The multimorbidity profile of South African women newly diagnosed with breast cancer

Oluwatosin A Ayeni; Shane A Norris; Maureen Joffe; Herbert Cubasch; Sarah Nietz; Ines Buccimazza; Urishka Singh; Sharon Čačala; Laura Stopforth; Wenlong Carl Chen; Valerie A McCormack; Daniel S O’Neil; Judith S Jacobson; Alfred I Neugut; Paul Ruff; Lisa K Micklesfield

doi:10.1002/ijc.32727

. Author manuscript; available in PMC: 2020 Nov 9.

Published in final edited form as: Int J Cancer. 2019 Oct 25;147(2):361–374. doi: 10.1002/ijc.32727

The multimorbidity profile of South African women newly diagnosed with breast cancer

Oluwatosin A Ayeni ^1,², Shane A Norris ^1,², Maureen Joffe ^1,^2,³, Herbert Cubasch ^1,^3,⁴, Sarah Nietz ^1,⁵, Ines Buccimazza ⁶, Urishka Singh ⁶, Sharon Čačala ⁷, Laura Stopforth ⁷, Wenlong Carl Chen ^8,⁹, Valerie A McCormack ¹⁰, Daniel S O’Neil ¹¹, Judith S Jacobson ¹², Alfred I Neugut ^12,^13,¹⁴, Paul Ruff ^1,^3,¹⁵, Lisa K Micklesfield ^1,²

^1.Non communicable Diseases Research Division, Wits Health Consortium (PTY) Ltd, Johannesburg, Gauteng, South Africa

^2.SAMRC/Wits Developmental Pathways for Health Research Unit, Department of Pediatrics, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, Gauteng, South Africa

^3.South Africa Medical Research Council Common Epithelial Cancers Research Centre, University of Witwatersrand, Johannesburg, Gauteng, South Africa.

^4.Department of Surgery, Chris Hani Baragwanath Academic Hospital and Faculty of Health Sciences, University of Witwatersrand, Johannesburg, Gauteng, South Africa

^5.Department of Surgery, Charlotte Maxeke Johannesburg Academic Hospital and Faculty of Health Sciences, University of Witwatersrand, Johannesburg, Gauteng, South Africa

^6.Departments of Surgery and Oncology, Inkosi Albert Luthuli Central Hospital, Durban and Ngwelezane Hospital, University of KwaZulu Natal, Empangeni, KwaZulu Natal, South Africa

^7.Departments of Surgery and Oncology, Grey’s Hospital, University of KwaZulu Natal, Pietermaritzburg, KwaZulu Natal, South Africa

^8.National Cancer Registry, National Health Laboratory Service, Johannesburg, South Africa

^9.Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa

^10.Section for Environment and Radiation, International Agency for Research on Cancer, Lyon, France

^11.University of Miami Miller School of Medicine, Department of Medicine, Miami, United States

^12.Department of Epidemiology, Mailman School of Public Health, Columbia University, New York NY, United States

^13.Herbert Irving Comprehensive Cancer Center, College of Physicians and Surgeons, Columbia University, New York NY, United States

^14.Department of Medicine, College of Physicians and Surgeons, Columbia University, New York NY, United States

^15.Division of Medical Oncology, Department of Medicine, Faculty of Health Sciences, University of Witwatersrand, Johannesburg, Gauteng, South Africa

Authors’ contributions

Conceptualization: OA, MJ, SAN, LKM

Formal analysis: OA, SAN, VAM, DSO

Funding acquisition: MJ, SAN, HC, DSO, PR, JSJ, AIN, LKM

Writing-original draft: OA, LKM

All authors reviewed, edited and approved final copy of the manuscript

^✉

Correspondence to: Dr Oluwatosin A. Ayeni, Non-communicable Diseases Research Division, Wits Health Consortium, 31 Princess of Wales Terrace, Parktown, Johannesburg, South Africa, Phone: +27 61 2239417, +27 11 2740000, Oayeni@witshealth.co.za

PMCID: PMC7649092 NIHMSID: NIHMS1638744 PMID: 31600408

Abstract

Multimorbidity in women with breast cancer may delay presentation, affect treatment decisions and outcomes. We described the multimorbidity profile of women with breast cancer, its determinants, associations with stage at diagnosis and treatments received. We collected self-reported data on five chronic conditions (hypertension, diabetes, cerebrovascular diseases, asthma/chronic obstructive pulmonary disease, tuberculosis), determined obesity using body mass index (BMI), and tested HIV status, in women newly diagnosed with breast cancer between January 2016 and April 2018 in 5 public hospitals in South Africa. We identified determinants of ≥2 of the 7 above mentioned conditions (defined as multimorbidity), multimorbidity itself with stage at diagnosis (advanced (III-IV) vs. early (0-II)) and multimorbidity with treatment modalities received. Among 2281 women, 1001 (44%) presented with multimorbidity. Obesity (52.8%), hypertension (41.3%), HIV (22.0%) and diabetes (13.7%) were the chronic conditions that occurred most frequently. Multimorbidity was more common with older age (OR=1.02; 95% CI 1.01–1.03) and higher household socioeconomic status (HSES) (OR=1.06; 95% CI 1.00–1.13). Multimorbidity was not associated with advanced stage breast cancer at diagnosis, but for self-reported hypertension there was less likelihood of being diagnosed with advanced stage disease in the adjusted model (OR 0.80; 95% CI 0.64–0.98). Multimorbidity was associated with first treatment received in those with early stage disease, p=0.003. The prevalence of multimorbidity is high among patients with breast cancer. Our findings suggest that multimorbidity had a significant impact on treatment received in those with early stage disease. There is need to understand the impact of multimorbidity on breast cancer outcomes.

Keywords: Breast cancer, chronic conditions, multimorbidity, stage at diagnosis and South African women

INTRODUCTION

Breast cancer is the most commonly diagnosed cancer and the leading cause of cancer death in women worldwide, accounting for 6.6% of all cancer deaths.¹ In South Africa, it is also the most common cancer among women, accounting for 20.8% of all female cancers. Age-standardized annual incidence rates are 18 per 100,000 in black women, 49 in women of mixed ancestral, 57 in the Asian women, and 88 in white South African women.²

The strongest determinant of breast cancer survival is stage at diagnosis^3–5 and over 50% of women in South Africa have advanced stage disease at diagnosis.⁶ A similar situation exists across sub-Saharan Africa (SSA), attributable in part to modifiable factors which include lack of early diagnosis strategies at the primary care level,⁷ inadequate knowledge and awareness of breast cancer, inadequate access to care, socioeconomic factors,^{5, 6, 8} as well as tumour biology.⁹

In addition to late diagnosis, another challenge to breast cancer control in South Africa may be the presence of chronic conditions at the time of breast cancer diagnosis. Multimorbidity has been defined as the presence of two or more chronic conditions in the same individual.¹⁰ In South Africa, the effective treatment of infectious diseases, such as human immunodeficiency virus (HIV), has extended life expectancy, leading to increases in the prevalence of age-related chronic conditions. The number of people with two or more chronic conditions, such as cancer, hypertension, diabetes, obesity, HIV and tuberculosis is also growing rapidly and the burden is worse in poor communities.¹¹ Patients with multimorbidity are at higher risk for inadequate disease management due to independent management of each disease within overburdened health systems, significant out of pocket costs and poor adherence for the patients who are likely to have to take more medications, adverse drug reactions, and potential drug-drug, drug-enzyme and drug-gene interactions affecting treatment efficacy.^{10, 12} Further, patients with multimorbidity may be at additional risk of sub-standard care due to a high possibility of obtaining conflicting advice on care and treatment.^{13, 14} The burden of multimorbidity, its determinants and impact on healthcare is well established in high income countries,^{15, 16} but there are few studies in low and middle income countries.^{17, 18} Many women with breast cancer have other chronic diseases at the time of their breast cancer diagnosis, and these conditions may affect the stage at diagnosis of breast cancer, treatment modality, and prognosis.^19–22 It has been demonstrated that patients with cancer and other chronic diseases have reduced quality of life and survival.^22–24

In our previous work describing the prevalence of multimorbid health conditions among 798 black South African women with and without breast cancer, although multimorbidity prevalence in cases was not higher than in the age-matched general population, the prevalence in cases were themselves high: 79.5% were overweight or obese, 59.2% were hypertensive, 46.6% had impaired fasting plasma glucose, 16.5% were HIV positive and 64.2% had ≥2 chronic conditions.²⁵ Other South African studies reported that the prevalence of multimorbidity increases with age²⁶ and is associated with higher household income.¹⁷

The prevalence and patterns of multimorbidity in a given cancer patient population have important implications for targeted patient-oriented prevention, diagnosis, treatment, disease control and improved outcome strategies.²⁷ The aim of this study is four-fold: (i) to describe the prevalence and patterns of multimorbidity in women newly diagnosed with breast cancer in five public hospitals in South Africa; (ii) to determine socio-demographic factors associated with different chronic conditions; (iii) to determine the factors associated with multimorbidity; and (iv) to assess whether multimorbidity is associated with breast cancer stage at diagnosis and first treatment received.

METHODS

We used data from a cohort of women enrolled in the ongoing South African Breast Cancer and HIV Outcomes study (SABCHO) collected between January 2016 and April 2018.²⁸ Five public hospital breast centres across Gauteng and KwaZulu-Natal provinces served as recruitment sites: Chris Hani Baragwanath Academic Hospital (Soweto hospital, Johannesburg), Charlotte Maxeke Johannesburg Academic Hospital (Central Johannesburg hospital), Addington Hospital and Inkosi Albert Luthuli Central Hospital in Durban (KwaZulu-Natal (KZN) Durban hospitals), Grey’s Hospital, Pietermaritzburg (KZN Pietermaritzburg hospital) and Ngwelezane Hospital (KZN rural hospital). The tertiary hospitals where all patients were enrolled on the study and received their treatments are located in city centres. The Central Johannesburg, KZN Durban hospitals and KZN Pietermaritzburg hospital all provide diagnostic services, surgery, chemotherapy, radiation therapy and endocrine treatments. The Soweto hospital, Johannesburg provides diagnostic, surgical and endocrine treatment services and patients have to travel to the Central Johannesburg hospital for chemotherapy and radiation treatments. The KZN rural hospital provides only diagnostic services, endocrine treatments and follow up surveillance of patients following surgical, chemotherapy and radiation therapy treatments at KZN Durban hospitals located more than 200km away. In the KwaZulu-Natal province most patients undergo diagnostic procedures in the district referral hospitals and surgery is performed either at district hospitals or at the tertiary hospital centres. Some Patients have to bare out of pocket transport costs to access their treatment while others travel using hospital transport. The women differ in education and socioeconomic status with the more rural communities accessing the KZN Pietermaritzburg and KZN rural hospitals generally less educated and more impoverished than urban communities” The patient referral demographics for each participating hospital are shown in table 1.

