Data are mean±SD or median [IQR].ERI, EPO Resistance Index, BP, Blood pressure; hsCRP, high sensitivity C Reactive Protein; IL-6, Interleukin 6.

*p test by Kruskal Wallis, except.\raster(60%)="rg2"Fisher's exact.ΨAdmission serum creatinine>265 µmol/L.Ω

Abbreviation: AaDO2, alveolar-arterial oxygen tension difference; AKIN, acute kidney injury network; APACHE II, acute physiology and chronic health evaluation II; CRRT: continuous renal replacement therapies; ECMO: extracorporeal membrane oxygenation; F, female; FiO2, fraction of inspired oxygen; GCS, Glasgow coma scale; Hb, hemoglobin; M, male; MAP, mean arterial pressure; NS, not significant; OSF, organ system failure; PaO2, partial pressure of oxygen; SCr, serum creatinine; SOFA, sequential organ failure assessment; TnI, troponin-I; UO, urine output; WBC, white blood cell count.*, exclude 6 patients under maintenance hemodialysis.

Means and standard deviations for cognitive measures and questionnaires in participants scoring above and below 25 on the MMSE.

Note: given numbers are mean±SD (range).Patient Demographics.

Demographic and socioeconomic characteristics of patients in the health system compared to pancreatic cancer patients.

Socio-demographic and clinical characteristics of population that requested inpatient rehabilitation.

BackgroundSegmentation of heterogeneous patient populations into parsimonious and relatively homogenous groups with similar healthcare needs can facilitate healthcare resource planning and development of effective integrated healthcare interventions for each segment. We aimed to apply a data-driven, healthcare utilization-based clustering analysis to segment a regional health system patient population and validate its discriminative ability on 4-year longitudinal healthcare utilization and mortality data.MethodsWe extracted data from the Singapore Health Services Electronic Health Intelligence System, an electronic medical record database that included healthcare utilization (inpatient admissions, specialist outpatient clinic visits, emergency department visits, and primary care clinic visits), mortality, diseases, and demographics for all adult Singapore residents who resided in and had a healthcare encounter with our regional health system in 2012. Hierarchical clustering analysis (Ward’s linkage) and K-means cluster analysis using age and healthcare utilization data in 2012 were applied to segment the selected population. These segments were compared using their demographics (other than age) and morbidities in 2012, and longitudinal healthcare utilization and mortality from 2013–2016.ResultsAmong 146,999 subjects, five distinct patient segments “Young, healthy”; “Middle age, healthy”; “Stable, chronic disease”; “Complicated chronic disease” and “Frequent admitters” were identified. Healthcare utilization patterns in 2012, morbidity patterns and demographics differed significantly across all segments. The “Frequent admitters” segment had the smallest number of patients (1.79% of the population) but consumed 69% of inpatient admissions, 77% of specialist outpatient visits, 54% of emergency department visits, and 23% of primary care clinic visits in 2012. 11.5% and 31.2% of this segment has end stage renal failure and malignancy respectively. The validity of cluster-analysis derived segments is supported by discriminative ability for longitudinal healthcare utilization and mortality from 2013–2016. Incident rate ratios for healthcare utilization and Cox hazards ratio for mortality increased as patient segments increased in complexity. Patients in the “Frequent admitters” segment accounted for a disproportionate healthcare utilization and 8.16 times higher mortality rate.ConclusionOur data-driven clustering analysis on a general patient population in Singapore identified five patient segments with distinct longitudinal healthcare utilization patterns and mortality risk to provide an evidence-based segmentation of a regional health system’s healthcare needs.

Abstract

The 1998 Ghana Demographic and Health Survey (GDHS) is the latest in a series of national-level population and health surveys conducted in Ghana and it is part of the worldwide MEASURE DHS+ Project, designed to collect data on fertility, family planning, and maternal and child health.

The primary objective of the 1998 GDHS is to provide current and reliable data on fertility and family planning behaviour, child mortality, children’s nutritional status, and the utilisation of maternal and child health services in Ghana. Additional data on knowledge of HIV/AIDS are also provided. This information is essential for informed policy decisions, planning and monitoring and evaluation of programmes at both the national and local government levels.

