Lexis Expansion: a prerequisite for analyzing time changing variables in a cohort study

Nitika, Nitika; Mishra, Sidharth Sekhar; Lohani, Pallavi

doi:10.3126/nje.v7i2.17974

Cited by 8 publications

(7 citation statements)

References 1 publication

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“…For the computation of LE, abridged life tables with five-year age groups were constructed, with a last open interval at age 85. The age-specific mortality rates were computed using Lexis expansion, to account for the ageing of population during follow up [38]. The DFLE and its variance were computed using Sullivan’s method [39].…”

Section: Methodsmentioning

confidence: 99%

Evolution of educational inequalities in life and health expectancies at 25 years in Belgium between 2001 and 2011: a census-based study

et al. 2019

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Background Reducing socio-economic health inequalities is a public health priority, necessitating careful monitoring that should take into account changes in the population composition. We analyzed the evolution of educational inequalities in life expectancy and disability-free life expectancy at age 25 (LE 25 and DFLE 25 ) in Belgium between 2001 and 2011. Methods The 2001 and 2011 census data were linked with the national register data for a five-year mortality follow up. Disability prevalence estimates from the health interview surveys (2001 to 2013) were used to compute DFLE according to Sullivan’s method. LE 25 and DFLE 25 were computed by educational level (EL). Absolute differentials of LE 25 and DFLE 25 were calculated for each EL and for each period, as well as composite inequality indices (CII) of population-level impact of inequality. Changes over the 10-year period were then calculated for each inequality index. Results The LE 25 increased in all ELs and both genders, except in the lowest EL for women. The increase was larger in the highest EL, leading in 2011 to 6.07 and 4.58 years for the low-versus-high LE 25 gaps respectively in men and women, compared to 5.19 and 3.76 in 2001, namely 17 and 22% increases. The upwards shift of the EL distribution led to a limited 7% increase of the CII among men but no change in women. The substantial increase of the DFLE 25 in males with high EL (+ 4.5 years) and the decrease of the DFLE 25 in women with low EL, results in a substantial increase of all considered DFLE 25 inequality measures in both genders. In 2011, DFLE 25 gaps were respectively 10.4 and 13.5 years in males and females compared to 6.51 and 9.30 in 2001, representing increases of 61 and 44% for the gaps, and 72 and 20% for the CII. Conclusion The LE 25 increased in all ELs, but at a higher pace in highly educated, leading to an increase in the LE 25 gaps in both genders. After accounting for the upwards shift of the educational distribution, the population-level inequality index increased only for men. The DFLE 25 increased only in highly educated men, and decreased in low educated women, leading to large increases of inequalities in both genders. A general plan to tackle health inequality should be set up, with particular efforts to improve the health of the low educated women. Electronic supplementary material The online version of this article (10.1186/s13690-019-0330-8) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

Evolution of educational inequalities in life and health expectancies at 25 years in Belgium between 2001 and 2011: a census-based study

et al. 2019

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“…Moreover, we split each participant’s follow up time into 5-year age-at-risk bands. The effect of age during the study (age-at-risk) is more appropriate because it determines current risk rather than age recorded at the start of the study (age-at-baseline) 18 .…”

Section: Discussionmentioning

confidence: 99%

“…The associations of baseline heart rate and change of heart rate with outcomes were assessed with multivariable Cox proportional hazards models. The multivariable models were stratified for age at risk (in 5-year intervals) 18 , gender, and adjusted for established risk factors of CVD, including education, smoking status, alcohol intake status, physical activity, family history of CVD, BMI, hypertension, hyperlipidemia, diabetes. In addition, we adjusted for baseline heart rate when modeling for change of heart rate.…”

Section: Methodsmentioning

confidence: 99%

Association of resting heart rate and its change with incident cardiovascular events in the middle-aged and older Chinese