Table 1:

Characteristics of the hospital sites

	Soweto hospital, Johannesburg¹	Central Johannesburg hospital²	KZN Durban hospitals³	KZN Pietermaritzburg hospital⁴	KZN rural hospital⁵
Location	Johannesburg (Gauteng province)	Johannesburg (Gauteng province)	Durban (KwaZulu-Natal Province)	Pietermaritz-burg (KwaZulu-Natal Province)	Empageni (KwaZulu-Natal Province)
Main population catchment area (N)	Soweto (3 million)	Johannesburg, East and Central (1.5 million)	Durban metropolitan, (3.5 million)	Western KwaZulu-Natal and distant rural communities (3.5 million),	Uthungulu, Umkhanyakude, Zululand (3 million)
Surgery	Surgery performed at this hospital	Surgery performed at this hospital	Surgery performed at this hospital and in secondary hospitals	Surgery performed at this hospital and in secondary hospitals	Surgery performed at this hospital
Oncology capacity (Chemotherapy and radiotherapy)	None. Patients are referred to Central Johannesburg hospital	Full oncology capacity	Full oncology capacity	Full oncology capacity	None. Patients are referred to KZN Durban hospitals.

Open in a new tab

Chris Hani Baragwanath Academic Hospital, Soweto Johannesburg

Charlotte Maxeke Johannesburg Academic Hospital

Addington Hospital and Inkosi Albert Luthuli Central Hospital, Durban, KwaZulu-Natal

⁴

Grey’s Hospital, Pietermaritzburg, KwaZulu-Natal

⁵

Ngwelezane Hospital, KwaZulu-Natal

This study was approved by the University of the Witwatersrand Human Research Ethics Committee (Approval Number: Ml50351, dated: 6th May 2015), and the Institutional Review Board of Columbia University (protocol number AAAQ1359, dated 1st January 2016). This study was performed in accordance with the Declaration of Helsinki.

Recruitment of participants

Women who were newly diagnosed with invasive breast cancer, were ≥18 years of age and had resided in South Africa for ≥ 5 years were eligible for enrolment in the SABCHO study. Patients who had a previous cancer diagnosis (excluding non-melanoma skin cancer or non-invasive cervical cancer), had previously received > 1 month of radiotherapy or chemotherapy, had previously received any endocrine therapy, or were unable to give informed consent, were excluded.

Study procedures

Women were enrolled on the day of the diagnostic visit following voluntary written informed consent. Participants consented to HIV testing. Those who tested positive were referred for ART initiation prior to commencement of cancer treatment. Those who refused HIV testing were still included in the study and HIV status was recorded as unknown.

Socio-demographic and anthropometric measures

Trained study staff collected detailed socio-demographic information (age, self-identified ethnicity, marital status, employment status, highest level of education completed, residential address); reproductive history (number of full term pregnancies, age at first full-term pregnancy, use of oral and injectable contraceptives, use of hormone replacement therapy) and behavioural factors (alcohol consumption, smoking). Tumour American Joint Committee on Cancer (7th edition) stage, grade, receptor status and first treatment received data were extracted from medical records.²⁹ Participants were grouped by stage at diagnosis as early (I-II) or advanced (III-IV).

Household socio-economic scores were determined using self-reported home ownership, car ownership, possession of a washing machine or microwave, presence of a flush toilet inside the home and presence of indoor running water. We assigned a score of 1 for each of these items and the scores were added out of a total of six. We documented the residential address of each participant at enrolment and computed the shortest straight-line distance to the enrolling hospital in kilometres (km).

We assessed seven chronic conditions: obesity, hypertension, diabetes, cerebrovascular disease, asthma/COPD, HIV infection and tuberculosis. HIV status was tested using the enzyme-linked immunosorbent assay through the National Health Laboratory Services. Body weight and height measurements were measured at enrolment, and obesity was defined as a body mass index (BMI) ≥30.0 kg/m2. Patients were asked if they were ever treated for tuberculosis and whether they had ever been diagnosed and treated for the other four conditions (hypertension, diabetes, cerebrovascular disease and asthma/COPD). We defined multimorbidity as having ≥ 2 of these seven chronic conditions.

Data availability:

Data for this study contain confidential patient information. Data supporting the results reported in this article may be requested from the corresponding author.

Statistical analysis

Determinants of each chronic condition and multimorbidity (≥2 chronic conditions) were examined in a multisite pooled analysis using bivariate and multivariate logistic regression. Odds ratios (OR) for multimorbidity were examined in 3 models by adding potentially associated factors in a stepwise fashion. Model 1 we examined all socio-demographic factors (age, ethnicity, marital status, highest level of education, employment status and household socio-economic status). Model 2 included socio-demographic and geographical factors (hospital site and residential distance from the hospital), and model 3 included socio-demographic, geographical factors and behavioural factors (alcohol intake and smoking). Finally, multivariable logistic regression models were used to examine whether chronic conditions and multimorbidity were associated with advanced stage breast cancer (stage III-IV). We included in the multivariate models variables for which p-values were <0.1 in bivariate analysis with advanced stage breast cancer. ORs were examined in two models (model A (unadjusted) and model B (which was adjusted for age, race, highest level of education and hospital site). We further examined the associations between multimorbidity and first treatment received (primary surgery, neoadjuvant chemotherapy or neoadjuvant endocrine therapy) by their stage at diagnosis using Pearson’s Chi-squared test. Analysis was performed using Stata version 15 (StataCorp Ltd, Texas, USA).

RESULTS

A total of 2281 women were included in the study: 795 (34.9%) from Soweto, Johannesburg hospital, 511 (22.4%) from Central Johannesburg hospital, 435 (19.1%) from KZN Durban hospitals, 462 (20.2%) from KZN Pietermaritzburg hospital and 77 (3.4%) from KZN rural hospital (Table 2). The mean age at diagnosis was 56.2 ± 14.3 years; women from the two Johannesburg hospitals compared to the KZN cities hospitals (Durban and Pietermaritzburg) were significantly younger and more likely to be married. In the combined sample, the majority (76.9%) of women were black Africans while KZN Durban hospitals had the highest percentage of Asians (39.4%) compared to other sites, and central Johannesburg hospital had the highest number of white women (17.0%).

Table 2:

Socio-demographic characteristics, prevalence of chronic conditions and stage at breast cancer diagnosis in women newly diagnosed with breast cancer in South Africa, by study-site.

