Inequalities in the prevalence of cardiovascular disease risk factors in Brazilian slum populations: A cross-sectional study

Jasper J L Chan; Linh Tran-Nhu; Charlie F M Pitcairn; Anthony A Laverty; Matías Mrejen; Julia M Pescarini; Thomas V Hone

doi:10.1371/journal.pgph.0000990

. 2022 Sep 8;2(9):e0000990. doi: 10.1371/journal.pgph.0000990

Inequalities in the prevalence of cardiovascular disease risk factors in Brazilian slum populations: A cross-sectional study

Jasper J L Chan ^1,^*, Linh Tran-Nhu ², Charlie F M Pitcairn ^1,³, Anthony A Laverty ⁴, Matías Mrejen ⁵, Julia M Pescarini ^6,⁷, Thomas V Hone ⁴

Editor: Peter Bai James⁸

PMCID: PMC10022010 PMID: 36962864

Abstract

Background

Social and environmental risk factors in informal settlements and slums may contribute to increased risk of cardiovascular disease (CVD). This study assesses the socioeconomic inequalities in CVD risk factors in Brazil comparing slum and non-slum populations.

Methods

Responses from 94,114 individuals from the 2019 Brazilian National Health Survey were analysed. The United Nations Human Settlements Programme definition of a slum was used to identify slum inhabitants. Six behavioural risk factors, four metabolic risk factors and doctor-diagnosed CVD were analysed using Poisson regression models adjusting for socioeconomic characteristics.

Results

Compared to urban non-slum inhabitants, slum inhabitants were more likely to: have low (less than five days per week) consumption of fruits (APR: 1.04, 95%CI 1.01–1.07) or vegetables (APR: 1.08, 95%CI 1.05–1.12); drink four or more alcoholic drinks per day (APR: 1.05, 95%CI 1.03–1.06); and be physically active less than 150 minutes per week (APR: 1.03, 95%CI 1.01–1.04). There were no differences in the likelihoods of doctor-diagnosed metabolic risk factors or CVD between the two groups in adjusted models. There was a higher likelihood of behavioural and metabolic risk factors among those with lower education, with lower incomes, and the non-White population.

Conclusions

Brazilians living in slums are at higher risk of behavioural risk factors for CVD, suggesting local environments might impact access to and uptake of healthy behaviours.

Introduction

Development and urbanisation have fundamentally affected demographics and lifestyles globally–especially in low- and middle- income countries (LMICs) [1]. Consequently, chronic non-communicable diseases (NCDs) are now a major health challenge [2]. Cardiovascular disease (CVD) is the leading contributor to global NCD mortality and morbidity–the number one cause of deaths globally and responsible for almost 18 million deaths annually [3]. Nearly 80% of all CVD deaths occur in LMICs, where the annual cost of management and treatment exceeds the total health expenditure per capita [1,4].

CVD stems from a combination of interlinked risk factors [5]. This includes non-modifiable risk factors, such as sex, age, and race [5], however, modifiable behavioural and metabolic risk factors play a larger role [6,7]. Behavioural risk factors include an unhealthy diet (e.g. lacking in fruits and vegetables), physical inactivity, tobacco use and alcohol consumption [5,6,8,9]. These lifestyle risk factors drive increases in intermediate metabolic risk factors such as obesity, hypertension, diabetes and high cholesterol [5,6,8]. Unaddressed, these risk factors increase the risk of CVD events such as heart disease or stroke [10–12].

Individuals living in slums and informal settlements are a growing population–nearly one billion people worldwide now live in slums, a figure expected to double by 2030 [13,14]. Slums have grown sizeably in LMICs due to inadequate urban planning and rapid urbanisation [13,15]. Slums are characterised by poverty, overcrowding, inadequate housing and limited access to safe water and sanitation [15–17]. There is also evidence that individuals living within them are at greater risk of CVD and have higher rates of behavioural CVD risk factors compared to populations in formal urban or rural settlements [18–21]. However, the health profiles and risk factors of slum populations are infrequently studied [13,22,23].

The wider social determinants of health are important determinants of CVD and associated risk factors [24]. Individuals of lower socioeconomic status (SES) experience increased morbidity and mortality on average [25]. Lower income, lower educational attainment, unemployment, and living in disadvantaged areas is associated with higher rates of CVD [26–28]. In many countries, the high costs of treating and managing CVDs can drive individuals and families into poverty, worsening SES, and passing disadvantage from one generation to the next [1,4,29,30]. Much of the literature on CVD and SES inequalities comes from high-income countries [28,31]. However, it is important to generate evidence from LMICs where globalisation has rapidly changed lifestyles in recent decades–there are large inequalities and sizeable slum populations, and access to high-quality healthcare is not universal [14,32].

Brazil is a country with large health and social inequalities [22,33]. Like many other countries, an aging population, obesity, unhealthy diets, and physical inactivity have contributed to a rising burden of CVDs–responsible for 28% of all NCD deaths in Brazil in 2016 [12,34,35]. Brazil is the country with the greatest degree of wealth inequality in South America where the majority of wealth belongs to the top one percent [36,37], and over a quarter of the population live in poverty [38]. Additionally, over 16% of Brazil’s urban population reside in slum settlements (favelas) [21,22,39]. However, few studies have examined the CVD risk factors and prevalence in these settings [17,22]. National studies demonstrate socioeconomic inequalities in NCD risk factors and health behaviours [12,40]. For example, rural populations have lower average incomes, are more exposed to infectious diseases, and face greater barriers to accessing healthcare. However, there are no country-wide studies comparing behavioural and metabolic CVD risk factors between rural, slum and non-slum urban inhabitants. Better knowledge of slum population health and their CVD risk factors is important for making progress towards the United Nations Sustainable Development Goals (SDGs) which include targets for reducing premature NCDs deaths (SDG 3), reducing inequality within countries (SDG 10), and upgrading slums (SDG 11) [41].

This study assesses the prevalence of behavioural and metabolic CVD risk factors across socioeconomic and demographic groups in Brazil in 2019. It investigates the associations between socioeconomic inequalities and the prevalence of CVD and its risk factors, specifically examining the inequalities between slum and non-slum inhabitants.

Methods

Ethics statement

Ethics approval for the PNS 2019 itself was approved by Brazil’s National Research Ethics Committee/National Health Council (opinion no. 3.529.376) with informed consent obtained for all respondents. Ethics approval for this specific study was not required as it utilised anonymised data in the public domain.

Study design

This is a cross-sectional analysis of the 2019 Brazilian National Health Survey–Pesquisa Nacional de Saúde (PNS).

