GENDER AND NONCOMMUNICABLE DISEASES IN REPUBLIC OF MOLDOVA

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GENDER AND NONCOMMUNICABLE

DISEASES IN REPUBLIC OF MOLDOVA

Analysis of STEPS data

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GENDER AND NONCOMMUNICABLE

DISEASES IN REPUBLIC OF MOLDOVA

Analysis of STEPS data

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disaggregated data that need to be unpacked through sociodemographic characteristics, because men and women are not homogenous groups. The report also recognizes gaps in evidence and calls for further analysis of the impact of gender-based inequalities.

KEYWORDS

NONCOMMUNICABLE DISEASES GENDER

SOCIOECONOMIC FACTORS RISK FACTORS

HEALTHY DIET ALCOHOL TOBACCO USE OBESITY

BLOOD PRESSURE REPUBLIC OF MOLDOVA

WHO/EURO:2020-1669-41420-56462

© World Health Organization 2020

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iii

ACKNOWLEDGMENTS iv

EXECUTIVE SUMMARY v

INTRODUCTION 1 NCDS CONSTITUTE THE MAIN BURDEN OF DISEASE FOR BOTH WOMEN AND MEN,

BUT THERE ARE IMPORTANT DIFFERENCES 4

DIFFERENCES IN BEHAVIOURAL AND BIOLOGICAL RISK FACTORS 5

SIGNIFICANT DIFFERENCES BETWEEN MEN AND WOMEN 6

PREVALENCE OF THREE OR MORE RISK FACTORS 7

MORTALITY RATES AMONG MEN AND WOMEN 8

DIFFERENCES IN SPECIFIC RISK FACTORS AMONG MEN AND WOMEN BETWEEN AGE GROUPS 9 DIFFERENCES IN THE WAY MEN AND WOMEN ACCESS SERVICES 19 DIFFERENCES IN MEN AND WOMEN NOT MEASURED FOR RISK FACTORS 20 LIFESTYLE ADVICE GIVEN BY A HEALTH-CARE PROFESSIONAL 24 CONCLUSIONS 27 REFERENCES 31 ANNEX 1. SUPPLEMENTARY TABLES 33

The editors of the series and of this report are Isabel Yordi Aguirre and Ivo Rakovac from the WHO Regional Office for Europe. They conceptualized the series’ publications, defined content, provided overall input, and reviewed and amended the content of the report to ensure alignment with overall WHO policy and guidance documents. The authors of the report are Brett J. Craig, WHO Regional Office for Europe, and Galina Obreja, Department of Social Medicine and Health Management, State University of Medicine and Pharmacy, Republic of Moldova. Overall support and leadership for this initiative was provided by João Breda, Head of the WHO European Office for Prevention and Control of Noncommunicable Diseases, Nino Berdzuli, Director of the Division of Country Health Programmes, and Natasha Azzopardi- Muscat, Director of the Division of Country Health Policies and Systems, WHO Regional Office for Europe.

Input was provided by: Daniela Demiscane, Ministry of Health, Labour and Social Protection, Republic of Moldova; Ion Salaru and Natalia Silitrari, National Agency for Public Health, Republic of Moldova; Rosemary Morgan, Johns Hopkins Bloomberg School of Public Health, United States of America; and Åsa Nihlén, Jill Farrington, Juan Tello and Enrique Loyola, WHO Regional Office for Europe.

The work to create the country profile was made possible by the generous support of the governments of the Russian Federation and Germany.

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v

This country profile for the Republic of Moldova presents an analysis of sex-disaggregated data linked with other variables, such as education and employment, gathered through the WHO STEPwise (STEPS) survey as part of the WHO Regional Office for Europe’s gender and noncommunicable diseases (NCDs) initiative to improve the response to NCDs in the Region through a gender approach.

It is the first gender analysis of NCD risk factor data for adults in the Republic of Moldova and makes an important contribution to, and serves as an evidence base for, international commitments on NCDs in accelerating action towards reducing the NCD burden and ensuring universal health coverage. It also contributes to raising awareness and building capacity among country-based researchers and policy-makers on the rationale for applying a gender analysis to health data.

A gender analysis of STEPS NCD risk-factor survey data describes how risk factors for chronic diseases differ between and among men and women by exploring and tracking the direction and magnitude of trends in risk factors and accessing services. It enables better planning and/or evaluation of gender-responsive health promotion or preventive campaigns and gender-responsive interventions.

The analysis in this country profile examined differences in risk factors and accessing services between men and women overall by age group, geographic location, education, income, employment status and ethnicity. Important differences hide even in sex-disaggregated data that need to be unpacked by including sociodemographic characteristics, because men and women are not homogenous groups.

Globally, more than 100 countries have collected data through the STEPS surveys, but this is the first time a more in-depth analysis from a gender perspective has been conducted. The following findings of the gender analysis therefore can be used to address specific needs and policy opportunities for the Republic of Moldova.

• Significantly higher percentages of men than women in most age groups engage in the behavioural risk factors for NCDs (like tobacco-smoking, alcohol consumption, insufficient levels of physical activity, insufficient intake of fruit and vegetables, adding salt to the diet and frequent consumption of processed foods), and significantly higher percentages of women than men in the older age groups are found with most of the biological risk factors (overweight and obesity, and raised blood pressure, glucose and cholesterol).

• While the percentage of men with multiple risk factors more than doubles from the 18–29 age group to the 60–69 group, the increase in percentage of women is more than 11 times greater between comparable age groups.

