 cm [18] and female participants >91.5 cm [19], indicating risk of metabolic syndrome
number of participants with both elevated GL and BP in addition to a waist circumference indicative of metabolic syndrome and/or classified as pre-obese/obese/severely obese
number of participants in outcomes 1, 2, and 5 who when contacted stated that they linked to primary healthcare for further diabetes and/or hypertension testing and care.

Statistical analysis

We used descriptive statistics to display the clinical and demographic characteristics of our study population. For our primary outcomes, we used descriptive statistics stratified by biological sex and crude and adjusted modified Poisson regression to assess predictors of elevated blood glucose, elevated blood pressure, and linkage-to-care. We modeled the outcome of elevated blood glucose as a function of age (18–29.9, 30–39.9, 40–49.9, 50–59.9, ≥60 years), sex, BMI (categorized as <25 vs. ≥25 kg/m²), smoking status (ever vs. never), elevated blood pressure at enrollment (diastolic ≥90 mmHg and systolic ≥140 mmHg), previous diabetes diagnosis at enrollment (self-reported) and previous hypertension diagnosis at enrollment (self-reported). The outcome of elevated blood pressure was adjusted for the same variables, with the exception of elevated blood pressure at enrollment being replaced by elevated blood glucose at enrollment. The outcome of linkage-to-care was modeled as a function of all covariates mentioned. All analyses were conducted using SAS v. 9.4.

Results

Cohort

A total of 1169 participants were enrolled during an eight-week recruitment period and screened for elevated blood glucose and elevated blood pressure. The median age of participants was 37.0 years [interquartile range (IQR):30.0–47.0] and 58.3% were men (Table 1). The median BMI for all participants was 26.5 kg/m² [IQR:22.8–31.1], 28.8% were identified as pre-obese, 26.9% as obese, and 3.7% as severely obese. There was a higher prevalence of obesity (41.8% vs. 16.3%, respectively) and severe obesity (7.2% vs. 1.2%, respectively) among females compared to males. A total of 237 (20.3%) of participants identified as ever having smoked, with the prevalence of smoking being higher in males (31.4%) compared to females (4.7%). The majority of participants were employed full-time (54.4%), which was also higher in males (59.5%) than in females (47.1%). The COVID-19 positivity rate was low at 0.9% (n = 11), and higher in females (1.7%) compared to males (0.4%).

Table 1. Characteristics and demographics of participants screened for elevated blood pressure and blood glucose at Germiston, South Africa taxi rank (N = 1168).

	Male	Female	Total
	n = 682 (58.3)	n = 486 (41.6)	N = 1168*
Age (years) (n,%)
18–29	146 (21.4)	114 (23.5)	260 (22.2)
30–39	218 (32.0)	167 (34.4)	386 (33.0)
40–49	180 (26.4)	115 (23.7)	295 (25.2)
50–59	106 (15.5)	61 (12.6)	167 (14.3)
≥60	32 (4.7)	29 (6.0)	61 (5.2)
Age (median; IQR)	38.0 (31.0–48.0)	36.0 (30.0–47.0)	37.0 (30.0–47.0)
COVID Status (n,%)
Positive	3 (0.4)	8 (1.7)	11 (0.9)
BMI categories (n,%)
underweight (<18.5 kg/m²)	36 (5.3)	6 (1.2)	42 (3.6)
normal (18.5–24.9 kg/m²)	326 (47.8)	104 (21.4)	430 (36.8)
pre-obese (25.0–29.9 kg/m²)	199 (29.2)	137 (28.2)	337 (28.8)
obese (30.0–39.9 kg/m²)	111 (16.3)	203 (41.8)	314 (26.9)
severely Obese (> = 40 kg/m²)	8 (1.2)	35 (7.2)	43 (3.7)
Missing	2 (0.3)	1 (0.2)	3 (0.3)
Body Mass Index (median; IQR)	24.4 (21.6–28.4)	29.7 (25.4–33.8)	26.5 (22.8, 31.1)
Employment (n,%)
full-time	406 (59.5)	229 (47.1)	636 (54.4)
part-time	65 (9.5)	53 (10.9)	118 (10.1)
student	13 (1.9)	22 (4.5)	35 (3.0)
Unemployed	198 (29.0)	182 (37.4)	380 (32.5)
Smoking Status (n,%)
ever	214 (31.4)	23 (4.7)	237 (20.3)
never	464 (68.0)	459 (94.4)	924 (70.0)
Missing	4 (0.6)	4 (0.8)	8 (0.7)
Patient Type (n,%)
commuter	591 (86.7)	437 (89.9)	1029 (88.0)
driver	52 (7.6)	0 (0.0)	52 (4.5)
vendor	28 (4.1)	15 (3.1)	62 (5.3)
other	11 (1.6)	34 (7.0)	26 (2.2)
Previous diabetes diagnosis at enrollment	6 (0.9)	17 (3.5)	23 (2.0)
Previous hypertension diagnosis at enrollment	49 (7.2)	75 (15.4)	124 (10.6)
Outcomes (n (%; 95% CI)
elevated blood glucose ¹	36 (5.3; 3.8–7.2)	28 (5.8; 3.9–8.1)	64 (5.5; 4.3–6.9)
elevated blood pressure ²	147 (21.6; 18.6–24.8)	87 (17.9; 14.6–21.5)	234 (20.0; 17.8–22.4)
elevated blood glucose no previous diabetes diagnosis ³	35 (5.2; 3.7–7.0)	25 (5.3; 3.6–7.7)	60 (5.2; 4.1–6.6)
elevated blood pressure no previous hypertension diagnosis ⁴	134 (21.2; 18.1–24.5)	68 (16.5; 13.2–20.4)	202 (19.3; 17.0–21.8)
classified as pre-obese, obese, and severely obese	318 (46.6; 42.9–50.4)	375 (77.3; 73.3–80.7)	694 (59.4; 53.4–62.0)
waist circumference indicative of metabolic syndrome ⁵	4 (0.6; 0.2–1.4)	4 (0.8; 0.3–2.0)	8 (0.7; 0.3–1.3)
≥3 or more risk factors above	9 (1.3; 0.6–2.4)	5 (1.0; 0.4–2.3)	14 (1.2; 0.7–2.0)
Linkage-to-care amongst those with elevated blood glucose ⁶	13 (37.1; 22.5–53.9)	5 (20.8; 8.1–40.3)	18 (30.0; 19.4–42.4)
Linkage-to-care amongst those with elevated blood pressure ⁷	20 (14.9; 9.6–21.7)	13 (19.1; 11.1–29.8)	33 (16.3; 11.7–21.9)

