Abstract

This study sought to develop a user-friendly decision-making tool to explore country-specific estimates for costs and economic consequences of different options for scaling screening and psychosocial interventions for women with common perinatal mental health problems in Malawi. We developed a simple simulation model using a structure and parameter estimates that were established iteratively with experts, based on published trials, international databases and resources, statistical data, best practice guidance and intervention manuals. The model projects annual costs and returns to investment from 2022 to 2026. The study perspective is societal, including health expenditure and productivity losses. Outcomes in the form of health-related quality of life are measured in Disability Adjusted Life Years, which were converted into monetary values. Economic consequences include those that occur in the year in which the intervention takes place. Results suggest that the net benefit is relatively small at the beginning but increases over time as learning effects lead to a higher number of women being identified and receiving (cost‑)effective treatment. For a scenario in which screening is first provided by health professionals (such as midwives) and a second screening and the intervention are provided by trained and supervised volunteers to equal proportions in group and individual sessions, as well as in clinic versus community setting, total costs in 2022 amount to US$ 0.66 million and health benefits to US$ 0.36 million. Costs increase to US$ 1.03 million and health benefits to US$ 0.93 million in 2026. Net benefits increase from US$ 35,000 in 2022 to US$ 0.52 million in 2026, and return-on-investment ratios from 1.05 to 1.45. Results from sensitivity analysis suggest that positive net benefit results are highly sensitive to an increase in staff salaries. This study demonstrates the feasibility of developing an economic decision-making tool that can be used by local policy makers and influencers to inform investments in maternal mental health

Description of data set

Iteratively, information was gathered from desk-based searches and from talking to and exchanging emails with experts in the maternal health field to establish a model structure and the parameter values. This included the development of an information request form that presents a list of parameters, parameter values and details about how the values were estimated and the data sources. We collected information on: Intervention’s effectiveness; prevalence rates; population and birth estimates; proportion of women attending services; salaries and reimbursement rates for staff and volunteers; details about training, supervision, intervention delivery (e.g., frequency, duration); unit costs, and data needed to derive economic consequences (e.g. women’s income, health weights). Data were searched from the following sources: published randomised controlled trials and meta-analyses; WHO published guidance and intervention manual; international databases and resources (WHO-CHOICE, Global Burden of Disease Database; International Monetary Fund; United Nations Treasury, World Bank, Global Investment Framework for Women’s and Children’s Health). We consulted two groups of experts: one group included individuals with clinical, research or managerial expertise in funding, managing, delivering, or evaluating screening of common mental health problems and PSIs; the second group included individuals from the Malawi Government, Ministry of Health Reproductive Health Unit and Non-Communicable Disease Committee and Mental Health Unit. The first group of experts provided information from research and administrative data systems concerned with implementing and evaluating screening for maternal mental health and the delivery of interventions. The second group of experts from the Malawi Government provided information on unit costs for hospital use and workforce data, as well as information on how training and supervision might be delivered at scale. Individuals were identified by colleagues of this team based or part-time based in Malawi, which included a psychiatrist specialising in perinatal mental health (co-author RS) and the coordinator of the African Maternal Mental Health Alliance (co-author DN), an organisation concerned with disseminating information and evidence on perinatal mental health to policy makers and influencers, and the wider public.

For a summary of the case study, please go to "Portfolio Project".

Context

This data analysis was meant to show that men have their own issues in society that are being ignored. The mental health has been declining especially for men. This decline worldwide maybe due to a multitude of other variables that may correlate such as: internet usage/social media usage, social belonging, work hours, dating apps, and physical health. This data analysis was meant to show that men have their own issues in society that are being ignored. This decline worldwide maybe due to a multitude of other variables that may correlate such as: internet usage/social media usage, social belonging, work hours, dating apps, and physical health. These variables may require a separate dataset going into more detail about them.