The long-term objectives of the survey include strengthening the technical capacity of the Ghana Statistical Service (GSS) to plan, conduct, process, and analyse the results of complex national sample surveys. Moreover, the 1998 GDHS provides comparable data for long-term trend analyses within Ghana, since it is the third in a series of demographic and health surveys implemented by the same organisation, using similar data collection procedures. The GDHS also contributes to the ever-growing international database on demographic and health-related variables.

Geographic coverage

National

Analysis unit

• Household
• Children under five years
• Women age 15-49
• Men age 15-59

Kind of data

Sample survey data

Sampling procedure

The major focus of the 1998 GDHS was to provide updated estimates of important population and health indicators including fertility and mortality rates for the country as a whole and for urban and rural areas separately. In addition, the sample was designed to provide estimates of key variables for the ten regions in the country.

The list of Enumeration Areas (EAs) with population and household information from the 1984 Population Census was used as the sampling frame for the survey. The 1998 GDHS is based on a two-stage stratified nationally representative sample of households. At the first stage of sampling, 400 EAs were selected using systematic sampling with probability proportional to size (PPS-Method). The selected EAs comprised 138 in the urban areas and 262 in the rural areas. A complete household listing operation was then carried out in all the selected EAs to provide a sampling frame for the second stage selection of households. At the second stage of sampling, a systematic sample of 15 households per EA was selected in all regions, except in the Northern, Upper West and Upper East Regions. In order to obtain adequate numbers of households to provide reliable estimates of key demographic and health variables in these three regions, the number of households in each selected EA in the Northern, Upper West and Upper East regions was increased to 20. The sample was weighted to adjust for over sampling in the three northern regions (Northern, Upper East and Upper West), in relation to the other regions. Sample weights were used to compensate for the unequal probability of selection between geographically defined strata.

The survey was designed to obtain completed interviews of 4,500 women age 15-49. In addition, all males age 15-59 in every third selected household were interviewed, to obtain a target of 1,500 men. In order to take cognisance of non-response, a total of 6,375 households nation-wide were selected.

Note: See detailed description of sample design in APPENDIX A of the survey report.

Mode of data collection

Face-to-face

Research instrument

Three types of questionnaires were used in the GDHS: the Household Questionnaire, the Women’s Questionnaire, and the Men’s Questionnaire. These questionnaires were based on model survey instruments developed for the international MEASURE DHS+ programme and were designed to provide information needed by health and family planning programme managers and policy makers. The questionnaires were adapted to the situation in Ghana and a number of questions pertaining to on-going health and family planning programmes were added. These questionnaires were developed in English and translated into five major local languages (Akan, Ga, Ewe, Hausa, and Dagbani).

The Household Questionnaire was used to enumerate all usual members and visitors in a selected household and to collect information on the socio-economic status of the household. The first part of the Household Questionnaire collected information on the relationship to the household head, residence, sex, age, marital status, and education of each usual resident or visitor. This information was used to identify women and men who were eligible for the individual interview. For this purpose, all women age 15-49, and all men age 15-59 in every third household, whether usual residents of a selected household or visitors who slept in a selected household the night before the interview, were deemed eligible and interviewed. The Household Questionnaire also provides basic demographic data for Ghanaian households. The second part of the Household Questionnaire contained questions on the dwelling unit, such as the number of rooms, the flooring material, the source of water and the type of toilet facilities, and on the ownership of a variety of consumer goods.

The Women’s Questionnaire was used to collect information on the following topics: respondent’s background characteristics, reproductive history, contraceptive knowledge and use, antenatal, delivery and postnatal care, infant feeding practices, child immunisation and health, marriage, fertility preferences and attitudes about family planning, husband’s background characteristics, women’s work, knowledge of HIV/AIDS and STDs, as well as anthropometric measurements of children and mothers.

The Men’s Questionnaire collected information on respondent’s background characteristics, reproduction, contraceptive knowledge and use, marriage, fertility preferences and attitudes about family planning, as well as knowledge of HIV/AIDS and STDs.

