Category: 3. Business

  • Mosquito survival from mark–recapture studies releasing at known age | Parasites & Vectors

    Mosquito survival from mark–recapture studies releasing at known age | Parasites & Vectors

    Scoping

    A scoping exercise was carried out to assess the feasibility of a synthesis of MR studies with respect to survival. A query of the Guerra et al. [9] database indicated the number of mark–recapture studies with or without known-age releases, which is presented in Table 1. Though known-age studies are a minority, 51 were available with potential information. The set of ages-at-release in any study was usually small: single-age release, more occasionally two or three. A prime requirement was that MR data were available that could be put into the form of a capture history matrix or m-array (these data structures are explained further in the section hereafter). Scoping found that publications never reported the capture history matrix and rarely the full m-array but often reported m-array information directly in a tabular form or indirectly in graphical form. Single-release experiments were much more common and would usually yield information for a single-row m-array. Recapture was usually carried out with similar apparatus and effort from occasion to occasion. Mosquitoes were usually killed on recapture; experiments with re-release of recaptured mosquitoes were very unusual. The exercise concluded that there were no strong obstacles to a pooled analysis of studies making use of an age-dependent CJS model, and the assumption of time-independent capture probability was defensible.

    Table 1 Counts of MR studies with or without known-age release from Ref. [9]

    Data

    The ‘capture history’ of the cohort(s) can be put in matrix form. This mark–recapture matrix forms a summary of the set of individual capture histories in the experiment, of which there are ({2}^{T}-1) possibilities where T is the number of recapture occasions. Under the assumptions of mark–recapture, an even more concise summary is provided by the ‘reduced m-array’ (henceforth ‘m-array’), which counts the numbers of mosquitoes released at i and next caught at j, without regard to the capture history prior to i or subsequent to j. The m-array is the usual form of reporting recapture data in publications.

    When only a single release is carried out, the data reported can be structured as a single-row m-array, and this is the most common format. In some experiments, the release and/or recapture occasions are irregularly spaced.

    An example m-array is shown here from Takagi et al. [14], in which three cohorts of 4-day old marked mosquitoes were released on three successive days (days 22, 23 and 24):

    23

    24

    25

    26

    27

    28

    29

    30

    31

    32

    33

    34

    35

    36

    37

    Uncaptured

    1

    4

    14

    2

    1

    12

    5

    6

    2

    		  

    1

    3

    		  

    0

    4

    241
     

    1

    2

    0

    0

    6

    3

    3

    1

    		  

    0

    0

    		  

    1

    0

    144
       

    7

    0

    1

    3

    2

    5

    1

    		  

    1

    0

    		  

    0

    0

    229

    The first to penultimate columns show a set of counts ({m}_{ij}) of animals released at occasion i and subsequently caught at occasion j. The final column shows the numbers released but not re-caught again over the course of the experiment. In this example, the final column was calculated from the supplied numbers released and numbers re-caught. No recaptures were attempted on days 32 and 35, so the columns are empty.

    A much fuller expression of the study information contains m-arrays by age (the ‘full’ or ‘generalised’ m-array, McRea and Morgan [13]). This structure is a set of counts with ({m}_{ij}left(aright)) denoting the number of individuals which, when released on day i at age a days, are next captured at j. Accompanying it is an array ({R}_{i}left(aright)), which is the number of individuals released on day i of age a days. The generalised m-array is almost never reported directly but could in principle be surmised from the study report. A reduced m-array for an experiment with cohorts of known age can be expressed as a generalised m-array.

    In the example above, the cohorts released were all of the same age on each occasion (multiple release, single age). An alternative is to release multiple ages simultaneously (single release, multiple ages), for which a full m-array data structure is required. Harrington et al. [15] simultaneously released ‘young’ and ‘old’ cohorts (3 and 13 days old, respectively) in Puerto Rico. The data can be formed to give the following R-array.

    and generalised m-array:

    Age

    2

    3

    4

    5

    6

    7

    8

    9

    10

    11

    Uncaptured

    3

    20

    15

    9

    0

    3

    0

    1

    1

    0

    1

    92

    13

    13

    3

    1

    1

    0

    0

    0

    0

    1

    0

    103

    Finally, different ages may be released at each occasion (single or multiple release and multiple age). For example, Eldridge and Reeves [16] released cohorts of ages 5, 1 and 2 on days 1, 4 and 7, respectively.

    The MR and release data were extracted from the original studies and then entered into R as arrays.

    A study may report more than one MR experiment, with releases separated in time and space. For example, Reisen et al. [17] reported separate experiments carried out in different months. When release cohorts overlap, for our study, a multiple-release m-array was formed where possible. Recaptures from separate releases were sometimes accumulated in the source publication in such a way that they could not be disaggregated and had to be treated as a single-release experiment, e.g. in Ref. [18].

    Other aggregation or conditioning factors include experimental site, sex, mosquito species and age. For example, Reisen et al. [17] released and recaptured male and female cohorts of Cu. tarsalis and Cu. quinquefasciatus. However, the recapture data were only reported fully for Cu. tarsalis females. Table 2 presents a brief summary of study and dataset-level characteristics.

    Table 2 Summary of included study characteristics

    Searching

    The references identified by Guerra et al. [9] were supplemented by a much smaller set of references collected ad hoc by the author (n = 26), and combined with a Web of Science search from 2014 to date [title terms: mosquito AND (surviv* OR longevity OR mortality)] to create the complete reference set, n = 188.

    A flow diagram of the search and selection process is provided in Additional File 1: Supplementary Fig. S1 (Appendix S2). Studies in which a survival-related experimental negative intervention (e.g. genetic modification) was apparent from the title were excluded. Studies in which age of release was unknown were excluded. For example, Takken et al. [19] captured mosquitoes from houses with aspirators prior to marking, so the ages of these adults at release were not known. Studies without useable MR information were excluded. For example, the authors of Marini et al. [20] reported recapture numbers in aggregated form, e.g. in the first experiment as 2–5, 6–9 and 10–21 day totals; these data could not be put into the usual m-array form by recapture day, and the study was therefore excluded.

    None of the selected datasets were exclusively concerned with males, and the majority contained female-specific MR information, so the analysis followed Ref. [9] in confining results to females. Similarly, the vast majority of datasets were in the three genera Anopheles, Aedes and Culex; other genera were excluded.

    Studies were then filtered according to conditions established by simulation (details below). For example, Takagi et al. [14] released three laboratory-reared cohorts 4–5 days after emergence. Criteria established on the basis of simulation results excluded this single-age study because of the older age of the mosquito cohorts and the inadequate size (< 500) of the release cohorts (296, 161 and 249).

    After exclusions, there were 73 MR datasets with ages known at release and, from these, 30 datasets of female mosquitoes with suitable MR information and experimental characteristics. The references supplying the final datasets are listed in Additional File 2.

    Analysis

    After the selection of studies described above, analysis is carried out in two stages. In the first stage, the parameters of a mark recapture model are estimated for each selected study, which include survival and capture parameters. The capture parameters have a modelling function, but the survival parameters are of primary interest. Study-specific capture probabilities are ascribed to each study, allowing study characteristics (experimental design, local conditions, etc.) to influence the data. For example, a study in which recapture uses baited recapture is allowed a different (probably higher) recapture probability to one that does not. In this way, important heterogeneity is modelled. In the second stage, the EL and its variance are estimated from the survival parameters, and this outcome is analysed by conventional meta-analysis.

    In the first stage, each study is analysed using the CJS model. In our analysis, the Weibull survival curve determines the values of the discrete survival parameters in the CJS model, so the parameters in the likelihood are reduced from a potentially large set of discrete survival parameters to the small set of Weibull parameters that they map to. A summary of symbols used is presented in Table 3.

    Table 3 Summary of symbols used (mostly from Ref. [13])

    Analysis uses the age-specific CJS likelihood equation [13, p. 74] written here as:

    $$L propto mathop prod limits_{a} mathop prod limits_{i = 1}^{T – 1} left{ {chi_{i} left( a right)^{{R_{i} left( a right) – sum m_{ij} left( a right)}} mathop prod limits_{j = i + 1}^{T} nu_{ij} left( a right)^{{m_{ij} left( a right)}} } right}$$

    where ({R}_{i}left(aright)) and ({m}_{ij}left(aright)) are data arrays with examples given in the Data section, and for a mosquito of age a when released at occasion i, ({nu }_{ij}left(aright)) is the probability of next recapture at j, and ({chi }_{i}left(aright)) is the probability, of not being caught afterwards, so (chi_{i} left( a right) = 1 – mathop sum limits_{j} nu_{ij} left( a right).)

    This is a multinomial likelihood and, leaving age aside for the purposes of explanation, includes:

    • the probability of no recapture (({chi }_{i})) raised to the power of the numbers not recaptured (({R}_{i}-sum {m}_{ij})); hence, the first term, and

    • the probabilities of recapture (({nu }_{ij})) raised to the power of the number of recaptures (({m}_{ij})); hence, the second term.

    The parameters in the current analysis are relatively simple compared with the general form: p is the probability of capture on any recapture occasion, which is assumed time-independent, and (underset{_}{phi }) is a vector of probabilities, with element (phi left[kright]) the probability of surviving from age k to k + 1.

    Then,

    $$nu_{ij} left( a right) = left( {1 – p} right)^{j – i – 1} p times mathop prod limits_{k = i}^{j – 1} phi left[ {a + k – i} right]$$

    Conventionally, discrete survival probabilities (({phi }_{k})) are used in MR analyses (see Ref. [13]). The analysis for age-dependence when interest lies in discrete age classes is set out by Pollock et al. [21], as is common in some fields (e.g. birds: immature and mature). Analysis with many age classes requires many parameters and associated limits on precision. Parametric age-dependence on a continuum has been additionally utilised in this paper because it provides a more compact parameterisation and potentially increased precision. The parameter vector for an individual study under the discrete survival formulation (with a time-independent capture model) is (left(p,{phi }_{1},…{phi }_{k}…right)), whereas under the compact parameterisation it is (for the Weibull survival model) (underset{_}{theta }=left(p,alpha ,eta right)).

