The prevalence and pattern of comorbid long-term conditions with low back pain and osteoarthritis in low- and middle-income countries: a systematic review and meta-analysis

Figures & data

Table 1. Eligibility Criteria.

	Inclusion criteria	Exclusion criteria
Study design	Epidemiology studies, e.g. cross-sectional or cohort studies	*Qualitative studies, case studies and abstract-only reports.
Participants and conditions of interest	Population: Adults, 18 years and older who have back pain, knee osteoarthritis (OA) or hip OA	*Children *Patients with an absence of musculoskeletal conditions or pain *Studies in specific subpopulations, e.g. cancer patients – will most commonly involve patients in secondary/tertiary care settings and may not be representative of the general population. *Studies based on individuals living in high-income countries.
Interventions or exposures	At least one comorbid long-term conditions (LTC), for example cardiovascular disease or diabetes, anxiety, depression. LTCs defined according to the International Classification of Diseases (ICD) criteria published by the WHO as any ongoing, long term or recurring conditions that can have a significant impact on people’s lives
Comparisons or control groups	Any, general population
Outcomes of interest	Primary outcomes *Prevalence and incidence of comorbid LTCs in those with musculoskeletal conditions (back pain, knee OA and hip OA) in LMICs Secondary outcomes: *Other outcomes: mortality, quality of life
Setting	Countries classified by World bank as low- or lower-middle-income (LMICs), at any point within the last 5 years (2013–2017).	High-income and upper-middle income countries according to World bank.

Figure 1. PRISMA study selection flow diagram.

Table 2. Characteristics of included studies.

