To quantify the burden of communicable diseases and characterize the most reported infections during public health emergency of floods in Pakistan.

The study’s design is a descriptive trend analysis. The study utilized the disease data reported to District Health Information System (DHIS2) for the 12 most frequently reported priority diseases under the Integrated Disease Surveillance and Response (IDSR) system in Pakistan.

In total, there were 1,532,963 suspected cases during August to December 2022 in flood-affected districts (n = 75) across Pakistan; Sindh Province reported the highest number of cases (n = 692,673) from 23 districts, followed by Khyber Pakhtunkhwa (KP) (n = 568,682) from 17 districts, Balochistan (n = 167,215) from 32 districts, and Punjab (n = 104,393) from 3 districts. High positivity was reported for malaria (79,622/201,901; 39.4%), followed by acute diarrhea (non-cholera) (23/62; 37.1%), hepatitis A and E (47/252; 18.7%), and dengue (603/3245; 18.6%). The crude mortality rate was 11.9 per 10 000 population (1824/1,532,963 [deaths/cases]).

The study identified acute respiratory infection, acute diarrhea, malaria, and skin diseases as the most prevalent diseases. This suggests that preparedness efforts and interventions targeting these diseases should be prioritized in future flood response plans. The study highlights the importance of strengthening the IDSR as a Disease Early Warning System through the implementation of the DHIS2.

The recognition of dementia as a multifactorial disorder encourages the exploration of new pathways to understand its origins. Social health might play a role in cognitive decline and dementia, but conceptual clarity is lacking and this hinders investigation of associations and mechanisms. Social health might provide a new perspective on social connectedness. The objective is to develop a conceptual framework for social health to advance conceptual clarity in future studies and to identify potentially modifiable risk and protective factors in the “Social Health And Reserve in the Dementia patient journey (SHARED)” project.

The methods include the process of building the conceptual framework. We used the following steps: underpinning for concept advancement, concept advancement by the development of a conceptual model, and exploration of its potential feasibility.

Underpinning of the concept drew from a synthesis of theoretical, conceptual and epidemiological work, and resulted in the definition of social health as well-being that relies on capacities both of the individual and the social environment. In the conceptual framework the abstract definition has been elaborated into more precisely defined domains at both the individual and the social environmental levels. This allowed to identify domain related social health characteristics or markers in epidemiological data bases and to investigate associations between these markers and cognitive decline and dementia. The associated social health markers represent potentially modifiable risk and protective factors. Examples are “social engagement” in the participation domain at the individual level, and “frequency of contact” in the structure domain, “exchange of support” in the function domain and “loneliness” in the appraisal domain at the environmental level. The conceptual framework facilitated identification of domain related markers in the SHARED project, thus showing its potential feasibility.

The conceptual framework provides guidance for future research and facilitates identification of potentially modifiable risk and protective factors. These may shape new avenues for preventive interventions. We highlight the paradigm of social health in dementia as a priority for dementia research.

To identify urinary catheter (UC)–associated urinary tract infection (CAUTI) incidence and risk factors.

A prospective cohort study.

The study was conducted across 623 ICUs of 224 hospitals in 114 cities in 37 African, Asian, Eastern European, Latin American, and Middle Eastern countries.

The study included 169,036 patients, hospitalized for 1,166,593 patient days.

Data collection took place from January 1, 2014, to February 12, 2022. We identified CAUTI rates per 1,000 UC days and UC device utilization (DU) ratios stratified by country, by ICU type, by facility ownership type, by World Bank country classification by income level, and by UC type. To estimate CAUTI risk factors, we analyzed 11 variables using multiple logistic regression.

Participant patients acquired 2,010 CAUTIs. The pooled CAUTI rate was 2.83 per 1,000 UC days. The highest CAUTI rate was associated with the use of suprapubic catheters (3.93 CAUTIs per 1,000 UC days); with patients hospitalized in Eastern Europe (14.03) and in Asia (6.28); with patients hospitalized in trauma (7.97), neurologic (6.28), and neurosurgical ICUs (4.95); with patients hospitalized in lower–middle-income countries (3.05); and with patients in public hospitals (5.89).

