Patterns and predictors of co-morbidities in Tuberculosis: A cross-sectional study in the Philippines

White, Laura V.; Edwards, Tansy; Lee, Nathaniel; Castro, Mary Christine; Saludar, Naomi R.; Calapis, Rugaiya W.; Faguer, B; Fuente, Nelson Dela; Mayoga, Ferdinand; Saito, Nobuo; Ariyoshi, Koya; Garfin, Anna Marie Celina; Solon, Juan Antonio A.; Cox, Sharon E.

doi:10.1038/s41598-020-60942-2

Cited by 36 publications

(33 citation statements)

References 23 publications

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“…In a cross-sectional study in England, more than 20% of diabetes was undiagnosed in all age groups older than 25 years, 47 and in lower-income settings, this proportion is likely to be much higher. 48 While our analysis provides numbers of people who could benefit from shielding due to underlying conditions, in practice, the low coverage of diagnosis and treatment for many chronic conditions in low-income settings means that age-based thresholds could play a key role. However, the choice of age threshold needs to be carefully balanced against the proportion of the working age population affected and the adverse mental health consequences that might be associated with long periods of isolation.…”

Section: Discussionmentioning

confidence: 99%

Global, regional, and national estimates of the population at increased risk of severe COVID-19 due to underlying health conditions in 2020: a modelling study

Clark¹,

Jit²,

Warren‐Gash³

et al. 2020

The Lancet Global Health

957

881

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Background The risk of severe COVID-19 if an individual becomes infected is known to be higher in older individuals and those with underlying health conditions. Understanding the number of individuals at increased risk of severe COVID-19 and how this varies between countries should inform the design of possible strategies to shield or vaccinate those at highest risk. Methods We estimated the number of individuals at increased risk of severe disease (defined as those with at least one condition listed as "at increased risk of severe COVID-19" in current guidelines) by age (5-year age groups), sex, and country for 188 countries using prevalence data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 and UN population estimates for 2020. The list of underlying conditions relevant to COVID-19 was determined by mapping the conditions listed in GBD 2017 to those listed in guidelines published by WHO and public health agencies in the UK and the USA. We analysed data from two large multimorbidity studies to determine appropriate adjustment factors for clustering and multimorbidity. To help interpretation of the degree of risk among those at increased risk, we also estimated the number of individuals at high risk (defined as those that would require hospital admission if infected) using age-specific infection-hospitalisation ratios for COVID-19 estimated for mainland China and making adjustments to reflect country-specific differences in the prevalence of underlying conditions and frailty. We assumed males were twice at likely as females to be at high risk. We also calculated the number of individuals without an underlying condition that could be considered at increased risk because of their age, using minimum ages from 50 to 70 years. We generated uncertainty intervals (UIs) for our estimates by running low and high scenarios using the lower and upper 95% confidence limits for country population size, disease prevalences, multimorbidity fractions, and infection-hospitalisation ratios, and plausible low and high estimates for the degree of clustering, informed by multimorbidity studies. Findings We estimated that 1•7 billion (UI 1•0-2•4) people, comprising 22% (UI 15-28) of the global population, have at least one underlying condition that puts them at increased risk of severe COVID-19 if infected (ranging from <5% of those younger than 20 years to >66% of those aged 70 years or older). We estimated that 349 million (186-787) people (4% [3-9] of the global population) are at high risk of severe COVID-19 and would require hospital admission if infected (ranging from <1% of those younger than 20 years to approximately 20% of those aged 70 years or older). We estimated 6% (3-12) of males to be at high risk compared with 3% (2-7) of females. The share of the population at increased risk was highest in countries with older populations, African countries with high HIV/AIDS prevalence, and small island nations with high diabetes prevalence. Estimates of the number of individuals at increased risk wer...

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Section: Discussionmentioning

confidence: 99%

Global, regional, and national estimates of the population at increased risk of severe COVID-19 due to underlying health conditions in 2020: a modelling study

Clark¹,

Jit²,

Warren‐Gash³

et al. 2020

The Lancet Global Health

957

881

View full text Add to dashboard Cite

show abstract

“…However, of the approximately 1 billion individuals aged 50+ years that we estimate to be at increased risk of severe COVID-19 disease (table 1) many may not be aware of their underlying condition. 35 For example, CKD has a very high prevalence in many countries, but more than 90% of these cases are likely to be undiagnosed. 23 Age thresholds could therefore play a critical role in shielding the large number of older individuals without a diagnosis.…”

Section: Discussionmentioning

confidence: 99%

How many are at increased risk of severe COVID-19 disease? Rapid global, regional and national estimates for 2020

