The Pan American Health Organization-adapted Hanlon method for prioritization of health programs

Public health surveillance and the data, information, knowledge, intelligence and wisdom paradigm

This article points out deficiencies in present-day definitions of public health surveillance, which include data collection, analysis, interpretation and dissemination, but not public health action. Controlling a public health problem of concern requires a public health response that goes beyond information dissemination. It is undesirable to have public health divided into data generation processes (public health surveillance) and data use processes (public health response), managed by two separate groups (surveillance experts and policy-makers). It is time to rethink the need to modernize the definition of public health surveillance, inspired by the authors' enhanced Data, Information, Knowledge, Intelligence and Wisdom model. Our recommendations include expanding the scope of public health surveillance beyond information dissemination to comprise actionable knowledge (intelligence); mandating surveillance experts to assist policy-makers in making evidence-informed decisions; encouraging surveillance experts to become policy-makers; and incorporating public health literacy training - from data to knowledge to wisdom - into the curricula for all public health professionals. Work on modernizing the scope and definition of public health surveillance will be a good starting point.

Prioritisation processes for programme implementation and evaluation in public health: A scoping review

Background

Programme evaluation is an essential and systematic activity for improving public health programmes through useful, feasible, ethical, and accurate methods. Finite budgets require prioritisation of which programmes can be funded, first, for implementation, and second, evaluation. While criteria for programme funding have been discussed in the literature, a similar discussion around criteria for which programmes are to be evaluated is limited. We reviewed the criteria and frameworks used for prioritisation in public health more broadly, and those used in the prioritisation of programmes for evaluation. We also report on stakeholder involvement in prioritisation processes, and evidence on the use and utility of the frameworks or sets of criteria identified. Our review aims to inform discussion around which criteria and domains are best suited for the prioritisation of public health programmes for evaluation.

Methods

We reviewed the peer-reviewed literature through OVID MEDLINE (PubMed) on 11 March 2022. We also searched the grey literature through Google and across key websites including World Health Organization (WHO), US Centers for Disease Control and Prevention (CDC), European Centre for Disease Prevention and Control (ECDC), and the International Association of National Public Health Institutes (IANPHI) (14 March 2022). Articles were limited to those published between 2002 and March 2022, in English, French or German.

Results

We extracted over 300 unique criteria from 40 studies included in the analysis. These criteria were categorised into 16 high-level conceptual domains to allow synthesis of the findings. The domains most frequently considered in the studies were “burden of disease” (33 studies), “social considerations” (30 studies) and “health impacts of the intervention” (28 studies). We only identified one paper which proposed criteria for use in the prioritisation of public health programmes for evaluation. Few prioritisation frameworks had evidence of use outside of the setting in which they were developed, and there was limited assessment of their utility. The existing evidence suggested that prioritisation frameworks can be used successfully in budget allocation, and have been reported to make prioritisation more robust, systematic, transparent, and collaborative.

Conclusion

Our findings reflect the complexity of prioritisation in public health. Development of a framework for the prioritisation of programmes to be evaluated would fill an evidence gap, as would formal assessment of its utility. The process itself should be formal and transparent, with the aim of engaging a diverse group of stakeholders including patient/public representatives.

JB. Leaving no one behind: a methodology for setting health inequality reduction targets for Sustainable Development Goal 3

OBJECTIVES

To present a methodology for the simultaneous setting of quantitative targets that reflect both an improvement in the national average of an indicator for Sustainable Development Goal 3 (SDG3), as well as a reduction in its geographic inequality.

METHODS

A five-step algorithm was developed: (a) calculate the national average annual percent change (AAPC) for an SDG3 indicator; (b) normatively define geographic strata from the subnational distribution of the indicator in a baseline year; (c) apply a proportional progressivity criterion to the AAPC to project the stratum-specific indicator value for the target year; (d) set the national target as the weighted average of the indicator in the subnational territorial units for the target year; and (e) set the inequality reduction targets by calculating the absolute and relative gaps between the bottom and top strata for the target year.

RESULTS

The algorithm was applied to SDG indicator 3.1.1 (maternal mortality ratio, MMR), disaggregated by Guatemala's 22 departments at the baseline year 2014 (MMR = 113 per 100,000 live births). By sustaining the AAPC rate attained from 2009 to 2014 (-4.3%) and focalizing its actions with territorial progressivity, by 2030 the country could reduce its MMR to 53 per 100,000 and its absolute and relative inequality gaps by 72% and 48%, respectively.

CONCLUSIONS

The proposed methodology allows for simultaneously setting targets for overall progress and inequality reduction in health, making explicit the primacy of the equity principle contained in the SDG commitment to leave no one behind, whose urgency takes on renewed relevance in the current pandemic scenario.

[Leaving no one behind: Methodology to set health inequality reduction targets for Sustainable Development Goal 3Não deixar ninguém para trás: uma metodologia para estabelecer metas de redução das desigualdades em saúde sob o Objetivo de Desenvolvimento Sustentável 3]

OBJECTIVES

Present methodology for the concurrent development of quantitative targets that reflect improvement in the national average of an indicator for Sustainable Development Goal 3 (SDG3), as well as a reduction in geographic inequality.

METHODS

A five-step algorithm was developed: a) calculate the national average annual percentage change (AAPC) for an SDG3 indicator; b) standardize the definition of geographic strata based on subnational distribution of the indicator in a base year; c) apply a criterion for proportional progress in the AAPC in order to project the stratum-specific indicator to the target year; d) set the national target as the weighted average of the indicator in the subnational territorial units for the target year; and e) develop inequality reduction targets by calculating absolute and relative gaps between the top and bottom strata for the target year.

RESULTS

The algorithm was applied to SDG indicator 3.1.1 (maternal mortality ratio, MMR), disaggregated by Guatemala's 22 departments for base year 2014 (MMR = 113/100,000 live births). By sustaining the average AAPC rate attained from 2009 to 2014 (-4.3%) and targeting its actions to territorial progress, the country would reduce its MMR to 53/100,000 by 2030 and its absolute and relative gaps by 72% and 48%, respectively.

CONCLUSIONS

The proposed methodology makes it possible to concurrently develop targets for the reduction of geographic inequalities in health and improvements in the national average, with explicit reference to the primacy of the principle of equity expressed in the SDGs' commitment to leaving no one behind, whose urgency is newly important in the current post-pandemic scenario.