We explored long-term employment status and income before and after depression diagnosis among men and women and at different working ages in Taiwan.

Data from 2006 to 2019 were obtained from the National Health Insurance Research Database (NHIRD). Individuals with newly diagnosed depressive disorder aged 15 to 64 years during the study period were identified. An equal number of individuals without depression were matched for their demographic and clinical characteristics. Employment outcomes included employment status, which was categorized into employed or unemployed, and annual income. Based on the occupation categories and monthly insurance salary recorded in the Registry for Beneficiaries of the NHIRD, a subject was defined as unemployed if he or she differed from the income earner or the occupation category was unemployed. Monthly income was defined as zero for unemployed subjects and proxied as monthly insurance salary for others. Annual income was the sum of monthly income in each observation year.

A total of 420,935 individuals with depressive disorder were included in the study, and an equal number of individuals with not diagnosed depression served as controls. Employment rate and income were lower in the depression group than in the control group before the year of diagnosis, with a difference of 5.7% in employment rate and USD 1,173 in annual income. This gap increased considerably after the year of diagnosis (7.3% in employment rate and USD 1,573 in annual incomes) and further widened in the subsequent years (8.1% in employment rate and USD 2,006 in annual incomes in the 5th following year). The drops in the employment rate and income caused by depression were more evident in men and older age groups than in women and younger age groups, respectively. However, the reduction in employment rate and income in the following years after the diagnosis was more considerable among younger age groups.

The effect of depression on employment status and income was significant during the year of diagnosis and continued afterwards. The effect on employment outcomes varied between genders and across all age groups.

Studying the effect of primary health care development when simultaneously implemented with health insurance schemes assesses effectiveness and use of health care services and gives us insight on how to develop such interventions in different countries.

To analyze the impact of health insurance and the family physician program on total hospitalizations, and the relation between avoidable hospitalizations and access to family physicians among the rural population in Iran.

We conducted an interrupted time series (ITS) analysis of monthly hospitalization rates between the years of 2003 and 2014 to assess the immediate and gradual effects of these reforms on total hospitalization rates in the rural areas of Tehran province. In addition, we used a sample of 22 570 hospitalizations between 2006 and 2013 to develop a logistic regression model to measure the association between access to a family physician and avoidable hospitalizations.

ITS analysis showed that there was an immediate increase of about 1.96 hospitalizations per 1000 inhabitants (P<0.0001, CI=1.58, 2.34) hospitalization rates after the reforms. This was followed by a significant increase of about 0.089 per 1000 inhabitants (P<0.0001, CI=0.07, 0.1). Hospitalization increase continued up to four years after the policy implementation. Following that, hospitalization rates decreased among the rural population (a decrease of 0.066 per 1000, P<0.0001, CI=−0.084, −0.048). Studying the hospitalizations that occurred between 2006 and 2013 showed that there were 4106 avoidable hospitalizations from among a sample of 22 570 hospitalizations. Results of logistic regression models including gender, age and access to family physician variables showed that there was no statistical relation between access to a family physician and avoidable hospitalizations.

Reforms had access effect and caused increased hospital services uses in people with unmet needs. Also the reforms did not decrease avoidable hospitalizations, and therefore had no efficiency effect.

When 2017 Hurricane Harvey struck the coastline of Texas on August 25, 2017, it resulted in 88 fatalities and more than US $125 billion in damage to infrastructure. The floods associated with the storm created a toxic mix of chemicals, sewage and other biohazards, and over 6 million cubic meters of garbage in Houston alone. The level of biohazard exposure and injuries from trauma among persons residing in affected areas was widespread and likely contributed to increases in emergency department (ED) visits in Houston and cities receiving hurricane evacuees. We investigated medical surge resulting from these evacuations in Dallas–Fort Worth (DFW) metroplex EDs.

We used data sourced from the North Texas Syndromic Surveillance Region 2/3 in ESSENCE to investigate ED visit surge following the storm in DFW hospitals because this area received evacuees from the 60 counties with disaster declarations due to the storm. We used the interrupted time series (ITS) analysis to estimate the magnitude and duration of the ED surge. ITS was applied to all ED visits in DFW and visits made by patients residing in any of the 60 counties with disaster declarations due to the storm. The DFW metropolitan statistical area included 55 hospitals. Time series analyses examined data from March 1, 2017–January 6, 2018 with focus on the storm impact period, August 14–September 15, 2017. Data from before, during, and after the storm were visualized spatially and temporally to characterize magnitude, duration, and spatial variation of medical surge attributable to Hurricane Harvey.

During the study period overall, ED visits in the DFW area rose immediately by about 11% (95% CI: 9%, 13%), amounting to ~16 500 excess total visits before returning to the baseline on September 21, 2017. Visits by patients identified as residing in disaster declaration counties to DFW hospitals rose immediately by 127% (95% CI: 125%, 129%), amounting to 654 excess visits by September 29, 2017, when visits returned to the baseline. A spatial analysis revealed that evacuated patients were strongly clustered (Moran’s I = 0.35, P < 0.0001) among 5 of the counties with disaster declarations in the 11-day window during the storm surge.

The observed increase in ED visits in DFW due to Hurricane Harvey and ensuing evacuation was significant. Anticipating medical surge following large-scale hurricanes is critical for community preparedness planning. Coordinated planning across stakeholders is necessary to safeguard the population and for a skillful response to medical surge needs. Plans that address hurricane response, in particular, should have contingencies for support beyond the expected disaster areas.

We analyzed the impact of opioid initiation on the prevalence of antipsychotic and benzodiazepine and related drug (BZDR) use among community-dwelling persons with Alzheimer's disease (AD).

We utilized the register-based Medication use and Alzheimer's disease (MEDALZ) cohort for this study. We included all community-dwelling persons diagnosed with AD during 2010–2011 in Finland initiating opioid use (n = 3,327) and a matched cohort of persons not initiating opioids (n = 3,325). Interrupted time series analyses were conducted to compare the prevalence of antipsychotic and BZDR use in 30-day periods within six months before opioid initiation to 30-day periods six months later.

Before opioid initiation, prevalence of antipsychotic use among opioid initiators was 13.3%, 18.3% at opioid initiation, and 17.3% six months later. Prevalences of BZDR use were 27.1% six months prior, 28.9% at opioid initiation, and 26.9% six months later. After opioid initiation, antipsychotic and BZDR use declined by 0.3 percentage points (pps, 95% confidence interval 0.1–0.5) and 0.4 pps (0.2–0.7) per month, respectively, until the end of the follow-up. Compared to persons not initiating opioid use, opioid initiation immediately resulted in an increase in prevalence of 1.9 pps (0.9–2.8) for antipsychotics and of 1.6 pps (0.9–2.2) for BZDR use. However, in total there was a comparative decrease of 0.5 pps (0.3–0.8) per month for antipsychotics and of 0.4 pps (0.2–0.6) for BZDR use until the end of the follow-up.

Our results suggest that opioid initiation may reduce antipsychotic and BZDR use among persons with AD.