Response rate

A total of 6,375 households were selected for the GDHS sample. Of these, 6,055 were occupied. Interviews were completed for 6,003 households, which represent 99 percent of the occupied households. A total of 4,970 eligible women from these households and 1,596 eligible men from every third household were identified for the individual interviews. Interviews were successfully completed for 4,843 women or 97 percent and 1,546 men or 97 percent. The principal reason for nonresponse among individual women and men was the failure of interviewers to find them at home despite repeated callbacks.

Note: See summarized response rates by place of residence in Table 1.1 of the survey report.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors, and (2) sampling errors. Nonsampling errors are the results of shortfalls made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 1998 GDHS to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 1998 GDHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95 percent of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 1998 GDHS sample is the result of a two-stage stratified design, and, consequently, it was necessary to use more complex formulae. The computer software used to calculate sampling errors for the 1998 GDHS is the ISSA Sampling Error Module. This module uses the Taylor linearization method of variance estimation for survey estimates that are means or proportions. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Age distribution of eligible and interviewed men - Completeness of reporting - Births by calendar years - Reporting of age at death in days - Reporting of age at death in months

Note: See detailed tables in APPENDIX C of the survey report.

BackgroundProportion of hospitalized COVID-19 patients receiving antimicrobial drug is significantly high despite evidence of low level of actual bacterial co-infection, potentially contributing to poor health outcome and global antimicrobial resistance.Materials and methodsA retrospective study was performed on antimicrobial agents prescribed to adult patients with confirmed COVID-19 admitted across three isolation facilities between 1 March 2020 and 30 April 2021 in Ibadan, Oyo state, Nigeria. From individual records, we evaluated patient demographics, COVID-19 risk factors, diagnostic testing, disease severity and antimicrobial utilization. The primary aim was to determine the prevalence of antimicrobial prescription as well as factors associated with antimicrobial prescribing in hospitalized patients with COVID-19 in Oyo state.ResultsIn total, 271 patients were included in this study. The median age of the population was 51 years (IQR; 32–62 years). The mean duration of hospital admission was 13 days (IQR: 10–14 days). Majority of participants were symptomatic (81.5%). All participants had a COVID-19 PCR test performed and none had bacterial culture performed. All patients received antimicrobial therapy across the entire cohort. The mean DOT per LOT across cohorts was 1.2 for mild cases, 1.4 for moderate cases and 1.3 for severe cases. Factors associated with the number of antimicrobials per prescription were being single (P = 0.02), being below 60 years of age (P = 0.04), mild COVID-19 symptoms (P < 0.001) and diabetes comorbidity (P = 0.03).ConclusionGiven the high rate of antimicrobial prescription and absence of bacteriological culture analysis in these patients, there is risk of development and spread of antimicrobial resistant. Continuous review of antimicrobial prescription is critical in the management of hospitalized COVID-19 patients.

Patient’s demographics N = 25: Age (Mean): 43.2 years.

Demographic characteristics for the whole patient population.

IntroductionThe interpretation of plain hip radiographs can vary widely among physicians. This study aimed to develop and validate a deep learning-based screening model for distinguishing normal hips from severe hip diseases on plain radiographs.MethodsElectronic medical records and plain radiograph from 2004 to 2012 were used to construct two patient groups: the hip disease group (those who underwent total hip arthroplasty) and normal group. A total of 1,726 radiographs (500 normal hip radiographs and 1,226 radiographs with hip diseases, respectively) were included and were allocated for training (320 and 783), validation (80 and 196), and test (100 and 247) groups. Four different models were designed–raw image for both training and test set, preprocessed image for training but raw image for the test set, preprocessed images for both sets, and change of backbone algorithm from DenseNet to EfficientNet. The deep learning models were compared in terms of accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), F1-score, and area under the receiver operating characteristic curve (AUROC).ResultsThe mean age of the patients was 54.0 ± 14.8 years in the hip disease group and 49.8 ± 14.9 years in the normal group. The final model showed highest performance in both the internal test set (accuracy 0.96, sensitivity 0.96, specificity 0.97, PPV 0.99, NPV 0.99, F1-score 0.97, and AUROC 0.99) and the external validation set (accuracy 0.94, sensitivity 0.93, specificity 0.96, PPV 0.95, NPV 0.93, F1-score 0.94, and AUROC 0.98). In the gradcam image, while the first model depended on unrelated marks of radiograph, the second and third model mainly focused on the femur shaft and sciatic notch, respectively.ConclusionThe deep learning-based model showed high accuracy and reliability in screening hip diseases on plain radiographs, potentially aiding physicians in more accurately diagnosing hip conditions.