• The association between geographic location and behavioural risk factors varies for men and women, while the general trend for biological risk factors is that prevalence is higher in rural areas, especially for women.

• A high education level for men and women is not associated with lower prevalence of behavioural risk factors. Higher prevalence of biological risk factors is observed for medium- and low-education women, but this is not the case for men.

EXECUTIVE SUMMARY

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• Prevalence also varies by ethnicity, with some ethnic groups lower in behavioural risk factors but higher in biological.

• There are gaps in some areas between exposure to risk and response from health services. A significantly higher percentage of men have not been measured for biological risk factors, while a significantly higher percentage of women have been given lifestyle advice on most behavioural risk factors by a health-care professional.

• More women and (especially) men in rural areas have not been measured for risk factors than in urban, with the percentage of urban men not differing from the percentages of women in rural and urban areas.

• Women and (especially) men in the low education level are being measured for risk factors less than in the high education level.

• Fewer low-income as well as unemployed men and women have been measured for risk factors, and the difference overall is greater for women.

• More men and women in ethnic minority groups have not been measured for risk factors.

• Improving access to services for women and men may therefore require that additional attention is paid to the following groups: men, starting in the 30–44 age group, women and (especially) men in rural areas, women and (especially) men in the low education level, low-income men and (especially) women, unemployed men and women, and ethnic minority women and (especially) men.

• Studies that specifically examine gender and social norms and gender inequality in these contexts can be used to complement this analysis by identifying driving and constraining factors for men and women in exposure to risk and access to services.

In addressing the areas identified in this report, cost-effective interventions like best-buy and other interventions recommended by WHO should be prioritized and tailored to the country-specific context to ensure uptake and efficiency. This would greatly contribute to the achievement of universal health coverage and the health-related Sustainable Development Goals.

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INTRODUCTION

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The WHO Regional Office for Europe launched a gender and noncommunicable diseases (NCDs) initiative in 2019 to improve the response to NCDs in the Region through a gender approach. Gender and rights-based approaches are imperative to accelerate transformative and sustainable progress towards achievement of the United Nations Sustainable Development Goals (SDGs). The strategy on women’s health and well-being in the WHO European Region (1) and the strategy on the health and well-being of men in the WHO European Region (2) strengthen the links between SDGs 3 and 5 in the WHO European Region while providing a comprehensive working framework for improving health and well-being in Europe through gender- responsive approaches.

Commitments by Member States of the WHO European Region to accelerate actions towards reducing NCDs build on the Action Plan for the Prevention and Control of Noncommunicable Diseases in the WHO European Region 2016–2025 (3) and high-level meetings, in particular Health Systems Respond to NCDs: Experience in the European Region (Sitges, Spain, 16–18 April 2018) (4) and the WHO European High-level Conference on Noncommunicable Diseases: Time to Deliver – Meeting Noncommunicable Disease Targets to Achieve the Sustainable Development Goals in Europe (Ashgabat, Turkmenistan, 9 April 2019) (5).

To support these commitments with evidence and knowledge exchange, country profiles of Armenia, Belarus, Georgia, Kyrgyzstan, the Republic of Moldova, Turkey, Ukraine and Uzbekistan have been created using a gender analysis on data gathered through the WHO STEPwise approach to Surveillance (STEPS) NCD risk-factor survey.

This country profile for the Republic of Moldova presents an analysis of sex-disaggregated data linked with other variables, such as geographic location, education, employment and marital status, gathered through the STEPS survey. The analysis allows identification of the main gender-based differences and highlights some of the areas that need further gender analysis. Evidence generated within the country profiles in the series is intended to provide an evidence base and rationale for countries to strengthen health systems and whole-of-government responses to prevent, detect, manage and control NCDs, particularly at primary-care levels, through gender-responsive actions (Fig. 1).

Source: WHO (6).

Gender unequal Perpetuates inequalities

Gender blind Ignores gender norms

Gender sensitive Acknowledges but does not address inequalities

Gender specific Considers women’s and men’s specific needs

Gender transformative Aims at transforming harmful gender norms, roles and relations

GENDER-RESPONSE POLICY

Considers genders, norms, roles and relations

Takes active measures to reduce harmful effects Fig. 1. WHO gender-response assessment scale

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GENDER AND NONCOMMUNICABLE DISEASES IN REPUBLIC OF MOLDOVA

3 INTRODUCTION

The analysis follows the key elements identified by the WHO Regional Office for Europe (7). A gender analysis considers socially constructed norms, roles, behaviours and attributes that a given society considers appropriate for women and men and how this implies differential degrees of power between and among women and men. It recognizes that women and men are not homogenous groups and that their health opportunities and risks vary according to social, economic, environmental and cultural influences throughout their lifetime, while also considering how gender intersects with other factors behind social inequalities, such as age, employment, education, ethnicity or place of residence.

The STEPS surveys (8) gather information on NCD risk factors to help plan and evaluate programmes and interventions by collecting standardized, high-quality risk-factor data to enable comparisons while allowing flexibility. The STEPS surveys consist of interviews (STEP 1), physical measurements such as blood pressure, weight and height (STEP 2) and biochemical measurements like blood glucose and cholesterol (STEP 3). An integrated approach is used, allowing an analysis of multiple risk factors simultaneously in a cost-efficient manner. WHO provides countries with a reference methodology for NCD surveillance and technical support for implementation.

