What must be done to intervene and ensure that history does not repeat itself for future populations? This week, you examine the impact of the historical roots of social disparities on health of populations in low-income countries. As you go through this week’s Learning Resources, think about what we can learn from history. This week, you consider developing a policy in a country you selected and think about various issues in practicing population health.
For your Final Project, share some of your ideas on how you can use the knowledge and insights gained in this course to promote positive social change in a community/country and the world. It is advisable to select a community/country other than the one where you live.
To prepare for the Final Project, review all the week’s Learning Resources and consider possible issues you might encounter when implementing a policy.
Final Project (7–10 pages), not including the cover and the references:
In developing a policy in the country you selected, consider the following:
Use APA formatting for your Final Project and to cite your resources. Expand on your insights utilizing the Learning Resources.
www.thelancet.com/lancetgh Vol 5 June 2017 e557
Smoking status and HIV in low-income and middle-income countries
In high-income settings, the prevalence of tobacco use has been shown to be significantly higher in people living with HIV than among HIV-negative individuals of the same age and sex distribution. This at-risk pattern is one of the biggest threats to the number of years of life saved with antiretroviral therapy (ART).1,2 Extrapolation of these findings to low-income and middle-income countries (LMICs) is risky because social, cultural, and behavioural factors influencing tobacco use differ widely across different regions. The epidemiology of tobacco use in HIV-positive individuals in LMICs has been sparsely reported, with limited representativeness and no or biased control populations.3–5 In The Lancet Global Health, Noreen Mdege and colleagues6 report an unprecedented estimation of tobacco use in people living with HIV, using nationally representative samples extracted from the Demographic and Health Surveys (DHS) from 28 countries on three continents. In addition to depicting the burden and diversity of tobacco use, the authors show significantly higher figures of tobacco use in people living with HIV compared with their HIV-negative counterparts, regardless of gender. These results confirm what has already been reported in high-income settings, and emphasise the need for adapted preventive measures and tobacco cessation programmes in LMICs.
Countries highly affected by the HIV epidemic usually have underfunded health-care systems and are overburdened with other major epidemics such as malaria and tuberculosis, and are therefore less inclined to invest in preventive measures against non- communicable diseases and their determinants. In this context, smoking-targeted preventive and cessation programmes are often limited or nonexistent. HIV care programmes represent by far the largest chronic care programmes rolled out in LMICs, potentially paving the way for an integrated panel of services targeting non- communicable diseases. Measures directed towards smoking avoidance and cessation can then be introduced and piloted before their extension and adaption to a larger set of health facilities.
Although Mdege and colleagues’ analysis6 of publicly available data provides a comprehensive presentation
of prevalence estimates of tobacco use in HIV-positive individuals in LMICs, the number of people living with HIV in the study represents less than 0·001% of the estimated 34 million people living with HIV in 2014 in these parts of the world; this limited size might lead to imprecision and potential bias in the prevalence estimates of tobacco use, especially outside of Africa.7 Although the data were fairly representative of the African region, data for southeast Asia were only available for India, leaving important uncertainties concerning the association between tobacco use and HIV infection in countries particularly affected by tobacco smoking—especially China. This report6 comes at a time when LMICs represent a major target for the tobacco industry.8 Southeast Asia is the widest market for the tobacco industry, and the Chinese tobacco market represents more cigarettes than all other LMICs combined.9
Additional data sources on tobacco use are needed for people living with HIV in LMICs. Achievements made by the international community to enable universal access to ART were accompanied by initiatives providing worldwide data on the follow-up of patients initiating ART. The International Epidemiology Databases to Evaluate AIDS (IeDEA), funded by the US National Institutes of Health, is a unique platform that has so far gathered data on more than 1 700 000 people living with HIV on ART, most of whom live in LMICs. This platform has successfully collected core information on ART exposure, and harmonisation is underway to standardise the collection of basic behavioural risk factors such as tobacco use. Data from observational cohorts participating in IeDEA have already provided regional estimates on tobacco use from west Africa,4 and in the future could contribute to a more robust and complementary estimation of tobacco use in people living with HIV, especially in the context of universal ART.10
Nevertheless, the DHS offer a good opportunity to access a somewhat representative control group of HIV- uninfected people and can be repeated over time using the same methodological approach. This use of DHS data is therefore a unique framework to conduct sound
For more on IeDEA see http://www.iedea.org
See Articles page e578
e558 www.thelancet.com/lancetgh Vol 5 June 2017
analyses for identification of trends in tobacco use and to measure the effect of smoking prevention and cessation programmes according to HIV infection status. To expand their analysis, Mdege and colleagues could also consider prevalence estimates of tobacco use in younger age groups because these groups are the most susceptible to smoking initiation. Additionally, the low prevalence of tobacco smoking reported in women compared with men in LMICs makes women—along with young people—a particular target for the tobacco industry, whether they live with HIV or not.8
Antoine Jaquet, *François Dabis Institut de Santé Publique, d’Epidémiologie et de Développement, University of Bordeaux, and Inserm, Bordeaux Population Health Research Center, UMR 1219, F-33000 Bordeaux, France [email protected]
We are investigators of the West Africa IeDEA collaboration, and declare no competing interests.