BackgroundAs the prevalence of atrial fibrillation (AF) increases worldwide and AF management becomes ever more diversified and personalised, insights into (regional) AF patient demographics and contemporary AF management are needed. This paper reports the current AF management and baseline demographics of a Belgian AF population recruited for a large multicenter integrated AF study (AF-EduCare/AF-EduApp study).MethodsWe analyzed data from 1,979 AF patients, assessed between 2018 and 2021 for the AF-EduCare/AF-EduApp study. The trial randomised consecutive patients with AF (irrespective of AF history duration) into three educational intervention groups (in person-, online-, and application-based), compared with standard care. Baseline demographics of both the included and excluded/refused patients are reported.ResultsThe mean age of the trial population was 71.2 ± 9.1 years, with a mean CHA2DS2-VASc score of 3.4 ± 1.8. Of all screened patients, 42.4% were asymptomatic at presentation. Being overweight was the most common comorbidty, present in 68.9%, while 65.0% were diagnosed with hypertension. Anticoagulation therapy was prescribed in 90.9% of the total population and in 94.0% of the patients with an indication for thromboembolic prophylaxis. Of the 1,979 assessed AF patients, 1,232 (62.3%) were enrolled in the AF-EduCare/AF-EduApp study, with transportation problems (33.4%) as the main reason for refusal/non-inclusion. About half of the included patients were recruited at the cardiology ward (53.8%). AF was first diagnosed, paroxysmal, persistent and permanent in 13.9%, 47.4%, 22.8% and 11.3%, respectively. Patients who refused or were excluded were older (73.3 ± 9.2 vs. 69.8 ± 8.9 years, p 

BackgroundThe quality of life(QoL) of patients with primary Sjögren’s syndrome(PSS) is affected by a variety of symptoms, and it is important to comprehensively assess the factors affecting patients’ QoL.The PSS-QoL is a specific tool for the assessment of patients’ QoL in the PSS. The purpose of this study was to cross-culturally adapt the PSS-QoL for the Chinese language,to establish a QoL assessment tool for PSS patients in Chinese culture and to test the reliability and validity of the PSS-QoL.MethodsOur study period was from January 17, 2024 to June 15, 2024. The study was designed as a two-stage observational study. Double forward and backward translations of the PSS-QoL were performed for cultural adaptation to the Chinese language,and the specific steps included forward and backward translations, coordination, expert correspondence, small-sample surveys, and corrections to form the final draft of the Chinese version of the PSS-QoL. From January 29, 2024 to June 15, 2024,for the evaluation of psychometric properties, 135 patients with PSS completed the Chinese version of the PSS-QoL,Short Form-12(SF-12) and EULAR Sjögren’s Syndrome Patient Reported Index (ESSPRI).After 2 to 4 weeks,15 patients with PSS completed the Chinese version of the PSS-QoL for the second time.We used SPSS 26.0 software to statistically analyze the data, including item analysis and reliability and validity tests.ResultsThe Chinese version of the PSS-QoL consists of 25 questions and can be divided into two main categories: physical (discomfort and dryness) and psychosocial. The mean score on the Chinese version of the PSS-QoL was 40.46±15.00. Among the 135 patients with PSS,92.60% were female, the mean (±SD) age was 52.76±12.74 years, and the disease duration was 5 (1.5, 9) years. There was good differentiation between the individual items and the Chinese version of the PSS-QoL,as the decision value of all the items ranged from -3.223 to -12.234 (p < 0.05), the correlation coefficient between the individual items and the whole questionnaire ranged from 0.315 to 0.730 (p < 0.05), the Chinese version of the PSS-QoL had good reliability and validity,as Cronbach’s α = 0.886, the Spearman-Brown coefficient was 0.782,the reliability of the individual items was 0.89,and the values of the I-CVI and S-CVI/AVE were all 1, indicating good psychometric properties. The construct validity between the Chinese version of the PSS-QoL and the ESSPRI was excellent (p = 0.506 ~0.687), and that between the PSS-QoL and the SF-12 was good(p = -0.464 ~ -0.673).ConclusionThe Chinese version of the PSS-QoL possesses good reliability and validity, and all the indicies meet the metrics and satisfy the psychometrically acceptable range;therefore, it can be used as a reliable instrument for assessing the QoL of patients with PSS in China.