A space dedicated just for men and another just for women to speak about their problems with help and constructive criticism for growth and for social belonging maybe required to improve the mental health of society (among other variables). This does not mean that the struggles of women are nonexistent. There are already a multitude of datasets and articles dedicated to some of the possible struggles of women from MSNBC, CNN, NBC, BBC, Netflix movies, and even popular secular music like recent songs WAP from Megan Thee Stallion, God is a Women by Arianna Grande, etc. This dataset's objective was not made to continue to light a flame between the already hostile relationships that modern men and women have with each other. Awareness without bias is the goal.

For the results, please read the portfolio project and leave comments.

Content

Where the data were obtained:

1. The first excel file was obtained from https://data.world/vizzup/mental-health-depression-disorder-data/workspace/file?filename=Mental+health+Depression+disorder+Data.xlsx

2. The second excel file was obtained from https://ourworldindata.org/grapher/male-vs-female-suicide

3. The third excel file was obtained from https://ourworldindata.org/suicide

4. The fourth excel file was obtained from https://ourworldindata.org/drug-use

Inspiration

I want to be the best data analyst ever, so criticism (regardless of the harshness), it will be greatly appreciated. What would you have added/improved on? Was it easy to understand? What else do you want me to make a dataset on?

This publication contains the official statistics about uses of the Mental Health Act(1) ('the Act') in England during 2020-21. Under the Act, people with a mental disorder may be formally detained in hospital (or 'sectioned') in the interests of their own health or safety, or for the protection of other people. They can also be treated in the community but subject to recall to hospital for assessment and/or treatment under a Community Treatment Order (CTO). In 2016-17, the way we source and produce these statistics changed. Previously these statistics were produced from the KP90 aggregate data collection. They are now primarily produced from the Mental Health Services Data Set (MHSDS). The MHSDS provides a much richer data source for these statistics, allowing for new insights into uses of the Act. However, some providers that make use of the Act are not yet submitting data to the MHSDS, or submitting incomplete data. Improvements in data quality have been made over the past year. NHS Digital is working with partners to ensure that all providers are submitting complete data and this publication includes guidance on interpreting these statistics. Please note: This publication covers the 2020-21 reporting year and, as such, it is likely the impact of COVID-19 may be evident as the national lockdown began on 23 March 2020. The time series data for people subject to detention does show a decrease in people subject to detention in March 2021 so the context of COVID-19 should be kept in mind when using and interpreting these statistics. Footnotes (1) The Mental Health Act 1983 as amended by the Mental Health Act 2007 and other legislation.

This report uses 2011 to 2012 National Survey on Drug Use and Health (NSDUH) to assess past month alcohol use and binge alcohol use among pregnant women aged 15 to 44 by trimester of pregnancy.