A gender analysis of STEPS NCD risk-factor survey data describes how risk factors for chronic diseases differ between and among men and women by exploring and tracking the direction and magnitude of trends in risk factors and how these differ between and among women and men. It enables better planning and/or evaluation of gender-responsive health promotion or preventive campaigns and gender-responsive interventions. At the same time, data reveal important differences between men and women in relation to health services access.

The survey in the Republic of Moldova was carried out from October 2013 to November 2013. A cluster sample design was used to produce nationally representative data for the age range of 18–69 years in the Republic of Moldova. The overall response rate was 83.5%, with 4807 adults participating in the survey.

The data were weighted for complex survey design, non-response rate and population distribution by age and sex.

The analysis examined differences between and among men and women in risk factors and accessing services. In addition to looking at overall differences between men and women in risk factors, the analysis examined differences in groups of behavioural and biological risk factors. Differences among men and among women were then analysed by age group and other sociodemographic variables for both individual risk factors and groups of risk factors. Overall and within-group differences were also analysed by sociodemographic variables for accessing services. Examining sex-disaggregated data not only for overall differences between men and women but also for differences within these groups across the life-course is necessary, because men and women are not homogenous groups. There are important differences hiding even in sex-disaggregated data that need to be unpacked by including sociodemographic characteristics.

The country profiles in this series are the first steps in mainstreaming gender, which is explained and further elaborated in the WHO manual Gender mainstreaming for health managers: a practical approach (6) (Fig. 2).

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NCDs constitute the main burden of disease for both women and men, but there are important differences

NCDs are the leading cause of death, disease and disability in the WHO European Region, and they are the greatest burden of disease in the Republic of Moldova. NCDs are estimated to account for 90% of all deaths in the Republic of Moldova (9), with cardiovascular diseases accounting for 59% of all deaths in the country.

Though improvements have been made, further action is needed to address the expected increase in the overall burden of NCDs (10). As of 2016, it is estimated that 33% of the adult population had raised blood pressure, 24% smoked tobacco, 12% were physically inactive, 20% were obese, 9% had diabetes and 15%

used alcohol harmfully (9).

The growing NCD burden has been met with a number of reforms to increase efficiency and improve service delivery (10). The prevalence of risk factors that account for NCDs nevertheless are different between and among men and women, and important differences exist in the ways men and women access health services.

Source: WHO (6).

ACCOUNTABILITY

Sexdisaggregated data + Gender

analysis + Gender-responsive action

ST EP S

Fig. 2. Gender mainstreaming steps

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DIFFERENCES IN BEHAVIOURAL

AND BIOLOGICAL RISK FACTORS

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For behavioural risk factors, the STEPS data focus specifically on tobacco use, harmful alcohol consumption, unhealthy diet (low fruit and vegetable consumption, diet high in salt and/or processed foods) and insufficient physical activity, and, for biological risk factors, overweight/obesity, raised blood pressure, raised blood glucose and raised cholesterol. Highlighting where the highest differences exist will help to uncover where inequitable gender norms, roles, behaviours and attributes are likely to have the greatest effect on risk factors.

Significant differences between men and women

The prevalence of these risk factors for men and women was examined and tested for significant differences (Fig. 3 and Annex 1, Table A1.1).

While the prevalence among men is significantly higher than for women in nearly all the behavioural risk factors (current tobacco use, alcohol and diet), the same trend is not found for the biological risk factors.

Prevalence is significantly higher for women in obesity and raised cholesterol, and there is no significant difference between men and women in overweight, raised blood pressure and raised blood glucose. The prevalence of raised blood pressure without medication is the only biological risk factor for which men are significantly higher than women.

BMI: body mass index. * Statistically significant difference.

0 10 20 30 40 50 60 70 80 90

Current tobacco use*

Alcohol*

Alcohol (heavy episodic)*

Unhealthy diet ( < 5 fruit/veg per day) Unhealthy diet (add salt)*

Unhealthy diet (processed food)*

Insufficient physical activity

Overweight (BMI ≥ 25) Behavioural risk factors

Biological risk factors

Obesity (BMI ≥ 30)*

Raised blood pressure (or on medication) Raised blood pressure NOT on medication Raised blood glucose (or on medication) Raised cholesterol (or on medication)

Men Women

Fig. 3. Prevalence of risk factors across countries with differences between men and women (%)

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7 DIFFERENCES IN BEHAVIOURAL AND BIOLOGICAL RISK FACTORS

Prevalence of three or more risk factors

Differences between men and women in the prevalence of NCD risk factors are also found in those more at risk due to the prevalence of multiple risk factors. In accordance with the STEPS methodology, selected risk factors were used to examine the prevalence of three or more risk factors in the population. These combined risk factors are:

• current daily smokers;

• fewer than five servings of fruit and vegetables per day;

• insufficient physical activity (< 150 minutes of moderate-intensity activity per week, or equivalent);

• overweight (body mass index (BMI) ≥ 25 kg/m²); and

• raised blood pressure (BP) (systolic BP ≥ 140 and/or diastolic BP ≥ 90 mmHg or currently on medication).

Overall, a significantly higher percentage of men (35.2%) have three or more risk factors compared to women (25.0%). A significantly lower percentage of men (5.7%) than women (10.4%) do not have any risk factors.

