Trends in prevalence of chronic disease and multimorbidity in Ontario,Canada

BACKGROUND:New case-mix tools from the Canadian Institute for Health Information offer a novel way of exploring the prevalence of chronic disease and multimorbidity using diagnostic data. We took a comprehensive approach to determine whether the prevalence of chronic disease and multimorbidity has been rising in Ontario, Canada.METHODS:In this observational study, we applied case-mix methodology to a population-based cohort. We used 10 years of patient-level data (fiscal years 2008/09 to 2017/18) from multiple care settings to compute the rolling 5-year prevalence of 85 chronic diseases and multimorbidity (i.e., the co-occurrence of 2 or more diagnoses). Diseases were further classified based on type and severity. We report both crude and age- and sex-standardized trends.RESULTS:The number of patients with chronic disease increased by 11.0% over the 10-year study period to 9.8 million in 2017/18, and the number with multimorbidity increased 12.2% to 6.5 million. Overall increases from 2008/09 to 2017/18 in the crude prevalence of chronic conditions and multimorbidity were driven by population aging. After adjustments for age and sex, the prevalence of patients with ≥ 1 chronic conditions decreased from 70.2% to 69.1%, and the prevalence of multimorbidity decreased from 47.1% to 45.6%. This downward trend was concentrated in minor and moderate diseases, whereas the prevalence of many major chronic diseases rose, along with instances of extreme multimorbidity (≥ 8 conditions). Age- and sex-standardized resource intensity weights, which reflect relative expected costs associated with patient diagnostic profiles, increased 4.6%.INTERPRETATION:Evidence of an upward trend in the prevalence of chronic disease was mixed. However, the change in case mix toward more serious conditions, along with increasing patient resource intensity weights overall, may portend a future need for population health management and increased health system spending above that predicted by population aging.

Multimorbidity exists when a patient is diagnosed with 2 or more chronic diseases. Patients with multimorbidity present challenges for physicians managing their care and, as the proportion of these patients in the population increases, for health care system planning. The prevalence of multimorbidity and chronic disease has been strongly associated with primary care use, specialist consultations, number and intensity of inpatient hospital admissions and other types of care.^1–7 Among beneficiaries of fee-for-service Medicare in the United States, expenditures for those with 4 or more chronic diseases were reported to be 66 times higher than for those with none.⁸ One study found that most health spending growth (77.6%) in the US between 1987 and 2011 could be attributed to patients with 4 or more diseases.⁹Several recent studies have estimated the prevalence of chronic disease and multimorbidity in Canada.^3,10–13 Rates of multimorbidity ranged from 10% to 25%, owing to differences in classification systems used to identify chronic disease, including the choice of conditions, and variations in study population. Lack of standardization in measures of chronic disease prevalence and multimorbidity has hampered the evaluation of trends over time and across settings.Ontario provides an ideal setting to evaluate trends in the prevalence of chronic disease because patients have access to a comprehensive set of publicly funded services. The Canadian Institute for Health Information (CIHI) has created a system that maps patient diagnosis data from all health care settings to a set of 226 clinically meaningful health conditions, covering the full spectrum of acute and chronic morbidity (Jeffrey Hatcher, Canadian Institute for Health Information, Ottawa: personal communication, 2017). CIHI’s system has been independently compared with the Johns Hopkins ACG System; CIHI’s system was deemed to be more specific and less sensitive in classifying diagnoses, making it more conservative in identifying health conditions (S. Cheng, ICES, unpublished data, 2016). The purpose of this study was to evaluate trends in the prevalence of chronic disease and multimorbidity in Ontario using CIHI’s comprehensive disease classification system.     

Prevalence,Determinants and Patterns of Multimorbidity in Primary Care: A Systematic Review of Observational Studies

Introduction

Multimorbidity is a major concern in primary care. Nevertheless, evidence of prevalence and patterns of multimorbidity, and their determinants, are scarce. The aim of this study is to systematically review studies of the prevalence, patterns and determinants of multimorbidity in primary care.

Methods

Systematic review of literature published between 1961 and 2013 and indexed in Ovid (CINAHL, PsychINFO, Medline and Embase) and Web of Knowledge. Studies were selected according to eligibility criteria of addressing prevalence, determinants, and patterns of multimorbidity and using a pretested proforma in primary care. The quality and risk of bias were assessed using STROBE criteria. Two researchers assessed the eligibility of studies for inclusion (Kappa  = 0.86).

Results

We identified 39 eligible publications describing studies that included a total of 70,057,611 patients in 12 countries. The number of health conditions analysed per study ranged from 5 to 335, with multimorbidity prevalence ranging from 12.9% to 95.1%. All studies observed a significant positive association between multimorbidity and age (odds ratio [OR], 1.26 to 227.46), and lower socioeconomic status (OR, 1.20 to 1.91). Positive associations with female gender and mental disorders were also observed. The most frequent patterns of multimorbidity included osteoarthritis together with cardiovascular and/or metabolic conditions.