    A Weibull survival model is assumed with shape and scale parameters (alpha) and (eta). There are several textbook parameterisations of the Weibull, and the one adopted here corresponds to that coded in R. Note that the symbol for the Weibull scale parameter ((sigma)) in the R parameterisation is replaced in this paper with (eta) because (sigma) is also commonly used for measures of dispersion. The Weibull distribution is fairly flexible though monotonic, and it includes the exponential as a special case when (alpha =1).

    For the Weibull, the continuous survival function is:

    $$Sleft( t right) = exp left( { – left( {t/eta } right)^{alpha } } right)$$

    The conditional survival over a time step is (Sleft(k+1right)/Sleft(kright)) [22, p. 31], so the equation:

    $$phi left( k right) = frac{{Sleft( {k + 1} right)}}{Sleft( k right)}$$

    connects the continuous survival model with the discrete apparent survival of the CJS, in which (phi left(kright)) represents the probability of an animal alive at age (k) surviving to (k+1).

    Weibull parameters are restricted to (alpha >0) and (eta >0). These constraints were implemented by numerical fitting with the Nelder–Mead method on the transformed variables (text{log}left(alpha right)) and (text{log}left(eta right)).

    The EL of a mosquito is given by (int Sleft(tright)text{d}t) and has an analytic solution for the Weibull model for known parameter values. To incorporate the parameter uncertainty in estimates of (alpha) and (eta), further analysis is required. The calculation in this paper of the variance of the conditional mean of the EL under a Weibull model is described in Additional File 1: Appendix S3.

    Meta-analysis was carried out using the metafor package in R. The meta-analysis on expected lifetimes used a log transformation for this positive-value outcome, with inverse-variance weighting. The variance of the log-transformed EL was approximated using the ‘delta method’, that is:

    $${text{var}} ;log left( {{text{EL}}} right) approx left{ {frac{{d,log left( {{text{EL}}} right)}}{{dleft( {{text{EL}}} right)}}} right}^{2} cdot {text{var}} left( {{text{EL}}} right) = frac{{{text{var}} left( {{text{EL}}} right)}}{{left( {{text{EL}}} right)^{2} }}$$

    The pooled estimate from the meta-analysis used a random-effects model to account for heterogeneity, which incorporates extra ‘between-study’ variation in the estimates.

    Each study receives its own (constant) capture probability, which means there are as many capture parameters as studies; however, it is the survival parameters that are of primary interest and the capture parameters serve a modelling function only. In the analysis with genus as a moderator, there is an average for each group (e.g. for genus Anopheles) shared by those studies.

    Three sensitivity analyses were carried out with alternative constraints:

    1. 1.

      For the overall model, with 0 < p < 0.05 and (alpha ge) 1. The analysis asserts increasing or constant mortality with age, and rules out capture probabilities > 0.05, which may be implausible.

    2. 2.

      For the genus-specific model, 0 < p < 0.05 and (alpha) >0.1. The boundary on low values of (alpha) is a practical step to help avoid numerical difficulties, as discussed elsewhere.

    3. 3.

      For the genus-specific model, a meta-analysis excluding any studies with (alpha <1), where simulations showed estimation, produced a high root mean square error (RSME) (Additional File 1: Appendix S4).

    Simulations

    Simulations of known-age MR experiments were carried out using known parameter values and known age, with time-independent capture probability and age-dependent survival. Four Weibull-derived survival models were used to generate simulated data, one exponential ((alpha =1)), two with larger shoulders and increasing mortality with age ((alpha >1)) and one where mortality fell with age ((alpha <1)). These survival curves are shown in Additional File 1: Supplementary Fig. S2 along with their parameter values. The capture probability was set to 0.01 throughout, and there were 1000 runs in each scenario. When summarising scenarios, simulated data were trimmed where (widehat{alpha }) > 30 or (widehat{eta })> 30. The proportion of simulations with these outliers was 0.13.

    In broad terms, the simulations showed that bias and variance reduces with more recapture occasions and larger cohort sizes, with younger release ages, and with more releases. The following inclusion criteria were adopted, when mosquitoes are released at known age, to give broadly accurate estimates (details below): releases at young age (le 3), a sufficient number of recapture occasions (ge 8) and

    1. 1.

      With single release, cohort size (Rge 1000)

    2. 2.

      With multiple releases, (Rge 500)

    The heuristic reasoning for allowing smaller cohort sizes for multiple release experiments (500 versus 1000 for single release) is that the resulting loss of efficiency from the smaller cohorts is somewhat balanced by further releases made within the same experiment. Studies that did not meet the inclusion criteria were excluded from the meta-analysis (see Appendix S2).

    The statistical performance of the main outcome of interest in the present study (text{EL}), along with results for (alpha) and (eta), is summarised in Appendix S4. Under the inclusion criteria, it can be seen that the bias of (widehat{text{EL}}) is low (magnitude (lesssim) 0.5). Furthermore the RMSE of EL is rather smaller than the RMSE of (alpha) when (alpha gtrsim 2) (scenarios a and b). However, when (alpha <1) (scenario d), the RMSE of (widehat{text{EL}}) is large. The main conclusion of the simulations is that the bias of (widehat{text{EL}}) may be reduced to low-moderate levels by the inclusion criteria but that the RMSE of (widehat{text{EL}}) is especially high when (alpha <1). This is the region where Weibull variables are inherently most variable, and any estimates can be very imprecise.

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  • 30-year-old founder reached out to 100 strangers in 100 days—it ‘totally changed my life,’ she says

    30-year-old founder reached out to 100 strangers in 100 days—it ‘totally changed my life,’ she says

    Reaching out to strangers is a daunting prospect for many, but Carly Valancy has a “really special love” for networking, she says.

    Valancy, 30, has had a wide-ranging career so far: she’s worked in tech, theater and marketing, before becoming the co-founder of Momentum Growth, a growth consultancy for female founders.

    Through each professional pivot, networking has been key to her success, she says.

    When she faced a career crossroads in her early 20s, Valancy challenged herself to contact one new person every day for 100 days, an idea inspired by Molly Beck’s networking strategy book “Reach Out.”

    The experience “totally changed my life,” she says.

    “Not only did it give me so many incredible opportunities, jobs and mentors, but it really gave me such a belief in myself — that I could ask for what I want, and I could reach out to a stranger and actually make a genuine connection.”

    Five years later, Valancy is trying the 100 days of networking challenge again. She began in October, and the challenge — which, she clarifies, only takes place on weekdays — will conclude in March on her 31st birthday.

    This time around, she already has a strong network by her side, so her main goal is to “plant seeds for my future self,” she says.

    Her strategy for the challenge

    The first time Valancy tried sending 100 messages in 100 days, she felt “chaotic and desperate to make a change.” By the end of it, she was feeling burnt out, she says.

    “A lot of people turn to networking when they’re in those desperate situations, which makes sense,” she says. “Maybe you were laid off from a job, or maybe you’ve moved to a new city and all of a sudden you’re like, ‘Oh my gosh, I need to be networking.’”

    This time, she’s being more intentional.

    Having a concrete list of goals is crucial, Valancy says. Hers are to find “dream clients” for her consulting firm, make her personal brand “more visible” and to create “amazing experiences” for her family.

    So far, Valancy has scheduled introductory meetings with potential clients, pitched herself to speak at a university, and secured sponsorship from her favorite baby brands for her son’s Formula 1-themed birthday party.

    Valancy doesn’t plan who she reaches out to ahead of time, she says: “I don’t want it to feel like a to do list.”

    Every day, she chooses a person to contact via LinkedIn, social media or email based on “just my curiosity of like, oh, this person seems really cool, or they’re doing work that I want to be doing in 5 years, or, they’ve written something that I really love.”

    Valancy learned from her last attempt at the 100 days challenge that “the coolest opportunities came from the most random places and the most random people,” she says.

    She logs every outreach message and response on Tether, an online platform she created to keep track of her networking efforts. Last time Valancy tried the challenge, she had a 70% response rate.

    “It can be so easy to let these connections or attempts for connection just totally slip through our fingers,” she says, but Tether helps her stay organized.

    How she overcomes networking nerves

    Many people resist reaching out to others because they’re afraid “of being rejected or being judged,” Valancy says.

    Even after sending hundreds of networking messages over the course of her career, Valancy still gets nervous: “The fear around putting yourself out there is so real, and to pretend it isn’t is such a lie,” she says.

    Instead of letting that hold her back, Valancy chooses to be open about her feelings instead.

    “It’s really disarming to just tell the truth, and to just say how you feel about attempting to connect with someone,” she says.

    Valancy has found that people are more likely to respond positively when she openly acknowledges how stressful and awkward networking can be.

    “I can’t tell you the amount of times I will reach out to someone and just be like, ‘You are way out of my league, and I feel so nervous to be reaching out to you right now,’” she says.

    “The second that we can kind of like let our guard down, be honest, and share that with the person that we’re trying to connect with — first of all, the better it feels, the more genuine and truthful it feels, but also the better it is received,” Valancy continues.

    Networking can feel “really icky” when people approach it from a transactional perspective, she says, but it doesn’t have to be that way.

    She describes her networking ethos as “the anti-sales bro approach”: “I just want to like feel like myself when I talk to other people,” Valancy says.

    The connections you make today can “have incredible effects on our life many years from now,” according to Valancy.

    “Life really, really is about who you know,” she says. “The professional things, the personal things — all of the best things in our life are made possible by the people around us, and by the company we keep.”