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*one participant is missing gender

¹ ≥11.1 mmol/L if measurement is random; ≥7.0 mmol/L if measurement is equivalent to fasting

² diastolic ≥90 mmHg and systolic ≥140 mmHg

³ denominator male n = 676 and female n = 469

⁴ denominator male n = 633 and female n = 411

⁵ >90 cm (males), >91.5 cm (females)

⁶ denominator male n = 35 and female n = 25

⁷ denominator male n = 134 and female n = 68

Primary outcomes

Combining participants with a previous diagnosis of diabetes (n = 23, 2.0%; 95% confidence interval (CI):1.3–2.9%) and those that had an elevated blood glucose measurement (n = 60, 5.2%; 95% CI:4.1–6.6%) at study enrollment, we estimated an overall indicative prevalence of diabetes of 7.1% (95% CI:5.7–8.7%) (Table 1 and Fig 1). The 60 participants or 72% of the total 83 participants unaware of their elevated blood glucose were predominately male (58.3%, 95% CI:45.6–70.3%). We found that women (3.5%; 95% CI:2.1–5.4%) had a higher prevalence of a previous diabetes diagnosis at study enrollment than their male counterparts (0.9%; 95% CI:0.4–1.8%). However, we saw no difference in the prevalence of elevated blood glucose levels by sex (female 5.8%; 95% CI:3.9–8.1% vs. male 5.3%; 95% CI:3.8–7.2%) amongst those with no known diabetes diagnosis at enrollment. A total of 4 (17.4%; 95% CI:5.8–36.8%) participants that had a previous diagnosis of diabetes had elevated blood sugar levels at enrollment, 3 (75.0%) of which were female.

When combining those with known hypertension at study enrollment (n = 124, 10.6%; 95% CI:8.9–12.5%) and those with elevated blood pressure (n = 202; 17.3%; 95% CI:15.2–19.5%), we get an overall prevalence of 27.9% (n = 326; 95% CI:25.4–30.1%) (Table 1 and Fig 2). As with elevated blood glucose, the majority of the 202 participants (or 62% of the total 326 participants) unaware of their elevated blood pressure were male (66.3%, 95% CI:59.6–72.6%). We found that women (15.4%; 95% CI:12.4–18.9%) had a higher prevalence of hypertension diagnosis at study enrollment than their male counterparts (7.2%; 95% CI:5.4–9.3%). However, we saw a more comparable relationship when stratifying elevated blood pressure levels by sex (female 17.9%; 95% CI:14.6–21.5% vs. male 21.6%; 95% CI:18.6–24.8%) amongst those with no known hypertension diagnosis. A total of 32 (25.8%; 95% CI:18.7–34.0%) participants who had a previous diagnosis of hypertension had uncontrolled blood pressure at enrollment, 59.4% (n = 19) of whom were female.

Having a waist circumference indicative of metabolic syndrome (>90 cm for males and >91.5 cm for females) was rare in our cohort (0.7%; 95% CI:0.3–1.3%) (Table 1), while the number of participants with elevated blood glucose and blood pressure in addition to a high waist circumference and/or classified as pre-obese/obese/severely obese was also rare at 1.2% (95% CI:0.7–2.0%).

Amongst those participants with elevated blood glucose that had no known previous diagnosis of diabetes (n = 60), 18 (n = 30.0%; 95% CI:19.4–42.4%) linked to care within three weeks of study enrollment (Table 1 and Fig 1), while amongst those with elevated blood pressure and no known hypertension diagnosis (n = 202), 33 (16.3%; 95% CI:11.7–21.9%) linked to care (Table 1 and Fig 2). Of those participants with elevated blood glucose, more males (n = 13, 37.1%; 95% CI:22.5–53.9%) sought out PHC services than females (n = 5; 20.8%; 95% CI:8.1–40.3%), but more females (n = 13; 19.1%; 95% CI:11.1–29.8%) with elevated blood pressure linked to PHC services than males (n = 20; 14.9%; 95% CI:9.6–21.7%) (Table 1).

Predictors of elevated blood glucose, elevated blood pressure and linkage-to-care

The modified Poisson regression model we used to assess predictors of elevated blood glucose suggests that those ≥30 years of age, individuals with elevated blood pressure at enrollment (adjusted risk ratio (aRR) 1.96; 95% CI:1.15–3.33) and those with a BMI ≥25kg/m² vs. <25kg/m² (aRR 2.05; 95% CI:1.06–3.95) were at increased risk of elevated blood glucose (Table 2). Our results, although imprecise, also suggest participants with a previous diagnosis of hypertension (aRR 1.54; 95% CI:0.81–2.95), those with a previous diagnosis of diabetes (aRR 2.24; 95% CI:0.77–6.57), and those that had ever smoked vs never smokers (aRR 1.59; 95% CI:0.85, 2.97) at enrollment were also at increased risk of elevated blood glucose.