Clark

Jit

Warren‐Gash

et al. 2020

Preprint

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Background The risk of severe COVID-19 disease is known to be higher in older individuals and those with underlying health conditions. Understanding the number of individuals at increased risk of severe COVID-19 illness, and how this varies between countries may inform the design of possible strategies to shield those at highest risk. Methods We estimated the number of individuals at increased risk of severe COVID-19 disease by age (5-year age groups), sex and country (n=188) based on prevalence data from the Global Burden of Disease (GBD) study for 2017 and United Nations population estimates for 2020. We also calculated the number of individuals without an underlying condition that could be considered at-risk because of their age, using thresholds from 50-70 years. The list of underlying conditions relevant to COVID-19 disease was determined by mapping conditions listed in GBD to the guidelines published by WHO and public health agencies in the UK and US. We analysed data from two large multimorbidity studies to determine appropriate adjustment factors for clustering and multimorbidity. Results We estimate that 1.7 (1.0 - 2.4) billion individuals (22% [15-28%] of the global population) are at increased risk of severe COVID-19 disease. The share of the population at increased risk ranges from 16% in Africa to 31% in Europe. Chronic kidney disease (CKD), cardiovascular disease (CVD), diabetes and chronic respiratory disease (CRD) were the most prevalent conditions in males and females aged 50+ years. African countries with a high prevalence of HIV/AIDS and Island countries with a high prevalence of diabetes, also had a high share of the population at increased risk. The prevalence of multimorbidity (>1 underlying conditions) was three times higher in Europe than in Africa (10% vs 3%). Conclusion Based on current guidelines and prevalence data from GBD, we estimate that one in five individuals worldwide has a condition that is on the list of those at increased risk of severe COVID-19 disease. However, for many of these individuals the underlying condition will be undiagnosed or not severe enough to be captured in health systems, and in some cases the increase in risk may be quite modest. There is an urgent need for robust analyses of the risks associated with different underlying conditions so that countries can identify the highest risk groups and develop targeted shielding policies to mitigate the effects of the COVID-19 pandemic.

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“…Within a cross-sectional study of Filipino people with active TB disease enrolled in TB-DOTS clinics (ISRCTN12506117), we investi-gated, as a primary objective, the prevalence of DM and moderate/severe acute undernutrition and factors associated with these comorbidities, reported elsewhere. 6 Here, we report into whether comorbidities of undernutrition, DM and moderate or severe anaemia and HIV infection are associated with reduced HrQoL in this population, as a secondary objective.…”

Section: Introductionmentioning

confidence: 94%

“…10 Data were entered directly into Open Data Kit software and uploaded to a secure server daily. Research nurses conducted anthropometry measurements; 6 weight to the nearest 0.1 kg; height, mid-upper arm circumference (MUAC) and waist and hip circumferences to the nearest 0.5 cm; and grip strength to the nearest kg (Jamar Hydraulic Hand Dynamometer; Lafayette Instruments, IN, USA) using the highest of three measurements of the dominant hand. Fingerprick blood samples were used to obtain haemoglobin (HemoCue 301;Ängelholm, Sweden), glycosylated haemoglobin (HbA1C) and C-reactive protein (CRP) (Alere Afinion AS100 Analyzer; Abbott Diagnostics, Abbott Park, IL, USA) and to conduct HIV screening (Standard Diagnostics Bioline HIV-1/2 Ag/Ab Combo Rapid Test kits) for those with unknown status and who provided additional consent.…”

Section: Data Collection and Definitions Of Comorbidity Exposuresmentioning

confidence: 99%

Effects of comorbidities on quality of life in Filipino people with tuberculosis

Edwards

White

Lee

et al. 2020

int j tuberc lung dis

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BACKGROUND: We investigated health-related quality of life (HrQoL) in Filipino people undergoing TB treatment, and whether HrQoL was negatively impacted by comorbidity with undernutrition, diabetes (DM) and anaemia.METHODS: Adult participants were enrolled in public facilities in Metro Manila (three sites) and Negros Occidental (two sites). Multivariate linear regression was used to model the four correlated domain scores from a WHOQOL-BREF questionnaire (physical, psychological, social, environmental). A forward-stepwise approach was used to select a final multivariable model with inclusion based on global tests of significance at P < 0.1.RESULTS: In 446 people on drug-susceptible TB treatment, DM and moderate/severe anaemia were not associated with HrQoL. After adjustment for age, sex, education, food insecurity, treatment adherence, inflammation, Category I or II TB treatment, treatment phase, current side effects and inhibited ability to work, moderate/severe undernutrition (body mass index < 17 kg/m2) was associated with lower HrQoL (P = 0.003) with reduced psychological (coefficient: −1.02, 95% CI −1.54 to −0.51), physical (−0.62, 95% CI −1.14 to −0.09) and environmental domain scores (−0.45, 95% CI −0.88 to −0.01). In 225 patients with known HIV status in Metro Manila, HIV was associated with modestly reduced HrQoL (P = 0.014).CONCLUSION: Nutritional status and food insecurity represent modifiable risk factors for poor HrQoL that may be alleviated through interventions.

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Patterns and predictors of co-morbidities in Tuberculosis: A cross-sectional study in the Philippines

Cited by 36 publications

References 23 publications

Global, regional, and national estimates of the population at increased risk of severe COVID-19 due to underlying health conditions in 2020: a modelling study

Global, regional, and national estimates of the population at increased risk of severe COVID-19 due to underlying health conditions in 2020: a modelling study

How many are at increased risk of severe COVID-19 disease? Rapid global, regional and national estimates for 2020

Effects of comorbidities on quality of life in Filipino people with tuberculosis

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