Copyright © The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.
1 Reddy KP, Parker RA, Losina E, et al. Impact of cigarette smoking and smoking cessation on life expectancy among people with HIV: A US-based modeling study. J Infect Dis 2016; 214: 1672–81.
2 Mdodo R, Frazier EL, Dube SR, et al. Cigarette smoking prevalence among adults with HIV compared with the general adult population in the United States: cross-sectional surveys. Ann Intern Med 2015; 162: 335–44.
3 Iliyasu Z, Gajida AU, Abubakar IS, Shittu O, Babashani M, Aliyu MH. Patterns and predictors of cigarette smoking among HIV-infected patients in northern Nigeria. Int J STD AIDS 2012; 23: 849–52.
4 Jaquet A, Ekouevi DK, Aboubakrine M, et al. Tobacco use and its determinants in HIV-infected patients on antiretroviral therapy in West African countries. Int J Tuberc Lung Dis 2009; 13: 1433–39.
5 Mwiru RS, Nagu TJ, Kaduri P, Mugusi F, Fawzi W. Prevalence and patterns of cigarette smoking among patients co-infected with human immunodeficiency virus and tuberculosis in Tanzania. Drug Alcohol Depend 2017; 170: 128–32.
6 Mdege ND, Shah S, Ayo-Yusuf OA, Hakim J, Siddiqi K. Tobacco use among people living with HIV: analysis of data from Demographic and Health Surveys from 28 low-income and middle-income countries. Lancet Glob Health 2017; 5: e578–92.
7 UNAIDS. Global AIDS Update 2016. Geneva: UNAIDS, 2016. 8 Gilmore AB, Fooks G, Drope J, Bialous SA, Jackson RR. Exposing and
addressing tobacco industry conduct in low-income and middle-income countries. Lancet 2015; 385: 1029–43.
9 Eriksen M, Mackay J, Schluger N, Islami F, Drope J. The Tobacco Atlas, 5th edn. Atlanta, GA: American Cancer Society, 2015.
10 WHO. Consolidated guidelines on the use of antiretroviral drugs for treating and preventing HIV infection: recommendations for a public health approach, 2nd edn. Geneva: World Health Organization, 2016.
RESEARCH ARTICLE Open Access
Prevalence of arthritis according to age, sex and socioeconomic status in six low and middle income countries: analysis of data from the World Health Organization study on global AGEing and adult health (SAGE) Wave 1 Sharon L. Brennan-Olsen1,2,3,4* , S. Cook1, M. T. Leech5, S. J. Bowe1, P. Kowal6,7, N. Naidoo6, I. N. Ackerman8, R. S. Page1,9, S. M. Hosking1, J. A. Pasco1,3 and M. Mohebbi1
Background: In higher income countries, social disadvantage is associated with higher arthritis prevalence; however, less is known about arthritis prevalence or determinants in low to middle income countries (LMICs). We assessed arthritis prevalence by age and sex, and marital status and occupation, as two key parameters of socioeconomic position (SEP), using data from the World Health Organization Study on global AGEing and adult health (SAGE).
Methods: SAGE Wave 1 (2007–10) includes nationally-representative samples of older adults (≥50 yrs), plus smaller samples of adults aged 18-49 yrs., from China, Ghana, India, Mexico, Russia and South Africa (n = 44,747). Arthritis was defined by self-reported healthcare professional diagnosis, and a symptom-based algorithm. Marital status and education were self-reported. Arthritis prevalence data were extracted for each country by 10-year age strata, sex and SEP. Country-specific survey weightings were applied and weighted prevalences calculated.