    Want to level up your AI skills? Sign up for Smarter by CNBC Make It’s new online course, How To Use AI To Communicate Better At Work. Get specific prompts to optimize emails, memos and presentations for tone, context and audience.

    Plus, sign up for CNBC Make It’s newsletter to get tips and tricks for success at work, with money and in life, and request to join our exclusive community on LinkedIn to connect with experts and peers.

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  • BNY to Speak at the Goldman Sachs Financial Services Conference

    BNY to Speak at the Goldman Sachs Financial Services Conference

    NEW YORK, Nov. 10, 2025 /PRNewswire/ — The Bank of New York Mellon Corporation (“BNY”) (NYSE: BK), a global financial services company, today announced that Robin Vince, Chief Executive Officer, will speak at the Goldman Sachs Financial Services Conference in New York at 10:00 a.m. ET on Wednesday, December 10, 2025. The discussion may include forward-looking statements and other material information.

    A live webcast of the audio portion of the conference will be available on the BNY website (www.bny.com/investorrelations). An archived version of the audio portion will be available on the BNY website approximately 24 hours after the live webcast and will remain available until January 9, 2026. 

    About BNY

    BNY is a global financial services company that helps make money work for the world – managing it, moving it and keeping it safe. For more than 240 years BNY has partnered alongside clients, putting its expertise and platforms to work to help them achieve their ambitions. Today BNY helps over 90% of Fortune 100 companies and nearly all the top 100 banks globally access the money they need. BNY supports governments in funding local projects and works with over 90% of the top 100 pension plans to safeguard investments for millions of individuals, and so much more. As of September 30, 2025, BNY oversees $57.8 trillion in assets under custody and/or administration and $2.1 trillion in assets under management.

    BNY is the corporate brand of The Bank of New York Mellon Corporation (NYSE: BK). Headquartered in New York City, BNY has been named among Fortune’s World’s Most Admired Companies and Fast Company’s Best Workplaces for Innovators. Additional information is available on www.bny.com.  Follow on LinkedIn or visit the BNY Newsroom for the latest company news.

    Contacts:

    Investors
    Marius Merz
    +1 212 298 1480
    marius.merz@bny.com

    Media
    Anneliese Diedrichs
    +1 646 468 6026
    anneliese.diedrichs@bny.com

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  • Kyndryl expands collaboration with Dow to accelerate application modernization with AI and automation

    Kyndryl expands collaboration with Dow to accelerate application modernization with AI and automation

    NEW YORK, Nov. 10, 2025 /PRNewswire/ — Kyndryl (NYSE: KD), a leading provider of mission-critical enterprise technology services, today announced the expansion of its nearly two-decade collaboration with Dow (NYSE: DOW), a global leader in materials science. Through this expanded engagement, Kyndryl will collaborate with Dow to modernize infrastructure applications leveraging AI and automation to boost operational agility and accelerate innovation across Dow’s technology stack.

    “Dow’s IT mission is to empower our teams with modern, intelligent solutions that drive productivity and innovation across our global operations,” said Chris Koniecny, Enterprise Applications & Technology IT Director at Dow. “By partnering with Kyndryl to modernize our application landscape and infuse AI and automation, we are taking a bold step forward in our digital transformation journey. This collaboration has also delivered measurable cost savings for Dow. Kyndryl’s expertise in providing end-to-end modernization and applications services across the enterprise and commitment make them the ideal choice for this next phase.”

    “We are proud to expand our collaboration with Dow,” said Gretchen Tinnerman, Managing Partner at Kyndryl. “By applying our advanced capabilities in application management, AI and automation, we are helping Dow drive enterprise-wide innovation, enhance efficiency and support its applications in becoming modern, resilient and future-ready.”

    Over the two decades, the collaboration has enabled Dow to modernize and enhance operational efficiency across its global operations. Kyndryl also manages and provides Kyndryl Consult services to Dow’s IT infrastructure including cloud, network, digital workplace, and security and resiliency services.

    About Kyndryl

    Kyndryl (NYSE: KD) is a leading provider of mission-critical enterprise technology services offering advisory, implementation and managed service capabilities to thousands of customers in more than 60 countries. As the world’s largest IT infrastructure services provider, the company designs, builds, manages and modernizes the complex information systems that the world depends on every day. For more information, visit www.kyndryl.com.

    Kyndryl press contact 
     [email protected]

    SOURCE Kyndryl

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  • S&P Global Adds Robert Moritz to its Board of Directors

    S&P Global Adds Robert Moritz to its Board of Directors

    NEW YORK, Nov. 10, 2025 /PRNewswire/ — S&P Global (NYSE: SPGI) announced today that its Board of Directors has approved the addition of Mr. Robert Moritz to the Board, effective March 1, 2026.

    Mr. Moritz has more than four decades of global leadership experience specifically in audit and assurance in the financial services, banking sectors and capital markets. Most recently, Mr. Moritz served as global Chairman of PricewaterhouseCoopers LLC (PwC) where he led the company’s global leadership teams – setting strategy and elevating PwC’s brand among its clients and stakeholders.

    Mr. Moritz is currently a member of Walmart’s Board of Directors, where he sits on the retailer’s audit and technology and e-commerce committees, and Northern Trust Corporation, as a member of the audit and human capital and compensation committees. In addition, he holds several not-for-profit Board seats, including at SUNY-Oswego College Foundation, his alma mater.

    “We’re thrilled to welcome Bob to our Board,” said Martina L. Cheung, President and CEO, S&P Global. “He brings extensive global experience and unique perspectives on the opportunities and risks facing global companies in today’s fast-paced environment.”  

    “We’re delighted that Bob will be joining our Board,” said Ian P. Livingston, Non-Executive Chairman of the Board of S&P Global. “His experience as a global financial services industry leader will be extremely valuable in helping S&P Global to manage the opportunities and challenges that lie ahead and the evolving needs of our clients.”

    “It’s a privilege to serve on the Board of a company that has built a trusted reputation in global markets anchored on integrity and independence,” said Bob Moritz. “I’m excited to collaborate with my fellow Board members, supporting Martina and S&P Global’s leaders as the company moves into its next phase of growth.”

    Mr. Moritz will serve on the S&P Global Board’s Audit and Nominating and Corporate Governance committees.

    About S&P Global

    S&P Global (NYSE: SPGI) provides Essential Intelligence. We enable governments, businesses and individuals with the right data, expertise and connected technology so that they can make decisions with conviction. From helping our customers assess new investments to guiding them through sustainability and energy transition across supply chains, we unlock new opportunities, solve challenges and Accelerate Progress for the world.

    We are widely sought after by many of the world’s leading organizations to provide credit ratings, benchmarks, analytics and workflow solutions in the global capital, commodity, and automotive markets. With every one of our offerings, we help the world’s leading organizations plan for tomorrow and today. For more information, visit www.spglobal.com.

    Investor Contact:

    Mark Grant 
    Senior Vice President, Investor Relations and Treasurer 
     +1 347-640-1521
    [email protected]

    Media Contact:

    April Kabahar 
    Global Head of Corporate Communications 
     +1 917-796-3121 
    [email protected]

    SOURCE S&P Global

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  • Exposure to high altitude is associated with an elevated risk of hip fracture: a retrospective cohort study using data from the CHARLS | BMC Geriatrics

    Exposure to high altitude is associated with an elevated risk of hip fracture: a retrospective cohort study using data from the CHARLS | BMC Geriatrics

    Study population and dataset

    The CHARLS is a prospective national cohort study that enrolled 17,708 participants in 2011, and three waves of follow-up were conducted in 2013, 2015, and 2018. Participants were randomly selected using a probability-proportional-to-size (PPS) technique and a four-stage random sample method. The workgroup selected 150 counties in 28 provinces. Administrative villages in rural areas and neighborhoods in urban areas were the primary sampling units (PSUs). Three PSUs within each county-level unit were selected using PPS sampling. Detailed information on the methodology and cohort profile has been reported previously [11].

    We collected data on participants enrolled in the baseline investigation who attended all three follow-up investigations and aged above 60 years old. Those with a history of cancer were excluded because the progression and treatment of cancer affect multiple organs and systems throughout the body; this aspect could have introduced substantial bias into our study. During the data cleaning process, we identified participants with implausible data regarding body weight and height, specifically heights less than 100 cm or weights less than 20 kg. These values were considered erroneous owing to likely data entry mistakes; therefore, participants with an abnormal body mass index (BMI) (BMI < 10 or BMI > 60) in the baseline investigation were excluded.

    Altitude data acquisition

    The participants’ place of residence was determined by their community ID. The latitude and longitude were acquired through amap api (https://restapi.amap.comv3/geocode), and the local altitude was acquired based on the latitude and longitude using geodata (version 0.5-8) [12] packages and the raster package (version 3.6–23) [13]. Participants were stratified into a low-altitude or high-altitude group based on a criterion of 1500 m, which was used in previous studies [14].

    Variable collection

    The primary outcome was hip fractures reported by the participants, which was defined by their answer to the question, “Have you fractured your hip since the last interview?” Participants choosing “yes” were considered to have experienced a hip fracture, and the time point the investigation occurred was recorded as the time the event happened.