In addition to overall differences between men and women in multiple risk factors, prevalence through the life-course is different for men and women. As expected, the percentage of men and women with three or more risk factors is higher in older than younger age groups. Significantly higher percentages of both men and women in the first three age groups have three or more risk factors than in the preceding age group, and in the first two age groups the prevalence for men is significantly higher than for women.

While the percentage of men steadily increases with each age group, the increase in percentage of women is more drastic, causing the significant difference in percentage between men and women to lessen and disappear in the two older age groups. The most significant decrease of the difference can be seen between age groups 30–44 and 45–59, with the accumulation of risk factors in women being more dramatic. While the percentage of men more than doubles from the 18–29 age group to the 60–69 group, from 23.1% to 53.1%, the percentage of women with three or more risk factors is more than 11 times greater between comparable age groups, from 4.8% to 57.1% (Fig. 4 and Annex 1, Table A1.2).

These combined risk factors, however, do not include all risk factors, such as alcohol consumption or raised cholesterol. Additionally, risk factors have different impacts on NCD morbidity and mortality. For example, the risk associated with smoking is higher at individual level than the risk associated with eating fewer than five servings of fruit and vegetables (11): further analysis therefore is warranted to examine differences in these risk factors between men and women as well as among men and women.

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Mortality rates among men and women

Though difficult to calculate, there is probably an influence of mortality rates on prevalence of risk factors in the population when examining differences between men and women through the life-course. The mortality rate for men is significantly higher than for women and increases in the older age groups (Fig. 5 and Annex 1, Table A1.3) (12).

0 10 20 30 40 50 60 70 80

18–29 30–44 45–59 60–69

Men Women

Fig. 4. Prevalence with three or more risk factors by age group (%)

0 10 20 30 40 50 60 70 80

20–29 30–44 45–59 60–69

Men Women

Fig. 5. Total mortality per 1000

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The higher mortality rates for men may account for some of the lessening of the gap observed between men and women with three or more risk factors in the older age groups.

Differences in specific risk factors among men and women between age groups

Not only do men and women experience multiple risk factors differently through the life-course, but their experience with individual risk factors is also different. Examining the differences between men and women in more detail and by age group regarding risk factors reveals further the importance of a gender analysis. The difference between age groups for either sex in each behavioural risk factor shows how many more men than women engage in nearly all risk factors across age groups (Fig. 6 and Annex 1, Table A1.4).

Differences between age groups in behavioural risk factors for men may be more pronounced than for women, such as with tobacco use, but there is more observed variance for alcohol consumption by age for women than for men. Overall, prevalence for men is higher in most behavioural risk factors.

The story for biological risk factors and age is quite different. The percentages of men and women with biological risk factors is significantly higher with each advancing age group (Fig. 7 and Annex 1, Table A1.5). More important is that the prevalence for men starts higher in the youngest age group, but for women it is higher for most biological risk factors in the older age groups.

These data show that prevalence of biological risk factors for women increases with older age groups more dramatically than with men. This applies not only to the population with multiple risk factors, but also to each individual risk factor. For example, though the prevalence of overweight for men is not significantly higher than for women overall, prevalence for men in the 18–29 age group is significantly higher (39.6%) than for women (25.0%). In the 60–69 age group, however, prevalence is significantly higher for women

Current tobacco use

Alcohol (heavy episodic) Alcohol

Unhealthy diet ( < 5 fruit/veg per day) Unhealthy diet (add salt) Unhealthy diet (processed food) Insufficient physical activity

0 10 20 30 40 50 60 70 80 90 100

Men Women 18–29 30–44 45–59 60–69

Fig. 6. Prevalence of behavioural risk factors by age group (%)

More men than women engage in behavioural risk factors across the life-course

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(85.2%) than for men (67.5%). With obesity, prevalence is not significantly different between men and women in the 18–29 age group (5.5% men, 9.5% women), but is significantly greater for women (50.4%) than for men (36.2%) in the 60–69 age group.

Differences between men and women are apparent across the life-course, but disaggregating data reveals additional differences among men and among women. Disaggregation by age group reveals specific groups of men and women who are more at risk and differences by sex. Other demographic categorizations, such as geographic location, education level, income level, employment status and ethnicity, further help identify differences between men and women and also differences within these groups.

GEOGRAPHIC LOCATION – URBAN AND RURAL

The geographic location of the population can be used to further examine the differences in risk factors not only between, but also among, men and women. Data on geographic location were collected in the STEPS survey and have been categorized into urban and rural for the purposes of analysis. While some differences between men and women in urban and rural areas are observed, these differences are not consistent across the different risk factors (Fig. 8 and Annex 1, Table A1.6). For example, prevalence of alcohol consumption and heavy episodic drinking is higher in rural areas for both men and women, but prevalence in risk factors associated with diet are generally higher in urban areas. While tobacco use does not vary by location for men (42.9% urban, 44.3% rural), prevalence among women in urban areas (10.4%) is significantly higher than in rural (1.3%). Prevalence of eating processed food high in salt is significantly higher for men in urban areas (41.7%) than rural (32.0%), but for women there is no significant difference (30.6% urban, 25.8% rural).