Results: Self-reported (lifetime) diagnosed arthritis was reported by 5003 women and 2664 men (19.9% and 14.1%, respectively), whilst 1220 women and 594 men had current symptom-based arthritis (4.8% and 3.1%, respectively). For men, standardised arthritis rates were approximately two- to three-fold greater than for women. The highest rates were observed in Russia: 38% (95% CI 36%–39%) for men, and 17% (95% CI 14%–20%) for women. For both sexes and in all LMICs, arthritis was more prevalent among those with least education, and in separated/divorced/widowed women.
Conclusions: High arthritis prevalence in LMICs is concerning and may worsen poverty by impacting the ability to work and fulfil community roles. These findings have implications for national efforts to prioritise arthritis prevention and management, and improve healthcare access in LMICs.
Keywords: Arthritis, Epidemiology, Prevalence, Socio-demographic characteristics, Low and middle income countries
* Correspondence: [email protected] 1Deakin University, Geelong, Australia 2Australian Institute for Musculoskeletal Science (AIMSS), The University of Melbourne-Western Precinct, Level 3, Western Centre for Health Research and Education (WCHRE) Building, C/- Sunshine Hospital, Furlong Road, St Albans, Melbourne, VIC 3021, Australia Full list of author information is available at the end of the article
© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Brennan-Olsen et al. BMC Musculoskeletal Disorders (2017) 18:271 DOI 10.1186/s12891-017-1624-z
Background Worldwide, musculoskeletal disorders represent a global threat to healthy ageing , and are ranked as the sec- ond most common cause of disability, measured by years lived with disability (YLDs) . Lower and middle in- come countries (LMICs) are not immune to the burden of musculoskeletal diseases, indeed the prevalence of this non-communicable disease (NCD) group is dramatically increasing in LMICs . The 2010 Global Burden of Disease (GBD) study reported that musculoskeletal dis- eases accounted for 19.2% of all YLDs in LMICs . Despite this, the majority of the global NCD initiatives do not include musculoskeletal diseases . Significantly contributing to the global disability burden associated with the musculoskeletal system are arthritis diseases. Arthritis is an umbrella term that encompasses in ex- cess of 100 different arthritic conditions which are a chronic, painful, and debilitating group of diseases. Arthritis, specifically osteoarthritis, is a significant contributor to global disability burden, and the YLDs attributable to osteoarthritis have increased by 75% from 1990 to 2013 , indicating this disease as a growing problem internationally. In combination with an increasing trajectory of arthritis prevalence [2, 4], growth in YLDs attributable to arthritis is due pri- marily to increased life expectancy worldwide, and prolonged exposure to arthritis risk factors . Compared to higher income countries, many LMICs
, where two-thirds of the world’s population resides, have a much lower capacity to pay for adequate health- care. Indeed, LMICs have 90% of the global burden of disease but only 12% of global health spending . In higher income countries, arthritis is associated with re- duced workplace productivity [8, 9]; however, for resi- dents of LMICs, arthritis imposes a potential additional burden by creating a vicious cycle that subsequently worsens poverty . For example, compared to higher income countries, and in context of scarce medical and social support systems, residents of LMICs with arthritis also experience reduced ability to access, afford or utilize treatments including analgesic and anti-inflammatory pharmacotherapies [11, 12], or arthroplasty for advanced disease [13, 14]. They also have, in context of workforce capacity limitations, less flexibility regarding working conditions or hours , and few if any options for early retirement, or social security ‘safety nets’ pertaining to minimum income, including financial and/or material goods. Whilst the majority of research regarding arthritis
prevalence has been undertaken in higher income coun- tries, recent data from the 2010 GBD Study provides some evidence that LMICs may have greater arthritis prevalence than higher income countries . Yet, while valuable population level estimates, extrapolation from
these GBD estimates is difficult given that they are based on published prevalence and incidence data from a small number of heterogeneous studies spanning different time periods in a limited number of LMIC . Further- more, data from multi-country studies of LMICs that have examined prevalence of arthritis across sociodemo- graphic factors are typically not readily available [18, 19], with the exception of a recent publication, which showed that more years of schooling and greater levels of wealth decreased the odds of having an undiagnosed NCD, including arthritis . Understanding the preva- lence of arthritis across different parameters of socioeco- nomic position (SEP) data would augment our global understanding of global arthritis prevalence, social deter- minants and burden. To date, country-specific arthritis prevalence across
parameters of SEP has not been systematically evalu- ated in large, nationally representative samples of populations from LMICs. This information is crucial for planning future healthcare delivery for high bur- den chronic conditions and to ensure sufficient health workforce capacity – both significant concerns in an ageing world . Comprehensive data have been collected in the World Health Organization (WHO) Study on global AGEing and adult health (SAGE) [20, 22, 23], thus providing an important re- source to investigate disease prevalence in large population samples from six LMICs. Using SAGE Wave 1, these analyses were undertaken to determine the prevalence of arthritis in LMICs according to age, sex, and socioeconomic position (SEP).