The study aimed to gain insight into the ways that narratives of self-responsibility were taken up and embodied - or alternatively, resisted - within economically disadvantaged communities; the ways that these narratives and associated welfare reforms impacted on mental distress; and the way that these narratives interconnected with the medicalisation and pathologisation of poverty-related distress. (1) The study involved sixteen focus groups with ninety-seven participants (aged 18-65) from economically disadvantaged communities to establish the source and impact of narratives of self-responsibility within people’s everyday lives (36 men, 61 women). (2)Fifty-seven low-income residents (aged 18-65) who had experienced poverty-related mental distress were also interviewed (26 men, 31 women) to understand the cause(s) of their distress, and their responses to this. Participants who were receiving mental health treatment at the time of the study, and participants who wanted more time to discuss their experiences were interviewed on two occasions (total interviews n=80), enabling us to track responses over time and facilitating the triangulation of data. All lived on low-incomes. Whilst not specifically asked to define their status in terms of class, people commonly defined themselves through characteristics or inferences usually associated with being ‘working class.’ (3) Interviews with General Practitioners (n=10) working in low income areas were undertaken to understand their experiences and the challenges they faced supporting mental health amongst patients experiencing poverty, and their perceptions of current treatment options. The provision of effective treatment and support for mental distress is a stated aim of the Department of Health and civil society organisations e.g. Mind. Yet despite a stated need to tackle health inequalities, current strategies e.g. Closing the Gap: Priorities for Essential Change in Mental Health (DoH 2014), frame mental distress as a psychological problem that lies within the individual concerned. This not only suggests that distress can be 'corrected' through medical treatment, but also masks the factors that often underlie the root causes of suffering e.g. poor living conditions, unemployment. At the same time, policies in place to restrict welfare support, and popular media e.g. Benefits Street, draw on moralising narratives that promote the idea that people are responsible for their own actions and circumstances. This research aims to explore how these moralising narratives impact on the ways that people in low-income communities perceive and respond to mental distress caused by material deprivation and social disadvantage, and to examine the impacts of this on their wellbeing. This was achieved through in-depth research in two low-income communities in the South West, which sought to understand: i) the way that moral narratives are defined and used or resisted in people's everyday lives in relation to mental distress; ii) the influence of moral narratives on people's decisions to seek medical support for distress; iii) how moral narratives manifest within GP consultations and influence treatment decisions and patient wellbeing; iv) which responses to mental distress have the potential to effectively support vulnerable populations, and to inform ethical debates on the medicalisation of distress in a way that benefits patients, and assists practitioners and policy makers seeking to support low-income communities. The DeStress Project was a two and half-year research project with two very low-income urban communities (one post-industrial, one coastal with a seasonal employment structure) in the UK’s south-west region. Ethics permission was obtained from the NHS Cambridgeshire and Hertfordshire Research Ethics Committee. The study aimed to gain insight into the ways that narratives of self-responsibility were taken up and embodied - or alternatively, resisted - within economically disadvantaged communities; the ways that these narratives and associated welfare reforms impacted on mental distress; and the way that these narratives interconnected with the medicalisation and pathologisation of poverty-related distress. (1) The study involved sixteen focus groups with ninety-seven participants (aged 18-65) from economically disadvantaged communities to establish the source and impact of narratives of self-responsibility within people’s everyday lives (36 men, 61 women). (2) Fifty-seven low-income residents (aged 18-65) who had experienced poverty-related mental distress were also interviewed (26 men, 31 women) to understand the cause(s) of their distress, and their responses to this. Of these participants, eighty one per cent had been prescribed antidepressants, whilst a further seven per cent had refused the prescription offered. The remaining thirteen per cent had been advised to self-refer to talking therapy, or had chosen to avoid interaction with health services. Potential participants were alerted to the study by community and health practitioners, social media and word-of-mouth and recruited through community groups and GP surgeries. Participants who were receiving mental health treatment at the time of the study, and participants who wanted more time to discuss their experiences were interviewed on two occasions (total interviews n=80), enabling us to track responses over time and facilitating the triangulation of data. In almost all cases, study participants had lived in an economically disadvantaged area throughout their lives, though older participants in one area had also lived there at a time when it was more prosperous. All lived on low-incomes. Whilst not specifically asked to define their status in terms of class, people commonly defined themselves through characteristics or inferences usually associated with being ‘working class.’ (3) Interviews with General Practitioners (n=10) working in low income areas were undertaken to understand their experiences and the challenges they faced supporting mental health amongst patients experiencing poverty, and their perceptions of current treatment options. Informal discussions with key service providers from health, education and social sectors were also undertaken to gain insight into their experiences of working with people living with the stresses of poverty. Sixteen focus groups with a total of ninety-seven participants, aged 18-65, from the two study sites (36 men and 61 women), with the gender ratio reflecting reported rates of common mental disorders in England (NHS Digital 2016) . Participants were recruited via community groups and settings, word of mouth and advertising on posters and social media. Participants were asked about the main health issues and stresses faced by local residents, how people respond to those stresses and their impact on wellbeing. In addition, eighty interviews were undertaken with fifty-seven residents (aged 18-65) who had experienced poverty-related distress (26 men, 31 women) to gain a more in-depth understanding of the source(s) of this distress, and their responses to it. Interviewees were recruited via the focus groups and word of mouth but also via GP surgeries to capture a broad range of views and experiences (including those who may be socially isolated). In the majority of cases, people had sought medical support for their distress, although two had chosen not to. Participants who were engaged in the health system for their distress at the time of the study, and participants who wanted more time to discuss their experiences were interviewed on two occasions, enabling us to capture any changes over time and understand the ongoing dynamic interaction between mental ill-health and welfare reform. The interviews and focus groups generated a rich body of narrative data that gives prominence to the voices and experiences of people living in low-income communities. This data has been supplemented with interviews with General Practitioners (n=10) to understand the challenges they face supporting people experiencing poverty-related distress.