First Author/Data Collection Period	Country, UN Geographical Subregion	Study Design	Brief aim of Study + Important notes on eligibility criteria + Data collection period	Relevant sample Size	Mean Age (SD)	Mean BMI	Proportion Male (%)	Work Status/Proportion in Work (%)	MSK Diagnosis	Primary Outcome Measure of Prevalence	Description of Measurement/Analysis
Abdel-Nasser et al. (1998)	Egypt, Northern Africa	Cross-Sectional	Determine the differences in depressive symptoms and depression between rheumatoid arthritis (RA) and osteoarthritis (OA) patients, and to analyse the contribution of sociodemographic and clinical variables to depression in RA patients. DCP NR.	40	39.7 (10.9)	NR	NR	NR	Knee OA	Point Prevalence	Date & time period of interviews/data collection not specified
Aboderin and Nanyonjo (2017)	Kenya, Eastern Africa	Cross-Sectional	Examine the prevalence, potential predictors and sequelae of MSK pain among older adults residing in two low-resource informal urban settlements or ‘slums’ in Nairobi Kenya. DCP separate surveys in 2006 and 2016.	761	NR: mode age range was 60–69 (NR)	NR	From two surveys at different timepoints: 2007 survey = 57.2%; 2016 = 62.8%	71.20%	LBP, Hip/Knee OA	Point Prevalence	Point prevalence measured via surveys at two separate points in 2006–2007 and 2016
Adebusoye, Ogunbode, and Alonge (2013)	Nigeria, Western Africa	Cross-Sectional	Determine the magnitude and risk factors associated with knee OA among adult patients presenting at the University College Hospital, Ibadan, Nigeria. DCP 2012 (January–March)	400	47.3 (16.4)	NR	40.50%	48.80%	Knee OA	Point/period Prevalence	Prevalence data collected between January and March 2012 using a semi-structured questionnaire to interview 400 respondents (though OA was diagnosed clinically using ACR criteria
Aggarwal et al. (2013)	India, Southern Asia	Cross-Sectional	Assess the prevalence and risk factors of LBP in students of a medical college in Delhi. DCP NR.	160	20.61 (2.6)	NR	53.80%	NR	LBP	Point Prevalence	Prevalence data collected from pre-tested structured questionnaire (self-report) containing items to assess for LBP and comorbidities. Date & time period of data collection not specified, only that every year group was contacted
Akintayo et al. (2019)	Nigeria, Western Africa	Cross-Sectional	Determine the prevalence of depression and its determinants among Nigerian patients with knee OA. NB: Excluded Patients with history of previous knee surgery, malignancy, known mental illness, inflammatory arthritis, systemic infection and other chronic diseases (diabetes, CLD, COPD) DCP NR.	250	59.9 (10.62)	30.82 ± 5.48	16.40%	77.60%	Knee OA	Point/period Prevalence	Prevalence data collected over a period of 3 months through PHQ-9 surveys (depression) and radiographic evaluation of OA via Kellgren-Lawrence criteria. BP measurements were obtained via clinical assessments following interview
El-Sayed et al. (2010)	Ethiopia, Eastern Africa	Cross-Sectional survery (using data from Gilgel Gibe Growth and Development Study/GGGDS) (Retrospective)	1) Assess the prevalence of BP and NP, and 2) assess the relations between symptoms of anxiety, depression, and post-traumatic stress (PTS) and BNP (back or neck pain). DCP NR.	900	NR (Mode age of PPTS was 20–29) (NR)	NR	NR	NR	LBP	Point Prevalence	Data acquired from Gilgel Gibe Growth and Development Study (GGGDS), which used questionnaires & interviews to gain anthropometric information from participants, and an additional survey to screen for a one-week history of BP or NP. Date of interviews/questionnaires NR
Iqbal et al. (2011)	Pakistan, Southern Asia	Cross-Sectional	Determine the frequency of factors associated with knee OA. NB: Excluded patients with family history of OA, worked as farmers, mill workers, jack-hammer operators, females taking HRT, diagnosis with hyperparathyroidism, haemochromatosis, or SLE. DCP 2007 (September) − 2008 (March).	100	56.28 (8.786)	29.434 ± 7.849	26.00%	NR	Knee OA	Point Prevalence	Prevalence measured from September 2017 - March 2018. All patients (already confirmed to have OA) were interviewed by the principal investigator to fill out a proforma for variables including age, gender, height, weight, BMI, diabetes mellitus, hypertension, dyslipidaemia, IHD, smoking and anaemia
Kusumaningtyas, Tamtomo, and Murti (2018)	Indonesia, South-Eastern Asia	Cross-Sectional	Analyse factors associated with the occurrence of OA in Surakarta, Central Java, using a path analysis model. DCP June 2018.	50	NR. Age is split into categories: <40 y/o and > 40 y/o. Frequency of these ages is provided. (NR)	74% had BMI > 25	52.00%	NR	Knee OA	Point Prevalence	Data collected from medical records in June 2018
Mahmoud et al. (2019)	Egypt, Northern Africa	Cross-Sectional	Evaluate health-related quality of life (HRQoL) in primary knee OA patients using the osteoarthritis knee hip quality of life (OAKHQoL) questionnaire and study its relation to clinical and radiographic parameters NB: Excluded patients with secondary knee OA, post-traumatic OA, knee surgery, hip disorders, systemic inflammatory disease, severe psychiatric or physical illness. DCP NR.	100	54.6 (10.4)	28.6 ± 2.7	25.00%	39.00%	Knee OA	Point Prevalence	Prevalence data obtained from survey carried out over 1 year
Narayan, Thabah, and Poduval (2017)	India, Southern Asia	Cross-Sectional	Assess the level of neuropathic pain in patients with knee OA and identify the clinical and sociodemographic factors associated with neuropathic pain. NB: Excluded patients who had secondary knee OA due to diseases such as RA, post-traumatic OA. DCP 2018 (June-July)	161	55.7 (8.8)	22.46 ± 4.63	30.41%	NR	Knee OA	Point Prevalence	Prevalence data obtained from survey carried out over June and July 2016
Omoke and Amaraegbulam (2016)	Nigeria, Western Africa	Retrospective database review (? cross-sectional)	Determine the etiology and pattern of presentation of LBP among patients seen in our orthopaedic outpatient clinics DCP 2003–2013.	291	45.8 (1.67)	NR	50.86%	82.13%	LBP	Incidence of Emerging COLTC	Retrospective study into the incidence of emerging comorbidities in those with low back pain between 2003–2013; data sourced via case notes of those who had between to the orthopaedic clinic at Federal Teaching Hospital (Abakaliki) between this time period
Sagheer et al. (2013)	Pakistan, Southern Asia	Prospective Cross-Sectional	Observe the prevalence of anxiety and depression in chronic LBP population at a tertiary care centre. NB: Excluded patients with acute back pain, age below 15 years and above 80 years, patients who had spine/back surgery, patients with spine fractures and patients with metabolic bone disease/disorders. DCP NR.	140	43.02 (13.34)	NR	52.85%	NR	LBP	Point Prevalence	All data collected through questionnaire including demographic details, followed by clinical evaluation with HADS score between January and June 2010
Shah, Kataria, and Joshi (2011)	India, Southern Asia	Cross-Sectional	Investigate the comorbidity of depression among patients suffering from chronic LBP. NB: Excluded patients with known history of depression prior to LBP onset, or poor understanding of English or Gujarati. DCP NR.	107	NR: mode age was ages 30–40, followed by 51–60 (NR)	NR	35.50%	NR	LBP	Incidence of Emerging COLTC	Study describes gathering incidence rate of depression in those with a 3 months history of chronic back pain in population of 107 patients; incidence of depression screened using questionnaire/survey & confirmed through consultation with psychiatrist
Yerima and Adelowo (2017)	Nigeria, Western Africa	Cross-Sectional	Determine the frequency of metabolic syndrome (Mets) among patients with knee OA and its relationship with pain and functional status. NB: Excluded patients with inflammatory arthritis, previous knee surgery, congenital knee/hip abnormalities or history of traumatic knee injury. DCP 2015 (March–October).	244	NR (Median age 50) (NR)	32.2 ± 5.95	25.80%	64.80%	Knee OA	Point Prevalence	Data collected between March and October 2015 (from patients satisfying ACR criteria for OA) via a ‘pre-tested semi-structured interviewer-diabetes mellitus questionnaire’