    Individual income, marital status, medical history, sex, BMI, smoking status, alcohol consumption, and age were selected as covariates for propensity score matching (PSM). Individual income was acquired from harmonized CHARLS data and evenly divided into five groups. Educational status was retrieved from the answer to the question: “What is the highest level of education completed?” and re-coded into preschool, primary, secondary, and higher education. Marital status was retrieved from the answer to the question: “What is your marital status?” and re-coded as unmarried, married, widowed, divorced (or long-term separation). History of stroke, cardiovascular dysfunction, falls, hip fractures, chronic lung diseases, and arthritis were retrieved from the answer to the question: “Have you been diagnosed with any of the following by a doctor?” The presence of specific diseases was determined based on whether the participant selected “Yes” to the following conditions: (1) “Chronic lung diseases, such as chronic bronchitis and emphysema (excluding tumors or cancer),” (2) “Heart attack, coronary heart disease, angina, congestive heart failure, or other heart problems,” (3) “Stroke,” and (4) “Arthritis or rheumatism.” BMI was calculated using the following formula: BMI (kg/m[2]) = body weight (kg)/body height (m)2.

    Propensity score matching

    Individual income, marital status, medical history, sex, BMI, smoking status, alcohol consumption, and age were selected for PSM. A general linear model was used to calculate the distance, and the nearest method was used for matching with a matching ratio of 1:8 (high altitude: low altitude). After matching, a balance test was performed to evaluate the imbalance between the two groups in the matched data. The MatchIt package [15] was used for matching, and the Cobalt package [16] was used for the balanced test and plotting.

    Statistical analysis

    Categorical variables are shown as counts (percentages, %), and continuous variables are shown as mean ± SD. To compare the differences in baseline data between the two groups, chi-square and t-tests were used for categorical and continuous variables, respectively. Kaplan–Meier survival analysis and Cox regression were used to compare the differences between the two groups regarding fall and hip fracture risks.

    Subgroup analysis was performed, and the participants were divided into subgroups according to sex, smoking history, and overweight status. The effects of high-altitude exposure on fall and hip fracture risks in different subgroups were evaluated using Cox regression analysis.

    To test the robustness of our results, we performed different sensitivity analyses as follows: (1) We randomly sampled 80% and 90% of all participants, respectively, and replicated the previously described analysis workflow. (2) Using optimal matching as the PSM method and a matching ratio of 1:8, we replicated the previously described analysis workflow. (3) Cox regression was performed with and without adjustments for covariates without matching the participants.

    All analyses were performed using R (version 4.3.1). The survival analysis was performed using the survival package (version 3.5-5) [17] and visualized using the Survminer package (version 0.4.9) [18]. The comparison of baseline characteristics and the generation of tables was performed using the gtsummary package (version 1.7.2) [19].

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  • Harnessing telemedicine to deliver patient-centered opioid agonist treatment within a community-based harm reduction service: a mixed methods evaluation | Harm Reduction Journal

    Harnessing telemedicine to deliver patient-centered opioid agonist treatment within a community-based harm reduction service: a mixed methods evaluation | Harm Reduction Journal

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  • Eating disorders symptoms and associated risk factors among medical students in France: a nationwide cross-sectional study | Journal of Eating Disorders

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  • Cost-Effectiveness Analysis and Priority Setting in the Transition fro

    Cost-Effectiveness Analysis and Priority Setting in the Transition fro

    Introduction

    During pregnancy, adequate maternal nutrition is required to provide the increased nutritional requirements necessary for metabolic maintenance and fetal growth.1–4 In 2020, the Indonesian Ministry of Health documented 4627 maternal deaths, indicating an increase relative to 2019.5 The increase is attributed to the poor nutritional status of pregnant women.6,7 Insufficient multiple micronutrients during pregnancy can increase the risk of complications, including anemia, which affects about 40% of pregnant women globally and peaks at 49% in Southeast Asia.8,9 Multiple micronutrient insufficiency could negatively impact pregnancy outcomes, leading to fetal loss, low birth weight (LBW), preterm birth, preeclampsia, small for gestational age, postpartum depression, elevated risk of neural tube defects, and increased mortality risk.1,10,11 These have been related to immunological development and inadequate neurodevelopmental outcomes in children.12,13 Inadequate development during childhood can extend into adolescence and adulthood, resulting in poor academic performance, reduced income, and reduced human capital.12

    To reduce the risk of various complications, the World Health Organization (WHO) has promoted the supplementation of pregnant women with multiple micronutrient supplementation (MMS) since 2016.14 The administration of MMS is recommended at a dosage of 180 tablets during the first six months of pregnancy.15 MMS is crucial for cellular metabolism, development, and maintaining normal physiological functioning in the human body. MMS is a micronutrient containing 15 vitamins and minerals that fulfill the body’s nutritional requirements. Micronutrient composition as specified by the United Nations International Multiple Micronutrient Antenatal Preparation – multiple micronutrient supplements (UNIMMAP – MMS) consists of vitamin A (800µg), vitamin D (5µg), vitamin E (10mg), vitamin C (70mg), vitamin B1 (1,4mg), vitamin B2 (1,4mg), vitamin B6 (1,6mg), vitamin B12 (2,6 µg), folic acid (400µg), iron (30mg), zinc (15mg), iodine (150µg), selenium (65µg), niacin 18mg, and copper (2mg).16–18 MMS plays essential roles for human reproduction from early pregnancy, facilitating gametogenesis, fertilization, embryogenesis, and the development of placental function, redox balance, and vascularization.2,19–21 MMS contributes a significant role in metabolic processes essential for cell proliferation, growth, and protein synthesis in early pregnancy, and it is crucial for the establishment of the fetal genome throughout gestation. Furthermore, MMS plays an essential role in organogenesis, the development of the fetal central nervous system, and early brain development. MMS is also critical in regulating hemoglobin metabolism and optimizing mitochondrial function during pregnancy.2,11 MMS was shown to reduce oxidative stress and improve mitochondrial function in a study conducted in Lombok, Indonesia.11 As a result, the risk of fetal loss or miscarriage was reduced by 10%, the risk of infant death was reduced by 18%, and the risk of LBW and premature birth was reduced by 14%. This was compared to using iron and folic acid (IFA) alone. Research conducted on a group of pregnant women who suffered from anemia revealed more substantial findings, including a reduction of the risk of fetal loss and neonatal mortality by 29%, a reduction of the risk of newborn death by 38%, and a reduction of the risk of low birth weight by 25%.2

    In October 2024, the Indonesian government started to implement MMS to improve maternal nutrition nationwide, though it remains in the initial phases.22–24 However, the transition from IFA to MMS tablets in Indonesia might take several years and require critical resources, including human capital and financial investment.25 MMS may receive significant government attention and influence healthcare policies. Consequently, the government requires an economic evaluation study as a critical component in the decision-making process. Given Indonesia’s extensive territory, high population density, and the unmet nutritional requirements for pregnant women across its provinces, a comprehensive economic evaluation is necessary to facilitate informed decision-making by policymakers in allocating budgets, resources, and areas of coverage for maternal and child health programs.14,20,26–29 Numerous studies in Bangladesh,14,26–28 India,28,29 Pakistan,28,29 Tanzania,29 Mali,29 and Burkina Faso14 have shown that MMS was a cost-effective intervention compared to IFA in enhancing maternal and child health.14,26–29 This is the first study aimed at evaluating the cost-effectiveness of transitioning from IFA to MMS in Indonesia for enhancing maternal and child health, and its implications for priority setting in Indonesia based on their incremental cost-effectiveness ratio (ICER).

    Methods

    Model Setting

    We applied an open-access online modeling MMS cost-benefit tool developed by Nutrition International to estimate the cost-effectiveness of MMS compared to IFA in Indonesia at the national and sub-national levels (38 provinces). The MMS Tool incorporates a comprehensive set of background data into its foundational model, which has passed quality assurance by technical specialists to guarantee its accuracy, recentness, and relevance.30–32 MMS tools supply national and global policymakers with context-specific assessments that evaluate whether antenatal MMS is more cost-effective compared to IFA.31,32 A 10-year time horizon was applied to assess the cost-effectiveness value, given the substantial effectiveness of MMS in pregnant women. In these scenarios, we applied a coverage scenario at 44% on a national level (baseline), reflecting the current national adherence level of the IFA program in Indonesia, which stands at 44%,33 and a 100% coverage scenario, which is a hypothetical highest cost scenario. The assumption of 180 tablets consumed by each pregnant woman was utilized following WHO recommendations.3,9 The cost-effectiveness analysis of transitioning from IFA to MMS utilized a population of approximately 211,351 pregnant women, as indicated by the 2023 Indonesian Health Survey data.33 We analyzed the projected economic results of MMS compared to IFA at both the national and provincial levels. Additionally, we calculated the ICER by dividing the incremental costs by the disability-adjusted life years (DALYs). The incremental cost-effectiveness ratio (ICER) was calculated using the standardized Cost-Benefit Tool developed by Nutrition International. This tool, which is publicly available on their website (accessible at: https://www.nutritionintl.org/learning-resources-home/mms-cost-benefit-tool/), generates ICER values automatically. Consequently, the results presented in the manuscript are the direct output from this validated tool. The economic value of DALYs averted, reflecting the total economic benefits of transitioning to MMS, is estimated using a monetised DALY approach based on the Value of Statistical Life (VSL). The VSL quantifies the monetary amount an individual is prepared to pay to prevent injury or illness, with variations observed across different countries. Various methods exist for calculating the VSL for a country. This MMS Tool utilizes country-level VSL estimates in LMICs from Viscusi and Masterman34 to derive the Value of a Statistical Life Year (VSLY) by dividing the VSL by the expected life expectancy at birth. The economic value of DALYs averted is calculated by taking the product of the estimated discounted DALYs averted in a particular scenario by the corresponding country’s VSLY. The calculation of the number of DALYs averted incorporates a discount rate of 3%.31,35

    Cost Data

    Costs are determined from the government’s perspective as the health system provider. The MMS cost-benefit modeling tool was designed according to fixed input cost parameters that covered three categories of costs: the cost of IFA, the cost of MMS, and the transition cost (Table 1). These costs are related to a transition from the IFA to the MMS program.30–32 All costs were converted to USD (2024). The cost of MMS was determined using the United Nations International Children’s Emergency Fund (UNICEF) Supply Catalogue.36 The highest price of MMS was set at USD 4 (IDR 61,600) for 180 tablets. The IFA cost was determined by the decree issued by the Indonesian Ministry of Health regarding drug claim prices. Price differences among regions in Indonesia arise from governmental authorities that adjust drug price claims according to logistics expenses, distribution factors, and geographical characteristics specific to each region.37 IFA supplementation requires 180 tablets to ensure 6 months of coverage, and it was priced at USD 3.31 (IDR 50,940).