An analysis of biological risk factors by geographic location further shows that urban and rural areas are not associated with risk factors the same for men and women (Fig. 9 and Annex 1, Table A1.7). The prevalence

Overweight (BMI ≥ 25)

Obesity (BMI ≥ 30)

Raised blood pressure (or on medication) Raised blood pressure NOT on medication Raised blood glucose (or on medication)

Raised cholesterol (or on medication)

0 10 20 30 40 50 60 70 80 90 100

Men Women 18–29 30–44 45–59 60–69

Fig. 7. Prevalence of biological risk factors by age group (%)

Prevalence of women with biological risk factors ends higher than men, even if it starts lower

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of obesity for women is significantly higher in rural areas (33.5%) than urban (22.9%), but for men there is no significant difference (16.4% in rural, 19.5% in urban). The prevalence of obesity among urban women is not significantly higher than for men in urban areas, showing that it is a subgroup that is primarily driving the overall difference between men and women.

The prevalence of overweight is also significantly different for both men and women between urban and rural areas. For men, however, prevalence is higher in urban areas (60.4% in urban, 52.4% in rural), which is the opposite of the association for women (62.4% in rural, 48.6% in urban). As is seen with disaggregation by age group and geographic location, important differences between men and women are hiding in the aggregated percentages of risk factors for men and women.

Current tobacco use

Alcohol

Alcohol (heavy episodic) Unhealthy diet ( < 5 fruit/veg per day) Unhealthy diet (add salt) Unhealthy diet (processed food) Insufficient physical activity

0 10 20 30 40 50 60 70 80 90 100

Men Women Rural Urban

Fig. 8. Prevalence of behavioural risk factors by geographic location (%)

The association of geographic location and behavioural risk factors is different for men and women

0 10 20 30 40 50 60 70 80 90 100

Fig. 9. Prevalence of biological risk factors by geographic location (%)

Geographic location can affect biological risk factors

differently for women and men

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EDUCATION LEVEL

The education level of the population can be used to examine further the differences in risk factors not only between men and women, but also within the groups of men and women. The Republic of Moldova has extremely high literacy rates (99.6% for men, 99.1% for women) and high enrolment in primary (86.4%

for boys, 86.2% for girls) and secondary education (78.3% for males, 77.6% for females). A significant difference is visible only at tertiary level (34.2% for males, 45.7% for females) (13).

Data on education level, determined by the highest level of education completed, were collected in the STEPS survey using country-specific categories. These categories have been matched to the levels of the International Standard Classification of Education (ISCED) (14) and then condensed to reflect the three levels of low, medium and high (Table 1 and 2).

The prevelence of behavioural risk factors for men and women varies by education level, depending on the risk factor and whether it is men or women in that level (Fig. 10 and Annex 1, Table A1.8). Prevalence of current tobacco use is significantly higher for men in the low education level while prevalence of insufficient physical activity for men is higher in the high education level. For women, differences by education are found in risk factors regarding diet. Prevalence of eating fewer than five servings of fruit and vegetables per day and adding salt are higher for women in the low education level. With most behavioural risk factors, prevalence is higher for men and women in the low or medium education levels.

STEPS survey categories ISCED levels

1 = no formal schooling/less than

primary school ISCED 0 = early childhood education 2 = primary school completed ISCED 1 = primary education 3 = gymnasium completed ISCED 2 = lower-secondary education 4 = lyceum/secondary school completed ISCED 3 = upper-secondary education

5 = college/vocational school completed ISCED 4 = post-secondary non-tertiary education ISCED 5 = short-cycle tertiary education 6 = university completed/postgraduate

degree

ISCED 6 = bachelor’s degree or equivalent tertiary education ISCED 7 = master’s degree or equivalent tertiary education ISCED 8 = doctoral degree or equivalent tertiary education Table 1. STEPS survey categories and ISCED levels

Education level for analysis STEPS survey categories ISCED levels Low level of education 1 = no formal schooling/less than primary school

2 = primary school completed 3 = gymnasium completed

4 = lyceum/secondary school completed

ISCED 0–3

Medium level of education 5 = college/vocational school completed ISCED 4–5 High level of education 6 = university completed/postgraduate degree ISCED 6–8 Table 2. Education level for analysis

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13

Additional differences in education levels are observed between and among men and women in relation to biological risk factors. Overall, the prevalence of biological factors for women tends to be lower in the high-education group, which is not necessarily the case for men with high-level education (Fig. 11 and Annex 1, Table A1.9).

With overweight, obesity and raised blood pressure, prevalence is lowest among the high education level for women and tends to be similar in the low and medium education levels. Prevalence among men in these same risk factors follows a different pattern, with prevalence varying by education level across risk factors.

Current tobacco use

Alcohol

0 10 20 30 40 50 60 70 80 90 100

Men Women Low Medium High

Fig. 10. Prevalence of behavioural risk factors by education level (%)

Prevalence is higher for both men and women in the low and medium education levels

0 10 20 30 40 50 60 70 80 90 100

Fig. 11. Prevalence of biological risk factors by education level (%)

Prevalence is higher for medium- and low-education men and (especially) women

DIFFERENCES IN BEHAVIOURAL AND BIOLOGICAL RISK FACTORS

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Additional differences that are not apparent in the overall differences in biological risk factors are observed when comparing education levels between men and women. For example, while the prevalence of overweight is significantly lower for high-education women, it is significantly higher for high-education men. While the prevalence of obesity is significantly lower for low-education men, it is significantly lower for high-education women. High-education women are not higher in obesity than men, showing that differences in prevalence of risk factors between men and women are often dependent on prevalence in groups among men and among women.

The prevalence of raised blood pressure is also significantly lower for high-education women than the other levels and for men at all levels. While overall there is no significant difference between raised blood pressure for men and women, by education level it can be seen again that men and women are not homogenous groups.