	Soweto, Johannesburg hospital (n=795)	Central Johannesburg hospital (n=511)	KZN Durban hospitals (n=436)	KZN Pietermaritzburg hospital n=462	KZN rural hospital n=77	Total N=2281	P value
Age in years, mean ± SD	55.1 ± 14.3	55.3 ± 14.2	58.1 ± 13.6	57.3 ± 14.7	55.6 ± 14.8	56.2 ± 14.3	0.002
Ethnicity
Asian	7 (0.9%)	18 (3.5%)	172 (39.4%)	63 (13.6%)	1 (1.3%)	261 (11.4%)	<0.001
Black	734 (92.3%)	378 (74.0%)	203 (46.6%)	364 (78.8%)	75 (97.4%)	1,754 (76.9%)
Mixed	42 (5.3%)	28 (5.5%)	17 (3.9%)	18 (3.9%)	0 (0.0%)	105 (4.6%)
White	12 (1.5%)	87 (17.0%)	44 (10.1%)	17 (3.7%)	1 (1.3%)	161 (7.1%)
Marital status
Married/co-habiting	329 (41.5%)	212 (41.5%)	156 (36.1%)	140 (30.5%)	24 (31.2%)	861 (37.9%)	0.001
Unmarried	464 (58.5%)	299 (58.5%)	276 (63.9%)	319 (69.5%)	53 (68.8%)	1,411(62.1%)
Highest level of education
Completion of informal/primary education	192 (24.2%)	90 (17.8%)	127 (29.7%)	180 (39.2%)	38 (52.1%)	627 (27.7%)	<0.001
Completion of high school/any tertiary education	602 (75.8%)	416 (82.2%)	301 (70.3%)	279 (60.8%)	35 (47.9%)	1,633 (72.3%)
Employment status
Unemployed	583 (73.3%)	335 (65.6%)	342 (78.4%)	373 (80.7%)	63 (81.8%)	1,696 (74.4%)	<0.001
Employed	212 (26.7%)	176 (34.4%)	94 (21.6%)	89 (19.3%)	14 (18.2%)	585 (25.6%)
^aHousehold socio-economic status (score 0–6), median(IQR)	4(2–5)	4(3–5)	4 (2–4)	2 (1–4)	1 (1–2)	2 (3–5)	<0.001
Residential distance from hospital site (km), median (IQR)	12.8 (6.8–27.2)	19.7 (8.7–34.7)	20.4 (16.056.1)	84.1 (8.3–155.6)	58.9 (29.1144.2)	20.6 (8.9–42.9)	<0.001
Have you ever consumed alcohol?
No	616 (77.5%)	407 (79.7%)	359 (82.3%)	382 (82.7%)	68 (88.3%)	1,832 (80.3%)	0.039
Yes	179 (22.5%)	104 (20.3%)	77 (17.7%)	80 (17.3%)	9 (11.7%)	449 (19.7%)
Have you ever smoked?
No	718 (90.3%)	427 (83.6%)	367 (84.2%)	413 (89.4%)	74 (96.1%)	1,999 (87.6%)	<0.001*
Yes	77 (9.7%)	84 (16.4%)	69 (15.8%)	49 (10.6%)	3 (3.9%)	282 (12.4%)
Stage at diagnosis
Early stage (0-II)	402 (50.6%)	196 (38.4%)	172 (40.0%)	181 (39.2%)	14 (18.4%)	965 (43.9%)	<0.001
Advanced stage (III & IV)	393 (49.4%)	315 (61.6%)	258 (60.0%)	281 (60.8%)	62 (81.6%)	1309 (56.1%)
Receptor subtype
Luminal A	111 (14.1%)	123 (24.6%)	152 (35.0%)	71 (15.4%)	19 (24.7%)	476 (21.1%)	<0.001
Luminal B	467 (59.5%)	232 (46.4%)	172 (39.7%)	255 (55.3%)	30 (38.9%)	1156 (51.2%)
HER2 enriched	94 (12.0%)	69 (13.8%)	50 (11.5%)	58 (12.6%)	14 (18.2%)	285 (12.6%)
Triple negative	113 (14.4%)	76 (15.2%)	60 (13.8%)	77 (16.7%)	14 (18.2%)	340 (15.1%)
Chronic conditions considered
Nutritional status (BMI)
^aUnderweight & Normal (≤24.9 kg/m²)	160 (21.8%)	98 (20.6%)	96 (23.4%)	109 (24.4%)	19 (24.7%)	482 (22.5%)	0.094
Overweight (25–29.9 kg/m²)	160 (21.8%)	133 (28.0%)	117 (28.5%)	103 (23.1%)	17 (22.1%)	530 (24.7%)
Obese (≥ 30.0 kg/m²)	415 (56.4%)	244 (51.4%)	197 (48.1%)	234 (52.5%)	41 (53.2%)	1,131 (52.8%)
Self-reported hypertension
No	487 (61.3%)	322 (63.0%)	224 (51.4%)	254 (55.0%)	51 (66.2%)	1,338 (58.7%)	<0.001
Yes	308 (38.7%)	189 (37.0%)	212 (48.6%)	208 (45.0%)	26 (33.8%)	943 (41.3%)
HIV status
Negative	564 (74.7%)	401 (82.5%)	342 (85.3%)	343 (75.4%)	45 (59.2%)	1,695 (78.0%)	<0.001
Positive	191 (25.3%)	85 (17.5%)	59 (14.7%)	112 (24.6%)	31 (40.8%)	478 (22.0%)
Self-reported diabetes
No	720 (90.6%)	465 (91.0%)	324 (74.3%)	388 (84.0%)	71 (92.2%)	1,968 (86.3%)	<0.001
Yes	75 (9.4%)	46 (9.0%)	112 (25.7%)	74 (16.0%)	6 (7.8%)	313 (13.7%)
Self-reported prior tuberculosis
No	742 (93.3%)	491 (96.1%)	394 (90.4%)	417 (90.3%)	70 (90.9%)	2,114 (92.7%)	0.002
Yes	53 (6.7%)	20 (3.9%)	42 (9.6%)	45 (9.7%)	7 (9.1%)	167 (7.3%)
Self-reported asthma/COPD
No	763 (96.0%)	482 (94.3%)	399 (91.5%)	444 (96.1%)	71 (92.2%)	2,159 (94.7%)	0.007
Yes	32 (4.0%)	29 (5.7%)	37 (8.5%)	18 (3.9%)	6 (7.8%)	122 (5.3%)
Self-reported cerebrovascular disease
No	773 (97.2%)	503 (98.4%)	412 (94.5%)	453 (98.1%)	74 (96.1%)	2,215 (97.1%)	0.005*
Yes	22 (2.8%)	8 (1.6%)	24 (5.5%)	9 (1.9%)	3 (3.9%)	66 (2.9%)

Open in a new tab

HSES: Household socio-economic status (possessions) score determined from: (home ownership=1, motor vehicle=1, microwave=1, washing machine=1, flush toilet=1, indoor running water=1) Denominator= 6 (1 for Yes, 0 for No).

54 women were underweight

KZN (KwaZulu-Natal)

Missing values for covariates were as follows: marital status (n=9), highest level of education (n=21), BMI (n=138), HIV status unknown (n=108), stage at diagnosis (n=7).

P value* (Fisher’s exact test).

BMI (Body mass index), COPD (Chronic obstructive pulmonary disease)

SD (standard deviation), IQR (interquartile range), km (kilometres)

In the combined sample, most of the women had at least a high school education (72.3%), but there was a significant difference between the sites as 52.1% of women attending KZN rural hospital had only completed primary education. Overall >50% of women were unemployed; women from the Johannesburg hospitals and KZN Durban hospitals had higher HSES than women from KZN Pietermaritzburg and KZN rural hospitals. Participants from KZN Pietermaritzburg hospital had the furthest distance to travel and women from Soweto, Johannesburg hospital travelled the shortest distance (84.1 vs. 12.8km) (Table 2). There was a significant difference between the sites for smoking and alcohol consumption, with the KZN rural hospital reporting the least for both compared to the other sites. Overall, 56.1% of the women presented with advanced stage breast cancer. Soweto, Johannesburg hospital was the only site where more than half (50.6%) presented at an early (stage I-II). Most (72.3%) of the women had oestrogen and progesterone receptor positive (Luminal A and B) breast cancer (Table 2).

Prevalence of chronic conditions

At enrolment, 19.0% of the women reported no chronic conditions (hypertension, diabetes, cerebrovascular diseases, asthma/chronic obstructive pulmonary disease, tuberculosis, HIV and obesity) ranging from 10.4% in KZN rural hospital to 25.6% at Central Johannesburg hospital (Figure 1 and supplementary table 1). In the combined sample, 52.8% of women were obese. The second most common chronic condition was hypertension (41%), which ranged from 34% at the KZN rural hospital to 48.6% at KZN Durban hospitals. HIV was the third most prevalent chronic condition (22.0%) but with considerable variation, highest (40.8%) at the KZN rural hospital and lowest at the KZN Durban hospitals (14.7%). Diabetes ranked as the fourth most prevalent chronic condition affecting 14% of women, with <10% at the two Johannesburg hospitals. Finally ranking fifth, sixth and seventh were tuberculosis, COPD/asthma and cerebrovascular disease, respectively, with each present in less than 10% of breast cancer patients at each site. Overall, 43.9% of breast cancer patients met our definition of multimorbidity (presence of ≥2 additional chronic conditions) and KZN Durban hospitals had the highest proportion (51.6%) (Figure 1 and supplementary table 1).

Figure 1. — Percentage of women newly diagnosed with breast cancer from each hospital site in South Africa, with none, one, two or more of obesity, self-reported hypertension, self-reported diabetes, self-reported cerebrovascular disease, self-reported asthma/COPD, HIV and self-reported prior tuberculosis.

KZN (KwaZulu-Natal)

Bivariate and multivariable logistic regression models of associations between socio-demographic variables and each chronic condition are presented in supplementary table 3 and table 3 respectively. In the multivariate analysis, with every 1 year increase in age, there was an 8% increase in the odds of hypertension, 4% increase in the odds of having diabetes, 4% increase in the odds of cerebrovascular disease, 6% reduced odds of HIV and 3% reduced odds of tuberculosis. When compared to the black women, white women had reduced odds of all the chronic conditions except asthma and the Asian women had increased odds of hypertension, diabetes and asthma/COPD. Of the three measures of socio-economic status, HSES was most frequently associated with the presence of a chronic condition. Women with higher HSES were more likely to be obese, hypertensive, diabetic, report asthma/COPD, and were less likely to be living with HIV (Table 3). The variance in the different chronic conditions that could be explained by age, race and socio-economic status ranged from 2% for obesity to 22% for HIV infection.

Table 3:

Pooled multivariate logistic regression model of socio-demographic characteristics and association with chronic conditions in women newly diagnosed with breast cancer in South Africa.

Socio-demographic characteristics	Obesity (BMI ≥ 30.0 kg/m2)	Hypertension	Diabetes	Cerebrovascular disease	Asthma/COPD	HIV positive	Tuberculosis
	OR (95% CI)	OR (95% CI)	OR (95% CI)	OR (95% CI)	OR (95% CI)	OR (95% CI)	OR (95% CI)
Age in years	1.00 (0.99–1.00)	1.08 (1.07–1.09)^b	1.04 (1.03–1.05)^b	1.04 (1.02–1.06)^b	0.99 (.98–1.01)	0.94 (0.93–0.95)^b	0.97 (0.95–0.98)^b
Race
White	1.0 (Ref)	1.0 (Ref)	1.0 (Ref)	1.0 (Ref)	1.0 (Ref)	1.0 (Ref)	1.0 (Ref)
Asian	0.8 (0.6–1.3)	3.3 (2.1–5.3)^b	9.4 (4.5–19.6)^b	1.1 (0.4–3.1)	1.0 (0.5–2.1)	0.3 (0.1–3.6)	0.8 (0.2–3.0)
Black	2.1 (1.4–2.9)^b	2.4 (1.6–3.5)^b	2.8 (1.4–5.8)^b	0.6 (0.2–1.6)	0.5 (0.3–0.9) ^a	16.4 (4.0–67.2)^b	2.5 (0.9–6.9)
Mixed	1.5 (0.9–2.6)	2.8 (1.6–5.0)^b	4.4 (1.9–10.3)^b	0.5 (0.1–2.8)	0.7 (0.3–1.8)	5.2 (1.1–25.7)^a	2.0 (0.5–7.2)
Highest level of education
Primary education and below	1.0 (Ref)	1.0 (Ref)	1.0 (Ref)	1.0 (Ref)	1.0 (Ref)	1.0 (Ref)	1.0 (Ref)
Secondary and above	1.2 (0.9–1.5)	1.0 (0.8–1.2)	0.9 (0.6–1.2)	0.8 (0.4–1.4)	0.8 (0.4–1.2)	1.2 (0.9–1.6)	0.7 (0.5–1.1)
Employment status
Unemployed	1.0 (Ref)	1.0 (Ref)	1.0 (Ref)	1.0 (Ref)	1.0 (Ref)	1.0 (Ref)	1.0 (Ref)
Employed	1.2 (0.9–1.4)	0.8 (0.6–1.0)	0.7 (0.5–0.9)^a	0.7 (0.3–1.6)	0.7 (0.4–1.2)	1.2 (0.9–1.5)	0.8 (0.6–1.2)
Household socio-economic status (score 0–6)	1.2 (1.1–1.2)^b	1.1 (1.0–1.1) ^a	1.1 (1.0–1.2) ^a	0.9 (0.8–1.1)	1.2 (1.0–1.3) ^a	0.8 (0.8–0.9)^b	0.9 (0.8–1.0)
R2	0.02	0.19	0.11	0.06	0.02	0.22	0.04