    Table 1 Breakdown of Costs (2024 USD/ IDR)

    Transition costs include a range of expenditures that extend beyond direct interactions between patients and providers. These include logistical expenses and administrative costs at national and provincial levels, as well as investments in training, nutrition education activities, media promotion, and supervision mechanisms.38,39 In this scenario, the transition cost is set with an additional 13% markup rate applied to the medicine and supply price (ie MMS price) to cover logistics and administrative costs. The costs associated with program transition can constitute an essential component of the total costs.38,39 Table 1 presents the cost data at both the national and provincial levels.

    Maternal Health Parameters

    The effect measure is captured as DALY averted. DALY quantifies the overall disease burden by integrating years of life lost due to premature mortality (YLLs) and years lived with disability (YLDs). One DALY lost signifies one year of healthy life lost; consequently, one DALY averted corresponds to the acquisition of one year of healthy life. Assessing the cost per DALY averted allows for the comparison of various health interventions and the evaluation of an intervention’s impact.26

    The approach examines supplements based on their effectiveness in affecting various health outcomes. This study utilized an open-access online MMS cost-benefit modeling tool developed by Nutrition International,30–32 which was designed according to fixed input maternal health parameters as identified in two published reviews, Keats et al, 201940 and Smith et al, 2017.41 The following maternal health parameters examined in this study, including life expectancy at 73.93 years,33 maternal anemia at 27.7%,33 preterm birth at 11.1%,33 SGA (small for gestational age) at 23.8%, LBW at 6.1%,33 stillbirth at 10.5 per 1000 births,42 maternal mortality at 189 per 100,000 live births,42 neonatal mortality (male and female) at 3.3–3.5 per 1000 live births,42 and infant mortality at 7.8 per 1000 live births.42 These numbers are based on national data from the literature. Sources of health outcomes data include the Indonesian Health Survey 202333 (ie, for preterm birth, maternal anemia, LBW, and life expectancy), the Indonesian Health Profile 202342 (ie, for stillbirth, maternal mortality, neonatal mortality, and infant mortality), and research journals. To determine which provinces should be prioritized in conducting the IFA to MMS transition program, the data collection results were divided into 38 Indonesian provinces (see Table 2).

    Table 2 Maternal Health Parameters

    Priority Setting

    We applied a cost-effectiveness league table to evaluate the prioritization of MMS implementation by comparing the ICER in each province to the national ICER. The cost-effectiveness league table can serve as a tool for resource and budget allocation. A cost-effectiveness league table is a commonly utilized tool for quantitatively ranking priorities based on efficacy, safety, and costs. Healthcare resources could be distributed according to the strategies listed in the league table, beginning with the province with the lowest ICERs and subsequently progressing to the provinces with higher ICERs in the ranking.43 In provincial-level scenarios, we adopted a baseline population coverage of 44%, consistent with the national level.

    Sensitivity Analysis

    We applied one-way deterministic sensitivity analyses to identify variables that might significantly affect results. We performed a one-way deterministic sensitivity analysis to investigate the effects of different input parameters on ICER. All parameters were adjusted by plus or minus 25% for DALYs lost and associated costs.44

    Results

    Maternal Health Outcomes

    Implementing MMS in the 44% and 100% coverage scenarios resulted in 54.897 and 124,766 DALYs averted in Indonesia, respectively. MMS was estimated to have an impact on both scenario (44% and 100% coverage scenarios), resulting in 20,305 and 46,148 DALYs averted in stillbirth, 11,965 and 27,194 DALYs averted in neonatal mortality, 12,879 and 29,271 DALYs averted in preterm birth, 106 and 241 DALYs averted in LBW, 5656 and 12,855 DALYs averted in infant mortality and 9061 and 20,592 DALYs averted in SGA, respectively (Figure S1).

    Cost-Effectiveness Analysis

    According to the perspective of the Indonesian government as the health system provider, the implementation of MMS yielded ICER values of USD 10 per DALY averted in the 44% coverage scenario. Implementing MMS under 100% coverage scenarios yielded an identical ICER value of USD 10 per DALY averted. Variations in coverage will affect the overall costs and benefits, both of which change linearly and proportionally. This is, provided that the ICER, defined as the cost per DALY averted, remains unchanged. The MMS implementation is considered highly cost-effective, as the ICERs remained significantly below the Indonesian one GDP per capita (USD 4870.13) in 2024.45

    Priority Setting

    The MMS implementation in each province is considered highly cost-effective. The ICER of each province in Indonesia is presented in a cost-effectiveness league table (supplementary material 2). Nevertheless, determining priority coverage areas is crucial due to budget constraints and significant disparities in monetary value across different provinces.46 The resource and budget allocation applied to the national ICER as a cost-effectiveness cut-off value. Provinces prioritized for the introduction of MMS in Indonesia are those with ICER values lower than the national ICER value (USD 10 per DALY averted). Therefore, the implementation of the MMS program is recommended to be prioritized in 18 provinces: Southwest Papua, Highlands Papua, Bali, West Java, South Kalimantan, North Maluku, West Papua, Aceh, North Sumatra, Central Java, Central Kalimantan, West Sulawesi, Maluku, Yogyakarta, East Java, North Kalimantan, South Sulawesi, and South Papua as illustrated in Table 3 and Figure 1.

    Table 3 Cost-Effectiveness League Table

    Figure 1 Priority setting for the MMS Program in Indonesia.

    Sensitivity Analysis

    Related to the sensitivity analysis, MMS cost and IFA cost seemed to be the most influential variables affecting the cost-effectiveness value in the implementation of MMS. Other variables that importantly influence the cost-effectiveness value are stillbirth, life expectancy (at birth), neonatal mortality (female), preterm birth, and small gestational age as presented in a tornado chart (Figure S2).

    Discussion

    Insufficient maternal nutritional intake significantly contributes to adverse birth outcomes.1,8,10,11 The WHO has advocated for the supplementation of pregnant women with MMS since 2016 to decrease the risk of numerous complications.9 Our findings show that the projected positive impacts of transitioning from IFA to MMS resulted in reduced mortality rates and adverse birth outcomes. The monetary investment necessary to realize these improvements indicates a cost-effective outcome. Additionally, priority setting based on cost-effectiveness was considered a reasonable option to guide decision-makers in resource allocation to maximize health outcomes, serving as a key consideration in strategic planning for achieving universal health coverage.48,49 Furthermore, the outcomes exhibited stability throughout one-way deterministic sensitivity analyses, indicating that these conclusions were reliable.

    Assuming that all pregnant women adhere to a regimen of 180 pills throughout their pregnancies, the transition from IFA to MMS is projected to 54,897 (44% coverage scenario) and 124,766 (100% coverage scenario) DALYs would be averted in Indonesia. The difference in DALYs averted between the 44% and 100% coverage scenarios is attributable to the differing number of women who benefit from the intervention in each case. With 100% coverage, a greater percentage of pregnant women receive MMS, resulting in a more significant overall decrease in negative maternal and neonatal health outcomes, thus yielding higher DALY estimates. Consuming 180 MMS tablets has played an essential role in pregnant women from early pregnancy in human reproduction, facilitating gametogenesis, fertilization, embryogenesis, placental development, function, redox balance, and vascularization.19–21,50 The growth and function of the placenta are crucial throughout pregnancy to reduce the risk of LBW (14%). MMS demonstrates improved mitochondrial function, which is associated with a reduced risk of premature birth (14%). MMS contributes to hemoglobin metabolism, thus decreasing the risk of anemia in pregnancy. MMS effectively reduces the risk of infant mortality (18%).1,8,10,11,50,51 It is crucial to highlight that, even at existing coverage levels, significant reductions in maternal and child mortality and morbidity are projected in Indonesia if there is high adherence to the prescribed amount of tablets.14 Adherence level is crucial for maximizing the health advantages of MMS intervention20 since medication non-adherence has been identified as a major contributor to health issues and economic burden.52 A report concerning the previous IFA program in Indonesia shows that the adherence rate for the consumption of 90 IFA tablets is merely 44%.33 Consequently, adherence to MMS consumption will be a potential challenge that must be addressed.

    As a country considering investment in MMS, Indonesia requires an assessment of the cost-effectiveness of transitioning from IFA to MMS.29 The MMS cost-benefit tool was utilized to quickly calculate predictions regarding maternal and child health outcomes, as well as the cost-effectiveness of MMS in comparison to IFA for pregnant women. The MMS cost-benefit tool works as an evidence-based modeling instrument designed to facilitate national and international policymakers’ access to data that aids in evaluating the cost-effectiveness of transitioning from IFA to MMS for pregnant women. The MMS cost-benefit tool presents a valuable resource for countries to perform comprehensive, sub-national, and ongoing analyses within the framework of implementation research on MMS.31

    Our findings prove that transitioning from IFA to MMS is highly cost-effective based on the threshold of one to three times Indonesia’s GDP per capita, in the absence of country-specific thresholds.53 Our analysis shows that the implementation of MMS under both 44% and 100% coverage scenarios produced an equal ICER value of USD 10 per DALY averted at the national level. The ICER is consistent across the two scenarios, as it is defined as the additional cost per DALY averted in relation to IFA supplementation. The proportional scaling of both costs and effects with coverage in the modeling framework ensures that the ratio of incremental costs to health benefits remains constant. This demonstrates the difference in the absolute number of DALYs between the two scenarios, despite the constancy of the ICER. The spending required to achieve these improvements, which is below one GDP per capita (USD 4870.13) in 2024, signifies a favorable cost-benefit ratio. Cost-effectiveness analysis provides a quantitative evaluation of both current and prospective efficiency in a health system.49 The cost-effectiveness of the IFA-MMS transition in reducing maternal and child mortality and morbidity (eg, neonatal mortality, infant mortality, LBW, preterm births, stillbirth, and SGA) has proven beneficial. Given that most lives saved would occur in the early stages of life, the policy of transitioning from IFA to MMS is worthy of consideration.