RA=Rheumatoid Arthritis. OA=Osteoarthritis. MSK=Musculoskeletal. LBP=Low Back Pain. CLD=Chronic Liver Disease. COPD=Chronic Obstructive Pulmonary Disease. BP=Back Pain. NP=Neck Pain. PTS=Post Traumatic Stress. BNP=Back or Neck Pain. HRT=Hormone Replacement Therapy. SLE=Systemic Lupus Erythematosus. HRQoL=Health-Related Quality of Life. OAKHQoL=Osteoarthritis Knee Hip Quality of Life. Mets=Metabolic Syndrome. NR=Not Reported. SD=Standard Deviation. Ppts=Participants. BMI=Body Mass Index. COLTC=Co-Occurring Long-term condition. ACR=American College of Rheumatology. PHQ-9=Patient Health Questionnaire-9. GGGDS=Gilgel Gibe Growth and Development Survey. IHD=Ischemic Heart Disease. HADS=Hospital Anxiety and Depression Scale, UN=United Nations. DCP=Data Collection Period.

Table 3. Risk of bias assessment.

Study Authors	Was the study’s target population a close representation of the population?	Was sampling frame a true representation	Was random selection used to select sample?	Was the likelihood of nonresponse bias minimal	Were data collected directly from subjects?	Was an acceptable case definition of Low Back Pain/004FA used?	Was the method that diagnosed cases shown to have validity and reliability?	Was the same mode of data collection used for all subjects?	Was the length of the shortest prevalence period for the parameter of interest appropriate?	Were the numerator(s)/ denominator(s) for the parameter of interest appropriate and clearly reported?	Quality Assessment Rating* (Qi Score)	Overall risk of bias Assessment
Abdel-Nasser et al. (1998)									-		5/9	High
Aboderin and Nanyonjo (2017)									-		7/9	Low
Adebusoye, Ogunbode, and Alonge (2013)											8/10	Low
Aggarwal et al. (2013)									-		6/9	Low
Akintayo et al. (2019)											9/10	Low
El-Sayed et al. (2010)											7/10	Low
Iqbal et al. (2011)									-		4/9	High
Kusumaningtyas, Tamtomo, and Murti (2018)									-		4/9	High
Mahmoud et al. (2019)									-		4/9	High
Narayan, Thabah, and Poduval (2017)									-		5/9	High
Omoke and Amaraegbulam (2016)									-		5/9	High
Sagheer et al. (2013)									-		6/9	Low
Shah, Kataria, and Joshi (2011)									-		4/9	High
Yerima and Adelowo (2017)											6/10	Low

Low risk Unclear risk High risk – Not applicable.

* A quality score out of 10 was produced for each study assessing period prevalence, and 9 for each study with data on point prevalence. A rating of 5 or less suggested high risk of bias, and a score of 6 or greater suggested a low risk of bias.

Figure 2a. Forest plot: Overall MSK and NCD comorbidities with indications of overall risk of bias per study.

Figure 3. Forest Plot: MSK prevalence with comorbid Hypertension with indications of overall risk of bias per study.

Figure 4. Forest Plot: MSK prevalence with comorbid Diabetes with indications of overall risk of bias per study.

Figure 5. Forest Plot: MSK prevalence with comorbid Depression and Anxiety with indications of overall risk of bias per study.

Figure 6. Forest Plot: Subgroup analysis of comorbid NCDs with specific MSK Pain (i.e. hip/knee OA and LBP) with indications of overall risk of bias per study.

Figure 2b. Forest plot: Overall MSK and NCD comorbidities (>2).

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