Open in a new tab

OR (Odds ratio), 95% CI (95% confidence interval), COPD (Chronic obstructive pulmonary disease),

Significant at p<0.05,

Significant at p<0.001

In table 4a, bivariate logistic regression showed positive associations between multimorbidity and age, marital status, hospital sites and negative associations with ethnicity, highest level of education, employment status, alcohol consumption and smoking. These significant associations at the bivariate level were further explored in multivariate logistic regressions in three models (Table 4b). In model 1 (socio-demographic variables only), age contributed independently to an increased odds of multimorbidity and being white was associated with a decreased odds of multimorbidity. In model 2 (socio-demographic and geographical factors) age and white ethnicity remained significant and being Asian was associated with a lower odds of multimorbidity. Women from KZN Durban hospitals (OR=1.90, 95% CI 1.43–2.53) and KZN Pietermaritzburg hospital (OR 1.49, 95% CI 1.13–1.98) had an increased odds of having multimorbidity and the factors in model 2 were able to explain 3.1% of the variance in multimorbidity. In model 3 (socio-demographic, geographical and behavioural variables), age, white and Asian ethnicity, enrolment at KZN Durban hospitals and KZN Pietermaritzburg hospital remained significant, although higher HSES was now associated with an increased odds of multimorbidity (OR=1.06, 95% CI 1.00–1.13), none of the behavioural factors were significant in the model (Table 4b).

Table 4a:

Pooled associations between socio-demographic, geographical and behavioural factors with chronic conditions (≥2 (multimorbidity) vs. <2) in women newly diagnosed with breast cancer.

Characteristics	< 2 chronic conditions N=1280	≥2 chronic conditions (multimorbidity) N=1001	Bivariate analysis OR (95% CI)	P value
Age in years, mean ± SD	54.4 ± 15.0	58.4 ±13.0	1.02 (1.01–1.03)	<0.001^*
Ethnicity (%)
Black	976 (76.2%)	778 (77.7%)	1.00 (Referent)
Asian	130 (10.2%)	131 (13.1%)	1.26 (0.97–1.64)	<0.001^*
Mixed	56 (4.4%)	49 (4.9%)	1.10 (0.74–1.63)
White	118 (9.2%)	43 (4.3%)	0.46 (0.32–0.66)
Marital status (%)
Married/co-habiting	513 (40.2%)	348 (34.9%)	1.00 (Ref)	0.010^*
Unmarried	763 (59.8%)	648 (65.1%)	1.25 (1.05–1.49)
Highest level of education (%)
Primary education and below	324 (24.4%)	303 (30.7%)	1.00 (Referent)	0.006^*
Secondary education and above	949 (74.6%)	684 (69.3%)	0.77 (0.64–0.93)
Employment status (%)
Unemployed	921 (71.9%)	775 (77.4%)	1.00 (Referent)	0.003^*
Employed	359 (28.1%)	226 (22.6%)	0.75 (0.62–0.91)
Household socio-economic status (score 0–6), median (IQR)	3(2–5)	4(2–5)	1.02 (0.97–1.07)	0.488
Hospital site (%)
Soweto, Johannesburg	472 (36.9%)	323 (32.3%)	1.00 (Referent)	<0.001^*
Central Johannesburg	318 (24.8%)	193 (19.3%)	0.89 (0.71–1.11)
KZN Durban	211 (16.5%)	225 (22.5%)	1.56 (1.23–1.97)
KZN Pietermaritzburg	240 (18.8%)	222 (22.2%)	1.35 (1.07–1.70)
KZN rural	39 (3.1%)	38 (3.8%)	1.42 (0.89–2.28)
Residential distance from hospital site (km), median (IQR)	19.9 (8.7–39.5)	21.8 (9.1–48.1)	1.00 (1.00–1.01)	0.033^*
Have you ever consumed alcohol? (%)
No	1004 (78.4%)	828 (82.7%)	1.00 (Ref)	0.011^*
Yes	276 (21.6%)	173 (17.3%)	0.76 (0.62–0.94)
Have you ever smoked? (%)				0.044^*
No	1106 (86.4%)	893 (89.2%)	1.00 (Ref)
Yes	174 (13.6%)	108 (10.8%)	0.77 (0.60–0.99)

Open in a new tab

OR (Odds ratio) = odds of reporting ≥2 chronic conditions compared with <2 chronic conditions,

variables significant at p<0.05.

Table 4b:

Pooled multiple logistic regression model of socio-demographic, geographical and behavioural factors associated with ≥2 chronic conditions (multimorbidity) in women newly diagnosed with breast cancer.

Characteristics	Model 1 (Socio-demographic factors) OR (95% CI)	Model 2 (Socio-demographic and geographical factors) OR (95% CI)	Model 3 (Socio-demographic, geographical and behavioural factors) OR (95% CI)
Age in years	1.02 (1.01–1.03)^b	1.02 (1.01–1.03)^b	1.02 (1.01–1.03)^b
Ethnicity
Black	1.00 (Ref)	1.00 (Ref)	1.00 (Ref)
Asian	1.05 (0.79–1.38)	0.70 (0.50–0.97)^a	0.70 (0.50–0.97)^a
Mixed	0.99 (0.66–1.50)	0.93 (0.61–1.40)	0.97 (0.63–1.49)
White	0.39 (0.27–0.58)^b	0.34 (0.23–0.51)^b	0.36 (0.24–0.54)^b
Marital status
Married/co-habiting	1.00 (Ref)	1.00 (Ref)	1.00 (Ref)
Unmarried	1.09 (0.90–1.30)	1.06 (0.89–1.28)	1.07 (0.89–1.29)
Highest level of education
Primary education and below	1.00 (Ref)	1.00 (Ref)	1.00 (Ref)
Secondary education and above	1.04 (0.84–1.29)	1.08 (0.87–1.35)	1.08 (0.87–1.35)
Employment status
Unemployed	1.00 (Ref)	1.00 (Ref)	1.00 (Ref)
Employed	0.93 (0.75–1.14)	0.93 (0.75–1.15)	0.93 (0.75–1.15)
Household socio-economic status (score 0–6)	1.01 (0.96–1.07)	1.06 (0.99–1.13)	1.06 (1.00–1.13)^b
Hospital site
Soweto, Johannesburg		1.00 (Ref)	1.00 (Ref)
Central Johannesburg		1.03 (0.81–1.31)	1.02 (0.80–1.30)
KZN Durban		1.90 (1.43–2.53)^b	1.89 (1.42–2.51)^b
KZN Pietermaritzburg		1.49 (1.13–1.98)^a	1.49 (1.12–1.97)^a
KZN rural		1.57 (0.94–2.63)	1.54 (0.92–2.57)
Residential distance from hospital site (km)		1.00 (0.99–1.00)	1.00 (0.99–1.00)
Have you ever consumed alcohol?
No			1.00 (Ref)
Yes			0.85 (0.67–1.07)
Have you ever smoked?
No			1.00 (Ref)
Yes			0.96 (0.71–1.29)
R²	0.028	0.031	0.033

Open in a new tab

OR (Odds ratio) = odds of reporting ≥2 chronic conditions compared with <2 chronic conditions

Significant at p<0.05,

Significant at p<0.001 KZN (KwaZulu-Natal)

Bivariate and multivariate logistic regression models of chronic conditions and multimorbidity associated with advanced stage breast cancer disease at diagnosis are presented in tables 5a and 5b respectively. In the bivariate analysis (Table 5a), obesity and hypertension were negatively associated with advanced stage breast cancer at diagnosis. Women who were HIV positive and women who reported prior tuberculosis had positive associations with advanced stage breast cancer at diagnosis. There was no significant association between multimorbidity and advanced stage breast cancer at diagnosis (OR=0.88, 95% CI 0.74–1.04) (Table 5a). The significant variables at p <0.1 were further explored in the multivariate model (Table 5b). In Model A, women who reported having hypertension had lower odds of advanced stage breast cancer and women who were HIV positive had higher odds of advanced stage breast cancer at diagnosis than those women without the disease. In model B, adjusting for age, race, highest level of education and hospital site, self-reported hypertension (OR=0.80, 95% CI 0.64–0.98) was the only chronic condition associated with advanced stage breast cancer disease (Table 5b).

Table 5a:

Pooled bivariate analyses of the association between chronic conditions and advanced stage disease amongst women newly diagnosed with breast cancer.

Characteristics	Bivariate analysis OR (95% CI)	P value
Obese	0.84 (0.70–0.99)	0.041^*
Self-reported hypertension	0.74 (0.62–0.87)	<0.001^*
Self-reported diabetes	0.80 (0.63–1.01)	0.064
Self-reported cerebrovascular disease	1.00 (0.61–1.64)	0.998
Self-reported asthma/COPD	0.82 (0.57–1.18)	0.286
HIV positive	1.52 (1.23–1.87)	<0.001^*
Self-reported prior tuberculosis	1.49 (1.07–2.08)	0.019^*
Multimorbidity	0.88 (0.68–1.10)	0.138

Open in a new tab

Significant at p<0.05, COPD (Chronic obstructive pulmonary disease)

Table 5b:

Pooled multivariate logistic regression models of chronic conditions associated with advanced stage disease (stages III-IV) in women newly diagnosed with breast cancer.