Results produced using a normalized code x descriptor analysis in Dedoose mixed-method analysis software.

Objective: Examine factors influencing late (> sixth month of gestation) entry into prenatal care by race/ethnicity and insurance payer. Methods: The study population was drawn from singleton live births in California from 2007 to 2012 in the birth cohort file maintained by the California Office of Statewide Health Planning and Development, which includes linked birth certificate and mother and infant hospital discharge records. The sample was restricted to infants delivered between 20 and 44 weeks gestation. Logistic regression was used to calculate relative risks (RR) and 95% confidence intervals (CI) for factors influencing late entry into prenatal care. Maternal age, education, smoking, drug or alcohol abuse/dependence, mental illness, participation in the Women, Infants and Children’s program and rural residence were evaluated for women entering prenatal care > sixth month of gestation compared with women entering  12-year education or age >34 years at term reduced the likelihood of late prenatal care entry (adjusted RRs 0.5–0.7). Drugs and alcohol abuse/dependence and residing in a rural county were associated with increased risk of late prenatal care across all subgroups (adjusted RRs 1.3–3.8). Participation in the Women, Infants, and Children’s program was associated with decreased risk of late prenatal care for women with public insurance (adjusted RRs 0.6–0.7), but increased risk for women with private insurance (adjusted RRs 1.4–2.1). Conclusions: The percent of women with late entry into prenatal care was consistently higher among women with public insurance. Younger women, women with  sixth month of gestation) entry into prenatal care by race/ethnicity and insurance payer. We found the percent of women with late entry into prenatal care was consistently higher among women with public insurance. Younger women, women with

Polycystic ovary syndrome or PCOS was first described in 1935 by Irving F. Stein and Michael L. Leventhal as Stein Leventhal Syndrome; ‘women with ovarian hyperthecosis presenting with persistent anovulation, obesity and hirsutism.’ Today, PCOS is the most common reproductive endocrinopathy of women, the most common cause of ovarian infertility and the cause of psychological and emotional distress. PCOS is associated with serious health risks in the short and long term including significantly increased risks for diabetes, cancer and cardiovascular disease, independent to body weight. For the health care system, the annual cost of PCOS in Australia was conservatively estimated as AU$400 million.

Methods: Questions were addressed following the survey of women with PCOS, two separate literature reviews and through a randomised controlled trial examining the effectiveness of a naturopathic herbal formula plus lifestyle intervention on a broad range of outcomes in overweight women with polycystic ovary syndrome.

An electronic survey of 493 women with PCOS assessed women’s use and attitudes toward complementary medicine and self-care (diet and exercise). Women were recruited from the Polycystic Ovary Syndrome Association of Australia (POSAA) and through social media PCOS support groups on social media.

A randomised controlled trial (RCT) compared the clinical effectiveness and safety of the addition of a new herbal combination to lifestyle intervention against lifestyle intervention alone in 122 overweight women with medically diagnosed PCOS. The primary outcome was menstrual regularity. Secondary outcomes included pregnancy rates, serum hormone and blood glucose concentrations, anthropometry, and psychological health outcomes at three months and live birth rates at nine months after the intervention period.

Results: Over two-thirds of women surveyed reported regular use of nutritional and herbal supplements to manage their PCOS. Although most respondents reported regular engagement with lifestyle interventions (diet and exercise) few reported satisfaction, and most expressed a desire for transparent information about complementary medicines.