The following variables were independently associated with CAUTI: Age (adjusted odds ratio [aOR], 1.01; P < .0001), female sex (aOR, 1.39; P < .0001), length of stay (LOS) before CAUTI-acquisition (aOR, 1.05; P < .0001), UC DU ratio (aOR, 1.09; P < .0001), public facilities (aOR, 2.24; P < .0001), and neurologic ICUs (aOR, 11.49; P < .0001).

CAUTI rates are higher in patients with suprapubic catheters, in middle-income countries, in public hospitals, in trauma and neurologic ICUs, and in Eastern European and Asian facilities.

Based on findings regarding risk factors for CAUTI, focus on reducing LOS and UC utilization is warranted, as well as implementing evidence-based CAUTI-prevention recommendations.

To identify central-line (CL)–associated bloodstream infection (CLABSI) incidence and risk factors in low- and middle-income countries (LMICs).

From July 1, 1998, to February 12, 2022, we conducted a multinational multicenter prospective cohort study using online standardized surveillance system and unified forms.

The study included 728 ICUs of 286 hospitals in 147 cities in 41 African, Asian, Eastern European, Latin American, and Middle Eastern countries.

In total, 278,241 patients followed during 1,815,043 patient days acquired 3,537 CLABSIs.

For the CLABSI rate, we used CL days as the denominator and the number of CLABSIs as the numerator. Using multiple logistic regression, outcomes are shown as adjusted odds ratios (aORs).

The pooled CLABSI rate was 4.82 CLABSIs per 1,000 CL days, which is significantly higher than that reported by the Centers for Disease Control and Prevention National Healthcare Safety Network (CDC NHSN). We analyzed 11 variables, and the following variables were independently and significantly associated with CLABSI: length of stay (LOS), risk increasing 3% daily (aOR, 1.03; 95% CI, 1.03–1.04; P < .0001), number of CL days, risk increasing 4% per CL day (aOR, 1.04; 95% CI, 1.03–1.04; P < .0001), surgical hospitalization (aOR, 1.12; 95% CI, 1.03–1.21; P < .0001), tracheostomy use (aOR, 1.52; 95% CI, 1.23–1.88; P < .0001), hospitalization at a publicly owned facility (aOR, 3.04; 95% CI, 2.31–4.01; P <.0001) or at a teaching hospital (aOR, 2.91; 95% CI, 2.22–3.83; P < .0001), hospitalization in a middle-income country (aOR, 2.41; 95% CI, 2.09–2.77; P < .0001). The ICU type with highest risk was adult oncology (aOR, 4.35; 95% CI, 3.11–6.09; P < .0001), followed by pediatric oncology (aOR, 2.51;95% CI, 1.57–3.99; P < .0001), and pediatric (aOR, 2.34; 95% CI, 1.81–3.01; P < .0001). The CL type with the highest risk was internal-jugular (aOR, 3.01; 95% CI, 2.71–3.33; P < .0001), followed by femoral (aOR, 2.29; 95% CI, 1.96–2.68; P < .0001). Peripherally inserted central catheter (PICC) was the CL with the lowest CLABSI risk (aOR, 1.48; 95% CI, 1.02–2.18; P = .04).

The following CLABSI risk factors are unlikely to change: country income level, facility ownership, hospitalization type, and ICU type. These findings suggest a focus on reducing LOS, CL days, and tracheostomy; using PICC instead of internal-jugular or femoral CL; and implementing evidence-based CLABSI prevention recommendations.

Current categorical classification systems of psychiatric diagnoses lead to heterogeneity of symptoms within disorders and common co-occurrence of disorders. We investigated the heterogeneous and overlapping nature of symptom endorsement in a population-based sample across three of the most common categories of psychiatric disorders: depressive disorders, anxiety disorders, and sleep–wake disorders using unsupervised machine learning approaches.

We assessed a total of 43 symptoms in a discovery sample of 6,602 participants of the population-based Rotterdam Study between 2009 and 2013, and in a replication sample of 3,005 participants between 2016 and 2020. Symptoms were assessed using the Center for Epidemiologic Studies Depression Scale, the Hospital Anxiety and Depression Scale, and the Pittsburgh Sleep Quality Index. Hierarchical clustering analysis was applied on test items and participants to investigate common patterns of symptoms co-occurrence, and further quantitatively investigated with clustering methods to find groups that may represent similar psychiatric phenotypes.