Conclusions

Well-established determinants of multimorbidity include age, lower socioeconomic status and gender. The most prevalent conditions shape the patterns of multimorbidity. However, the limitations of the current evidence base means that further and better designed studies are needed to inform policy, research and clinical practice, with the goal of improving health-related quality of life for patients with multimorbidity. Standardization of the definition and assessment of multimorbidity is essential in order to better understand this phenomenon, and is a necessary immediate step.     

Multimorbidity combinations,costs of hospital care and potentially preventable emergency admissions in England: A cohort study

BackgroundPatients with multimorbidities have the greatest healthcare needs and generate the highest expenditure in the health system. There is an increasing focus on identifying specific disease combinations for addressing poor outcomes. Existing research has identified a small number of prevalent “clusters” in the general population, but the limited number examined might oversimplify the problem and these may not be the ones associated with important outcomes. Combinations with the highest (potentially preventable) secondary care costs may reveal priority targets for intervention or prevention. We aimed to examine the potential of defining multimorbidity clusters for impacting secondary care costs.Methods and findingsWe used national, Hospital Episode Statistics, data from all hospital admissions in England from 2017/2018 (cohort of over 8 million patients) and defined multimorbidity based on ICD-10 codes for 28 chronic conditions (we backfilled conditions from 2009/2010 to address potential undercoding). We identified the combinations of multimorbidity which contributed to the highest total current and previous 5-year costs of secondary care and costs of potentially preventable emergency hospital admissions in aggregate and per patient. We examined the distribution of costs across unique disease combinations to test the potential of the cluster approach for targeting interventions at high costs. We then estimated the overlap between the unique combinations to test potential of the cluster approach for targeting prevention of accumulated disease. We examined variability in the ranks and distributions across age (over/under 65) and deprivation (area level, deciles) subgroups and sensitivity to considering a smaller number of diseases.There were 8,440,133 unique patients in our sample, over 4 million (53.1%) were female, and over 3 million (37.7%) were aged over 65 years. No clear “high cost” combinations of multimorbidity emerged as possible targets for intervention. Over 2 million (31.6%) patients had 63,124 unique combinations of multimorbidity, each contributing a small fraction (maximum 3.2%) to current-year or 5-year secondary care costs. Highest total cost combinations tended to have fewer conditions (dyads/triads, most including hypertension) affecting a relatively large population. This contrasted with the combinations that generated the highest cost for individual patients, which were complex sets of many (6+) conditions affecting fewer persons. However, all combinations containing chronic kidney disease and hypertension, or diabetes and hypertension, made up a significant proportion of total secondary care costs, and all combinations containing chronic heart failure, chronic kidney disease, and hypertension had the highest proportion of preventable emergency admission costs, which might offer priority targets for prevention of disease accumulation. The results varied little between age and deprivation subgroups and sensitivity analyses.Key limitations include availability of data only from hospitals and reliance on hospital coding of health conditions.ConclusionsOur findings indicate that there are no clear multimorbidity combinations for a cluster-targeted intervention approach to reduce secondary care costs. The role of risk-stratification and focus on individual high-cost patients with interventions is particularly questionable for this aim. However, if aetiology is favourable for preventing further disease, the cluster approach might be useful for targeting disease prevention efforts with potential for cost-savings in secondary care.

Jonathan Stokes and co-workers explore patterns of multimorbidity and implications for the organization and costs of care.     

The effect of physical multimorbidity,mental health conditions and socioeconomic deprivation on unplanned admissions to hospital: a retrospective cohort study

Background:

Multimorbidity, the presence of more than 1 long-term disorder, is associated with increased use of health services, but unplanned admissions to hospital may often be undesirable. Furthermore, socioeconomic deprivation and mental health comorbidity may lead to additional unplanned admissions. We examined the association between unplanned admission to hospital and physical multimorbidity, mental health and socioeconomic deprivation.

Methods:

We conducted a retrospective cohort study using data from 180 815 patients aged 20 years and older who were registered with 40 general practices in Scotland. Details of 32 physical and 8 mental health morbidities were extracted from the patients’ electronic health records (as of Apr. 1, 2006) and linked to hospital admission data. We then recorded the occurrence of unplanned or potentially preventable unplanned acute (nonpsychiatric) admissions to hospital in the subsequent 12 months. We used logistic regression models, adjusting for age and sex, to determine associations between unplanned or potentially preventable unplanned admissions to hospital and physical multimorbidity, mental health and socioeconomic deprivation.

Results:

We identified 10 828 (6.0%) patients who had at least 1 unplanned admission to hospital and 2037 (1.1%) patients who had at least 1 potentially preventable unplanned admission to hospital. Both unplanned and potentially preventable unplanned admissions were independently associated with increasing physical multimorbidity (for ≥ 4 v. 0 conditions, odds ratio [OR] 5.87 [95% confidence interval (CI) 5.45–6.32] for unplanned admissions, OR 14.38 [95% CI 11.87–17.43] for potentially preventable unplanned admissions), mental health conditions (for ≥ 1 v. 0 conditions, OR 2.01 [95% CI 1.92–2.09] for unplanned admissions, OR 1.80 [95% CI 1.64–1.97] for potentially preventable unplanned admissions) and socioeconomic deprivation (for most v. least deprived quintile, OR 1.56 [95% CI 1.43–1.70] for unplanned admissions, OR 1.98 [95% CI 1.63–2.41] for potentially preventable unplanned admissions).