Yuan

Shen

Zhang

et al. 2019

Sci Rep

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Whether heart rate change is associated with cardiovascular disease (CVD) in the general population is unclear. We conducted a prospective cohort study to assess the association of resting heart rate and its change with incident CVD in the middle-aged and older Chinese. Resting heart rate was measured during the baseline survey (September 2008 to June 2010) and the resurvey (2013). Incident CVD was followed up until December 31, 2016. Finally, a total of 20,828 participants were included in the analyses of baseline heart rate and 9132 participants were included in the analyses of heart rate change. The associations of baseline heart rate and heart rate change with incident CVD were assessed with multivariable Cox proportional hazards models. Compared with moderate baseline heart rate (65 to 80 bpm), low baseline heart rate (<65 bpm) was associated with higher risk of CVD (HR, 1.19; 95% CI, 1.07–1.32). Compared with stable heart rate (−5 to 15 bpm) in the moderate baseline heart rate group (65 to 80 bpm), an increase of heart rate (>15 bpm) in high baseline heart rate group (>80 bpm) (HR, 1.67; 95% CI, 1.02–2.71) or a decrease of heart rate (<−5 bpm) in low baseline heart rate group (<65 bpm) (HR, 2.48; 95% CI, 1.27–4.82) was associated with higher risk of CVD. In conclusion, low resting heart rate is associated with higher risk of CVD. Both continuous increase in high baseline heart rate and decrease in low baseline heart rate are associated with higher risk of CVD.

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“…Time-to-event analyses were used to determine time from ART initiation to non-retention over a six-months follow-up period. Because some individuals started ART during the pre-COVID-19 period, but their follow-up crossed over into the COVID-19 period, Lexis expansion was applied and follow-up time split accordingly, either to pre-COVID-19 or COVID-19 periods [31]. Univariable and multivariable survival regression models were used to determine effect of COVID-19 period on non-retention.…”

Section: Methodsmentioning

confidence: 99%

Impact of the Covid-19 Pandemic on Routine Hiv Care and Antiretroviral Treatment Outcomes in Kenya: A Nationally Representative Analysis

Kimanga,

Makory,

Hassan

et al. 2023

Preprint

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BackgroundThe COVID-19 pandemic adversely disrupted global health service delivery. We aimed to assess impact of the pandemic on same-day HIV diagnosis/ART initiation, six-months non-retention and initial virologic non-suppression (VnS) among individuals starting antiretroviral therapy (ART) in Kenya.MethodsIndividual-level longitudinal service delivery data were analysed. Random sampling of individuals aged >15 years starting ART between April 2018 – March 2021 was done. Date of ART initiation was stratified into pre-COVID-19 (April 2018 – March 2019 and April 2019 – March 2020) and COVID-19 (April 2020 – March 2021) periods. Mixed effects generalised linear, survival and logistic regression models were used to determine the effect of COVID-19 pandemic on same-day HIV diagnosis/ART initiation, six-months non-retention and VnS, respectively.ResultsOf 7,046 individuals sampled, 35.5%, 36.0% and 28.4% started ART during April 2018 – March 2019, April 2019 – March 2020 and April 2020 – March 2021, respectively. Compared to the pre-COVID-19 period, the COVID-19 period had higher same-day HIV diagnosis/ART initiation (adjusted risk ratio [95% CI]: 1.09 [1.04–1.13], p<0.001) and lower six-months non-retention (adjusted hazard ratio [95% CI]: 0.66 [0.58–0.74], p<0.001). Of those sampled, 3,296 (46.8%) had a viral load test done at a median 6.2 (IQR, 5.3–7.3) months after ART initiation. Compared to the pre-COVID-19 period, there was no significant difference in VnS during the COVID-19 period (adjusted odds ratio [95% CI]: 0.79 [95%% CI: 0.52–1.20], p=0.264).ConclusionsIn the short term, the COVID-19 pandemic did not have an adverse impact on HIV care and treatment outcomes in Kenya. Timely, strategic and sustained COVID-19 response may have played a critical role in mitigating adverse effects of the pandemic and point towards maturity, versatility and resilience of the HIV program in Kenya. Continued monitoring to assess long-term impact of the COVID-19 pandemic on HIV care and treatment program in Kenya is warranted.

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Lexis Expansion: a prerequisite for analyzing time changing variables in a cohort study

Cited by 8 publications

References 1 publication

Evolution of educational inequalities in life and health expectancies at 25 years in Belgium between 2001 and 2011: a census-based study

Evolution of educational inequalities in life and health expectancies at 25 years in Belgium between 2001 and 2011: a census-based study

Association of resting heart rate and its change with incident cardiovascular events in the middle-aged and older Chinese

Impact of the Covid-19 Pandemic on Routine Hiv Care and Antiretroviral Treatment Outcomes in Kenya: A Nationally Representative Analysis

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