Methods Study population and design SAGE Wave 1 (2007–10) is a longitudinal study with na- tionally representative samples of persons aged 50+ years and a smaller sample of adults aged 18–49 years that in- cludes 44,747 adults aged ≥18 years from China, Ghana, India, Mexico, Russian Federation and South Africa . Multistage cluster sampling strategies were used with households as sampling units. Households were classi- fied into one of two mutually exclusive categories: i) all persons aged 50 years and older were selected from “older” households, and ii) one person aged 18–49 years was selected from each “younger” household. An older or younger household was defined by the age of the re- spondent targeted for individual interview. Household- level and person-level analysis weights were calculated for each country. This research was performed in ac- cordance with the Declaration of Helsinki. The WHO and the respective implementing agency in each country provided ethics approvals. Written, informed consent was obtained from all participants.
Brennan-Olsen et al. BMC Musculoskeletal Disorders (2017) 18:271 Page 2 of 12
Data collection in WHO SAGE Using a standardized survey instrument to ensure consistency, and based on standardized methods, inter- viewer training and translation protocols, face-to-face in- terviews were conducted in China (2008–10; response 93%), Ghana (2008–09; response 81%), India (2007–08; response 68%), Mexico (2009–10; response rate 53%), the Russian Federation (2007–10; response 83%) and South Africa (2007–08; response 75%), as previously published . Full details regarding the probability sampling design, cluster sampling strategies and country-specific areas included in SAGE have been pub- lished elsewhere . Briefly, the SAGE questionnaire consisted of household, individual and proxy question- naires, a verbal autopsy, and appendices: the domains of which are summarised in Table 1 .
Arthritis status: self-reported and symptom-based For the current analyses, self-reported diagnosis of arth- ritis (lifetime) was based on participant responses to the question; “Have you ever been diagnosed with/told by a health care professional you have arthritis (a disease of the joints; or by other names rheumatism or osteoarthritis)?” As a secondary endpoint, a symptom-based determination of arthritis (yes/no for current within the previous 12 months) was also employed, by applying an algorithm developed by the WHO SAGE study team ; questions and the algorithm are presented in Table 2.
Socioeconomic position SEP was measured using two key parameters of marital status and educational attainment: the latter used due to the inextricable link between education and skilled vs. un- skilled labour, and thus financial remuneration for work. Self-reported marital status was categorised for analyses into three groups of: (i) never married, (ii) currently mar- ried or cohabitating, and (iii) separated/divorced or
widowed. Participants were asked if they had ever been to school; for those that indicated ‘yes’, they were also asked to identify the highest level of education completed. Educa- tion was categorised as (i) ‘no formal schooling’, (ii) less than primary school, or primary school completed, (iii) sec- ondary school completed, or high school (or equivalent) completed, or (iv) college, pre-university or university com- pleted, or post-graduate degree completed. Education levels were mapped to an international standard .
Statistical analyses Arthritis (self-reported and symptom-based) prevalence and 95% confidence intervals (95%CI) were calculated by implementing household level analysis weights separ- ately for each of the six countries across 10-year age strata (the 20–29 year age group was expanded to also include those aged 18–19 years), sex, marital status and education. Country-specific survey weightings were applied, and weighted prevalence calculated for each country. Adjustment of prevalence estimates for differ- ences in the age structure across countries was accom- plished by age-standardisation, using the direct method of standardisation  and the WHO World Standard Population distribution (%) as standard population . Ten-year intervals were used for age categorisation.
