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Analysis of longitudinal data to evaluate a policy change.

ABSTRACT: Longitudinal data analysis methods are powerful tools for exploring scientific questions regarding change and are well suited to evaluate the impact of a new policy. However, there are challenging aspects of policy change data that require consideration, such as defining comparison groups, separating the effect of time from that of the policy, and accounting for heterogeneity in the policy effect. We compare currently available methods to evaluate a policy change and illustrate issues specific to a policy change analysis via a case study of laws that eliminate gun-use restrictions (shall-issue laws) and firearm-related homicide. We obtain homicide rate ratios estimating the effect of enacting a shall-issue law, which vary between 0.903 and 1.101. We conclude that in a policy change analysis it is essential to select a mean model that most accurately characterizes the anticipated effect of the policy intervention, thoroughly model temporal trends, and select methods that accommodate unit-specific policy effects. We also conclude that several longitudinal data analysis methods are useful to evaluate a policy change, but not all may be appropriate in certain contexts. Analysts must carefully decide which methods are appropriate for their application and must be aware of the differences between methods to select a procedure that generates valid inference.

SUBMITTER: French B

PROVIDER: S-EPMC3415557 | biostudies-literature | 2008 Oct

REPOSITORIES: biostudies-literature

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Analysis of longitudinal data to evaluate a policy change.

French Benjamin B Heagerty Patrick J PJ

Statistics in medicine 20081001 24

Longitudinal data analysis methods are powerful tools for exploring scientific questions regarding change and are well suited to evaluate the impact of a new policy. However, there are challenging aspects of policy change data that require consideration, such as defining comparison groups, separating the effect of time from that of the policy, and accounting for heterogeneity in the policy effect. We compare currently available methods to evaluate a policy change and illustrate issues specific t ...[more]

PMID: 18618416

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Project description:BackgroundCOVID-19 transmission rates in South Asia initially were under control when governments implemented health policies aimed at controlling the pandemic such as quarantines, travel bans, and border, business, and school closures. Governments have since relaxed public health restrictions, which resulted in significant outbreaks, shifting the global epicenter of COVID-19 to India. Ongoing systematic public health surveillance of the COVID-19 pandemic is needed to inform disease prevention policy to re-establish control over the pandemic within South Asia.ObjectiveThis study aimed to inform public health leaders about the state of the COVID-19 pandemic, how South Asia displays differences within and among countries and other global regions, and where immediate action is needed to control the outbreaks.MethodsWe extracted COVID-19 data spanning 62 days from public health registries and calculated traditional and enhanced surveillance metrics. We use an empirical difference equation to measure the daily number of cases in South Asia as a function of the prior number of cases, the level of testing, and weekly shifts in variables with a dynamic panel model that was estimated using the generalized method of moments approach by implementing the Arellano-Bond estimator in R.ResultsTraditional surveillance metrics indicate that South Asian countries have an alarming outbreak, with India leading the region with 310,310 new daily cases in accordance with the 7-day moving average. Enhanced surveillance indicates that while Pakistan and Bangladesh still have a high daily number of new COVID-19 cases (n=4819 and n=3878, respectively), their speed of new infections declined from April 12-25, 2021, from 2.28 to 2.18 and 3.15 to 2.35 daily new infections per 100,000 population, respectively, which suggests that their outbreaks are decreasing and that these countries are headed in the right direction. In contrast, India's speed of new infections per 100,000 population increased by 52% during the same period from 14.79 to 22.49 new cases per day per 100,000 population, which constitutes an increased outbreak.ConclusionsRelaxation of public health restrictions and the spread of novel variants fueled the second wave of the COVID-19 pandemic in South Asia. Public health surveillance indicates that shifts in policy and the spread of new variants correlate with a drastic expansion in the pandemic, requiring immediate action to mitigate the spread of COVID-19. Surveillance is needed to inform leaders whether policies help control the pandemic.

