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An efficient allocation of limited resources in low-income settings offers the opportunity to improve population-health outcomes given the available health system capacity. Efforts to achieve this are often framed through the lens of "health benefits packages" (HBPs), which seek to establish which services the public healthcare system should include in its provision. Analytic approaches widely used to weigh evidence in support of different interventions and inform the broader HBP deliberative process however have limitations. In this work, we propose the individual-based Thanzi La Onse (TLO) model as a uniquely-tailored tool to assist in the evaluation of Malawi-specific HBPs while addressing these limitations. By mechanistically modelling-and calibrating to extensive, country-specific data-the incidence of disease, health-seeking behaviour, and the capacity of the healthcare system to meet the demand for care under realistic constraints on human resources for health available, we were able to simulate the health gains achievable under a number of plausible HBP strategies for the country. We found that the HBP emerging from a linear constrained optimisation analysis (LCOA) achieved the largest health gain-∼8% reduction in disability adjusted life years (DALYs) between 2023 and 2042 compared to the benchmark scenario-by concentrating resources on high-impact treatments. This HBP however incurred a relative excess in DALYs in the first few years of its implementation. Other feasible approaches to prioritisation were assessed, including service prioritisation based on patient characteristics, rather than service type. Unlike the LCOA-based HBP, this approach achieved consistent health gains relative to the benchmark scenario on a year- to-year basis, and a 5% reduction in DALYs over the whole period, which suggests an approach based upon patient characteristics might prove beneficial in the future.

IntroductionMathematical modelling is a commonly utilised tool to predict the impact of policy on health outcomes globally. Given the persistently high levels of maternal and perinatal morbidity and mortality in sub-Saharan Africa, mathematical modelling is a potentially valuable tool to guide strategic planning for health and improve outcomes.MethodsThe aim of this scoping review was to explore the characteristics of mathematical models and modelling studies evaluating the impact of maternal and/or perinatal healthcare interventions or services on health-related outcomes in the region. A search across three databases was conducted on 2nd November 2023 which returned 8660 potentially relevant studies, from which 60 were included in the final review. Characteristics of these studies, the interventions which were evaluated, the models utilised, and the analyses conducted were extracted and summarised.ResultsFindings suggest that the popularity of modelling within this field is increasing over time with most studies published after 2015 and that population-based, deterministic, linear models were most frequently utilised, with the Lives Saved Tool being applied in over half of the reviewed studies (n = 34, 57%). Much less frequently (n = 6) models utilising system-thinking approaches, such as individual-based modelling or systems dynamics modelling, were developed and applied. Models were most applied to estimate the impact of interventions or services on maternal mortality (n = 34, 57%) or neonatal mortality outcomes (n = 39, 65%) with maternal morbidity (n = 4, 7%) and neonatal morbidity (n = 6, 10%) outcomes and stillbirth reported on much less often (n = 14, 23%).DiscussionGoing forward, given that healthcare delivery systems have long been identified as complex adaptive systems, modellers may consider the advantages of applying systems-thinking approaches to evaluate the impact of maternal and perinatal health policy. Such approaches allow for a more realistic and explicit representation of the systems- and individual- level factors which impact the effectiveness of interventions delivered within health systems.

IntroductionThe COVID-19 pandemic and the restriction policies implemented by the Government of Malawi may have disrupted routine health service utilisation. We aimed to find evidence for such disruptions and quantify any changes by service type and level of health care.MethodsWe extracted nationwide routine health service usage data for 2015-2021 from the electronic health information management systems in Malawi. Two datasets were prepared: unadjusted and adjusted; for the latter, unreported monthly data entries for a facility were filled in through systematic rules based on reported mean values of that facility or facility type and considering both reporting rates and comparability with published data. Using statistical descriptive methods, we first described the patterns of service utilisation in pre-pandemic years (2015-2019). We then tested for evidence of departures from this routine pattern, i.e., service volume delivered being below recent average by more than two standard deviations was viewed as a substantial reduction, and calculated the cumulative net differences of service volume during the pandemic period (2020-2021), in aggregate and within each specific facility.ResultsEvidence of disruptions were found: from April 2020 to December 2021, services delivered of several types were reduced across primary and secondary levels of care-including inpatient care (-20.03% less total interactions in that period compared to the recent average), immunisation (-17.61%), malnutrition treatment (-34.5%), accidents and emergency services (-16.03%), HIV (human immunodeficiency viruses) tests (-27.34%), antiretroviral therapy (ART) initiations for adults (-33.52%), and ART treatment for paediatrics (-41.32%). Reductions of service volume were greatest in the first wave of the pandemic during April-August 2020, and whereas some service types rebounded quickly (e.g., outpatient visits from -17.7% to +3.23%), many others persisted at lower level through 2021 (e.g., under-five malnutrition treatment from -15.24% to -42.23%). The total reduced service volume between April 2020 and December 2021 was 8 066 956 (-10.23%), equating to 444 units per 1000 persons.ConclusionWe have found substantial evidence for reductions in health service delivered in Malawi during the COVID-19 pandemic which may have potential health consequences, the effect of which should inform how decisions are taken in the future to maximise the resilience of healthcare system during similar events.