Table 2. Crude and adjusted risk ratios assessing predictors of elevated blood glucose and blood pressure (n = 1169).

	elevated blood glucose		elevated blood pressure
	RR (95% CI)	aRR (95% CI)	RR (95% CI).	aRR (95% CI)
Age (years)
18–29	ref	ref	ref	ref
30–39	1.97 (0.78–4.96)	1.42 (0.55–3.65)	2.46 (1.44–4.20)	2.06 (1.20–3.54)
40–49	2.56 (1.02–6.46)	1.69 (0.65–4.39)	4.51 (2.68–7.58)	3.50 (2.07–5.92)
50–59	4.05 (1.58–10.34)	2.38 (0.89–6.35)	4.49 (2.58–7.79)	3.24 (1.84–5.70)
≥60	4.13 (1.33–12.81)	2.03 (0.60–6.88)	4.76 (2.48–9.17)	3.44 (1.74–6.79)
Gender
Male	ref	ref	ref	Ref
Female	1.09 (0.66–1.78)	1.07 (0.61–1.89)	0.83 (0.64–1.08)	0.66 (0.50–0.88)
Previous hypertension diagnosis
No	ref	Ref	ref	Ref
yes	2.14 (1.17–3.94)	1.54 (0.81–2.95)	1.34 (0.92–1.94)	1.09 (0.74–1.61)
Previous diabetes diagnosis
No	ref	ref	ref	Ref
yes	3.29 (1.20–9.05)	2.24 (0.77–6.57)	1.09 (0.45–2.64)	0.72 (0.28–1.81)
Elevated blood pressure at enrollment
No	ref	ref	--	--
yes	2.53 (1.53–4.18)	1.96 (1.15–3.33)	--	--
Elevated blood glucose diagnosis at enrollment
No	--	--	ref	Ref
yes	--	--	2.04 (1.35–3.10)	1.60 (1.05–2.44)
Body Mass Index
<25.0 kg/m²	ref	ref	ref	Ref
≥25.0 kg/m²	2.68 (1.46–4.93)	2.05 (1.06–3.95)	2.43 (1.78–3.31)	2.23 (1.61–3.08)
Smoking Status
never	ref	ref	ref	ref
ever (current or former)	1.31 (0.74–2.31)	1.59 (0.85–2.97)	0.80 (0.57–1.13)	0.77 (0.53–1.10)

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Our models used to assess predictors of elevated blood glucose show that those ≥30 vs. <30 years of age, participants with elevated blood glucose (aRR 1.60; 95% CI:1.05–2.44) and individuals with a BMI ≥25kg/m² vs. <25kg/m² (aRR 2.23; 95% CI:1.61–3.08) at enrollment were at increased risk of elevated blood pressure levels (Table 2). We also found that females had a 34% decreased risk (aRR 0.66; 95% CI 0.50–0.88) of elevated blood pressure levels when compared to their male counterparts.

For the outcome of linkage-to-care, our results, although imprecise most likely due to small sample size, suggest that those ≥40 years of age, participants with a previous diagnosis of hypertension (aRR 1.79; 95% CI:0.91–3.49) and individuals with a BMI ≥25kg/m² vs. <25kg/m² (aRR 2.06; 95% CI:0.86–4.93) were more likely to linkage-to-care (Table 3). We also found, although imprecise, that those with only elevated blood pressure at enrollment were less likely to seek care than those with only elevated blood sugar (aRR 0.68; 95% CI:0.31–1.51), while those with both elevated blood glucose and blood pressure were more likely to seek care compared to those with only elevated blood glucose (aRR 1.23; 95% CI:0.46–3.29).

Table 3. Predictors of linkage-to-care amongst those with elevated blood glucose and/or blood pressure (n = 234).

	Linked to Care
	RR (95% CI)	aRR* (95% CI)
Age
<40	ref	ref
≥40	2.28 (1.14–4.53)	2.01 (0.99–4.11)
Comorbidities
elevated blood glucose	ref	Ref
elevated blood pressure	0.83 (0.38–1.78)	0.68 (0.31–1.51)
both	1.87 (0.75–4.65)	1.23 (0.46–3.29)
Previous hypertension diagnosis
No	ref	Ref
yes	1.99 (1.05–3.79)	1.79 (0.91–3.49)
Previous diabetes diagnosis
No	ref	Ref
yes	1.49 (0.36–6.12)	0.86 (0.20–3.71)
Gender
Male	ref	Ref
female	1.25 (0.73–2.15)	1.00 (0.55–1.82)
BMI
<25.0 kg/m²	ref	Ref
≥25.0 kg/m²	2.35 (1.00–5.50)	2.06 (0.86–4.93)
Smoking Status
never	ref	Ref
ever (current or former)	0.89 (0.42–1.89)	0.78 (0.34–1.67)

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*age was collapsed to <40 vs. ≥40 years due to smaller sample size

Discussion

Recognition and prevalence of NCDs have risen throughout sub-Saharan Africa. Many NCDs can be prevented or treated early on with low-cost interventions, yet implementation of such care has been limited throughout the region due to an already overburdened health care system, further impacted by the COVID-19 pandemic. Our study provides evidence of the feasibility of leveraging existing COVID-19 health screening infrastructure in South Africa to provide simple screening for elevated blood pressure and elevated blood glucose outside of health facilities at a high traffic taxi rank in Germiston. While community COVID-19 screening efforts might fluctuate or eventually stop in the months to come, the screening intervention could be easily added into other health staff-led mobile testing effort, such as those for HIV and tuberculosis.

We screened 1169 participants during an eight-week period and found an overall indicative prevalence of diabetes of 7.1% (combining previous diagnosis of diabetes (2%) and those that had an elevated blood glucose measurement (5.2%) at study enrollment. Our estimate was slightly lower than what has been previously reported for South Africa (11% [2, 20]), but in line with more recent estimates from sub-Saharan Africa (7.2%) [21]. We found females had a higher prevalence of previously diagnosed diabetes (3.5%) compared to males (0.9%) at enrollment. Research suggests that this may be attributable to men having a higher risk of dying from other causes (e.g., HIV, tuberculosis, accidents) prior to being diagnosed with diabetes, or due to differences in health seeking behavior by sex [22]. Once we removed participants with known diabetes at enrollment, we found estimates of elevated blood glucose to be comparable between men and women in our study, which is consistent with previous research [23].