The systematic review of the literature revealed evidence from 18 RCTs (1109 women) for six types of nutritional supplements (vitamin D, Omega 3, calcium, chromium, vitamins and inositol) and four herbal medicines (Camellia sinensis, Cimicifuga racemosa Cinnamon cassia and Mentha spicata) for the management of symptoms associated with PCOS. The quality of studies was low to moderate, 11 meta-analyses were applied to 11 outcomes. The strongest evidence was found for inositol for the treatment of a hyperandrogenism, infertility, normalising metabolic hormones and for omega three fish oils for treatment of high cholesterol in women with PCOS. Findings highlighted the lack of robust evidence for many natural health supplements and that outcomes for inositol and omega three may contribute lower grade evidence to the evidence-based guidelines for the management of women with PCOS.

One hundred and twenty-two women with PCOS were randomised to receive herbal medicine plus lifestyle intervention or lifestyle intervention alone. At three months there was a significant improvement in menstrual regularity for women taking the additional herbal medicine compared to women using lifestyle intervention alone with a moderate to large treatment effect. Significant improvements for secondary outcomes included fasting insulin, anthropometric characteristics (BMI, body weight, and waist circumference), quality of life (PCOSQ), depression, anxiety and stress (DASS 21) and pregnancy rates. Overall, the herbal medicine was well tolerated however two women were withdrawn due to non-serious side effects. Methodological strengths included sufficient power, low attrition and intention to treat analyses. This thesis presents preliminary evidence for the enhanced effectiveness of lifestyle intervention following the addition of a new herbal formulation for overweight women with PCOS. Significantly improved outcomes included menstrual regularity, metabolic hormones, anthropometry, blood pressure, pregnancy rates, psychological profile and quality of life.

This entry includes 2 data-sets that were published in 3 papers.

1. Data collected from a randomised control trial that compared the effectiveness of a naturopathic herbal medicine supplement in addition to lifestyle intervention, compared to lifestyle alone for menstrual regularity in 122 overweight women with polycystic ovarian syndrome.

2. A survey of 496 women with PCOS describing their prevalence and patterns of complementary medicine use

3. A survey of 496 women with PCOS describing their experiences of lifestyle intervention.

The survey data-set could be used to further describe women with PCOS who are living in the community, and associated physical and psychological health characteristic ot other associations. It is a reasonably sized sample of 493 responses.

The RCT dataset could be used to generate a power calculation for a placebo-controlled efficacy RCT. The IP was commercialised by an Australian company Mediherb Pty Ltd, who manufactured a product called PCOSupport that is registered (listed) with the TGA

https://www.tga.gov.au/resources/artg/395997

The ANZCTR entry: Trial Id: ACTRN12612000122853 https://www.anzctr.org.au/trial/MyTrial.aspx The effectiveness of naturopathic herbal medicine and a lifestyle intervention, compared to lifestyle intervention alone for oligomenorrhoea, serum hormones, anthropometric, reproductive, blood pressure, quality of life and adverse outcomes in overweight women with polycystic ovary syndrome (PCOS).

As of October 2021, women had the highest share of mental health disorders in India, amounting to ** percent and ** percent for stress and anxiety health disorder respectively. Comparatively, ** percent of men had depression as compared to women with ** percent during the same time period.

