Scale economies with regard to price adjustment costs and the speed of price adjustment in Australian manufacturing

This paper aims to examine the impact of firm size, industry concentration and the length of production on industry speed of price adjustment. To motivate the paper, an industry pricing model in error correction form is derived from firm pricing behaviour. As a new development, firms are assumed to have price adjustment costs that are a function of their size. The empirical model is estimated using two‐digit Australian manufacturing industry data for the period 1994:3 to 2006:1. The results suggest that the industry speed of price adjustment is positively related to firm size and negatively related to industry concentration and the production lag. Implied values for industry speeds of price adjustment are generally small when compared to other country industry studies. However, the industry average median lag of 7.1 quarters indicates a slightly faster speed of price adjustment than the estimate for the Australian consumer price index by Dwyer and Leong (2001 Dwyer, J. and Leong, K. 2001. Changes in the determination of inflation in Australia 1–44. Reserve Bank of Australia Research Discussion Paper 2001‐02 [Google Scholar]).     

A real-world analysis of healthcare costs and relative risk of hyperprolactinemia associated with antipsychotic treatments in the United States

Abstract

Aims: Antipsychotic medications are associated with an increased risk of hyperprolactinemia, but differ in their propensity to cause this complication. This study aimed to assess the economic burden of hyperprolactinemia, and to compare its risk among adult patients using atypical antipsychotics (AAs) with a mechanism of action associated with no/low vs high/moderate prolactin elevation.

Methods: This retrospective cohort study was based on US Commercial and Medicaid claims databases. Healthcare costs were compared between matched hyperprolactinemia and hyperprolactinemia-free cohorts using a two-part model. Risk of hyperprolactinemia was compared between patients receiving AAs with a mechanism of action associated with no/low (no/low prolactin elevation cohort) vs high/moderate prolactin elevation (high/moderate prolactin cohort) using logistic regression.

Results: In the commercially insured sample, compared to the hyperprolactinemia-free cohort (n?=?499), the hyperprolactinemia cohort (n?=?499) was associated with incremental total healthcare costs of $5,732 ($20,081 vs $14,349; p?=?.004), and incremental medical costs of $3,861 ($13,218 vs $9,357; p?=?.040), mainly driven by hyperprolactinemia-related costs. In the Medicaid-insured sample, compared to the hyperprolactinemia-free cohort, the hyperprolactinemia cohort was associated with incremental total healthcare costs of $10,773 ($30,763 vs $19,990; p?=?.004), and incremental medical costs of $9,246 ($20,859 vs $11,613; p?=?.004), mainly driven by hyperprolactinemia-related and mental health-related costs. The odds of hyperprolactinemia in the no/low prolactin elevation cohort were 4–5-times lower than that in the high/moderate prolactin elevation cohort (odds ratio =0.21; p?<?.001).

Limitations: Hyperprolactinemia may be under-reported in claims data.

Conclusions: Hyperprolactinemia is associated with substantial healthcare costs. AAs associated with no/low prolactin elevation reduce the risk of hyperprolactinemia by 4–5-times compared to AAs associated with moderate/high prolactin elevation. Treatment options with minimal impact on prolactin levels may contribute to reducing hyperprolactinemia burden in AA-treated patients.