Our overall hypertension prevalence was 27.9% (combining those with known hypertension at study enrollment (10.6%) and those with elevated blood pressure (17.3%)), which is comparable to those previously reported out of South Africa [24–26]. We found that women (15.4%) had a higher prevalence of previous diagnosed hypertension at study enrollment than men (7.2%), also consistent with previous work [24–28]. These differences by sex in the prevalence of hypertension, like diabetes, could be associated with higher magnitudes of obesity and physical inactivity among women in our study population [24–28]. However, we saw the relationship become more comparable (overlapping confidence intervals in our estimates) when stratifying elevated blood pressure levels by sex (female 16.5% vs. male 21.2%) amongst those with no known hypertension diagnosis, consistent with previous research [29].

One of the most interesting findings of our study were the additional 60 (72% of the total 83 individuals potentially living with diabetes) and additional 202 (62% of the total 326 individuals potentially living with hypertension) participants in our cohort being newly diagnosed with either condition, equating to almost one quarter of screened participants being unaware of their elevated blood glucose or blood pressure levels. This observation is common in many studies of diabetes [23, 30] and hypertension [28, 31] in sub-Saharan Africa. With more than 50% of people living with diabetes unaware of their condition [23, 32, 33], and 7% to 56% of people living with hypertension being unaware of their blood pressure status [25, 34]. We found that those unaware of their blood glucose or blood pressure levels were predominately male, possibly attributable to men generally being less engaged in the health system than women [23].

We found 17.4% of those with a prior diabetes diagnosis and 26.0% of those with a prior diagnosis of hypertension at enrollment did not have their condition under control, the majority of which were female. Our estimates are lower than what has been reported previously in sub-Saharan Africa (uncontrolled diabetes ranging from 18.1% to 30.3% [20, 30] and uncontrolled hypertension ranging from 50% to 93% [32–34]). The difference between our estimates and those previously reported could be that most of the previous estimates are from household surveys with a denominator comprised of everyone who was found in testing as part of the survey, while our denominator was comprised of individuals of people who knew they had hypertension and/or diabetes. It also could be that disease control once someone is engaged with the health system is higher in this setting, or those who self-report a prior diagnosis are more likely to be engaged in care and adherent to treatment, even in the context of the COVID-19 pandemic.

Linkage-to-care was poor in our study. It could be that moving testing for diabetes and hypertension into an individual’s commute makes it more difficult to link-to-care compared to being screened and diagnosed at a health facility where an individual could be enrolled in care for their condition and initiated onto treatment immediately, at the same clinic. A recent study assessing the intermediary cost-effectiveness of distributing HIV self-test kits and onward linkage to confirmatory testing and treatment services through 11 distribution models in South Africa reported that moving the location of HIV testing from the facility to the community helped close the gap in knowledge of HIV status but increased the gap in the next step, from diagnosis to engaging in care, as the health facility was still far away from the community and severe barriers to care continued to exist [35]. Additionally, available evidence suggests that the COVID-19 pandemic severely impacted control and prevention programs for other conditions in South Africa, including diabetes and hypertension [36, 37], due to higher demand for COVID-19 related health services, process changes including. screening of patients at the gate, longer than usual waiting times and line cutoffs, the need to book a visit in advance) and overall hesitancy of individuals to visit health facilities for any services during the pandemic.

Additionally, although our sample size of patients referred to care after an elevated blood glucose and/or blood pressure measurement was small (n = 234) and we cannot draw strong conclusions, the predictors of linkage-to-care that we identified were comparable to what has been previously reported for chronic health conditions in this setting [20, 30]. Those ≥40 years, participants with a previous hypertension diagnosis at enrollment and those with a BMI ≥25 kg/m² were more likely to seek further care for their condition in our study.

There are many strengths of our study. We provide evidence that any engagement with the health system, however incidental, can be used as an opportunity to screen for additional diseases or co-morbidities. Additionally, our results might be generalizable to the general population in Southern African and possibly more broadly into other regions of sub-Saharan Africa that have high prevalence of obesity. It is important to consider our study alongside its limitations. First, a single measurement of blood glucose of blood pressure cannot be used to diagnose diabetes or hypertension. In order to get a definitive diagnosis, further formal laboratory measurements and clinical evaluations would be necessary, and in order to receive a full diagnosis and/or treatment the individual must link to the health care system [9, 16, 17]. Second, prior diagnoses of diabetes and hypertension were self-reported by participants; therefore, response bias is possible. Finally, there may be a recruitment bias, as taxi ranks generally service employed people travelling to or from work, as evidenced by the vast majority (64.5%) of participants in our study who were employed full- or part-time. Likely as a result of this, we enrolled more males (58.3%) than females into our study, which is reflective of men more likely to be employed than women in South Africa [38].

Conclusion

In our proof of concept study, opportunistically leveraging the existing intervention of COVID-19 screening in South Africa to provide screening for hypertension and diabetes, we detected elevated blood glucose in 5.1% and elevated blood pressure in 17.3% of our participants with no known previous diagnosis of either condition, equating to almost 25% of screened participants being unaware of their elevated blood glucose or blood pressure levels. Though we identified these cases, there was poor linkage-to-care following the screening. Future research should evaluate options such as digital technologies to facilitating screening and linkage-to-care for diagnoses made outside of health facilities, and evaluate the feasibility and cost-effectiveness of this type of screening which can easily be extended to other community screening efforts beyond COVID-19- such as those for HIV and tuberculosis.

Data Availability

Anonymized datasets used and/or analysed during the current study are available at OpenBU, Boston University’s open access repository (https://hdl.handle.net/2144/46066).

Funding Statement

ATB, MM, VTM, FV, AG and GMR received funding from FIND through a grant from the German Federal Ministry of Economic Cooperation and Development. ATB received funding from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) grant 1K01DK116929-01A1.