Goal 3Ensure healthy lives and promote well-being for all at all agesTarget 3.1: By 2030, reduce the global maternal mortality ratio to less than 70 per 100,000 live birthsIndicator 3.1.1: Maternal mortality ratioSH_STA_MORT: Maternal mortality ratioIndicator 3.1.2: Proportion of births attended by skilled health personnelSH_STA_BRTC: Proportion of births attended by skilled health personnel (%)Target 3.2: By 2030, end preventable deaths of newborns and children under 5 years of age, with all countries aiming to reduce neonatal mortality to at least as low as 12 per 1,000 live births and under-5 mortality to at least as low as 25 per 1,000 live birthsIndicator 3.2.1: Under-5 mortality rateSH_DYN_IMRTN: Infant deaths (number)SH_DYN_MORT: Under-five mortality rate, by sex (deaths per 1,000 live births)SH_DYN_IMRT: Infant mortality rate (deaths per 1,000 live births)SH_DYN_MORTN: Under-five deaths (number)Indicator 3.2.2: Neonatal mortality rateSH_DYN_NMRTN: Neonatal deaths (number)SH_DYN_NMRT: Neonatal mortality rate (deaths per 1,000 live births)Target 3.3: By 2030, end the epidemics of AIDS, tuberculosis, malaria and neglected tropical diseases and combat hepatitis, water-borne diseases and other communicable diseasesIndicator 3.3.1: Number of new HIV infections per 1,000 uninfected population, by sex, age and key populationsSH_HIV_INCD: Number of new HIV infections per 1,000 uninfected population, by sex and age (per 1,000 uninfected population)Indicator 3.3.2: Tuberculosis incidence per 100,000 populationSH_TBS_INCD: Tuberculosis incidence (per 100,000 population)Indicator 3.3.3: Malaria incidence per 1,000 populationSH_STA_MALR: Malaria incidence per 1,000 population at risk (per 1,000 population)Indicator 3.3.4: Hepatitis B incidence per 100,000 populationSH_HAP_HBSAG: Prevalence of hepatitis B surface antigen (HBsAg) (%)Indicator 3.3.5: Number of people requiring interventions against neglected tropical diseasesSH_TRP_INTVN: Number of people requiring interventions against neglected tropical diseases (number)Target 3.4: By 2030, reduce by one third premature mortality from non-communicable diseases through prevention and treatment and promote mental health and well-beingIndicator 3.4.1: Mortality rate attributed to cardiovascular disease, cancer, diabetes or chronic respiratory diseaseSH_DTH_NCOM: Mortality rate attributed to cardiovascular disease, cancer, diabetes or chronic respiratory disease (probability)SH_DTH_NCD: Number of deaths attributed to non-communicable diseases, by type of disease and sex (number)Indicator 3.4.2: Suicide mortality rateSH_STA_SCIDE: Suicide mortality rate, by sex (deaths per 100,000 population)SH_STA_SCIDEN: Number of deaths attributed to suicide, by sex (number)Target 3.5: Strengthen the prevention and treatment of substance abuse, including narcotic drug abuse and harmful use of alcoholIndicator 3.5.1: Coverage of treatment interventions (pharmacological, psychosocial and rehabilitation and aftercare services) for substance use disordersSH_SUD_ALCOL: Alcohol use disorders, 12-month prevalence (%)SH_SUD_TREAT: Coverage of treatment interventions (pharmacological, psychosocial and rehabilitation and aftercare services) for substance use disorders (%)Indicator 3.5.2: Alcohol per capita consumption (aged 15 years and older) within a calendar year in litres of pure alcoholSH_ALC_CONSPT: Alcohol consumption per capita (aged 15 years and older) within a calendar year (litres of pure alcohol)Target 3.6: By 2020, halve the number of global deaths and injuries from road traffic accidentsIndicator 3.6.1: Death rate due to road traffic injuriesSH_STA_TRAF: Death rate due to road traffic injuries, by sex (per 100,000 population)Target 3.7: By 2030, ensure universal access to sexual and reproductive health-care services, including for family planning, information and education, and the integration of reproductive health into national strategies and programmesIndicator 3.7.1: Proportion of women of reproductive age (aged 15–49 years) who have their need for family planning satisfied with modern methodsSH_FPL_MTMM: Proportion of women of reproductive age (aged 15-49 years) who have their need for family planning satisfied with modern methods (% of women aged 15-49 years)Indicator 3.7.2: Adolescent birth rate (aged 10–14 years; aged 15–19 years) per 1,000 women in that age groupSP_DYN_ADKL: Adolescent birth rate (per 1,000 women aged 15-19 years)Target 3.8: Achieve universal health coverage, including financial risk protection, access to quality essential health-care services and access to safe, effective, quality and affordable essential medicines and vaccines for allIndicator 3.8.1: Coverage of essential health servicesSH_ACS_UNHC: Universal health coverage (UHC) service coverage indexIndicator 3.8.2: Proportion of population with large household expenditures on health as a share of total household expenditure or incomeSH_XPD_EARN25: Proportion of population