Characteristics	Model A OR (95% CI)	P value	Model B OR (95% CI)	P value
Obese	0.92 (0.77–1.09)	0.340	0.85 (0.71–1.03)	0.098
Self-reported hypertension	0.77 (0.64–0.92)	0.005^*	0.80 (0.64–0.98)	0.036^*
HIV positive	1.36 (1.09–1.71)	0.008^*	1.07 (0.83–1.38)	0.582
Self-reported prior tuberculosis	1.32 (0.93–1.88)	0.123	1.22 (0.84–1.78)	0.286
R²	0.013		0.040

Open in a new tab

Significant at p<0.05, model A (unadjusted), model B (adjusted for age on presentation, race, highest level of education and hospital site)

In women with early stage (stages 0-II) breast cancer, there was a significantly higher proportion of women with multimorbidity who received neoadjuvant endocrine therapy (18.2%) compared to those without multimorbidity who received neoadjuvant endocrine treatment (10.5%), p=0.003 (Table 6).

Table 6:

Association between multimorbidity and first treatment received by stage at diagnosis in women newly diagnosed with breast cancer.

	Stages 0-II (Early stage)				Stages III & IV (Advanced stage)
Multimorbidity	Primary surgery	Neoadjuvant chemotherapy	Neoadjuvant endocrine therapy	P value	Primary surgery	Neoadjuvant chemotherapy	Neoadjuvant endocrine therapy	P value
	N=662 (%)	N=141 (%)	N=131 (%)		N=125 (%)	N=881 (%)	N=206 (%)
No (< 2 chronic conditions)	372 (73.7)	80 (15.8)	53 (10.5)	0.003	67 (9.6)	520 (74.7)	109 (15.7)	0.184
Yes (≥ 2 chronic conditions)	290 (67.6)	61 (14.2)	78 (18.2)		58 (11.2)	361 (70.0)	97 (18.8)
^*Total	662 (70.9)	141 (15.1)	131 (14.0)		125 (10.3)	881 (72.7)	206 (17.0)

Open in a new tab

Total for stages 0-II (early stage) = 934, total for stages III & IV (advanced stage) = 1212. Some 135 women had missing treatment information. Row percentages shown

DISCUSSION

In this study, we found that among women newly diagnosed with breast cancer from 5 public hospitals across South Africa, more than 80% reported at least one chronic condition and 43.9% had two or more chronic conditions. Older women, higher HSES, and women enrolled at the KZN Durban and Pietermaritzburg hospitals, had higher odds of multimorbidity, while the Asian and white women were less likely to present with multimorbidity when compared to black women. Hypertensive women were less likely to have advanced stage breast cancer at diagnosis, whilst multimorbidity was not associated with advanced stage breast cancer at diagnosis.

The burden of multimorbidity in our cohort was high with 43.9% of the whole sample reporting two or more chronic conditions. Multimorbidity is of increasing concern in South Africa and this high prevalence of non-communicable and communicable multimorbidity has significant implications for the mode of health care delivery and the health system. Severe chronic conditions narrow treatment choices and are associated with poor quality of life and survival, especially in postmenopausal women.^{24, 30} In South Africa chronic disease management is meant to be delivered using the integrated chronic disease management approach;³¹ however this is yet to be achieved as people with multimorbidity receive fragmented and inefficient health care delivery.^{32, 33} There is a need for an effective integrated and comprehensive array of health services spanning from primary to tertiary levels.

The most common chronic condition reported in this sample of women with breast cancer was obesity (52.8%), which was much higher than report from the recent South African national survey with obesity prevalence of 41.7% in adult women (ranging from 30.6% among the white women to 49.2% among the Asian women).³⁴

Our results indicate that multimorbidity in women with breast cancer is associated with socio-demographic characteristics (age, race and HSES) and hospital site where they are seeking health care. It is well accepted that multimorbidity is associated with increasing age, due to age-related chronic health conditions.³⁵ The Asians in South Africa have been reported to have a high burden of chronic conditions,¹⁷ which is in keeping with our findings for individual chronic conditions in this population group. This is primarily the result of high prevalence of hypertension and diabetes in the Asian group; HIV prevalence was low. A study conducted among Asian South Africans living in Durban also reported a high burden of hypertension (47.5%), diabetes (20.1%), and obesity (32.4%).³⁶ Despite this high finding of the prevalence of individual chronic conditions in Asian women in our cohort, they were still less likely to present with multimorbidity (OR=0.70, 95% CI 0.50–0.97) compared to the black African women. This may be due to the higher prevalence of obesity in the black women,³⁷ and equally high burden of hypertension. South Africa has the highest burden of hypertension in people >50 years of age in SSA,^{27, 38} HIV-infection and tuberculosis were almost exclusive to the black African women in our cohort. This double burden of communicable and non-communicable diseases in women with breast cancer is of concern and can ultimately impact on treatment, quality of life and survival.

We found that women with higher HSES were more likely to present with multimorbidity, independent of ethnicity. There is a dearth of knowledge on the association between socioeconomic status and multimorbidity in South Africa; however our finding is in contrast to other South African studies that reported that poorer women and those socioeconomically deprived bear a greater burden of multimorbidity.^{39, 40} Women socioeconomically deprived had 50% increased odds of having multimorbidity compared to those not socioeconomically deprived.⁴⁰ This may be due to different definitions and mode of measurement of socio-economic status in the studies.

Patients also experience varying delays within the referral network, as reflected in significant differences in the proportions of advanced stage (stages III & IV) at presentation of patients at the different sites (ranging from 50% (lowest) at the Soweto, Johannesburg hospital and 73% (highest) at the KZN Pietermaritzburg and KZN rural hospital. These challenges are compounded for patients with high multimorbidity who have to visit other clinics for their chronic medications apart for their breast cancer treatments.

Some of our findings on the association between individual chronic conditions, multimorbidity, and stage of breast cancer diagnosis are consistent with previous work in South Africa and SSA. Although obesity was associated with a reduced risk of being diagnosed with advanced stage breast cancer in the bivariate analysis, when included in the multivariable analysis with other chronic conditions this was no longer significant. This is in keeping with studies from Africa and high income countries^41–43 where obesity did not emerge as being associated with advanced stage disease at breast cancer diagnosis. Obesity is a risk factor for luminal type breast cancers, which tend to be slower growing and less likely to be diagnosed at advanced stage.^{44, 45} Obesity is not a comorbidity that has been shown to increase contact with health services in our setting; we therefore cannot imply its protective effect from regular contact with the health facilities.

Women who reported being hypertensive were less likely to have advanced stage of breast cancer at diagnosis, before and after adjusting for potential covariates. This may imply regular contact with the health systems that may result in patient breast cancer education by the health services or a surveillance effect leading to earlier stage at diagnosis.¹⁹ This may also be related to tumour biology because breast cancer in younger women (with less hypertension prevalence) tend to be the more aggressive subtypes.^46–48 HIV infection, prior tuberculosis, diabetes, asthma/COPD, cerebrovascular disease and multimorbidity was not associated with advanced stage breast cancer at diagnosis.

Our findings show that multimorbidity was significantly associated with first treatment received in women with early stage breast cancer. Among women with multimorbidity with early stage breast cancer, a large proportion were placed on less aggressive neoadjuvant endocrine therapy. Older women with multimorbidity are more likely to have poor Eastern Cooperative Oncology Group (ECOG) performance status and not able to withstand the stress of surgery or chemotherapy and are therefore not likely to be placed on standard more aggressive treatments.⁴⁹

Some limitations need to be noted. As the data on the various chronic conditions was self-reported the prevalence of these conditions may be higher than reported. The list of chronic conditions was not comprehensive, and therefore certain health conditions may not have been detected and may result in potential confounding. We had to rely on patient self-reports of their diagnosed comorbidities and that they were on treatment. In South Africa hospital records are not electronically centralised and patient records from different clinics are not readily accessible for diagnostic and treatment details. Another limitation was that in the interview we did not specify if the patient had a current history of tuberculosis or previous history, whilst not ideal, it was not a common prior condition so did not affect overall results, as seen in supplementary table 2 where the tuberculosis rows and columns are all relatively small. Despite these limitations, the strength of this study include the uniquely large sample of South African women with breast cancer and inclusion of hospitals in different geographical regions. We described the burden and pattern of multimorbidity, and difference in chronic conditions between different hospital settings and have also measured factors associated with individual chronic conditions as well as multimorbidity. Further this study provides data on the association between these chronic conditions and multimorbidity, and advanced stage breast cancer at diagnosis, in South Africa women.

Our finding give important insight into demographic and socio-economic factors associated with multimorbidity in women with breast cancer in South Africa. There is need for policymakers to evaluate the effect of multimorbidity on the health system and its impact on the limited health care resources.

In conclusion, we have demonstrated a high prevalence of multimorbidity in women newly diagnosed with breast cancer from different public hospital settings in South Africa, with many of these chronic conditions being associated with socio-demographic factors. Women that were hypertensive were less likely to be diagnosed with advanced stage breast cancer. Our findings contribute to the literature in understanding socio-demographic factors associated with multimorbidity in South Africa. There is need to understand how multimorbidity may impact breast cancer outcomes.

Supplementary Material

Ayeni supp table 1

NIHMS1638744-supplement-Ayeni_supp_table_1.png^{(29.2KB, png)}

Ayeni supp table 2

NIHMS1638744-supplement-Ayeni_supp_table_2.png^{(50.9KB, png)}

Ayeni supp table 3

NIHMS1638744-supplement-Ayeni_supp_table_3.png^{(38.5KB, png)}

Novelty and impact statement.