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PLoS One. doi: 10.1371/journal.pone.0287794.r001

Decision Letter 0

Mobolanle Balogun

23 Mar 2023

PONE-D-22-33612Integration of point-of-care screening for type 2 diabetes mellitus and hypertension with COVID-19 rapid antigen screening in Johannesburg, South AfricaPLOS ONE

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Reviewer #1: In this paper Brennan et al sought to evaluate the yield and linkage-to-care for diabetes and hypertension screening alongside a study assessing the use of rapid antigen tests for COVID-19 in taxi ranks in Johannesburg, South Africa. Overall, the paper is interesting and easy to follow. I do however have some remarks.

1. Throughout the manuscript and abstract elevated blood glucose is defined as fasting glucose >7 or random glucose >11.1 mmol/L, and elevated blood pressure as systolic >140 or diastolic >90 mmHg. However, the diagnostic criteria for both diabetes and hypertension include the cutoff-value i.e, glucose ≥7 and/or ≥11.1 mmol/L, and blood pressure ≥140 and/or ≥90 mmHg. Unless there is a strong rationale not to use the diagnostic cutoffs, I suggest reporting the results throughout the manuscript based on the diagnostic cutoffs.

2. Abstract, findings: The authors write that the prevalence of diabetes and hypertension was 7.1% and 27.9% respectively. This is not correct as a diagnosis of diabetes and hypertension require repeated elevated values. I suggest rephrasing the sentence.

3. Methods, first paragraph: Could you please provide more details how the blood pressure was measured? Was it measured after some rest, in seated, standing, or supine position? Were manual or automatic blood pressure meters used? Were previous diabetes and/or hypertension diagnoses self-reported?

4. Methods, outcomes: Could you please provide a reference to the waist circumference cutoffs indicative of the metabolic syndrome?

5. Methods, statistical analysis & Table 2: As I understand, the outcomes in the Poisson regression models were elevated blood glucose and elevated blood pressure, not diabetes and hypertension (se comment #3)? This also needs to be clarified in Table 2 as there are discrepancies in the table title and the column headings, and in the results and discussion sections. Could you also please add what statistical software that was used in the analyses?

6. Results, primary outcomes third paragraph: Typo – “in our cohort” is duplicated.

7. Results, primary outcomes fourth paragraph: Inconsistency regarding how many participants with elevated blood glucose that had no known previous diagnosis of diabetes who linked to care. In table 1, it says 18 participants but, in the text, and in Figure 1 there were 19 participants. Also, the text says it was 13 males and 5 females.

8. Results, predictors, first paragraph: The aRR of several of the reported predictors have wide 95% CI including 1, meaning that the effect of those variables are non-significant and therefore cannot be interpreted as predictors of increased risk of elevated blood glucose. Only the aRR regarding elevated blood pressure at enrollment and BMI ≥25 were significantly increased. Please only report significant findings as predictors of elevated blood pressure (as was done in the second paragraph).

9. Results, predictors, third paragraph: As I understand the results, no predictors of linkage-to-care were found, as none of the aRR for the mentioned variables in the paragraph and Table 3 were significantly increased or decreased. Please only report significant findings as predictors.

10. Discussion, first sentences in paragraphs one and two: Please see comment #3.

11. Discussion, sixth paragraph: Please rephrase and only report significant findings as predictors.

12. Discussion, last paragraph: Could the blood pressures have been elevated as the participants were also tested by potentially painful or unpleasant nasopharyngeal covid tests? If so, this might be added as a potential limitation.

13. Table 1: The table title states N=1168 but total N=1169.

14. References: #33 is not numbered and #34 seem to be missing in the reference list.

15. I couldn’t find the anonymized datasets at the OpenBU repository. Could you please provide a detailed link to the dataset?

Reviewer #2: This is a well written manuscript describing disease prevalence and awareness and linkage to care for diabetes and hypertension at a community-based COVID-19 testing venue in South Africa. The methods are well described and this manuscript fully meets the requirements set forth by PLOS ONE for publishing scientifically sound studies. I have a few suggestions for consideration about the implications of this work that the authors may consider.

1. Was there information collected on prior screening for diabetes or hypertension? This would be interesting to help understand the component causes of low rates of diagnosis (lack of screening vs screening that failed to identify these conditions).

2. Though still not adequate, as the authors note, diabetes and hypertension control among those aware of their diagnosis was much higher than seen in many other published studies, including those cited. What do the authors make of this finding? I wonder if disease control once someone is engaged with the health system is higher in this setting, or if simply those who self-report a prior diagnosis are also more likely to be engaged in care and adherent to treatment.

3. Linkage was quite low and while mentioned briefly in the discussion, I think this should be discussed further. Linkage was also much lower than other studies including older studies from a different context in South Africa (e.g. Govindasamy PLOS ONE 2013, among others). It would be worth discussing linkage in the present study in the context of other community-based hypertension and diabetes screening studies.

4. Further, regarding the low linkage rate, is there any other information about this particular context that may provide insight into the low rates of linkage to care (beyond the individual factors that are reported)? Distance from the taxi stand where recruitment occurred to the referral clinic? Distance from where participants lived if they were commuting from another part of the city? Availability of medications in the clinic? Reputation of the clinic in the community? Linkage to other clinics separate from the one to which they were referred? Further discussion of the potential factors contributing to low linkage to care could help readers contextualize this finding and be hypothesis-generating for potential interventions.

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Reviewer #1: No

Reviewer #2: No

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PLoS One. 2023 Jul 7;18(7):e0287794. doi: 10.1371/journal.pone.0287794.r002

Author response to Decision Letter 0

26 Apr 2023

We thank the reviewer for their kind assessment

We apologize for the oversight on our part. The cut-offs are set at the values you specified and should have read as fasting glucose >7 or random glucose >11.1 mmol/L, and elevated blood pressure as systolic >140 or diastolic >90 mmHg. We have changed this throughout the manuscript.