    Currently, MMS is in the initial phases of implementation in Indonesia. The findings of this study support decision-making on the possibility of MMS being expanded as a key focus within Indonesia’s national health program, serving as a primary strategy for lowering the risk of numerous complications in pregnant women.48,49,54 The findings of this study align with previous studies conducted in Bangladesh,14,26–28 Burkina Faso,14 Pakistan,28,29 India,28,29 Mali,29 and Tanzania,29 which indicated that transitioning from IFA to MMS is considered a cost-effective intervention to enhance maternal and child health. This is the first study conducted in Indonesia addressing this issue.

    Related to the sensitivity analyses, the results in this study showed that the cost-effectiveness value was sensitive to changes in MMS cost. Numerous variables can influence MMS costs, including procurement regulations, the volume, and consistency of purchasing bargains with tablet ingredient suppliers, packaging methods, and tablet quantities.14,18,36,55–57 Consequently, the introduction of MMS at accessible costs is essential, particularly for low- and middle-income countries (LMICs), including Indonesia.55 Scale economies in tablet production are crucial therefore, the domestic manufacture of MMS in Indonesia should be initiated to provide a consistent, affordable, and high‐quality supply of MMS, while supporting the expansion of MMS coverage.14,58 Domestic manufacture of MMS could be accomplished by following the standards established by the MMS Technical Advisory Group, alongside a comprehensive technical understanding of the manufacturing prerequisites for the UNIMMAP–MMS product, and the methodologies to ensure that the produced product achieves its expected quality.14,18

    As Islam is the major religion among Indonesia’s population, ensuring halal compliance is essential. The UNIMMAP–MMS product can be produced in compliance with Halal standards established by local authorities.18 Indonesia is home to over 207 million Muslims, representing 87.2% of the population.59 Halal is an essential concept for Muslim consumers concerning the products they consume, including pharmaceutical ingredients. In contemporary medicine, these ingredients must be completely free of porcine (0%) and contain less than 1% alcohol.60,61

    In October 2024, the Indonesian Ministry of Health announced the immediate initiation of MMS in Indonesia, a program currently in its early stages.22–24 Switching from IFA to MMS tablets in Indonesia may require several years and substantial resources, particularly personnel and budgetary expenditure, given the country’s expansive territory and dense population.25 Due to logistical and budget limitations, determining priority coverage areas is essential to mitigate the disease burden associated with nutritional deficiencies among pregnant women. This involves identifying higher-risk groups and regions where preventive measures are expected to be most effective, thereby maximizing public health returns on investment.20,46

    Priority settings should focus on optimizing population health, and the availability of further knowledge regarding cost-effectiveness will enhance decision-making and result in improved health outcomes as one consideration in strategic planning.48,49 Cost-effectiveness evidence in each province enables policy-makers to assess the effective and efficient use of available resources. It also guides optimal investment strategies to meet health targets and achieve universal health coverage within the constraints of limited resources, ensuring the optimal allocation of financial resources in the healthcare sector.38,49 Universal health coverage signifies that every individual has access to a comprehensive array of quality health services as required, without experiencing financial difficulties.62 Based on the results of the cost-effectiveness analysis, it’s recommended to prioritize introducing the MMS program in 18 provinces where the ICER is below Indonesia’s national ICER of USD 10. The analysis commences with the province exhibiting the lowest ICERs, subsequently advancing to those with higher ICERs in the rank order.43 Public health strategies prioritize introducing MMS programs, especially in provinces with poor maternal health outcomes. Expanding MMS programs in LMICs necessitates advancements in supply chain logistics and improved availability and access to health services.15,63 The prioritization of MMS implementation allows for the distribution of healthcare resources under the strategies, starting with the province exhibiting the lowest cost-effectiveness and subsequently advancing to those with higher cost-effectiveness in the ranking.

    To the best of our knowledge, our study is the first to assess cost-effectiveness analysis regarding the transition from IFA to MMS during pregnancy in Indonesia at the national and sub-national levels (38 provinces). The primary strength of this study is the use of country-specific data, which enables policymakers to make informed decisions regarding finance and resource allocation by prioritizing selected coverage areas in MMS implementation. Specifically, MMS programs have not yet been included in the national healthcare insurance coverage unit, and our study offers valuable insights into the implications of their potential inclusion. With regards to the shifting from IFA to MMS supplementation in Indonesia, our cost-effectiveness study demonstrates that the implementation of MMS is more advantageous than IFA, aligning with the WHO’s recommendation to improve the quality of maternal and child health.

    Nevertheless, this study has several limitations. The first limitation concerns a proposed transition cost that has been estimated based on population size to assess context-specific expenses associated with initiating a new program. These expenses include developing training components, establishing new policies and regulations, and training healthcare personnel; however, actual costs may differ. In the absence of more reliable data on transition costs, we perform a costing exercise for transition activities informed by a published article on the cost-effectiveness of interventions aimed at improving maternal, newborn, and child health outcomes: a WHO Choosing Interventions that are Cost-Effective (CHOICE) analysis for Eastern Sub-Saharan Africa and South-East Asia.38 Consequently, we may have underestimated the transition cost. As for the second limitation, this study did not address MMS adherence. The analysis presumes that all “covered” pregnant women receive and take precisely 180 tablets. Achieving all consumption of a precise dosage of tablets by all covered women will be challenging and does not seem to occur systematically. Some pregnant women may take fewer than 180 tablets. Referring to the previous IFA program in Indonesia, a study indicates that the adherence level to the consumption of 90 IFA tablets is only 44%.33 The costs associated with transitioning from IFA to MMS will rise, while the expected benefits will remain unchanged. Further attempts are required to address these issues. The third limitation is that the modeling tool for cost-benefit analysis, developed by Nutrition International, does not allow the inclusion of additional maternal health parameters that could be significant for assessing the effectiveness of MMS in pregnant women. The model focuses on health outcomes of interest, as identified in two published reviews, Keats et al, 201940 and Smith et al, 2017.41 The fourth limitation is that probabilistic sensitivity analysis (PSA) cannot be performed using the open-access online MMS cost-benefit tool developed by Nutrition International because the tool is designed primarily to provide deterministic estimates based on fixed input parameters rather than full probabilistic modeling. While the tool incorporates rigorous methodologies to estimate health impacts, costs, and cost-effectiveness, it does not support Monte Carlo simulations or the input of probability distributions for parameters that allow quantification of uncertainty across multiple model inputs simultaneously. However, the deterministic approach can be used to provide clear, interpretable results such as benefit-cost ratios and incremental cost per DALY averted based on fixed assumptions, but without performing full probabilistic uncertainty analysis. More complex PSA requiring simulation of parameter distributions and joint uncertainty are typically conducted offline using statistical software, as seen in some detailed cost-effectiveness studies of MMS interventions. Thus, while the tool does conduct some sensitivity analyses by varying key assumptions, full probabilistic sensitivity analysis is not supported in its open-access online version.

    This study provides recommendations for policymakers in Indonesia to decide on following comprehensive policies to improve maternal and child health outcomes. Health enhancements throughout gestation and early childhood might lower the likelihood of poor health and disease, hence supporting life expectancy.64 The additional budgetary requirement poses significant challenges to implement in a country with constrained healthcare budgets for maternal and child health programs. The MMS implementation strategy could start with a 44% coverage scenario across 18 prioritized provinces and gradually expand to 100% coverage for all pregnant women in Indonesia. This approach is critical for transitioning from the IFA to the MMS program. Transitioning from IFA to MMS represents the ideal approach for enhancing maternal and child health outcomes.54 We are encouraged that the reviewer acknowledges the policy relevance of our findings and their potential to inform resource allocation and program planning for maternal nutrition interventions. Further study is required to incorporate a concise perspective on the necessity of supplementary qualitative or operational studies, which could highlight the significance of converting economic findings into practical implementation.

    Conclusions

    This study showed the ICER value of USD 10 per DALY averted for both scenarios (44% and 100% coverage), concluding that the transition from IFA to the MMS program was confirmed to be a highly cost-effective intervention. These results should facilitate decision-making that prioritizes maternal and child health. The MMS implementation strategy could start with a 44% coverage scenario across 18 prioritized provinces and gradually expand to 100% coverage for all pregnant women in Indonesia. This strategy is essential for the transition from the IFA to the MMS program in Indonesia. It is essential to identify several potential barriers to the implementation of the MMS program in Indonesia, including the adequacy of supply chain logistics, the improvement of health service availability and access, and the need for better adherence to MMS consumption.

    Ethical Clearance

    Data were obtained from publicly accessible documents, and human participants were not involved in this investigation. Consequently, ethical considerations regarding human participants were not necessary. Nevertheless, efforts were made to guarantee that the data were collected and analyzed in a manner that was both ethical and transparent.

    Disclosure

    The authors report no conflicts of interest in this work.

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    64. Dundas R, Boroujerdi M, Browne S, et al. Evaluation of the Healthy Start voucher scheme on maternal vitamin use and child breastfeeding: a natural experiment using data linkage. Public Heal Res. 2023;11(11):1–101.