Interpretation:

Physical multimorbidity was strongly associated with unplanned admission to hospital, including admissions that were potentially preventable. The risk of admission to hospital was exacerbated by the coexistence of mental health conditions and socioeconomic deprivation.Multimorbidity — usually defined as the presence of more than 1 long-term disorder — is becoming the norm rather than the exception as populations age.^1,2 A recent study found that most people older than 65 years of age had multimorbidity, and the mean number of comorbidities per person increased with age;¹ however, multimorbidity is not confined to older adults.³Multimorbidity is associated with a range of adverse outcomes. People with multimorbidity have worse physical, social and psychological quality of life⁴ and increased mortality.⁵ Mental health conditions often accompany and exacerbate long-term physical conditions, leading to poor health outcomes, reduced quality of life and increased costs.^1,6,7 Furthermore, health services are largely organized to provide care for single diseases, particularly in hospitals or under specialist care. Indeed, many aspects of care are poor for patients with multimorbidity.^8–10 This situation may be further aggravated among patients who are socioeconomically disadvantaged, because they often have poorer health and higher health care needs, while also experiencing poorer provision of services, than their more advantaged counterparts.¹¹ A lack of social and personal resources, coupled with multiple stresses, makes coping difficult for these patients,¹² and the multiplicity of physical, psychological and social problems means that family physicians sometimes struggle to support patients with multimorbidity in deprived settings.¹³Multimorbidity is associated with increased use of health services; however, whereas high use of primary and specialist ambulatory care may be seen as an appropriate response to multimorbidity, frequent unplanned admissions to hospital will often be undesirable.¹⁴ Unfortunately, there are relatively few large studies that have examined the association between multimorbidity and unplanned hospital admissions.^15–17 Moreover, such studies did not separately examine physical and mental health morbidity and did not account for the additional effect of socioeconomic deprivation — shortcomings we hope to have addressed. Using linked routine clinical primary care and hospital data, we sought to determine the association between unplanned admissions to hospital and physical multimorbidity, as well as any additional effect of mental health morbidity and socioeconomic deprivation.     

Multimorbidity Patterns in a National Representative Sample of the Spanish Adult Population

Background

In the context of population aging, multimorbidity has emerged as a growing concern in public health. However, little is known about multimorbidity patterns and other issues surrounding chronic diseases. The aim of our study was to examine multimorbidity patterns, the relationship between physical and mental conditions and the distribution of multimorbidity in the Spanish adult population.

Methods

Data from this cross-sectional study was collected from the COURAGE study. A total of 4,583 participants from Spain were included, 3,625 aged over 50. An exploratory factor analysis was conducted to detect multimorbidity patterns in the population over 50 years of age. Crude and adjusted binary logistic regressions were performed to identify individual associations between physical and mental conditions.

Results

Three multimorbidity patterns rose: ‘cardio-respiratory’ (angina, asthma, chronic lung disease), ‘mental-arthritis’ (arthritis, depression, anxiety) and the ‘aggregated pattern’ (angina, hypertension, stroke, diabetes, cataracts, edentulism, arthritis). After adjusting for covariates, asthma, chronic lung disease, arthritis and the number of physical conditions were associated with depression. Angina and the number of physical conditions were associated with a higher risk of anxiety. With regard to multimorbidity distribution, women over 65 years suffered from the highest rate of multimorbidity (67.3%).

Conclusion

Multimorbidity prevalence occurs in a high percentage of the Spanish population, especially in the elderly. There are specific multimorbidity patterns and individual associations between physical and mental conditions, which bring new insights into the complexity of chronic patients. There is need to implement patient-centered care which involves these interactions rather than merely paying attention to individual diseases.     

Socioeconomic inequalities in prevalence and development of multimorbidity across adulthood: A longitudinal analysis of the MRC 1946 National Survey of Health and Development in the UK