We thank the reviewer for their comment. We updated the results section in the abstract to match the results in the main text. It now reads as follows,

“1169 participants were enrolled and screened for diabetes and hypertension. Combining participants with a previous diagnosis of diabetes (n=23, 2%; 95% CI:1.3-2.9%) and those that had an elevated BG measurement (n=60, 5.2%; 95% CI:4.1-6.6%) at study enrollment, we estimated an overall indicative prevalence of diabetes of 7.1% (95% CI:5.7-8.7%). When combining those with known hypertension at study enrollment (n=124, 10.6%; 95% CI:8.9-12.5%) and those with elevated BP (n=202; 17.3%; 95% CI:15.2-19.5%), we arrive at an overall prevalence of hypertension of 27.9% (95% CI:25.4-30.1%). Only 31.7% of those with elevated BG and 16.0% of those with elevated BP linked-to-care.”

The blood pressure measurement was taken while the patient was seated and with a manual blood pressure device with cuff. We have updated the sentence in the methods section on page 3 to state the following,

“…and measured blood pressure (taken while the individual was seated via a once-off measurement with a manual blood pressure device with cuff)”.

We updated the methods section to reflect that previous hypertension and diabetes were self-reported. The statement in statistical analysis section of the paper on page 4 now reads as follows,

“We modeled the outcome of diabetes as a function of age (18-29.9, 30-39.9, 40-49.9, 50-59.9, >60 years), sex, BMI (categorized as <25 vs. >25 kg/m2), smoking status (ever vs. never), elevated blood pressure at enrollment (diastolic >90 mmHg and systolic >140 mmHg), previous diabetes diagnosis at enrollment (self-reported) and previous hypertension diagnosis at enrollment (self-reported).”

4. Methods, outcomes: Could you please provide a reference to the waist circumference cutoffs indicative of the metabolic syndrome?

We have added the following references to the paper and have referenced them in the section where we define our outcomes in our methods (page 4).

The reference for our cut point of 90.0 cm for men has been added as citation #18: Kalk WJ, Joffe BI, Sumner AE. The waist circumference of risk in black South African men is lower than in men of European ancestry. Metab Syndr Relat Disord. 2011 Dec;9 (6):491-5. doi: 10.1089/met.2011.0063. Epub 2011 Aug 29. PMID: 21875336; PMCID: PMC3225062.

The reference for our cut point of 91.5 cm for females has been added as citation #19: Crowther NJ, Norris SA. The current waist circumference cut point used for the diagnosis of metabolic syndrome in sub-Saharan African women is not appropriate. PLoS One. 2012;7 (11):e48883. doi: 10.1371/journal.pone.0048883. Epub 2012 Nov 8. PMID: 23145009; PMCID: PMC3493601.

We have updated the header in the Table 2 and updated the statistical analysis section on page 4 to read as follows,

“Poisson regression to assess predictors of elevated blood glucose, elevated blood pressure, and linkage-to-care.” We also added the following to the end of the section, “All analyses were conducted using SAS v. 9.4.”

6. Results, primary outcomes third paragraph: Typo – “in our cohort” is duplicated.

The sentence on page 5 of the results now reads as follows,

“Having a waist circumference indicative of metabolic syndrome (>90 cm for males and >91.5 cm for females) was rare in our cohort (0.7%; 95% CI:0.3-1.3%) (Table 1)”

We have updated the text, table and figure to reflect 13 males and 5 females linked to care. We updated the text in the results section on page 5 to read,

“Amongst those participants with elevated blood glucose that had no known previous diagnosis of diabetes (n=60), 18 (n=30.0%; 95% CI:19.4-42.4%) linked to care within three weeks of study enrollment (Figure 1), while amongst those with elevated blood pressure and no known hypertension diagnosis (n=202), 33 (16.3%; 95% CI:11.7-21.9%) linked to care (Figure 2). Of those participants with elevated blood glucose, more males (n=13, 37.1%; 95% CI:22.5-53.9%) sought out PHC services than females (n=5; 20.0%; 95% CI:7.7-38.9%), but more females (n=13; 19.1%; 95% CI:11.1-29.8%) with elevated blood pressure linked to PHC services than males (n=20; 14.9%; 95% CI:9.6-21.7%) (Table 1).”

We thank the reviewer for their comment. We have updated the language on page 6 in that paragraph to be less definitive,

“The modified Poisson regression model we used to assess predictors of diabetes suggests that those >30 years of age, individuals with elevated blood pressure (aRR 1.96; 95% CI:1.15-3.33) and those with a BMI >25kg/m2 vs. <25kg/m2 (aRR 2.05; 95% CI:1.06-3.95) (Table 2). Our results, although imprecise, also suggest participants with a previous diagnosis of hypertension (adjusted risk ratio (aRR) 1.54; 95% CI:0.81-2.95), those with a previous diagnosis of diabetes (aRR 2.24; 95% CI:0.77-6.57), and those that had ever smoked vs never smokers (aRR 1.59; 95% CI:0.85, 2.97) at enrollment were at increased risk of elevated blood glucose.”

We appreciate the reviewer’s feedback. We have updated the language on page 6 in that paragraph to be less definitive,

“For the outcome of linkage-to-care, our results, although imprecise, suggest that those >40 years of age, participants with a previous diagnosis of hypertension (aRR 1.78; 95% CI:0.91-3.49) and individuals with a BMI >25kg/m2 vs. <25kg/m2 (aRR 1.98; 95% CI:0.83-4.73) were predictors of linkage-to-care (Table 3). Additionally, those with elevated blood pressure alone at enrollment were less likely to seek care than those with elevated blood sugar alone (aRR 0.68; 95% CI:0.31-1.51), while those with both elevated blood glucose and blood pressure were more likely to seek care than those with elevated blood glucose alone (aRR 1.23; 95% CI:0.46-3.29).“

10. Discussion, first sentences in paragraphs one and two: Please see comment #3.

We apologize but we are unclear on how comment #3 above relates to the first sentence in paragraph 1 of the discussion. Comment #3 is in reference to how blood pressure was measured and how previous diagnoses of hypertension and diabetes were reported.