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  • Current State and Challenges of Dental Intervention in Aspiration Pneu

    Current State and Challenges of Dental Intervention in Aspiration Pneu

    Introduction

    The world is trending toward an aging society, and this trend is expected to continue.1 In Japan, this impact is particularly pronounced, with projections suggesting that by 2060, nearly 40% of the population will be older than 65 years.2 In addition, the incidence of aspiration pneumonia in Japan increased by 53.7% between 2005 and 2019.3 The United States has a similar substantial burden: a recent epidemiological analysis identified over 1.1 million aspiration pneumonia-related deaths from 1999 to 2017, averaging about 58,000 deaths per year.4 Although the incidence decreased between 2002 and 2012, the median total cost of hospitalization for patients aged ≥ 65 years doubled over the decade, rising from US $16,173 to US $30,280.5 Addressing these issues has become an urgent global challenge.4,6

    Generally, the risk of aspiration pneumonia is influenced by diminished swallowing function and the intraoral environment.7 In particular, disruption of the oral microbiome and biofilm formation is a core mechanism linking oral conditions to systemic health.8 Poor oral hygiene plays a pivotal role in this process by increasing the oropharyngeal bacterial load. Pathogenic microorganisms that proliferate in unclean mouths, including anaerobes, such as Fusobacterium spp., and Gram-negative bacilli, such as Klebsiella pneumoniae, can be aspirated into the lungs, especially in patients with dysphagia or an altered gag reflex.9 Therefore, it has been shown that indicators of good oral hygiene, such as receiving regular professional dental cleanings and frequent toothbrushing (≥3 times a day), are independently associated with lower pneumonia incidence.10 Also, specialized oral health care provided by dentists and dental hygienists has been shown to significantly reduce the incidence of aspiration pneumonia during the perioperative period for esophageal cancer.11 Furthermore, recent reports suggest that oral health care by dentists can prevent the recurrence of aspiration pneumonia, highlighting the critical importance of dental intervention in the management of aspiration pneumonia.12–14

    Given the clear link between oral health interventions and pneumonia, dental professionals have a vital role to play in the multidisciplinary management of aspiration pneumonia. However, some barriers persist in achieving collaboration between physicians and dentists. One challenge is the separation of the healthcare system: in many countries, dentistry has operated largely in parallel to medicine rather than integrated within it.15 Also, the number of dentists in hospitals is limited: only 28% of hospitals in Japan currently employ full-time dentists.16 Furthermore, another barrier is the limited awareness and training across professions. Some physicians and nurses underestimate the impact of oral hygiene on systemic health or view oral care as a “nursing task” rather than a medical priority.14,17

    Hospitalists, through their interventions, have been reported to reduce the length of hospital stays, improve the quality of medical care, and have a favorable impact on healthcare economics.18,19 Also, it is shown that hospitalist management of pneumonia is associated with shorter length of stay and lower costs compared with non-hospitalist care.20 Although they are familiar with aspiration pneumonia, to the best of our knowledge, there have been no investigations into the frequency or specific nature of hospitalists’ collaboration with dentists in the management of aspiration pneumonia.

    Therefore, we aimed to clarify the state of dental involvement in aspiration pneumonia management and identify challenges that need to be addressed.

    Materials and Methods

    Study Design, Setting, and Participants

    This study was an observational cross-sectional survey based on questionnaires sent to all individuals listed on the Japanese Society of Hospital General Medicine (JSHGM) mailing lists. JSHGM is primarily responsible for the board certification of hospitalists, whereas the Japan Primary Care Association (JPCA) is mainly responsible for the board certification of family physicians. Many hospitals in Japan belong to both organizations. Since this study focused on hospitalists, the survey was conducted among members of JSHGM,21 as it was considered suitable for surveying hospitalists in Japan. In this study, hospitalists were defined as general medicine doctors working in a hospital, which was defined as a hospital with 20 or more beds.22 The participants of this study were Japanese hospitalists. We included data collected from August 23 to November 15, 2023. Individuals who did not provide consent and those who were not hospitalists were excluded.

    Survey Instrument Variable

    In this study, we developed an original questionnaire to collect baseline data on participants’ basic attributes and work environments, as well as outcome data on the extent to which dentistry-related practices are being implemented (Appendix Figure 1). Baseline data included age, gender, years of experience as a doctor, type of hospital (community-based hospital or university hospital), the presence or absence of an oral surgery department, the number of full-time dentists (0, 1, 2, or 3 or more), and the availability of dental hygienists.

    For the outcome data, we examined whether the following actions, considered important steps in consulting an oral surgery department when treating aspiration pneumonia, were performed: oral evaluation, use of oral healthcare assessment tools, checking dentures, identifying the patient’s primary care dentist, recommending a dental visit after discharge, and encouraging a dental visit for patients with relevant medical histories. These items were evaluated in 20% increments (0%, 1–20%, 21–40%, 41–60%, 61–80%, 81–100%) and recategorized into four groups: 0% as “never”, 1–40% as “sometimes”, 41–80% as “often”, and 81–100% as “always”.

    Our primary outcome was whether respondents made any dental referral for aspiration-pneumonia management (0% vs 1–100%). A prespecified secondary outcome was routine referral, defined as a referral frequency of 81–100%. Explanatory variables comprised (i) system-level resources—the presence of an oral surgery department, the number of full-time dentists, the presence of dental hygienists, and hospital type; (ii) clinician practices—six oral-care activities (oral evaluation, use of an oral health assessment tool, denture check, identifying the patient’s primary-care dentist, recommending a dental visit after discharge, and encouraging a dental visit in those with a relevant history), each recorded on a six-category frequency scale and recoded as never/sometimes/often/always; and (iii) demographics—age, sex, and years in practice.

    We evaluated whether respondents initiated dental referrals for the management of aspiration pneumonia by classifying their referral frequency into 20% increments (0%, 1–20%, 21–40%, 41–60%, 61–80%, 81–100%). Those who referred between 1% and 100% were assigned to the “refer group”, while those with 0% referral were categorized as the “non-refer group”. Within the Refer group, individuals referring 81–100% of the time were identified as the “routinely refer group”. Furthermore, the referral rate was examined among respondents who reported performing multiple actions in the always group. Specifically, we identified respondents who consistently performed both “oral evaluation” and “use of oral healthcare assessment tools” or both “oral evaluation” and “checking dentures” in the always group. Among these respondents, the routinely refer group was calculated and compared with the proportion in the non-refer group.

    Respondents were also asked, through multiple-choice questions, to indicate their reasons for referring or not referring. These options were determined through discussions involving TM, ST, and TM (Matsumoto). We then analyzed the clinical practices (“always”) routinely performed by hospitalists in the Refer group. Furthermore, we analyzed the sum of these practices.

    Data Analysis

    All statistical analyses were performed using JMP version 18.1 (SAS Institute, Cary, NC). Patient-level variables are presented as medians and interquartile ranges (IQR) for continuous variables and as numbers and percentages for categorical variables. For continuous variables, the Mann–Whitney U-test was used after normality was assessed. Statistical significance was defined as a p-value of < 0.05 using a two-tailed test. Regarding missing data, analyses were conducted using only the non-missing portions of the dataset.

    Ethics

    The study was approved by the ethics committee of the Ashikaga Red Cross Hospital (No.2024–34) and was conducted in accordance with the principles of the Declaration of Helsinki. All participants reviewed the study document detailing data anonymization, voluntary participation, and the dissemination of research results prior to participation. Only participants who provided informed consent (opt-in) were included in the study. Additionally, participants could withdraw from the study at any time.

    Results

    A total of 370 hospitalists participated in this study. Of these, 18 worked in clinics and were excluded from the analysis, leaving 352 participants in the final study sample.

    Among the participants, 305 (86.7%) were male, and the median age (IQR) was 48 (40–56) years. The median number of years of practice was 22 (13–31) years. A total of 255 (72.4%) worked at community-based hospitals, and 234 (66.4%) worked at institutions with an affiliated oral surgery department. Regarding the number of full-time dentists, hospitals with three or more full-time dentists were common, accounting for 159 participants (45.1%). Finally, 237 participants (67.3%) reported having dental hygienists available at their workplaces.

    Among these participants, 141 (40.1%) referred patients for dental consultation as part of their treatment for aspiration pneumonia. In this group, the following factors showed significant differences. Hospitalists whose hospitals had an affiliated oral surgery department were more likely to make referrals (affiliated: 118 [50.4%] vs unaffiliated: 23 [19.5%], P<0.001). Similarly, those working in hospitals with full-time dentists showed a higher referral rate (0 full-time dentists: 26 [20.8%], 1 full-time dentist: 12 [42.9%], 2 full-time dentists: 25 [62.5%], 3 or more full-time dentists: 78 [49.1%], P<0.001). A similar difference was observed regarding the presence of dental hygienists (presence: 118 [49.8%] vs absence: 23 [20%]; P<0.001). Moreover, hospitalists at university hospitals were more likely to make referrals than those at community-based hospitals (university hospitals: 51 [52.6%] vs community-based hospitals: 90 [35.3%]; P<0.01) (Table 1).

    Table 1 Participant Characteristics

    Furthermore, among the participants, the proportion of those who routinely performed the following practices in aspiration pneumonia care (always group) was as follows: 138 (39.2%) reported conducting oral evaluations themselves for patients admitted with aspiration pneumonia. Of these, 37 (11.3%) used the oral health assessment tool. In addition, 169 (48.0%) checked whether patients had dentures. Only 30 (8.5%) recommended a dental visit after discharge, and 36 (10.2%) encouraged dental visits for patients with a history of aspiration pneumonia (Table 2).