“Multimorbidity in women with breast cancer increases with age and may delay presentation, influence treatment decisions and outcome. This study shows that 44% of women newly diagnosed with breast cancer presented with multimorbidity, with obesity, hypertension, HIV and diabetes most prevalent. With effective treatment of HIV in South Africa leading to an increase in the prevalence of age related chronic conditions, there is a need to evaluate the effect of multimorbidity not only on patients but also on the health system, and its impact on the scarce health care resources”

Acknowledgement:

We are grateful to the patients who provided the data for this study and to the team at the breast unit of Chris Hani Baragwanath Academic Hospital, Charlotte Maxeke Johannesburg Academic Hospital, Addington Hospital and Inkosi Albert Luthuli Central Hospitals, Grey’s Hospital, Pietermaritzburg and Ngwelezane Hospital, who have served and continue to serve the patients with skill and devotion.

Funding:

This work was supported by NIH grant (NCI 1R01CA192627) http://grantome.com/grant/NIH/R01-CA192627-01 to Drs. Jacobson, Joffe, Neugut and Ruff; (ii) the South African Medical Research Council/University of the Witwatersrand Common Epithelial Cancer Research Centre (MRC/WITS CECRC) http://www.mrc.ac.za/epithelial/Epithelial.htm led by Dr Ruff; and (iii) the Cancer Association of South Africa (CANSA) grant “Down-staging and improving survival of breast cancer in South Africa” http://www.cansa.org.za/downstaging-and-improving-survival-of-breast-cancerin-sa-dr-herbert-cubasch(Dr Cubasch) ; (iv) a Conquer Cancer Foundation of the American Society of Clinical Oncology 2018 Young Investigator Award to Dr. O’Neil; (v) Lisa K. Micklesfield is supported by the South African Medical Research Council and (vi) Shane A. Norris is supported by the DST-NRF Centre of Excellence in Human Development at the University of the Witwatersrand, Johannesburg, South Africa.

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The Wits Health Consortium (PTY) Ltd provided support in the form of payroll administration of salaries from grant funds for authors OA, MJ, SAN, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

List of abbreviations

BMI: body mass index
HIV: human immunodeficiency virus
HSES: household socio-economic status
KZN: KwaZulu-Natal
OR: odds ratio
CI: confidence interval
SD: standard deviation
IQR: interquartile range
SSA: sub-Saharan Africa
SABCHO: South African Breast Cancer and HIV Outcomes study
COPD: Chronic obstructive pulmonary disease
CVD: cerebrovascular disease

Footnotes

Informed consent: All participants provided written informed consent

Conflict of Interest:

Dr. O’Neil declares personal fees from Ipsen outside the submitted work. Dr. Neugut receives consultant fees from Otsuka Pharmaceuticals, United Biosource Corp, Hospira, and Eisai and serves on the scientific advisory board of EHE International. All other authors declare no conflict of interest.

Publisher's Disclaimer: Disclaimer: Where authors are identified as personnel of the International Agency for Research on Cancer / World Health Organization, the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy or views of the International Agency for Research on Cancer / World Health Organization.

REFERENCES

1.Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians. 2018;68(6):394–424. [DOI] [PubMed] [Google Scholar]
2.South African National Cancer Registry. Cancer in South Africa. Johannesburg, South Africa: South African National Cancer Registry; 2014. [Google Scholar]
3.Cubasch H, Dickens C, Joffe M, Duarte R, Murugan N, Chih MT, Moodley K, Sharma V, Ayeni O, Jacobson JS, Neugut A, McCormack V, Ruff P Breast cancer survival in Soweto, Johannesburg, South Africa: a receptor-defined cohort of women diagnosed from 2009 to 11. Cancer epidemiology. 2018;52:120–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Iqbal J, Ginsburg O, Rochon PA, Sun P, Narod SA. Differences in breast cancer stage at diagnosis and cancer-specific survival by race and ethnicity in the United States. Jama. 2015;313(2):165–73. [DOI] [PubMed] [Google Scholar]
5.McKenzie F, Zietsman A, Galukande M, Anele A, Adisa C, Parham G, Pinder L, Cubasch H, Joffe M, Kidaaga F, Lukande R, Offiah A, Egejuru R, Shibemba A, Schuz J, Anderson B, Dos Santos Silva I, McCormack V Drivers of advanced stage at breast cancer diagnosis in the multicountry A frican breast cancer-disparities in outcomes (ABC‐DO) study. International journal of cancer. 2018;142(8):1568–79. [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Joffe M, Ayeni O, Norris SA, McCormack VA, Ruff P, Das I, Neugut AI, Jacobson JS, Cubasch H Barriers to early presentation of breast cancer among women in Soweto, South Africa. PloS one. 2018;13(2):e0192071. [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Opoku SY, Benwell M, Yarney J Knowledge, attitudes, beliefs, behaviour and breast cancer screening practices in Ghana, West Africa. Pan African Medical Journal. 2012;11(1). [PMC free article] [PubMed] [Google Scholar]
8.Merkin SS, Stevenson L, Powe N Geographic socioeconomic status, race, and advanced-stage breast cancer in New York City. American Journal of Public Health. 2002;92(1):64–70. [DOI] [PMC free article] [PubMed] [Google Scholar]
9.Curtis E, Quale C, Haggstrom D, Smith-Bindman R Racial and ethnic differences in breast cancer survival: how much is explained by screening, tumor severity, biology, treatment, comorbidities, and demographics? Cancer: Interdisciplinary International Journal of the American Cancer Society. 2008;112(1):171–80. [DOI] [PMC free article] [PubMed] [Google Scholar]
10.World Health Organisation. Multimorbidity:Technical Series on Safer Primary Care. Geneva, Switzerland: World Health Organisation; 2016. Report No.: CC BY-NC-SA 3.0 IGO. [Google Scholar]
11.Mayosi BM, Flisher AJ, Lalloo UG, Sitas F, Tollman SM, Bradshaw D The burden of non-communicable diseases in South Africa. The lancet. 2009;374(9693):934–47. [DOI] [PubMed] [Google Scholar]
12.Bahar MA, Setiawan D, Hak E, Wilffert B Pharmacogenetics of drug-drug interaction and drug-drug-gene interaction: a systematic review on CYP2C9, CYP2C19 and CYP2D6. Pharmacogenomics. 2017;18(7):701–39. [DOI] [PubMed] [Google Scholar]
13.McCormick WC, Boling PA. Multimorbidity and a Comprehensive Medicare Care-Coordination Benefit. Journal of the American Geriatrics Society. 2005;53(12):2227–8. [DOI] [PubMed] [Google Scholar]
14.Starfield B, Lemke KW, Bernhardt T, Foldes SS, Forrest CB, Weiner JP. Comorbidity: implications for the importance of primary care in ‘case’management. The Annals of Family Medicine. 2003;1(1):8–14. [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Salisbury C, Johnson L, Purdy S, Valderas JM, Montgomery AA. Epidemiology and impact of multimorbidity in primary care: a retrospective cohort study. Br J Gen Pract. 2011;61(582):e12–e21. [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Schäfer I, Hansen H, Schön G, Höfels S, Altiner A, Dahlhaus A, Gensichen J, Riedel-Heller S, Weyerer S, Blank WA. The influence of age, gender and socio-economic status on multimorbidity patterns in primary care. First results from the multicare cohort study. BMC health services research. 2012;12(1):89. [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Alaba O, Chola L The social determinants of multimorbidity in South Africa. International journal for equity in health. 2013;12(1):63. [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Khanam MA, Streatfield PK, Kabir ZN, Qiu C, Cornelius C, Wahlin Å Prevalence and patterns of multimorbidity among elderly people in rural Bangladesh: a cross-sectional study. Journal of health, population, and nutrition. 2011;29(4):406. [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Gurney J, Sarfati D, Stanley J The impact of patient comorbidity on cancer stage at diagnosis. British journal of cancer. 2015;113(9):1375. [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Sheppard AJ, Chiarelli AM, Marrett LD, Nishri ED, Trudeau ME. Stage at diagnosis and comorbidity influence breast cancer survival in First Nations women in Ontario, Canada. Cancer Epidemiology and Prevention Biomarkers. 2011;20(10):2160–7. [DOI] [PubMed] [Google Scholar]
21.Sarfati D, Koczwara B, Jackson C The impact of comorbidity on cancer and its treatment. CA: a cancer journal for clinicians. 2016;66(4):337–50. [DOI] [PubMed] [Google Scholar]
22.Søgaard M, Thomsen RW, Bossen KS, Sørensen HT, Nørgaard M The impact of comorbidity on cancer survival: a review. Clinical epidemiology. 2013;5(Suppl 1):3. [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Braithwaite D, Moore DH, Satariano WA, Kwan ML, Hiatt RA, Kroenke C, Caan BJ. Prognostic impact of comorbidity among long-term breast cancer survivors: results from the LACE study. Cancer Epidemiology and Prevention Biomarkers. 2012;21(7):1115–25. [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Fu M, Axelrod D, Guth A, Cleland C, Ryan C, Weaver K, Qiu J, Kleinman R, Scagliola J, Palamar J, Melkus G Comorbidities and quality of life among breast cancer survivors: a prospective study. Journal of personalized medicine. 2015;5(3):229–42. [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Ayeni O, Joffe M, Cubasch H, Rinaldi S, Taljaard C, Vorster E, Romieu I, Norris S Prevalence of comorbidities in women with and without breast cancer in Soweto, South Africa: Results from the SABC study. SAMJ: South African Medical Journal. 2019;109(4):264–71. [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Oni T, Youngblood E, Boulle A, McGrath N, Wilkinson RJ, Levitt NS. Patterns of HIV, TB, and non-communicable disease multimorbidity in peri-urban South Africa-a cross sectional study. BMC infectious diseases. 2015;15(1):20. [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Prados-Torres A, Calderón-Larranaga A, Hancco-Saavedra J, Poblador-Plou B, van den Akker M Multimorbidity patterns: a systematic review. Journal of clinical epidemiology. 2014;67(3):254–66. [DOI] [PubMed] [Google Scholar]
28.Cubasch H, Ruff P, Joffe M, Norris S, Chirwa T, Nietz S, Sharma V, Duarte R, Buccimazza I, Čačala S, Stopforth L, Tsai W, Stavsky E, Crew K, Jacobson J, Neugut A South African breast cancer and HIV outcomes study: Methods and baseline assessment. Journal of global oncology. 2016;3(2):114–24. [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Edge SB, Compton CC. The American Joint Committee on Cancer: the 7th edition of the AJCC cancer staging manual and the future of TNM. Annals of surgical oncology. 2010;17(6):1471–4. [DOI] [PubMed] [Google Scholar]
30.Yancik R, Wesley MN, Ries LA, Havlik RJ, Edwards BK, Yates JW. Effect of age and comorbidity in postmenopausal breast cancer patients aged 55 years and older. Jama. 2001;285(7):885–92. [DOI] [PubMed] [Google Scholar]
31.Mahomed O, Asmall S Development and implementation of an integrated chronic disease model in South Africa: lessons in the management of change through improving the quality of clinical practice. International Journal of Integrated Care. 2015;15(4). [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Draper CA, Draper CE, Bresick GF. Alignment between chronic disease policy and practice: Case study at a primary care facility. PloS one. 2014;9(8):e105360. [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Mahomed OH, Asmall S, Voce A Sustainability of the integrated chronic disease management model at primary care clinics in South Africa. African journal of primary health care & family medicine. 2016;8(1). [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Statistics South Africa. Demographic and Health Survey 2016 Key Findings. Pretoria, South Africa: Statistics South Africa; 2016. [Google Scholar]
35.Uijen AA, van de Lisdonk EH. Multimorbidity in primary care: prevalence and trend over the last 20 years. The European journal of general practice. 2008;14(sup1):28–32. [DOI] [PubMed] [Google Scholar]
36.Prakaschandra DR, Esterhuizen TM, Motala AA, Gathiram P, Naidoo DP. High prevalence of cardiovascular risk factors in Durban South African Indians: The Phoenix lifestyle project. South African Medical Journal. 2016;106(3):284–9. [DOI] [PubMed] [Google Scholar]
37.Human Sciences Research Council. South African National Health and Nutrition Examination Survey. Pretoria, South Africa: Human Sciences Research Council; 2013. [Google Scholar]
38.Lloyd-Sherlock P, Ebrahim S, Grosskurth H Is hypertension the new HIV epidemic? : Oxford University Press; 2014. [DOI] [PMC free article] [PubMed] [Google Scholar]
39.Ataguba JE-O Inequalities in multimorbidity in South Africa. International journal for equity in health. 2013;12(1):64. [DOI] [PMC free article] [PubMed] [Google Scholar]
40.Weimann A, Dai D, Oni T A cross-sectional and spatial analysis of the prevalence of multimorbidity and its association with socioeconomic disadvantage in South Africa: A comparison between 2008 and 2012. Social Science & Medicine. 2016;163:144–56. [DOI] [PMC free article] [PubMed] [Google Scholar]
41.Amin A BMI and breast cancer in upper Egypt. Al-Azhar Assiut Medical Journal. 2016;14(1):33. [Google Scholar]
42.Cui Y, Whiteman MK, Flaws JA, Langenberg P, Tkaczuk KH, Bush TL. Body mass and stage of breast cancer at diagnosis. International journal of cancer. 2002;98(2):279–83. [DOI] [PubMed] [Google Scholar]
43.Leung J, Martin J, McLaughlin D Rural-urban disparities in stage of breast cancer at diagnosis in A ustralian women. Australian Journal of Rural Health. 2016;24(5):326–32. [DOI] [PubMed] [Google Scholar]
44.Babu K, Anand A, Lakshmaiah K, Lokanatha D, Jacob L, Babu M, Lokesh K, Rudresha H, Rajeev L, Saldanha S, Giri G, Koppaka D, Panwar D, Kumar R Correlation of BMI with breast cancer subtype and tumour size. ecancermedicalscience. 2018;12. [DOI] [PMC free article] [PubMed] [Google Scholar]
45.Nattenmüller C, Kriegsmann M, Sookthai D, Fortner R, Steffen A, Walter B, Johnson T, Kneisel J, Katzke V, Bergmann M, Sinn H, Schirmacher P, Herpel E, Boeing H, Kaaks R, Kühn T Obesity as risk factor for subtypes of breast cancer: results from a prospective cohort study. BMC cancer. 2018;18(1):616. [DOI] [PMC free article] [PubMed] [Google Scholar]
46.Anders C, Hsu D, Broadwater G, Acharya C, Foekens J, Zhang Y, Wang Y, Marcom P, Marks J, Febbo P, Nevins J, Potti A, Blackwell K Young age at diagnosis correlates with worse prognosis and defines a subset of breast cancers with shared patterns of gene expression. Journal of clinical oncology. 2008;26(20):3324–30. [DOI] [PubMed] [Google Scholar]
47.Assi HA, Khoury KE, Dbouk H, Khalil LE, Mouhieddine TH, El Saghir NS. Epidemiology and prognosis of breast cancer in young women. Journal of thoracic disease. 2013;5(Suppl 1):S2. [DOI] [PMC free article] [PubMed] [Google Scholar]
48.Collins L, Marotti J, Gelber S, Cole K, Ruddy K, Kereakoglow S, Brachtel E, Schapira L, Come S, Winer E, Partridge A Pathologic features and molecular phenotype by patient age in a large cohort of young women with breast cancer. Breast cancer research and treatment. 2012;131(3):1061–6. [DOI] [PubMed] [Google Scholar]
49.Koroukian S,M, Bakaki P,M, Schluchter M,D, Owusu C Treatment and survival patterns in relation to multimorbidity in patients with locoregional breast and colorectal cancer. Journal of Geriatric oncology 2011; 2 (3): 200–8. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Ayeni supp table 1