with large household expenditures on health (greater than 25%) as a share of total household expenditure or income (%)SH_XPD_EARN10: Proportion of population with large household expenditures on health (greater than 10%) as a share of total household expenditure or income (%)Target 3.9: By 2030, substantially reduce the number of deaths and illnesses from hazardous chemicals and air, water and soil pollution and contaminationIndicator 3.9.1: Mortality rate attributed to household and ambient air pollutionSH_HAP_ASMORT: Age-standardized mortality rate attributed to household air pollution (deaths per 100,000 population)SH_STA_AIRP: Crude death rate attributed to household and ambient air pollution (deaths per 100,000 population)SH_STA_ASAIRP: Age-standardized mortality rate attributed to household and ambient air pollution (deaths per 100,000 population)SH_AAP_MORT: Crude death rate attributed to ambient air pollution (deaths per 100,000 population)SH_AAP_ASMORT: Age-standardized mortality rate attributed to ambient air pollution (deaths per 100,000 population)SH_HAP_MORT: Crude death rate attributed to household air pollution (deaths per 100,000 population)Indicator 3.9.2: Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene (exposure to unsafe Water, Sanitation and Hygiene for All (WASH) services)SH_STA_WASH: Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene (deaths per 100,000 population)Indicator 3.9.3: Mortality rate attributed to unintentional poisoningSH_STA_POISN: Mortality rate attributed to unintentional poisonings, by sex (deaths per 100,000 population)Target 3.a: Strengthen the implementation of the World Health Organization Framework Convention on Tobacco Control in all countries, as appropriateIndicator 3.a.1: Age-standardized prevalence of current tobacco use among persons aged 15 years and olderSH_PRV_SMOK: Age-standardized prevalence of current tobacco use among persons aged 15 years and older, by sex (%)Target 3.b: Support the research and development of vaccines and medicines for the communicable and non-communicable diseases that primarily affect developing countries, provide access to affordable essential medicines and vaccines, in accordance with the Doha Declaration on the TRIPS Agreement and Public Health, which affirms the right of developing countries to use to the full the provisions in the Agreement on Trade-Related Aspects of Intellectual Property Rights regarding flexibilities to protect public health, and, in particular, provide access to medicines for allIndicator 3.b.1: Proportion of the target population covered by all vaccines included in their national programmeSH_ACS_DTP3: Proportion of the target population with access to 3 doses of diphtheria-tetanus-pertussis (DTP3) (%)SH_ACS_MCV2: Proportion of the target population with access to measles-containing-vaccine second-dose (MCV2) (%)SH_ACS_PCV3: Proportion of the target population with access to pneumococcal conjugate 3rd dose (PCV3) (%)SH_ACS_HPV: Proportion of the target population with access to affordable medicines and vaccines on a sustainable basis, human papillomavirus (HPV) (%)Indicator 3.b.2: Total net official development assistance to medical research and basic health sectorsDC_TOF_HLTHNT: Total official development assistance to medical research and basic heath sectors, net disbursement, by recipient countries (millions of constant 2018 United States dollars)DC_TOF_HLTHL: Total official development assistance to medical research and basic heath sectors, gross disbursement, by recipient countries (millions of constant 2018 United States dollars)Indicator 3.b.3: Proportion of health facilities that have a core set of relevant essential medicines available and affordable on a sustainable basisSH_HLF_EMED: Proportion of health facilities that have a core set of relevant essential medicines available and affordable on a sustainable basis (%)Target 3.c: Substantially increase health financing and the recruitment, development, training and retention of the health workforce in developing countries, especially in least developed countries and small island developing StatesIndicator 3.c.1: Health worker density and distributionSH_MED_DEN: Health worker density, by type of occupation (per 10,000 population)SH_MED_HWRKDIS: Health worker distribution, by sex and type of occupation (%)Target 3.d: Strengthen the capacity of all countries, in particular developing countries, for early warning, risk reduction and management of national and global health risksIndicator 3.d.1: International Health Regulations (IHR) capacity and health emergency preparednessSH_IHR_CAPS: International Health Regulations (IHR) capacity, by type of IHR capacity (%)Indicator 3.d.2: Percentage of bloodstream infections due to selected antimicrobial-resistant organismsiSH_BLD_MRSA: Percentage of bloodstream infection due to methicillin-resistant Staphylococcus aureus (MRSA) among patients seeking care and whose