Project description:BackgroundTo date, public health policies implemented during the COVID-19 pandemic have been evaluated on the basis of their ability to reduce transmission and minimise economic harm. We aimed to assess the association between COVID-19 policy restrictions and mental health during the COVID-19 pandemic.MethodsIn this longitudinal analysis, we combined daily policy stringency data from the Oxford COVID-19 Government Response Tracker with psychological distress scores and life evaluations captured in the Imperial College London-YouGov COVID-19 Behaviour Tracker Global Survey in fortnightly cross-sections from samples of 15 countries between April 27, 2020, and June 28, 2021. The mental health questions provided a sample size of 432 642 valid responses, with an average of 14 918 responses every 2 weeks. To investigate how policy stringency was associated with mental health, we considered two potential mediators: observed physical distancing and perceptions of the government's handling of the pandemic. Countries were grouped on the basis of their response to the COVID-19 pandemic as those pursuing an elimination strategy (countries that aimed to eliminate community transmission of SARS-CoV-2 within their borders) or those pursuing a mitigation strategy (countries that aimed to control SARS-CoV-2 transmission). Using a combined dataset of country-level and individual-level data, we estimated linear regression models with country-fixed effects (ie, dummy variables representing the countries in our sample) and with individual and contextual covariates. Additionally, we analysed data from a sample of Nordic countries, to compare Sweden (that pursued a mitigation strategy) to other Nordic countries (that adopted a near-elimination strategy).FindingsControlling for individual and contextual variables, higher policy stringency was associated with higher mean psychological distress scores and lower life evaluations (standardised coefficients β=0·014 [95% CI 0·005 to 0·023] for psychological distress; β=-0·010 [-0·015 to -0·004] for life evaluation). Pandemic intensity (number of deaths per 100 000 inhabitants) was also associated with higher mean psychological distress scores and lower life evaluations (standardised coefficients β=0·016 [0·008 to 0·025] for psychological distress; β=-0·010 [-0·017 to -0·004] for life evaluation). The negative association between policy stringency and mental health was mediated by observed physical distancing and perceptions of the government's handling of the pandemic. We observed that countries pursuing an elimination strategy used different policy timings and intensities compared with countries pursuing a mitigation strategy. The containment policies of countries pursuing elimination strategies were on average less stringent, and fewer deaths were observed.InterpretationChanges in mental health measures during the first 15 months of the COVID-19 pandemic were small. More stringent COVID-19 policies were associated with poorer mental health. Elimination strategies minimised transmission and deaths, while restricting mental health effects.FundingNone.

Project description:BACKGROUND: National malaria control programmes must deal with the complex process of changing national malaria treatment guidelines, often without guidance on the process of change. Selecting a replacement drug is only one issue in this process. There is a paucity of literature describing successful malaria treatment policy changes to help guide control programs through this process. OBJECTIVES: To understand the wider context in which national malaria treatment guidelines were formulated in a specific country (Peru). METHODS: Using qualitative methods (individual and focus group interviews, stakeholder analysis and a review of documents), a retrospective analysis of the process of change in Peru's anti-malarial treatment policy from the early 1990's to 2003 was completed. RESULTS: The decision to change Peru's policies resulted from increasing levels of anti-malarial drug resistance, as well as complaints from providers that the drugs were no longer working. The context of the change occurred in a time in which Peru was changing national governments, which created extreme challenges in moving the change process forward. Peru utilized a number of key strategies successfully to ensure that policy change would occur. This included a) having the process directed by a group who shared a common interest in malaria and who had long-established social and professional networks among themselves, b) engaging in collaborative teamwork among nationals and between nationals and international collaborators, c) respect for and inclusion of district-level staff in all phases of the process, d) reliance on high levels of technical and scientific knowledge, e) use of standardized protocols to collect data, and f) transparency. CONCLUSION: Although not perfectly or fully implemented by 2003, the change in malaria treatment policy in Peru occurred very quickly, as compared to other countries. They identified a problem, collected the data necessary to justify the change, utilized political will to their favor, approved the policy, and moved to improve malaria control in their country. As such, they offer an excellent example for other countries as they contemplate or embark on policy changes.

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Analysis of longitudinal data to evaluate a policy change.

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Analysis of longitudinal data to evaluate a policy change.

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