NIHMS1638744-supplement-Ayeni_supp_table_1.png^{(29.2KB, png)}

Ayeni supp table 2

NIHMS1638744-supplement-Ayeni_supp_table_2.png^{(50.9KB, png)}

Ayeni supp table 3

NIHMS1638744-supplement-Ayeni_supp_table_3.png^{(38.5KB, png)}

Data Availability Statement

Data for this study contain confidential patient information. Data supporting the results reported in this article may be requested from the corresponding author.

[R1] 1.Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians. 2018;68(6):394–424. [DOI] [PubMed] [Google Scholar]

[R2] 2.South African National Cancer Registry. Cancer in South Africa. Johannesburg, South Africa: South African National Cancer Registry; 2014. [Google Scholar]

[R3] 3.Cubasch H, Dickens C, Joffe M, Duarte R, Murugan N, Chih MT, Moodley K, Sharma V, Ayeni O, Jacobson JS, Neugut A, McCormack V, Ruff P Breast cancer survival in Soweto, Johannesburg, South Africa: a receptor-defined cohort of women diagnosed from 2009 to 11. Cancer epidemiology. 2018;52:120–7. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R4] 4.Iqbal J, Ginsburg O, Rochon PA, Sun P, Narod SA. Differences in breast cancer stage at diagnosis and cancer-specific survival by race and ethnicity in the United States. Jama. 2015;313(2):165–73. [DOI] [PubMed] [Google Scholar]

[R5] 5.McKenzie F, Zietsman A, Galukande M, Anele A, Adisa C, Parham G, Pinder L, Cubasch H, Joffe M, Kidaaga F, Lukande R, Offiah A, Egejuru R, Shibemba A, Schuz J, Anderson B, Dos Santos Silva I, McCormack V Drivers of advanced stage at breast cancer diagnosis in the multicountry A frican breast cancer-disparities in outcomes (ABC‐DO) study. International journal of cancer. 2018;142(8):1568–79. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R6] 6.Joffe M, Ayeni O, Norris SA, McCormack VA, Ruff P, Das I, Neugut AI, Jacobson JS, Cubasch H Barriers to early presentation of breast cancer among women in Soweto, South Africa. PloS one. 2018;13(2):e0192071. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R7] 7.Opoku SY, Benwell M, Yarney J Knowledge, attitudes, beliefs, behaviour and breast cancer screening practices in Ghana, West Africa. Pan African Medical Journal. 2012;11(1). [PMC free article] [PubMed] [Google Scholar]

[R8] 8.Merkin SS, Stevenson L, Powe N Geographic socioeconomic status, race, and advanced-stage breast cancer in New York City. American Journal of Public Health. 2002;92(1):64–70. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R9] 9.Curtis E, Quale C, Haggstrom D, Smith-Bindman R Racial and ethnic differences in breast cancer survival: how much is explained by screening, tumor severity, biology, treatment, comorbidities, and demographics? Cancer: Interdisciplinary International Journal of the American Cancer Society. 2008;112(1):171–80. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R10] 10.World Health Organisation. Multimorbidity:Technical Series on Safer Primary Care. Geneva, Switzerland: World Health Organisation; 2016. Report No.: CC BY-NC-SA 3.0 IGO. [Google Scholar]

[R11] 11.Mayosi BM, Flisher AJ, Lalloo UG, Sitas F, Tollman SM, Bradshaw D The burden of non-communicable diseases in South Africa. The lancet. 2009;374(9693):934–47. [DOI] [PubMed] [Google Scholar]

[R12] 12.Bahar MA, Setiawan D, Hak E, Wilffert B Pharmacogenetics of drug-drug interaction and drug-drug-gene interaction: a systematic review on CYP2C9, CYP2C19 and CYP2D6. Pharmacogenomics. 2017;18(7):701–39. [DOI] [PubMed] [Google Scholar]

[R13] 13.McCormick WC, Boling PA. Multimorbidity and a Comprehensive Medicare Care-Coordination Benefit. Journal of the American Geriatrics Society. 2005;53(12):2227–8. [DOI] [PubMed] [Google Scholar]