Data source

Responses from the 2019 PNS were obtained from the website of the Brazilian Institute of Geography and Statistics (IBGE). The PNS is a country-wide household-based survey conducted by IBGE and the Ministry of Health. It collects information on the health, healthcare access and usage, and lifestyles of the Brazilian population [42]. A three-stage probability sample selection process was used. First, primary sampling units (PSU) composed of one or more census tracts were selected by random sampling. Second, 12–18 households were randomly selected from each PSU. Third and finally, one individual aged 15 years or older was selected from each household for a computer-assisted personal interview. Interviewed participants answered questionnaires on housing, socioeconomic and demographic characteristics, health status and behaviours. The PNS is publicly available from IBGE: https://www.ibge.gov.br/estatisticas/sociais/saude/9160-pesquisa-nacional-de-saude.html?=&t=downloads.

A total of 293,725 individuals from 108,457 households were surveyed across Brazil. Of these, 94,114 individuals answered the individual questionnaires and were included in this study. Responses were weighted to be representative of the national Brazilian population [43].

Outcomes

Eleven binary outcomes, including ten modifiable risk factors and doctor-diagnosed CVD, were derived as outcomes in this analysis. The risk factors were divided into six behavioural and four metabolic ones. Risk factors were chosen based on evidence of their association with CVD [2,5–8]. The six behavioural risk factors were self-reported: low weekly consumption of vegetables (<5 days per week); low weekly consumption of fruits (<5 days per week); high weekly consumption of red meat (≥3 days per week); currently smoking tobacco; heavy consumption of alcoholic drinks (≥4 drinks per day); and low weekly physical activity (<150 minutes per week). The categorisation for alcohol consumption was based on evidence from the United States National Institute on Alcohol Abuse and Alcoholism, which defined heavy alcohol consumption as four or more drinks per day [44–46]. For physical activity, the categorisation was based on the World Health Organization (WHO) recommendation of at least 150 minutes per week of moderate-intensity physical activity [47].

Four metabolic risk factor variables were included: overweight (body mass index (BMI) over 25kg/m²) [48]; doctor-diagnosed hypertension; doctor-diagnosed diabetes; and doctor-diagnosed high cholesterol. Both height and weight were measured at the interview to determine BMI. The doctor-diagnosed conditions were determined in the interview as: “Has a doctor ever diagnosed you with…”. Doctor-diagnosed CVD was also included as an outcome and was defined as an individual who reported ever having been diagnosed with either heart disease or stroke.

Variables of interest

The United Nations Human Settlements Programme (UN-Habitat) definition of slums was used to identify respondents living in slums and has been used in similar studies in Brazil [49,50]. It defines slums as an environment where inhabitants lack access to one or more of these conditions: improved water source, improved sanitation facilities, sufficient living area, housing durability and security of tenure (Table 1) [51]. Data on a lack of security of tenure was not available and not included. In this study, if an individual met any of the criteria and were living in an urban area, they were considered slum residents. Those who did not meet the criteria were categorised as either urban non-slum or rural residents.

Table 1. Slum residence definition.

UN-Habitat definition	Variable	Full PNS survey question	Responses considered for slums
Lack of access to improved water source	Water supply	What is the main form of water supply for this household?	Shallow, water table or cacimba
			Source or spring
			Another
	Water distribution network	Is this household connected to the general water distribution network?	No
	Arriving water	How does the water used in this household arrive?	Not piped
Lack of access to improved sanitation	Bathroom or sanitation room	How many bathrooms or sanitation rooms for residents to use are located on the ground or property?	None
	Dejections	Do residents use a toilet or hole located on the ground or property for dejections?	No
	Drainage	Where does the sullage from the bathroom, toilet or hole go?	Rudimentary pit
			Ditch
			River, lake, stream or sea
			Another
Lack of sufficient living area	Number of rooms	How many rooms does this household have?	≥3 people per room^*
Lack of sufficient living area	Number of household members	Number of household members	≥3 people per room^*
Lack of housing durability	Wall material	What is the predominant material on the external walls of the household?	Uncoated rammed earth
	Wall material		Harnessed wood
	Roof material	What is the predominant material on the roof of the household?	Zinc, aluminium or sheet metal
	Floor material	What is the predominant material on the floor of the household?	Earth

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This was based on the United Nations Human Settlements Programme (UN-Habitat) definition of slum households with indicators mapped to questions from the 2019 Brazilian National Health Survey, Pesquisa Nacional de Saúde (PNS).

*Overcrowding is defined by UN-Habitat as three or more people per room [52].

Ten socioeconomic and demographic variables were derived from the survey: area (slum, urban non-slum, rural); sex (male, female); age (15–29 years, 30–39, 40–49, 50–59, 60–69, 70 or older); self-reported race/ethnicity (White, Black, Pardo/mixed (Brown), other (Asian, Indigenous or not reported)); highest level of education (illiterate, elementary education, high school education, higher education); currently in the workforce (yes, no); household income per capita (≤ half minimum wage, > half but ≤1 minimum wage, >1 but ≤2 minimum wage, >2 minimum wage); household registered to the Family Health Strategy (FHS) (yes, no, do not know); private healthcare (yes, no); and the last time a doctor was consulted (≤1 year, >1 but ≤2 years, >2 years or never consulted). Those who identified as Asian or Indigenous were grouped together with those who ignored the question due to small numbers. In 2020, the annual minimum wage in Brazil was USD 5,198.40 after adjusting for purchasing power [53]. The FHS is Brazil’s main primary healthcare program and consists of primary care services delivered through multidisciplinary teams assigned to a certain area, comprising of approximately 3,500 individuals [54,55].

Data analysis

All analyses accounted for clustered sampling of the survey and weighted for non-response. Descriptive data analysis explored the prevalence of the socioeconomic characteristics, behavioural risk factors, metabolic risk factors and CVD outcomes in the study population. Weighted prevalence estimates (%) and 95% confidence intervals (95%CIs) are reported. Prevalence estimates were explored across slum, urban non-slum and rural populations.

Adjusted Poisson regression models were used to analyse the associations between demographic and socioeconomic variables with different CVD outcomes. Poisson regression models were chosen due to the high prevalence of many risk factors and to obtain prevalence ratios which are more interpretable. Models were repeated for each of the eleven outcomes, and all models were adjusted for location of residence (urban slum, urban non-slum, rural), sex, age, race/ethnicity, educational level, employment status, and household income per capita. Collinearity of variables were tested and found to be unproblematic as all variance inflation factors were less than 2. Adjusted prevalence ratios (APRs) with their respective 95%CIs and p-value are reported. The unadjusted regression models can be found in the supporting information (S1 and S2 Tables). In supplementary analyses, differential associations between slum residence and the eleven outcomes were explored across the regions of Brazil. Models were repeated with interactions between the variable for location of residence (urban slum, urban non-slum, rural) and region of residence (North, Northeast, Southeast, South and Central west). Prediction prevalence estimates from interacted Poisson regression models were plotted (S1 Fig).

Stata/IC 16.1 was used to analyse the data.

Results

The responses from 94,114 individuals were included. According to the UN-Habitat definition, 14.4% (95%CI 13.7–15.0) of the Brazilian population are slum residents (Table 2).