First, clustering analyses of the questionnaire items suggested a three-cluster solution representing clusters of “mixed” symptoms, “depressed affect and nervousness”, and “troubled sleep and interpersonal problems”. A highly similar clustering solution was independently established in the replication sample. Second, four groups of participants could be separated, and these groups scored differently on the item clusters.

We identified three clusters of psychiatric symptoms that most commonly co-occur in a population-based sample. These symptoms clustered stable over samples, but across the topics of depression, anxiety, and poor sleep. We identified four groups of participants that share (sub)clinical symptoms and might benefit from similar prevention or treatment strategies, despite potentially diverging, or lack of, diagnoses.

Rates of ventilator-associated pneumonia (VAP) in low- and middle-income countries (LMIC) are several times above those of high-income countries. The objective of this study was to identify risk factors (RFs) for VAP cases in ICUs of LMICs.

Prospective cohort study.

This study was conducted across 743 ICUs of 282 hospitals in 144 cities in 42 Asian, African, European, Latin American, and Middle Eastern countries.

The study included patients admitted to ICUs across 24 years.

In total, 289,643 patients were followed during 1,951,405 patient days and acquired 8,236 VAPs. We analyzed 10 independent variables. Multiple logistic regression identified the following independent VAP RFs: male sex (adjusted odds ratio [aOR], 1.22; 95% confidence interval [CI], 1.16–1.28; P < .0001); longer length of stay (LOS), which increased the risk 7% per day (aOR, 1.07; 95% CI, 1.07–1.08; P < .0001); mechanical ventilation (MV) utilization ratio (aOR, 1.27; 95% CI, 1.23–1.31; P < .0001); continuous positive airway pressure (CPAP), which was associated with the highest risk (aOR, 13.38; 95% CI, 11.57–15.48; P < .0001); tracheostomy connected to a MV, which was associated with the next-highest risk (aOR, 8.31; 95% CI, 7.21–9.58; P < .0001); endotracheal tube connected to a MV (aOR, 6.76; 95% CI, 6.34–7.21; P < .0001); surgical hospitalization (aOR, 1.23; 95% CI, 1.17–1.29; P < .0001); admission to a public hospital (aOR, 1.59; 95% CI, 1.35-1.86; P < .0001); middle-income country (aOR, 1.22; 95% CI, 15–1.29; P < .0001); admission to an adult-oncology ICU, which was associated with the highest risk (aOR, 4.05; 95% CI, 3.22–5.09; P < .0001), admission to a neurologic ICU, which was associated with the next-highest risk (aOR, 2.48; 95% CI, 1.78–3.45; P < .0001); and admission to a respiratory ICU (aOR, 2.35; 95% CI, 1.79–3.07; P < .0001). Admission to a coronary ICU showed the lowest risk (aOR, 0.63; 95% CI, 0.51–0.77; P < .0001).

Some identified VAP RFs are unlikely to change: sex, hospitalization type, ICU type, facility ownership, and country income level. Based on our results, we recommend focusing on strategies to reduce LOS, to reduce the MV utilization ratio, to limit CPAP use and implementing a set of evidence-based VAP prevention recommendations.

Cerebrovascular disease is regarded as a potential cause of late-life depression. Yet, evidence for associations of neuroimaging markers of vascular brain disease with depressive symptoms is inconclusive. We examined the associations of neuroimaging markers and depressive symptoms in a large population-based study of middle-aged and elderly persons over time.

A total of 4943 participants (mean age = 64.6 ± 11.1 years, 55.7% women) from the Rotterdam Study were included. At baseline, total brain volume, gray matter volume, white matter volume, white matter hyperintensities volume, cortical infarcts, lacunar infarcts, microbleeds, white matter fractional anisotropy, and mean diffusivity (MD) were measured with a brain MRI (1.5T). Depressive symptoms were assessed twice with the Center for Epidemiologic Studies Depression scale (median follow-up time: 5.5 years, IQR = 0.9). To assess temporal associations of neuroimaging markers and depressive symptoms, linear mixed models were used.