We assume the reviewer means the 3rd sentence in paragraph 1 and that we need to update our reference to hypertension and diabetes to elevated blood pressure and elevated blood glucose. We updated the following sentence in the first paragraph of the discussion on page 6,

“Our study provides evidence of the feasibility of leveraging existing COVID-19 health screening infrastructure in South Africa to provide simple screening for elevated blood pressure and elevated blood glucose outside of health facilities at a high traffic taxi rank in Germiston.”

Same for the 1st sentence of the 2nd paragraph on page 6. We assume that the reviewer is referring to changing the language around diabetes diagnosis. We updated the following sentence in the second paragraph of the discussion,

“We screened 1169 participants during an eight-week period and found an overall indicative prevalence of diabetes of 7.1% (combining previous diagnosis of diabetes (2%) and those that had an elevated blood glucose measurement (5.2%) at study enrollment).”

We made the same change in reference to hypertension diagnosis. We updated the following sentence in the third paragraph of the discussion on page 7,

“Our overall hypertension prevalence was 27.9% (combining those with known hypertension at study enrollment (10.6%) and those with elevated BP (17.3%)), which is comparable to those previously reported out of South Africa (22-24).”

11. Discussion, sixth paragraph: Please rephrase and only report significant findings as predictors.

We thank the reviewer for their comment. We are clear in our language about the imprecise estimates in our discussion in reference to predictors of linkage to care. We state the following in paragraph 6 of the discussion on page 7,

“Although our sample size of patients referred to care after an elevated blood glucose and/or blood pressure measurement was small (n=234) and we cannot draw strong conclusions, the predictors of linkage-to-care that we identified were comparable to what has been previously reported for chronic health conditions in this setting (20,30).”

We thank the reviewer for their comment. Blood pressure measurements were taken prior to the administration of the nasopharyngeal swab for COVID-19 testing. We added the following to the first paragraph of the methods section to clarify study procedure’s,

“All study relevant measures were recorded before nasopharyngeal swabs for COVID-19 testing were administered, eliminating the role these swabs could have had on elevating blood pressure.”

13. Table 1: The table title states N=1168 but total N=1169.

We updated the total in the subheading to N=1168 to match the title. Since we stratified the table by sex and data on sex was missing for one individual, we changed the numbers to reflect that.

14. References: #33 is not numbered and #34 seem to be missing in the reference list.

We apologize for the error. The references have been updated as follows:

#33 Gómez-Olivé, F. X., Ali, S. A., Made, F., Kyobutungi, C., Nonterah, E., Micklesfield, L., Alberts, M., Boua, R., Hazelhurst, S., Debpuur, C., Mashinya, F., Dikotope, S., Sorgho, H., Cook, I., Muthuri, S., Soo, C., Mukomana, F., Agongo, G., Wandabwa, C., Afolabi, S., … AWI-Gen and the H3Africa Consortium (2017). Regional and Sex Differences in the Prevalence and Awareness of Hypertension: An H3Africa AWI-Gen Study Across 6 Sites in Sub-Saharan Africa. Global Heart, 12 (2), 81–90.

#34 Statistics South Africa. P0211 – Quarterly labour force survey (QLFS), 4th quarter 2022. 25 November 2008. Available at: https://www.statssa.gov.za/?page_id=1861&PPN=P0211&SCH=72944.

15. I couldn’t find the anonymized datasets at the OpenBU repository. Could you please provide a detailed link to the dataset?

You can locate the datasets at the following OpenBU link https://hdl.handle.net/2144/46066. We have updated the link in the paper.

We thank the reviewer for their comment and agree that it would be interesting to have that information. Unfortunately, we did not ask about previous screening, only about previously known diagnosis of hypertension and diabetes.

We thank the reviewer for their comment. We updated the 5 paragraph of the discussion on page 7 to read as follows,

“We found 17.4% of those with a prior diabetes diagnosis and 26.0% of those with a prior diagnosis of hypertension at enrollment did not have their condition under control, the majority of which were female. Our estimates are lower than what has been reported previously in sub-Saharan Africa (uncontrolled diabetes ranging from 18.1% to 30.3% (20,30) and uncontrolled hypertension ranging from 50% to 93% (32-34)). The difference between our estimates and those previously reported could be that most of the previous estimates are from household surveys with a denominator comprised of everyone who was found in testing as part of the survey, while our denominator was comprised of individuals of people who know they have hypertension and/or diabetes. It also could be that disease control once someone is engaged with the health system is higher in this setting, or those who self-report a prior diagnosis are more likely to be engaged in care and adherent to treatment, even in the context of the COVID-19 pandemic.”

We thank the reviewer for their comment. We agree that linkage to care was low. We have added the following to paragraph 6 of the discussion on page 7,

“Linkage-to-care was poor in our study. It could be that moving testing for diabetes and hypertension into an individual’s commute makes it more difficult to link-to-care compared to being screened and diagnosed at a health facility where an individual could be enrolled in care for their condition and initiated onto treatment immediately, at the same clinic. A recent study assessing the intermediary cost-effectiveness of distributing HIV self-test kits and onward linkage to confirmatory testing and treatment services through 11 distribution models in South Africa reported that moving the location of HIV testing from the facility to the community helped close the gap in knowledge of HIV status but increased the gap in the next step, from diagnosis to engaging in care, as the health facility was still far away from the community and severe barriers to care continued to exist (36). Additionally, available evidence suggests that the COVID-19 pandemic severely impacted control and prevention programs for other conditions in South Africa, including diabetes and hypertension (37,38), due to higher demand for COVID-19 related health services, process changes including. screening of patients at the gate, longer than usual waiting times and line cutoffs, the need to book a visit in advance) and overall hesitancy of individuals to visit health facilities for any services during the pandemic.”