BackgroundWe aimed to estimate multimorbidity trajectories and quantify socioeconomic inequalities based on childhood and adulthood socioeconomic position (SEP) in the risks and rates of multimorbidity accumulation across adulthood.Methods and findingsParticipants from the UK 1946 National Survey of Health and Development (NSHD) birth cohort study who attended the age 36 years assessment in 1982 and any one of the follow-up assessments at ages 43, 53, 63, and 69 years (N = 3,723, 51% males). Information on 18 health conditions was based on a combination of self-report, biomarkers, health records, and prescribed medications. We estimated multimorbidity trajectories and delineated socioeconomic inequalities (based on childhood and adulthood social class and highest education) in multimorbidity at each age and in longitudinal trajectories.Multimorbidity increased with age (0.7 conditions at 36 years to 3.7 at 69 years). Multimorbidity accumulation was nonlinear, accelerating with age at the rate of 0.08 conditions/year (95% CI 0.07 to 0.09, p < 0.001) at 36 to 43 years to 0.19 conditions/year (95% CI 0.18 to 0.20, p < 0.001) at 63 to 69 years. At all ages, the most socioeconomically disadvantaged had 1.2 to 1.4 times greater number of conditions on average compared to the most advantaged. The most disadvantaged by each socioeconomic indicator experienced an additional 0.39 conditions (childhood social class), 0.83 (adult social class), and 1.08 conditions (adult education) at age 69 years, independent of all other socioeconomic indicators. Adverse adulthood SEP was associated with more rapid accumulation of multimorbidity, resulting in 0.49 excess conditions in partly/unskilled compared to professional/intermediate individuals between 63 and 69 years. Disadvantaged childhood social class, independently of adulthood SEP, was associated with accelerated multimorbidity trajectories from age 53 years onwards.Study limitations include that the NSHD cohort is composed of individuals of white European heritage only, and findings may not be generalizable to the non-white British population of the same generation and did not account for other important dimensions of SEP such as income and wealth.ConclusionsIn this study, we found that socioeconomically disadvantaged individuals have earlier onset and more rapid accumulation of multimorbidity resulting in widening inequalities into old age, with independent contributions from both childhood and adulthood SEP.

Amal Khanolkar and co-workers study associations between multimorbidity and socioeconomic position in the UK.     

Associations between multimorbidity and adverse health outcomes in UK Biobank and the SAIL Databank: A comparison of longitudinal cohort studies

BackgroundCohorts such as UK Biobank are increasingly used to study multimorbidity; however, there are concerns that lack of representativeness may lead to biased results. This study aims to compare associations between multimorbidity and adverse health outcomes in UK Biobank and a nationally representative sample.Methods and findingsThese are observational analyses of cohorts identified from linked routine healthcare data from UK Biobank participants (n = 211,597 from England, Scotland, and Wales with linked primary care data, age 40 to 70, mean age 56.5 years, 54.6% women, baseline assessment 2006 to 2010) and from the Secure Anonymised Information Linkage (SAIL) databank (n = 852,055 from Wales, age 40 to 70, mean age 54.2, 50.0% women, baseline January 2011). Multimorbidity (n = 40 long-term conditions [LTCs]) was identified from primary care Read codes and quantified using a simple count and a weighted score. Individual LTCs and LTC combinations were also assessed. Associations with all-cause mortality, unscheduled hospitalisation, and major adverse cardiovascular events (MACEs) were assessed using Weibull or negative binomial models adjusted for age, sex, and socioeconomic status, over 7.5 years follow-up for both datasets.Multimorbidity was less common in UK Biobank than SAIL (26.9% and 33.0% with ≥2 LTCs in UK Biobank and SAIL, respectively). This difference was attenuated, but persisted, after standardising by age, sex, and socioeconomic status. The association between increasing multimorbidity count and mortality, hospitalisation, and MACE was similar between both datasets at LTC counts of ≤3; however, above this level, UK Biobank underestimated the risk associated with multimorbidity (e.g., mortality hazard ratio for 2 LTCs 1.62 (95% confidence interval 1.57 to 1.68) in SAIL and 1.51 (1.43 to 1.59) in UK Biobank, hazard ratio for 5 LTCs was 3.46 (3.31 to 3.61) in SAIL and 2.88 (2.63 to 3.15) in UK Biobank). Absolute risk of mortality, hospitalisation, and MACE, at all levels of multimorbidity, was lower in UK Biobank than SAIL (adjusting for age, sex, and socioeconomic status). Both cohorts produced similar hazard ratios for some LTCs (e.g., hypertension and coronary heart disease), but UK Biobank underestimated the risk for others (e.g., alcohol-related disorders or mental health conditions). Hazard ratios for some LTC combinations were similar between the cohorts (e.g., cardiovascular conditions); however, UK Biobank underestimated the risk for combinations including other conditions (e.g., mental health conditions). The main limitations are that SAIL databank represents only part of the UK (Wales only) and that in both cohorts we lacked data on severity of the LTCs included.ConclusionsIn this study, we observed that UK Biobank accurately estimates relative risk of mortality, unscheduled hospitalisation, and MACE associated with LTC counts ≤3. However, for counts ≥4, and for some LTC combinations, estimates of magnitude of association from UK Biobank are likely to be conservative. Researchers should be mindful of these limitations of UK Biobank when conducting and interpreting analyses of multimorbidity. Nonetheless, the richness of data available in UK Biobank does offers opportunities to better understand multimorbidity, particularly where complementary data sources less susceptible to selection bias can be used to inform and qualify analyses of UK Biobank.