Table 2. Distribution of study population’s area of residence.

	n	Weighted %	95%CI
Area
Slum	16,627	14.4	13.7–15.0
Urban non-slum	56,082	71.6	70.8–72.4
Rural	21,405	14.1	13.7–14.5

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Source: Brazilian National Health Survey Pesquisa Nacional de Saúde (PNS) 2019. Shows numbers of individuals (n) and the weighted percentage (%) prevalence estimates. 95%CI—95% confidence interval. Percentages may not equal 100% due to rounding.

The self-reported prevalence and distribution of the socioeconomic and demographic characteristics is shown in Table 3. The average age of the participants was 46.2 years, with individuals living in slums generally younger. Slums had the highest prevalence of participants identifying as Black (12.1%) or Pardo/mixed (57.0%). In terms of education, nearly half of slum populations reported having high school (36.9%) or higher education (11.3%) which was lower than urban non-slums populations but higher than rural populations. Rural populations had the greatest proportion of illiteracy (13.3%) and over half of the population had elementary education (56.9%) but did not progress further with only 4.3% having higher education. Rural populations also had the greatest proportion of unemployment (44.2%) compared to slums and urban non-slums (37.6% and 33.7% respectively). Over two thirds of the population in slums (69.4%) earned the minimum wage or less, compared to 78.5% in rural areas and 43.8% in urban non-slum areas. In slums, 70.3% of households were registered with the FHS compared 78.6% in rural areas and 56.7% in urban non-slums. Conversely, urban non-slums had the highest access to private healthcare (33.3%) compared to 12.8% of respondents in slums and 6.0% of rural respondents. The proportion of individuals who had a doctor consultation within the last year was highest in urban non-slums (82.6% of respondents) compared to 75.2% in slums and 70.7% in rural areas.

Table 3. Socioeconomic and demographic characteristics of study population by area of residence.

		Slum	Urban non-slum	Rural
	n	Weighted % (95%CI)	Weighted % (95%CI)	Weighted % (95%CI)
Sex
Male	44,752	46.4 (45.2–47.7)	45.9 (45.2–46.7)	53.5 (52.2–54.8)
Female	49,362	53.6 (52.3–54.8)	54.1 (53.3–54.8)	46.5 (45.2–47.8)
Age
15–29	18,648	28.8 (27.5–30.2)	25.6 (24.8–26.4)	27.9 (26.6–29.2)
30–39	18,904	20.2 (19.2–21.2)	19.9 (19.3–20.5)	19.2 (18.4–20.1)
40–49	17,282	17.6 (16.6–18.5)	17.2 (16.6–17.7)	16.8 (16.2–17.5)
50–59	16,136	16.2 (15.4–17.2)	16.4 (15.9–16.9)	15.0 (14.3–15.7)
60–69	12,799	9.4 (8.8–10.1)	11.9 (11.5–12.4)	11.4 (10.7–12.1)
70+	10,345	7.8 (7.2–8.4)	9.0 (8.6–9.5)	9.7 (9.2–10.3)
Race/ethnicity
White	34,320	29.7 (28.2–31.2)	47.7 (46.8–48.7)	32.1 (30.8–33.5)
Black	10,730	12.1 (11.3–13.0)	11.3 (10.8–11.9)	10.9 (10.1–11.9)
Pardo/mixed (Brown)	47,660	57.0 (55.5–58.6)	39.3 (38.4–40.1)	56.0 (54.6–57.5)
Other	1,404	1.2 (1.0–1.6)	1.7 (1.5–1.9)	0.9 (0.7–1.1)
Education level (complete or incomplete)
Illiterate	7,870	8.5 (7.7–9.4)	3.8 (3.6–4.1)	13.3 (12.7–14.1)
Elementary education	36,829	43.3 (41.8–44.7)	32.2 (31.4–33.0)	56.9 (55.6–58.1)
High school education	31,128	36.9 (35.6–38.3)	39.4 (38.6–40.1)	25.5 (24.3–26.7)
Higher education	18,287	11.3 (10.5–12.1)	24.6 (23.6–25.5)	4.3 (3.9–4.7)
Currently employed
Yes	59,172	62.4 (61.2–63.6)	66.3 (65.6–67.1)	55.8 (54.7–57.0)
No	34,942	37.6 (36.4–38.8)	33.7 (33.0–34.4)	44.2 (43.0–45.3)
Household income per capita
≤ half minimum wage	24,592	34.3 (32.8–35.8)	16.3 (15.6–17.0)	46.4 (45.2–47.7)
> half but ≤ 1 minimum wage	27,417	35.1 (33.9–36.3)	27.5 (26.8–28.3)	32.0 (30.9–33.1)
> 1 but ≤ 2 minimum wage	23,248	21.6 (20.4–22.7)	31.0 (30.3–31.8)	16.0 (15.1–16.9)
> 2 minimum wage	18,833	9.1 (8.4–9.9)	25.2 (24.2–26.2)	5.6 (5.2–6.1)
Household registered to the Family Health Strategy
Yes	59,358	70.3 (68.4–72.2)	56.7 (55.3–58.2)	78.6 (76.9–80.2)
No	23,424	19.7 (18.1–21.4)	31.1 (29.8–32.4)	13.0 (11.8–14.4)
Do not know	11,332	10.0 (9.1–11.0)	12.2 (11.5–12.9)	8.4 (7.4–9.4)
Private healthcare
Yes	21,333	12.8 (11.9–13.9)	33.3 (32.3–34.4)	6.0 (5.3–6.6)
No	72,781	87.2 (86.1–88.1)	66.7 (65.6–67.7)	94.0 (93.4–94.7)
The last time a doctor was consulted
≤1 year	73,561	75.2 (74.1–76.4)	82.6 (81.9–83.2)	70.7 (69.4–72.0)
>1 but ≤2 years	9,885	11.1 (10.4–11.9)	9.1 (8.7–9.6)	12.1 (11.3–12.9)
>2 years or never consulted	10,668	13.6 (12.6–14.6)	8.3 (7.9–8.7)	17.2 (16.1–18.3)

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The prevalence of modifiable CVD risk factors was examined by area of residence (Fig 1). Compared to urban non-slum populations, individuals in slums and rural areas had a higher (unadjusted) prevalence of: low fruits consumption (61.7% (slum) and 64.9% (rural), compared to 52.8% (urban non-slum)); low vegetables consumption (53.0%, 57.3%, and 42.2%); smoking (12.8%, 12.9%, and 11.9%); heavy alcohol consumption (82.9%, 81.0%, and 74.4%); and physical inactivity (76.1%, 83.1%, and 70.1%). Only for high red meat consumption, was the prevalence highest in urban non-slums (65.8% (urban non-slum), 60.0% (slum), and 62.8% (rural). For metabolic CVD risk factors (Fig 2), 55.4% of slum residents and 57.3% of urban non-slum residents were overweight compared to rural residents (49.1%). For doctor-diagnosed hypertension, diabetes, high cholesterol and CVD, the prevalence was higher in urban environments than rural areas.