INCOME LEVEL

An analysis by income level can also be used to examine differences among men and women due to differences in lifestyle and access to resources. Five household income quintiles were used in the STEPS survey; based on the average income levels and adjusting for household size, categories of high income (monthly income greater than 1500 Moldovan Leu (MDL)) and low income (less than 1500 MDL) were created for this analysis.

The estimated average annual earned income per capita for women in the Republic of Moldova is approximately 75% of what it is for men (the equivalent of Int$ 5600 for women and Int$ 7500 for men) (13). Disaggregating the STEPS survey data by employment levels and sex reveals how the prevalence of behavioural risk factors varies in some groups and not in others (Fig. 12 and Annex 1, Table A1.10).

Current tobacco use

Alcohol

0 10 20 30 40 50 60 70 80 90 100

Men Women Low High

Fig. 12. Prevalence of behavioural risk factors by income level (%)

Prevalence tends to be higher for low-income men and women across risk factors

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Most differences in behavioural risk factors by income level are not significant, but the trend is for higher prevalence for low-income men and women. The prevalence of adding salt shows again the important differences hiding in the aggregate. While prevalence is significantly higher for men (28.0%) than women (20.3%) overall, by income level the significant difference is between low-income men (32.2%) and high- income women (16.6%). Prevalence for high-income men (23.7%) and low-income women (23.7%) is not significantly different.

An analysis of biological risk factors by income level and sex shows again that prevalence tends to be higher for low-income men and women, but there are some differences (Fig. 13 and Annex 1, Table A1.11).

Some significant differences were also found among the biological risk factors by income level. Prevalence of overweight is significantly higher for high-income than low-income men, but there is no significant difference between low- and high-income women.

Prevalence of raised blood pressure for low-income men (44.3%) is significantly higher than for high- income men (36.9%). Likewise, prevalence for low-income women (45.2%) is significantly higher than for high-income women (33.5%). The association of income level therefore is similar for men and women in some risk factors, while in others the association is different.

EMPLOYMENT STATUS

An analysis by income level can be enriched with an additional analysis by employment status. Data on employment status were collected in the STEPS survey, and the categories have been condensed for analysis into employed (government employee, nongovernment employee, self-employed), non-paid (non-paid, student, homemaker), retired and unemployed (unemployed (able or unable to work)).

0 10 20 30 40 50 60 70 80 90 100

Men Women Low High

Fig. 13. Prevalence of biological risk factors by income level (%)

Prevalence tends to be higher for low-income men and women, but there are differences

(24)

In the Republic of Moldova, labour-force participation is 49.4% of men and 44.6% of women. Those not currently employed but seeking work comprises 2.6% of women and 3.6% of men, though 35.8% of women and 29.7% of men are part-time workers and women engage in unpaid work more than twice as much as men (13). The Republic of Moldova experiences high labour migration, and, while the percentage of migrants has been higher among men, it is also increasing for women (15). Labour migration may be contributing to differences in risk factors and accessing services between men and women, urban and rural areas, and in employment and income groups.

Disaggregating the STEPS survey data by employment levels and sex reveals how the prevalence of behavioural risk factors varies in some groups and not in others (Fig. 14 and Annex 1, Table A1.12).

Prevalence of behavioural risk factors tends to be higher for unemployed men and women.

Prevalence for retired men and women is similar to prevalence in the older age groups. Prevalence is higher for unemployed men and women, but there are differences: for example, a significantly higher percentage of unemployed women (68.4%) than employed (55.1%) consume alcohol, but there is no significant difference for men (77.3% unemployed, 71.9% employed). Prevalence of heavy episodic drinking, however, is significantly higher for men who are unemployed (41.2%) than those who are employed (28.2%).

Additionally, prevalence of adding salt and eating processed food high in salt are significantly higher for unemployed women. Prevalence of adding salt and eating processed foods are significantly higher for unemployed women than employed, but there is no difference for men.

More differences among biological risk factors are observed for women than for men, and prevalence is generally higher for unemployed women than for employed women (Fig. 15 and Annex 1, Table A1.13).

Prevalence of biological risk factors tends to be highest for retired men and women and lowest for non- paid men and women, most likely due to the corresponding age groups.

Current tobacco use

Alcohol

0 10 20 30 40 50 60 70 80 90 100

Men Women Employed Non-paid Retired Unemployed

Fig. 14. Prevalence of behavioural risk factors by employment status (%)

Prevalence of risk factors is higher for unemployed men and women

(25)

The trend is that while prevalence for unemployed men and women is higher than for employed, the difference is generally greater for women. The association between employment status and biological risk factors may be greater for women than for men. For example, while prevalence of raised blood pressure is higher for unemployed men and women than for those who are employed, the difference is greater for women (57.2% unemployed, 36.1% employed) than it is for men (46.3% unemployed, 37.9% employed).

Additionally, prevalence of obesity for unemployed women is significantly higher (39.4%) than for employed women (26.8%).

ETHNICITY

An analysis by ethnicity can also be used to examine additional differences, because ethnicity can be a social influence on health behaviours and gender roles may vary across ethnic groups. Data on ethnicity were collected in the STEPS survey, and the ethnic groups with sufficiently high numbers of participants (Moldovan/Romanian, Russian, Ukrainian, Other) have been used for analysis.

Some significant differences are observed in behavioural risk factors by ethnicity, and it varies by risk factors and ethnic group (Fig. 16 and Annex 1, Table A1.14).