    Table 2 Regarding the Treatment of Aspiration Pneumonia by Japanese Hospitalists

    A total of 126 (89.4%) participants were referred for oral healthcare, 85 (60.3%) for denture adjustment, 61 (43.3%) for swallowing function assessment, and 58 (41.1%) for tooth extraction. Meanwhile, 95 (45.0%) did not make referrals because they consulted other healthcare professionals, such as speech-language therapists and nurses. Additionally, 91 (43.1%) cited a lack of a habit of making referrals and 87 (41.2%) mentioned the absence of a dentist in the hospital as reasons for not referring patients (Table 3).

    Table 3 The Reasons for Each Group of Patients Who Were (a) Referred to a Dentist or (b) Not Referred to a Dentist

    The referral frequency results for those who routinely referred patients were as follows: among those in the always oral evaluation group, 75 (54.3%) referred patients with aspiration pneumonia. Within this group, 25 (69.4%) who routinely checked for a primary care dentist had a higher referral rate. Similarly, in the always group, 22 (59.5%) who used the oral healthcare assessment tool referred patients when they developed aspiration pneumonia. Moreover, 14 (73.7%) in this group who routinely consulted their primary care dentists also had a high referral rate (Table 4).

    Table 4 Comparison of Medical Care Between Groups That are Always and are Not Introduced

    Discussion

    This study investigated the involvement of dentists in the management of aspiration pneumonia among hospitalists in Japan. Our findings revealed that the most common reason for not referring patients was consultation with other non-dental professionals. This study identified the hospital and practice characteristics of hospitalists who routinely referred patients to dentists.

    Reasons Why Hospitalists Do Not Refer Patients to Dentists

    In this study, the main reasons for not referring patients to dentists were consultation with other medical professionals, such as speech-language therapists and nurses, lack of a referral habit, and absence of dentists within the hospital. The first reason was reliance on others. Oral bacteria predispose patients to aspiration pneumonia, and previous studies have suggested that oral health care provided by dentists can reduce bacterial levels.23,24 Although oral rehabilitation by speech-language therapists has been shown to shorten hospital stay and improve outcomes,25 they are unable to provide oral health care themselves, making it difficult to fully address oral health issues. In contrast, while 80.2% of nurses recognized the importance of oral health care in preventing aspiration pneumonia, studies suggest that collaborative care with dentists is more effective than nurse-led care alone.12,26 In a Japanese stroke unit, embedding a full-time dentist to develop and lead an oral care system (including nurse training and standardized techniques) significantly lowered pneumonia incidence compared to periods without dentist involvement.14 These studies emphasize the importance of oral health care by dentists.

    The second reason was the lack of referral habits. Studies in Japan and the United States have highlighted the limited collaboration between hospitalists and dentists in routine clinical practice.27,28 Similarly, our study suggests that the lack of a habit of referring patients to dentists may be reflected in the low referral rates observed during aspiration pneumonia management. In contrast, a qualitative study from Germany indicated that while hospitalists did not perceive the need for collaboration with dentists, they considered such collaboration important.29 This suggests that the lack of collaboration between hospitalists and dentists may be driven by hospitalists’ attitudes. To address this challenge, implementing systematic referral prompts in electronic health records (EHR) may be beneficial. A study of oral health promotion in primary care found that automatically bundling dental referrals with related medical orders in EHR systems significantly improved referral consistency.30 One Japanese hospital’s program assigned a dentist to its acute stroke ward and defined procedures for nurses to request dental intervention promptly for any patient with compromised oral health.14 Additionally, borrowing core elements from antimicrobial stewardship programs, which are widely implemented to address antibiotic resistance, may be beneficial.31 These interventions reduce the referral barrier, potentially enhancing dental referral rates during aspiration pneumonia management.

    The third reason was the absence of dentists in hospitals. The proportion of dentists working in hospitals is as low as 0.4% and 3.0% in the United States and Japan, respectively.32,33 Furthermore, only about 28% of hospitals with dental departments have full-time dentists.16 This highlights the current shortage of hospital-based dentists capable of providing specialized oral health care. Teledentistry, a hub-and-spoke model in which intra-oral images and other clinical data are transmitted electronically from “spoke” wards to a central (hub) dentist, enables a university-hospital dentist to monitor and advise on the oral health of inpatients in surrounding hospitals even when no onsite dentist is available.34 Although there is limited direct evidence linking teledentistry to a reduction in the risk of aspiration pneumonia, it can enable hospitals to access dental care services remotely, potentially improving oral health management for at-risk patients.

    Background of Hospitalists Who Refer Patients to Dentists

    Several key characteristics were identified among hospitalists who referred patients to dentists. The first was the number of dentists in the hospital. Referral rates were higher in hospitals with dentists. Dentists have been reported to desire greater collaboration with their doctors.29 Our study found that hospitals with two or more dentists had higher referral rates than hospitals with no dentists or only one dentist. This suggests that hospitals with a larger dental workforce may be better equipped to provide a wider range of oral healthcare services, such as oral hygiene management and swallowing function support. The second factor was the presence of dental hygienists. Oral health care provided by dental hygienists has been shown to be effective in preventing aspiration pneumonia.35,36 Additionally, dental hygienists have been reported to play a key managerial role in medical-dental collaborations.37 The presence of dental hygienists may improve access to oral health care and facilitate collaboration between medical and dental professionals. The third factor was hospital type. Our findings indicate that hospitalists working in university hospitals had higher referral rates than those working in community-based hospitals. In Japan, university hospitals are legally required to offer 16 medical departments, including dentistry,38 whereas community-based hospitals have no such requirement. Consequently, university hospitals are guaranteed to have at least one dentist, which may have contributed to a more established referral habit. Additionally, university hospitals in Japan often treat patients with multiple underlying conditions, resulting in increased complexity and potentially higher referral rates. Moreover, because referrals to other specialties are more common for diseases outside one’s own expertise, this practice may have contributed to the higher referral rate observed in our study.39

    Actions Taken by Hospitalists Who Refer Patients for Dental Consultation

    A key finding of this study was the clear association between hospitalists’ attention to oral health in daily practice and the likelihood of referring patients to dentists. Hospitalists who routinely performed oral evaluations had higher referral rates than those who did not. Furthermore, among those who not only used the oral healthcare assessment tool but also confirmed whether patients had a primary care dentist, the referral rate exceeded 70%, suggesting that performing both actions as part of routine care further increased the likelihood of referral. Therefore, it is critical to conduct oral healthcare assessments. Paying attention to oral health can increase referral rates.

    The oral healthcare assessment tool is a screening method that evaluates oral health across eight categories and can be used for patients with dementia.40 Studies have shown that patients with Oral Health Assessment Tool (OHAT) scores of 3 or higher have significantly lower 60-day survival rates post-hospitalization compared to those with lower scores.41 Additionally, OHAT scores have been found to be significantly worse in patients with aspiration pneumonia compared to those with other types of community-acquired pneumonia.42 However, the utilization of OHAT remains low, with fewer than 5% of initial outpatient visits, including OHAT assessments.43 The findings of this study suggest that when hospitalists prioritize oral health using assessment tools and primary care dentists, they are more likely to refer patients to dentists. In Australian acute-care hospitals, national safety-and-quality guidance stipulates that an oral-health assessment, performed with a validated tool such as the OHAT, must be completed and documented within 24 hours of admission, alongside other vital signs.44 Consistent with earlier quality-improvement projects,30 embedding two mandatory EHR pop-up prompts at admission: (i) confirmation that the oral-health assessment is complete and (ii) documentation of whether the patient has a primary-care dentist, would ensure these checkpoints are actioned in real time. Designating these two assessments as mandatory Day-1 tasks within the admission order set is expected to expedite dental referrals and reduce the incidence of aspiration pneumonia.

    Limitations

    This study had some limitations. First, it focused on hospitalists in Japan, and the findings may not be generalizable to other countries with different healthcare systems and cultural backgrounds. Second, the survey targeted hospitalists primarily from general medicine departments, meaning that physicians from other medical specialties were excluded. This limitation should be considered when interpreting the results. Moreover, the questionnaire was designed by two of the authors and reviewed by the other authors. Because this is the original version, no external reference exists.

    Additionally, there is a possibility of selection bias, as the physicians who participated in this study may have had a greater interest in the research topic. Consequently, the perspectives of hospitalists with lower motivation to participate in such surveys may not have been adequately captured. Furthermore, because this study was based on a cross-sectional, self-report survey, it did not establish a causal relationship between actual clinical behavior and dental referrals. Nevertheless, there has never been a survey on medical-dental collaboration with a large number of respondents before, making this research an important baseline for future study.

    Future research should include a broader range of medical professionals, such as physicians from various specialties. Moreover, prospective studies are necessary to clarify the causal relationship between hospitalists’ actions and dental consultations.

    Conclusions

    This study investigated the status of collaboration between hospitalists and dentists in Japan regarding the management of aspiration pneumonia. The results revealed that organizational factors, such as the presence of a dental specialist, and individual factors, such as the degree to which an oral evaluation was performed, significantly affected the referral rates to dentists in aspiration pneumonia management. Attention to the oral environment during examinations may improve dental collaboration.

    Abbreviations

    JSHGM, Japanese Society of Hospital General Medicine; JPCA, Japanese Primary Care Association; OHAT, Oral Health Assessment Tool.

    Data Sharing Statement

    The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.

    Ethics Approval and Informed Consent

    The study was approved by the ethics committee of the Ashikaga Red Cross Hospital (No.2024-34) and was conducted in accordance with the principles of the Declaration of Helsinki. All participants reviewed the study document detailing data anonymization, voluntary participation, and the dissemination of research results prior to participation. Only participants who provided informed consent (opt-in) were included in the study. Additionally, the participants could withdraw from the study at any time.

    Author Contributions

    All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising, or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

    Funding

    This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

    Disclosure

    The authors report no conflicts of interest in this work.

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