Peter Hanlon and colleagues compare the associations between multimorbidity and adverse health outcomes in UK Biobank and the SAIL Databank.     

Prevalence and Correlates of Physical Inactivity among Older Adults in Rio Grande do Sul,Brazil

BackgroundCurrent information on the epidemiology of physical inactivity among older adults is lacking, making it difficult to target the inactive and to plan for interventions to ameliorate adverse effects.ObjectivesTo present statewide representative findings on the prevalence of physical inactivity among older community residents, its correlates and associated health service use.MethodsA representative non-institutionalized random sample of 6963 individuals in Rio Grande do Sul, Brazil, aged ≥60 years, was interviewed face-to-face. Information was obtained on demographic characteristics, social resources, health conditions and behaviors, health service use, and physical inactivity. Controlled logistic regression was used to determine the association of physical inactivity with these characteristics.ResultsOverall, 62% reported no regular physical activity. Physical inactivity was significantly more prevalent among women, older persons, those with lower education and income, Afro-Brazilians (73%; White: 61%; “other”: 64%), those no longer married, and was associated with multiple individual health conditions and impaired activities of daily living (ADL). In adjusted analyses, associations remained for sociodemographic characteristics, social participation, impaired self-rated health, ADL, vision, and depression (odds ratios (OR) 1.2–1.7). Physically inactive respondents were less likely to report outpatient visits (OR 0.81), but more likely to be hospitalized (OR 1.41).ConclusionsPhysical inactivity is highly prevalent, particularly among Afro -Brazilians. It is associated with adverse sociodemographic characteristics; lack of social interaction; and poor self-rated health, ADL, vision, and depression; although not with other health conditions. Self-care may be neglected, resulting in hospitalization.     

Incidence and Predictors of Multimorbidity in the Elderly: A Population-Based Longitudinal Study

Background

We aimed to calculate 3-year incidence of multimorbidity, defined as the development of two or more chronic diseases in a population of older people free from multimorbidity at baseline. Secondly, we aimed to identify predictors of incident multimorbidity amongst life-style related indicators, medical conditions and biomarkers.

Methods

Data were gathered from 418 participants in the first follow up of the Kungsholmen Project (Stockholm, Sweden, 1991–1993, 78+ years old) who were not affected by multimorbidity (149 had none disease and 269 one disease), including a social interview, a neuropsychological battery and a medical examination.

Results

After 3 years, 33.6% of participants who were without disease and 66.4% of those with one disease at baseline, developed multimorbidity: the incidence rate was 12.6 per 100 person-years (95% CI: 9.2–16.7) and 32.9 per 100 person-years (95% CI: 28.1–38.3), respectively. After adjustments, worse cognitive function (OR, 95% CI, for 1 point lower Mini-Mental State Examination: 1.22, 1.00–1.48) was associated with increased risk of multimorbidity among subjects with no disease at baseline. Higher age was the only predictor of multimorbidity in persons with one disease at baseline.

Conclusions

Multimorbidity has a high incidence at old age. Mental health-related symptoms are likely predictors of multimorbidity, suggesting a strong impact of mental disorders on the health of older people.     

Multimorbidity Patterns in Elderly Primary Health Care Patients in a South Mediterranean European Region: A Cluster Analysis

Objective

The purpose of this study was to identify clusters of diagnoses in elderly patients with multimorbidity, attended in primary care.

Design

Cross-sectional study.

Setting

251 primary care centres in Catalonia, Spain.

Participants

Individuals older than 64 years registered with participating practices.

Main outcome measures

Multimorbidity, defined as the coexistence of 2 or more ICD-10 disease categories in the electronic health record. Using hierarchical cluster analysis, multimorbidity clusters were identified by sex and age group (65–79 and ≥80 years).

Results

322,328 patients with multimorbidity were included in the analysis (mean age, 75.4 years [Standard deviation, SD: 7.4], 57.4% women; mean of 7.9 diagnoses [SD: 3.9]). For both men and women, the first cluster in both age groups included the same two diagnoses: Hypertensive diseases and Metabolic disorders. The second cluster contained three diagnoses of the musculoskeletal system in the 65- to 79-year-old group, and five diseases coincided in the ≥80 age group: varicose veins of the lower limbs, senile cataract, dorsalgia, functional intestinal disorders and shoulder lesions. The greatest overlap (54.5%) between the three most common diagnoses was observed in women aged 65–79 years.

Conclusion

This cluster analysis of elderly primary care patients with multimorbidity, revealed a single cluster of circulatory-metabolic diseases that were the most prevalent in both age groups and sex, and a cluster of second-most prevalent diagnoses that included musculoskeletal diseases. Clusters unknown to date have been identified. The clusters identified should be considered when developing clinical guidance for this population.     