Fig 2 — Source: Brazilian National Health Survey *Pesquisa Nacional de Saúde* (PNS) 2019. CVD–doctor-diagnosed with either heart disease or stroke.

Table 4 shows results from adjusted Poisson regression models on behavioural CVD risk factors. The unadjusted regression can be found in S1 Table. In fully-adjusted models and compared to urban non-slum populations, residents of slums had: 8.3% higher prevalence of having low vegetables consumption (APR: 1.083, 95%CI 1.048–1.119); 3.7% higher prevalence of having low fruits consumption (APR: 1.037 95%CI 1.010–1.065); 4.5% higher prevalence of having heavy alcohol consumption (APR: 1.045, 95%CI 1.028–1.062); and 2.5% higher prevalence of being physically inactive (APR: 1.025, 95%CI 1.007–1.044). Individuals from rural areas also had a higher prevalence of behavioural risk factors compared to urban non-slums. However, both rural and slum inhabitants had a lower prevalence of smoking (APR: 0.806, 95%CI 0.742–0.875 and APR: 0.920, 95%CI 0.847–0.999 respectively). Regarding other SES factors, women had a higher prevalence of high alcohol consumption (APR: 1.046, 95%CI 1.032–1.061) and being physically inactive (APR: 1.079, 95%CI 1.062–1.096), but had a lower prevalence of low fruits and vegetables consumption (APR: 0.844, 95% CI 0.827–0.861 and APR: 0.835, 95%CI 0.814–0.856 respectively), high red meat consumption (APR: 0.855, 95%CI 0.841–0.870), and smoking (APR: 0.637, 95%CI 0.599–0.677). There was a higher prevalence of low fruits and vegetables consumption and heavy alcohol use among the non-white population. Individuals with higher levels of education had a lower prevalence of low levels of fruits and vegetables consumption, smoking tobacco, consuming high amounts of alcohol, or being physically inactive. Generally, as household income increased, the prevalence of behavioural CVD risk factors decreased.

Table 4. Poisson regression models on the association between slums and behavioural cardiovascular disease risk factors.

	Low Vegetables Consumption	Low Fruits Consumption	High Red Meat Consumption	Currently Smoke	Heavy Alcohol Consumption	Low Physical Exercise
	APR	APR	APR	APR	APR	APR
	95%CI	95%CI	95%CI	95%CI	95%CI	95%CI
Area
Non-slum urban (Ref)	1	1	1	1	1	1
Slum	1.083^***	1.037^**	0.962^**	0.920^*	1.045^***	1.025^**
	1.048–1.119	1.010–1.065	0.934–0.990	0.847–0.999	1.028–1.062	1.007–1.044
Rural	1.100^***	1.037^**	1.036^*	0.806^***	0.996	1.077^***
	1.067–1.135	1.012–1.062	1.009–1.063	0.742–0.875	0.981–1.011	1.060–1.095
Sex
Male (Ref)	1	1	1	1	1	1
Female	0.835^***	0.844^***	0.855^***	0.637^***	1.046^***	1.079^***
	0.814–0.856	0.827–0.861	0.841–0.870	0.599–0.677	1.032–1.061	1.062–1.096
Age
15–29 (Ref)	1	1	1	1	1	1
30–39	0.830^***	0.920^***	0.980	1.225^***	1.007	1.101^***
	0.799–0.863	0.893–0.947	0.956–1.004	1.101–1.364	0.984–1.030	1.073–1.131
40–49	0.753^***	0.821^***	0.936^***	1.261^***	0.983	1.120^***
	0.724–0.783	0.795–0.848	0.912–0.961	1.129–1.407	0.960–1.007	1.091–1.150
50–59	0.696^***	0.741^***	0.865^***	1.492^***	0.973^*	1.155^***
	0.667–0.727	0.715–0.768	0.840–0.890	1.335–1.666	0.951–0.996	1.125–1.187
60–69	0.668^***	0.651^***	0.853^***	1.364^***	0.977^*	1.196^***
	0.638–0.701	0.624–0.679	0.825–0.881	1.214–1.533	0.953–1.001	1.161–1.231
70+	0.593^***	0.544^***	0.851^***	0.729^***	1.007	1.293^***
	0.561–0.627	0.517–0.572	0.818–0.886	0.629–0.845	0.980–1.034	1.254–1.334
Race/ethnicity
White (Ref)	1	1	1	1	1	1
Black	1.129^***	1.057^**	0.965^*	1.007	1.059^***	0.976^*
	1.084–1.176	1.023–1.093	0.936–0.994	0.909–1.115	1.033–1.084	0.954–0.999
Pardo/mixed (Brown)	1.096^***	1.026^*	0.959^***	1.009	1.071^***	0.975^**
	1.063–1.131	1.002–1.050	0.941–0.977	0.941–1.082	1.055–1.087	0.958–0.991
Other	1.020	1.007	0.866^**	1.033	1.070	1.030
	0.891–1.167	0.902–1.123	0.783–0.958	0.808–1.320	0.981–1.168	0.964–1.101
Education level
Illiterate (Ref)	1	1	1	1	1	1
Elementary education	0.797^***	0.818^***	1.107^***	0.810^***	0.941^***	0.976^*
	0.764–0.831	0.790–0.847	1.055–1.161	0.735–0.892	0.925–0.956	0.957–0.996
High school education	0.761^***	0.762^***	1.122^***	0.495^***	0.917^***	0.884^***
	0.727–0.797	0.734–0.792	1.068–1.179	0.441–0.557	0.898–0.936	0.862–0.906
Higher education	0.667^***	0.651^***	1.117^***	0.400^***	0.828^***	0.752^***
	0.628–0.709	0.619–0.684	1.060–1.178	0.345–0.465	0.804–0.852	0.727–0.778
Currently employed
Yes (Ref)	1	1	1	1	1	1
No	1.039^**	0.980	0.941^***	0.798^***	1.095^***	0.946^***
	1.010–1.068	0.957–1.004	0.920–0.963	0.740–0.859	1.078–1.113	0.928–0.964
Household income per capita
≤ half minimum wage (Ref)	1	1	1	1	1	1
> half but ≤ 1 minimum wage	0.892^***	0.929^***	1.165^***	0.911^*	0.990	0.951^***
	0.865–0.919	0.906–0.953	1.134–1.197	0.838–0.991	0.973–1.006	0.935–0.968
> 1 but ≤ 2 minimum wage	0.767^***	0.846^***	1.241^***	0.832^***	0.944^***	0.917^***
	0.739–0.796	0.821–0.871	1.205–1.278	0.757–0.914	0.926–0.963	0.898–0.937
> 2 minimum wage	0.649^***	0.721^***	1.264^***	0.782^***	0.851^***	0.812^***
	0.615–0.685	0.689–0.754	1.222–1.307	0.696–0.877	0.829–0.873	0.789–0.837
Number of observations	90,824	90,824	90,824	90,824	90,824	90,824

Open in a new tab

Source: Brazilian National Health Survey Pesquisa Nacional de Saúde (PNS) 2019. APR–Adjusted prevalence ratio; 95%CI– 95% Confidence interval; Ref–Reference category

*p<0.05

**p<0.01

***p<0.001

Low Vegetables Consumption–Vegetables consumption <5 days per week; Low Fruits Consumption–Fruits consumption <5 days per week; High Red Meat Consumption–Red meat consumption ≥3 days per week; Currently Smoke–Currently smoke tobacco; Heavy Alcohol Consumption–Alcohol consumption ≥4 per day; Low Physical Exercise–Exercise <150 minutes per week.