[R14] 14.Starfield B, Lemke KW, Bernhardt T, Foldes SS, Forrest CB, Weiner JP. Comorbidity: implications for the importance of primary care in ‘case’management. The Annals of Family Medicine. 2003;1(1):8–14. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R15] 15.Salisbury C, Johnson L, Purdy S, Valderas JM, Montgomery AA. Epidemiology and impact of multimorbidity in primary care: a retrospective cohort study. Br J Gen Pract. 2011;61(582):e12–e21. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R16] 16.Schäfer I, Hansen H, Schön G, Höfels S, Altiner A, Dahlhaus A, Gensichen J, Riedel-Heller S, Weyerer S, Blank WA. The influence of age, gender and socio-economic status on multimorbidity patterns in primary care. First results from the multicare cohort study. BMC health services research. 2012;12(1):89. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R17] 17.Alaba O, Chola L The social determinants of multimorbidity in South Africa. International journal for equity in health. 2013;12(1):63. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R18] 18.Khanam MA, Streatfield PK, Kabir ZN, Qiu C, Cornelius C, Wahlin Å Prevalence and patterns of multimorbidity among elderly people in rural Bangladesh: a cross-sectional study. Journal of health, population, and nutrition. 2011;29(4):406. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R19] 19.Gurney J, Sarfati D, Stanley J The impact of patient comorbidity on cancer stage at diagnosis. British journal of cancer. 2015;113(9):1375. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R20] 20.Sheppard AJ, Chiarelli AM, Marrett LD, Nishri ED, Trudeau ME. Stage at diagnosis and comorbidity influence breast cancer survival in First Nations women in Ontario, Canada. Cancer Epidemiology and Prevention Biomarkers. 2011;20(10):2160–7. [DOI] [PubMed] [Google Scholar]

[R21] 21.Sarfati D, Koczwara B, Jackson C The impact of comorbidity on cancer and its treatment. CA: a cancer journal for clinicians. 2016;66(4):337–50. [DOI] [PubMed] [Google Scholar]

[R22] 22.Søgaard M, Thomsen RW, Bossen KS, Sørensen HT, Nørgaard M The impact of comorbidity on cancer survival: a review. Clinical epidemiology. 2013;5(Suppl 1):3. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R23] 23.Braithwaite D, Moore DH, Satariano WA, Kwan ML, Hiatt RA, Kroenke C, Caan BJ. Prognostic impact of comorbidity among long-term breast cancer survivors: results from the LACE study. Cancer Epidemiology and Prevention Biomarkers. 2012;21(7):1115–25. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R24] 24.Fu M, Axelrod D, Guth A, Cleland C, Ryan C, Weaver K, Qiu J, Kleinman R, Scagliola J, Palamar J, Melkus G Comorbidities and quality of life among breast cancer survivors: a prospective study. Journal of personalized medicine. 2015;5(3):229–42. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R25] 25.Ayeni O, Joffe M, Cubasch H, Rinaldi S, Taljaard C, Vorster E, Romieu I, Norris S Prevalence of comorbidities in women with and without breast cancer in Soweto, South Africa: Results from the SABC study. SAMJ: South African Medical Journal. 2019;109(4):264–71. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R26] 26.Oni T, Youngblood E, Boulle A, McGrath N, Wilkinson RJ, Levitt NS. Patterns of HIV, TB, and non-communicable disease multimorbidity in peri-urban South Africa-a cross sectional study. BMC infectious diseases. 2015;15(1):20. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R27] 27.Prados-Torres A, Calderón-Larranaga A, Hancco-Saavedra J, Poblador-Plou B, van den Akker M Multimorbidity patterns: a systematic review. Journal of clinical epidemiology. 2014;67(3):254–66. [DOI] [PubMed] [Google Scholar]

[R28] 28.Cubasch H, Ruff P, Joffe M, Norris S, Chirwa T, Nietz S, Sharma V, Duarte R, Buccimazza I, Čačala S, Stopforth L, Tsai W, Stavsky E, Crew K, Jacobson J, Neugut A South African breast cancer and HIV outcomes study: Methods and baseline assessment. Journal of global oncology. 2016;3(2):114–24. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R29] 29.Edge SB, Compton CC. The American Joint Committee on Cancer: the 7th edition of the AJCC cancer staging manual and the future of TNM. Annals of surgical oncology. 2010;17(6):1471–4. [DOI] [PubMed] [Google Scholar]

[R30] 30.Yancik R, Wesley MN, Ries LA, Havlik RJ, Edwards BK, Yates JW. Effect of age and comorbidity in postmenopausal breast cancer patients aged 55 years and older. Jama. 2001;285(7):885–92. [DOI] [PubMed] [Google Scholar]

[R31] 31.Mahomed O, Asmall S Development and implementation of an integrated chronic disease model in South Africa: lessons in the management of change through improving the quality of clinical practice. International Journal of Integrated Care. 2015;15(4). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R32] 32.Draper CA, Draper CE, Bresick GF. Alignment between chronic disease policy and practice: Case study at a primary care facility. PloS one. 2014;9(8):e105360. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R33] 33.Mahomed OH, Asmall S, Voce A Sustainability of the integrated chronic disease management model at primary care clinics in South Africa. African journal of primary health care & family medicine. 2016;8(1). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R34] 34.Statistics South Africa. Demographic and Health Survey 2016 Key Findings. Pretoria, South Africa: Statistics South Africa; 2016. [Google Scholar]

[R35] 35.Uijen AA, van de Lisdonk EH. Multimorbidity in primary care: prevalence and trend over the last 20 years. The European journal of general practice. 2008;14(sup1):28–32. [DOI] [PubMed] [Google Scholar]

[R36] 36.Prakaschandra DR, Esterhuizen TM, Motala AA, Gathiram P, Naidoo DP. High prevalence of cardiovascular risk factors in Durban South African Indians: The Phoenix lifestyle project. South African Medical Journal. 2016;106(3):284–9. [DOI] [PubMed] [Google Scholar]

[R37] 37.Human Sciences Research Council. South African National Health and Nutrition Examination Survey. Pretoria, South Africa: Human Sciences Research Council; 2013. [Google Scholar]

[R38] 38.Lloyd-Sherlock P, Ebrahim S, Grosskurth H Is hypertension the new HIV epidemic? : Oxford University Press; 2014. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R39] 39.Ataguba JE-O Inequalities in multimorbidity in South Africa. International journal for equity in health. 2013;12(1):64. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R40] 40.Weimann A, Dai D, Oni T A cross-sectional and spatial analysis of the prevalence of multimorbidity and its association with socioeconomic disadvantage in South Africa: A comparison between 2008 and 2012. Social Science & Medicine. 2016;163:144–56. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R41] 41.Amin A BMI and breast cancer in upper Egypt. Al-Azhar Assiut Medical Journal. 2016;14(1):33. [Google Scholar]

[R42] 42.Cui Y, Whiteman MK, Flaws JA, Langenberg P, Tkaczuk KH, Bush TL. Body mass and stage of breast cancer at diagnosis. International journal of cancer. 2002;98(2):279–83. [DOI] [PubMed] [Google Scholar]

[R43] 43.Leung J, Martin J, McLaughlin D Rural-urban disparities in stage of breast cancer at diagnosis in A ustralian women. Australian Journal of Rural Health. 2016;24(5):326–32. [DOI] [PubMed] [Google Scholar]

[R44] 44.Babu K, Anand A, Lakshmaiah K, Lokanatha D, Jacob L, Babu M, Lokesh K, Rudresha H, Rajeev L, Saldanha S, Giri G, Koppaka D, Panwar D, Kumar R Correlation of BMI with breast cancer subtype and tumour size. ecancermedicalscience. 2018;12. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R45] 45.Nattenmüller C, Kriegsmann M, Sookthai D, Fortner R, Steffen A, Walter B, Johnson T, Kneisel J, Katzke V, Bergmann M, Sinn H, Schirmacher P, Herpel E, Boeing H, Kaaks R, Kühn T Obesity as risk factor for subtypes of breast cancer: results from a prospective cohort study. BMC cancer. 2018;18(1):616. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R46] 46.Anders C, Hsu D, Broadwater G, Acharya C, Foekens J, Zhang Y, Wang Y, Marcom P, Marks J, Febbo P, Nevins J, Potti A, Blackwell K Young age at diagnosis correlates with worse prognosis and defines a subset of breast cancers with shared patterns of gene expression. Journal of clinical oncology. 2008;26(20):3324–30. [DOI] [PubMed] [Google Scholar]

[R47] 47.Assi HA, Khoury KE, Dbouk H, Khalil LE, Mouhieddine TH, El Saghir NS. Epidemiology and prognosis of breast cancer in young women. Journal of thoracic disease. 2013;5(Suppl 1):S2. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R48] 48.Collins L, Marotti J, Gelber S, Cole K, Ruddy K, Kereakoglow S, Brachtel E, Schapira L, Come S, Winer E, Partridge A Pathologic features and molecular phenotype by patient age in a large cohort of young women with breast cancer. Breast cancer research and treatment. 2012;131(3):1061–6. [DOI] [PubMed] [Google Scholar]

[R49] 49.Koroukian S,M, Bakaki P,M, Schluchter M,D, Owusu C Treatment and survival patterns in relation to multimorbidity in patients with locoregional breast and colorectal cancer. Journal of Geriatric oncology 2011; 2 (3): 200–8. [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

The multimorbidity profile of South African women newly diagnosed with breast cancer

Oluwatosin A Ayeni, MBChB, M.Sc

Shane A Norris, PhD

Maureen Joffe, PhD

Herbert Cubasch, MD,

Sarah Nietz, MD, MMed

Ines Buccimazza, MBChB

Urishka Singh, MBChB

Sharon Čačala, MBChB

Laura Stopforth, MBChB

Wenlong Carl Chen, M.Sc

Valerie A McCormack, PhD

Daniel S O’Neil, MD, MPH

Judith S Jacobson, DrPH, MBA

Alfred I Neugut, MD, PhD

Paul Ruff, MBBCh, MMed

Lisa K Micklesfield, PhD

Abstract

INTRODUCTION

METHODS

Table 1:

Recruitment of participants

Study procedures

Socio-demographic and anthropometric measures

Data availability:

Statistical analysis

RESULTS

Table 2:

Prevalence of chronic conditions

Figure 1.

Table 3:

Table 4a:

Table 4b:

Table 5a:

Table 5b:

Table 6:

DISCUSSION

Supplementary Material

Novelty and impact statement.

Acknowledgement:

List of abbreviations

Footnotes

REFERENCES

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases