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Sex and gender have a significant relationship to health and health outcomes for women, men, and sexually and gender-diverse people. Sex relates to biological attributes, whether born female or male, while gender identity relates to how someone feels and experiences their gender, which may or may not be different to their physiology or sex at birth. Biological characteristics expose women and men to different health risks and health conditions. Gender also exposes people to different health risks, and gender inequity impacts on their potential to achieve health and well-being.
Sexual health nurses are employed to work in a range of practice settings and work with diverse population groups. Sexual and reproductive health care is considered a human right and is fundamental to positive well-being. The nurses role in sexual and reproductive health varies between settings within and across different jurisdictons. Work settings include dedicated sexual health clinics, family planning services, community health centres, women’s health services, correctional services, general practices and tertiary education settings. In some juristictions, nurses also provide care in publicly funded sexual health clinics aimed at providing services to specific priority population groups to increase their access to services and reduce the prevalence of adverse sexual and reproductive health outcomes including sexually transmitted infections and unplanned pregnancy.
The role of occupational health nurses is to improve mental and physical health outcomes and the well-being of workers. These benefits can often extend to family and community. Workers’ health is impacted by several factors including fatigue, gender, culture, age, language, living conditions, access to nutritious food, level of physical activity, sleep patterns, personal health practices and coping strategies, levels of social support and inclusion, personal safety and freedom from violence.
There is a growing focus on understanding the complexity of dietary patterns and how they relate to health and other factors. Approaches that have not traditionally been applied to characterise dietary patterns, such as latent class analysis and machine learning algorithms, may offer opportunities to characterise dietary patterns in greater depth than previously considered. However, there has not been a formal examination of how this wide range of approaches has been applied to characterise dietary patterns. This scoping review synthesised literature from 2005 to 2022 applying methods not traditionally used to characterise dietary patterns, referred to as novel methods. MEDLINE, CINAHL and Scopus were searched using keywords including latent class analysis, machine learning and least absolute shrinkage and selection operator. Of 5274 records identified, 24 met the inclusion criteria. Twelve of twenty-four articles were published since 2020. Studies were conducted across seventeen countries. Nine studies used approaches with applications in machine learning, such as classification models, neural networks and probabilistic graphical models, to identify dietary patterns. The remaining studies applied methods such as latent class analysis, mutual information and treelet transform. Fourteen studies assessed associations between dietary patterns characterised using novel methods and health outcomes, including cancer, cardiovascular disease and asthma. There was wide variation in the methods applied to characterise dietary patterns and in how these methods were described. The extension of reporting guidelines and quality appraisal tools relevant to nutrition research to consider specific features of novel methods may facilitate consistent reporting and enable synthesis to inform policies and programs.
In Europe, organic food must comply with specific regulations which do not include nutritional criteria. The ability of organic food to meet the nutritional needs of children is not assessed. This narrative review discusses the nutritional composition (macronutrients, micronutrients) of organic food compared with conventional products and its clinical relevance with a paediatric focus, as well as the health impact of these differences and of contaminants which interfere with metabolism. Other potential differences, particularly regarding the direct/indirect exposure to other contaminants in conventional food, are not addressed in this review. The composition of some organic food may differ from conventional food. Protein content was lower in cereals and eggs. A lower n-6:n-3 polyunsaturated fat (PUFA) ratio was observed in milk, meat and eggs. Long-chain PUFA and vitamin E may be higher in milk, meat and fish, as well as some minerals and antioxidants (phenolic compounds, vitamin C) in fruits, vegetables and starchy food and carotenoids in fruits and vegetables. Epidemiological studies suggest an association between organic diets and lower prevalence of childhood obesity, type 2 diabetes and metabolic syndrome, whereas the protective effect on allergy and cancer is controversial. Some organic food may be of greater nutritional interest for children’s diet than conventional food. Standardised studies comparing food composition and diet in children are needed. Considering the lower toxicologic risk and the sustainability of organic food, the Committee on Nutrition encourages the use of organic food, provided that such food is affordable, alongside specific baby food which is subject to strict specific European Union regulations.
Health technology assessment (HTA) organizations generate guidelines to inform healthcare practices toward improved health outcomes. This review sought to identify and classify outcomes of guidelines from HTA organizations within published research.
Methodology
We performed a systematic mixed studies review of empirical studies that (a) referred to a published guideline from an HTA organization and (b) reported an outcome resulting from a guideline. We searched the published literature in English or French within seven databases. Outcome types were classified within five dimensions of an existing framework for online health information (e.g., relevance, cognitive/affective impact, and use). Subdimensions were inductively developed. A two-phase sequential data synthesis was performed. Phase 1: a hybrid deductive–inductive thematic analysis identified the types of outcomes and displayed their relationships on a concept map. Phase 2: descriptive statistics were tabulated by the type of outcome.
Results
A total of 6,719 records were retrieved through searches on 6 February 2023. After screening, we included 120 observational studies (twenty-one qualitative, ninety-four quantitative, and five mixed methods). Phase 1 identified twenty-nine types of outcomes. The most frequently reported outcomes were within the organizational dimension (reported in ninety-four studies). The most common subdimensions were “Referrals” (thirty-eight occurrences), the “Quality of Prescriptions” (fifteen occurrences), and the “Quality of Diagnosis” (eight occurrences). For Phase 2, we could only generate descriptive statistics on seventeen outcomes. These were almost equally distributed among positive, neutral, and negative effects. Our results contribute to knowledge about the outcomes of HTA guidelines and options for documenting and measuring them in future evaluations.
In the previous chapter we alluded to what is sometimes called ‘secondary’ prevention, where instead of trying to prevent disease from occurring, we try to detect it earlier, in the hope that this will enable more effective treatment and thus improved health outcomes. This is an aspect of public health that has great intuitive appeal, especially for serious conditions such as cancer, where the options for primary prevention can be very limited. However, screening programs are usually very costly exercises and they do not always deliver the expected benefits in terms of improved health outcomes. In this chapter we introduce you to the requirements for implementing a successful screening program and to some of the problems that we encounter when trying to determine whether such a program is actually beneficial in practice.
Cognitive decline is intricately linked to various factors such as obesity, stress, poor sleep, and circadian rhythm misalignment, which are interrelated in their impact on cognitive health. Irregular food-intake timing further compounds these issues. The practice of prolonged nightly fasting (PNF) may help synchronize food intake with circadian rhythms, potentially mitigating adverse effects of cognitive decline and associated factors.
Methods:
A pilot nationwide, remotely delivered, 2-arm randomized controlled trial was conducted to assess the 8-week outcomes of cognition, stress, and sleep, after a PNF intervention (14-hr nightly fast, 6 nights/week, no calories after 8 pm) compared to a health education (HED) control condition. Participants were living with memory decline, stress, and obesity and had weekly check-in calls to report fasting times (PNF) or content feedback (HED).
Results:
Participants were enrolled from 37 states in the US; N = 58, 86% women, 71% white, 93% non-Latinx, mean (SD) age 50.1 (5.1) years and BMI 35.6 (3.6) kg/m2. No group differences existed at baseline. Linear mixed-effects models were used to compare outcome change differences between groups. Compared to the HED control, the PNF intervention was associated with improved sleep quality (B = −2.52; SE = 0.90; 95% CI −4.30–−0.74; p = 0.006). Perceived stress and everyday cognition significantly changed over time (p < 0.02), without significant difference by group.
Discussion:
Changing food intake timing to exclude nighttime eating and promote a fasting period may help individuals living with obesity, memory decline, and stress to improve their sleep. Improved sleep quality may lead to additional health benefits.
The associations of prior homelessness with current health are unknown. Using nationally representative data collected in private households in England, this study aimed to examine Common Mental Disorders (CMDs), physical health, alcohol/substance dependence, and multimorbidities in people who formerly experienced homelessness compared to people who never experienced homelessness.
Methods
This cross-sectional study utilised data from the 2007 and 2014 Adult Psychiatric Morbidity Surveys. Former homelessness and current physical health problems were self-reported. Current CMDs, alcohol dependence and substance dependence were ascertained using structured validated scales. Survey-weighted logistic regression was used to compare multimorbidities (conditions in combination) for participants who formerly experienced homelessness with those who had never experienced homelessness, adjusting for sociodemographic characteristics, smoking status and adverse experiences. Population attributable fractions (PAFs) were calculated.
Results
Of 13,859 people in the sample, 535 formerly experienced homelessness (3.6%, 95% CI 3.2–4.0). 44.8% of people who formerly experienced homelessness had CMDs (95% CI 40.2–49.5), compared to 15.0% (95% CI 14.3–15.7) for those who had never experienced homelessness. There were substantial associations between prior homelessness and physical multimorbidity (adjusted odds ratio [aOR] 1.98, 95% CI 1.53–2.57), CMD–physical multimorbidity (aOR 3.43, 95% CI 2.77–4.25), CMD–alcohol/substance multimorbidity (aOR 3.53, 95% CI 2.49–5.01) and trimorbidity (CMD–alcohol/substance–physical multimorbidity) (aOR 3.26, 95% CI 2.20–4.83), in models adjusting for sociodemographic characteristics and smoking. After further adjustment for adverse experiences, associations attenuated but persisted for physical multimorbidity (aOR 1.40, 95% CI 1.10–1.79) and CMD–physical multimorbidity (aOR 1.55, 95% CI 1.20–2.00). The largest PAFs were observed for CMD–alcohol/substance multimorbidity (17%) and trimorbidity (16%).
Conclusions
Even in people currently rehoused, marked inequities across multimorbidities remained evident, highlighting the need for longer-term integrated support for people who have previously experienced homelessness.
Type 2 diabetes (T2D) is a global health burden, more prevalent among individuals with attention deficit hyperactivity disorder (ADHD) compared to the general population. To extend the knowledge base on how ADHD links to T2D, this study aimed to estimate causal effects of ADHD on T2D and to explore mediating pathways.
Methods
We applied a two-step, two-sample Mendelian randomization (MR) design, using single nucleotide polymorphisms to genetically predict ADHD and a range of potential mediators. First, a wide range of univariable MR methods was used to investigate associations between genetically predicted ADHD and T2D, and between ADHD and the purported mediators: body mass index (BMI), childhood obesity, childhood BMI, sedentary behaviour (daily hours of TV watching), blood pressure (systolic blood pressure, diastolic blood pressure), C-reactive protein and educational attainment (EA). A mixture-of-experts method was then applied to select the MR method most likely to return a reliable estimate. We used estimates derived from multivariable MR to estimate indirect effects of ADHD on T2D through mediators.
Results
Genetically predicted ADHD liability associated with 10% higher odds of T2D (OR: 1.10; 95% CI: 1.02, 1.18). From nine purported mediators studied, three showed significant individual mediation effects: EA (39.44% mediation; 95% CI: 29.00%, 49.73%), BMI (44.23% mediation; 95% CI: 34.34%, 52.03%) and TV watching (44.10% mediation; 95% CI: 30.76%, 57.80%). The combination of BMI and EA explained the largest mediating effect (53.31%, 95% CI: −1.99%, 110.38%) of the ADHD–T2D association.
Conclusions
These findings suggest a potentially causal, positive relationship between ADHD liability and T2D, with mediation through higher BMI, more TV watching and lower EA. Intervention on these factors may thus have beneficial effects on T2D risk in individuals with ADHD.
There is a populist narrative that authoritarian regimes were better able to respond to the challenges of the COVID-19 pandemic because of their strict enforcement powers, compliance of citizens, and speed of autocratic decision-making in a crisis. Research evidence to date on this assertion is however inconclusive or inconsistent. This paper analyses data from democratic and authoritarian countries with the aim of finding out whether autocratic regimes, using greater stringency measures (policy interventions to tackle COVID-19), had better public health outcomes than their counterparts. The results show that authoritarian regimes performed better in tackling the pandemic in terms of infection and death rates than their counterparts. However, we did not find any empirical evidence on the moderation effect of trust in government on the relationship between stringency measures against COVID-19 and policy outcomes. This result might be due to the lack of data transparency in authoritarian countries.
Prior research, largely focused on US male veterans, indicates an increased risk of cardiovascular disease among individuals with post-traumatic stress disorder (PTSD). Data from other settings and populations are scarce. The objective of this study is to examine PTSD as a risk factor for incident major adverse cardiovascular events (MACEs) in South Africa.
Methods
We analysed reimbursement claims (2011–2020) of a cohort of South African medical insurance scheme beneficiaries aged 18 years or older. We calculated adjusted hazard ratios (aHRs) for associations between PTSD and MACEs using Cox proportional hazard models and calculated the effect of PTSD on MACEs using longitudinal targeted maximum likelihood estimation.
Results
We followed 1,009,113 beneficiaries over a median of 3.0 years (IQR 1.1–6.0). During follow-up, 12,662 (1.3%) persons were diagnosed with PTSD and 39,255 (3.9%) had a MACE. After adjustment for sex, HIV status, age, population group, substance use disorders, psychotic disorders, major depressive disorder, sleep disorders and the use of antipsychotic medication, PTSD was associated with a 16% increase in the risk of MACEs (aHR 1.16, 95% confidence interval (CI) 1.05–1.28). The risk ratio for the effect of PTSD on MACEs decreased from 1.59 (95% CI 1.49–1.68) after 1 year of follow-up to 1.14 (95% CI 1.11–1.16) after 8 years of follow-up.
Conclusion
Our study provides empirical support for an increased risk of MACEs in males and females with PTSD from a general population sample in South Africa. These findings highlight the importance of monitoring cardiovascular risk among individuals diagnosed with PTSD.
Chapter 19 explores differences in spending on healthcare and health outcomes across countries. The main result of interest from these comparisons is that the United States spends vastly more than other OECD countries but gets only middle of the road health outcomes. The second part of the chapter explores leading theories as to why this is the case. The final part of the chapter compares different strategies for setting up a healthcare and health insurance sector: the Bismarck model, the Beveridge model, national health insurance, and hybrid models.
Acute asthma exacerbation is one of the most common reasons for paediatric emergency room visits and hospital admissions in the United States of America.
Objective:
To assess the impact of CHD on outcomes of children hospitalised for acute asthma exacerbation.
Methods:
Children primarily admitted for acute asthma exacerbation were sampled from 2006, 2009, 2012, and 2016 kid inpatient database of the Healthcare Cost and Utilization Project using ICD codes. The disease outcomes were compared between those with and without CHD using multivariate logistic regressions in Stata version 17.
Results:
There were a total of 639,280 acute asthma exacerbation admissions, of which 5,907 (0.92%) had CHD. The mortality rate was 0.079% for patients without CHD and 0.72% for those with co-existing CHD. Children with CHD had higher odds of mortality (5.51, CI 3.40–8.93, p < 0.001), acute respiratory failure (2.84, CI 2.53–3.20; p < 0.001), need for invasive mechanical ventilation (4.58, CI 3.80–5.52; p < 0.001), acute kidney injury (adjusted odds ratio 3.03, CI 3.03–7.44; p < 0.001), and in-hospital cardiac arrest (adjusted odds ratio 4.52, CI 2.49–8.19; p < 0.001) when compared with those without CHD. The adjusted mean length of hospital stays (CI 2.91–3.91; p < 0.001) and hospital charges (95% CI $31060–$47747) among children with acute asthma exacerbation and CHD were significantly higher than in those without CHD.
Conclusion and Significance:
CHD is an independent predictor of mortality, more severe disease course, and higher hospital resource utilisation. Strategies that improve CHD care will likely improve the overall health outcomes of children with CHD hospitalised for acute asthma exacerbation.
Cost-effectiveness analysis (CEA) is the standard framework for informing the efficient allocation of scarce healthcare resources. The importance of considering all relevant intervention strategies and appropriate incremental comparisons have both long been recognized in CEA. Failure to apply methods correctly can lead to suboptimal policies. Our objective is to assess if CEAs of infant pneumococcal vaccination apply appropriate methods with respect to the completeness of strategies assessed and incremental comparisons between them.
Methods
We conducted a systematic search of the PubMed, Scopus, Embase, and Web of Science databases and performed a comparative analysis of the retrieved pneumococcal vaccination CEAs. We checked the appropriateness of the incremental analyses by attempting to replicate the published incremental cost-effectiveness (CE) ratios from the reported costs and health effects.
Results
Our search returned twenty-nine eligible articles. Most studies failed to recognize one or more intervention strategies (n = 21). Incremental comparisons were questionable in four CEAs and insufficient reporting of cost and health effect estimates was identified in three studies. Overall, we only found four studies that made appropriate comparisons between all strategies. Lastly, study findings appear to be strongly associated with manufacturer sponsorship.
Conclusions
We found considerable scope for improvement regarding strategy comparison in the infant pneumococcal vaccination literature. To prevent overestimation of the CE of new vaccines, we urge greater adherence to existing guidelines recommending that all available strategies are evaluated to capture relevant comparators for CE evaluation. Closer adherence to existing guidelines will generate better evidence, leading to more effective vaccination policies.
The psychoactive properties of cannabis have been known forever. Since 1987, several prospective studies have suggested an increased risk of psychosis among cannabis users, with alternative explanations failing to account for such an effect. A cause–effect relationship has thus been implied. Further evidence has indicated that there is a dose–response relationship, and high-potency cannabis varieties confer the greatest risk of psychosis. As cannabis use has become more common over the last decades, one would expect a related increase in the number of schizophrenia cases. However, evidence in this regard remains equivocal for several reasons, including relying on databases that are not primarily designed to address such question and the issue that solid information regarding the incidence of schizophrenia is a relatively recent acquisition. Recent years have seen the development of online web publications, such as Google Trends and “Our World in Data”, where data are explorable and interactable for tracking and comparing trends over specific periods and world regions. By using such databases, we believe that the question whether changes in cannabis use are associated with changes in schizophrenia rates can be answered, at least partly. Therefore, we tested these tools by evaluating trends in cannabis use and both cases and prevalence of schizophrenia in the United Kingdom, one of the countries where the incident rates for psychotic disorder have been suggested to be particularly increased by cannabis consumption. Crossing data from these tools revealed that interest in cannabis has been growing at the country level for over 10 years, with a parallel overlapping raise in psychosis cases and prevalence. Following up on this example, let us think of how many public health opportunities these public resources may offer. The question now is whether public health interventions for the benefit of the general population will follow suit.
Cardiometabolic diseases are responsible for the majority of premature deaths in people with schizophrenia. This study aimed to quantify the fatal burden of ischaemic heart disease (IHD), stroke and diabetes attributable to schizophrenia.
Methods
Comparative Risk Assessment methodology from the Global Burden of Disease (GBD) study was used to calculate attributable burden; pooled relative risks (RRs) for IHD, stroke and diabetes were estimated via meta-regression, which were combined with GBD schizophrenia prevalence estimates to calculate the deaths and years of life lost (YLLs) caused by these health outcomes that were attributable to schizophrenia. The proportion of explained all-cause fatal burden and corresponding unexplained burden was also calculated.
Results
The pooled RRs for IHD, stroke and diabetes mortality were 2.36 [95% uncertainty interval (UI) 1.77 to 3.14], 1.86 (95% UI 1.36 to 2.54) and 4.08 (95% UI 3.80 to 4.38) respectively. Schizophrenia was responsible for around 50 000 deaths and almost 1.5 million YLLs globally in 2019 from these health outcomes combined. IHD, stroke and diabetes together explained around 13% of all deaths and almost 11% of all YLLs attributable to schizophrenia, resulting in 320 660 (95% UI 288 299 to 356 517) unexplained deaths and 12 258 690 (95% UI 10 925 426 to 13 713 646) unexplained YLLs.
Conclusions
Quantifying the physical disease burden attributable to schizophrenia provides a means of capturing the substantial excess mortality associated with this disorder within the GBD framework, contributing to an important evidence base for healthcare planning and practice.
Not only is nature essential for human existence, but many of its functions and contributions are irreplaceable. Studying the impact of these changes on individuals and communities, researchers and public health officials have largely focused on physical health. Our aim is to better understand how climate change also exacerbates many social and environmental risk factors for mental health and psychosocial problems, and can lead to emotional distress, the development of new mental health conditions and a worsening situation for people already living with these conditions.
Methods
We considered all possible direct and indirect pathways by which climate change can affect mental health. We built a framework which includes climate change-related hazards, climate change-related global environmental threats, social and environmental exposure pathways, and vulnerability factors and inequalities to derive possible mental health and psychosocial outcomes.
Results
We identified five approaches to address the mental health and psychosocial impacts of climate change which we suggest should be implemented with urgency: (1) integrate climate change considerations into policies and programmes for mental health, to better prepare for and respond to the climate crisis; (2) integrate mental health and psychosocial support within policies and programmes dealing with climate change and health; (3) build upon global commitments including the Sustainable Development Goals, the Paris Agreement and the Sendai Framework for Disaster Risk Reduction; (4) implement multisectoral and community-based approaches to reduce vulnerabilities and address the mental health and psychosocial impacts of climate change; and (5) address the large gaps that exist in funding both for mental health and for responding to the health impacts of climate change.
Conclusions
There is growing evidence of the various mechanisms by which climate change is affecting mental health. Given the human impacts of climate change, mental health and psychosocial well-being need to be one of the main focuses of climate action. Therefore, countries need to dramatically accelerate their responses to climate change, including efforts to address its impacts on mental health and psychosocial well-being.
Eating disorders (EDs) and substance use disorders (SUDs) often co-occur, and both involve somatic diseases. So far, no study has considered whether comorbid SUDs may impact somatic disease risk in patients with EDs. Therefore, this study aimed to examine the impact of comorbid SUDs on the risk of 11 somatic disease categories in patients with anorexia nervosa (AN), bulimia nervosa (BN) and unspecified eating disorder (USED) compared to matched controls.
Methods
A retrospective cohort study was conducted using Danish nationwide registries. The study population included 20 759 patients with EDs and 83 036 controls matched on month and year of birth, sex and ethnicity. Hazard ratios (HRs) were calculated to compare the risk of being diagnosed with a somatic disease (within 11 categories defined by the ICD-10) following first ED diagnosis (index date) between ED patients and controls both with and without SUDs (alcohol, cannabis or hard drugs).
Results
The ED cohort and matched controls were followed for 227 538 and 939 628 person-years, respectively. For ED patients with SUDs, the risk pattern for being diagnosed with different somatic diseases (relative to controls without SUDs) varied according to type of ED and SUD [adjusted HRs ranged from 0.95 (99% CI = 0.57; 1.59) to 4.17 (2.68, 6.47)]. The risk estimates observed among ED patients with SUDs were generally higher than those observed among ED patients without SUDs [adjusted HRs ranged from 1.08 (99% CI = 0.95, 1.22) to 2.56 (2.31, 2.84)]. Abuse of alcohol only had a non-synergistic effect on six disease categories in AN patients and five in BN and USED patients. Abuse of cannabis (with/without alcohol) had a non-synergistic effect on five disease categories in AN and BN patients and two in USED patients. Abuse of hard drugs (with/without alcohol or cannabis) had a non-synergistic effect on nine disease categories in AN patients, eight in BN patients and seven in USED patients.
Conclusions
The present study documents non-synergistic but not synergistic harmful somatic consequences of SUDs among patients with different EDs, with AN and hard drugs being the most predominant factors. Hence, EDs and SUDs did not interact and result in greater somatic disease risk than that caused by the independent effects. Since EDs and SUDs have independent effects on many somatic diseases, it is important to monitor and treat ED patients for SUD comorbidity to prevent exacerbated physical damage in this vulnerable population.