Results Country-specific numbers and proportions of the total 44,747 participants (total 57.1% women), were; China n = 15,050 (33.6%), Ghana n = 5573 (12.5%), India n = 12,198 (27.3%), Mexico n = 2752 (6.1%), the Russian Federation n = 4947 (11.1%), and South Africa n = 4227 (9.5%). Across the entire study population, 5003 women and 2664 men had (lifetime) self-reported arthritis (19.9% and 14.1%, respectively), whilst 1220 women and 594 men
Table 1 Questionnaire sections included in the SAGE Wave 1 standardized survey instrument 
Household roster Questions regarding the dwelling, income, transfers [of family members] in and out of the household, assets and expenditures
Questions regarding health and its determinants, disability, work history, risk factors, chronic conditions, caregiving, subjective well-being, health care utilization and health systems responsiveness
Questions regarding health, functioning, chronic conditions, and health care utilization
Verbal autopsy Performed to ascertain the probable cause of death for deaths in the household in the 24 months prior to interview or between interview waves
Appendices Includes show-cards to assist with the interviews
Table 2 Symptom-based questions and the related algorithm to ascertain prevalent arthritis, developed as part of the World Health Organization SAGE Wave 1 
Question number Question text and algorithm
1 During the last 12 months, have you experienced pain, aching, stiffness or swelling in or around the joints (like arms, hands, legs or feet) which were not related to an injury and lasted for more than a month?
2 During the last 12 months, have your experienced stiffness in the joint in the morning after getting up from bed, or after a long rest of the joint without movement?
3 Did this stiffness last for more than 30 min?
4 Did this stiffness go away after exercise or movement in the joint?
Algorithm If a participant responded with ‘yes’ to questions 1 and/or 2, and responded with ‘yes’ to question 3 and ‘no’ to question 4, then the participant was categorised as having arthritis
Brennan-Olsen et al. BMC Musculoskeletal Disorders (2017) 18:271 Page 3 of 12
were identified as having (within previous 12 months) symptom-based arthritis (4.8% and 3.1%, respectively). Table 3 presents the country-specific proportional
responses (non-weighted) to the four symptom-based questions (see Table 2), that were included in the algorithm to determine symptom-based arthritis. For women, proportions that reported ‘any pain during the last 12 months’ or ‘any stiffness during the last 12 months’ were lowest for Mexico (28.4% [95% CI 26.3%– 30.9%] and 23.3% [95% CI 20.9%–26.0%], respectively) and highest for the Russian Federation (48.4% [95% CI 46.4%–50.4%] and 50.5% [95% CI 48.8%–52.1%], respect- ively). For men, the proportions that reported ‘any pain during the last 12 months’ or ‘any stiffness during the last 12 months’ were lowest for Mexico (20.1% [95% CI 17.5%–23.0%] and 16.1% [95% CI%CI 14.1%–18.3%], re- spectively) and highest for the Russian Federation (32.9% [95% CI 30.5%–35.5%] and 34.6% [95% CI 32.4%– 36.9%], respectively). Table 4 presents the country-specific and sex-stratified
prevalence of self-reported arthritis (weighted), across age strata, educational attainment and marital status. For both sexes in each country, arthritis prevalence increased proportionally with advancing age; with the exception of women from China and men and women from South Africa who had the greatest prevalence in the age group of 60–69 years, all other groups showed a peak in arthritis prevalence in the oldest age group ≥70 years. For women, the prevalence by country ranged from 22.9% (95% CI 11.2%–41.1%) in Mexico to 45.7% (95% CI 39.1%–52.3%) in the Russian Federation. For men, prevalence ranged from 9.7% (95% CI 6.3%–14.5%) in Mexico to 37.8% (95% CI 30.3%–46.0%) in the Russian Federation. In each country, women who had never been formally schooled or had completed less than primary school had the highest prevalence of arthritis compared to those with a greater level of educational at- tainment. Higher arthritis prevalence was consistently observed for women that were separated, divorced or widowed (range: Russian Federation 36.4% [95% CI 29.1%–44.4%] to Ghana 11.7% [95% CI 8.9%–15.1%]) compared to those that were never married or currently married (range: China 0.9% [95% CI 0.3%–3.0%] to South Africa 12.1% [95% CI 5.5%–24.7%]). Similar to women, men that had never been formally schooled had the highest arthritis prevalence, with the exception of men from the Russian Federation, for whom the greatest prevalence was observed in those that had completed all or some primary school level education (39.6% [95% CI 21.3%–61.4%]), however these numbers were small. Compared to other categories, men that were never married had the lowest arthritis prevalence (range: Mexico 0.1% [95% CI 0.0%–0.5%] to India 3.9% [95% CI 1.5%–9.5%]). In China and India, men that were