Alcohol Consumption among HIV-Infected Persons in a Large Urban HIV Clinic in Kampala Uganda: A Constellation of Harmful Behaviors

IntroductionAlcohol use by persons living with HIV/AIDS (PLWHA) negatively impacts the public health benefits of antiretroviral therapy (ART). Using a standardized alcohol assessment tool, we estimate the prevalence of alcohol use, identify associated factors, and test the association of alcohol misuse with sexual risk behaviors among PLWHA in Uganda.MethodsA cross-section of PLWHA in Kampala were interviewed regarding their sexual behavior and self-reported alcohol consumption in the previous 6 months. Alcohol use was assessed using the alcohol use disorders identification test (AUDIT). Gender-stratified log binomial regression analyses were used to identify independent factors associated with alcohol misuse and to test whether alcohol misuse was associated with risky sexual behaviors.ResultsOf the 725 subjects enrolled, 235 (33%) reported any alcohol use and 135 (18.6%) reported alcohol misuse, while 38 (5.2%) drank hazardous levels of alcohol. Alcohol misuse was more likely among subjects not yet on ART (adjusted prevalence ratio [aPR] was 1.65 p=0.043 for males and 1.79, p=0.019 for females) and those with self-reported poor adherence (aPR for males=1.56, p=0.052, and for females=1.93, p=0.0189). Belonging to Pentecostal or Muslim religious denominations was protective against alcohol misuse compared to belonging to Anglican and Catholic denominations in both sexes (aPR=0.11 for men, p<0.001, and aPR=0.32 for women, p=0.003). Alcohol misuse was independently associated with reporting risky sexual behaviors (aPR=1.67; 95% CI: 1.07–2.60, p=0.023) among males, but not significant among females (aPR=1.29; 95% CI: 0.95–1.74, p=0.098). Non-disclosure of HIV positive status to sexual partner was significantly associated with risky sex in both males (aPR=1.69; p=0.014) and females (aPR 2.45; p<0.001).ConclusionAlcohol use among PLWHA was high, and was associated with self-reported medication non-adherence, non-disclosure of HIV positive status to sexual partner(s), and risky sexual behaviors among male subjects. Interventions targeting alcohol use and the associated negative behaviors should be tested in this setting.     

Impaired Health-Related Quality of Life in Elderly Women is Associated With Multimorbidity: Results From the Korean National Health and Nutrition Examination Survey

BackgroundMultimorbidity is a common problem in elderly populations and is significantly associated with functional decline, disability, and mortality. However, the sex-specific characteristics of multimorbidity and its effect on patients' quality of life (QOL) have not been clearly established.MethodsWe analyzed the Korean National Health and Nutrition Examination Survey database. EuroQol 5D (a standardized health outcomes measurement instrument that includes 2 dimensions, the EuroQol 5 Dimension [EQ-5D] index score and the EuroQol visual analogue scale [EQ-VAS]) was used to evaluate QOL. Multimorbidity was evaluated using data on blood pressure measurements, blood chemistry examinations, and anthropometric assessments, as well as a survey that assessed health status.ResultsA total of 1419 patients aged ≥65 years were included in the analysis (age = 72.40 [0.19] years; 39.3% men). Multimorbidity was significantly associated with being a woman; however, it was not associated with age. The EQ-5D index score and EQ-VAS score were significantly lower in patients with multimorbidity, especially among the elderly women. The inverse association between QOL and the number of chronic diseases was maintained without a floor effect. Hypertension was the most common disease; however, QOL was significantly associated with musculoskeletal disease, stroke, and depression, all of which were more common in female patients. There was no significant difference in QOL between men and women with similar levels of comorbidity.ConclusionBoth the amount and pattern of chronic diseases have been associated with QOL in elderly populations. Elderly women have low levels of QOL due to multimorbidity and a higher prevalence of chronic disease, which is related to impaired QOL.     

Epidemiology of Schistosoma mansoni infection in Ituri Province,north-eastern Democratic Republic of the Congo

BackgroundSchistosomiasis, caused by Schistosoma mansoni, is of great significance to public health in sub–Saharan Africa. In the Democratic Republic of Congo (DRC), information on the burden of S. mansoni infection is scarce, which hinders the implementation of adequate control measures. We assessed the geographical distribution of S. mansoni infection across Ituri province in north-eastern DRC and determined the prevailing risk factors.Methods/Principal findingsTwo province–wide, community–based studies were conducted. In 2016, a geographical distribution study was carried out in 46 randomly selected villages across Ituri. In 2017, an in–depth study was conducted in 12 purposively–selected villages, across the province. Households were randomly selected, and members were enrolled. In 2016, one stool sample was collected per participant, while in 2017, several samples were collected per participant. S. mansoni eggs were detected using the Kato–Katz technique. In 2017, a point–of–care circulating cathodic S. mansoni antigen (POC–CCA) urine test was the second used diagnostic approach. Household and individual questionnaires were used to collect data on demographic, socioeconomic, environmental, behavioural and knowledge risk factors.Of the 2,131 participants in 2016, 40.0% were positive of S. mansoni infection. Infection prevalence in the villages ranged from 0 to 90.2%. Of the 707 participants in 2017, 73.1% were tested positive for S. mansoni. Prevalence ranged from 52.8 to 95.0% across the health districts visited. Infection prevalence increased from north to south and from west to east. Exposure to the waters of Lake Albert and the villages’ altitude above sea level were associated with the distribution.Infection prevalence and intensity peaked in the age groups between 10 and 29 years. Preschool children were highly infected (62.3%). Key risk factors were poor housing structure (odds ratio [OR] 2.1, 95% 95% confidence interval [CI] 1.02–4.35), close proximity to water bodies (OR 1.72, 95% CI 1.1–2.49), long-term residence in a community (OR 1.41, 95% CI 1.11–1.79), lack of latrine in the household (OR 2.00, 95% CI 1.11–3.60), and swimming (OR 2.53, 95% CI 1.20–5.32) and washing (OR 1.75, 95% CI 1.10–2.78) in local water bodies.Conclusions/SignificanceOur results show that S. mansoni is highly endemic and a major health concern in Ituri province, DRC. Infection prevalence and intensity, and the prevailing socioeconomic, environmental, and behavioural risk factors in Ituri reflect intense exposure and alarming transmission rates. A robust plan of action is urgently needed in the province.     

