The old state pension system treated women like second-class citizens – now it's payback time

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The old state pension system treated women like second-class citizens – now it's payback time
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This is a story about an old state pension system in which women have been second-class citizens 🖊️ stevewebb1

In particular, many women should have seen an increase in their state pension when their husband retired or when he died, but this never happened. Over half a billion pounds has been underpaid to widows, and nearly half a billion pounds to married women. And these problems were only unearthed when I, working in partnership with the websiteAfter some initial reluctance, the DWP eventually admitted systematic problems and has started a multi-year project to put things right.

In light of this, in 2022, for the first time in 15 years, the DWP started doing more detailed checks on state pension payments, including phoning people on low pensions to check what was going on. To their shock, the DWP started to realise that a whole group of people should have been getting national insurance “credits” for raising children but this was missing from their record. The Government now thinks around 210,000 people have been underpaid to the tune of £1.3bn.

To put this right, HMRC is commencing a letter-writing campaign to target those who may be missing out and encouraging them to claim their NI credits. The National Audit Office think the exercise may not be completed until 2027-28. There are many lessons to be learned. A key one is that there needs to be a change in attitude at the DWP. Even this week, it was trying to play down the scale of the problem. This was a tin-eared response. The only proper reaction would have been a heartfelt apology to every individual who has missed out, and a real commitment to proper checks so that retirees can be confident that the amount they are being paid is actually correct.

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Development of a predictive model for identifying women vulnerable to HIV in Chicago - BMC Women's HealthDevelopment of a predictive model for identifying women vulnerable to HIV in Chicago - BMC Women's HealthIntroduction Researchers in the United States have created several models to predict persons most at risk for HIV. Many of these predictive models use data from all persons newly diagnosed with HIV, the majority of whom are men, and specifically men who have sex with men (MSM). Consequently, risk factors identified by these models are biased toward features that apply only to men or capture sexual behaviours of MSM. We sought to create a predictive model for women using cohort data from two major hospitals in Chicago with large opt-out HIV screening programs. Methods We matched 48 newly diagnosed women to 192 HIV-negative women based on number of previous encounters at University of Chicago or Rush University hospitals. We examined data for each woman for the two years prior to either their HIV diagnosis or their last encounter. We assessed risk factors including demographic characteristics and clinical diagnoses taken from patient electronic medical records (EMR) using odds ratios and 95% confidence intervals. We created a multivariable logistic regression model and measured predictive power with the area under the curve (AUC). In the multivariable model, age group, race, and ethnicity were included a priori due to increased risk for HIV among specific demographic groups. Results The following clinical diagnoses were significant at the bivariate level and were included in the model: pregnancy (OR 1.96 (1.00, 3.84)), hepatitis C (OR 5.73 (1.24, 26.51)), substance use (OR 3.12 (1.12, 8.65)) and sexually transmitted infections (STIs) chlamydia, gonorrhoea, or syphilis. We also a priori included demographic factors that are associated with HIV. Our final model had an AUC of 0.74 and included healthcare site, age group, race, ethnicity, pregnancy, hepatitis C, substance use, and STI diagnosis. Conclusions Our predictive model showed acceptable discrimination between those who were and were not newly diagnosed with HIV. We identified risk factors such as recent pregnancy
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