Table 5 shows the adjusted Poisson regression results for metabolic risk factors for CVD and doctor-diagnosed CVD. The unadjusted regression can be found in S2 Table. In fully-adjusted models, there was no statistically significant difference in all self-reported doctor-diagnosed conditions (overweight, hypertension, diabetes, high cholesterol and CVD) between slums and urban non-slums. However, individuals living in rural areas had a lower prevalence of all metabolic risk factors and CVD. Females had a higher prevalence of hypertension (APR: 1.262, 95%CI 1.218–1.309), diabetes (APR: 1.097, 95%CI 1.013–1.189) and high cholesterol (APR: 1.413, 95%CI 1.334–1.497). The prevalence of having hypertension was significantly higher in the non-white population. Notably, there were no significant differences in the prevalence of high cholesterol and CVD across education levels. Across education levels, illiterate individuals had a lower prevalence of being overweight, whilst increasing educational attainment was associated with a lower prevalence of hypertension and diabetes. Unemployed individuals had a higher prevalence of hypertension, diabetes, high cholesterol, and CVD yet had a lower prevalence of being overweight. There were no clear patterns by household income, except for high cholesterol which was associated with increasing income.

Table 5. Poisson regression models on the association between slums and metabolic cardiovascular disease risk factors.

	Overweight	Hypertension	Diabetes	High Cholesterol	CVD
	APR	APR	APR	APR	APR
	95%CI	95%CI	95%CI	95%CI	95%CI
Area
Non-slum urban (Ref)	1	1	1	1	1
Slum	0.992	0.987	1.030	1.044	1.006
	0.966–1.019	0.941–1.034	0.942–1.127	0.969–1.125	0.891–1.136
Rural	0.890^***	0.908^***	0.731^***	0.911^*	0.791^***
	0.865–0.916	0.871–0.946	0.665–0.804	0.847–0.980	0.711–0.881
Sex
Male (Ref)	1	1	1	1	1
Female	0.967^**	1.262^***	1.097^*	1.413^***	0.923
	0.947–0.988	1.218–1.309	1.013–1.189	1.334–1.497	0.846–1.007
Age
15–29 (Ref)	1	1	1	1	1
30–39	1.676^***	3.026^***	3.017^***	1.682^***	1.690^**
	1.610–1.745	2.624–3.489	2.215–4.110	1.421–1.991	1.233–2.315
40–49	1.784^***	5.756^***	5.557^***	2.953^***	2.866^***
	1.710–1.861	4.984–6.647	4.171–7.405	2.527–3.451	2.159–3.804
50–59	1.765^***	9.358^***	11.40^***	4.434^***	5.266^***
	1.686–1.847	8.248–10.62	8.556–15.18	3.808–5.164	4.029–6.883
60–69	1.797^***	12.53^***	16.00^***	5.191^***	7.234^***
	1.715–1.883	11.06–14.20	12.06–21.23	4.447–6.061	5.502–9.511
70+	1.582^***	14.30^***	16.40^***	4.446^***	10.38^***
	1.500–1.669	12.60–16.22	12.17–22.10	3.756–5.262	7.895–13.65
Race/ethnicity
White (Ref)	1	1	1	1	1
Black	1.036*	1.188^***	1.120	0.951	0.996
	1.002–1.071	1.122–1.257	0.999–1.255	0.862–1.051	0.878–1.131
Pardo/mixed (Brown)	1.008	1.079^***	1.069	0.980	0.954
	0.984–1.033	1.038–1.122	0.984–1.161	0.922–1.041	0.869–1.047
Other	0.932	1.143^*	1.445^*	1.005	0.964
	0.825–1.053	1.007–1.297	1.064–1.962	0.780–1.295	0.668–1.392
Education level
Illiterate (Ref)	1	1	1	1	1
Elementary education	1.052^**	1.050^*	0.998	1.037	1.075
	1.010–1.097	1.003–1.099	0.896–1.113	0.949–1.133	0.950–1.216
High school education	1.077^**	0.944^*	0.794^**	0.947	0.973
	1.026–1.130	0.887–1.004	0.690–0.913	0.852–1.052	0.828–1.143
Higher education	1.156*	0.841^***	0.624^***	0.966	0.905
	1.003–1.112	0.779–0.908	0.520–0.748	0.859–1.086	0.751–1.091
Currently employed
Yes (Ref)	1	1	1	1	1
No	0.931^***	1.159^***	1.426^***	1.194^***	1.644^***
	0.904–0.959	1.106–1.215	1.290–1.577	1.121–1.272	1.479–1.827
Household income per capita
≤ half minimum wage (Ref)	1	1	1	1	1
> half but ≤ 1 minimum wage	1.041^**	1.030	1.002	1.115^*	1.065
	1.012–1.070	0.974–1.089	0.904–1.111	1.021–1.217	0.936–1.212
> 1 but ≤ 2 minimum wage	1.065^***	0.989	0.955	1.188^***	0.970
	1.031–1.100	0.938–1.042	0.852–1.070	1.089–1.295	0.843–1.117
> 2 minimum wage	1.035	0.975	1.040	1.309^***	0.973
	0.998–1.074	0.912–1.042	0.891–1.214	1.173–1.462	0.820–1.155
Number of observations	89,933	88,714	84,052	82,860	90,824

Open in a new tab

Source: Brazilian National Health Survey Pesquisa Nacional de Saúde (PNS) 2019. APR–Adjusted prevalence ratio; 95%CI– 95% Confidence interval; Ref–Reference category

*p<0.05

**p<0.01

***p<0.001

CVD–doctor-diagnosed with either heart disease or stroke.

When examining the results by region, there were quite large differences across Brazil (S1 Fig). Compared the wealthier South, Southeast and Central west regions, the North and Northeast regions had generally lower levels of vegetable consumption, lower red meat consumption, lower smoking, and higher excessive drinking. There was a general trend of lower hypertension and CVD.

By region and area of residence, many confidence intervals were overlapping or showed similar trends to the overall pooled regressions. However, there were a few notable findings. Compared to non-slum urban and rural populations, slum populations in the North had higher vegetable consumption, whilst slum populations in the Northeast and South-east had lower red meat consumption. Over 85% of rural populations in the central west had high red meat consumption compared to ~50% of rural northern populations. Excessive drinking varied from ~87% of non-slum urban populations in the North to ~66% of rural populations in the South. Across the regions, rural populations were more likely to have low physical activity, and in the North and Northeast rural populations were less likely to have a BMI over 25.