The epidemiological status of urogenital schistosomiasis among reproductive aged individuals in the Tiko Health Area- a semi-urban setting in the Mount Cameroon area

BackgroundUrogenital schistosomiasis (UGS) caused by S. haematobium has enormous reproductive health consequences including infertility. Reproductive aged individuals are a neglected group and not included in control programs in Cameroon. This study investigated the prevalence and severity of S. haematobium infection in the context of gender and socio-economic structures that shape behaviour among reproductive aged individuals living in Tiko, a semi-urban setting, Cameroon.Methodology/Principal findingsA cross-sectional study was carried out in the Tiko Health District (THD) between May to September 2019. Consenting individuals were enrolled using a convenient sampling technique and administered a semi-structured questionnaire to document data on socio-demographic and stream contact behaviour. A urine sample was collected and screened for the presence of S. haematobium ova using reagent strips, filtration and microscopy. The overall prevalence of S. haematobium infection was 22.8% (95% CL: 19.27–26.73) with geometric mean egg load of 18.74 (range: 1–1600) per 10ml of urine. Younger age group (15 – 20years) (OR: 5.13; 95% CL: 1.35–19.42), male (OR: 2.60 3.07; 95% CL: 1.54–4.40) and awareness of UGS (OR: 1.73; 95% CL: 1.02–2.95) were associated with higher odds of exposure to infection. Significantly higher intensity of infection was seen in males, singles and in the age group 15–30 years. It is worth noting that males carried out more activities which entailed longer duration in streams.Conclusion/SignificanceThe prevalence obtained shows that Tiko is a moderate-risk area for UGS with underlying morbidity-inducing infection intensity. The severity of the infection is more in males. Awareness of the disease is not enough to protect these communities from infection, but provision of public infrastructures and health education will limit contact with infested water and thus curtail the infection. There is an urgent need to involve all age groups in control programs.     

Metabolic Syndrome in South African Patients with Severe Mental Illness: Prevalence and Associated Risk Factors

Background

There is a surge of cardiovascular disease (CVD) in Africa. CVD is the leading cause of mortality among patients with severe mental illness (SMI) in developed countries, with little evidence from the African context.

Objective

To determine the prevalence and risk factors for MetS among South African patients with SMI.

Method

In a cross sectional study, individuals with SMI treated with antipsychotics and a control group without a mental illness, matched for age, gender and ethnicity were evaluated for MetS using the 2009 Joint Interim statement (JIS) criteria.

Results

Of the 276 study group subjects, 65.9% were male, 84.1% black African, 9.1% white, 5.4% of Indian descent and 1.5% coloured (mixed race) with a mean age of 34.7 years (±12.5). Schizophrenia was the most common diagnosis (73.2%) and 40% were taking first generation antipsychotics. The prevalence of MetS was 23.2% (M: 15.4%, F: 38.3%) in the study group and 19.9% (M: 11.9%, F: 36.3%) in the control group (p = 0.4). MetS prevalence was significantly higher in study subjects over 55 years compared to controls (p = 0.03). Increased waist circumference (p< 0.001) and low high density lipoprotein (HDL) cholesterol (p = 0.003) were significantly more prevalent in study subjects compared to controls. In study subjects, risk factors associated with MetS included age (OR: 1.09, 95% CI 1.06–1.12, p < 0.001), female gender (OR: 2.19, 95% CI 1.06–4.55, p = 0.035) and Indian descent (OR: 5.84, 95% CI 1.66–20.52, p = 0.006) but not class of antipsychotic (p = 0.26).

Conclusion

The overall MetS prevalence was not increased in patients with SMI compared to controls; however, the higher prevalence of the individual components (HDL cholesterol and waist circumference) suggests an increased risk for CVD, especially in patients over 55 years.     