ATT of suicide for BFP participation in the original cohort from 2004–2015.

Suicide IRR for BFP participation in the matched and original cohorts from 2004–2015.

1OR: Odds Ratio. CI: Confidence Interval.

Whilst some studies have identified gender-specific differences, there is no consensus about gender-specific determinants for prevalence rates or concomitant symptoms of chronic tinnitus such as depression or anxiety. However, gender-associated differences in psychological response profiles and coping strategies may differentially affect tinnitus chronification and treatment success rates. Thus, understanding gender-associated differences may facilitate a more detailed identification of symptom profiles, heighten treatment response rates, and help to create access for vulnerable populations that are potentially less visible in clinical settings. Our research questions are: RQ1: how do male and female tinnitus patients differ regarding tinnitus-related distress, depression severity, and treatment response, RQ2: to what extent are answers to questionnaires administered at baseline associated with gender, and RQ3: which baseline questionnaire items are associated with tinnitus distress, depression, and treatment response, while relating to one gender only? In this work, we present a data analysis workflow to investigate gender-specific differences in N = 1,628 patients with chronic tinnitus (828 female, 800 male) who completed a 7-day multimodal treatment encompassing cognitive behavioral therapy (CBT), physiotherapy, auditory attention training, and information counseling components. For this purpose, we extracted 181 variables from 7 self-report questionnaires on socio-demographics, tinnitus-related distress, tinnitus frequency, loudness, localization, and quality as well as physical and mental health status. Our workflow comprises (i) training machine learning models, (ii) a comprehensive evaluation including hyperparameter optimization, and (iii) post-learning steps to identify predictive variables. We found that female patients reported higher levels of tinnitus-related distress, depression and response to treatment (RQ1). Female patients indicated higher levels of tension, stress, and psychological coping strategies rates. By contrast, male patients reported higher levels of bodily pain associated with chronic tinnitus whilst judging their overall health as better (RQ2). Variables measuring depression, sleep problems, tinnitus frequency, and loudness were associated with tinnitus-related distress in both genders and indicators of mental health and subjective stress were found to be associated with depression in both genders (RQ3). Our results suggest that gender-associated differences in symptomatology and treatment response profiles suggest clinical and conceptual needs for differential diagnostics, case conceptualization and treatment pathways.

Description of nonbeneficiaries (non-BFP) and beneficiaries of the BFP in the original and matched cohorts from 2004 to 2015.

Introduction: Female infertility has a devastating impact on the physical and mental health of individuals and national fertility. However, most of the previous studies on this subject were conducted on rather small sample sizes and have certain limitations. Therefore, we aimed to determine the prevalence of female infertility in 204 countries and territories from 1990 to 2019. Methods: We examined female infertility in terms of prevalence, age-standardized prevalence rates (ASR), and disability-adjusted life-years (DALYs) across different age groups in 204 countries and territories from 1990 to 2019 using data from the Global Health Data Exchange query tool. Results: From 1990 to 2019, ASR and DALYs for female infertility increased globally. At the socio-demographic index (SDI) quintile level, middle-SDI and high-middle-SDI countries exhibited a faster increase in the ASR of female infertility. In 2019, with the highest female infertility rate recorded among those between the ages of 30–34 years and the lowest among those between the ages of 45–49 years. In 2019, high-income North America recorded the highest proportion of primary infertility, while East Asia recorded the lowest proportion. Limitations: First, the GBD database lacks data for some countries and regions. Second, data access and quality differ across locations. Third, the causes of infertility are not comprehensive, data on Klinefelter in GBD2019 in relation to primary infertility was 0. Conclusion: Globally, the prevalence of DALYs and age-standardized female infertility increased from 1990 to 2019.