currently married had the highest prevalence (11.9% [95% CI 9.4%–14.8%], and 8.8% [95% CI 7.2%–10.7%], respectively), whilst for all other countries, men that were separated, divorced or widowed were observed to have the highest arthritis prevalence (highest: Russian Federation 33.5% [95% CI 13.3%–62.3%]). Table 5 presents the country-specific and sex-stratified
prevalence of symptom-based arthritis prevalence (weighted), across age strata, educational attainment and marital status, for each LMIC. Patterns of symptom- based arthritis prevalence were similar to self-reported arthritis for both sexes; however, prevalence was lower than observed for self-reported arthritis. Figure 1 presents a box plot of the age-standardised
rates of self-reported arthritis, stratified by sex, across each country (crude and age-standardised rates are presented in Additional file 1: Online Table S1). For five of the six LMICs, the standardised rates of arthritis for men were approximately twice that observed for women; the excep- tion was Ghana, where men had rates three times greater than those observed for women (12% [95% CI 11%–13%] vs. 4% [95% CI 3%–5%]). The highest rates of arthritis were observed in the Russian Federation: for men the rate was 38% (95% CI 36%–39%) and for women it was 17% (95% CI 14%–20%).
Discussion We present the prevalence of arthritis across age, sex and different parameters of SEP in a large population- based study spanning six LMICs. Across the countries and for both sexes, higher arthritis prevalence was con- sistently associated with older age and lower educational attainment, whilst higher prevalence was also observed in women, but not men, that were separated, divorced, or widowed. The pattern between advancing age and increasing
arthritis prevalence in LMICs appears similar to the pat- tern observed in higher income countries . However, after age-standardisation, we observed in our current study that the rates of arthritis in LMICs were greater than those reported in higher income countries, specific- ally for men from China, India, the Russian Federation and South Africa. Compared to higher income countries, higher age-standardised rates of arthritis were also ob- served for women from the Russian Federation; however, for the remaining five LMICs, rates appeared to be simi- lar to those observed from higher income countries. Our results indicate the importance of age-standardisation when reporting prevalence data, in order that fair com- parisons can be applied when discussing whether any disparities in diseases exist between countries. In addition to the peak of arthritis prevalence observed in older age groups, we observed a sizeable proportion of arthritis in younger age groups; prevalence that would
Brennan-Olsen et al. BMC Musculoskeletal Disorders (2017) 18:271 Page 4 of 12
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Ru ss ia n Fe d er at io n (n
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c A n y p ai n d u rin
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m o n th s?
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29 .1 %
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38 .2 %
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29 .2 %
(2 8. 0%
– 30 .4 % )
28 .4 %
(2 6. 3%
– 30 .9 % )
48 .4 %
(4 6. 4%
– 50 .4 % )
36 .5 %
(3 4. 6%
– 38 .4 % )
c A n y st iff n es s d u rin
g la st 12
m o n th s?
(Y es )
24 .2 %
(2 3. 2%
– 25 .2 % )
43 .5 %
(4 1. 5%
– 45 .6 % )
29 .7 %
(2 8. 5%
– 30 .8 % )
23 .3 %
(2 0. 9%
– 26 .0 % )
50 .5 %
(4 8. 8%
– 52 .1 % )
33 .2 %
(3 1. 2%
– 35 .3 % )
d D id
st iff n es s la st fo r > 30
m in ? (Y es )
24 .7 %
(2 2. 4%
– 27 .1 % )
38 .1 %
(3 5. 6%
– 40 .7 % )
33 .3 %
(3 0. 9%
– 35 .2 % )
26 .1 %
(2 1. 8%
– 31 .0 % )
45 .3 %
(4 2. 8%
– 47 .9 % )
36 .3 %
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d D id
st iff n es s g o aw
ay af te r m o ve m en
t? (N o )
19 .2 %
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