A smaller total brain volume (β = −0.107, 95% CI −0.192 to −0.022), larger white matter hyperintensities volume (β = 0.047, 95% CI 0.010–0.084), presence of cortical infarcts (β = 0.194, 95% CI 0.047–0.341), and higher MD levels (β = 0.060, 95% CI 0.022–0.098) were cross-sectionally associated with more depressive symptoms. Longitudinal analyses showed that small total brain volume (β = −0.091, 95% CI −0.167 to −0.015) and presence of cortical infarcts (β = 0.168, 95% CI 0.022–0.314) were associated with increasing depressive symptoms over time. After stratification on age, effect sizes were more pronounced at older ages.

Neuroimaging markers of white matter microstructural damage were associated with depressive symptoms longitudinally in this study of middle-aged and elderly persons. These associations were more pronounced at older ages, providing evidence for the role of white matter structure in late-life depressive symptomatology.

The coronavirus disease-2019 (COVID-19) pandemic and accompanying lockdown restrictions impacted social life significantly. We studied associations of sociodemographic factors, mental and social health markers, and brain structure with social health trajectories during the COVID-19 pandemic.

Prospective longitudinal population-based cohort study.

Community-dwelling inhabitants of Rotterdam, the Netherlands.

Repeated questionnaires including questions on social health were sent to Rotterdam Study participants from April 2020 onwards. Social health data at study baseline were available for 5017 participants (mean age: 68.7 ± 11.3; 56.9% women).

Determinants were assessed in routine Rotterdam Study follow-up (1990–2020), including global brain volumes in a subset of participants (N = 1720). We applied linear mixed models and generalized estimating equations to quantify associations between determinants and trajectories of loneliness, perceived social isolation and social connectedness over three time points from April 22nd to July 31st 2020.

Loneliness prevalence was 27.9% in April 2020 versus 12.6% prepandemic. Social isolation (baseline mean 4.7 ± 2.4) and loneliness scores (baseline mean 4.9 ± 1.5) decreased over time, whereas social connectedness trajectories remained stable. Depressive symptoms, female sex, prepandemic loneliness, living alone, and not owning a pet were independently associated with lower social connectedness and higher social isolation and loneliness at COVID-19 baseline, but recovery of social health was similar for all determinants. Larger intracranial volume was associated with higher social connectedness.

Despite baseline differences for specific determinants, older adults showed similar recovery of loneliness and social isolation alongside stable social connectedness over time during the pandemic. Social health is multidimensional, especially during a global health crisis.

In older populations disturbed 24-h activity rhythms, poor sleep, and depressive symptoms are often lingering and co-morbid, making treatment difficult. To improve insights into these commonly co-occurring problems, we assessed the bidirectional association of sleep and 24-h activity rhythms with depressive symptoms in middle-aged and elderly persons.

In 1734 participants (mean age: 62.3 ± 9.3 years, 55% women) from the prospective Rotterdam Study, 24-h activity rhythms and sleep were estimated with actigraphy (mean duration: 146 ± 19.6 h), sleep quality with the Pittsburgh Sleep Quality Index, and depressive symptoms with the Center for Epidemiological Studies Depression scale. Repeated measures were available for 947 participants (54%) over a median follow-up of 6 years (interquartile range = 5.6–6.3). Linear-mixed models were used to assess temporal associations of 24-h activity rhythms and sleep with depressive symptoms in both directions.

High 24-h activity rhythm fragmentation (IV) (B = 1.002, 95% confidence interval (CI) = 0.641–1.363), long time in bed (TIB) (B = 0.111, 95% CI = 0.053–0.169), low sleep efficiency (SE) (B = −0.015, 95% CI = −0.020 to −0.009), long sleep onset latency (SOL) (B = 0.009, 95% CI = 0.006–0.012), and low self-rated sleep quality (B = 0.112, 95% CI = 0.0992–0.124) at baseline were associated with increasing depressive symptoms over time. Conversely, more depressive symptoms at baseline were associated with an increasing 24-h activity rhythm fragmentation (B = 0.002, 95% CI = 0.001–0.003) and TIB (B = 0.009, 95% CI = 0.004–0.015), and a decreasing SE (B = −0.140, 95% CI = −0.196 to −0.084), SOL (B = 0.013, 95% CI = 0.008–0.018), and self-rated sleep quality (B = 0.193, 95% CI = 0.171–0.215) over time.

This study demonstrates a bidirectional association of 24-h activity rhythms, actigraphy-estimated sleep, and self-rated sleep quality with depressive symptoms over a time frame of multiple years in middle-aged and elderly persons.