Novel community health worker strategy for HIV service engagement in a hyperendemic community in Rakai,Uganda: A pragmatic,cluster-randomized trial

BackgroundEffective implementation strategies are needed to increase engagement in HIV services in hyperendemic settings. We conducted a pragmatic cluster-randomized trial in a high-risk, highly mobile fishing community (HIV prevalence: approximately 38%) in Rakai, Uganda, to assess the impact of a community health worker-delivered, theory-based (situated Information, Motivation, and Behavior Skills), motivational interviewing-informed, and mobile phone application-supported counseling strategy called “Health Scouts” to promote engagement in HIV treatment and prevention services.Methods and findingsThe study community was divided into 40 contiguous, randomly allocated clusters (20 intervention clusters, n = 1,054 participants at baseline; 20 control clusters, n = 1,094 participants at baseline). From September 2015 to December 2018, the Health Scouts were deployed in intervention clusters. Community-wide, cross-sectional surveys of consenting 15 to 49-year-old residents were conducted at approximately 15 months (mid-study) and at approximately 39 months (end-study) assessing the primary programmatic outcomes of self-reported linkage to HIV care, antiretroviral therapy (ART) use, and male circumcision, and the primary biologic outcome of HIV viral suppression (<400 copies/mL). Secondary outcomes included HIV testing coverage, HIV incidence, and consistent condom use. The primary intent-to-treat analysis used log-linear binomial regression with generalized estimating equation to estimate prevalence risk ratios (PRR) in the intervention versus control arm. A total of 2,533 (45% female, mean age: 31 years) and 1,903 (46% female; mean age 32 years) residents completed the mid-study and end-study surveys, respectively. At mid-study, there were no differences in outcomes between arms. At end-study, self-reported receipt of the Health Scouts intervention was 38% in the intervention arm and 23% in the control arm, suggesting moderate intervention uptake in the intervention arm and substantial contamination in the control arm. At end-study, intention-to-treat analysis found higher HIV care coverage (PRR: 1.06, 95% CI: 1.01 to 1.10, p = 0.011) and ART coverage (PRR: 1.05, 95% CI: 1.01 to 1.10, p = 0.028) among HIV–positive participants in the intervention compared with the control arm. Male circumcision coverage among all men (PRR: 1.05, 95% CI: 0.96 to 1.14, p = 0.31) and HIV viral suppression among HIV–positive participants (PRR: 1.04, 95% CI: 0.98 to 1.12, p = 0.20) were higher in the intervention arm, but differences were not statistically significant. No differences were seen in secondary outcomes. Study limitations include reliance on self-report for programmatic outcomes and substantial contamination which may have diluted estimates of effect.ConclusionsA novel community health worker intervention improved HIV care and ART coverage in an HIV hyperendemic setting but did not clearly improve male circumcision coverage or HIV viral suppression. This community-based, implementation strategy may be a useful component in some settings for HIV epidemic control.Trial registrationClinicalTrials.gov {"type":"clinical-trial","attrs":{"text":"NCT02556957","term_id":"NCT02556957"}}NCT02556957.

Larry Chang and co-workers study an intervention by which community health workers aim to promote engagement in HIV treatment and prevention services in Uganda.     

Examining methodology to identify patterns of consulting in primary care for different groups of patients before a diagnosis of cancer: An exemplar applied to oesophagogastric cancer

BackgroundCurrent methods for estimating the timeliness of cancer diagnosis are not robust because dates of key defining milestones, for example first presentation, are uncertain. This is exacerbated when patients have other conditions (multimorbidity), particularly those that share symptoms with cancer. Methods independent of this uncertainty are needed for accurate estimates of the timeliness of cancer diagnosis, and to understand how multimorbidity impacts the diagnostic process.MethodsParticipants were diagnosed with oesophagogastric cancer between 2010 and 2019. Controls were matched on year of birth, sex, general practice and multimorbidity burden calculated using the Cambridge Multimorbidity Score. Primary care data (Clinical Practice Research Datalink) was used to explore population-level consultation rates for up to two years before diagnosis across different multimorbidity burdens. Five approaches were compared on the timing of the consultation frequency increase, the inflection point for different multimorbidity burdens, different aggregated time-periods and sample sizes.ResultsWe included 15,410 participants, of which 13,328 (86.5 %) had a measurable multimorbidity burden. Our new maximum likelihood estimation method found evidence that the inflection point in consultation frequency varied with multimorbidity burden, from 154 days (95 %CI 131.8–176.2) before diagnosis for patients with no multimorbidity, to 126 days (108.5–143.5) for patients with the greatest multimorbidity burden. Inflection points identified using alternative methods were closer to diagnosis for up to three burden groups. Sample size reduction and changing the aggregation period resulted in inflection points closer to diagnosis, with the smallest change for the maximum likelihood method.DiscussionExisting methods to identify changes in consultation rates can introduce substantial bias which depends on sample size and aggregation period. The direct maximum likelihood method was less prone to this bias than other methods and offers a robust, population-level alternative for estimating the timeliness of cancer diagnosis.