Regression Coefficients for Social Determinants of Hospital-Level Pancreatic Cancer Care Utilization.

Multivariable Regression Coefficients for Social Determinants Impacting Utilization on population level.

PurposeThis study determined whether initiation of pharmacologic treatment was delayed for newly diagnosed osteoporosis patients during the COVID-19 pandemic.Methods1,189 patients ≥50 years with newly diagnosed osteoporosis using dual-energy x-ray absorptiometry (DXA) screening at a single academic institution were included. Patients with previous osteoporosis were excluded. Patients diagnosed between March 1, 2018—January 31, 2020 (pre-pandemic cohort, n = 576) were compared to those diagnosed between March 1, 2020—January 31, 2022 (pandemic cohort, n = 613). Age, sex, race, ethnicity, ordering providers (primary vs specialty), and pharmacological agents were evaluated. Primary outcomes included proportion of patients prescribed therapy within 3 and 6-months of diagnosis, and mean time from diagnosis to treatment initiation.ResultsThe pre-pandemic cohort had more White patients (74.3 vs 68.4%, p = .02) and no differences between remaining demographic variables. Only 40.5% of newly diagnosed patients initiated pharmacologic therapy within 6 months. Patients treated at 3-months (31.8 vs 35.4%, p = 0.19) and 6-months (37.8 vs 42.9, p = 0.08) were comparable between cohorts (47.2 vs 50.2% p = 0.30). Mean time from diagnosis to treatment initiation was similar (46 vs 45 days, p = 0.72). There were no treatment differences based on gender, race, or ethnicity or between ordering providers (65.1 vs 57.4% primary care, p = 0.08). Bisphosphonates were most often prescribed in both cohorts (89% vs 82.1%).ConclusionsThis is the first study assessing COVID-19’s impact on pharmacologic treatment of newly diagnosed osteoporosis. 40.5% of newly diagnosed patients were treated pharmacologically within six months of diagnosis, and the pandemic did not significantly affect treatment rates.

Adjusted mean difference of COVID-19 hospital length of stay and odds ratio of COVID-19 in-hospital mortality (95% CI) [p-value].

IntroductionA health and lifestyle advisor service embedded within primary care was piloted in Kingston-upon-Hull from January 2021. We aimed to evaluate the first two years of service delivery by identifying patient demographics referred to the service, reason for referral, determine uptake and retention rates, and monitor individual lifestyle-related risk factor changes following discharge.MethodsAnonymised data were extracted from the SystmOne database for all patients referred to the service between January 2021 and January 2023.ResultsIn the initial two years of the service, 705 unique patients were referred at a mean rate of ∼29 per month. Each unique patient received a median (robust median absolute deviation; [MAD]) of 3 (Steel N, et al 2018) planned consultations prior to discharge over this period. The majority of referrals were for symptom management and health promotion purposes (95%). Of those referred, 69% attended their appointments, and 14% did not attend. The majority of referrals were white British (55%), however, the service did receive a substantial number of referrals from minority ethnic groups, with only 67% of referrals speaking English as their main language. Eighteen distinct languages were spoken. Most referrals were classified as class I obese (59.4%). Across initial and final appointments, median (robust MAD) systolic blood pressure was 130 (15) mmHg and 130 (15) mmHg, and median (robust MAD) waist circumference was 103.0 (13.3) cm and 101.0 (13.3) cm.ConclusionThe evaluation highlighted the demand for this service embedded within primary care settings in Kingston-upon-Hull. Service engagement was evident, and a large proportion of those who engaged were from minority ethnic groups. A high proportion of referrals presented with obesity and/or hypertension which requires further investigation.