Discussion

This study found that, compared to non-slum urban populations, Brazilian slum and rural populations had a greater likelihood of having many behavioural CVD risk factors (including low fruits and vegetables consumption, heavy alcohol consumption (only slum) and physical inactivity). Compared to non-slum urban populations, there were no differences in the likelihood of doctor-diagnosed metabolic risk factors or CVD for slum populations, however rural populations had a lower prevalence across all metabolic risk factors and CVD. Sizeable SES inequalities in CVD risk factors and doctor-diagnosed CVD exist in Brazil by sex, race, education and income.

The findings of an association between slum residence and higher rates of behavioural CVD risk factors (which persisted after adjusting for socioeconomic factors) is generally concordant with other studies and are likely to be partially explained by the slum environment. Studies from Kenyan and Nepalese slums found slum populations reported lower fruits and vegetables consumption, higher heavy alcohol consumption, and more physical inactivity [56,57]. Low fruits and vegetables consumption suggest barriers to accessing healthy foods in slums [58]. Poor transport infrastructure, limited public services and unsafe neighbourhoods in slums have been found to affect local food availability [17,59]. Violence can also deter local businesses and violent drug-related crime is a particular challenge in Brazilian favelas [60]. Violence and unsafe neighbourhoods can also deter exercise–concordant with evidence from Kenyan slums [58]. Furthermore, the high population density of slums limits space for exercise and gyms in slums are often unaffordable or unavailable [20]. In this study, slum dwellers were also more likely to have excessive alcohol consumption–similar to evidence from Indian slums [61]. In Brazil, the availability and advertisement of alcohol is widespread, with alcohol consumption increasing over the last decade [40]. Stress from poor quality housing and insecure neighbourhoods may contribute to increased alcohol consumption [62,63].

In adjusted models, there were no differences between slum dwellers and urban non-slum residents in the likelihood of being overweight or having doctor-diagnosed hypertension, diabetes, high cholesterol, or CVD. However, there was a significantly reduced prevalence in the rural population of these metabolic risk factors. This suggests socioeconomic factors, such as age, sex, income, education and employment are more important determinants of these risk factors than household location. Other studies show similar results, including from the favela Pau da Lima in Salvador which reported the prevalence of diagnosed hypertension or high cholesterol was no different between the urban slum and non-slum [17]. Evidence from some Kenyan slums showed that the majority of the population had low CVD risk [23], whilst in Haiti, slum populations had a low prevalence of obesity and diabetes [64]. One explanation could relate to survey questions asking if conditions were doctor diagnosed, and socio-cultural attitudes may reduce healthcare use and diagnosis, resulting in under-estimation of the true prevalence in slum and rural populations [10,11,65,66]. For example, if community members are of similarly poor health, risk factors and symptoms can become the norm making atypical symptoms hard to recognise–particularly relevant in slums with overcrowding and increased environmental risk factors [64–67]. Precarious employment, which is likely associated with slum residence, can also inhibit healthcare seeking, even if direct costs of healthcare are low as in Brazil [68,69]. In Brazil, barriers to accessing high-quality care remain and are associated with SES, affecting the likelihood of diagnoses [70]. This likely explains the lower prevalence of metabolic risk factors or CVD in rural populations. For example, accessibility and quality of care are likely correlated with neighbourhood factors such as violence, poverty, transport, geographical remoteness, and other local services [67,71]. Public health services in Brazil, particularly in rural or poor urban areas, are often short-staffed, reducing quality and can result in misdiagnoses and inadequate treatment [66,72]. These factors may also explain some of the differences found by region.

This study found strong socioeconomic patterning across other characteristics, in line with other evidence. For example, increasing education and income were generally associated with lower CVD behavioural risk factors, and a lower likelihood of being hypertensive or diabetic. This patterning is generally found in other contexts and countries [12,41,57,73,74]. Men were more likely to smoke, have diets low in fruits and vegetables, and be physically active–consistent with other studies in Brazil [40]. Other studies also find individuals identifying as of Black or Pardo/mixed ethnicity/skin colour have higher prevalence of the behavioural risk factors, hypertension and obesity [41]. This likely stems from additional accumulation of social disadvantage from institutional and structural racism in Brazil [74,75].

This study has limitations. First, the data is self-reported and subject to recall and reporting biases. Second, the cross-sectional nature of this study precludes causal interpretation of the associations found and unmeasured factors could potentially explain the findings. Third, the identification of slum inhabitants involved adapting survey questions resulting in potential for misclassification. While no validation studies of this approach for identifying slum inhabitants have been carried out, we used the UN-Habitat definition, which is widely accepted and has been used in previously published work for assessing slum populations and their health [13,49,50,76,77]. Fourth, the reliance on “doctor-diagnosed” to ascertain health conditions likely does not capture true prevalence–especially for lower SES individuals. Fifth, important CVD risk factors were not included in the survey such as a family history of CVDs, and there is scope for further research on other risk factors. Six, some of the survey questions only captured broad measures of food consumption or risk factors, such as only asking the number of alcoholic drinks consumed and not the alcohol type or only the number of study years and not the quality of education received.

There are policy and clinical implications for this work. People living in slums are more likely to be Black or Pardo, of lower income, and with lower levels of education. With their poorer health outcomes, they should be better targeted and monitored by health services. Higher rates of some behavioural risk factors, not explained by SES, in slum populations should be a target of public health and clinical interventions that, if properly tailored, can have a positive impact. Existing programmes such as supervised physical training through the ‘Carioca Academy Program’ in Rio de Janeiro have already shown to reduce CVD risk [78]. Policies such as the National School Meal Programme in Brazil have resulted in greater access to healthier foods, improved nutrition education and the promotion of local family farming [79,80]. There is also a potentially higher burden of undiagnosed CVD and metabolic risk factors in slum populations. Lack of data on informal urban settlements and the health profiles of these populations means local healthcare services can be poorly targeted, inadequate or culturally inappropriate [81]. Further research should better investigate the specific factors driving poorer behavioural risk factors in slums and also wider inequalities. It is important to understand why greater behavioural risk factors do not translate into higher rates of diagnosed metabolic risk factors within slums, including identifying the roles of health services and environmental factors. Lastly, there should be attention and research involving individuals in non-permanent housing to better understand slum health and the health of the most deprived, vulnerable populations.

Conclusion

Individuals living in slum populations have higher rates of behavioural CVD risk factors, but similar rates of doctor-diagnosed metabolic risk factors and CVD to urban non-slum populations. Slum environments are likely risk factors for poorer behavioural health outcomes. Large inequalities in both behavioural and metabolic risk factors exist across other socioeconomic characteristics in Brazil. Tackling socioeconomic inequalities in CVD risk factors remains a priority including addressing the factors driving poorer health behaviours in slum populations.

