The unknown denominator problem in population studies of disease frequency

Overcoming denominator problems in refugee settings with fragmented electronic records for health and immigration data: a prediction-based approach

BACKGROUND

Epidemiological studies in refugee settings are often challenged by the denominator problem, i.e. lack of population at risk data. We develop an empirical approach to address this problem by assessing relationships between occupancy data in refugee centres, number of refugee patients in walk-in clinics, and diseases of the digestive system.

METHODS

Individual-level patient data from a primary care surveillance system (PriCarenet) was matched with occupancy data retrieved from immigration authorities. The three relationships were analysed using regression models, considering age, sex, and type of centre. Then predictions for the respective data category not available in each of the relationships were made. Twenty-one German on-site health care facilities in state-level registration and reception centres participated in the study, covering the time period from November 2017 to July 2021.

RESULTS

445 observations ("centre-months") for patient data from electronic health records (EHR, 230 mean walk-in clinics visiting refugee patients per month and centre; standard deviation sd: 202) of a total of 47.617 refugee patients were available, 215 for occupancy data (OCC, mean occupancy of 348 residents, sd: 287), 147 for both (matched), leaving 270 observations without occupancy (EHR-unmatched) and 40 without patient data (OCC-unmatched). The incidence of diseases of the digestive system, using patients as denominators in the different sub-data sets were 9.2% (sd: 5.9) in EHR, 8.8% (sd: 5.1) when matched, 9.6% (sd: 6.4) in EHR- and 12% (sd 2.9) in OCC-unmatched. Using the available or predicted occupancy as denominator yielded average incidence estimates (per centre and month) of 4.7% (sd: 3.2) in matched data, 4.8% (sd: 3.3) in EHR- and 7.4% (sd: 2.7) in OCC-unmatched.

CONCLUSIONS

By modelling the ratio between patient and occupancy numbers in refugee centres depending on sex and age, as well as on the total number of patients or occupancy, the denominator problem in health monitoring systems could be mitigated. The approach helped to estimate the missing component of the denominator, and to compare disease frequency across time and refugee centres more accurately using an empirically grounded prediction of disease frequency based on demographic and centre typology. This avoided over-estimation of disease frequency as opposed to the use of patients as denominators.

Applying a zero-corrected, gravity model estimator reduces bias due to heterogeneity in healthcare utilization in community-scale, passive surveillance datasets of endemic diseases

Data on population health are vital to evidence-based decision making but are rarely adequately localized or updated in continuous time. They also suffer from low ascertainment rates, particularly in rural areas where barriers to healthcare can cause infrequent touch points with the health system. Here, we demonstrate a novel statistical method to estimate the incidence of endemic diseases at the community level from passive surveillance data collected at primary health centers. The zero-corrected, gravity-model (ZERO-G) estimator explicitly models sampling intensity as a function of health facility characteristics and statistically accounts for extremely low rates of ascertainment. The result is a standardized, real-time estimate of disease incidence at a spatial resolution nearly ten times finer than typically reported by facility-based passive surveillance systems. We assessed the robustness of this method by applying it to a case study of field-collected malaria incidence rates from a rural health district in southeastern Madagascar. The ZERO-G estimator decreased geographic and financial bias in the dataset by over 90% and doubled the agreement rate between spatial patterns in malaria incidence and incidence estimates derived from prevalence surveys. The ZERO-G estimator is a promising method for adjusting passive surveillance data of common, endemic diseases, increasing the availability of continuously updated, high quality surveillance datasets at the community scale.

Epidemiology of Asbestosis between 2010-2014 and 2015-2019 Periods in Colombia: Descriptive Study

Background

Asbestosis is a prevalent worldwide problem, but scarce data sourced from developing countries are available. We describe the sociodemographic characteristics and patterns in the occurrence of care provided for asbestosis in Colombia during the periods 2010-2014 and 2015-2019 to establish the behavior, trends, and variables associated with concentrations among people attended by asbestosis.

Methods

A retrospective descriptive study was carried out with data from the Integrated Social Protection Information System (SISPRO) for two 5-year periods. People attended by asbestosis (ICD-10: J61) were identified; the frequency of patient visits, sociodemographic characteristics, case distribution patterns, and trends in both five-year periods were described, as was the crude frequency (cFr, 95% CI) of asbestosis (1,000,000 people/year) in both five-year periods (cFr ratio, 95% CI).