A significantly higher percentage of ethnic Russian women use tobacco than those with other ethnicities, while for men there are no significant differences between ethnic groups. Significantly more Moldovan/

Romanian men (71.7%) and men in the Other category (71.6%) consume alcohol than do ethnic Ukrainian men (52.8%). Prevalence for ethnic Ukrainian men is not significantly higher than that of women across ethnic groups, again showing that men and women are not homogenous groups. Prevalence of eating processed food is higher for ethnic Russian men and men in the Other category than Moldovan/Romanian and Ukrainian men.

0 10 20 30 40 50 60 70 80 90 100

Fig. 15. Prevalence of biological risk factors by employment status (%)

Prevalence is higher for unemployed men and (especially) women

(26)

Ethnic Ukrainian men and women are lower than other ethnic groups in several behavioural risk factors, but prevalence for them tends to be higher across biological risk factors. Additionally, prevalence for Moldovan/Romanian men and women tends to be higher across biological risk factors compared to other ethnic groups (Fig. 17 and Annex 1, Table A1.15).

Though there are less significant differences in biological risk factors by ethnicity for both men and women, an overall trend is higher prevalence for both men and women in the ethnic Ukrainian group, though there are differences.

Current tobacco use

Alcohol

0 10 20 30 40 50 60 70 80 90 100

Men Women Moldovan/

Romanian Russian Ukrainian Other

Fig. 16. Prevalence of behavioural risk factors by ethnicity (%)

Prevalence varies by ethnicity for men and women

0 10 20 30 40 50 60 70 80 90 100

Men Women Moldovan/

Fig. 17. Prevalence of biological risk factors by ethnicity (%)

Prevalence tends to be higher for ethnic Ukrainian men and women, but there are differences

(27)

DIFFERENCES IN THE WAY MEN

AND WOMEN ACCESS SERVICES

(28)

In addition to the differences observed between and among men and women in NCD risk factors, significant differences are also found between men and women in accessing services for NCDs. A significantly higher percentage of men report never having had their blood pressure, blood glucose and cholesterol measured by a health-care professional (Fig. 18 and Annex 1, Table A1.16).

Differences in men and women not measured for risk factors

The groups can be examined further to identify target populations that may be facing barriers to accessing services (Fig. 19 and Annex 1, Table A1.17).

0 20 40 60 80 100

Blood pressure

not measured Blood glucose

not measured Cholesterol

not measured Men Women

Fig. 18. Percentage not measured for risk factors by a health-care professional

Blood pressure not measured

Blood glucose not measured

Cholesterol not measured

0 10 20 30 40 50 60 70 80 90 100

Men Women 18–29 30–44 45–59 60–69

Fig. 19. Percentage not measured for risk factors by age group

Trends in men and women not measured are different in the older than younger age groups

(29)

21 DIFFERENCES IN THE WAY MEN AND WOMEN ACCESS SERVICES

It is not surprising that the percentages of both men and women who have not been measured for these risk factors decreases with each age group. The benefit of this analysis, however, is to expose the significant differences between men and women at each age group and to identify which age groups are significantly different for both men and women. This reveals that the trends in accessing services differ between men and women across the life-course.

For example, the percentage of men who have not been measured for blood glucose and cholesterol is only significantly higher than women in the 45–59 and 60–69 age groups. This means that the percentages of men who have not been measured does not decrease with each ascending age group as much as it does for women in those age groups. The trend with blood pressure is different. Significantly more men than women have not been measured in the 18–29 and 30–44 age groups, but there are no significant differences in the 45–59 and 60–69 age groups.

GEOGRAPHIC LOCATION – URBAN AND RURAL

Further differences can be seen when those not being measured for risk factors are examined by geographic location (Fig. 20 and Annex 1, Table A1.18). More men in rural areas have not been measured than those in urban areas, but there is no difference for women.

The trend is that men in rural areas have a higher percentage of having never been measured for risk factors than men in urban areas. The percentages of men in urban areas not measured are not significantly higher than those of women in both rural and urban, showing that the higher percentage of men in rural areas not measured is driving the overall difference between men and women.

EDUCATION LEVEL

Men and women with different education levels have not been measured for risk factors to the same extent. While both men and women in the low education level have higher percentages of having not been measured than the other levels, the difference is more pronounced for men (Fig. 21 and Annex 1, Table A1.19).

0 10 20 30 40 50 60 70 80 90 100

Fig. 20. Percentage not measured for risk factors by geographic location

Fewer men in rural areas are measured than urban men and all women

(30)

For blood glucose and cholesterol, the percentages of low-education men and women not measured are significantly higher than for the other levels. The percentages of high-education men not measured are not significantly higher than the percentages of high- and medium-education women. Medium-education men are also significantly higher than medium-education women, showing a need for attention to both low- and medium-education men as well as low-education women.

INCOME LEVEL

Just as education level can present barriers in accessing services for men and women, income level may also play a role due to its association with accessing resources. Overall, higher percentages of men and women in the low-income level have not been measured for risk factors (Fig. 22 and Annex 1, Table A1.20).

The difference between low- and high-income groups is overall greater for women than for men. The percentages of low-income women not measured are also not significantly lower than the percentages of high-income men not measured.