Supporting information

S1 Fig. Figures on post-regression predicted prevalence of risk factors by region and area of residence (urban slum, urban non-slum, rural).

(DOCX)

Click here for additional data file.^{(842.4KB, docx)}

S1 Table. Unadjusted regression models on the association between slums and behavioural cardiovascular disease risk factors.

(DOCX)

Click here for additional data file.^{(26KB, docx)}

S2 Table. Unadjusted regression models on the association between slums and metabolic cardiovascular disease risk factors.

(DOCX)

Click here for additional data file.^{(24KB, docx)}

S3 Table. STROBE checklist for cross-sectional studies.

(DOC)

Click here for additional data file.^{(82.5KB, doc)}

Acknowledgments

JC would like to thank Dr Oyinlola Oyebode, Mrs Marylois Beard, Miss Aditi Rajgopal, Miss Mia Wong, Mr Shinya Monno, and Mr Sam Wong for all their help, assistance and encouragement.

Data Availability

All data is from the 2019 Brazilian Health Survey - Pesquisa Nacional de Saúde (PNS). The PNS is publicly available from the Brazilian Institute of Geography and Statistics (IBGE): https://www.ibge.gov.br/estatisticas/sociais/saude/9160-pesquisa-nacional-de-saude.html?=&t=downloads.

Funding Statement

The authors received no specific funding for this work.

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PLOS Glob Public Health. doi: 10.1371/journal.pgph.0000990.r001

Decision Letter 0

Peter Bai James

11 May 2022

PGPH-D-22-00446

Inequalities in the prevalence of cardiovascular disease risk factors in Brazilian slum populations: A cross sectional study

PLOS Global Public Health

Dear Dr.Chan,

Thank you for submitting your manuscript to PLOS Global Public Health. After careful consideration, we feel that it has merit but does not fully meet PLOS Global Public Health’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by 23th May 2022. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at globalpubhealth@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pgph/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

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Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

We look forward to receiving your revised manuscript.

Kind regards,

Peter Bai James, PhD

Academic Editor

PLOS Global Public Health

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Reviewers' comments:

Reviewer's Responses to Questions

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1. Does this manuscript meet PLOS Global Public Health’s publication criteria? Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe methodologically and ethically rigorous research with conclusions that are appropriately drawn based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

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

Reviewer #1: No

Reviewer #2: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available (please refer to the Data Availability Statement at the start of the manuscript PDF file)?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception. The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

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

PLOS Global Public Health does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: In this manuscript Chan et al., present the results of their analysis of inequalities in the prevalence of self-reported cardiovascular disease risk factors (CVRFs, both behavioral and doctor-diagnosed) attributable to demographic and socioeconomic factors, specifically examining the inequalities between slum and non-slum inhabitants, using for this purpose publicly available data from the 2019 Brazilian National Health Survey (BNHS). This secondary analysis is innovative in documenting the burden, distribution, and clustering of CVRFs among marginalized populations studied in a well-designed and conducted nationwide representative survey, whose results have important implications for public health policy in the Latin American region, considering the large fraction of population currently living in the precarious conditions that characterize slums.

The main exposure of the analysis is residence as compared to non-residence in a slum, both levels corresponding to urban settings; however, the data set also provides information about rural dwellers which was incorporated into the analysis and reported in the results’ section but without further discussion in the corresponding section. If there was no hypothesis behind rural residency as an exposure, what was the reason for keeping it in the analysis? I am not suggesting that rural residency is not an interesting and important exposure, in fact, rural-urban inequities in CVFRs are rampant in Latin America and your results support this point; however, considering the focus of your analysis, I suggest either developing the discussion about the findings on the rural-urban contrast or excluding rural residency from the exposure at all. The latest can be done by creating a subpopulation within the data set that contains the levels "slum" and "non-slum" (both urban) and conduct the analysis in that subset without excluding rural dwellers.

From the statistical point of view, in estimating prevalence the authors correctly accounted for the sampling design of the survey by incorporating sampling weights (and presumably, clustering); however, at the time of estimating associations, it is not clear to me why they opted for the odds ratios (from logistic regression) instead of prevalence ratios (from, for instance, binomial regression) considering that the first measure of association overestimates the magnitude of relationships in scenarios where outcomes are highly prevalent, as is the case for most of the CVRFs. Also, prevalence ratios are more “natural” estimates of what it is being studied from a cross-sectional design and easier to communicate than odds ratios to the attempted audience of the paper. In consequence, I strongly recommend estimating and presenting prevalence ratios instead of odds ratios.

The discussion, in what refers to the "slum" versus "non-slum" residency contrast, is focused on the main findings of the analysis and framed in the most relevant evidence, pondering the results in light of the limitations of a cross-sectional design and self-reported data on exposures and health outcomes; however, I do not consider the use of regression models a limitation but a strength of the analysis (see lines 388-391). Finally, the authors did an excellent job at developing both policy and clinical implications for their findings.

Minor comments.

In the “Data analysis” section consider making explicit how the complex sampling design of the survey was incorporated into the analysis (i.e., clustering), as well as which variables were included into the adjusted models (only stated in line 389).

In the “Results” section, line 230, perhaps the authors meant to say, “Over two thirds…”.

Reviewer #2: This manuscript assessed data from Brazil's most recent health demographic survey to appraise the association between cardiovascular risk factors and housing conditions. The theme is relevant to global public health; the study is well organized and reads smoothly. This reviewer agrees with the main methodological options adopted here, with special mention to the choice of the database and the statistical analysis. Also, the robust assertion about study limitations is a strong point.

I also underscore the original use of the survey’s data to classify slum households according to UN-Habitat criteria as a study’s strength, which can instruct further research on the subject.

Aiming to contribute to the authors and improve even further this excellent manuscript, I offer two discretionary suggestions.

1. I missed the report on unadjusted odds ratios as supplemental tables. I understand the difficulty of reporting too many regression models. The study collates eleven outcome variables and seven factors. However, socioeconomic factors such as education, employment status, and income correlate among themselves. The problem increases when we consider that race/ethnicity and slum residence can also be associated with the remaining socioeconomic covariates. Excessive autocorrelation can interfere with regression models’ goodness-of-fit indicators. Assessing variance inflation, direct acyclic graphs, and comparing unadjusted and adjusted regression results are tools to attenuate this problem.

2. Brazil is a large country with substantial geographic inequalities. I wonder whether the results reported here would hold for the poorer North and Northeast regions to the same extent as the remaining, wealthier regions. The database is sufficiently large and allows this assessment. Maybe the authors will not have difficulties running the same analytical routines for each geographic region and reporting the results in supplemental tables if the results are eventually relevant.