We agree that this information would be useful to have. However, when we referred patients to care, it was not to a specific clinic near the taxi rank, but rather we gave them a referral form with the details of the glucose and blood pressure measurements and then encouraged them to follow-up at their local clinic, which may have or may not have been close to the taxi rank. We think the paragraph added in response to comment #3 above can help add context.

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PLoS One. doi: 10.1371/journal.pone.0287794.r003

Decision Letter 1

Mobolanle Balogun

29 May 2023

PONE-D-22-33612R1Integration of point-of-care screening for type 2 diabetes mellitus and hypertension with COVID-19 rapid antigen screening in Johannesburg, South AfricaPLOS ONE

Dear Dr. Brennan,

Please address the further comments presented by Reviewer 2. In addition, the phrase "although imprecise most likely due to small sample size" which is stated in the last paragraph of the results is better suited as a limitation.

Please submit your revised manuscript by Jul 13 2023 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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Reviewers' comments:

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Reviewer #1: (No Response)

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

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6. Review Comments to the Author

Reviewer #1: The manuscript has improved and been clarified in the revised version. I just have some minor remarks regarding the results section about Predictors of diabetes, hypertension and linkage-to-care:

1. As stated in the methods section and tables the predictors are about elevated blood glucose, elevated blood pressure and linkage-to-care and I suggest rephrasing the subheading accordingly.

2. The last bit of the first sentence in the first paragraph seem to be missing.

3. As the 95% CIs are wide and include 1 indicating no effect, I suggest toning down the results described in the last sentence of the third paragraph in a similar way as the author have done with other non-significant findings. For example, an aRR of 0.68 with 95% CI 0.31–1.51 really means that individuals could be anywhere from 69% less likely to 51% more likely to seek care.

Reviewer #2: (No Response)

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Reviewer #2: No

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PLoS One. 2023 Jul 7;18(7):e0287794. doi: 10.1371/journal.pone.0287794.r004

Author response to Decision Letter 1

3 Jun 2023

We thank the reviewer for reviewing our manuscript a second time and providing useful feedback.

1. As stated in the methods section and tables the predictors are about elevated blood glucose, elevated blood pressure and linkage-to-care and I suggest rephrasing the subheading accordingly.

We thank the reviewer for their comment. We have made changes throughout the manuscript to make it clear that our study is assessing elevated blood glucose, blood pressure and linkage-to-care and not diabetes or hypertension.

2. The last bit of the first sentence in the first paragraph seem to be missing.

The sentence now reads, “The modified Poisson regression model we used to assess predictors of diabetes elevated blood glucose suggests that those >30 years of age, individuals with elevated blood pressure at enrollment (adjusted risk ratio (aRR) 1.96; 95% CI:1.15-3.33) and those with a BMI >25kg/m2 vs. <25kg/m2 (aRR 2.05; 95% CI:1.06-3.95) were at increased risk of elevated blood glucose (Table 2).

We thank the reviewer for their comment. The sentence now reads, “We also found, although imprecise, that those with only elevated blood pressure at enrollment were less likely to seek care than those with only elevated blood sugar (aRR 0.68; 95% CI:0.31-1.51), while those with both elevated blood glucose and blood pressure were more likely to seek care compared to those with only elevated blood glucose (aRR 1.23; 95% CI:0.46-3.29).

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PLoS One. doi: 10.1371/journal.pone.0287794.r005

Decision Letter 2

Mobolanle Balogun

13 Jun 2023

Integration of point-of-care screening for type 2 diabetes mellitus and hypertension with COVID-19 rapid antigen screening in Johannesburg, South Africa

PONE-D-22-33612R2

Dear Dr. Brennan,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Mobolanle Balogun

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PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0287794.r006

Acceptance letter

Mobolanle Balogun

29 Jun 2023

PONE-D-22-33612R2

Integration of point-of-care screening for type 2 diabetes mellitus and hypertension with COVID-19 rapid antigen screening in Johannesburg, South Africa

Dear Dr. Brennan:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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on behalf of

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Data Availability Statement

Anonymized datasets used and/or analysed during the current study are available at OpenBU, Boston University’s open access repository (https://hdl.handle.net/2144/46066).

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PERMALINK

Integration of point-of-care screening for type 2 diabetes mellitus and hypertension with COVID-19 rapid antigen screening in Johannesburg, South Africa

Alana T Brennan

Beatrice Vetter

Mohammed Majam

Vanessa T Msolomba

Francois Venter

Sergio Carmona

Kekeletso Kao

Adena Gordon

Gesine Meyer-Rath

Roles

Abstract

Aims

Methods

Results

Conclusion

Introduction

Methods

Informed consent

Ethics statement

Study population

Outcomes

Statistical analysis

Results

Cohort

Table 1. Characteristics and demographics of participants screened for elevated blood pressure and blood glucose at Germiston, South Africa taxi rank (N = 1168).

Primary outcomes

Fig 1. Number of clients with elevated blood glucose level indicative of diabetes mellitus (i.e., ≥11.1 mmol/L if measurement is random; ≥7.0 mmol/L if measurement is equivalent to fasting) amongst those with no known previous diabetes diagnosis at study enrollment (N = 1169).

Fig 2. Number of clients with elevated blood pressure indicative of hypertension (i.e. diastolic ≥90 mmHg; systolic ≥140 mmHg) amongst those with no known previous diagnosis of hypertensive at study enrollment (N = 1169).

Predictors of elevated blood glucose, elevated blood pressure and linkage-to-care

Table 2. Crude and adjusted risk ratios assessing predictors of elevated blood glucose and blood pressure (n = 1169).

Table 3. Predictors of linkage-to-care amongst those with elevated blood glucose and/or blood pressure (n = 234).

Discussion

Conclusion

Data Availability

Funding Statement

References

Decision Letter 0

Mobolanle Balogun

Roles

Author response to Decision Letter 0

Decision Letter 1

Mobolanle Balogun

Roles

Author response to Decision Letter 1

Decision Letter 2

Mobolanle Balogun

Roles

Acceptance letter

Mobolanle Balogun

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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