Results

During the period 2010-2019, 765 people attended by asbestosis were identified; there were 308 people attended by asbestosis between 2010-2014 (cFr: 2.20, 1.96-2.47), and ther were 457 people attended by asbestos between 2015-2019 (cFr: 3.14, 2.92-3.50). In both periods, the estimated cFr in men was nine times the estimated cFr in women. The cFr increased in the 2015-2019 period (cFr_ratio: 1.23, 1.06-1.43). Compared with the 2010-2014 period, the cFr of asbestosis increased in women (cFr_ratio: 1.44, 1.03-2.01), in the Andean (cFr_ratio: 1.61, 1.35-1.95) and Caribbean regions (cFr_ratio: 1. 66, 1.21-2.30), in the urban area (cFr_ratio: 1.24, 1.05-1.48), and in the age groups 45-59 years (cFr_ratio: 1.34, 1.001-1.79) and ≥60 years (cFr_ratio: 1.43, 1.13-1.83).

Discussion

During two five-year periods, the cFr of asbestosis was higher in men; between the first and second five-year periods, it increased significantly, especially in urbanized geographic areas and in populations aged ≥45 years. The estimates possibly reflect the effect of disease latency or the expected impact of public health policies to monitor asbestos exposure and complications.

Harmonising electronic health records for reproducible research: challenges, solutions and recommendations from a UK-wide COVID-19 research collaboration

BACKGROUND

The CVD-COVID-UK consortium was formed to understand the relationship between COVID-19 and cardiovascular diseases through analyses of harmonised electronic health records (EHRs) across the four UK nations. Beyond COVID-19, data harmonisation and common approaches enable analysis within and across independent Trusted Research Environments. Here we describe the reproducible harmonisation method developed using large-scale EHRs in Wales to accommodate the fast and efficient implementation of cross-nation analysis in England and Wales as part of the CVD-COVID-UK programme. We characterise current challenges and share lessons learnt.

METHODS

Serving the scope and scalability of multiple study protocols, we used linked, anonymised individual-level EHR, demographic and administrative data held within the SAIL Databank for the population of Wales. The harmonisation method was implemented as a four-layer reproducible process, starting from raw data in the first layer. Then each of the layers two to four is framed by, but not limited to, the characterised challenges and lessons learnt. We achieved curated data as part of our second layer, followed by extracting phenotyped data in the third layer. We captured any project-specific requirements in the fourth layer.

RESULTS

Using the implemented four-layer harmonisation method, we retrieved approximately 100 health-related variables for the 3.2 million individuals in Wales, which are harmonised with corresponding variables for > 56 million individuals in England. We processed 13 data sources into the first layer of our harmonisation method: five of these are updated daily or weekly, and the rest at various frequencies providing sufficient data flow updates for frequent capturing of up-to-date demographic, administrative and clinical information.

CONCLUSIONS

We implemented an efficient, transparent, scalable, and reproducible harmonisation method that enables multi-nation collaborative research. With a current focus on COVID-19 and its relationship with cardiovascular outcomes, the harmonised data has supported a wide range of research activities across the UK.

Quantifying the risk of falls and injuries for amputees beyond annual fall rates-A longitudinal cohort analysis based on person-step exposure over time

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People with lower-limb loss even with community walking ability have high annual fall and injury rates.

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True fall and injury risk may be obscured if exposure to risk measured by person-steps over time is not considered.

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Risk was higher for amputees with limited walking ability per person-step exposure over time.

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Incorporating person-step exposure over time clarifies fall and injury risk level.

People with lower-limb loss (PLL) have high annual fall and injury rates. People with transtibial amputations have better walking function than those with transfemoral amputations but paradoxically incur more fall-related injuries. Risk exposure, however, has not been previously considered. This study examined whether all-cause fall and injury incidence per person-step exposure over time varied in PLL of different walking abilities. The prospective cohort design, conducted at a major medical center, included five assessments 1-month apart. Walking ability level was categorized by Houghton Scale scores: ≥9 indicating community walking and ≤ 8 indicating limited community-household walking. Accelerometer-measured daily step counts were collected via StepWatch4 monitors. The main outcome measures, self-reported all-cause falls and injuries were assessed using the standard National Health Injury Survey. Generalized estimating equations, using Poisson distributions and log of step count as an offset, determined fall and injury incidence rate ratio [IRR] according to walking ability level. Ten people, aged 33–63 years with amputations of different causes and levels, were assessed monthly over five months. The community walking group (n = 6) had six falls and seven injuries; the limited community walking group (n = 4) had four falls and three injuries. For PLL, limited community walking ability was associated with higher incidence of falls (IRR = 6.10, 95%CI = 1.12–33.33, p = 0.037) and injuries (IRR = 8.56, 95%CI = 1.73–42.40, p = 0.009) when accounting for person-steps. Considering per person-step exposure over time added precision to fall and injury risk assessment that clarified the risks: PLL with limited community walking ability have higher fall and injury risks.