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6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Reviewer #1: Yes: Victor Herrera

Reviewer #2: Yes: Jose Leopoldo Ferreira Antunes

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PLOS Glob Public Health. 2022 Sep 8;2(9):e0000990. doi: 10.1371/journal.pgph.0000990.r002

Author response to Decision Letter 0

10 Jun 2022

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file.^{(16.5KB, docx)}

PLOS Glob Public Health. doi: 10.1371/journal.pgph.0000990.r003

Decision Letter 1

Peter Bai James

18 Jul 2022

PGPH-D-22-00446R1

Inequalities in the prevalence of cardiovascular disease risk factors in Brazilian slum populations: A cross sectional study

PLOS Global Public Health

Dear Dr. Chan,

Please submit your revised manuscript by the 1st of August 2022. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at globalpubhealth@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pgph/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

We look forward to receiving your revised manuscript.

Kind regards,

Peter Bai James, PhD

Academic Editor

PLOS Global Public Health

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments (if provided):

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: All comments have been addressed

Reviewer #3: (No Response)

**********

2. Does this manuscript meet PLOS Global Public Health’s publication criteria? Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe methodologically and ethically rigorous research with conclusions that are appropriately drawn based on the data presented.

Reviewer #2: Yes

Reviewer #3: Yes

**********

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

Reviewer #2: Yes

Reviewer #3: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available (please refer to the Data Availability Statement at the start of the manuscript PDF file)?

Reviewer #2: Yes

Reviewer #3: (No Response)

**********

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

Reviewer #2: Yes

Reviewer #3: Yes

**********

6. Review Comments to the Author

Reviewer #2: (No Response)

Reviewer #3: This is an interesting and well-written manuscript about the relationship between residence in a slum and two forms of CVD risk factors (behavioral and doctor-diagnosed conditions) from the 2019 PNS. While the paper is clear and thorough, there are a few ways it may be further strengthened:

1. Has this approach to defining a slum resident been validated in prior studies (not the definition persay, but this retrospective attempt to define urban residents as slum-dwellers). in the limitations, the authors alluded to prior studies that did this but would be even more convincing if they said how well this approach performed previously.

2. Additionally, the the derivation of the four of the metabolic risk factors was a little bit unclear. Was BMI measured in the survey and used to define overweight? Were the "doctor diagnosed" conditions only self-report (the limitations indicates this is likely the case)? Though mentioned in the limitations, this seems pretty important. Were there biomarkers measured in the survey that could further bolster these definitions or be used as a second way to categorize people as having these risk factors?

3. Table 3 might benefit from the addition of a statistical comparison test.

4. The inclusion of the rural population as a comparison is interesting but sometimes may distract from the focus on slum residents. I wonder if the authors should have made the findings for rural residents (as a comparison) more explicit in the paper and especially in the discussion.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

Do you want your identity to be public for this peer review? If you choose “no”, your identity will remain anonymous but your review may still be made public.

For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #2: Yes: Jose Leopoldo Ferreira Antunes

Reviewer #3: No

**********

PLOS Glob Public Health. 2022 Sep 8;2(9):e0000990. doi: 10.1371/journal.pgph.0000990.r004

Author response to Decision Letter 1

24 Jul 2022

Attachment

Submitted filename: Response to Reviewers II.docx

Click here for additional data file.^{(17.8KB, docx)}

PLOS Glob Public Health. doi: 10.1371/journal.pgph.0000990.r005

Decision Letter 2

Peter Bai James

29 Jul 2022

PGPH-D-22-00446R2

Inequalities in the prevalence of cardiovascular disease risk factors in Brazilian slum populations: A cross sectional study

PLOS Global Public Health

Dear Dr. Chan,

Please submit your revised manuscript by the 4th August 2022. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at globalpubhealth@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pgph/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

We look forward to receiving your revised manuscript.

Kind regards,

Peter Bai James, PhD

Academic Editor

PLOS Global Public Health

Journal Requirements:

1. Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments (if provided):

Please attach a supplementary file that shows that your study adheres to the STROBE guideline for cross-sectional study. You can access a copy of the STROBE checklist for cross-sectional studies via this link below.

STROBE - Strengthening the reporting of observational studies in epidemiology (strobe-statement.org)

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Reviewers' comments:

PLOS Glob Public Health. 2022 Sep 8;2(9):e0000990. doi: 10.1371/journal.pgph.0000990.r006

Author response to Decision Letter 2

6 Aug 2022

Attachment

Submitted filename: Response to Reviewers III.docx

Click here for additional data file.^{(12.1KB, docx)}

PLOS Glob Public Health. doi: 10.1371/journal.pgph.0000990.r007

Decision Letter 3

Peter Bai James

9 Aug 2022

Inequalities in the prevalence of cardiovascular disease risk factors in Brazilian slum populations: A cross-sectional study

PGPH-D-22-00446R3

Dear Chan,

We are pleased to inform you that your manuscript 'Inequalities in the prevalence of cardiovascular disease risk factors in Brazilian slum populations: A cross-sectional study' has been provisionally accepted for publication in PLOS Global Public Health.

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Associated Data

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

Supplementary Materials

S1 Fig. Figures on post-regression predicted prevalence of risk factors by region and area of residence (urban slum, urban non-slum, rural).

(DOCX)

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S1 Table. Unadjusted regression models on the association between slums and behavioural cardiovascular disease risk factors.

(DOCX)

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S2 Table. Unadjusted regression models on the association between slums and metabolic cardiovascular disease risk factors.

(DOCX)

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S3 Table. STROBE checklist for cross-sectional studies.

(DOC)

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Attachment

Submitted filename: Response to Reviewers.docx

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Attachment

Submitted filename: Response to Reviewers II.docx

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Submitted filename: Response to Reviewers III.docx

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Data Availability Statement

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PERMALINK

Inequalities in the prevalence of cardiovascular disease risk factors in Brazilian slum populations: A cross-sectional study

Jasper J L Chan

Linh Tran-Nhu

Charlie F M Pitcairn

Anthony A Laverty

Matías Mrejen

Julia M Pescarini

Thomas V Hone

Roles

Abstract

Background

Methods

Results

Conclusions

Introduction

Methods

Ethics statement

Study design

Data source

Outcomes

Variables of interest

Table 1. Slum residence definition.

Data analysis

Results

Table 2. Distribution of study population’s area of residence.

Table 3. Socioeconomic and demographic characteristics of study population by area of residence.

Fig 1. Weighted prevalence of self-reported cardiovascular disease (CVD) behavioural risk factors by area of residence.

Fig 2. Weighted prevalence of self-reported cardiovascular disease (CVD) metabolic risk factors and doctor-diagnosed CVD by area of residence.

Table 4. Poisson regression models on the association between slums and behavioural cardiovascular disease risk factors.

Table 5. Poisson regression models on the association between slums and metabolic cardiovascular disease risk factors.

Discussion

Conclusion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Peter Bai James

Roles

Author response to Decision Letter 0

Decision Letter 1

Peter Bai James

Roles

Author response to Decision Letter 1

Decision Letter 2

Peter Bai James

Roles

Author response to Decision Letter 2

Decision Letter 3

Peter Bai James

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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