IntroductionIn 2016, the WHO published recommendations increasing the number of recommended antenatal care (ANC) visits per pregnancy from four to eight. Prior to the implementation of this policy, coverage of four ANC visits has been suboptimal in many low-income settings. In this study we explore socio-demographic factors associated with early initiation of first ANC contact and attending at least four ANC visits ("ANC4+") in Malawi using the Malawi Demographic and Health Survey (MDHS) data collected between 2004 and 2016, prior to the implementation of new recommendations.MethodsWe combined data from the 2004-5, 2010 and 2015-16 MDHS using Stata version 16. Participants included all women surveyed between the ages of 15-49 who had given birth in the five years preceding the survey. We conducted weighted univariate, bivariate and multivariable logistic regression analysis of the effects of each of the predictor variables on the binary endpoint of the woman attending at least four ANC visits and having the first ANC attendance within or before the four months of pregnancy (ANC4+). To determine whether a factor was included in the model, the likelihood ratio test was used with a statistical significance of P< 0.05 as the threshold.ResultsWe evaluated data collected in surveys in 2004/5, 2010 and 2015/6 from 26386 women who had given birth in the five years before being surveyed. The median gestational age, in months, at the time of presenting for the first ANC visit was 5 (inter quartile range: 4-6). The proportion of women initiating ANC4+ increased from 21.3% in 2004-5 to 38.8% in 2015-16. From multivariate analysis, there was increasing trend in ANC4+ from women aged 20-24 years (adjusted odds ratio (aOR) = 1.27, 95%CI:1.05-1.53, P = 0.01) to women aged 45-49 years (aOR = 1.91, 95%CI:1.18-3.09, P = 0.008) compared to those aged 15-19 years. Women from richest socio-economic position ((aOR = 1.32, 95%CI:1.12-1.58, PConclusionThe proportion of women reporting ANC4+ and of key ANC interventions in Malawi have increased significantly since 2004. However, we found that most women did not access the recommended number of ANC visits in Malawi, prior to the 2016 WHO policy change which may mean that women are less likely to undertake the 2016 WHO recommendation of 8 contacts per pregnancy. Additionally, our results highlighted significant variation in coverage according to key socio-demographic variables which should be considered when devising national strategies to ensure that all women access the appropriate frequency of ANC visits during their pregnancy.

Objectives: To undertake an descriptive analysis of the health needs, healthcare practices and barriers to accessing healthcare faced by women in Lower Napo River Region, Peru, and to understand health literacy regarding cervical cancer and the need for more effective cervical cancer screening services. Methods: We performed a community-based needs assessment adapting Demographic and Health survey methodology with additional questions determining female health literacy on cervical cancer and assessing the availability and need for cervical cancer screening services. We surveyed women (N = 121) across all households in six communities along the Lower Napo River, Loreto, Peru, in May 2015. Data were collected as part of the larger Amazon Community Based Participation Cervical Cancer Screen-and-Treat Programme . Survey data were compared to national results from ENDES 2014. Results: Comparison between our findings and the ENDES 2014 survey highlighted considerable inequality between indigenous or mixed indigenous, rural populations in Loreto, Peru, and national population data averages over level of formal education, literacy, barriers to accessing healthcare and maternal and sexual health. Even though only 5.9% (N = 7/117) of women had no formal health insurance coverage, money was reported as the leading barrier accessing healthcare (N = 88/117, 75.2%). Health literacy regarding cervical and breast cancer was poor. A high proportion of women highlighted fear of screening processes (70.8%, N = 80/113) and lack of available services (53.6%, N = 60/112) as barriers to cervical cancer screening. Conclusion: Although progress has been made in improving healthcare access in Peru, such gains have not been experienced equitably and women living in remote communities face persistent marginalization regarding their health. There is a significant need for education related to and screening for cervical cancer in this region that is tailored to the reality of women’s lives in remote communities in Loreto.

Abstract Background To make the best use of health resources, it is crucial to understand the healthcare needs of a population—including how needs will evolve and respond to changing epidemiological context and patient behaviour—and how this compares to the capabilities to deliver healthcare with the existing workforce. Existing approaches to planning either rely on using observed healthcare demand from a fixed historical period or using models to estimate healthcare needs within a narrow domain (e.g., a specific disease area or health programme). A new data-grounded modelling method is proposed by which healthcare needs and the capabilities of the healthcare workforce can be compared and analysed under a range of scenarios: in particular, when there is much greater propensity for healthcare seeking. Methods A model representation of the healthcare workforce, one that formalises how the time of the different cadres is drawn into the provision of units of healthcare, was integrated with an individual-based epidemiological model—the Thanzi La Onse model—that represents mechanistically the development of disease and ill-health and patients’ healthcare seeking behaviour. The model was applied in Malawi using routinely available data and the estimates of the volume of health service delivered were tested against officially recorded data. Model estimates of the “time needed” and “time available” for each cadre were compared under different assumptions for whether vacant (or established) posts are filled and healthcare seeking behaviour. Results The model estimates of volume of each type of service delivered were in good agreement with the available data. The “time needed” for the healthcare workforce greatly exceeded the “time available” (overall by 1.82-fold), especially for pharmacists (6.37-fold) and clinicians (2.83-fold). This discrepancy would be largely mitigated if all vacant posts were filled, but the large discrepancy would remain for pharmacists (2.49-fold). However, if all of those becoming ill did seek care immediately, the “time needed” would increase dramatically and exceed “time supply” (2.11-fold for nurses and midwives, 5.60-fold for clinicians, 9.98-fold for pharmacists) even when there were no vacant positions. Conclusions The results suggest that services are being delivered in less time on average than they should be, or that healthcare workers are working more time than contracted, or a combination of the two. Moreover, the analysis shows that the healthcare system could become overwhelmed if patients were more likely to seek care. It is not yet known what the health consequences of such changes would be but this new model provides—for the first time—a means to examine such questions.

The cost and complexity of whole genome sequencing limits its use in identifying and validating sequences used for genetic engineering and synthetic biology. Here the authors present Prymetime, an integrated workflow to sequence engineered strains and identify engineering in metagenomes.