0 10 20 30 40 50 60 70 80 90 100

Fig. 21. Percentage not measured for risk factors by education level

More low-education men and women have not been measured

0 10 20 30 40 50 60 70 80 90 100

Men Women Low High

Fig. 22. Percentage not measured for risk factors by income level

More low-income men and women are not measured for risk factors

(31)

EMPLOYMENT STATUS

An analysis by employment status can help further identify how access to resources may contribute to barriers in access for both men and women. Overall, higher percentages of unemployed men and women have not been measured for risk factors (Fig. 23 and Annex 1, Table A1.21).

The percentages of unemployed men and women who have not been measured for blood glucose and cholesterol are significantly higher than those for employed men and women. Being unemployed or underemployed may be a barrier for both men and women accessing services.

ETHNICITY

To determine if there are also differences in the ways in which men and women from different ethnic groups access services, this same analysis has been done by ethnicity (Fig. 24 and Annex 1, Table A1.22).

The general trend for men and women not measured for risk factors by ethnicity is that percentages are higher for the Other category. More ethnic Ukrainian women have been measured than women in other ethnicities. There is more variance for men, and fewer ethnic Russian men have not had their blood glucose and cholesterol measured than men in other ethnicities.

0 10 20 30 40 50 60 70 80 90 100

Fig. 23. Percentage not measured for risk factors by ethnicity

More unemployed men and women are not measured for risk factors

0 10 20 30 40 50 60 70 80 90 100

Men Women Moldovan/

Fig. 24. Percentage not measured for risk factors by ethnicity

“Other” men and women are not measured as much, and remaining ethnicities vary

(32)

Lifestyle advice given by a health-care professional

Men and women access services differently, and the responses they receive when they access services can also differ. The STEPS survey gathered information on whether men and women had been given lifestyle advice when they had visited a health-care professional. The topics under lifestyle advice can be compared with the prevalence of related risk factors (Table 3) to examine more differences between sexes.

In one lifestyle topic (avoiding tobacco use), a significantly higher percentage of men than women have been given advice, while a significantly higher percentage of women have been given advice on the remaining topics (reducing salt, eating more fruit and vegetables, engaging in more physical activity and managing body weight).

On tobacco, a significantly higher percentage of men (50.4%) than women (32.1%) report being counselled, but while this percentage is higher than the prevalence of men who use tobacco (43.6%), women report being counselled at nearly six times the prevalence for women with that risk factor (5.6%). A higher percentage of men (28.0%) than women (20.3%) reportedly add salt to their food; significantly fewer men (54.7%), however, are given advice compared to women (61.2%) (Fig. 25 and Annex 1, Table A1.23).

This may be due to primary health-care protocols addressing women’s health that require the provider to discuss tobacco use, or it may be due to women accessing services more than men. Additionally, social stigma surrounding women using tobacco may affect responses in the STEPS survey.

The percentages of those receiving lifestyle advice regarding tobacco use, dietary salt and physical activity are higher than the prevalence of the related risk factors, while the percentages of those receiving lifestyle advice regarding eating fruit and vegetables and body weight are not higher than the prevalence of those related risk factors. The difference in lifestyle advice given to men and women, and the corresponding prevalence of the related risk factors, warrants further analysis.

A review of primary health-care protocols and guidelines revealed that nearly all protocols and guidelines include giving lifestyle advice on tobacco use for both men and women. The percentages of men and especially women who reported receiving this lifestyle advice in STEPS, however, is lower than the percentages receiving advice on most other topics. On the other hand, while giving advice on reducing added salt is included in only a few protocols, it reportedly is higher than many of the other lifestyle advice

Lifestyle advice topic Related risk factor

Quit using tobacco or don’t start Current tobacco use

Reduce salt in your diet Unhealthy diet (added salt)

Eat at least five servings of fruit and/or vegetables each day Unhealthy diet (< 5 fruit/veg) Start or do more physical activity Insufficient physical activity Maintain a healthy body weight or lose weight Overweight (BMI ≥ 25) Table 3. Lifestyle advice topics and prevalence of related risk factors

(33)

topics addressed. The difference between lifestyle advice given on physical activity and the prevalence of the related risk factor could be explained in part by the fact that most of the protocols include physical activity as a topic for counselling for both men and women.

0 20 40 60 80 100

Tobacco Diet

(fruit/veg)

(salt)Diet Physical Body weight

activity Advice given to men

Advice given to men Related risk factor for men Related risk factor for women Advice given to women

Fig. 25. Percentage of lifestyle advice given for related risk factors

Higher percentage of women given lifestyle advice than men on most risk factors

(34)

(35)

GENDER AND NONCOMMUNICABLE DISEASES IN REPUBLIC OF MOLDOVA

GENDER AND NONCOMMUNICABLE

DISEASES IN REPUBLIC OF MOLDOVA

Analysis of STEPS data

GENDER AND NONCOMMUNICABLE

DISEASES IN REPUBLIC OF MOLDOVA

Analysis of STEPS data

CONTENTS

EXECUTIVE SUMMARY

INTRODUCTION

NCDs constitute the main burden of disease for both women and men, but there are important differences

ST EP S

DIFFERENCES IN BEHAVIOURAL

AND BIOLOGICAL RISK FACTORS

Significant differences between men and women

Prevalence of three or more risk factors

Mortality rates among men and women

Differences in specific risk factors among men and women between age groups

DIFFERENCES IN THE WAY MEN

AND WOMEN ACCESS SERVICES

Differences in men and women not measured for risk factors

Lifestyle advice given by a health-care professional

CONCLUSIONS